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title: Human 3D nucleus pulposus microtissue model to evaluate the potential of pre-conditioned
nasal chondrocytes for the repair of degenerated intervertebral disc
authors:
- Jesil Kasamkattil
- Anna Gryadunova
- Raphael Schmid
- Max Hans Peter Gay-Dujak
- Boris Dasen
- Morgane Hilpert
- Karoliina Pelttari
- Ivan Martin
- Stefan Schären
- Andrea Barbero
- Olga Krupkova
- Arne Mehrkens
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC9971624
doi: 10.3389/fbioe.2023.1119009
license: CC BY 4.0
---
# Human 3D nucleus pulposus microtissue model to evaluate the potential of pre-conditioned nasal chondrocytes for the repair of degenerated intervertebral disc
## Abstract
Introduction: An in vitro model that appropriately recapitulates the degenerative disc disease (DDD) microenvironment is needed to explore clinically relevant cell-based therapeutic strategies for early-stage degenerative disc disease. We developed an advanced 3D nucleus pulposus (NP) microtissues (µT) model generated with cells isolated from human degenerating NP tissue (Pfirrmann grade: 2–3), which were exposed to hypoxia, low glucose, acidity and low-grade inflammation. This model was then used to test the performance of nasal chondrocytes (NC) suspension or spheroids (NCS) after pre-conditioning with drugs known to exert anti-inflammatory or anabolic activities.
Methods: NPµTs were formed by i) spheroids generated with NP cells (NPS) alone or in combination with ii) NCS or iii) NC suspension and cultured in healthy or degenerative disc disease condition. Anti-inflammatory and anabolic drugs (amiloride, celecoxib, metformin, IL-1Ra, GDF-5) were used for pre-conditioning of NC/NCS. The effects of pre-conditioning were tested in 2D, 3D, and degenerative NPµT model. Histological, biochemical, and gene expression analysis were performed to assess matrix content (glycosaminoglycans, type I and II collagen), production and release of inflammatory/catabolic factors (IL-6, IL-8, MMP-3, MMP-13) and cell viability (cleaved caspase 3).
Results: The degenerative NPµT contained less glycosaminoglycans, collagens, and released higher levels of IL-8 compared to the healthy NPµT. In the degenerative NPµT, NCS performed superior compared to NC cell suspension but still showed lower viability. Among the different compounds tested, only IL-1Ra pre-conditioning inhibited the expression of inflammatory/catabolic mediators and promoted glycosaminoglycan accumulation in NC/NCS in DDD microenvironment. In degenerative NPµT model, preconditioning of NCS with IL-1Ra also provided superior anti-inflammatory/catabolic activity compared to non-preconditioned NCS.
Conclusion: The degenerative NPµT model is suitable to study the responses of therapeutic cells to microenvironment mimicking early-stage degenerative disc disease. In particular, we showed that NC in spheroidal organization as compared to NC cell suspension exhibited superior regenerative performance and that IL-1Ra pre-conditioning of NCS could further improve their ability to counteract inflammation/catabolism and support new matrix production within harsh degenerative disc disease microenvironment. Studies in an orthotopic in vivo model are necessary to assess the clinical relevance of our findings in the context of IVD repair.
## 1 Introduction
Low back pain (LBP) is experienced by $80\%$ of the world population at least once in their life and it is one of the costliest diseases for the healthcare system (Frapin et al., 2019). $40\%$ of the chronic LBP cases are due to degeneration of the intervertebral disc (IVD) (Schwarzer et al., 1995; Luoma et al., 2000). During IVD degeneration, the extracellular matrix (ECM) of the nucleus pulposus (NP) tissue, especially the proteoglycans (PG), is degraded due to the imbalance of catabolic and anabolic activities (Struglics and Hansson, 2012; Dowdell et al., 2017) and the cell density in the IVD decreases over time (Zhao et al., 2007). The gradual onset of IVD degeneration is considered as a part of the natural course of ageing (Sakai and Andersson, 2015). However, with progressing degeneration, the NP tissue could become inflamed and/or herniate through annulus fibrosus (AF) and press against the nerve roots causing pain (Nakazawa et al., 2018). The association of IVD degeneration with inflammation and pain is referred to as degenerative disc disease (DDD). Surgical strategies to treat DDD often do not improve patient’s quality of life, as they could accelerate degeneration of adjacent IVDs (van den Eerenbeemt et al., 2010; Deyo and Mirza, 2006). In order to prevent surgery, minimally invasive biological therapies should be developed and applied at relatively early stage (Frapin et al., 2019). Ideally, these will restore the structure and function of the mildly affected NP (Pfirrman grade 2–3), allowing the IVD to hydrate and regain the height, as well as reduce the catabolic shift (Binch et al., 2021). However, despite the advancement in biological IVD repair (Binch et al., 2021), no therapy has been widely adopted clinically yet (Meisel et al., 2019; Urits et al., 2019; Eisenstein et al., 2020).
Numerous in vitro, ex vivo, and in vivo studies have investigated the effects of anti-catabolic or anti-inflammatory factors on IVD repair (Seki et al., 2009; Walter et al., 2015; Evashwick-Rogler et al., 2018). The aim was to either directly suppress the expression of catabolic enzymes i.e., matrix metalloproteases (MMPs) and a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) (Klawitter et al., 2012; Kim et al., 2013) or by downregulating pro-inflammatory mediators (i.e., tumor necrosis factor alpha (TNF-α), interleukin-1 (IL-1) (Le Maitre et al., 2007a; Sinclair et al., 2011). Although these anti-catabolic and anti-inflammatory therapeutic strategies are promising, they are not sufficient enough to regenerate the IVD function since the resident cells often fail to restore their ability to synthesize ECM (Frapin et al., 2019). Pro-anabolic strategies using growth factors i.e., transforming growth factor β (TGFβ) or growth differentiation factor 5 (GDF-5) have been also investigated to induce matrix formation within the NP tissue with encouraging in vitro, ex vivo and in vivo results (Thompson et al., 1991; Nishida et al., 1998; Li et al., 2004; Chujo et al., 2006; Liang et al., 2010). As an example, clinical studies evaluated the safety, tolerability and efficacy of GDF-5 injection into degenerating IVD, with no major adverse events directly related to GDF-5 injection as well as moderate improvement of pain and disability (https://clinicaltrials.gov; NCT01158924, NCT00813813, NCT01182337, and NCT01124006). However, these pro-anabolic approaches are hampered by the limited amounts of healthy/metabolically active cells in the degenerated NP. Therefore, a single intradiscal injection of biological factors with anti-catabolic, anti-inflammatory and pro-anabolic effects combined with healthy therapeutic cells, which survive and produce ECM within the DDD microenvironment, could be a better approach to repair the NP tissue. Nevertheless, translation of cell-based approaches for IVD repair still faces several critical challenges, mainly related to (i) selection of a therapeutic cell source with good performance within the DDD microenvironment and (ii) a lack of an in vitro model that appropriately mimics the course of the disease and at the same time allows for clinically relevant incorporation of therapeutic cells (Buckley et al., 2018; Smith et al., 2018; Thorpe et al., 2018).
To repopulate the NP tissue with cells, endogenous stem/progenitor cell recruitment by injecting chemokine ligands (i.e., chemokine (C-C motif) ligand 5 (CCL5) or C-X-C motif chemokine 12 (CXCL12)) (Gruber et al., 2014; Frapin et al., 2020; Zhou et al., 2020) or exogenous cell injection strategies have been explored over the last two decades (Frapin et al., 2019). For exogenous cell injection, differentiated cell sources (NP, AF, articular chondrocytes (AC)) as well as stem/stromal cells (derived e.g. from bone marrow or adipose tissue) in combination with or without scaffolds were investigated (Williams et al., 2021; Kasamkattil et al., 2022). However, for both endogenous and exogenous cell supplementation, healthy cells are either not available in sufficient number, donor site morbidity arises, and/or cell survival within the harsh DDD microenvironment is reduced (Sakaguchi et al., 2005; Wuertz et al., 2009; Vadala et al., 2012; Sakai and Andersson, 2015; Vadala et al., 2019). These limitations can be overcome by using nasal chondrocytes (NC), isolated from autologous nasal septum cartilage with minimal donor site morbidity (Siegel et al., 2000; Homicz et al., 2002; Fulco et al., 2014). NC show superior viability over AC and mesenchymal stromal cells (MSCs) in simulated DDD microenvironment, thus representing a robust cell population with a likelihood of survival post injection (Gay et al., 2019). We have demonstrated that spheroids formed with NC (hereafter referred to as nasal chondrocyte spheroids, NCS) generate own matrix, and survive and fuse with NP microtissues in DDD microenvironment (Gryadunova et al., 2021). Notably, NCS are injectable into the IVD using a spinal needle, without losing their structural integrity (Gryadunova et al., 2021). Therefore, NCS represent a promising alternative for single-injection-based IVD repair strategy.
The first step towards developing a functional cell-based strategy for IVD repair includes testing in in vitro models. At this stage, general proof of principle, intercellular communications, cell functions, and cell behavior are investigated. For the appropriate design of the in vitro models, the selection of ideal cell source, culture system and culture condition are of key importance. Also, the right choice of species from which resident/therapeutic cells are being isolated for in vitro culture has to be considered because it is known that species-specific responses could lead to different outcomes (Bach et al., 2017; Rosenzweig et al., 2017).
Interaction between IVD and therapeutic cells is commonly investigated in 2D and 3D co-culture models, tissue explants as well as organ culture models (Thorpe et al., 2018). 3D culture models restore the IVD cell phenotype and allow to reproduce in vivo spatial distribution of the IVD cells, which makes them more physiologically relevant and predictive than 2D monolayer cultures (Ravi et al., 2015). Different 3D in vitro co-culture models have been used to study the interaction between therapeutic and IVD cells (Choi et al., 2011; Naqvi and Buckley, 2015; Li et al., 2019). As an example, direct co-culture of human bone marrow stromal cells (BMSCs) with bovine NP cells (ratio: 1:1) encapsulated in 3D alginate beads revealed that hypoxia and co-culture could lead to BMSCs differentiation into NP-like phenotype (Stoyanov et al., 2011). In order to overcome several disadvantages of the alginate bead model, such as lack of reproducibility and uniformity of quality and size of the microspheres (Lee et al., 2001), the 3D pellet culture model could be used (Vadala et al., 2008; Svanvik et al., 2010; Watts et al., 2013). However, even the direct co-culture pellet model does not simulate the in vivo situation properly since therapeutic cells are not supposed to differentiate with differentiating NP cells but should rather be introduced to an already differentiated NP microtissue. Tissue explants and organ culture models are excellent to test local tissue responses, integration and delivery of therapeutic cells into IVD tissue and also in regard to biological and cellular functions (Zhang et al., 2008; Le Maitre et al., 2009a; van Dijk et al., 2014; van Dijk et al., 2017). However, due to the handling and complexity of tissue/organ culture, they are not well suited for more fundamental cellular mechanistic studies.
Selecting the ideal culture condition to mimic the DDD microenvironment is essential to study the potential of therapeutic cell for IVD regeneration. The harsh NP microenvironment is characterized by avascularity, hypoxia, low glucose level, acidity, inflammation, high osmolality and restricted biomechanics (Dou et al., 2021). Several studies have assessed the performance of the therapeutic cells within in vitro models simulating some of the parameters present in the DDD microenvironment (Vadala et al., 2019). Nevertheless, it has been shown that less than $15\%$ of the in vitro studies include solely of these parameters thus not mimic the harsh IVD microenvironment properly (Thorpe et al., 2018).
Since available 3D models are still not satisfactory to study the responses of therapeutic cells to the harsh NP microenvironment, we aim to develop a practical yet sufficiently complex 3D NP microtissues (µT) model. Within this NPµT model we investigate the responses of therapeutic cell suspensions as well as cell spheroids (NC vs. NCS) to the DDD microenvironment. Furthermore, we test the efficacy of different clinically relevant compounds to improve NC function once exposed to DDD microenvironment (Figure 1). We demonstrate that the model is suitable to test pre-conditioning strategies that enhance the NP repair potential of therapeutic cells.
**FIGURE 1:** *Experimental design. (A) NP microtissue (NPμT) model: single spheroids were generated by culturing NC (NCS) and NP cells (NPS) for 3 days or 2 weeks respectively, in healthy condition. Afterwards, NPS were pooled with NCS/NC to form NPµT (+ NCS/NC susp). (B) NC were preconditioned with anti-inflammatory or pro-anabolic compounds in (B1) 2D culture and (B2) 3D culture. (B3) IL-1Ra-preconditioned NCS (pNCS) were implemented in the NPµT model. (C) Histological, biochemical, and gene expression analyses were performed. Created with BioRender.com.*
## 2.1.1 Tissue harvest and cell isolation
Tissues were collected following local ethical committee approval (EKNZ-2015305, University Hospital Basel). After obtaining informed consent from all donors, human nasal septal cartilage tissue was harvested from the patients undergoing rhinoplasty (total $$n = 6$$, Supplmentary Table S1). NP tissue was acquired from donors undergoing surgery for DDD. Harvested NP tissues were graded using Pfirrmann scale (Urrutia et al., 2016). NP tissues with Pfirrmann grade 2–3 (mild/moderate degeneration) were used in this study (total $$n = 6$$, Supplementary Table S2). NC and NP cells were isolated after digestion in collagenase type II ($0.15\%$ for NC; $0.05\%$ for NP cells for 22 h), and expanded in NC and NP expansion medium respectively (composition in Supplementary Material) up to passage 3.
## 2.1.2 Lentiviral transduction of nasal chondrocytes
In order to distinguish NC from NP cells, NC were labelled with mEmerald fluorescent protein using lentiviral transduction. Lentiviral expression vector was generated by subcloning mEmerald coding sequence by PCR from pmEmerald-LifeAct-7 vector (Addgene, 54148) into pLVX lentiviral vector (Clontech, United States). For lentivirus production, Lenti-X 293T cells (Clontech, United States) were transfected with lentiviral expression vector and 3rd generation packaging plasmids prMDLg/pREE, pRSV-Rec, and pMD2.G (Addgene, 12251, 12253, and 12259, respectively) using Lipofectamine 2000 (Lifetech, 11668019). After 72 h, the supernatant containing lentiviral particles was collected, and lentiviral titer was assessed by ELISA using Quick Titer Lentivirus titer kit (Cell Biolabs, VPK-1070). Lentiviral transduction of NC was performed according to previously established protocol, reported affecting neither proliferation nor the differentiation capacity of chondrocytes (Miot et al., 2010). Briefly, NC were seeded in 6-well plates as 2.5 × 106 cells/well and transduced with mEmerald-lentivirus at MOI 5 in the presence of 8 µg/ml polybrene, which yields ≥$95\%$ transduction efficiency (TEf). TEf was monitored by yielded the percentage of green-positive cells by flow cytometry (Aria III, BD) 3 days post-transduction.
## 2.2 Drug screening in 2D culture of nasal chondrocytes
NC ($$n = 3$$) were cultured in 6-well plates (0.1 × 106 cells/well) for 24 h in NC expansion medium (composition in Supplementary Material) and pre-treated with the FDA-approved drugs interleukin 1 receptor antagonist (IL-1Ra), growth and differentiation factor 5 (GDF-5), amiloride, metformin, and celecoxib at different concentrations (Table 1) for the last 3 h (21 h + 3 h). Afterwards, the drugs were removed and DDD mimicking condition (= hypoxia, low glucose, and medium supplemented with 1ow-grade pro-inflammatory cytokines TNFα, IL-1β, IL-6, all 100 pg/ml, full composition in Supplementary Material) was applied to the cells for further 24 h. Then, the cells were harvested and analyzed by reverse transcription quantitative PCR (RTqPCR) (chapter 4.3.1).
**TABLE 1**
| Drug | Desired Effect | Concentrations |
| --- | --- | --- |
| Amiloride | Inhibitor of acid sensing ion channel 1/3 (Guo et al., 2019) which could prevent acid-induced decrease in cell proliferation and ECM gene expression (Liu et al., 2017) | 10, 100 µM |
| Metformin | Inducer of inflammation resistant phenotype (Das et al., 2019) known for its chondroprotective properties (Park et al., 2019) | 10, 100, 1000 µM |
| Celecoxib | Intradiscal delivery of celecoxib-loaded microspheres restored IVD integrity in preclinical canine model (Tellegen et al., 2018) | 10, 100, 1000 µM |
| IL-1Ra | Inflammation inhibitor (Le Maitre et al., 2006) with matrix protective properties in intact human degenerate IVD explants (Le Maitre et al., 2007a) | 10, 100, 500 ng/mL |
| GDF-5 | Inducing NP-specific ECM forming effects (Le Maitre et al., 2009b) and driving differentiation of therapeutic cells towards NP-like cells (Colombier et al., 2016) | 1, 10, 100 ng/mL |
## 2.3.1 Fabrication of nucleus pulposus and nasal chondrocyte spheroids
Nucleus pulposus spheroids (NPS) formation: 25′000 NP cells/well were seeded in $2\%$ PolyHEMA (Sigma, P3932) coated 96-well plates and the formed spheroids were cultured for 14 days in NP differentiation medium (composition in Supplementary Material) in Thermo Scientific™ Heracell™ 150i CO2 incubator (37°C; $5\%$ CO2, $20\%$ O2). Media was changed twice a week. Nasal chondrocyte spheroids (NCS) formation: 12’500 NC/well were seeded in $2\%$ PolyHEMA (Sigma, P3932) coated 96-well plates and the formed spheroids were cultured for 3 days in NC differentiation medium (composition in Supplementary Material) in Thermo Scientific™ Heracell™ 150i CO2 incubator (37°C; $5\%$ CO2, $20\%$ O2) without medium change.
## 2.3.2 Preconditioning of nasal chondrocyte spheroids
NCS were formed in 96 well plates in NC differentiation medium (composition in Supplementary Material) for 3 days (Gryadunova et al., 2021) with/without GDF-5 (100 ng/mL), IL-1Ra (500 ng/mL) or the combination of both drugs. Then, the drugs were removed. Preconditioned NCS were either placed directly in DDD-mimicking condition (composition in Supplementary Material) for further 7 days and analyzed (chapter 4.3.2), or introduced into the NP microtissues (NPµT) model (see 4.3.3).
## 2.3.3 Nucleus pulposus microtissue model
NPµT was cultured either alone or in combination with NCS or NC suspension. 16 NPS were pooled in a polypropylene conical tube to form the NPµT. 8 NPS and 16 NCS were pooled to form the NP µT + NCS. 8 NPS and 0.2 × 106 NC cells were pooled to form the NPµT + NC cell suspension. The total number of the cells in each formed microtissues was 0.4 × 106. The aggregates were cultured for 14 days in 0.5 mL of either healthy (normoxia, high glucose, NHG) or DDD-mimicking condition (= hypoxia, low glucose, and medium supplemented with 1ow-grade pro-inflammatory cytokines TNFα, IL-1β, IL-6, all 100 pg/mL, full composition in Supplementary Material). Hypoxia is native to both healthy and degenerated NP, so we used this condition to mimic DDD (Chen et al., 2014; Kwon et al., 2017). The medium was changed twice per week.
## 2.4.1 Gene expression analysis
ECM genes (aggrecan, collagen type II) are downregulated in IVD degenerative condition (Le Maitre et al., 2007b) and inflammatory/catabolic genes (IL-6, IL-8, MMP3, MMP13) are upregulated (Goupille et al., 1998; Roberts et al., 2000; Le Maitre et al., 2007b; Teixeira et al., 2018). Thus, these targets were analyzed to verify whether pre-conditioning of NC (either in monolayer or 3D spheroidal organization) could modulate ECM degradation and inflammation. Total RNA from 0.2 M cells was extracted using the RNeasy Mini Kit (Quiagen, 74106), according to the manufacturer’s protocol. The RNA yield and purity were measured on a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific, United States). SuperScriptTM III Reverse Transcriptase kit (Invitrogen, 18080093) was used to reverse-transcribe 0.5 μg of RNA into cDNA in a 30 μL volume. 10 ng of cDNA/well was mixed with TaqManTM Universal PCR Master Mix (Applied Biosystems, 4304437), RNase-free water, and TaqMan primers (ACAN: Hs00153936; COL2A1: Hs00264051; COL1A1: Hs00164004; MMP3: Hs00968305_m1; MMP13: Hs00233992_m1; IL-8: Hs00174103_m1; IL-6: Hs00985639_m1) in a total volume of 10 μL and used for quantitative real-time polymerase chain reaction performed on a 7300 Real-time PCR System (Applied Biosystems, United States). For each sample, Ct values of the target were subtracted from the Ct values of a housekeeping gene (human GAPDH, Hs02758991, Applied Biosystems) to derive the ∆Ct. Gene expression was quantified relative GAPDH (2−ΔΔCT) and relative to control (2−ΔΔCT).
## 2.4.2 Biochemical content quantification
NPµT (+NCS/NC suspension) were digested for 16 h at 56°C in 1 mg/mL proteinase K solution [1 mg/ml proteinase K (Sigma-Aldrich, P2308) in 50 mM Tris (Sigma-Aldrich, A5456-3) with 1 mM EDTA (Fluka, 03680), 1 mM iodoacetamide (Sigma-Aldrich, I-1149) and 10 mg/mL pepstatin A (Sigma-Aldrich, P5318)]. Glycosaminoglycan (GAG) content was determined spectrophotometrically using Blyscan GAG Assay (Biocolor, B1000). DNA content was measured using the CyQuant Cell Proliferation Assay Kit (Invitrogen, C7026), with bacteriophage λ DNA as a standard, according to the manufacturer’s protocol. Total collagen content was quantified using the Hydroxyproline (HYP) Assay Kit (Sigma-Aldrich, MAK008) according to manufacturer’s protocol. Both GAG and HYP contents were normalized either to single NCS or to DNA content.
## 2.4.3 Enzyme-linked immunosorbent assay (ELISA) and luminex
The amounts of MMP13, IL-8, and IL-1Ra in the cell culture media were quantified using ELISA. MMP13 and IL-8 are known to be released in IVD tissues experiencing catabolic shift (Le Maitre et al., 2004; Krock et al., 2019). SensoLyte Plus™ 520 MMP13 Assay Kit (Catalog #: 72019) was used for fluorometric detection of total MMP13, performed according to the manufacturer’s protocol. Human IL-8 ELISA Set (555244, BD) with ELISA reagent set B (550534, BD) were used to detect IL-8 according to the manufacturer’s instructions. IL-1Ra was quantified using the Human Luminex Discovery Assay (Magnetic Luminex Assay 2 Plex, bio-techne, LXSAHM-02) according to the manifacturer’s instructions.
## 2.4.4 Histology and immunohistochemistry
Samples were fixed in $4\%$ paraformaldehyde (01-1,000, formafix), washed in phosphate buffered saline (PBS), embedded in Richard-Allan Scientific HistoGel™ (HG-4000-012, ThermoFisher) and processed using Tissue Processing Center TPC 15 Duo (Medite, Germany). 4 μm-thick sections were cut (Microm HM 430 or Microm HM 340E) and collected on poly-L-lysine coated glass slides (J2800AMNZ, Fishersci). After dehydration, safranin-O/fast green (SafO/FG; SafO: 84120, Sigma; FG: F-7252, Sigma) stain with hematoxylin (J.T. Baker, MFCD00078111) nuclear counterstaining was performed to visualize the PG within the sections. Widefield microscopy (Nikon Ti2, Japan; acquisition software: Nikon NIS; Camera: Nikon DS-Ri2; Objective: ×20 or 40x; NA: 0.95) was applied for imaging. Images were processed using Fiji/ImageJ software (NIH, Bethesda, MD). For immunohistochemistry, sections were subjected to enzymatic epitope retrieval and blocked with $1\%$ bovine serum albumin (BSA) (A9647, Sigma) supplemented with triton X-100 (1:1,000, 93418, Sigma), followed by application of primary antibodies anti-cleaved caspase 3 (1:300, polyclonal, 9661, Cell Signalling) and anti-GFP (1:1,500, GFP-1020, Aves). Respective matching secondary antibodies Alexa Fluor 647- or 488-conjugated (1:500, polyclonal, A21245, Invitrogen and 103–605-155, Jackson) were used, with DAPI as a nuclear counterstain. Widefield fluorescence microscopy (Nikon Ti2, Japan; acquisition software: Nikon NIS; Camera: Photometrics Prime 95B; Objective: 20 or 40x; NA: 0.95) was applied for imaging. Images were processed using Fiji/ImageJ software (NIH, Bethesda, MD).
## 2.4.5 Image quantification
Multiplexed fluorescence images from tissue sections were analyzed with QuPath version 0.3.0, an open source software for whole-slide images (Bankhead et al., 2017). Immunopositive areas containing NCS were used as regions of interest (ROIs). StarDist extension, a deep-learning-based model for nuclei detection, was applied to the DAPI channel of fluorescent images to calculate the amount of cells in ROI. For each image, the mean intensity value representing cleaved caspase 3 (cCas3) was obtained. The mean fluorescence intensity was normalized to either number of cell detections or the area. The percentage of cCas3-positive areas was assessed by adjusting software built-in pixel classifier. 29 images were analyzed for experimental group 1 (Figure 2E), 8 images for group 2 (Figure 3E), and 28 images for group 3 (Figure 6G).
**FIGURE 2:** *Development of 3D degenerative NP microtissue (µT) model. (A) NPµT was formed for 14d in NHG or DDD condition using NP spheroids (NPS) pre-cultured in healthy (NHG) condition for 14d. After 14d in NHG or DDD condition, NPµT was analyzed (B) biochemically by quantifying glycosaminoglycans (GAG) and collagens (HYP) (n = 6, mean ± SD, *p < 0.05, ANOVA). (C) Catabolic shift in degenerative NPµT was assessed by measuring IL-8 release in culture medium on day 3, 7, 11 and 14. Dashed line represents IL-8 release in NHG control (no IL-8 detected) (n = 5, mean ± SD, *p < 0.05 vs. NHG ctrl., ANOVA). (D) NPµT formed in (D1) NHG or (D2) DDD condition was stained with SafO/FG ((D1’’) zoomed D1 showing healthy cells producing proteoglycans, (D2’’) zoomed D2 showing cells with moderate proteoglycan production) and immunofluorescence ((D1’’-2’’) DAPI and (D1’’’-D2’’’) cleaved caspase 3 (cCas3) visualising nuclei (in blue) and apoptotic cells (in red), respectively) (n =6). (E) Quantification of cCas3 staining in NPµT (n = 6, mean ± SD, *p < 0.05, ANOVA).* **FIGURE 3:** *Assessing the responses of NC in 3D in vitro degenerative NPµT model. (A) NP spheroids (NPS) were formed for 2 weeks and NC spheroids (NCS) were formed for 3 days in healthy condition. After pooling NPS with either NCS or NC cell suspension (NC susp.), the aggregates were cultured for 2 weeks in either healthy or degenerated condition. Afterwards, (B) Glycosaminoglycans (GAG) and hydroxyproline (HYP) were quantified. Dashed lines represent value of healthy control. Asteriks indicate significance compared to healthy control (NP: n = 6, NC: n = 3, mean ± SD, *p < 0.05 vs. NHG ctrl., ANOVA). (C) Catabolic shift was assessed by measuring IL-8 release on day 3, 7, and 14. No IL-8 release was detected in healthy condition (NP: n = 6, NC: n = 3, mean ± SD, *p < 0.05, ANOVA). (D) SafO/FG staining of NPµT containing NCS accumulating proteoglycans (D1) and (D2) less/no proteoglycans. Immunofluorescence staining visualising (D’) GFP transduced NC, (D’’) nuclei (DAPI), and (D’’’) apoptotic cells (cleaved caspase 3 (cCas3)). Black and white squares depict zoomed-in subsections of (D1’’’) less apoptotic and (D2’’’) more apoptotic NCS (NP: n = 1, NC: n = 3). (E) Semi-quantification of cCas3 staining in D1’’’ and D2’’’ (NP: n = 1, NC: n = 3, mean ± SD, *p < 0.05, Mann-Whitney U test).*
## 2.4.6 Cell viability assay
The cell viability of NCS cultured in DDD mimicking conditions was assessed using the CellTiter-Glo Luminescent Cell Viability Assay (Promega, G7570) on day 0, 3, and 7 according to the manufacturer’s protocol. Briefly, the microplate with NCS was left at room temperature (RT) for 30 min prior to examination. Four empty wells were filled with 100 µl of the corresponding medium to obtain a value of background control. CellTiter-Glo Reagent and medium (ratio 1:1) were added to each well and mixed to induce cell lysis. Afterwards, the plate was incubated for 1 hour at RT to stabilize the luminescent signal. Luminescence was recorded using SPARK Multimode-Microplate Reader (Tecan, Switzerland). Luminescence signal proportional to cellular ATP generation was expressed in relative light units (RLU) and normalized to control (DDD ctr) (chapter 4.3.2).
## 2.5 Statistical analysis
All data were analyzed using GraphPad Prism software ver. 8.0.1 (GraphPad Software, Inc., La Jolla, Ca) and reported as mean ± SD. The following tests were used to assess the statistical significance: for normally distributed data, analysis of variance (ANOVA) followed by Sidak’s post hoc test (group analysis); for non-normally distributed data obtained from semi-quantitative image analysis, a non-parametric Mann-Whitney U test. Numerical values of probability (p) smaller than 0.05 were considered as statistically significant.
## 3.1 Development of 3D in vitro degenerative nucleus pulposus microtissue model
To recapitulate the conditions during early stage IVD degeneration such as the pro-inflammatory/catabolic shift, onset of ECM degradation and mild apoptosis (Dou et al., 2021), pre-formed spheroids consisting of NP cells (NPS) were pooled to form NPµT at NHG (control group) or DDD (early stage IVD degeneration group) for 14d (Figure 2A). This 2-stage NPµT formation allows for generation of larger microtissues with low risk of necrotic core formation. This configuration also permits straightforward incorporation of therapeutic cells into 3D NP microenvironment with cell-produced ECM and factors playing a key role in NP degeneration such as low nutrition, acidity, hypoxia and pro-inflammatory cytokines. Histological and quantitative analysis revealed that NPµT formed in DDD microenvironment accumulated significantly less GAG and collagens compared to healthy NPµT (Figure 2B, D). NP cells also experienced catabolic shift (as demonstrated by enhanced release of IL-8), which confirmed the presence of the harsh DDD microenvironment (Figure 2C). Even though apoptotic cells were detected in subsections of the NPµT cultured in DDD microenvironment (Figure 2D‴), semi-quantification of the cleaved caspase 3 staining on the whole section revealed no significant upregulation of apoptosis in NPµT cultured in DDD compared to control (Figure 2E).
## 3.2 Assessing the performance of nasal chondrocytes in NP microtissue model
The NPµT model was designed to study the long-term effects of DDD microenvironment on putative therapeutic cells. As promising cell type for NP repair, NC were implemented in the model (Vedicherla and Buckley, 2017; Gay et al., 2019; Gryadunova et al., 2021). To evaluate possible differences in responses to DDD microenvironment, NC were incorporated in the model either as cell suspension (NPµT + NC cell suspension) or spheroids (NPµT + NCS). *To* generate the model, NPS were pooled with NCS or NC suspension and cultured for 2 weeks either in healthy (NHG) control condition, consisting of normoxia ($20\%$ O2) and high glucose (4.5 mg/ml), or in the aforementioned degenerative (DDD) condition (Figure 3A). Accumulation of ECM components within degenerative NPµT co-cultures was compared to NHG control and between NCS and NC suspension groups (Figure 3B). GAG and collagen content in NPµT + NCS group did not significantly differ from healthy control, while GAG and collagen in NPµT + NC suspension group was significantly reduced. No significant differences in GAG and collagen between NPµT + NCS and NPµT + NC cell suspension were detected, although trends towards higher ECM content in NPµT + NCS were observed. Addition of NCS to degenerative NPµT tended to increase GAG content, compared to degenerative NPµT only. Catabolic shift was measured by the release of IL-8, typical for DDD (Krock et al., 2019). On day 3 a trend towards reduced release of IL-8 by the NPµT + NCS (9 ± 5 ng/mL) and NC cell suspension (29 ± 17 ng/mL) was detected compared to NPµT (194 ± 47 ng/mL) (Figure 3C). On day 7 the IL-8 released by NPµT + NCS was significantly lower (280 ± 117 ng/mL) compared to NPµT + NC suspension (816 ± 488 ng/mL) but no difference could be shown on day 14. In NHG control, no IL-8 release was detected at any time point. As the incorporation of NCS into degenerative NPµT tended to increase GAG content, further (immuno-) histological analysis of NPµT + NCS group was performed. The staining indicated that NCS within the DDD microenvironment could accumulate proteoglycans (Figure 3D1) however not all of them to the same extent (Figure 3D2). cCas3 quantification in NPµT + GFP-NCS showed high presence of cells with apoptotic traits within GAG-negative areas (Figures 3D’’’2, E). To a limited extent, NCS survived and accumulated ECM compared to ECM content in NCS before implementation in the NPµT model (Supplementary Figure S1).
The 3D in vitro degenerative NPµT model was developed and tested using promising therapeutic cell type (NC). NC could be distinguished from NP cells within the model, allowing to explore the responses of both cell types to DDD microenvironment as well as the fate of the therapeutic NC. Furthermore, the model could be used to assess two cell configurations (cell suspension and spheroids). Our data suggested that even if NCS in the degenerated NPµT acquired apoptotic traits, their overall performance in the DDD microenvironment was superior to the one of NC suspension. One possibility to improve NCS performance for clinical use, while keeping regulatory requirements feasible, is to use pre-conditioning with FDA approved drugs. To optimize NCS function within the DDD microenvironment, the drugs should equip NC with anti-inflammatory and anti-catabolic resistance and/or enhance their anabolic activity.
## 3.3 Preconditioning of nasal chondrocytes to optimize their function in the nucleus pulposus
Cell preconditioning using hypoxia, inflammatory mediators, pharmacological drugs and chemical agents has been investigated to improve cell function, survival, and therapeutic efficacy in IVD field (Noronha et al., 2019). In order to facilitate clinical translation, we selected pre-conditioning of therapeutic NC using FDA approved drugs with anti-inflammatory, anti-catabolic, and/or anabolic activities, namely GDF-5, IL-1Ra, metformin, amiloride, and celecoxib. GDF-5 injection was shown to increase ECM accumulation in the IVD in clinical settings and IL-1Ra was reported to reduce anti-inflammatory and anti-catabolic factor release in vitro, ex vivo, and in vivo, partly by inhibiting the p38 MAPK activity (Le Maitre et al., 2007a; Studer et al., 2007). Celecoxib was shown to aid in restoring IVD integrity (Tellegen et al., 2018), amiloride could prevent acid-induced decrease in cell proliferation and ECM gene expression (Liu et al., 2017), and metformin was used to induce inflammation resistant phenotypes (Das et al., 2019; Park et al., 2019). A 3-stage experiment was designed i) to identify compounds/concentrations active in NC (2D screening), ii) to study the effects of pre-conditioning on NCS cultured in DDD microenvironment (3D pre-conditioning), and finally iii) to evaluate the function of pre-conditioned NCS using the compounds selected in i&ii within the degenerative NPµT model.
## 3.3.1 Effects of compounds on nasal chondrocytes (2D drug screening)
NC were pre-treated with increasing concentrations of GDF-5, IL-1Ra, metformin, celecoxib, and amiloride for 3 h. Afterwards the drugs were removed, DDD mimicking condition was introduced to the cells for further 24 h, and the expression of anabolic (aggrecan: ACAN; collagen type II A1: COL2A1) and catabolic genes (matrix metallopeptidase 13: MMP-3; interleukin 6: IL-6) were analyzed. Metformin, amiloride and celecoxib pre-treated NC showed no significant modulation of tested genes (Supplementary Figure S2). GDF-5 (100 ng/mL) pre-treatment significantly upregulated ACAN and showed trend towards increased COL2A1 expression in the NC cultured in DDD mimicking condition (Figure 4A). In IL-1Ra (500 ng/mL) pre-treated NCS, a trend towards downregulated IL-6 and MMP-3 gene expression was detected (Figure 4B). Therefore, both GDF-5 (100 ng/mL) and IL-1Ra (500 ng/mL) were considered suitable for pre-conditioning of NCS.
**FIGURE 4:** *2D drug screening: Effects of drug pre-treatment on NC in DDD-mimicking conditions. Gene expression (relative to control) of anabolic genes (ACAN, COL2A1) and catabolic genes (MMP-3, IL-6) in NC pre-treated with (A) GDF-5 or (B) IL-1Ra. NHG: healthy condition, DDD: DDD mimicking condition (n = 3, mean ± SD, *p < 0.05, ANOVA).*
## 3.3.2 Preconditioning of nasal chondrospheres with IL-1Ra and GDF-5
NCS were pre-conditioned during their formation with IL-1Ra (500 ng/mL, putative anti-inflammatory activity), GDF-5 (100 ng/mL, putative anabolic activity), or the combination of both, and introduced to DDD mimicking conditions for 7 days. Pro-inflammatory/catabolic (IL-8, MMP-3) and anabolic (COL2A1, COL1A1, ACAN) gene expression, IL-8 release, GAG and total collagen content in the NCS were assessed on day 0 (before implementing in DDD condition), day 3 and 7. Furthermore, NCS viability was determined, with no significant difference between pre-treated NCS and DDD control.
## 3.3.2.1 IL-1Ra pre-conditioning
IL-1Ra preconditioning of NCS did not influence the expression of tested genes on day 0 (Figures 5A, C). In DDD condition, IL-1Ra pre-conditioned NCS significantly downregulated IL-8 and MMP-3 on day 3 and tended to reduce it on day 7 (Figure 5A). Pre-conditioning of NCS with IL-1Ra significantly downregulated the release of IL-8 protein on day 3 and on day 7 (trend), confirming gene expression data (Figure 5B). Although no significant effects on anabolic genes were observed (Figure 5C), IL-1Ra pre-conditioned NCS contained significantly more GAG (but not collagen) on day 7 compared to days 0 and 3 (Figure 5D), indicating that these NCS could accumulate ECM in DDD condition possibly via anti-catabolic action of IL-1Ra.
**FIGURE 5:** *(Continued)*
## 3.3.2.2 GDF-5 pre-conditioning
Significant upregulation of ACAN expression (Figure 5C) and a trend towards downregulated IL-8 and MMP-3 expression (Figure 5A) were observed on day 0, before the GDF-5 pre-treated NCS were implemented in DDD condition. In DDD condition, GDF-5 pre-conditioning of NCS had no effect on the expression of tested genes (Figures 5A, C), nor IL-8 release (Figure 5B) or ECM accumulation (Figure 5D).
## 3.3.2.3 IL-1Ra and GDF-5 pre-conditioning
At day 0, significant upregulation of COL1A1 and ACAN expression (Figure 4C) and reduced IL-8 and MMP-3 expression (not significant) were detected, likely due to the effects of GDF-5. In DDD condition, IL-1Ra + GDF-5 pre-conditioning significantly downregulated IL-8 and MMP3 genes in day 3 NCS (likely due to IL-1Ra) (Figure 4A) and tended to upregulate COL2A1 on days 3 and 7, possibly as a result of combination treatment (Figure 4C). The significant downregulation of IL-8 release from IL-1Ra + GDF-5 NCS on day 3 and on day 7 (trend) confirmed gene expression data, as expected effects of IL-1Ra (Figure 5B). Despite expectations, ECM accumulation in IL-1Ra + GDF-5 pre-conditioned NCS during 7 days was not significantly different from the corresponding controls (Figure 5E).
Altogether, GDF-5 pre-conditioning exerted some anabolic responses (i.e., increased ACAN expression). However, they were lost during NCS culture in DDD condition. IL-1Ra pre-conditioning downregulated pro-inflammatory (the expression of IL-8 on gene and protein level) and catabolic traits (reduced MMP-3 gene expression), which could allow for GAG accumulation in DDD condition (Risbud and Shapiro, 2014). Histological analysis also indicated superior effect on structural and cellular integrity when NCS was pre-conditioned with IL-1Ra (day 3 and 7) compared to control (Figure 5F). Since it was feasible to achieve anabolic effects in NCS without GDF-5 pre-conditioning, we proceeded to test the performance of IL-1Ra pre-conditioned NCS within the degenerative 3D in vitro NPµT model.
## 3.3.3 Performance of NCS pre-conditioned with IL-1Ra within degenerative NP microtissue model
NCS or IL-1Ra pre-conditioned NCS (pNCS) were implemented in the NPµT model and cultured for 2 weeks in DDD microenvironment. Before implementation into the microtissue model, ECM content in NCS and pNCS was comparable (Supplementary Figure S3). Interestingly, throughout their co-culture in the NPµT model, pNCS released IL-1Ra up to day 7, possibly as a result of its entrapment in newly generated ECM of pNCS (Martino et al., 2014; Tan et al., 2021) (Figure 6C). During co-culture in DDD condition, the NPµT + pNCS released significantly less IL-8 on day 3 and 7 compared to NPµT + NCS without pre-conditioning (Figure 6A). Similar trend was observed for MMP-13 release (Figure 6B). However, on day 14, the amount of released IL-8 became comparable between NPµT + pNCS and NPµT + NCS, which could be explained by significant decrease of IL-1Ra release from pNCS at this timepoint (Figure 6C).
**FIGURE 6:** *Implementation of pre-conditioned NCS (pNCS) in NPµT model. (A) IL-1Ra, (B) MMP-13, (C) IL-1Ra release from NPµT + NCS/pNCS was assessed on day 3, 7, and 14 (NP: n = 1, NC: n = 3, mean ± SD, *p < 0.05, ANOVA). (D) Glycosaminoglycan (GAG) and hydroxyproline (HYP) quantification, normalised to NPµT + NCS (NP: n = 1, NC: n = 3, mean ± SD, *p < 0.05, ANOVA). SafO/FG staining of NPµT containing (E) NCS and (F) pNCS, visualising proteoglycans. Immunofluorescence staining visualising (E’&F’) GFP transduced NC, (E’’&F’’) nuclei (DAPI), and (E’’’&F’’’) apoptotic cells (cleaved caspase 3, cCas3) within NPµT + NCS/pNCS. (G) Quantification of cCas3 staining (NP: n = 1, NC: n = 3, mean ± SD, *p < 0.05, ANOVA).*
No significant difference in GAG and total collagen content was detected between NPµT + pNCS and NPµT + NCS (Figure 6D) after 2 weeks culture in DDD condition. Consistently, no difference in GAG content could be observed histologically (Figures 6E, F). Regarding NCS viability, no difference in cCas3 staining intensity could be visualized (Figures 6E’’’, F’’’). However, a trend towards reduced apoptosis within NPµT containing pNCS could be seen when the images were semi-quantified (Figure 6G).
Although the biological half-life of IL-1Ra protein has been reported to be 4–6 h (Akash et al., 2013), NPµT co-cultured with IL-1Ra pre-conditioned NCS exhibited superior anti-inflammatory/anti-catabolic properties, compared to NPµT with non-preconditioned NCS. However, IL-1Ra pre-conditioning was not sufficient to significantly increase the ECM content in NPµT, which could have been the consequence of reduced IL-1Ra release/activity in time, together with the presence of inflamed NP cells.
## 4 Discussion
We developed a new 3D in vitro degenerative NPµT model allowing to investigate the responses of therapeutic cells (in this case NC) to DDD microenvironment. Our NPµT model contains hypoxia, acidity, low-grade inflammation as well as 3D degenerated human NP cells with cell-made matrix, pre-stimulated with DDD condition. This model exhibits important features of early-stage IVD degeneration including degrading ECM and catabolic shift (Buckley et al., 2018; Ju et al., 2020). Therapeutic cells can be incorporated into the NPµT model either as cell suspensions or spheroids, acquiring/maintaining 3D organization of target NP tissue. In this model, NC cell suspension showed inferior GAG and collagen accumulation and an increased catabolic shift compared to NCS, supporting the use of NCS for clinical IVD repair. As spheroidal organization did not completely recover early-stage DDD signs, we attempted to improve the performance of NCS by drug pre-conditioning during their formation. From five tested candidates (amiloride, celecoxib, IL-1Ra, metformin, GDF-5), a clinically available anti-inflammatory drug IL-1Ra was evaluated as the most promising. Pre-conditioning of NCS with IL-1Ra further downregulated pro-inflammatory and catabolic responses, which could allow for GAG accumulation in DDD-mimicking condition. Moreover, pre-conditioned NCS exhibited long-term IL-1Ra release associated with reduced IL-8 and MMP13, which underlines the importance of anti-inflammatory pre-conditioning for IVD repair.
In patients, early stage DDD could still be restored using cell therapy (Buckley et al., 2018). An ideal in vitro model of early-stage DDD should accurately simulate the target tissue, by using NP cells from a patient with an appropriate Pfirrmann grade (Schwarzer et al., 1995; Luoma et al., 2000), as well as degenerative microenvironmental cues such as low glucose and oxygen levels, pH, and low-grade inflammation (Buckley et al., 2018). 3D alginate beads and pellet cultures are commonly used in IVD research to study the interaction of NP cells with a therapeutic cell source of interest in vitro (Gantenbein-Ritter and Chan, 2012; Ouyang et al., 2017). In alginate bead co-cultures, expanded NP and cells of interest are mixed and then differentiated together within the beads, while the alginate represents an exogenous ECM (Arkesteijn et al., 2015; Naqvi and Buckley, 2015). Alternatively, a transwell system is used for indirect co-cultures, to study paracrine interactions between both cell types encapsulated within alginate beads separately (Stoyanov et al., 2011; Song et al., 2015). The pellet culture model overcomes several disadvantages of alginate beads such as lack of reproducibility and uniformity of quality/size of the microspheres (Lee et al., 2001). The pellet culture system was extensively used for direct co-culture studies, where the therapeutic cells were mixed together with the expanded NP cells and centrifuged in a tube to form a pellet, thus differentiated together thereafter (Vadala et al., 2008; Chen et al., 2009; Svanvik et al., 2010). However, the simple pellet culture model does not accurately mimic the early stage in vivo DDD, thus is not ideal for investigating/testing cell therapies.
In clinical settings, the therapeutic cells are introduced to an already mature degenerative NP tissue containing differentiated NP cells and NP cell-produced matrix. Furthermore, the therapeutic cells create initial contacts with the ECM of the NP tissue rather than directly with the NP cells (Thorpe et al., 2018). Therefore, in order to mimic this situation in vitro, we designed new 3D degenerative NPµT model. To simulate clinical early-stage DDD, we first re-differentiated NP cells in spheroidal organization and allowed them to accumulate cell-produced ECM, to which the therapeutic cells could be introduced at later stage. We also stimulated the resulting NPS with hypoxia, low glucose, acidity, and low-grade inflammation, to recapitulate the chemical properties of degenerated NP (Buckley et al., 2018). Pooling the NPS with the therapeutic cells allows to create NP niches where the cells of interest could integrate into. Furthermore, the NPµT model also contains low nutrition, acidity, hypoxia and low-grade inflammation, thus can be used to study the long-term effect of degenerative NP microenvironment on therapeutic cells. As such, this straightforward yet sufficiently complex NPµT model overcomes limitations of currently used models.
For early stage IVD repair, therapeutic cells have to reside, survive, resist inflammation and produce ECM within the degenerative NP tissue. In previous studies, NC showed superior viability in simulated DDD microenvironment over commonly used MSCs and AC, thus represent a promising cell source for IVD repair (Vedicherla and Buckley, 2017; Gay et al., 2019; Borrelli and Buckley, 2020; Gryadunova et al., 2021). In degenerative IVD condition, NC were reported to produce a ratio of low collagen to high GAG content whereas AC produce less favorable high collagen ratios (Vedicherla and Buckley, 2017). We have previously demonstrated the potential of NC as spheroids (NCS) for IVD repair (Gryadunova et al., 2021). However, these studies were performed partially in absence of the NP cells, which could modulate the behavior of NC, or only the short-term DDD mimicking conditions were applied (Gay et al., 2019; Gryadunova et al., 2021). In the current study we implemented NC either as NCS or NC suspension in the degenerative NPµT model. In the NPµT, NCS survived and produced proteoglycans only partially, possibly due to the inflamed microenvironment which is known to affect cell viability and ECM degradation (Wang et al., 2020).
We have also shown that pre-conditioning of NCS is necessary to improve their ability to counteract inflammation, increase cell survival, and/or accumulate ECM. 2D drug screening revealed two promising candidates, IL-1Ra (anti-inflammatory, (Le Maitre et al., 2006; Le Maitre et al., 2007a)) and GDF-5 (anabolic, (Le Maitre et al., 2009b; Colombier et al., 2016)), with GDF-5 being eliminated at next stage (3D) due to its inferior effects in NCS pre-conditioning tests. We expected anabolic effect of GDF-5 on NCS, as benefits of GDF-5 in cartilage and IVD repair are well described (Li et al., 2004; Wang et al., 2004; Chujo et al., 2006; Cui et al., 2008; Liang et al., 2010; Guo et al., 2021; Sun et al., 2021). IL-1Ra preconditioned NCS were further implemented into the NPµT model. Overall IL-1Ra preconditioning inhibited pro-inflammatory and catabolic responses of NCS (IL-8, MMP-3, MMP-13), which suggested IL-1Ra interference with MAPK/ERK and NFκB signaling pathways via binding to the IL-1 receptor, consistent with literature (Le Maitre et al., 2006; Le Maitre et al., 2007a; Risbud and Shapiro, 2014). While IL-1Ra promoted NCS GAG accumulation in DDD mimicking condition, it failed to produce similar significant effects in NPµT model. This result indicates that degenerated NP microtissue indeed influences the performance of therapeutic cells (compared to only chemical DDD-mimicking condition), thus NP microtissue should be present in vitro during preclinical therapeutic testing.
The biological half-life of IL-1Ra protein has been reported to be 4–6 h (Akash et al., 2013) which is too short for clinical IVD repair. Several approaches have been taken to prolong the half-life of IL-1Ra such as fusing IL-1Ra with proteins (elastin-like polypeptides, human serum albumin, albumin domain antibodies) or by combining it with biodegradable polymers (poly (D,L-lactidide-co-glycolide), PLGA, polyethylene glycol (PEG), thermo-reversible gel) to prolong its steady-state sustained release at the site of administration (Akash et al., 2013). However, another approach could be to entrap IL-1Ra within the ECM of the spheroids by pre-conditioning NCS during their formation time, as in our study. The entrapped IL-1Ra could be slowly released (Joshi et al., 2018), supporting cells to counteract inflammation for longer time periods. In our NPµT model, the anti-inflammatory protection by IL-1Ra lasted up to 14 days and appeared to inversely correlate with the release of IL-8, suggesting that the IL-1Ra entrapped within the NCS was consumed.
Currently our NPµT model has several limitations that should be addressed in the future. We used a ratio of 1:1 ratio for NP/NC cells based on literature (Strassburg et al., 2010; Tao et al., 2013; Dai et al., 2014; Ouyang et al., 2017). However, it was also reported that a ratio of 75:25 NP/MSC cells leads to optimized MSC differentiation towards NP phenotype (Richardson et al., 2006), thus it still has to be determined which ratio is optimal for co-culture studies of NP cells with therapeutic cells in NPµT model. Another limitation is that our microtissues were cultured in static conditions. Applying compressive loading to microtissues might even better mimic early stage IVD degeneration and increase cell survival due to enhanced nutrient diffusion and waste removal within the NPµT model.
The 3D in vitro degenerative NPµT model aims to substitute the use of current alginate and pellet culture systems for preclinical in vitro investigations of IVD cell therapies. The model allows to study the survival and performance of (primed) therapeutic cells within the NP microenvironment mimicking early stage IVD degeneration. An orthotopic animal model will be required to compare the function of therapeutic cells in the NPµT model vs. in vivo. As long-term follow-up, a sheep model will be used to evaluate whether IL-1Ra preconditioning of NCS would provide durable IVD repair or NCS will have to be combined with other strategies, e.g. enabling further increase of NCS anabolic activity.
## 5 Conclusion
Currently, there is no standardized 3D in vitro NP model available to study the responses of therapeutic cells to DDD microenvironment. In this study we developed a 3D model which includes differentiated NP cells, cell-produced matrix, and environmental cues associated with DDD microenvironment. By implementing NC within the model, we showed that the NC in spheroidal organization are superior to NC in suspension and have potential to survive and accumulate ECM components within DDD microenvironment. Furthermore, we provided evidence after testing five FDA approved drugs that IL-1Ra pre-conditioning of NCS provides anti-inflammatory and anti-catabolic effects. In future studies, the survival and function of IL-1Ra pre-conditioned NCS will be investigated within ex vivo bovine IVD explants cultured under loading and in an animal model.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by EKNZ-2015305, University Hospital Basel. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JK: Data acquisition, analysis, investigation, methodology, writing—original draft; AG: Data analysis, funding acquisition, writing—review and editing; RS: Data acquisition, data analysis, writing—review and editing; MHPG-D: Methodology, writing—review and editing; BD: Methodology; KP: Resources, funding acquisition, writing—review and editing; IM: Resources, funding acquisition, supervision, writing—review and editing; SS: Resources, supervision; AB: Conceptualization, resources, supervision, writing—review and editing; OK: Conceptualization, data acquisition, analysis, investigation, funding acquisition, supervision, writing—review and editing; AM: Funding acquisition, resources, supervision, writing—review and editing.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fbioe.2023.1119009/full#supplementary-material
## References
1. Akash M. S., Rehman K., Chen S.. **IL-1Ra and its delivery strategies: Inserting the association in perspective**. *Pharm. Res.* (2013) **30** 2951-2966. DOI: 10.1007/s11095-013-1118-0
2. Arkesteijn I. T., Smolders L. A., Spillekom S., Riemers F. M., Potier E., Meij B. P.. **Effect of coculturing canine notochordal, nucleus pulposus and mesenchymal stromal cells for intervertebral disc regeneration**. *Arthritis Res. Ther.* (2015) **17** 60. DOI: 10.1186/s13075-015-0569-6
3. Bach F. C., Laagland L. T., Grant M. P., Creemers L. B., Ito K., Meij B. P.. **Link-N: The missing link towards intervertebral disc repair is species-specific**. *PLoS One* (2017) **12** e0187831. DOI: 10.1371/journal.pone.0187831
4. Bankhead P., Loughrey M. B., Fernandez J. A., Dombrowski Y., McArt D. G., Dunne P. D.. **QuPath: Open source software for digital pathology image analysis**. *Sci. Rep.* (2017) **7** 16878. DOI: 10.1038/s41598-017-17204-5
5. Binch A. L. A., Fitzgerald J. C., Growney E. A., Barry F.. **Cell-based strategies for IVD repair: Clinical progress and translational obstacles**. *Nat. Rev. Rheumatol.* (2021) **17** 158-175. DOI: 10.1038/s41584-020-00568-w
6. Borrelli C., Buckley C. T.. **Injectable disc-derived ECM hydrogel functionalised with chondroitin sulfate for intervertebral disc regeneration**. *Acta Biomater.* (2020) **117** 142-155. DOI: 10.1016/j.actbio.2020.10.002
7. Buckley C. T., Hoyland J. A., Fujii K., Pandit A., Iatridis J. C., Grad S.. **Critical aspects and challenges for intervertebral disc repair and regeneration-Harnessing advances in tissue engineering**. *JOR Spine* (2018) **1** e1029. DOI: 10.1002/jsp2.1029
8. Chen J. W., Li B., Yang Y. H., Jiang S. D., Jiang L. S.. **Significance of hypoxia in the physiological function of intervertebral disc cells**. *Crit. Rev. Eukaryot. Gene Expr.* (2014) **24** 193-204. DOI: 10.1615/critreveukaryotgeneexpr.2014010485
9. Chen S., Emery S. E., Pei M.. **Coculture of synovium-derived stem cells and nucleus pulposus cells in serum-free defined medium with supplementation of transforming growth factor-beta1: A potential application of tissue-specific stem cells in disc regeneration**. *Spine (Phila Pa 1976)* (2009) **34** 1272-1280. DOI: 10.1097/brs.0b013e3181a2b347
10. Choi E. H., Park H., Park K. S., Park K. S., Kim B. S., Han I. B.. **Effect of nucleus pulposus cells having different phenotypes on chondrogenic differentiation of adipose-derived stromal cells in a coculture system using porous membranes**. *Tissue Eng. Part A* (2011) **17** 2445-2451. DOI: 10.1089/ten.tea.2010.0625
11. Chujo T., An H. S., Akeda K., Miyamoto K., Muehleman C., Attawia M.. **Effects of growth differentiation factor-5 on the intervertebral disc-**. *Spine (Phila Pa 1976)* (2006) **31** 2909-2917. DOI: 10.1097/01.brs.0000248428.22823.86
12. Colombier P., Clouet J., Boyer C., Ruel M., Bonin G., Lesoeur J.. **TGF-β1 and GDF5 act synergistically to drive the differentiation of human adipose stromal cells toward**. *Stem Cells* (2016) **34** 653-667. DOI: 10.1002/stem.2249
13. Cui M., Wan Y., Anderson D. G., Shen F. H., Leo B. M., Laurencin C. T.. **Mouse growth and differentiation factor-5 protein and DNA therapy potentiates intervertebral disc cell aggregation and chondrogenic gene expression**. *Spine J.* (2008) **8** 287-295. DOI: 10.1016/j.spinee.2007.05.012
14. Dai J., Wang H., Liu G., Xu Z., Li F., Fang H.. **Dynamic compression and co-culture with nucleus pulposus cells promotes proliferation and differentiation of adipose-derived mesenchymal stem cells**. *J. Biomech.* (2014) **47** 966-972. DOI: 10.1016/j.jbiomech.2014.01.023
15. Das V., Kroin J. S., Moric M., McCarthy R. J., Buvanendran A.. **AMP-activated protein kinase (AMPK) activator drugs reduce mechanical allodynia in a mouse model of low back pain**. *Reg. Anesth. Pain Med.* (2019) **44** 1010-1014. DOI: 10.1136/rapm-2019-100839
16. Deyo R. A., Mirza S. K.. **Trends and variations in the use of spine surgery**. *Clin. Orthop. Relat. Res.* (2006) **443** 139-146. DOI: 10.1097/01.blo.0000198726.62514.75
17. Dou Y., Sun X., Ma X., Zhao X., Yang Q.. **Intervertebral disk degeneration: The microenvironment and tissue engineering strategies**. *Front. Bioeng. Biotechnol.* (2021) **9** 592118. DOI: 10.3389/fbioe.2021.592118
18. Dowdell J., Erwin M., Choma T., Vaccaro A., Iatridis J., Cho S. K.. **Intervertebral disk degeneration and repair**. *Neurosurgery* (2017) **80** S46-S54. DOI: 10.1093/neuros/nyw078
19. Eisenstein S. M., Balain B., Roberts S.. **Current treatment options for intervertebral disc pathologies**. *Cartilage* (2020) **11** 143-151. DOI: 10.1177/1947603520907665
20. Evashwick-Rogler T. W., Lai A., Watanabe H., Salandra J. M., Winkelstein B. A., Cho S. K.. **Inhibiting tumor necrosis factor‐alpha at time of induced intervertebral disc injury limits long‐term pain and degeneration in a rat model**. *JOR Spine* (2018) **1** e1014. DOI: 10.1002/jsp2.1014
21. Frapin L., Clouet J., Delplace V., Fusellier M., Guicheux J., Le Visage C.. **Lessons learned from intervertebral disc pathophysiology to guide rational design of sequential delivery systems for therapeutic biological factors**. *Adv. Drug Deliv. Rev.* (2019) **149-150** 49-71. DOI: 10.1016/j.addr.2019.08.007
22. Frapin L., Clouet J., Chedeville C., Moraru C., Samarut E., Henry N.. **Controlled release of biological factors for endogenous progenitor cell migration and intervertebral disc extracellular matrix remodelling**. *Biomaterials* (2020) **253** 120107. DOI: 10.1016/j.biomaterials.2020.120107
23. Fulco I., Miot S., Haug M. D., Barbero A., Wixmerten A., Feliciano S.. **Engineered autologous cartilage tissue for nasal reconstruction after tumour resection: An observational first-in-human trial**. *Lancet* (2014) **384** 337-346. DOI: 10.1016/s0140-6736(14)60544-4
24. Gantenbein-Ritter B., Chan S. C.. **The evolutionary importance of cell ratio between notochordal and nucleus pulposus cells: An experimental 3-D co-culture study**. *Eur. Spine J.* (2012) **21** S819-S825. DOI: 10.1007/s00586-011-2026-9
25. Gay M. H., Mehrkens A., Rittmann M., Haug M., Barbero A., Martin I.. **Nose to back: Compatibility of nasal chondrocytes with environmental conditions mimicking a degenerated intervertebral disc**. *Eur. Cell. Mater* (2019) **37** 214-232. DOI: 10.22203/ecm.v037a13
26. Goupille P., Jayson M. I., Valat J. P., Freemont A. J.. **Matrix metalloproteinases: The clue to intervertebral disc degeneration?**. *Spine (Phila Pa 1976)* (1998) **23** 1612-1626. DOI: 10.1097/00007632-199807150-00021
27. Gruber H. E., Hoelscher G. L., Ingram J. A., Bethea S., Norton H. J., Hanley E. N.. **Production and expression of RANTES (CCL5) by human disc cells and modulation by IL-1-beta and TNF-alpha in 3D culture**. *Exp. Mol. Pathol.* (2014) **96** 133-138. DOI: 10.1016/j.yexmp.2014.01.002
28. Gryadunova A., Kasamkattil J., Gay M. H. P., Dasen B., Pelttari K., Mironov V.. **Nose to spine: Spheroids generated by human nasal chondrocytes for scaffold-free nucleus pulposus augmentation**. *Acta Biomater.* (2021) **134** 240-251. DOI: 10.1016/j.actbio.2021.07.064
29. Guo Y., Meng Y., Liu H., Wang B., Ding C., Rong X.. **Acid-sensing ion channels mediate the degeneration of intervertebral disc via various pathways-A systematic review**. *Channels (Austin)* (2019) **13** 367-373. DOI: 10.1080/19336950.2019.1664038
30. Guo S., Cui L., Xiao C., Wang C., Zhu B., Liu X.. **The mechanisms and functions of GDF-5 in intervertebral disc degeneration**. *Orthop. Surg.* (2021) **13** 734-741. DOI: 10.1111/os.12942
31. Homicz M. R., Schumacher B. L., Sah R. L., Watson D.. **Effects of serial expansion of septal chondrocytes on tissue-engineered neocartilage composition**. *Otolaryngol. Head. Neck Surg.* (2002) **127** 398-408. DOI: 10.1067/mhn.2002.129730
32. Joshi N., Yan J., Levy S., Bhagchandani S., Slaughter K. V., Sherman N. E.. **Towards an arthritis flare-responsive drug delivery system**. *Nat. Commun.* (2018) **9** 1275. DOI: 10.1038/s41467-018-03691-1
33. Ju D. G., Kanim L. E., Bae H. W.. **Intervertebral disc repair: Current concepts**. *Glob. Spine J.* (2020) **10** 130S-6S. DOI: 10.1177/2192568219872460
34. Kasamkattil J., Gryadunova A., Martin I., Barbero A., Scharen S., Krupkova O.. **Spheroid-based tissue engineering strategies for regeneration of the intervertebral disc**. *Int. J. Mol. Sci.* (2022) **23** 2530. DOI: 10.3390/ijms23052530
35. Kim J. S., Ellman M. B., Yan D., An H. S., Kc R., Li X.. **Lactoferricin mediates anti-inflammatory and anti-catabolic effects via inhibition of IL-1 and LPS activity in the intervertebral disc**. *J. Cell. Physiol.* (2013) **228** 1884-1896. DOI: 10.1002/jcp.24350
36. Klawitter M., Quero L., Klasen J., Liebscher T., Nerlich A., Boos N.. **Triptolide exhibits anti-inflammatory, anti-catabolic as well as anabolic effects and suppresses TLR expression and MAPK activity in IL-1β treated human intervertebral disc cells**. *Eur. Spine J.* (2012) **21** S850-S859. DOI: 10.1007/s00586-011-1919-y
37. Krock E., Millecamps M., Anderson K. M., Srivastava A., Reihsen T. E., Hari P.. **Interleukin-8 as a therapeutic target for chronic low back pain: Upregulation in human cerebrospinal fluid and pre-clinical validation with chronic reparixin in the SPARC-null mouse model**. *EBioMedicine* (2019) **43** 487-500. DOI: 10.1016/j.ebiom.2019.04.032
38. Kwon W. K., Moon H. J., Kwon T. H., Park Y. K., Kim J. H.. **The role of hypoxia in angiogenesis and extracellular matrix regulation of intervertebral disc cells during inflammatory reactions**. *Neurosurgery* (2017) **81** 867-875. DOI: 10.1093/neuros/nyx149
39. Le Maitre C. L., Freemont A. J., Hoyland J. A.. **Localization of degradative enzymes and their inhibitors in the degenerate human intervertebral disc**. *J. Pathol.* (2004) **204** 47-54. DOI: 10.1002/path.1608
40. Le Maitre C. L., Freemont A. J., Hoyland J. A.. **A preliminary**. *Int. J. Exp. Pathol.* (2006) **87** 17-28. DOI: 10.1111/j.0959-9673.2006.00449.x
41. Le Maitre C. L., Hoyland J. A., Freemont A. J.. **Interleukin-1 receptor antagonist delivered directly and by gene therapy inhibits matrix degradation in the intact degenerate human intervertebral disc: An**. *Arthritis Res. Ther.* (2007) **9** R83. DOI: 10.1186/ar2282
42. Le Maitre C. L., Pockert A., Buttle D. J., Freemont A. J., Hoyland J. A.. **Matrix synthesis and degradation in human intervertebral disc degeneration**. *Biochem. Soc. Trans.* (2007) **35** 652-655. DOI: 10.1042/bst0350652
43. Le Maitre C. L., Baird P., Freemont A. J., Hoyland J. A.. **An**. *Arthritis Res. Ther.* (2009) **11** R20. DOI: 10.1186/ar2611
44. Le Maitre C. L., Freemont A. J., Hoyland J. A.. **Expression of cartilage-derived morphogenetic protein in human intervertebral discs and its effect on matrix synthesis in degenerate human nucleus pulposus cells**. *Arthritis Res. Ther.* (2009) **11** R137. DOI: 10.1186/ar2808
45. Lee J. Y., Hall R., Pelinkovic D., Cassinelli E., Usas A., Gilbertson L.. **New use of a three-dimensional pellet culture system for human intervertebral disc cells: Initial characterization and potential use for tissue engineering**. *Spine (Phila Pa 1976)* (2001) **26** 2316-2322. DOI: 10.1097/00007632-200111010-00005
46. Li X., Leo B. M., Beck G., Balian G., Anderson G. D.. **Collagen and proteoglycan abnormalities in the GDF-5-deficient mice and molecular changes when treating disk cells with recombinant growth factor**. *Spine (Phila Pa 1976)* (2004) **29** 2229-2234. DOI: 10.1097/01.brs.0000142427.82605.fb
47. Li X., Wu A., Han C., Chen C., Zhou T., Zhang K.. **Bone marrow-derived mesenchymal stem cells in three-dimensional co-culture attenuate degeneration of nucleus pulposus cells**. *Aging (Albany NY)* (2019) **11** 9167-9187. DOI: 10.18632/aging.102390
48. Liang H., Ma S. Y., Feng G., Shen F. H., Joshua Li X.. **Therapeutic effects of adenovirus-mediated growth and differentiation factor-5 in a mice disc degeneration model induced by annulus needle puncture**. *Spine J.* (2010) **10** 32-41. DOI: 10.1016/j.spinee.2009.10.006
49. Liu J., Tao H., Wang H., Dong F., Zhang R., Li J.. **Biological behavior of human nucleus pulposus mesenchymal stem cells in response to changes in the acidic environment during intervertebral disc degeneration**. *Stem Cells Dev.* (2017) **26** 901-911. DOI: 10.1089/scd.2016.0314
50. Luoma K., Riihimaki H., Luukkonen R., Raininko R., Viikari-Juntura E., Lamminen A.. **Low back pain in relation to lumbar disc degeneration**. *Spine (Phila Pa 1976)* (2000) **25** 487-492. DOI: 10.1097/00007632-200002150-00016
51. Martino M. M., Briquez P. S., Guc E., Tortelli F., Kilarski W. W., Metzger S.. **Growth factors engineered for super-affinity to the extracellular matrix enhance tissue healing**. *Science* (2014) **343** 885-888. DOI: 10.1126/science.1247663
52. Meisel H. J., Agarwal N., Hsieh P. C., Skelly A., Park J. B., Brodke D.. **Cell therapy for treatment of intervertebral disc degeneration: A systematic review**. *Glob. Spine J.* (2019) **9** 39S-52S. DOI: 10.1177/2192568219829024
53. Miot S., Gianni-Barrera R., Pelttari K., Acharya C., Mainil-Varlet P., Juelke H.. *Methods* (2010) **16** 11-21. DOI: 10.1089/ten.tec.2008.0698
54. Nakazawa K. R., Walter B. A., Laudier D. M., Krishnamoorthy D., Mosley G. E., Spiller K. L.. **Accumulation and localization of macrophage phenotypes with human intervertebral disc degeneration**. *Spine J.* (2018) **18** 343-356. DOI: 10.1016/j.spinee.2017.09.018
55. Naqvi S. M., Buckley C. T.. **Differential response of encapsulated nucleus pulposus and bone marrow stem cells in isolation and coculture in alginate and chitosan hydrogels**. *Tissue Eng. Part A* (2015) **21** 288-299. DOI: 10.1089/ten.tea.2013.0719
56. Nishida K., Kang J. D., Suh J. K., Robbins P. D., Evans C. H., Gilbertson L. G.. **Adenovirus-mediated gene transfer to nucleus pulposus cells. Implications for the treatment of intervertebral disc degeneration**. *Spine (Phila Pa 1976)* (1998) **23** 2437-2442. DOI: 10.1097/00007632-199811150-00016
57. Noronha N. C., Mizukami A., Caliari-Oliveira C., Cominal J. G., Rocha J. L. M., Covas D. T.. **Priming approaches to improve the efficacy of mesenchymal stromal cell-based therapies**. *Stem Cell. Res. Ther.* (2019) **10** 131. DOI: 10.1186/s13287-019-1224-y
58. Ouyang A., Cerchiari A. E., Tang X., Liebenberg E., Alliston T., Gartner Z. J.. **Effects of cell type and configuration on anabolic and catabolic activity in 3D co-culture of mesenchymal stem cells and nucleus pulposus cells**. *J. Orthop. Res.* (2017) **35** 61-73. DOI: 10.1002/jor.23452
59. Park M. J., Moon S. J., Baek J. A., Lee E. J., Jung K. A., Kim E. K.. **Metformin augments anti-inflammatory and chondroprotective properties of mesenchymal stem cells in experimental osteoarthritis**. *J. Immunol.* (2019) **203** 127-136. DOI: 10.4049/jimmunol.1800006
60. Ravi M., Paramesh V., Kaviya S. R., Anuradha E., Solomon F. D.. **3D cell culture systems: Advantages and applications**. *J. Cell. Physiol.* (2015) **230** 16-26. DOI: 10.1002/jcp.24683
61. Richardson S. M., Walker R. V., Parker S., Rhodes N. P., Hunt J. A., Freemont A. J.. **Intervertebral disc cell-mediated mesenchymal stem cell differentiation**. *Stem Cells* (2006) **24** 707-716. DOI: 10.1634/stemcells.2005-0205
62. Risbud M. V., Shapiro I. M.. **Role of cytokines in intervertebral disc degeneration: Pain and disc content**. *Nat. Rev. Rheumatol.* (2014) **10** 44-56. DOI: 10.1038/nrrheum.2013.160
63. Roberts S., Caterson B., Menage J., Evans E. H., Jaffray D. C., Eisenstein S. M.. **Matrix metalloproteinases and aggrecanase: Their role in disorders of the human intervertebral disc**. *Spine (Phila Pa 1976)* (2000) **25** 3005-3013. DOI: 10.1097/00007632-200012010-00007
64. Rosenzweig D. H., Tremblay Gravel J., Bisson D., Ouellet J. A., Weber M. H., Haglund L.. **Comparative analysis in continuous expansion of bovine and human primary nucleus pulposus cells for tissue repair applications**. *Eur. Cell. Mater* (2017) **33** 240-251. DOI: 10.22203/ecm.v033a18
65. Sakaguchi Y., Sekiya I., Yagishita K., Muneta T.. **Comparison of human stem cells derived from various mesenchymal tissues: Superiority of synovium as a cell source**. *Arthritis Rheum.* (2005) **52** 2521-2529. DOI: 10.1002/art.21212
66. Sakai D., Andersson G. B.. **Stem cell therapy for intervertebral disc regeneration: Obstacles and solutions**. *Nat. Rev. Rheumatol.* (2015) **11** 243-256. DOI: 10.1038/nrrheum.2015.13
67. Schwarzer A. C., Aprill C. N., Derby R., Fortin J., Kine G., Bogduk N.. **The prevalence and clinical features of internal disc disruption in patients with chronic low back pain**. *Spine (Phila Pa 1976)* (1995) **20** 1878-1883. DOI: 10.1097/00007632-199509000-00007
68. Seki S., Asanuma-Abe Y., Masuda K., Kawaguchi Y., Asanuma K., Muehleman C.. **Effect of small interference RNA (siRNA) for ADAMTS5 on intervertebral disc degeneration in the rabbit anular needle-puncture model**. *Arthritis Res. Ther.* (2009) **11** R166. DOI: 10.1186/ar2851
69. Siegel N. S., Gliklich R. E., Taghizadeh F., Chang Y.. **Outcomes of septoplasty**. *Otolaryngol. Head. Neck Surg.* (2000) **122** 228-232. DOI: 10.1016/s0194-5998(00)70244-0
70. Sinclair S. M., Shamji M. F., Chen J., Jing L., Richardson W. J., Brown C. R.. **Attenuation of inflammatory events in human intervertebral disc cells with a tumor necrosis factor antagonist**. *Spine (Phila Pa 1976)* (2011) **36** 1190-1196. DOI: 10.1097/brs.0b013e3181ebdb43
71. Smith L. J., Silverman L., Sakai D., Le Maitre C. L., Mauck R. L., Malhotra N. R.. **Advancing cell therapies for intervertebral disc regeneration from the lab to the clinic: Recommendations of the ORS spine section**. *JOR Spine* (2018) **1** e1036. DOI: 10.1002/jsp2.1036
72. Song K., Gu T., Shuang F., Tang J., Ren D., Qin J.. **Adipose-derived stem cells improve the viability of nucleus pulposus cells in degenerated intervertebral discs**. *Mol. Med. Rep.* (2015) **12** 4664-4668. DOI: 10.3892/mmr.2015.3895
73. Stoyanov J. V., Gantenbein-Ritter B., Bertolo A., Aebli N., Baur M., Alini M.. **Role of hypoxia and growth and differentiation factor-5 on differentiation of human mesenchymal stem cells towards intervertebral nucleus pulposus-like cells**. *Eur. Cell. Mater* (2011) **21** 533-547. DOI: 10.22203/ecm.v021a40
74. Strassburg S., Richardson S. M., Freemont A. J., Hoyland J. A.. **Co-culture induces mesenchymal stem cell differentiation and modulation of the degenerate human nucleus pulposus cell phenotype**. *Regen. Med.* (2010) **5** 701-711. DOI: 10.2217/rme.10.59
75. Struglics A., Hansson M.. **MMP proteolysis of the human extracellular matrix protein aggrecan is mainly a process of normal turnover**. *Biochem. J.* (2012) **446** 213-223. DOI: 10.1042/bj20120274
76. Studer R. K., Aboka A. M., Gilbertson L. G., Georgescu H., Sowa G., Vo N.. **p38 MAPK inhibition in nucleus pulposus cells: a potential target for treating intervertebral disc degeneration**. *Spine (Phila Pa 1976)* (2007) **32** 2827-2833. DOI: 10.1097/brs.0b013e31815b757a
77. Sun K., Guo J., Yao X., Guo Z., Guo F.. **Growth differentiation factor 5 in cartilage and osteoarthritis: A possible therapeutic candidate**. *Cell. Prolif.* (2021) **54** e12998. DOI: 10.1111/cpr.12998
78. Svanvik T., Henriksson H. B., Karlsson C., Hagman M., Lindahl A., Brisby H.. **Human disk cells from degenerated disks and mesenchymal stem cells in co-culture result in increased matrix production**. *Cells Tissues Organs* (2010) **191** 2-11. DOI: 10.1159/000223236
79. Tan J. L., Lash B., Karami R., Nayer B., Lu Y. Z., Piotto C.. **Restoration of the healing microenvironment in diabetic wounds with matrix-binding IL-1 receptor antagonist**. *Commun. Biol.* (2021) **4** 422. DOI: 10.1038/s42003-021-01913-9
80. Tao Y. Q., Liang C. Z., Li H., Zhang Y. J., Li F. C., Chen G.. **Potential of co-culture of nucleus pulposus mesenchymal stem cells and nucleus pulposus cells in hyperosmotic microenvironment for intervertebral disc regeneration**. *Cell. Biol. Int.* (2013) **37** 826-834. DOI: 10.1002/cbin.10110
81. Teixeira G. Q., Pereira C. L., Ferreira J. R., Maia A. F., Gomez-Lazaro M., Barbosa M. A.. **Immunomodulation of human mesenchymal stem/stromal cells in intervertebral disc degeneration: Insights from a proinflammatory/degenerative**. *Spine (Phila Pa 1976)* (2018) **43** E673-E682. DOI: 10.1097/brs.0000000000002494
82. Tellegen A. R., Rudnik-Jansen I., Beukers M., Miranda-Bedate A., Bach F. C., de Jong W.. **Intradiscal delivery of celecoxib-loaded microspheres restores intervertebral disc integrity in a preclinical canine model**. *J. Control Release* (2018) **286** 439-450. DOI: 10.1016/j.jconrel.2018.08.019
83. Thompson J. P., Oegema T. R., Bradford D. S.. **Stimulation of mature canine intervertebral disc by growth factors**. *Spine (Phila Pa 1976)* (1991) **16** 253-260. DOI: 10.1097/00007632-199103000-00001
84. Thorpe A. A., Bach F. C., Tryfonidou M. A., Le Maitre C. L., Mwale F., Diwan A. D.. **Leaping the hurdles in developing regenerative treatments for the intervertebral disc from preclinical to clinical**. *JOR Spine* (2018) **1** e1027. DOI: 10.1002/jsp2.1027
85. Urits I., Capuco A., Sharma M., Kaye A. D., Viswanath O., Cornett E. M.. **Stem cell therapies for treatment of discogenic low back pain: A comprehensive review**. *Curr. Pain Headache Rep.* (2019) **23** 65. DOI: 10.1007/s11916-019-0804-y
86. Urrutia J., Besa P., Campos M., Cikutovic P., Cabezon M., Molina M.. **The Pfirrmann classification of lumbar intervertebral disc degeneration: An independent inter- and intra-observer agreement assessment**. *Eur. Spine J.* (2016) **25** 2728-2733. DOI: 10.1007/s00586-016-4438-z
87. Vadala G., Studer R. K., Sowa G., Spiezia F., Lucu C., Denaro V.. **Coculture of bone marrow mesenchymal stem cells and nucleus pulposus cells modulate gene expression profile without cell fusion**. *Spine* (2008) **33** 870-876. DOI: 10.1097/brs.0b013e31816b4619
88. Vadala G., Sowa G., Hubert M., Gilbertson L. G., Denaro V., Kang J. D.. **Mesenchymal stem cells injection in degenerated intervertebral disc: Cell leakage may induce osteophyte formation**. *J. Tissue Eng. Regen. Med.* (2012) **6** 348-355. DOI: 10.1002/term.433
89. Vadala G., Ambrosio L., Russo F., Papalia R., Denaro V.. **Interaction between mesenchymal stem cells and intervertebral disc microenvironment: From cell therapy to tissue engineering**. *Stem Cells Int.* (2019) **2019** 1-15. DOI: 10.1155/2019/2376172
90. van den Eerenbeemt K. D., Ostelo R. W., van Royen B. J., Peul W. C., van Tulder M. W.. **Total disc replacement surgery for symptomatic degenerative lumbar disc disease: A systematic review of the literature**. *Eur. Spine J.* (2010) **19** 1262-1280. DOI: 10.1007/s00586-010-1445-3
91. van Dijk B., Potier E., Licht R., Creemers L., Ito K.. **The effect of a cyclooxygenase 2 inhibitor on early degenerated human nucleus pulposus explants**. *Glob. Spine J.* (2014) **4** 33-39. DOI: 10.1055/s-0033-1359724
92. van Dijk B. G. M., Potier E., van Dijk M., Creemers L. B., Ito K.. **Osteogenic protein 1 does not stimulate a regenerative effect in cultured human degenerated nucleus pulposus tissue**. *J. Tissue Eng. Regen. Med.* (2017) **11** 2127-2135. DOI: 10.1002/term.2111
93. Vedicherla S., Buckley C. T.. *Tissue Cell.* (2017) **49** 503-513. DOI: 10.1016/j.tice.2017.05.002
94. Walter B. A., Purmessur D., Likhitpanichkul M., Weinberg A., Cho S. K., Qureshi S. A.. **Inflammatory kinetics and efficacy of anti-inflammatory treatments on human nucleus pulposus cells**. *Spine (Phila Pa 1976)* (2015) **40** 955-963. DOI: 10.1097/brs.0000000000000932
95. Wang H., Kroeber M., Hanke M., Ries R., Schmid C., Poller W.. **Release of active and depot GDF-5 after adenovirus-mediated overexpression stimulates rabbit and human intervertebral disc cells**. *J. Mol. Med. Berl.* (2004) **82** 126-134. DOI: 10.1007/s00109-003-0507-y
96. Wang Y., Che M., Xin J., Zheng Z., Li J., Zhang S.. **The role of IL-1β and TNF-α in intervertebral disc degeneration**. *Biomed. Pharmacother.* (2020) **131** 110660. DOI: 10.1016/j.biopha.2020.110660
97. Watts A. E., Ackerman-Yost J. C., Nixon A. J.. **A comparison of three-dimensional culture systems to evaluate**. *Tissue Eng. Part A* (2013) **19** 2275-2283. DOI: 10.1089/ten.tea.2012.0479
98. Williams R. J., Tryfonidou M. A., Snuggs J. W., Le Maitre C. L.. **Cell sources proposed for nucleus pulposus regeneration**. *JOR Spine* (2021) **4** e1175. DOI: 10.1002/jsp2.1175
99. Wuertz K., Godburn K., Iatridis J. C.. **MSC response to pH levels found in degenerating intervertebral discs**. *Biochem. Biophys. Res. Commun.* (2009) **379** 824-829. DOI: 10.1016/j.bbrc.2008.12.145
100. Zhang Y., Phillips F. M., Thonar E. J., Oegema T., An H. S., Roman-Blas J. A.. **Cell therapy using articular chondrocytes overexpressing BMP-7 or BMP-10 in a rabbit disc organ culture model**. *Spine (Phila Pa 1976)* (2008) **33** 831-838. DOI: 10.1097/brs.0b013e31816b1f38
101. Zhao C. Q., Wang L. M., Jiang L. S., Dai L. Y.. **The cell biology of intervertebral disc aging and degeneration**. *Ageing Res. Rev.* (2007) **6** 247-261. DOI: 10.1016/j.arr.2007.08.001
102. Zhou Z., Zeiter S., Schmid T., Sakai D., Iatridis J. C., Zhou G.. **Effect of the CCL5-releasing fibrin gel for intervertebral disc regeneration**. *Cartilage* (2020) **11** 169-180. DOI: 10.1177/1947603518764263
|
---
title: Ultrasound appearance of the kidney among radiology department attendees of
a tertiary centre in Malawi
authors:
- Laura Carey
- Bright Tsidya
- Bazwell Nkhalema
- Sylvester Kaimba
- Karen Chetcuti
- Elizabeth Joekes
- Benno Kreuels
- Marc Henrion
- Jamie Rylance
journal: Wellcome Open Research
year: 2023
pmcid: PMC9971658
doi: 10.12688/wellcomeopenres.18455.2
license: CC BY 4.0
---
# Ultrasound appearance of the kidney among radiology department attendees of a tertiary centre in Malawi
## Abstract
Background: Diagnostic and therapeutic decisions in nephrology in low-resource settings are frequently based on ultrasound assessment of kidney size. An understanding of reference values is critical, particularly given the rise of non-communicable disease and the expanding availability of point-of-care ultrasound. However, there is a paucity of normative data from African populations. We determined estimates of kidney ultrasound measures, including kidney size based on age, sex, and HIV status, among apparently healthy outpatient attendees of Queen Elizabeth Central hospital radiology department, Blantyre, Malawi.
Methods: We performed a cross-sectional cohort study of 320 adults attending the radiology department between October 2021 and January 2022. Bilateral kidney ultrasound was performed on all participants using a portable Mindray DP-50 machine and a 5MHz convex probe. The sample was stratified by age, sex, and HIV status. Predictive linear modelling was used to construct reference ranges for kidney size estimating the central 95 percentiles of 252 healthy adults. Exclusion criteria for the healthy sample were known kidney disease, hypertension, diabetes, BMI > 35, heavy alcohol intake, smoking and ultrasonographic abnormalities.
Results: There were $\frac{162}{320}$ ($51\%$) male participants. The median age was 47 (interquartile range [IQR] 34-59). Among people living with HIV $\frac{134}{138}$ ($97\%$) were receiving antiretroviral therapy. Men had larger average kidney sizes: mean 9.68 cm (SD 0.80 cm), compared to 9.46 cm (SD 0.87 cm) in women ($$p \leq 0.01$$). Average kidney sizes in people living with HIV were not significantly different from those who were HIV-negative, 9.73 cm (SD 0.93 cm) versus 9.58 cm (SD 0.93 cm) ($$p \leq 0.63$$).
Conclusions: *This is* the first report of the apparently healthy kidney size in Malawi. Predicted kidney size ranges may be used for reference in the clinical assessment of kidney disease in Malawi.
## Amendments from Version 1
The article has been updated to incorporate suggestions from reviewers. In response to reviewer 1, we now only refer to practices within Malawi, and have made more explicit the potential inclusion of undiagnosed diabetes in the healthy sample as a limitation. In response to reviewer 2, we have clarified the reason for stratification by HIV status, expanded the ultrasound methods section to include the quality control process, and included in the limitations that we did not formally assess interobserver variability. Tables 1, 3 and 4 have been presented stratified by sex as suggested. We have expanded the limitations section to include the potential for undiagnosed hypertension, diabetes and kidney disease within the healthy sample. We have mentioned the relatively small sample size for predicting size by age category and sex.
## Introduction
Patients with kidney failure routinely undergo ultrasonography as part of their assessment. In Malawi when laboratory tests for kidney function are delayed or unavailable, therapeutic decisions are frequently made on ultrasound assessment. Kidney ultrasound is standard practice in Malawi as part of the assessment of acute kidney injury to identify chronic damage in the face of competition for dialysis beds. Patients with evidence of chronic kidney impairment are unlikely to be prioritized for kidney replacement therapy. Therefore, in Malawi, abnormal kidney size and appearance on ultrasound is often used as a surrogate marker for chronic kidney disease.
An understanding of reference kidney size values is critical, particularly given the rise of non-communicable disease and the expanding availability of point-of-care ultrasound in the region. A number of studies have reported reference values for kidney size in healthy adults measured by ultrasonography 1– 4. Data on ultrasound kidney size measurements for Africa, however, are scarce. Furthermore, there are established physiological differences between populations which underlie the need for population-based estimates for examining kidney size. For example, for the same height, the vital capacity and the forced expiratory volume in one second are about $14\%$ smaller in adults of African lineage compared to Caucasian and Asian populations 5. The purpose of this study was to investigate the normal ultrasound measurements of the kidney among adults in Malawi.
## Methods
Queen Elizabeth Central Hospital (QECH) is a 1,300-bed government hospital providing free healthcare to Blantyre. QECH is the largest tertiary and teaching hospital in the country which manages severe trauma cases from the Southern and Eastern regions, and less severe cases from areas located near the hospital 6. Malawi is a low-income country in South-East Africa, with an estimated adult HIV prevalence of $9\%$ 7.
At QECH, we performed a cross sectional study of adults (≥18 years) attending the radiology department for any imaging modality, mostly relating to accidents and injury. Patients were approached for recruitment, Monday-Friday, 0700-1700. Exclusion criteria were people lacking capacity to consent with no proxy consent available. Radiology department attendees for imaging after accidents were targeted as they are less likely to have pre-existing kidney pathologies compared to other groups within the hospital. The sample was stratified by age, sex, and HIV status. The sample was stratified by HIV status to enable a separate reference range for those living with HIV.
## Sampling and laboratory methods
Point-of-care HIV testing was done for those with unknown status or no recent negative test. Data on serum creatinine or estimated glomerular filtration rate were not available.
## Ultrasound
Bilateral kidney ultrasound was performed by departmental sonographers experienced in performing kidney ultrasound, using a portable Mindray DP-50 machine and a 5MHz convex probe. For each individual, we evaluated the left and right kidney size, presence of hydronephrosis, loss of corticomedullary differentiation, echogenicity, and any other significant abnormality (such as cysts or pyonephrosis).
The examination was performed with the patient supine and the longitudinal dimensions of the kidneys were visually estimated to represent the largest longitudinal section. Quality control was performed before and after initiation of the study by experts to assess adequate image quality. Prior to commencement, a series of test images were reviewed by two experts for quality of view, detection of abnormalities and accuracy of length measurement. Where there was disparity, feedback was given to sonographers and further training in image acquisition. On data completion, $10\%$ images were randomly selected for external review with expert opinion taken as the ‘gold standard’ against which to benchmark the accuracy of the sonographers’ measurements. Images with insufficient quality as deemed by experts were rejected ($$n = 4$$).
Because kidney length is related to body height, the relative kidney length was calculated using the kidney length: body height ratio (KBR) by dividing the absolute kidney length (millimetres) by the body height (centimetres) for each kidney 3.
## Exclusion
For normal size range estimates, to represent a ‘healthy’ population as closely as possible, participants were excluded after recruitment if they reported diabetes, current heavy smoking (> 20 cigarettes/day) or heavy alcohol intake (> 50 alcohol drinks/week), and if body mass index (BMI) > 35. Data did not contribute to normal range estimates where there were significant imaging abnormalities; hydronephrosis, suspected pyonephrosis, and loss of corticomedullary differentiation.
## Statistical analysis
Statistical analyses were performed using R version 4.0.2 8. Summary statistics were calculated for the cohort, described using either median and interquartile range (IQR) or mean and standard deviation for continuous variables depending on data distribution, and proportions for categorical variables. Two-sample t-tests or non-parametric tests, depending on data distribution, were used to compare variables between groups.
*To* generate estimates of expected mean kidney size, predictive linear modelling was used to estimate the central 95 th percentile for mean kidney size based on age and sex. To quantify the uncertainty of the lower and upper limits of these ranges, we fitted the same linear model to 1,000 bootstrap samples of the healthy dataset. Bootstrapped $95\%$ confidence intervals were then constructed around the upper and lower limits of the prediction interval. Finally, a linear model was used to generate model fits of kidney size across the age ranges according to both sex and HIV status.
## Sample size
The known mean kidney bipolar length in adult males in the USA is 12.40 cm with a standard deviation of 0.90 cm 9. We aimed to estimate the bipolar length in the Malawi population with a margin of error of 0.15 cm using the following formula, where n is the sample size, z 2 a/2 = 1.96 ($95\%$ confidence level), σ 2 = 0.90 and $d = 0.15$ cm.
The number needed was inflated to 160 to cover for $15\%$ unusable data. To recruit 50:50 HIV positive to negative, the total sample size was 320.
## Results
Between 27 October 2021 and 31 January 2022, 320 participants were recruited. The study flow chart is summarised in Figure 1. Table 1 summarises the baseline characteristics of the participants. There were $\frac{162}{320}$ ($51\%$) male participants. The median age was 47 (interquartile range [IQR]34-59). Of those whose HIV status was positive, $\frac{138}{320}$ ($43\%$), $\frac{134}{138}$ ($97\%$) were receiving antiretroviral therapy. Tuberculosis (TB) history was known for $\frac{317}{320}$, $\frac{303}{320}$ ($95\%$) had no prior TB history, $\frac{8}{320}$ ($3\%$) either received prior treatment or were receiving current treatment for TB, and $\frac{6}{320}$ ($2\%$) were diagnosed but never treated.
**Figure 1.:** *Study flow diagram demonstrating the number of participants recruited and selected for the healthy sample for normal range predictions.* TABLE_PLACEHOLDER:Table 1.
Social characteristics, symptoms, and reasons for attending the radiology department are shown in Table 2. Place of residence was known in $\frac{296}{320}$, $\frac{218}{320}$ ($68\%$), reported living in an urban locality, $\frac{78}{320}$ ($24\%$) reported living rurally. proximity to the lake was known in $\frac{240}{320}$, $\frac{14}{320}$ ($4\%$) reported living near Lake Malawi, where schistosomiasis is endemic.
**Table 2.**
| Social | Unnamed: 1 |
| --- | --- |
| Current smoker, n (%) | 15/320 (5%) |
| Smoking unknown, n (%) | 23/320 (7%) |
| Drinks alcohol, n (%) | 62/320 (19%) |
| Alcohol intake unknown, n (%) | 22/320 (7%) |
| Occupation | |
| Professionals, n (%) | 45/320 (14%) |
| Sales and services, n (%) | 13/320 (4%) |
| Craft and related trades, n (%) | 11/320 (3%) |
| Labourer, n (%) | 7/320 (2%) |
| Service workers, n (%) | 6/320 (2%) |
| Armed forces, n (%) | 5/320 (2%) |
| Agriculture, n (%) | 5/320 (2%) |
| Machine operators, n (%) | 3/320 (1%) |
| Legislators/officials/managers, n (%) | 2/320 (1%) |
| Other/no occupation, n (%) | 223/320 (70%) |
| Place of residence | |
| Urban, n (%) | 218/320 (68%) |
| Rural, n (%) | 78/320 (24%) |
| Unknown, n (%) | 24/320 (8%) |
| Reason for attending | |
| Fall, n (%) | 98/320 (31%) |
| Road traffic accident, n (%) | 59/320 (18%) |
| Assault, n (%) | 22/320 (7%) |
| Bike injury, n (%) | 5/320 (2%) |
| Collapsed structure, n (%) | 3/320 (1%) |
| Burn, n (%) | 1/320 (0%) |
| Other, n (%) | 132/320 (41%) |
| Symptoms | |
| None, n (%) | 287/320 (90%) |
| Abdomen/Lower back pain, n (%) | 8/320 (3%) |
| Fever, n (%) | 6/320 (2%) |
| Chest pain, n (%) | 3/320 (1%) |
| Cough, n (%) | 2/320 (1%) |
| Headache, n (%) | 2/320 (1%) |
| Other, n (%) | 9/320 (3%) |
| Unknown, n (%) | 3/320 (1%) |
| Imaging | |
| X-ray, n (%) | 192/320 (60%) |
| Ultrasound, n (%) | 106/320 (33%) |
| Other, n (%) | 22/320 (7%) |
Physiology and ultrasound variables are provided in Table 3. The prevalence of hydronephrosis, increased echogenicity and loss of corticomedullary differentiation was low (2-$6\%$). The mean size of the right kidney was 9.38 cm (SD 0.98 cm) in women, and 9.61 cm (SD 0.93 cm) in men. The mean size of the left kidney was 9.54 cm (SD 0.97 cm) in women and 9.76 cm (SD 0.90 cm) in men. Men had larger average kidney sizes: mean 9.68 cm (SD 0.80 cm), compared to 9.46 cm (SD 0.87 cm) in women ($$p \leq 0.01$$). Average kidney sizes in HIV-positive participants were not significantly different from those who were HIV negative, 9.73 cm (SD 0.93 cm) versus 9.58 cm (SD 0.93 cm) ($$p \leq 0.63$$).
**Table 3.**
| Variable | Overall | Female | Male |
| --- | --- | --- | --- |
| Height (cm) | 162.40 (SD 99.01) | 158 (153-163) | 167 (160-172) |
| Weight (kg) | 67.30 (IQR 57.00-76.67) | 64 (56-80) | 63 (59-73) |
| Body mass index (kg m -2) | 24.00 (IQR 22.00-28.00) | 26 (23-31) | 24 (21-26) |
| Systolic blood pressure (mm Hg) | 136.00 (SD 23.61) | 130 (116-158) | 138 (125-156) |
| Diastolic blood pressure (mm Hg) | 80.00 (SD 12.11) | 80 (75-89) | 81 (74-89) |
| Average kidney size (cm) | 9.58 (SD 0.83) | 9.46 (SD 0.87) | 9.68 (SD 0.80) |
| Kidney size left (cm) | 9.66 (SD 0.94) | 9.54 (SD 0.97) | 9.76 (SD 0.90) |
| Kidney size right (cm) | 9.50 (SD 0.96) | 9.38 (SD 0.98) | 9.61 (SD 0.93) |
| Kidney length: height right (KBR) | 0.59 (SD 0.06) | 0.59 (SD 0.06) | 0.58 (SD 0.06) |
| Kidney length: height left (KBR) | 0.60 (SD 0.06) | 0.60 (SD 0.06) | 0.59 (SD 0.06) |
| Loss of corticomedullary differentiation, n (%) | 6/320 (2%) | 2/158 (1%) | 4/162 (2%) |
| Increased echogenicity, n (%) | 19/320 (6%) | 9/158 (6%) | 10/162 (6%) |
| Hydronephrosis, n (%) | 6/320 (2%) | 3/158 (2%) | 3/162 (2%) |
| Pyonephrosis, n (%) | 1/320 (0%) | 1/158 (1%) | 0/162 (0%) |
Absolute and relative kidney lengths are shown in Table 4. Absolute average (left and right) kidney lengths by sex and predicted range estimates with $95\%$ confidence interval and bootstrapped upper and lower $95\%$ prediction interval are shown in Table 5. The residuals plot for the multivariable model is in the GitHub repository (Extended data) 10. Figure 2 and Figure 3 show the kidney size range estimates dependent on age, sex, and HIV status.
## Discussion
This is the first report of ultrasound appearances of normal kidneys in a Malawian population. In our cohort, kidneys were larger in males compared to females. Prevalence of ultrasound abnormalities such as hydronephrosis, increased echogenicity and loss of corticomedullary differentiation was low. Kidney size was not significantly different in people living with HIV versus those without HIV, meaning the table of predicted ranges can be applied to both groups. This may be related to the successful scale up and subsequent high antiretroviral therapy coverage in our cohort, $97\%$ ($\frac{134}{138}$), compared to the subnational estimate of $92\%$ for Blantyre 11.
We found kidney sizes in our Malawi cohort to be smaller than in a Nigerian population, which reported 10.20 cm (SD 0.81 cm) and 9.85 cm (SD 0.90 cm) for left and right kidneys 12. We also found kidney sizes in Malawi to be smaller than populations outside of SSA. For example, in the US the mean kidney bipolar length on ultrasound has been reported as 11.20 cm and 11.00 cm for left and right kidneys 9, in Kuwait 10.71 cm (SD 1.00 cm) cm and 10.68 cm (SD 1.40 cm) for left and right kidneys 2, and in Copenhagen, 11.20 cm and 10.90 cm for left and right kidneys 1.
These differences may be explained, in part, by population differences in height; however very few studies report relative kidney size, and none in Africa. After accounting for height using kidney length: body height ratio (KBR) our data suggest smaller relative kidney sizes among Malawians compared to European populations. For example, data from Croatia suggest KBRs in adults younger than 60 without kidney disease, are between 0.60 and 0.74 for the left kidney and 0.57 to 0.72 for the right kidney 3. In a Swiss autopsy series of 635 adults without diabetes or known kidney disease, mean (standard deviation) KBRs were 0.67 (0.07) for men and 0.69 (0.07) for women 13.
There were limitations to our study. We were unable to measure kidney function to confirm absence of pre-existing kidney disease. However, we excluded participants with comorbidities, social behaviours, and ultrasound abnormalities likely to affect glomerular filtration rate (GFR). The healthy sample may have included participants with kidney impairment. We relied on self-reporting of hypertension and diabetes and were unable to perform glucose measurements for diabetes screening. It is therefore possible that some participants with undiagnosed hypertension and diabetes contributed to the healthy sample. The quality control process did not include a formal assessment of interobserver variability. Only $10\%$ of images were reviewed by experts for quality. There may be images remaining of insufficient quality which were not assessed. The relatively small sample for predicting kidney size within age and sex categories and sex, and the lower proportion of HIV-positive participants in the younger age categories may have biased the size estimates. We did not collect data on CD4 count or viral load, and future studies should aim to correlate kidney size with stage of HIV infection.
We recruited participants attending a tertiary centre for imaging following accidents as they were less likely to have a pre-existing kidney disease than other hospital-based cohorts. Future studies should aim to develop nomograms for adults and children, derived from a larger demographic sample. Ideally, these would also include GFR measurement, CD4 count and viral load for HIV positive participants.
In conclusion, we demonstrate the range of kidney sizes expected in adult Malawians without known kidney disease. We found a low prevalence of ultrasound abnormalities in our population. Our predicted size estimates within age categories can be referred to in the assessment of patients with kidney failure.
## Ethical statement
Participants gave written informed consent under ethical approvals from the College of Medicine Research Ethics Committee, University of Malawi (P$\frac{.03}{19}$/2625) and the Liverpool School of Tropical Medicine Ethics Committee [18-062]. Study information including purposes, benefits and risk was provided to all participants in both English and Chichewa.
## Underlying data
Zenodo: careyla/Normal-kidney: v1.0.0, https://doi.org/10.5281/zenodo.7231616 10 This project contains the following underlying data:
## Extended data
Zenodo: careyla/Normal-kidney: v1.0.0, https://doi.org/10.5281/zenodo.7231616 10 This project contains the following extended data: Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).
## Analysis code
Analysis code available from: https://github.com/careyla/Normal-kidney/tree/v1.0.0 *Archived analysis* code at time of publication: https://doi.org/10.5281/zenodo.7231616 10 License: MIT
## Reporting guidelines
Zenodo: STROBE checklist for ‘Ultrasound appearance of the kidney among radiology department attendees of a tertiary centre in Malawi’, https://doi.org/10.5281/zenodo.7231616 10
## References
1. Emamian SA, Nielsen MB, Pedersen JF. **Kidney dimensions at sonography: correlation with age, sex, and habitus in 665 adult volunteers.**. (1993.0) **160** 83-86. DOI: 10.2214/ajr.160.1.8416654
2. El-Reshaid W, Abdul-Fattah H. **Sonographic assessment of renal size in healthy adults.**. (2014.0) **23** 432-436. DOI: 10.1159/000364876
3. Miletić D, Fuckar Z, Sustić A. **Sonographic measurement of absolute and relative renal length in adults.**. (1998.0) **26** 185-189. DOI: 10.1002/(sici)1097-0096(199805)26:4<185::aid-jcu1>3.0.co;2-9
4. Brandt TD, Neiman HL, Dragowski MJ. **Ultrasound assessment of normal renal dimensions.**. (1982.0) **1** 49-52. DOI: 10.7863/jum.1982.1.2.49
5. Quanjer PH, Stanojevic S, Cole TJ. **Multi-ethnic reference values for spirometry for the 3–95-yr age range: the global lung function 2012 equations.**. (2012.0) **40** 1324-1343. DOI: 10.1183/09031936.00080312
6. Chokotho LC, Mulwafu W, Nyirenda M. **Establishment of trauma registry at Queen Elizabeth Central Hospital (QECH), Blantyre, Malawi and mapping of high risk geographic areas for trauma.**. (2019.0) **10** 33-41. DOI: 10.5847/wjem.j.1920-8642.2019.01.005
7. Gupta-Wright A, Corbett EL, van Oosterhout JJ. **Rapid urine-based screening for tuberculosis in HIV-positive patients admitted to hospital in Africa (STAMP): a pragmatic, multicentre, parallel-group, double-blind, randomised controlled trial.**. (2018.0) **392** 292-301. DOI: 10.1016/S0140-6736(18)31267-4
8. **R: The R Project for Statistical Computing.**
9. Cheong B, Muthupillai R, Rubin MF. **Normal values for renal length and volume as measured by magnetic resonance imaging.**. (2007.0) **2** 38-45. DOI: 10.2215/CJN.00930306
10. 10
careyla:
careyla/Normal-kidney: v1.0.0 (v1.0.0).
Zenodo.
[data set].2022.
10.5281/zenodo.7231616. (2022.0). DOI: 10.5281/zenodo.7231616
11. **HIV sub-national estimates viewer**
12. Zira JD, Salisu AY, Sani MU. **ULTRASONOGRAPHIC NOMOGRAM OF THE KIDNEYS IN APPARENTLY HEALTHY ADULTS IN A NIGERIAN POPULATION.**. (2017.0) **27**
13. Kalucki SA, Lardi C, Garessus J. **Reference values and sex differences in absolute and relative kidney size. A Swiss autopsy study.**. (2020.0) **21** 289. DOI: 10.1186/s12882-020-01946-y
|
---
title: 'Effectiveness and implementation of a lifestyle modification intervention
for women with isolated impaired fasting glucose: Study protocol for a hybrid type
2 study in Kerala, India'
authors:
- Elezebeth Mathews
- Thirunavukkarasu Sathish
- Anjaly Joseph
- Bhagieshwari Kodapally
- Jissa Vinoda Thulaseedharan
- KM Venkat Narayan
- Brian Oldenburg
- Kavumpurathu Raman Thankappan
journal: Wellcome Open Research
year: 2022
pmcid: PMC9971662
doi: 10.12688/wellcomeopenres.17631.1
license: CC BY 4.0
---
# Effectiveness and implementation of a lifestyle modification intervention for women with isolated impaired fasting glucose: Study protocol for a hybrid type 2 study in Kerala, India
## Abstract
Background: Isolated impaired fasting glucose (i-IFG) constitutes a major group in the prediabetic spectrum among Indians, and thus it is imperative to identify effective diabetes prevention strategies. This study aims to evaluate the effects of an intensive community-based lifestyle modification program on regression to normoglycemia among women with i-IFG, compared to a control group at 24 months. The study also aims to evaluate the implementation of the intervention, via both process and implementation outcomes.
Methods: We will use a hybrid design (Effectiveness-implementation hybrid type 2 trial) to test the effectiveness and implementation of the lifestyle modification intervention. Effectiveness is evaluated using a randomized controlled trial among 950 overweight or obese women, aged 30 to 60 years, with i-IFG on an oral glucose tolerance test in the Indian state of Kerala. The intervention involves an intensive lifestyle modification program through group and individually mentored sessions using behavioural determinants and behavioural change techniques. The intervention group will receive the intervention for a period of 12 months and the control group will receive general health advice through a health education booklet. Data on behavioural, clinical, and biochemical measures will be collected using standard methods at 12 and 24 months. The primary outcome will be regression to normoglycemia at 24 months, as defined by the American Diabetes Association criteria.
Discussion: This study will provide the first evidence on the effects of lifestyle interventions on regression to normoglycemia in people with i-IFG among Indians.
CTRI registration: CTRI/$\frac{2021}{07}$/035289 ($\frac{30}{07}$/2021)
## Introduction
Type 2 diabetes mellitus (T2DM), currently affects almost 537 million adults worldwide with a projected increase of nearly $50\%$ by 2045 1. Low- and middle-income countries (LMICs) such as India are disproportionately affected, with the large majority ($81\%$) of people with T2DM living in these countries 1. India has the second-largest number of people (74 million) living with T2DM and this is projected to increase to 125 million people by 2045 1. In addition, T2DM poses a significant economic burden in LMICs, affecting not just the health care system but also individuals and families with increased out-of-pocket spending for diabetes care 2. Therefore, much importance has been given to diabetes prevention, with a greater research focus on individuals who are at high risk for diabetes.
Prediabetes, a high-risk metabolic state for diabetes, comprises impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), wherein the glucose levels are higher than those considered to be normal, but lower than the threshold for T2DM 3. A recent review of 19 studies globally showed that among those with prediabetes, the average proportions of isolated IFG (i-IFG), isolated IGT (i-IGT), and combined IFG and IGT were $43.9\%$, $41.0\%$, and $13.5\%$, respectively, in Caucasians, and $29.2\%$, $49.4\%$, and $18.2\%$, respectively, in Asians 4. A large-scale study in 15 states of India showed that i-IFG is the most common form of prediabetes among adults, with a prevalence of $21\%$ 5. On average, nearly 5–$10\%$ of people with prediabetes develop T2DM every year, although the progression rate varies by population characteristics (including ethnicity) and the definition of prediabetes 6. With no effective intervention, nearly $70\%$ of people with prediabetes would eventually progress to develop T2DM 6.
Lifestyle modification is recognized as an effective non-pharmacologic intervention to prevent or delay the onset of diabetes among individuals with prediabetes 7. Most landmark lifestyle modification randomized controlled trials (RCTs) for diabetes prevention have been conducted in individuals with IGT 8, with very limited evidence in the i-IFG group. Lifestyle modification in the trials for those with IGT have focused mainly on weight loss and promoting physical activity, although the intervention intensity, goals, and delivery methods have varied widely between the trials. These trials have shown that diabetes incidence could be reduced between $28.5\%$ and $58\%$ in the intervention group compared with the control group 8. More importantly, a clinically meaningful reduction in diabetes incidence with lifestyle modification has been shown to persist in those with IGT even after 30 years of follow-up 9.
Only a few RCTs have evaluated the effects of lifestyle modification on diabetes prevention in people with i-IFG 10. RCTs conducted among 641 overweight Japanese 11 and 578 overweight Indian adults 12 showed hazard ratios of 1.17 ($95\%$ CI 0.50-2.74, $$n = 379$$) and 0.88 ($95\%$ CI 0.43-1.20, $$n = 166$$) respectively, in those with i-IFG at 3 years. Similarly, a trial among 880 adults with prediabetes in the UK showed a hazard ratio of 0.52 ($95\%$ CI 0.15-1.83, $$n = 108$$) at 3 years in those with i-IFG 13. The Kerala Diabetes Prevention Program (K-DPP) from India showed a relative risk of 0.95 ($95\%$ CI 0.68, 1.33) in people with i-IFG ($$n = 579$$) at 2 years among 1007 high-risk individuals 14. However, the above-mentioned findings were from the sub-group analyses, which are constrained by small sample sizes, a limited number of events, and confounding 15.
While preventing the progression of prediabetes to diabetes is important, regression to normoglycemia is also essential to achieve, even if transient, as this has been shown to be significantly associated with reduction in the development of diabetes 10. To our knowledge, there are no data on regression to normoglycemia with lifestyle modification in the i-IFG group among Indians, an ethnic group with i-IFG being the most common prediabetes phenotype 5, 14. In addition to being at high risk for diabetes, individuals with i-IFG are at an increased risk of developing micro- and macro-vascular complications, and of all-cause mortality 6.
This study aims to evaluate the effects of an intensive community-based lifestyle modification program on regression to normoglycemia among women with i-IFG compared to a control group at 24 months. We will also evaluate the effects of the intervention on improving cardiometabolic risk factors. The study will use the RE-AIM framework to evaluate Reach, Adoption, Implementation and Maintenance 16 and the intervention fidelity using process measures.
This two-arm parallel group randomized controlled trial targets women as the behavioural risk factors of overweight or obesity and physical inactivity are generally higher among women, compared to men, in the Indian context 17 and the intervention outcomes reported so far are poorer among women 12 in India.
## Study design and setting
The study uses a hybrid design (Hybrid type 2) 18 that tests the effectiveness and implementation of a lifestyle modification program. A randomized controlled trial will evaluate the effectiveness of the intervention, and the intervention implementation and fidelity will be evaluated using implementation outcomes and process measures, respectively.
The study was registered with Clinical Trials Registry, India (CTRI/$\frac{2021}{07}$/035289, on 30 th July 2021). The trial will be reported in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines 19.
The study will be conducted in Kasaragod district of Kerala (Figure 1). Kerala, the southernmost Indian state, has a higher prevalence of T2DM (nearly $20\%$) and a greater burden of several cardiometabolic risk factors than most other Indian states 17. Further, the state is in the most advanced stage of epidemiological transition in the country 20 and said to be a harbinger of the future burden of diabetes and other chronic diseases in India 20. Thus, Kerala provides an ideal place to implement and evaluate lifestyle modification programs.
**Figure 1.:** *Map of the study area.
A. India political.
B. Kasaragod district (shaded) in the State of Kerala.
C. Taluks of Kasaragod district.*
Figure 2 shows the CONSORT diagram of the trial. Kasaragod is the northernmost district of Kerala, with a population of 1,307,375, a sex ratio of 1079 women for every 1000 men, and a literacy rate of $90\%$ 21. Kasaragod has four taluks (sub-district) with 777 wards (lowest administrative division with approximately 1300 individuals in each ward) 21. From these four taluks, two were randomly selected, namely Hosdurg taluk and Kasaragod taluk. There are 465 wards in these two taluks, amongst which those wards within 20km distance from the institute were included (269 wards) considering the logistics and feasibility. Out of 269 wards, 25 wards with at least 1000 individuals in each ward were randomly selected. From these selected wards, individuals with i-IFG will be identified and recruited to the trial with a minimum of 23 participants per ward.
**Figure 2.:** *CONSORT diagram of the trial.*
## Study participants
Inclusion criteria: 1) Women aged 30–60 years; 2) overweight or obese (waist circumference ≥80 cm) 22; 3) no prior history of diabetes; 4) not taking any glucose-lowering medications; 5) no prior history of gestational diabetes mellitus (GDM); 6) able to read, write and speak Malayalam, the local language; 7) consents to participate in the trial; and 8) diagnosed with i-IFG (fasting plasma glucose [FPG] 5.6-6.9 mmol/l and 2-hr plasma glucose [2-hr PG] <7.8 mmol/l) on a 2-hr oral glucose tolerance test (OGTT) 3.
Exclusion criteria: 1) Women with known T2DM; 2) having GDM; 3) prior history of GDM; 4) breastfeeding women; 5) having major chronic illnesses including mental disorders, that are likely to impede consenting and participation in the intervention program; 6) taking medications that could alter glucose metabolism (e.g., corticosteroids); and 7) diagnosed with normoglycemia, IGT, and T2DM, as per the American Diabetes *Association criteria* 3 on the OGTT.
## Sample size and randomization
We assumed that the cumulative incidence of regression to normoglycemia at two years would be $18\%$ in the control group (based on unpublished data from the K-DPP trial) and that there would be a $50\%$ relative risk in conversion to normoglycemia with the intervention. The sample size required in each study group was 475 with a type 1 error of $5\%$, at least $80\%$ power, a contamination rate of $15\%$, and a $10\%$ loss to follow-up. The sample size required with same estimates and $90\%$ power in each group is 625, and will be recruited, if logistically feasible.
The estimated sample size of 950 participants will be randomly allocated in a 1:1 ratio to the control group and the intervention group using a computer-generated randomization sequence using Microsoft excel by an independent statistician not involved in the trial. The independent statistician will generate the randomized sequence and allocate the individuals to the randomized groups. The participants and the Principal Investigator (EM) will be blinded to the allocation sequence until informed written consent for study participation is obtained. The outcome assessors, data entry personnel and statisticians who analyse the data will be blinded throughout the study as they are not part of the team that delivers intervention or manage the project.
This study uses individual randomization in order to avoid the methodological challenges of cluster randomization, including the clustering effect on statistical power and selection bias, possibility of imbalanced study groups, and the dilution effect 23. Trial conduct solutions will be put in place to address potential contamination, if any, between the study groups with individual randomization. This include using different staff for each group, education of the participants against contamination 23, and getting signed nondisclosure agreement from the intervention participants regarding the type of intervention being received. Furthermore, analytical methods that adjust for contamination will also be used. We will also quantify the contamination using a treatment fidelity framework based on the criteria advocated by the Behaviour Change Consortium 24 and NICE guidance on behaviour change 25.
## Recruitment of participants and data collection
Screening and recruitment of participants and data collection will be conducted in 2 stages as described below: Stage 1: Home visits. All women in the age group of 30–60 years will be selected from the voters' list of the selected wards. Eligibility for participation in the study will be ascertained during home visits using a screening questionnaire (includes demographics and eligibility criteria) by trained staff. Only one participant per household (randomly selected) will be screened. Those who meet the eligibility criteria and provide consent for study participation will be screened with a point-of-care glucometer (OneTouch Select Plus, LifeScan Inc.) and their waist circumference will be measured using SECA 201 ergonomic retractable tape with using a standard protocol 26. Those who are overweight or obese (waist circumference ≥80 cm) 22 and with a random capillary blood glucose of >110 mg/dl 27 will be invited to undergo a 2-hr OGTT in clinics organized in their neighbourhoods. These individuals will be considered to be at high risk for having prediabetes or diabetes.
Stage 2: Clinics. The high-risk individuals will attend clinics organized in their neighbourhoods, where they will undergo a 2-hr OGTT, body composition, anthropometric, and blood pressure measurements, and interviews to complete the questionnaires. Those diagnosed with T2DM on the OGTT will be referred to the nearby health facility for treatment and care. Body fat composition will be assessed using TANITA DC 360 (pole). Blood pressure will be measured using an OMRON HEM-7120 blood pressure monitor using the standard protocol 26.
The weight and height will be measured using SECA 813 flat scale and SECA 213 stadiometer, respectively, with a standard protocol 26. Blood samples will be collected in a fasting state (8–10 hours of fasting) and will be centrifuged within 30 mins after collection and transported to a lab accredited by the National Accreditation Board for Testing and Calibration Laboratories (NABL) 28. Questionnaires will be administered by trained staff to collect information on demographics, diet 29, physical activity 26, tobacco use 26, alcohol use 26, diabetes knowledge 30, prediabetes knowledge 31, health-related quality of life (HRQoL) 32; self-efficacy for managing chronic diseases 33, risk perception for diabetes 34, social support 35, and sleep hygiene 36 using standardized questionnaires (Table 1). Those who are diagnosed with diabetes at 12 th and 24 th month assessment will be referred to the nearby health facility for treatment and care, and will continue to participate in the trial. All assessments done at the baseline will be repeated at 12 months and 24 months.
**Table 1.**
| Outcome measures | Variable | Tools/tests used | Baseline | Regular assessment | 12 months | 24 months |
| --- | --- | --- | --- | --- | --- | --- |
| Primary outcome for effectiveness | Primary outcome for effectiveness | Primary outcome for effectiveness | Primary outcome for effectiveness | Primary outcome for effectiveness | Primary outcome for effectiveness | Primary outcome for effectiveness |
| Regression of i-IFG to normoglycemia at 12 and 24 months | Fasting plasma glucose and 2-hr post-load plasma glucose | OGTT in a NABL accredited laboratory 28 | ✓ | | ✓ | ✓ |
| Secondary outcomes for effectiveness | Secondary outcomes for effectiveness | Secondary outcomes for effectiveness | Secondary outcomes for effectiveness | Secondary outcomes for effectiveness | Secondary outcomes for effectiveness | Secondary outcomes for effectiveness |
| Incidence of diabetes | Fasting plasma glucose, 30 minutes and 2-hr post- load plasma glucose, HbA1c | NABL accredited laboratory 28 | ✓ | | ✓ | ✓ |
| Insulin sensitivity | Fasting insulin, 2-h insulin, Homa IR | NABL accredited laboratory 28 | ✓ | | ✓ | ✓ |
| Beta cell function | Fasting insulin, 2hr insulin, Homa B | | ✓ | | ✓ | ✓ |
| Lipid profile | Total cholesterol, triglycerides, HDL-cholesterol, and LDL-cholesterol | NABL accredited laboratory 28 | ✓ | | ✓ | ✓ |
| Liver function tests | Aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT) | NABL accredited laboratory 28 | ✓ | | ✓ | ✓ |
| Blood Pressure | Systolic and diastolic blood pressure | OMRON electronic blood pressure monitor | ✓ | | ✓ | ✓ |
| Anthropometrics | a. Weight b. Height c. Waist circumference | a. SECA weighing scale b. SECA stadiometer c. SECA non-elastic tape | ✓ | | ✓ | ✓ |
| Body composition measures | a. Fat percent b. Muscle mass | TANITA body fat analyser | ✓ | | ✓ | ✓ |
| Psychosocial variables | a. Diabetes knowledge b. Knowledge on prediabetes c. Health-related quality of life d. Self-efficacy/self-empowerment e. Risk-perception for diabetes f. Social support g. Sleep Hygiene | a. 24 Item Diabetes Knowledge Questionnaire 30 b. Prediabetes Knowledge Questionnaire 31 c. WHO QoL BREF scale 32 d. Health Lifestyle and Personal Control Questionnaire 33 e. Scale by Hivert et al. 34 f. Scale by Sarason et al. 35 g. Sleep Hygiene Index scale by Mastin et al. 36 | ✓ | | ✓ | ✓ |
| Behavioural outcomes for effectiveness and implementation | Behavioural outcomes for effectiveness and implementation | Behavioural outcomes for effectiveness and implementation | Behavioural outcomes for effectiveness and implementation | Behavioural outcomes for effectiveness and implementation | Behavioural outcomes for effectiveness and implementation | Behavioural outcomes for effectiveness and implementation |
| | a. Diet b. Physical activity and sedentary behaviour c. Tobacco use d. Alcohol use | a. Food frequency questionnaire 25 b. Global Physical Activity Questionnaire 26 c. WHO STEPS questionnaire 26 d. WHO STEPS questionnaire 26 | ✓ | | ✓ | ✓ |
| Implementation outcomes (at community, provider and beneficiary levels) | Implementation outcomes (at community, provider and beneficiary levels) | Implementation outcomes (at community, provider and beneficiary levels) | Implementation outcomes (at community, provider and beneficiary levels) | Implementation outcomes (at community, provider and beneficiary levels) | Implementation outcomes (at community, provider and beneficiary levels) | Implementation outcomes (at community, provider and beneficiary levels) |
| | a. Reach a1. Participants approached a2. Frequency of contact with the panchayat b. Effectiveness b1. Improved knowledge on T2DM prevention b2. Risk perception b3. Self-efficacy b4. Social Support c. Adoption c1 Individuals attending the sessions (Number) c2. Individuals who set goals c3 Individuals achieving the behavioural targets c4. Change in knowledge on diabetes and its risk factors and behavioural targets d. Implementation d1. Peer mentor trainings (quality) d2. Participants group sessions (quality) d3. Sessions conducted d4. Peer mentor selection d5. Support received by peer mentors e. Maintenance e 1. Individuals achieving behavioural targets | a1. Database on recruitment a2. Database on meetings b1. 24 Item Diabetes Knowledge Questionnaire 30 b.2. Risk perception Scale 34 b.3 Health Lifestyle and Personal Control Questionnaire 33 b4. Social Support scale 35 c1. Participant attendance data c2. Participants feedback report c3. Behavioural change report 24, 25 c4. Pre and post knowledge change evaluation report (Participant) d1. Peer mentors feedback report, Pre and post knowledge change evaluation report (Peer Mentors) d2. Participants feedback report, d3. Session attendance data d4. 1. Peer mentors characteristics d5. Participant feedback report e1. Behavioural assessment data 24, 25 | ✓ ✓ ✓ ✓ | ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ | ✓ ✓ ✓ ✓ | ✓ ✓ ✓ ✓ ✓ |
Staff conducting physical measurements and administering questionnaires at baseline and follow-up visits will be blinded to participants’ group allocation. Intervention staff will not be engaged in the data collection process and the statistician engaged in data analysis will be blinded to the group allocation.
All study materials can be found in the *Extended data* 37.
## Outcomes
The primary outcome for the effectiveness study will be normoglycemia, defined as FPG<5.6 mmol/l and 2-hr PG<7.6 mmol/l, at 24 months 3. Secondary outcomes will include incidence of T2DM (fasting plasma glucose ≥ 7.0 mmol/l or 2hr PG ≥ 11.1 mmol/l or HbA1c ≥$6.5\%$), insulin sensitivity, beta cell function, weight, body mass index, waist circumference, FPG, 2-hr PG, blood pressure, body composition measures (fat percent and muscle mass) and psychosocial variables (health-related quality of life, self-efficacy/self-empowerment, risk-perception for diabetes, social support and sleep hygiene). Implementation outcomes include reach, effectiveness, adoption, implementation and maintenance of the intervention 16.
## Study groups
Control group. Participants in the control group will receive a health information leaflet at baseline in local language (Malayalam) on strategies for diabetes prevention. No further engagement will be there in the control group apart from follow-up assessments at 12 and 24 months.
Intervention group. The study participants in the intervention group will receive an intensive lifestyle modification program for a period of 12 months through group and individually mentored sessions. The intervention will include behavioural determinants such as self-efficacy, risk perception, social support, and behavioural change techniques, including knowledge enhancement, self-monitoring, goal setting and review, and peer support 38 (Table 2). The intervention will be intense as the engagement is not just limited to group sessions, but also involves individualized support through peer mentors and individualized instructions through a mobile application, specifically made for this trial.
**Table 2.**
| Program Goals | Personal learning and Social environmental change objectives | Determinants based on theory and evidence | Behaviour change techniques | Feasible and culturally acceptable strategies to enhance engagement and implementation |
| --- | --- | --- | --- | --- |
| a. Weight loss by 5–7% 6 b. Reduction in waist circumference by ≥4 cm 22 c. Reduction in body mass index 6 by ≥0.5 kg/m 2 d. Increased consumption of fruit and vegetables (>5 servings/day) 39 e. Increased physical activity through walking, exercise, and culturally appropriate activities (>150 minutes/ week) 40 f. Improved sleep hygiene 41 | Personal learning objectives • Increase in awareness of risk factors of T2DM • Improve risk perception on T2DM • Improve self-efficacy in making lifestyle changes Social Environment change objectives • Enhance peer support for behaviour change • Enhance household/family support for behaviour change • Facilitate opportunities for healthy lifestyle in collaboration with group members • Empowerment for diabetes prevention | • Risk perception • Self-efficacy • Social support • Availability and accessibility of facilities for physical activity and healthy food options | • Provide information on the risk factors of T2DM • Provide information on the dietary and physical activity targets for individuals. • Self-monitoring • Goal setting and goal review with emphasis on participants and family member outcomes • Peer support • Social and practical support from family, neighbourhood members and community organizations (Panchayats) • Engage and empower family and group members to increase availability and accessibility of healthy food options and physical activity options | Individual-level • Educational sessions that focus on ‘modifiable’ determinants of risk on diabetes. • Sessions scheduled in local neighbourhoods (e.g. a reading room or Kudumbashree’s meeting rooms) according to work, family and other cultural needs of participants Interpersonal-level (family) • Group-based delivery • Inclusion of family members in the sessions • Enabling ongoing peer and social support, with family members and friends of participants • Kitchen garden training to facilitate vegetable consumption and increase enjoyable physical activity • Forming walking groups or other activity groups such as yoga or aerobic dance groups as appropriate. |
Theory of intervention. The intervention program objectives, theory-based methods, and strategies for intervention engagement and implementation are given in Table 3. Briefly, the intervention was adapted from a successfully conducted lifestyle-based diabetes prevention trial in Kerala, the K-DPP 38 Program objectives will be achieved through “personal learning” and “environmental change” using evidence-based behaviour change techniques with strategies targeting participants at individual, interpersonal and community levels.
**Table 3.**
| Session | Individual/ Group | Theme | Activity (AV aid) | Duration | Facilitator |
| --- | --- | --- | --- | --- | --- |
| 1 | G | 1.1 Introduce the project 1.2 Rapport building 1.3 Knowledge enhancement | 1.1.1 Introduction about the project, requirements and commitments 1.2.1 Building rapport with the participants 1.2.2 Identification of peer mentor 1.3.1 Discuss about diabetes and prediabetes (IFG & IGT) (Flipchart & Mobile based application) 1.3.2 Discuss about risk factors of diabetes (Flipchart & Mobile based application) | 20 Min 15 Min 30 Min 20 Min 20 Min | Research Team |
| 2 | G | 2.1 Risk factor Modification- Unhealthy break/>diet 2.2 Self-monitoring of dietary intake 2.3 Knowledge enhancement on dietary allowances and recommendation for fat/sugar/ vegetables/fruits/salt/rice) 2.4 Goal setting 2.5 Diet monitoring | 2.2.1 Self-monitoring work sheet (Flipchart & participant workbook) 2.3.1 Education on healthy diet practices [session+ activity] (Flip chart & Mobile based application) 2.4.1 Individualised goal setting (Flipchart & participant workbook) 2.5.1 Personalized diet planning tool using an interactive mobile application platform. | 20 Min 20 Min 20 Min 15 Min | Research Team |
| 3 | I | 3.1. Diet goal monitoring and revisiting the goals | 3.1.1. Goal monitoring (participant workbook) 3.1.2. Identification of barriers 3.1.3. Goal resetting (if needed) | 10 Min 10 Min 10 Min | Peer mentor |
| 4 | G | 4.1 Demonstration of healthy diet option (Salad preparation) 4.2 Physical activity promotion 4.3 Self-monitoring 4.4 Knowledge enhancement on strategies to improve PA 4.5 Goal Setting | 4.1.1 Demonstrate the preparation of healthy diet (salad) 4.2.1 Discuss on physical activity and its importance (Flip chart) 4.3.1 PA self-monitoring worksheet (participant workbook) 4.4.1 Discuss on the types of physical activities that are culturally appropriate and feasible (Flipchart & participant workbook) 4.5.1 Individualised goal setting (Participant workbook) | 20 Min 15 Min 15 Min 25 Min 15 Min per participant | Research Team |
| 5 | I | 5.1 PA goal monitoring and revisiting the goals 5.2 Diet goal monitoring and revisiting the goals | 5.1.1 Goal monitoring (Participant workbook) 5.1.2 Identification of barriers 5.1.3 Goal resetting (if needed) 5.2.1 Goal monitoring (Participant workbook) 5.2.2 Identification of barriers 5.2.3 Goal resetting (if needed) | 30 Min per participant | Peer mentor |
| 6 | 1 | 6.1 PA goal monitoring and revisiting the goals 6.2 Diet goal monitoring and revisiting the goals | 6.1.1 Goal monitoring (Participant workbook) 6.1.2 Identification of barriers 6.1.3 Goal resetting (if needed) 6.2.1 Goal monitoring (Participant workbook) 6.2.2 Identification of barriers 6.2.3 Goal resetting (if needed) | 30 Min per participant | Peer mentor |
| 7 | G | 7.1 Knowledge enhancement - Effect of stress on diabetes and other chronic diseases 7.2 Strategies for Stress Management 7.3 Strategies to enhance Sleep hygiene | 7.1.1 Awareness on impact of stress and importance of managing it (Flipchart & m health app) 7.2.1 Identification of Stress factors (Flipchart) 7.2.2 Demonstration of various stress management techniques (Breathing exercise and yoga) 7.3.1 Educate about the importance of maintaining sleep hygiene (Flipchart) | 20 Min 20 Min 10 Min 15 Min | Research Team |
| 8 | I | 8.1 PA goal monitoring and revisiting the goals 8.2 Diet goal monitoring and revisiting the goals | 8.1.1 Goal monitoring (Participant workbook) 8.1.2 Identification of barriers 8.1.3 Goal resetting (if needed) 8.2.1 Goal monitoring (Participant workbook) 8.2.2 Identification of barriers 8.2.3 Goal resetting (if needed) | 30 Min per participant | Peer mentor |
| 9 | I | 9.1 PA goal monitoring and revisiting the goals 9.2 Diet goal monitoring and revisiting the goals | 9.1.1 Goal monitoring (Participant workbook) 9.1.2 Identification of barriers 9.1.3 Goal resetting (if needed) 9.2.1 Goal monitoring (Participant workbook) 9.2.2 Identification of barriers 9.2.3 Goal resetting (if needed) | 30 Min per participant | Peer mentor |
| 10 | G | 10.1 Tobacco and alcohol cessation | 10.1.1 Create awareness on impact of alcohol and tobacco in the development of T2DM 10.1.2 Refer the participants who use tobacco and alcohol to cessation clinics (if needed) | 20 Min | Research Team |
| 11 | I | 11.1 PA goal monitoring and revisiting the goals 11.2 Diet goal monitoring and revisiting the goals | 11.1.1 Goal monitoring (Participant workbook) 11.1.2 Identification of barriers 11.1.3 Goal resetting (if needed) 11.2.1 Goal monitoring (Participant workbook) 11.2.2 Identification of barriers 11.2.3 Goal resetting (if needed) | 30 Min per participant | Peer mentor |
| 12 | I | 12.1 PA goal monitoring and revisiting the goals 12.2 Diet goal monitoring and revisiting the goals | 12.1.1 Goal monitoring (Participant workbook) 12.1.2 Identification of barriers 12.1.3 Goal resetting (if needed) 12.2.1 Goal monitoring (Participant workbook) 12.2.2 Identification of barriers 12.2.3 Goal resetting (if needed) | 30 Min per participant | Peer mentor |
## Intervention content
Figure 3 shows a thematic representation of the program goals with the targets and the strategies adopted at individual and group level using the behaviour techniques. The intervention will focus primarily on key behavioural risk factors, including unhealthy diet, physical inactivity, tobacco use, alcohol use, and sleep. Based on the dietary recommendations for the prevention of T2DM 39 and pertinent research findings among individuals with IFG, the dietary intervention will include consumption of a low-calorie diet (~1500 calories per day i.e., 500 calories lower than the daily requirement for women) 39, 42– 44 and consuming food with low glycaemic index. Other dietary recommendations include changing the quality of dietary fat from using saturated to unsaturated, increasing the intake of whole grains and foods rich in fibre and decreasing the intake of sugar-sweetened beverages, sweets, and highly processed products 45– 47. The dietary goals are: <$30\%$ of total energy intake from fat, 400–600 grams of fruit and vegetable intake a day, 5 cups (400 gms of cooked rice intake per day), <25 gms of free sugar intake a day, and <5 gms of salt intake per day 45. The adherence to diet will be assessed every month using a 24-hour dietary recall 48. Other measures of intervention goal include increasing physical activity to the recommended levels of at least 150 minutes of moderate-vigorous physical activity per week 40. 7–9 hours of sleep at night 41, 49, and no use of alcohol and tobacco.
**Figure 3.:** *Thematic representation of the program goals, targets and individual-group tailored strategies using behaviour change techniques*
## Intervention delivery
The intervention will be delivered through 12 sessions (individual and group based), one session per month, over a period of 1 year (Table 3). Intervention delivery will be supported using pretested and piloted educational materials such as flip charts for group-based sessions and a mobile based application for individual sessions. The mobile application will also serve as an interactive platform for a personalized diet planning and reporting.
The group sessions (45–60 minutes duration) will be organised in the participants’ neighbourhood, mostly in homes or local health centres, delivered by the research team (postgraduates in public health/ social work degree) in the initial phase, followed by trained volunteers as “peer mentors”. Peer mentor, a group nominated volunteer, will undergo a five-day capacity building training program to guide and assist the participants in making realistic goals for lifestyle modification with the support of the extended community stakeholders.
## Evaluation framework
In addition to the baseline, 12th month and 24 th month assessment for the primary and secondary outcomes, process evaluation of core interventions at community, peer mentor and participant levels will be undertaken (Figure 4). Evaluation process will be facilitated through regular monitoring via participant feedback report, peer mentor feedback report, feedback on quality of training, pre- and post-training knowledge evaluation and other interactions (mobile app/telephonic contact).
**Figure 4.:** *Theory of change Framework.*
## Data analysis
Data will be collected and entered using a data entry template in an ODK platform by the data collectors and managed in a cloud-based server which will only be accessed by the Principal Investigator (EM). Subsequent to the data collection, the data will be used only using a participant identity number and all the personal identifiers will be masked. Only deidentified data will be shared with other investigators, if required. Data quality will be ensured during data collection and data analysis. During the data collection, the research team will verify the data for missing values and if present, will be rectified then and there. Furthermore, randomly identified $5\%$ of the participants data will be verified with the participants through telephone for quality check. The data from the ODK platform will be exported to SPSS and data cleaning will be done manually. Any outliers or implausible values will be identified and will be checked with participants over phone, if necessary.
The analysis will follow the intention-to-treat (ITT) principle. Characteristics of the participants at baseline will be compared between study groups using descriptive statistics. The primary outcome will be analysed using the generalized estimating equations (GEE) with an appropriate working correlation structure and a binomial family with 'log' link function to estimate the relative risk (and $95\%$ confidence interval [CI], p value). Standard errors will be based on Huber-White sandwich estimator, which will provide valid CIs even in case of misspecification of the correlation structure 50. For secondary outcomes, continuous variables will be analysed using mixed-effects linear regression models, which will include all available data at baseline, 12 and 24 months. Study group (intervention vs. control), timepoint (follow-up vs. baseline) and a study group-by-time point interaction will be specified as fixed effects. Random effects will be specified for wards, to account for the clustered study design, and for participants, to account for correlation between the repeated measurements on the same individual. Categorical variables will be analyzed using log-binomial models. All p values reported will be 2 tailed, and a p of 0.05 will be considered statistically significant. Analyses will be performed with Stata version 14.2 (StataCorp LP, College Station, Texas, USA).
## Ethics approval
The study was approved by the Institutional Human Ethics Committee of the Central University of Kerala (CUK/IHEC/$\frac{2019}{034}$_A, 21 st November 2019). Written informed consent will be obtained from all study participants. The risk from the intervention to the participants is anticipated to be negligible as the intervention involves only lifestyle modification and no pharmacological drugs. Data safety monitoring will be done by the research team.
## Discussion
This paper describes the protocol for an intense lifestyle intervention program among women with i-IFG to facilitate regression to normoglycemia at 24 months. This study will provide the first evidence on the effects of lifestyle intervention in regressing i-IFG to normoglycemia among Indians. The findings of the study will be disseminated through public engagement, reports and publications.
## Strengths and limitations
This is one of the first studies globally to evaluate the effects of a novel lifestyle intervention among women with i-IFG. Further, the community-based nature of the intervention would facilitate future sustainability and scalability in India and other similar settings. However, since the study population comprises only women, the findings cannot be generalized to men.
## Trial status
The trial is currently in the screening phase.
## Underlying data
No underlying data are associated with this article.
## Extended data
OSF: Randomized Controlled Trial on lifestyle modification intervention for women with isolated impaired fasting glucose. https://doi.org/10.17605/OSF.IO/8K9XJ 37.
This project contains the following extended data: Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
## References
1. **IDF Diabetes Atlas.**. (2019.0)
2. Bommer C, Sagalova V, Heesemann E. **Global Economic Burden of Diabetes in Adults: Projections From 2015 to 2030.**. (2018.0) **41** 963-970. DOI: 10.2337/dc17-1962
3. **2. Classification and Diagnosis of Diabetes:**. (2021.0) **44** S15-S33. DOI: 10.2337/dc21-S002
4. Yip WCY, Sequeira IR, Plank LD. **Prevalence of Pre-Diabetes across Ethnicities: A Review of Impaired Fasting Glucose (IFG) and Impaired Glucose Tolerance (IGT) for Classification of Dysglycaemia.**. (2017.0) **9** 1273. DOI: 10.3390/nu9111273
5. Anjana RM, Deepa M, Pradeepa R. **Prevalence of diabetes and prediabetes in 15 states of India: results from the ICMR-INDIAB population-based cross-sectional study.**. (2017.0) **5** 585-596. DOI: 10.1016/S2213-8587(17)30174-2
6. Tabák AG, Herder C, Rathmann W. **Prediabetes: a high-risk state for diabetes development.**. (2012.0) **379** 2279-2290. DOI: 10.1016/S0140-6736(12)60283-9
7. Glechner A, Keuchel L, Affengruber L. **Effects of lifestyle changes on adults with prediabetes: A systematic review and meta-analysis.**. (2018.0) **12** 393-408. DOI: 10.1016/j.pcd.2018.07.003
8. Haw JS, Galaviz KI, Straus AN. **Long-term Sustainability of Diabetes Prevention Approaches: A Systematic Review and Meta-analysis of Randomized Clinical Trials.**. (2017.0) **177** 1808-1817. DOI: 10.1001/jamainternmed.2017.6040
9. Gong Q, Zhang P, Wang J. **Morbidity and mortality after lifestyle intervention for people with impaired glucose tolerance: 30-year results of the Da Qing Diabetes Prevention Outcome Study.**. (2019.0) **7** 452-461. DOI: 10.1016/S2213-8587(19)30093-2
10. Campbell MD, Sathish T, Zimmet PZ. **Benefit of lifestyle-based T2DM prevention is influenced by prediabetes phenotype.**. (2020.0) **16** 395-400. DOI: 10.1038/s41574-019-0316-1
11. Saito T, Watanabe M, Nishida J. **Lifestyle modification and prevention of type 2 diabetes in overweight Japanese with impaired fasting glucose levels: a randomized controlled trial.**. (2011.0) **171** 1352-1360. DOI: 10.1001/archinternmed.2011.275
12. Weber MB, Ranjani H, Staimez LR. **The Stepwise Approach to Diabetes Prevention: Results From the D-CLIP Randomized Controlled Trial.**. (2016.0) **39** 1760-1767. DOI: 10.2337/dc16-1241
13. Davies MJ, Gray LJ, Troughton J. **A community based primary prevention programme for type 2 diabetes integrating identification and lifestyle intervention for prevention: the Let's Prevent Diabetes cluster randomised controlled trial.**. (2016.0) **84** 48-56. DOI: 10.1016/j.ypmed.2015.12.012
14. Thankappan KR, Sathish T, Tapp RJ. **A peer-support lifestyle intervention for preventing type 2 diabetes in India: A cluster-randomized controlled trial of the Kerala Diabetes Prevention Program.**. (2018.0) **15** e1002575. DOI: 10.1371/journal.pmed.1002575
15. Sathish T, Tapp RJ, Shaw JE. **Do lifestyle interventions reduce diabetes incidence in people with isolated impaired fasting glucose?**. (2021.0) **23** 2827-2828. DOI: 10.1111/dom.14529
16. Glasgow RE, Harden SM, Gaglio B. **RE-AIM planning and evaluation framework: adapting to new science and practice with a 20-year review.**. (2019.0) **7** 64. DOI: 10.3389/fpubh.2019.00064
17. Sarma PS, Sadanandan R, Thulaseedharan JV. **Prevalence of risk factors of non-communicable diseases in Kerala, India: results of a cross-sectional study.**. (2019.0) **9** e027880. PMID: 31712329
18. Curran GM, Bauer M, Mittman B. **Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact.**. (2012.0) **50** 217-226. DOI: 10.1097/MLR.0b013e3182408812
19. Schulz KF, Altman DG, Moher D. **CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials.**. (2010.0) **152** 726-732. DOI: 10.7326/0003-4819-152-11-201006010-00232
20. **Nations within a nation: variations in epidemiological transition across the states of India, 1990-2016 in the Global Burden of Disease Study.**. (2017.0) **390** 2437-2460. DOI: 10.1016/S0140-6736(17)32804-0
21. **Census 2011, India.**. (2011.0)
22. **Waist circumference and waist-hip ratio report of a WHO expert consultation.**. (2011.0)
23. Magill N, Knight R, McCrone P. **A scoping review of the problems and solutions associated with contamination in trials of complex interventions in mental health.**. (2019.0) **19** 4. DOI: 10.1186/s12874-018-0646-z
24. Bellg AJ, Borrelli B, Resnick B. **Enhancing treatment fidelity in health behavior change studies: best practices and recommendations from the NIH Behavior Change Consortium.**. (2004.0) **23** 443-51. DOI: 10.1037/0278-6133.23.5.443
25. **Behaviour change: general approaches.**. (2007.0)
26. **STEPwise Approach to NCD Risk Factor Surveillance (STEPS)Manual**. (2021.0)
27. Somannavar S, Ganesan A, Deepa M. **Random capillary blood glucose cut points for diabetes and pre-diabetes derived from community-based opportunistic screening in India.**. (2009.0) **32** 641-643. DOI: 10.2337/dc08-0403
28. **National Accreditation Board for Testing and Calibration Laboratories**
29. Iqbal R, Ajayan K, Bharathi AV. **Refinement and validation of an FFQ developed to estimate macro- and micronutrient intakes in a south Indian population.**. (2009.0) **12** 12-8. DOI: 10.1017/S1368980008001845
30. Arora S, Marzec K, Gates C. **Diabetes knowledge in predominantly Latino patients and family caregivers in an urban emergency department.**. (2011.0) **21** 1-6. PMID: 21462722
31. AlSaleh E, Alnaser M, Alsalman A. **A community-based prediabetes knowledge assessment among Saudi adults in Al-Ahsa region, 2018.**. (2021.0) **10** 2032. DOI: 10.4081/jphr.2021.2032
32. **Development of the World Health Organization WHOQOL-BREF quality of life assessment. The WHOQOL Group.**. (1998.0) **28** 551-558. DOI: 10.1017/s0033291798006667
33. Darviri C, Alexopoulos EC, Artemiadis AK. **The Healthy Lifestyle and Personal Control Questionnaire (HLPCQ): a novel tool for assessing self-empowerment through a constellation of daily activities.**. (2014.0) **14** 995. DOI: 10.1186/1471-2458-14-995
34. Hivert MF, Warner AS, Shrader P. **Diabetes Risk Perception and Intention to Adopt Healthy Life styles Among Primary Care Patients.**. (2009.0) **32** 1820-1822. DOI: 10.2337/dc09-0720
35. Sarason IG, Levine HM, Basham RB. **Assessing social support: The Social Support Questionnaire.**. (1983.0) **44** 127-139
36. Mastin DF, Bryson J, Corwyn R. **Assessment of sleep hygiene using the Sleep Hygiene Index.**. (2006.0) **29** 223-227. DOI: 10.1007/s10865-006-9047-6
37. Mathews E. **Randomized Controlled Trial on lifestyle modification intervention for women with isolated impaired fasting glucose**. (2022.0). DOI: 10.17605/OSF.IO/FQ7KC
38. Mathews E, Thomas E, Absetz P. **Cultural adaptation of a peer-led lifestyle intervention program for diabetes prevention in India: the Kerala diabetes prevention program (K-DPP).**. (2018.0) **17** 974. DOI: 10.1186/s12889-017-4986-0
39. Bantle JP, Wylie-Rosett J. **Nutrition Recommendations and Interventions for Diabetes: a position statement of the American Diabetes Association.**. (2008.0) **31 Suppl 1** S61-S78. DOI: 10.2337/dc08-S061
40. **Global recommendations on physical activity for health**. (2018.0)
41. **Sleep Hygiene**. (2019.0)
42. Wang LL, Wang Q, Hong Y. **The Effect of Low-Carbohydrate Diet on Glycemic Control in Patients with Type 2 Diabetes Mellitus.**. (2018.0) **10** 661. DOI: 10.3390/nu10060661
43. Steven S, Taylor R. **Restoring normoglycaemia by use of a very low calorie diet in long- and short-duration Type 2 diabetes.**. (2015.0) **32** 1149-1155. DOI: 10.1111/dme.12722
44. Ebbeling CB, Feldman HA, Klein GL. **Effects of a low carbohydrate diet on energy expenditure during weight loss maintenance: randomized trial.**. (2018.0) **363** k4583. DOI: 10.1136/bmj.k4583
45. Guess N, Perreault L, Kerege A. **Dietary fatty acids differentially associate with fasting versus 2-hour glucose homeostasis: implications for the management of subtypes of prediabetes.**. (2016.0) **11** e0150148. DOI: 10.1371/journal.pone.0150148
46. **Standards of Medical Care in Diabetes-2016 Abridged for Primary Care Providers.**. (2016.0) **34** 3-21. DOI: 10.2337/diaclin.34.1.3
47. Basiak A, Różańska D, Połtyn-Zaradna K. **Comparison of intake of food groups between participants with normoglycemia, impaired fasting glucose, and type 2 diabetes in PURE Poland population.**. (2019.0) **39** 315-324. DOI: 10.1007/s13410-018-0675-5
48. **Dietary guidelines for Indians**. (2011.0)
49. Watson NF, Badr MS, Belenky G. **Recommended amount of sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society.**. (2015.0) **38** 843-844. DOI: 10.5665/sleep.4716
50. Desai M, Bryson SW, Robinson T. **On the use of robust estimators for standard errors in the presence of clustering when clustering membership is misspecified.**. (2013.0) **34** 248-256. DOI: 10.1016/j.cct.2012.11.006
51. **Guidelines for Reporting Outcomes in Trial Protocols: The SPIRIT-Outcomes 2022 Extension.**. (2022.0) **328** 2345-2356. DOI: 10.1001/jama.2022.21243
|
---
title: 'Vending machine backgrounds: nudging healthier beverage choices'
authors:
- Ryan Calabro
- Eva Kemps
- Ivanka Prichard
- Marika Tiggemann
journal: Current Psychology (New Brunswick, N.j.)
year: 2023
pmcid: PMC9971671
doi: 10.1007/s12144-023-04420-8
license: CC BY 4.0
---
# Vending machine backgrounds: nudging healthier beverage choices
## Abstract
Soft drink overconsumption is a growing public health concern. The present research investigated whether priming nudges could decrease soft drink choices from a vending machine. We compared the effect of six vending machine wraps (Mount Franklin ™ logo, Coca-Cola™ logo, picture of water, picture of soft drink, blue, red) on beverage choice against a black (control) computerised vending machine display. In two studies, young adult participants (17 – 25 years) were recruited from [removed for blind review] (Study 1, $$n = 142$$, Study 2, $$n = 232$$). Participants were randomly allocated to choose a beverage from one of the wrap conditions. They also rated how much the beverage was liked and how often it was consumed (Study 1), or rated the refreshing value, healthiness, taste, and energy of each beverage in the vending machine (Study 2). We predicted that wraps referencing water would produce healthier choices and those referencing soft drink would result in unhealthier choices. Contrary to these predictions, the type of vending machine wrap did not significantly influence beverage choice in Study 1. However, viewing the black vending machine wrap resulted in significantly more caffeine-based selections in Study 2. Other significant predictors of the choice of beverage were how often the beverages were consumed and how much they were liked (Study 1), as well as their perceptions of the taste, healthiness and refreshing value (Study 2). The finding that the black vending machine produced more caffeine-based beverage choices demonstrates, in principle, that color-based priming nudges could influence beverage choices.
## Introduction
Around the world, there is growing public health concern about the overconsumption of soft drinks (drinks that contain carbonated water and are high in sugar) (Tahmassebi & BaniHani, 2020). Soft drinks are primarily consumed by adolescents and young adults (Australian Bureau of Statistics, 2018). Consuming too much soft drink can lead to various health issues because of the high content of sugar, such as diabetes, tooth decay, and obesity (Basu et al., 2013; Çetinkaya & Romaniuk, 2020). In an attempt to mitigate this public health concern, the World Health Organization (WHO, 2015) made a strong recommendation that people limit their daily intake of free (added) sugar to $10\%$ of their total energy intake, equating to roughly 50 g or 12 tsp of sugar.
Current strategies to reduce soft drink consumption include taxation, restricting access, and using front-of-package labels. Taxing soft drink has become increasingly common around the world, with evidence supporting its effectiveness in reducing soft drink consumption (Teng et al., 2019). However, soft drink taxation has been shown to shift purchasing behavior to other high sugar items (Nakhimovsky et al., 2016). In addition, although restricting access to soft drinks in schools can successfully reduce soft drink consumption, it does so only within the school environment, with consumption often shifting outside of school (Micha et al., 2018). Front-of-package labels have also been shown to effectively reduce soft drink purchases in lab based studies, with some countries now mandating nutritional warning labels on soft drinks (Temple, 2020). However, there is limited research to support the effectiveness of these warning labels in real world applications (Temple, 2020).
The aforementioned approaches tend to focus on large and policy driven changes. Other less common strategies focus on altering the environment or the presentation of soft drinks. These closely align with the principle of ‘nudging’ and are less obvious to consumers than removing the option to choose soft drinks or increasing the financial cost for those who choose them. Nudging is a behavior change strategy that attempts to alter a person’s behavior in a predictable way without removing any options or changing economic incentives (Hummel & Maedche, 2019). Nudging has shown some success in bringing about behavior change across a range of domains such as road safety, energy use and healthy food choices (Hansen & Jespersen, 2013; Hummel & Maedche, 2019).
One clear benefit of nudging is that it can encourage a sense of autonomy and thus is less likely to result in reactance; engaging in the desired behavior (e.g., healthy eating) involves little or no effort. In addition, in comparison to other behavior change strategies, nudging requires less self-regulation or self-control, which would otherwise lead to a state of fatigue known as ego-depletion (Moller et al., 2006). Furthermore, nudging does not make use of reward or punishment to encourage the desired behavior. According to Deci and Flaste [1995], pursuing a behavior for something other than reward or punishment leads to the behavior being performed more, with increased enjoyment while doing so. Moreover, recent research has made a link between self-support approaches (such as nudging) and better psychological outcomes (Behzadnia & FatahModares, 2022).
There are many different types of nudges (for a review see Wilson et al., 2016). One particular type and the focus of the present study is a priming nudge. Priming nudges are subconscious cues that can be physical, verbal, or sensory, which are designed to subtly guide a particular choice. They differ from other types of nudges because they do not change the default option, provide an incentive, enforce commitment, or establish norms. Instead, examples of priming nudges include changing the location of food items on a menu (Gynell et al., 2022), changing the shelf positions of food and beverages in shops and supermarkets, and using color to promote certain food consumption behaviors (Wilson et al., 2016).
To date, several studies have shown that priming nudges can successfully promote the consumption of healthier beverages. Two of these reported an effect of the color of plates and cups on the amount participants consumed from them (Akyol et al., 2018; Genschow et al., 2012). A third found that the existence of snack and beverage brand logos in schools was associated with an increased consumption of unhealthy foods (e.g., chocolate, and salty snacks), but not beverages (e.g., soft drink) (Minaker et al., 2011). Brown and Tammineni [2009] examined a range of factors designed to increase the number of healthy beverage purchases from vending machines in primary schools. These included reducing the availability of soft drinks and reducing the price of healthy beverages, as well as changing the vending machine wraps (the surround of the drink display) to reflect physical activity, feature school logos, or feature one of the healthier beverage choices through branding. Brown and Tammineni [2009] found that soft drink purchases decreased and healthy beverage purchases increased. However, as the study did not isolate the effect of the vending machine wrap (primes) from changes made to availability and pricing, it remains unclear which factor(s) were responsible for the healthier beverage choices.
The aim of the present research was to investigate specifically the effect of vending machine wraps on beverage choice. Vending machines are readily available across many different environments, such as shopping centres, factories, office buildings, airports, and schools (Statistic Brain Research Institute, 2017), thereby providing easy access to on-the-go snacks and drinks. People often purchase soft drinks from vending machines (Grand View Research, 2019), and may be unknowingly influenced by features incorporated into the wrap of the vending machine. Yet research into the effects of priming nudges on selections from vending machines is limited. A more general review of factors that influence vending machine choices (Hua & Ickovics, 2016) identified only one study that focused on priming nudges, namely that of Brown and Tammineni [2009] mentioned above.
In the present research, in two studies (one laboratory, one online) we manipulated the wrap on a computerised vending machine display to feature priming nudges (beverage brand logo, picture of a beverage in a glass, or the color of a brand of beverage). In each study, participants were randomly allocated to one of seven vending machine wrap conditions and asked to choose a beverage from the vending machine without considering price. Based on the previous research (Akyol et al., 2018; Brown & Tammineni, 2009; Genschow et al., 2012), we hypothesised that brand logos, pictured beverages, or associated brand color wraps could influence beverage choices. Specifically, we predicted that participants would be more likely to choose water (a healthy option) from vending machine wraps that featured the Mount Franklin™ (a popular brand of water in Australia) logo, a picture of water in a glass, or were colored blue (a visual representation of water/Mt. Franklin™ brand) (Hypothesis 1). We also predicted that participants would be more likely to select a soft drink (an unhealthy option) from vending machine wraps that featured the Coca-Cola™ logo, a picture of soft drink (Coca-Cola™) in a glass, or were colored red (a visual representation of soft drink/Coca-Cola™ brand), relative to a black colored (control) vending machine (Hypothesis 2). In addition, we measured how much participants liked the various beverages in the vending machine and how often they consumed them (Study 1), and how healthy, tasty, refreshing and energising they perceived the beverages to be (Study 2).
## Participants
Participants were 142 undergraduate students at [removed for blind review] who took part for course credit or a $5 reimbursement. Participants were recruited via a university research participation system for a study investigating the psychology of drink choices from vending machines. Participation was limited to young adults (17 – 25 years) to capture the primary consumers of soft drinks. The sample consisted of 118 women and 24 men with a mean age of 19.77 years (SD = 1.92). The mean BMI of the sample was 24.27 kg/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{m}}^{2}$$\end{document}m2 (SD = 6.20).
Participants were 232 undergraduate students at [removed for blind review] who took part for course credit or a $5 reimbursement. Participants were again recruited from [removed for blind review] the online research participation system for a study investigating the psychology of drink choices from vending machines. The sample was again limited to young adults (17 – 25 years) and consisted of 186 women and 46 men with a mean age of 19.39 years (SD = 2.08). Mean BMI of the sample was 24.85 kg/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{m}}^{2}$$\end{document}m2 (SD = 6.00).
## Design
The experiment used a between-subjects design. Participants were randomly allocated to one of seven experimental conditions by the random allocation feature in the survey creation software Qualtrics. The seven vending machines wraps were Mount Franklin™ logo, Coca-Cola™ logo, picture of water, picture of soft drink, blue, red, or black. The dependent variable was the type of beverage chosen (water, soft drink, caffeine-based).
## Vending Machine Wrap Displays
Seven vending machine wraps were created: Mount Franklin™ logo, Coca-Cola™ logo, picture of water, picture of soft drink, blue, red, or black. As shown in Fig. 1, the two branding wraps featured a Mount Franklin™ or Coca-Cola™ logo and their respective slogan on a colored background that was blue for Mount Franklin™ and red for Coca-Cola™. The pictured beverage wraps featured a picture of water or soft drink (Coca-Cola™) in a glass on a black background. The color wraps were either blue or red. The control was black, i.e., the color of a vending machine without a wrap. Fig. 1The seven vending machine conditions Each vending machine image was displayed in portrait orientation on a touchscreen computer. This allowed for a realistic representation and a larger overall image size. The beverage arrangement was identical across all vending machine conditions and was based on observations of typical vending machines in Australia. These commonly feature two rows of water, two rows of soft drink and one row of beverages that are high in caffeine. Thus, the beverage selection included water (Mount Franklin™), soft drinks (Coca-Cola™, Vanilla Coca-Cola™ and Sprite™), as well as a well-known energy drink (Mother™), and coffee beverages (Barista Bros™ Iced Coffee and Barista Bros™ Double Espresso). Because of its high caffeine content, the energy drink was categorised along with the two coffee beverages as a ‘caffeine-based’ beverage.
## Beverage Choice Task
Participants were instructed to imagine that they were in front of a real vending machine and to choose a beverage that they would like to drink straight away, without consideration of price. Participants made their selection by touching their beverage of choice on the screen and were then asked why they chose that particular beverage. Such choice tasks in the food domain have shown high test–retest reliability (Foerde et al., 2018).
Participants were then asked to rate how often they typically consume each of the beverages in the vending machine display (rated on a 7-point Likert scale ranging from ‘never’ to ‘daily’). They were also asked to rate how much they like each of the beverages (rated on a 100 mm visual analogue scale ranging from ‘not at all’ to ‘very much’). Such ratings in the food consumption domain have demonstrated a high test–retest reliability (Foerde et al., 2018).
## Background Information
Participants reported their age and gender, and the last time they drank anything (estimated to the nearest 15 min). They also rated how thirsty they were on a 100 mm visual analogue scale ranging from ‘not at all thirsty’ to ‘extremely thirsty’.
## Procedure
The study received ethics approval from [removed for blind review] University’s Social and Behavioral Research Ethics Committee (approval number 8391) and was run in accordance with the National Statement on Ethical Conduct in Human Research [2007]. The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Written informed consent was obtained from all individual participants included in the study. It was conducted in the Food Research Laboratory at [removed for blind review]. Figure 2 provides a flow-chart overview of the procedure. Participants were seated in front of a touchscreen computer. After reading the study information and providing written informed consent to participate in the study, participants provided some basic demographic information. They then completed the beverage choice task and post-choice questions. Finally, the participant’s height was measured by the researcher, and the participant weighed themselves and entered their weight in kg into the survey. Body mass index (BMI; kg/m2) was calculated using these measurements. All questionnaires were administered via Qualtrics and participants completed the study in 10–15 min. Fig. 2A flow chart of the procedure used in Studies 1 and 2
## Data analysis
To test the effect of condition (Mount Franklin™ logo, Coca-Cola™ logo, picture of water, picture of soft drink, blue, red, or black) on beverage choice (water, soft drink, caffeine-based), a multiple logistic regression was conducted using the IBM Statistical package for Social Sciences (SPSS, version 26) software, with significance levels set at $p \leq 0.05.$ *This analysis* was the most suitable due to the categorical nature of the independent and dependent variables. In addition, this type of analysis allowed us to control for potential confounds such as thirst and gender. All comparisons were conducted using the control (black) vending machine as the comparison condition, and water as the comparison beverage choice.
## Beverage choice
Overall participants mostly chose water ($45.1\%$), followed by soft drinks ($38.2\%$) and caffeine-based beverages ($16.7\%$). Table 1 provides a breakdown of beverage choices per condition. The most commonly reported reason for choosing a particular beverage was because it was liked ($10.4\%$), or because it was considered refreshing ($9.7\%$), energising ($8.3\%$), tasty ($7.6\%$), or healthy ($6.3\%$).Table 1Percentage of beverage type chosen in each condition for Study 1ConditionWaterSoft DrinkCaffeine-basedMt. Franklin™ logo$47.6\%$$47.6\%$$4.8\%$Coca-Cola™ logo$47.6\%$$38.1\%$$14.3\%$Picture of Water$47.6\%$$33.3\%$$19.0\%$Picture of Soft Drink$57.1\%$$28.6\%$$14.3\%$Blue$50.0\%$$15.0\%$$35.0\%$Red$35.0\%$$55.0\%$$10.0\%$Black$30.0\%$$50.0\%$$20.0\%$ Participants mostly chose soft drinks ($42.6\%$), closely followed by water ($41.3\%$), with fewer choosing caffeine-based beverages ($16.2\%$). Table 3 provides a breakdown of beverage choices per condition. The most commonly reported reason for choosing a beverage was that it was liked ($12.3\%$), followed by it being energising ($8.5\%$), healthy ($8.0\%$), thirst quenching ($8.0\%$), and refreshing ($7.2\%$).Table 3Percentage of beverage type chosen in each condition for Study 2ConditionWaterSoft DrinkCaffeine-basedMt. Franklin™ logo$34.4\%$$53.1\%$$12.5\%$Coca-Cola™ logo$45.5\%$$36.4\%$$18.2\%$Picture of Water$44.1\%$$41.2\%$$14.7\%$Picture of Soft Drink$28.6\%$$51.4\%$$20.0\%$Blue$50.0\%$$35.3\%$$14.7\%$Red$51.5\%$$42.4\%$$6.1\%$Black$35.3\%$$38.2\%$$26.5\%$
## Effect of vending machine condition on beverage choice
A multinomial regression was conducted to test the effect of multiple factors (vending machine wrap condition, gender, thirst, beverage liking, frequency of consumption) on beverage choice (water, soft drink, caffeine-based). The overall model explained a significant $69.4\%$ of the variance, X2 (28, $$n = 142$$) = 131.059, $p \leq 0.001$, and did not violate the Pearson (X2 (252, $$n = 142$$) = 220.795, $$p \leq 0.922$$) or Deviance (X2 (252, $$n = 142$$) = 158.757, $$p \leq 1.000$$) tests for goodness of fit.
The analysis showed that condition was not a significant predictor of beverage choice (see Table 2). Not surprisingly, how much water and soft drink were liked, and how often soft drink and caffeine-based beverages were typically consumed were significant predictors of beverage choice. More specifically, the parameter estimates show that participants who chose soft drink reported liking soft drink significantly more, $b = 0.50$, $$p \leq 0.004$$, OR = 1.052 ($95\%$ CI: 1.016, 1.088), and water significantly less, b = -0.36, $$p \leq 0.007$$, OR = 0.964 ($95\%$ CI: 0.939, 0.990), than those who chose water. The parameter estimates further show that how often a caffeine-based beverage was consumed significantly predicted a caffeine-based choice over a water choice, $b = 2.502$, $$p \leq 0.016$$, OR = 12.211 ($95\%$ CI: 1.588, 93.905).Table 2Study 1 multinomial logistic regression results to predict beverage choices (water, soft drink, caffeine-based)EffectModel Fitting Criteria (-2 Log Likelihood of Reduced Model)Chi-SquaredfSigCondition170.61011.85312.458Gender160.8392.0822.353Thirst Rating158.858.1022.950Liking Ratings Water168.1059.3482.009** Soft Drink168.7229.9652.007** Caffeine-based163.9105.1532.076Frequency of Consumption Water161.0872.3302.312 Soft Drink167.6368.8802.012* Caffeine-based171.41812.6612.002**The reference category is water. * $p \leq .05$, **$p \leq .01$ A multinomial regression was conducted to test the effect of multiple factors (vending machine wrap condition, gender, thirst, and the ratings of how healthy, refreshing, tasty and energising each beverage was) on beverage choice (water, soft drink, caffeine-based). The overall model explained a significant $52.4\%$ of the variance, X2 (40, $$n = 232$$) = 141.355, $p \leq 0.001$, and did not violate the Pearson (X2 (422, $$n = 232$$) = 426.068, $$p \leq 0.436$$) or Deviance (X2 (422, $$n = 232$$) = 332.534, $$p \leq 1.000$$) tests for goodness of fit.
Overall, condition was a significant predictor ($$p \leq 0.046$$) of beverage choice (see Table 4). Specifically, the parameter estimates show that participants made significantly more caffeine-based choices than water choices in the black condition, compared to the water, b = -1.908, $$p \leq 0.044$$, OR = 0.148 ($95\%$ CI: 0.023, 0.952), red, b = -3.672, $$p \leq 0.001$$, OR = 0.025 ($95\%$ CI: 0.003, 0.229), or blue, b = -2.639, $$p \leq 0.007$$, OR = 0.071 ($95\%$ CI: 0.010, 0.486) conditions. Gender was also a significant predictor of beverage choice ($$p \leq 0.007$$), with the parameter estimates showing that men were more likely to choose caffeine-based beverages, $b = 1.883$, $$p \leq 0.003$$, OR = 6.574 ($95\%$ CI: 1.888, 22.890) than women. In addition, how tasty (water, soft drink, caffeine-based), refreshing (soft drink, caffeine-based), and healthy (water) beverages were significantly predicted beverage choice; energy ratings were not a significant predictor. Table 4Study 2 multinomial logistic regression results to predict beverage choices (water, soft drink, caffeine-based)EffectModel Fitting Criteria (-2 Log Likelihood of Reduced Model)Chi-SquaredfSigCondition353.84621.31212.046*Gender342.4169.8822.007**Thirst Rating333.8431.3082.520Health Ratings Water345.75913.2252.001** Soft Drink338.1275.5922.061 Caffeine-based333.074.5392.764Refreshing Ratings Water334.4641.9302.381 Soft Drink343.92911.3942.003** Caffeine-based346.61614.0822.001**Taste Ratings Water344.12311.5892.003** Soft Drink352.61820.0842.000*** Caffeine-based359.32426.7902.000***Energy Ratings Water336.3543.8192.148 Soft Drink333.268.7342.693 Caffeine-based335.9193.3852.184The reference category is water. * $p \leq .05$, **$p \leq .01$, ***$p \leq .001$
## Discussion
Study 1 investigated whether priming nudges incorporated into vending machine wraps could influence beverage choice behavior. In contrast to Hypothesis 1, vending machine wraps that featured priming nudges related to healthy beverages (Mount Franklin™ logo, picture of water in a glass, or a blue colored wrap) did not result in more healthy beverage choices. Likewise, in contrast to Hypothesis 2, priming nudges related to unhealthy beverages (Coca-Cola™ logo, picture of Coca-Cola™ in a glass, or a red colored wrap) did not result in more unhealthy beverage choices. Instead, participants chose beverages that they liked and regularly consumed. The most common reasons for beverage choice were related to the refreshing, energising, taste, and health aspects of the beverage. We therefore examined these factors directly as potential drivers of beverage choice in Study 2. In contrast to the lab-based set-up of Study 1, Study 2 was conducted online to mitigate against any social desirability bias in beverage choices from the presence of the experimenter in the room.
In line with the findings of Study 1, the results of Study 2 did not support the hypotheses. Specifically, vending machine wraps that featured priming nudges related to healthy beverages (Mount Franklin™ logo, picture of water in a glass, or a blue colored wrap) did not result in more healthy beverage choices, and priming nudges related to unhealthy beverages (Coca-Cola™ logo, picture of Coca-Cola™ in a glass, or a red colored wrap) did not result in more unhealthy beverage choices. However, the black colored (control) vending machine did influence beverage choice, with relatively more people choosing caffeine-based beverages in this condition. We speculate that the black color of the vending machine may have influenced caffeine-related thoughts (due to caffeine being associated with the color black) and therefore potentially resulted in more caffeine-based choices. In addition, men were more likely to choose caffeine-based beverages than women. Participants also chose beverages they considered to be tasty, refreshing, and healthy.
## Design, materials, procedure, and data analysis
Study 2 was conducted fully online during COVID-19 restrictions. This carried the advantages of greater anonymity and reduced possible social desirability and demand effects. The study again received ethics approval from [removed for blind review] University’s Social and Behavioral Research Ethics Committee (approval number 8391) and was run in accordance with the National Statement on Ethical Conduct in Human Research [2007]. The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Written informed consent was obtained from all individual participants included in the study. Design, materials, procedure, and data analysis were the same as in Study 1, except that the liking and frequency ratings were replaced by ratings of how healthy, tasty, refreshing and energising each beverage featured in the vending machine was perceived to be, based on the most common reported reasons for choosing a beverage in Study 1. These were measured using 100 mm visual analogue scales ranging from ‘not at all’ to ‘very much’.
## General discussion
The present studies aimed to examine whether priming nudges featured on a vending machine wrap could influence beverage choice behavior. The findings from Study 1 showed that vending machine wraps had no significant effect on beverage choices, while Study 2 found a significant effect for the black vending machine (control) condition only. Specifically, participants who viewed the black vending machine chose relatively more caffeine-based beverages than water. Not surprisingly, beverage choices were also driven by how much a beverage was liked, how often it was consumed (Study 1), and perceptions of its taste, refreshing qualities and healthiness (Study 2).
We had predicted that participants would be more likely to choose water (a healthy option) from vending machine wraps that featured the Mount Franklin™ (a popular brand of water in Australia) logo, a picture of water in a glass, or were colored blue (a visual representation of water/Mt. Franklin™ brand) compared to a black colored (control) vending machine (Hypothesis 1). Conversely, we had predicted that participants would be more likely to choose a soft drink (an unhealthy option) from vending machine wraps that featured the Coca-Cola™ logo, a picture of soft drink (Coca-Cola™) in a glass, or were colored red (a visual representation of soft drink/Coca-Cola™ brand), relative to a black colored (control) vending machine (Hypothesis 2). The results of neither study supported these hypotheses. Thus the present findings are inconsistent with those of Brown and Tammineni [2009]. In contrast to the present studies which isolated the effect of changes in vending machine wrap, Brown and Tammineni [2009] reported on the effect of these nudges in combination with changes in beverage availability and pricing. Our findings suggest that the effects on beverage choice observed by Brown and Tammineni [2009] were most likely due to the changes in the pricing and availability of beverages rather than their vending machine wraps. Future research should test each of the above factors independently. The present findings are, however, consistent with those of Minaker et al. [ 2011] who found no association between the presence of beverage logos and beverage consumption in schools (although there was an association between the presence of snack logos and unhealthy food consumption). In a similar vein, Stöckli et al. [ 2016] showed that the presentation of a poster depicting a health-evoking nature scene, physical activity or skinny Giacometti sculptures on a vending machine resulted in healthier snack food choices. Together these findings suggest that priming nudges on vending machines may be more effective for food choices than beverage choices.
The finding that the black vending machine resulted in more caffeine choices in Study 2 supports in a general way the previously reported color effects by Genschow et al. [ 2012] and Akyol et al. [ 2018]. Although we had predicted that the blue and red prime wraps would influence beverage choice positively and negatively, respectively, we found instead that the black vending machine increased caffeine-based choices. It seems likely that the black color of the vending machine may have primed or cued caffeine because it is readily associated with the color of coffee. Future research could usefully examine whether other colors could similarly influence beverage choices from vending machines; for example, a green vending machine (a color associated with healthiness) might encourage healthier beverage choices.
Interestingly, the effect of the black vending machine on caffeine-based choices was only observed in Study 2. One difference between the two studies is that participants could complete Study 1 only during working hours (9am – 5 pm), whereas they could complete Study 2 at any time of the day. Participants who completed Study 2 at night may have had a greater desire for caffeine-based beverages due to a need for energy at that time. There was indeed a large proportion of caffeine choices (> $50\%$) after 10 pm. As a result, participants may have been more susceptible to the black vending machine condition priming caffeine related thoughts.
Not surprisingly, participants’ beverage choices in Study 1 were largely based on how often they consumed the beverages and how much they were liked. These two factors suggest.
that beverage choices are mostly habitual, as has been shown previously for sugar sweetened beverage consumption (Dono et al., 2021; Zhen et al., 2011). In Study 2, participants’ perceptions of how healthy, tasty, and refreshing the beverages were contributed to their choices, which fits with previous research that has linked unhealthy beverage consumption behavior to perceptions of the taste of beverage (Block et al., 2013; Dono et al., 2020). Looking at the patterns of perceptions in more detail, not surprisingly water choices were the only beverage choice associated with healthiness. In contrast, soft drinks and caffeine-based beverage choices were primarily associated with how refreshing and tasty they are. This difference likely reflects the focus of marketing strategies that promote soft drinks and caffeine-based beverages as tasty and refreshing. It is therefore possible that bottled water could be made a more attractive choice if its refreshing nature was more strongly empathized. *More* generally, attempts to shift individuals towards healthier beverages might usefully target and increase people’s perceptions of how tasty, refreshing, and healthy beverages are.
At a practical level, the priming nudges incorporated into the vending machine wraps in the present studies may not have been sufficiently strong to override existing habitual consumption behaviors. However, the finding that a black vending machine produced more caffeine-based beverage choices demonstrates, in principle, that color-based priming nudges might be a useful tool. In the present case, if one wanted to reduce the choices of caffeine-based beverages, one could avoid the color black on vending machines, a suggestion that could be tested in a field study. If effective, this type of priming nudge may then extend to other environments such as restaurant menus, supermarkets shelves, and drink fridges in cafes. Any such change (e.g., changing the color of a display) would be easily implementable, and does not depend on government regulation or policy change. Future research might specifically examine other colors in more powerful designs. In addition, although the focus of the present studies was on soft drink choices, it should be noted that the caffeine-based beverages in the vending machine displays also contained a high amount of sugar (in addition to caffeine), similar to that of soft drink (more than 40 g of sugar per serve). Therefore, these caffeine-based beverages share the same negative health consequences as soft drinks, in addition to the potential cardiovascular risks associated with overconsumption of caffeine (Poole et al., 2017).
The present research has some limitations. First, the samples consisted of predominantly female students of mostly average weight. Hence, the priming nudges (e.g., logos, pictured beverages, and color) may differentially affect beverage choices of children and older adults. Thus, future research should investigate whether the current findings generalise to other demographic groups. Second, the limited range of beverages offered in the vending machine displays may have restricted participant choice but was modelled on actual vending machines. In addition, some brands of beverage were exclusive to the Australian market. Third, participants in the present studies never had the opportunity to consume their chosen drink. Nevertheless, hypothetical choices have been shown to activate similar brain systems as real choices (Kang et al., 2011), and thus are a valid predictor of actual choices. Fourth, the present studies were conducted in the lab or online. Future research should seek to test the effects of vending machine wraps on actual machines in the field. Finally, priming nudges may influence motivational regulations that contribute to healthy beverage choice behavior. In particular, future research could adopt a Self-Determination Theory (Deci & Ryan, 1985) approach to investigate whether priming nudges affect beverage choices through autonomous or controlled motivation.
In conclusion, the present studies offer little evidence that vending machine wraps can shift beverage choices. However, they might do under certain circumstances. Specifically, the black vending machine wrap resulted in more caffeine-based choices than water and thus should be avoided. This is a promising start for priming nudges as a pathway towards promoting healthier beverage consumption behavior.
## References
1. Akyol A, Ayaz A, Inan-Eroglu E, Cetin C, Samur G. **Impact of three different plate colors on short-term satiety and energy intake: A randomized controlled trial**. *Nutrition Journal* (2018.0) **17** 46-46. DOI: 10.1186/s12937-018-0350-1
2. Australian Bureau of Statistics. (2018). Dietary behavior. Australian Bureau of Statistics. Retrieved February 15, 2022, from https://www.abs.gov.au/statistics/health/health-conditions-and-risks/dietary-behaviour/latest-release#articles
3. Basu S, McKee M, Galea G, Stuckler D. **Relationship of soft drink consumption to global overweight, obesity, and diabetes: A cross-national analysis of 75 countries**. *The American Journal of Public Health* (2013.0) **103** 2071-2077. DOI: 10.2105/AJPH.2012.300974
4. Behzadnia, B., & FatahModares, S. (2022). A Self-Support Approach to Satisfy Basic Psychological Needs During Difficult Situations. PsyArXiv. 10.31234/osf.io/un6vp
5. Block JP, Gillman MW, Linakis SK, Goldman RE. **“If it tastes good, I’m drinking it”: Qualitative study of beverage consumption among college students**. *Journal of Adolescent Health* (2013.0) **52** 702-706. DOI: 10.1016/j.jadohealth.2012.11.017
6. Brown DM, Tammineni SK. **Managing sales of beverages in schools to preserve profits and improve children's nutrition intake in 15 mississippi schools**. *Journal of the American Dietetic Association* (2009.0) **109** 2036-2042. DOI: 10.1016/j.jada.2009.09.008
7. Çetinkaya H, Romaniuk P. **Relationship between consumption of soft and alcoholic drinks and oral health problems**. *Central European Journal of Public Health* (2020.0) **28** 94-102. DOI: 10.21101/cejph.a5745
8. Deci EL, Flaste R. *Why we do what we do: The dynamics of personal autonomy* (1995.0)
9. Deci EL, Ryan RM. *Intrinsic motivation and self-determination in human behavior* (1985.0)
10. Dono J, Ettridge K, Wakefield M, Pettigrew S, Coveney J, Roder D, Durkin S, Wittert G, Martin J, Miller C. **Nothing beats taste or convenience: A national survey of where and why people buy sugary drinks in Australia**. *Australian and New Zealand Journal of Public Health* (2020.0) **44** 291-294. DOI: 10.1111/1753-6405.13000
11. Dono J, Ettridge KA, Wakefield M, Pettigrew S, Coveney J, Roder D, Durkin S, Wittert G, Martin J, Miller CL. **Intentions to reduce sugar-sweetened beverage consumption: The importance of perceived susceptibility to health risks**. *Public Health Nutrition* (2021.0) **24** 5663-5672. DOI: 10.1017/S1368980021000239
12. Foerde K, Gianini L, Wang Y, Wu P, Shohamy D, Walsh BT, Steinglass JE. **Assessment of test-retest reliability of a food choice task among healthy individuals**. *Appetite* (2018.0) **123** 352-356. DOI: 10.1016/j.appet.2018.01.010
13. Genschow O, Reutner L, Wänke M. **The color red reduces snack food and soft drink intake**. *Appetite* (2012.0) **58** 699-702. DOI: 10.1016/j.appet.2011.12.023
14. Grand View Research. (2019). Vending machine market size, share & trends analysis report by application (Offices, Commercial Places), by payment mode, by region (MEA, APAC, North America), and segment forecasts, 2019–2025. Retrieved February 15, 2022, from https://www.grandviewresearch.com/industry-analysis/global-vending-machine-market
15. Gynell I, Kemps E, Prichard I. **The effectiveness of implicit interventions in food menus to promote healthier eating behaviors: A systematic review**. *Appetite* (2022.0) **173** 105997-105997. DOI: 10.1016/j.appet.2022.105997
16. Hansen P, Jespersen A. **Nudge and the manipulation of choice: A framework for the responsible use of the nudge approach to behavior change in public policy**. *European Journal of Risk Regulation* (2013.0) **4** 3-28. DOI: 10.1017/S1867299X00002762
17. Hua SV, Ickovics JR. **Vending machines: A narrative review of factors influencing items purchased**. *Journal of the Academy of Nutrition and Dietetics* (2016.0) **116** 1578-1588. DOI: 10.1016/j.jand.2016.06.378
18. Hummel D, Maedche A. **How effective is nudging? A quantitative review on the effect sizes and limits of empirical nudging studies**. *Journal of Behavioral and Experimental Economics* (2019.0) **80** 47-58. DOI: 10.1016/j.socec.2019.03.005
19. Kang, M. J., Rangel, A., Camus, M., & Camerer, C. F. (2011). Hypothetical and real choice differentially activate common valuation areas. The Journal of Neuroscience, 31(2), 461–468. 10.1523/JNEUROSCI.1583-10.2011
20. Micha R, Karageorgou D, Bakogianni I, Trichia E, Whitsel LP, Story M, Peñalvo JL, Mozaffarian D. **Effectiveness of school food environment policies on children’s dietary behaviors: A systematic review and meta-analysis**. *PLoS ONE* (2018.0) **13** 1-12. DOI: 10.1371/journal.pone.0194555
21. Minaker LM, Storey KE, Raine KD, Spence JC, Forbes LE, Plotnikoff RC, McCargar LJ. **Associations between the perceived presence of vending machines and food and beverage logos in schools and adolescents’ diet and weight status**. *Public Health Nutrition* (2011.0) **14** 1350-1356. DOI: 10.1017/S1368980011000449
22. Moller AC, Deci EL, Ryan RM. **Choice and ego-depletion: The moderating role of autonomy**. *Personality & Social Psychology Bulletin* (2006.0) **32** 1024-1036. DOI: 10.1177/0146167206288008
23. Nakhimovsky, S. S., Feigl, A. B., Avila, C., O’Sullivan, G., Macgregor-Skinner, E., & Spranca, M. (2016). Taxes on sugar-sweetened beverages to reduce overweight and obesity in middle-income countries: A systematic review. PLoS One, 11. 10.1371/journal.pone.0163358
24. Poole R, Kennedy OJ, Roderick P, Fallowfield JA, Hayes PC, Parkes J. **Coffee consumption and health: Umbrella review of meta-analyses of multiple health outcomes**. *The BMJ* (2017.0) **359** 5024-5040. DOI: 10.1136/bmj.j5024
25. Statistic Brain Research Institute. (2017). Vending machine industry statistics. Retrieved February 15, 2022, from https://www.statisticbrain.com/vending-machine-industry-statistics/
26. Stöckli S, Stämpfli AE, Messner C, Brunner TA. **An (un)healthy poster: When environmental cues affect consumers’ food choices at vending machines**. *Appetite* (2016.0) **96** 368-374. DOI: 10.1016/j.appet.2015.09.034
27. Tahmassebi JF, BaniHani A. **Impact of soft drinks to health and economy: A critical review**. *European Archives of Paediatric Dentistry* (2020.0) **21** 109-117. DOI: 10.1007/s40368-019-00458-0
28. Temple NJ. **Front-of-package food labels: A narrative review**. *Appetite* (2020.0) **144** 104485. DOI: 10.1016/j.appet.2019.104485
29. Teng AM, Jones AC, Mizdrak A, Signal L, Genç M, Wilson N. **Impact of sugar-sweetened beverage taxes on purchases and dietary intake: Systematic review and meta-analysis**. *Obesity Reviews* (2019.0) **20** 1187-1204. DOI: 10.1111/obr.12868
30. WHO. (2015). Guideline: Sugars intake for adults and children. Retrieved February 15, 2022, from http://apps.who.int/iris/bitstream/10665/149782/1/9789241549028_eng.pdf
31. Wilson AL, Buckley E, Buckley JD, Bogomolova S. **Nudging healthier food and beverage choices through salience and priming. Evidence from a systematic review**. *Food Quality and Preference* (2016.0) **51** 47-64. DOI: 10.1016/j.foodqual.2016.02.009
32. Zhen C, Wohlgenant MK, Karns S, Kaufman P. **Habit formation and demand for sugar-sweetened beverages**. *American Journal of Agricultural Economics* (2011.0) **93** 175-193. DOI: 10.1093/ajae/aaq155
|
---
title: '“My Body, My Rhythm, My Voice”: a community dance pilot intervention engaging
breast cancer survivors in physical activity in a middle-income country'
authors:
- María Alejandra Rubio
- Carlos M. Mejía-Arbeláez
- Maria A. Wilches-Mogollon
- Sergio Moreno
- Carolyn Finck
- Lisa G. Rosas
- Sol A. Romero
- Paula Guevara
- Santiago Cabas
- Oscar Rubiano
- Alberto Flórez-Pregonero
- José G. León
- Luis Fernando Alarcón
- Robert Haile
- Olga L. Sarmiento
- Abby C. King
journal: Pilot and Feasibility Studies
year: 2023
pmcid: PMC9971676
doi: 10.1186/s40814-023-01253-x
license: CC BY 4.0
---
# “My Body, My Rhythm, My Voice”: a community dance pilot intervention engaging breast cancer survivors in physical activity in a middle-income country
## Abstract
### Background
Interventions to promote physical activity among women breast cancer survivors (BCS) in low- to middle-income countries are limited. We assessed the acceptability and preliminary effectiveness of a theory-driven, group-based dance intervention for BCS delivered in Bogotá, Colombia.
### Methods
We conducted a quasi-experimental study employing a mixed-methods approach to assess the 8-week, 3 times/week group dance intervention. The effect of the intervention on participants’ physical activity levels (measured by accelerometry), motivation to engage in physical activity, and quality of life were evaluated using generalized estimating equation analysis. The qualitative method included semi-structured interviews thematically analyzed to evaluate program acceptability.
### Results
Sixty-four BCS were allocated to the intervention ($$n = 31$$) or the control groups ($$n = 33$$). In the intervention arm, $84\%$ attended ≥ $60\%$ of sessions. We found increases on average minutes of moderate-to-vigorous physical activity per day (intervention: +8.99 vs control: −3.7 min), and in ratings of motivation (intervention change score = 0.45, vs. control change score= −0.05). BCS reported improvements in perceived behavioral capabilities to be active, captured through the interviews.
### Conclusions
The high attendance, behavioral changes, and successful delivery indicate the potential effectiveness, feasibility, and scalability of the intervention for BCS in Colombia.
### Trial registration
ClinicalTrial.gov NCT05252780, registered on Dec 7th, 2021—retrospectively registered unique protocol ID: P20CA217199-9492018.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40814-023-01253-x.
## Key messages regarding feasibility
What uncertainties existed regarding the feasibility?*There is* uncertainty about the implementation of a dance-based intervention to promote physical activity among breast cancer survivors (BCS) in a community setting in Colombia, as well as the assessment of the acceptability and effectiveness through a mixed-methods study. What are the key feasibility findings?The proposed intervention was successfully delivered and assessed, showing high attendance, acceptability, and behavioral changes. What are the implications of the feasibility findings for the design of the main study?The feasibility findings are promising to promote the systematic uptake and evaluation of My Body theory-driven intervention for breast cancer survivors in the community setting in Colombia.
## Background
Breast cancer is the most common cancer among women worldwide [1]. In Latin America, breast cancer incidence has been rising in most countries over the last decade, increasing between 2012 and 2018 from 52.1 to 56.8 new cases per 100,000 women [2, 3]. In Colombia, during the period 2011–2018, the breast cancer incidence rate increased from 33.8 to 44.1 cases per 100,000 women, and the mortality rate increased from 9.9 to 11.9 deaths per 100,000 women [4, 5]. Furthermore, the 5-year net survival for women diagnosed with breast cancer decreased in Colombia from $79.1\%$ in 2005 to $72.2\%$ for the women diagnosed during the 2010–2014 period [6].
A large body of evidence supports the beneficial effects of physical activity for breast cancer survivors (BCS), including improvements in survival, physical functioning, psychological complaints, and overall quality of life (QoL) [7, 8]. Worldwide evidence-based exercise guidance for cancer survivors recommends daily physical activity, as it contributes to reductions in body fat, metabolic and sex hormones, growth factors, and inflammation, as well as broader biopsychosocial outcomes (i.e., it reduces symptoms of depression/anxiety, insomnia, fatigue, and improves well-being) [9, 10]. However, many BCS are not meeting the physical activity recommendations [11]. Evidence from high-income countries shows low physical activity participation and more likelihood to engage in sedentary behavior among BCS [12, 13]. Among Colombian BCS, low physical activity levels have also been reported [14]; however, to the best of our knowledge, there are no prevalence estimates of physical activity among BCS in Colombia. We know that $57\%$ of Colombian adult women do not meet the current guidelines [15], and the most reported barriers to regular physical activity for Colombian women include time management, caregiving roles, safety concerns, and inadequate recreational facilities [16, 17].
The challenge of adhering to an active lifestyle has led to the development and assessment of theory-driven interventions promoting physical activity tailored for BCS as a way to help individuals adopt and maintain healthy behaviors [18]. Thus far, physical activity programs tailored to the needs and preferences of BCS have been lacking in Latin America, a region characterized by a disproportionately lower prevalence of physical activity among women compared to men [19]. Behavioral interventions undertaken in the USA and other high-income countries have achieved increases in physical activity levels in BCS using diverse cognitive-behavioral techniques [20, 21]. A systematic review and meta-analysis of 27 interventions designed to promote physical activity behavior change in cancer survivors concluded that interventions with more intense supervision (i.e., in-person, frequent interactions) tended to produce larger effects on behavior change [22], and “action planning,” “graded tasks,” and “social support” were identified as promising intervention components that facilitated behavior change [22]. In addition, qualitative studies evaluating elements of program adherence success among interventions targeting BCS have underscored the group-based environment, which can facilitate social support and camaraderie of others [23] in addition to physical activity and emotional modeling that facilitates positive psychosocial well-being [24]. Group-based interventions targeting BCS have been developed mainly in high-income countries [25], suggesting its potential in regions like Latin America, where social support and other interpersonal processes have been positively related to initiating and maintaining physical activity among women [26, 27].
From an ecological perspective, behavioral interventions, in addition to targeting individuals, should also affect interpersonal, organizational, and environmental factors influencing health behavior, while addressing the question of how practitioners can best integrate theory into large-scale public health programs. Colombia is an international leader in developing innovative, publicly funded, community-based physical activity programs, some of which have been in place for more than 20 years [28], and are now being implemented in other low- and middle-income countries (LMICs), as well as higher income nations. These group-based physical activity programs have shown promise in promoting physical activity among Latin American women by incorporating dance in guided sessions [29]. However, the role of health behavior theories in the ongoing programs has not been clear or has been secondary to practical concerns. In this context, a relevant next step is to design a theory-driven intervention to promote physical activity among Colombian BCS by leveraging the existing community-based approach. A culturally tailored, group-based dance intervention to promote physical activity is one approach that appears to be feasible, acceptable, and potentially effective to increase physical activity in Latin America, as dance is a well-known motivating form of physical activity [30, 31].
The aim of the “My body, My Rhythm, My Voice” (from now on referred to as My Body) pilot study was to assess the acceptability and preliminary effectiveness of a theory-driven, group-based dance intervention for BCS delivered by a governmental entity in Bogotá, Colombia. Using a mixed methods approach, we aimed to [1] assess the individual-level primary outcomes for this pilot including physical activity levels, QoL, and motivation to engage in physical activity and [2] assess the acceptability of the program from a socioecological perspective, including semi-structured interviews inquiring about barriers and facilitators at the individual, interpersonal, and community levels. This study is innovative in that, to our knowledge, it is the first to pilot-test a theory-driven community-based physical activity program for BCS in Latin America. The research was conducted prior to the COVID-19 pandemic.
## Study setting
In Colombia, a middle-income country [32], public institutions from the healthcare and sports/recreation sectors offer community-based programs aimed at physical activity promotion using a life-course approach [28, 33]. In addition, breast cancer-related stakeholders offer diverse activities as private initiatives to increase BCS’s quality of life. In the My Body study, we facilitated cross-sectoral partnerships between local entities to implement an 8-week evidence-based behavioral physical activity program for BCS using a community-based dance intervention. The municipal Recreovía dance-based physical activity program [29], publicly funded by Bogotá’s Sports and Recreation Institute, was in charge of My Body program delivery.
## Study design
The study piloted a two-armed, quasi-experimental trial comparing an intervention to a usual care control arm. This pilot study employed a convergent mixed-methods approach [34] to integrate pre- and post-quantitative and qualitative data methods. The quantitative methods included pre-test–post-test assessments. The qualitative methods included pre- and post-intervention semi-structured interviews with both intervention and control arms to understand facilitators and barriers to program acceptability. We summarized the quantitative and qualitative results in a table to gain a more thorough understanding of contextual factors that could influence the intervention outcomes. All participants signed informed consent, and the study was approved by the Universidad de los Andes ethics committee, Act Number 949 of 2018.
## The “My Body, My Rhythm, My Voice” physical activity behavioral intervention
Following a socioecological approach to promote physical activity behavior change, My Body was co-created through cross-sector collaboration among academic researchers and stakeholders from local and national public and private healthcare and sports/recreation institutions, healthcare service providers, and community organizations supporting cancer survivors. Members of the interdisciplinary and cross-sectoral research team (e.g., physiotherapist, epidemiologist, psychologist, respiratory therapist, physician, anthropologist) had monthly meetings ($$n = 8$$). Methodological details concerning the co-creation have been reported elsewhere [35]. Based on the discussions and a systematic review of the literature, we designed the 8-week, 3 times/week dancing-based physical activity intervention, mostly informed by the social cognitive and self-determination theories [36, 37]. The goal of the intervention was to gradually increase all participants’ physical activity levels to achieve significant health benefits. For this reason, the set of strategies to facilitate behavior change was deployed progressively, week by week, through the interaction of the dance-based physical activity sessions and a psychoeducational booklet designed for BCS, described as follows (online resource 1).
According to the guidelines of the American College of Sports Medicine for cancer patients [9], physical activity intensity increases were progressive and moderate. Sessions were 45 min initially, followed by 5-min increases per week, until reaching 60 min by the fifth week, plus 10-min warm-up and 10-min cool-down activities. Physical activity intensity increases were determined using maximum heart rate (HR) based on reference equations for BCS and considering the relationship between the expected exercise HR and the speed of a given song accompanying the group dance movements [38]. Based on these determinations, the playlist music was selected according to each song’s tempo (beats per minute [BPM]) and expected HR to maintain a comfortable level of exertion during the exercise [39]. Including culturally relevant dancing rhythms such as salsa and champeta, the music BPM progressively increased as shown in online resource 1. The instructors had been previously certified in dancing-based physical activity instruction by the Recreovía program. Participants were encouraged to invite their caregivers or another supportive person to attend the physical activity sessions with them.
The behavioral science research team members designed a psychoeducational booklet for BCS containing activities to foster behavior change (e.g., goal setting, action planning, enhancing decisional balance for being active, self-monitoring) [22, 36, 37]. The psychoeducational booklet was given to participants at the beginning of the intervention, and once per week, a researcher attended the session to follow up and guide participants’ use of the booklet and to provide motivational and behavioral instruction and support (online resource 1). The booklet was also used to train the physical activity instructors on behavior change strategies. As part of the physical activity intervention, the participants were taught by their instructor during each class how to utilize behavioral and cognitive self-regulatory skills to increase and maintain their physical activity participation (e.g., action planning, coping planning, counter conditioning, self-evaluation).
No specific instructions were given to the usual care group concerning physical activity behavior change beyond any given by their health care providers.
## Recruitment of breast cancer survivors
An a priori sample size of 30 participants per study group was based primarily on logistical and budgetary constraints for the pilot study. BCS were invited to the study through word of mouth, flyers, and social media from partner organizations. Eligible women were BCS at least 6 months post-treatment completion, more than 18 years of age, living in Bogotá, and willing to attend the program and the assessments. To enhance diversity in this first-generation study, individuals were eligible irrespective of whether they were currently meeting physical activity guidelines or not. Exclusion criteria were the presence of metastatic disease and other health conditions for which community physical activity was contraindicated. Medical clearance for participation provided by sports medicine physicians, who were members of the research team, was required. Women who did not receive physician approval were not included in the study. Given the need to respect partner organizations’ concerns about providing differential interventions to their patients through randomization, the first half of the participants with physician approval was allocated to the intervention group, while the second half was allocated to a waitlist control group. Participants in the intervention group received modest incentives in sessions 1, 12, and 24 (e.g., a water bottle, sun cap, drawstring bag), as well as a small financial remuneration for transportation to each attended community session (equalling approximately $1.25 USD per session). Participants in the control group received the physical activity intervention at the end of the 8-week study period after completing the pre-post-study assessments. Due to time and funding constraints, no outcome measurements were collected for the waitlist control group when they received their program.
## Quantitative assessment
Trained personnel evaluated participants at Universidad de los Andes’ campus facilities and at the intervention location, which was a residential community center, administered by Bogotá’s Sports and Recreation Institute. We collected accelerometry measurements, as well as self-reported questionnaires, that captured sociodemographic characteristics, medical history, and QoL (all described below). All measurements were performed at baseline (time 0) and at 8 weeks (time 1, i.e., immediately post-intervention).
## Physical activity levels/accelerometry
To assess participants’ physical activity levels, participants wore the Actigraph GT3X or GT3X+ Accelerometers (ActiGraph, Pensacola, FL, USA) for 7 consecutive days from awakening to bedtime using an elasticized belt around the waist at the right mid-axillary line. The accelerometer was removed when bathing or sleeping. For wear-time validation, a minimum of 4 weekdays and a weekend day with at least 10 h of wear during the waking time was required. Accelerometers were initialized to collect data at a sampling frequency of 80 Hz, downloaded in 1-s epochs, and grouped in 60-s epochs for analysis. After the data collection, we validated the time of use with an algorithm programmed in R (version 3.3.2). The data were scored using the Freedson cut-points for adults [40].
Additionally, to measure physical activity levels during dancing sessions, two participants, randomly selected during each session, wore an accelerometer with an elasticized belt around the waist at the right mid-axillary line.
## Motivation to engage in physical activity
Self-determination theory (SDT) conceptualizes motivation as a continuum ranging from amotivation (complete lack of self-determination to perform the behavior) to a high level of intrinsic motivation (high self-determination, the behavior is driven by enjoyment) [37, 41, 42]. As part of this continuum, extrinsic motivation is differentiated as four regulatory processes that range in level of internalization and self-determination (external, introjected, identified, integrated regulation) [42]. To assess motivational regulation for physical activity, we used the validated Spanish version of the Behavioral Regulation in Exercise Questionnaire-3 (BREQ-3) [43], a 23-item inventory assessing the above SDT-relevant constructs. Responses to each item were reported on a 5-point scale ranging from 0 (not true for me) to 4 (very true for me).
Significant between-group increases were noted for intrinsic motivation for exercise (0.45 vs −0.05) and for identified regulation (i.e., high self-determination, the behavior is executed because it is enjoyable and of interest to the individual) (0.27 vs −0.03) for the intervention versus the control group (Table 2).
## Quality of life
To assess health-related QoL, we used the official Colombian Spanish translation of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) [44]. It incorporates five functioning subscales (physical, role, cognitive, emotional, and social functioning), three symptom subscales (fatigue, pain, and nausea/vomiting), six single symptoms (dyspnea, appetite loss, insomnia, constipation, and diarrhea), an item for illness-related financial difficulties, and a two-item general health/global QoL subscale. Each item was reported on a 4-point scale ranging from 1 (not at all) to 4 (very much), except for the general health subscale, for which response options range from 1 (very poor) to 7 (excellent). All the scores are transformed into the range of 0–100. High scores on the functioning subscales and the global health status/QoL subscale represent higher QoL, while high scores on the symptom subscales indicate high levels of symptomatology, hence, poorer QoL. We also used a subscale of the EORTC questionnaire specific to breast cancer patients regarding physical symptoms of lymphedema; it comprises seven items with responses also ranging from 1 (not at all) to 4 (very much). We followed the questionnaire manuals to perform statistical analyses and compared results to normative values for the Colombian population [44].
The results of the EORTC QLQ-C30 did not show a change in the global score (Table 2). However, there are some specific positive results comparing changes between groups. For the physical functioning subscale scores (+2.67 vs −2.53) and insomnia (−12.00 vs 3.45), the change between the intervention versus control group was statistically significant. Additionally, although statistically non-significant, the intervention group also showed an important difference in gastrointestinal function. Compared with the control group, the intervention group had fewer diarrhea (−6.67 vs 3.45) and constipation (1.33 vs −4.60) complaints. These results indicate a pattern in some symptoms towards higher QoL among the intervention versus the control group. Adjust effect for pain and dyspnea could not be estimated due to changes were not found.
## Sociodemographic characteristics
We collected surveys inquiring about sociodemographic variables including age, education, socioeconomic level, employment status, and healthcare access.
Study participants were primarily educated women (most of them [$62.5\%$] had completed high school or higher education programs), with a mean age of 56.4 years (SD ± 9.5). Half of them were single ($50\%$), and participant households were more likely to be in the middle socioeconomic stratum ($62.5\%$) as opposed to higher ($0\%$) or lower ($37.5\%$) socioeconomic strata. Most of the women ($60.9\%$) were not formally employed, although a large percentage of them worked as caregivers or housekeepers ($60.0\%$). Most of the women received healthcare services through self-paid insurance plans (contributive regimens) ($78.1\%$). When examining the participant’s history of cancer, the average time since cancer diagnosis was 6.4 years (± 5.6). Women from the intervention and control groups did not differ significantly on any of the variables measured at baseline (Table 1).Table 1Baseline individual characteristics by group from the “My body, My Rhythm, My Voice” studyCharacteristicIntervention ($$n = 31$$)Control ($$n = 33$$)Mean/nSD/%Mean/nSD/%Age (years)57.028.7055.8410.32Marital status Single, divorced, or widowed1548.391751.52 Living with a partner or married1651.611648.48Socioeconomic level Low1548.39927.27 Middle1651.612472.73Educationa Less than high school1341.941133.33 High school graduate412.90515.15 Higher education1445.161751.52Employment statusb Not working for payment1858.062163.64 Working for payment1341.941236.36Type of social security Contributive (employer-based)2374.192781.82 Subsidized825.81618.18 Time since diagnosis (years)5.884.096.976.78Values are presented as mean and standard deviation, or number and percentage*p value for significance of baseline differences between groups, as tested using χ2 (categorical data) or t test (continuous data)aEducation: less than high school to some/no high school. Higher education—technical/vocational program, college degree, graduate degreebEmployment status: not working for payment—retired, unemployed, homemaker. Working for payment—employed full-time, employed part-time, freelancer
## Statistical analyses
Descriptive statistics (mean, standard deviation, absolute frequency, and percentage) were calculated for each outcome variable at baseline and post-intervention. To characterize the population, we compared the intervention with the control group on sociodemographics at baseline using independent t test or chi-square procedures depending on the nature and distribution of the variable. Generalized estimating equations (GEEs) with non-structure correlation were used to analyze changes in primary behavioral outcomes (e.g., physical activity levels) and secondary behavioral outcomes (e.g., motivation to engage in physical activity), and QoL across the intervention period occurring within subjects. GEE was used for its ability to provide a population-averaged effect from repeatedly measured data of multiple subjects in absence of normality. A coefficient interaction between the time of observation and the presence/absence of intervention and $95\%$ confidence intervals (CI) were calculated to determine the possible effects of the intervention in this population. Stata®, version 16.0, and R Core Team, version 4.0, were used to analyze the data.
## Acceptability assessment: interviews
Women in the intervention and control groups participated in the baseline interviews, while only the intervention group participated in follow-up interviews pertaining to the acceptability of the intervention (total interviews = 37). We used a semi-structured interviewing technique to ensure in-depth insights about BCS perspectives towards physical activity, their anticipated and actual experienced barriers and facilitators to engage in the physical activity intervention, their expected and reported benefits from the physical activity intervention, and the perceived positive and negative aspects of the program.
A social scientist on the study team (MAR) led the interviews, which had an average participation of three BCS and 18-min duration each. Interviews were conducted in small groups, given the purpose of generating collective insights regarding program acceptability. All interviews were audio-recorded, transcribed verbatim, and thematically analyzed using Excel thematic matrices (Microsoft Corporation, 2018). Four researchers, who were separate from the interview facilitator, independently conducted the analysis. The total number of transcripts was divided in two, and each half was duplicated and analyzed by two researchers. Transcripts were thematically analyzed according to the socioecological model [45] using the following categories and subthemes: perceived barriers to engage in the intervention (intrapersonal, interpersonal, community level), perceived facilitators (intrapersonal, interpersonal, community level), perceived benefits (physical, mental, and social), and program recommendations. Researchers participated in weekly meetings to discuss interpretations and resolve discrepancies.
## Study recruitment and attrition
The participants were recruited from March 2019 to September 2019 (Fig. 1). The combined recruitment efforts with partner organizations yielded 685 BCS eligible to participate in the study. Of this pool of individuals, 553 BCS were excluded because they declined or were not interested in participating, or were unreachable by phone. For the remaining eligible participants ($$n = 132$$), telephone screens were completed to determine study eligibility. For eligible participants, an appointment was made with a project sports medicine physician to obtain medical clearance for participation in the program. Of those who were eligible, 87 attended the medical evaluation. After receiving the medical evaluation, 24 BCS were excluded because the project physician did not approve of their participation in the study or because the individuals subsequently decided not to participate for personal reasons (including not being able to commit to the study or not being interested). After consent was obtained, the 64 eligible and consenting participants were allocated to the two study arms, achieving a sample size of 33 participants in the control group and 31 participants in the intervention group. Fig. 1Consort flow diagram of “My body, My Rhythm, My Voice” study population Attrition after study enrolment was $15.6\%$ overall ($\frac{10}{64}$), with six and four participants not completing follow-up in the intervention and control groups, respectively, owing to medical treatments (e.g., eye surgery) ($$n = 3$$), relocations ($$n = 2$$), or beginning a new job ($$n = 5$$). All participants with complete data from the pre- and post-intervention measurements were included in the analyses; thus, the final analytic sample included 25 participants in the intervention group and 29 participants in the control group. Regarding physical activity class attendance rates, $84\%$ of the intervention group attended ≥ $60\%$ of the sessions (15 or more of the 24 sessions). Twenty-five percent of women had family members and friends join at least one session.
## Changes in physical activity levels using accelerometry
From baseline to 8-week post-test, significant improvements were noted in average minutes of accelerometry-derived moderate to vigorous physical activity (MVPA) per day for the intervention versus the control group (+8.99 vs −3.71 min) (Table 2). Based on the subsample of women randomly chosen to wear accelerometers during physical activity sessions, women in the intervention group were recorded as having an average of 13.47 MVPA minutes per session. Online resource 2 shows MVPA records for the physical activity intervention sessions. Table 2Evaluated outcomes by group from the study My body, My Rhythm, My VoiceVariablesIntervention groupChange ScoreControl groupChange Score Adjust effectbBaselineaPost-interventionaBaselineaPost-interventionaMeanSDMeanSDPost.-Bas. MeanSDMeanSDPost.-Bas. CoefficientConfidence Interval $95\%$Physical activity (accelerometry) Average sedentary time per week (hours per day)12.534.0112.514.500.2610.583.9010.393.77−0.190.05−1.021.13 Average time of MVPA per week (minutes per day)24.1916.2533.1821.170.2433.4619.9729.7417.73−3.7113.013.3322.69Self-regulation in physical activity (BREQ-3)0.31 Intrinsic regulation3.390.983.840.220.213.790.443.740.36−0.050.500.110.89 Integrated regulation3.380.943.640.51−0.393.690.453.620.60−0.070.33−0.060.71 Identified regulation3.650.643.890.270.263.850.293.820.37−0.030.270.020.53 Introjected regulation1.060.971.370.930.240.960.791.161.050.210.10−0.420.63 External regulation0.460.620.670.960.310.300.460.600.680.30−0.09−0.460.27 Amotivation0.860.960.470.520.210.430.600.370.54−0.06−0.33−0.720.06Quality of life (EORTC QLQ-C30) Global quality of life functioning82.0016.2681.6716.32−0.3381.6119.2184.7717.263.16−3.49−10.973.98 Physical functioning86.4010.2789.0710.342.6794.716.0192.187.98−2.535.201.458.94 Role functioning92.0014.5391.3317.43−0.6794.8310.0695.9811.491.5−1.82−9.766.12 Emotional functioning76.3319.9477.6722.271.3384.2012.2781.3216.91−2.874.21−3.0711.48 Cognitive functioning83,3320,9785.3314.692.0089.6611.2887.9314.01−1.723.72−4.5211.97 Social functioning89.3321.9888.6722.42−0.6792.5314.4992.5312.270.00−0.67−13.4012.07 Fatigue21.7816.1918.6717.49−3.1113.0316.5515.3314.972.30−5.41−13.142.32 Nausea and vomiting3.338.334.679.031.331.156.191.154.300.001.33−3.656.32 Pain22.6719.7722.6719.770.0012.6423.4212.6423.420.000.00Non estimable Dyspnea4.0011.064.0011.060.001.156.191.156.190.000.00Non estimable Insomnia24.0026.3912.0018.95−12.0017.2421.1220.6924.263.45−15.45−27.00−3.89 Appetite loss6.6713.6113.3319.256671.156.192.308.601.155.52−2.0413.07 Constipation6.6719.258.0014.531.3310.3418.055.7512.81-4.605.93−3.2015.06 Diarrhea8.0017.431.336.67−6.674.6014.708.0519.223.45−10.11−20.650.42 Financial difficulties22.6731.5113.3321.52−9.3313.7924.438.0519.22−5.75−3.59−15.838.66 Arm21.3320.0119.1120.16−2.2213.0317.3312.6416.99−0.38−1.84−12.729.05 Breast14.6718.9816.6721.382.0017.8219.7612.9312.32−4.896.89−3.9717.74Values are presented as mean and standard deviation, or number and percentageMVPA moderate to vigorous physical activityaData based on study participants completing both baseline and the 8-week follow-upbBased on a GEE analysis: Yij = β0 + β1 × group + β2 × time + β3 × (group × time)
## Barriers and facilitators to engage in physical activity program
Table 3 provides details of the reported barriers (i.e., time management, limited self-efficacy, limited social support) and facilitators (i.e., enjoyment, resilience skills, peer care network) to engage in the My Body program. Of note, in addition to identifying barriers, BCS mentioned having used the following motivational coping strategies: [1] focusing on positive attitudes towards self-care that allow them to satisfy their own needs for enjoyment and personal time and being aware of the benefits of prioritizing their own well-being; [2] using available personal, family, and social resources such as better time management, making agreements with family members to distribute tasks, and speaking to employers to make their working hours flexible so that they could regularly participate in the program; and [3] developing home-based alternatives by recording sessions and sharing videos to practice at home. Table 3Reported barriers, facilitators, and benefits of My Body physical activity programIntrapersonalInterpersonalCommunityExemplar quotesBarriers to engage inMy Bodyphysical activity programTime management: restricted according to medical appointmentsLimited social support (family, employers)*Socioeconomic status* given the personal competing priorities to invest money and time“I say, what more to do than clean the house, sweep, do laundry, go get the kids, the grandkids from school, come back…what more exercise than what I already do. ”Limited self-efficacy: not feeling able to danceIntrahousehold gender disparities to distribute care and home tasksLack of physical activity programs offered within the health care systemFears and risk perceptions from family and some healthcare professionalsFacilitators to engage inMy Bodyphysical activity programPersonal enjoyment through dancingGroup-based physical activity practice enhancing social support represented in program companions (friends, family)Motivated knowledgeable staff trained to address breast cancer related issues and physical activity“I’d think: the cancer, 20 rounds of chemo, 10 rounds of radiotherapies, how would I be able to even lift a finger? Then when I saw myself in this program I thought ‘no way, this is great’. Knowing that not everything is bad, that after all what cancer put me through, I was able to dance again”Positive beliefs regarding physical activityGuided physical activity changing risk perceptions and strengthening motivationInterdisciplinary team (sports physicians, psychologists, respiratory therapists) providing safety, respect, and affectionThe resilience process associated to the diseasePeer care network (emotional support and role modelling)Location of the sessions was accessible by public transportationPerceived benefits from engaging inMy Bodyphysical activity programIncreased energy (vitality) and endurance to perform daily tasks“This program truly helps and should be growing even more. I don’t know where to go, if I should go to where I got treatment, to the chemo room and tell them ‘look, there’s a program for this, to help us with our self-esteem because it goes so low’. I’d love to spread the word because I suffered a lot and I don’t want others to go through what I went through. On the contrary, I want to walk into those chemo rooms where people are pitying themselves and tell them, ‘no, we’re going to get through this and get healthy and pretty’. ”Decreased pain and recovery of movement for the arms, knees, hips, and feetImprovements in flexibility, joint mobility, coordination, and agilityImprovements in the sleep cycle
## Perceived benefits of the program
According to the BCS, My Body enabled positive changes in their perceptions of physical activity. A number acknowledged that, instead of causing discomfort, exercise creates wellness. Some of the women described how they had believed that daily tasks and housekeeping chores provided a sufficient physical activity to maintain optimal health. They reported that through engaging in the My Body program they viewed physical activity during leisure time as a pleasant habit of personal enjoyment, which can be better maintained using the self-regulation skills that they were taught as part of the physical activity program.
Benefits primarily reported by BCS as part of the semi-structured interviews were related to a better perception and management of their body and rising awareness and control over their movements and capabilities (Table 3). Some participants who said that they tended to feel isolated, highlighted improvements in their social skills and connectedness. They described a shift from relating to their bodies from a place of fear, to feeling capable and comfortable in their bodies. The program enabled positive attitudes (e.g., discipline, personal agency, self-confidence) and improved self-image and mood, and motivation to leave home. *In* general, the narratives of the participants indicated that the physical activity dance sessions enabled changes in their self-belief systems, which contributed to the strengthening of the following outcomes: [1] self-care, by acquiring a space for personal enjoyment; [2] self-efficacy, by feeling capable of generating changes in important health behaviors; [3] self-esteem, by generating a sense of worth and pride, and celebrating their own achievements; and [4] self-concept, by recognizing their own physical, psychological, and social capacities, and valuing positive behaviors such as personal coping strategies and empathy towards others.
## Program recommendations
During the post-program interviews, the intervention women underscored the following areas as successful aspects of the program: [1] the professionals implementing it (instructors, members of the research team), as they encouraged participants within a context of worth and trust; [2] the sessions, which were perceived as innovative, attractive, challenging, and fun; [3] practicing physical activity with peers, as they could identify with each other’s experiences and provide/receive support; [4] being able to invite family members and friends to join the program sessions and share a fun space; and [5] the communication channels created between the research team and the participants, such as WhatsApp, which generated a feeling of worth and allowed access to recorded videos of the sessions. Regarding suggested program improvements, a few participants mentioned the location was too far from their home, some said the time schedule was inconvenient for people with working schedules, and some expressed the nutrition workshops should include practical sessions such as recipe preparation. Also, some BCS expressed willingness to perform as physical activity promotors whether disseminating the program with other BCS or facilitating its implementation in other cities, using their word of mouth to communicate experienced benefits. Table 4 summarizes issues and lessons learned about the intervention by triangulating quantitative and qualitative results. Table 4Integration of quantitative and qualitative results from the study My body, My Rhythm, My VoiceProgram outcomesQuantitative measuresQualitative measuresLessons learnedEngagement of breast cancer survivorsRecruitmentRecruitment rate$\frac{64}{553}$*Thematic analysis* of reported barriers to engage in physical activity“A lot of the time you stop yourself from doing something because of the fears that others instill in you. (…) So many people do this, they tell you ‘oh you can’t do this with your pain, you can’t warm up’ no, no, no, you can’t do anything no, no, no. (…) I started researching and it turns out that when you get surgery you have to start exercising right away unlike what other people tell you. It’s important for Doctors to become aware of this, not just tell you no, no, no, they should motivate you instead”. ( Intervention group, interview #3).Recruitment of a diverse group of women was difficult due to misperceptions of physical activity and its potential benefits during and post cancer. For future interventions, it is relevant to engage health care professionals and providers as physical activity promotion agents. Participant characteristicsMiddle socioeconomic level$62.17\%$*Thematic analysis* of BCS narratives“I’d say: it doesn’t matter, if I have to make sacrifices, I’m going to do it, I’m going to finish [the intervention] and that’s what I did and now I feel fulfilled to have been able to do it, even if I had to sacrifice a lot, because I’m always thinking about everyone else and not myself.” ( Intervention group, interview #5)Relevant background characteristics of the BCS who participated in My Body include their socioeconomic level, employment status, medical history, and time management practices regarding personal competing priorities (i.e., self-care, household caregiving duties, work, medical schedule).Not working for payment$60.85\%$Overweight/obese$80\%$Time since diagnosis (years)5.43Participant retentionAttendance to ≥$60\%$ of My Body sessions$84.40\%$*Thematic analysis* of facilitators to engage in physical activity. “I felt fulfilled with everything we did and all the dancing (...) it was my exit from home. I used to spend all day lying down. This has been very exciting and beautiful for me. I tell everyone in my house that I'm sorry because it's over.” ( Intervention group, interview #8).“It motivates you to know that it’s led by people who know what they’re doing. They’re always looking out for you, asking if you got back safe or why you’re not there yet. The attention that they give us by asking how we’re doing, how things are going, how did we do, that’s something you sometimes don’t even get from family.” ( Intervention group, interview #13).The high retention rate among the Intervention group after starting My Body program might be related to facilitators reported at all levels: individual (enjoyment through dancing, positive beliefs regarding physical activity, the resilience process associated to the disease), interpersonal (group-based guided physical activity practice, peer care network), and community (motivated knowledgeable staff, interdisciplinary team, accessible place).Effects of the intervention among breast cancer survivors in the physical activity groupChange in mean physical activity levelsAverage time of MVPA per day8.99 minThematic analysis of BCS perceived benefits“I felt really good because honestly, my leg would give up on me a lot before. I was going to get surgery but with this I’ve felt much better, I can walk now, and I feel like I’ve been reborn. I’ve walked a lot and already feel so good.” ( Intervention group, interview #5)“Learning to know our bodies has been important too. Learning to know our own body and its rhythm. If I couldn’t do something, then it was ok and when I could do it, it was at my own pace.” ( Intervention group, interview #1)*As a* result of the intervention, BCS on average added 9 minutes to their average time of MVPA per day, when compared with the control group. Change in motivation to engage in physical activityIntrinsic regulation score0.45Thematic analysis of BCS’ perceptions of physical activity“I learned that you’ve got to let go of the idea that you can’t do something. I used to think I couldn’t dance, and it turns out I can, and it makes me happy. You really need to free yourself from all those things that are holding you back.” ( Intervention group, interview #6)“Learning to have discipline and will power. For example, there were 3 classes left and I had all this leg pain, but I’d say no, I have to do this and finish it." ( Intervention group, interview #4)“I was a dancer as well (…). So, I’d think: the cancer, 20 rounds of chemo, 10 rounds of radiotherapies, how would I be able to even lift a finger? Then when I saw myself in this program I thought ‘no way, this is great’. Knowing that not everything is bad, that after all that cancer put me through, I was able to dance again. I love dancing” (Intervention group, interview #2)The increases in the intrinsic motivation to engage in physical activity among BCS were possibly related to the experienced changes towards perceiving physical activity as a pleasant habit of personal enjoyment and self-care, positive attitudes (e.g. discipline, will, self-confidence), improving self-image and mood, and motivation to leave home among their perceptions of physical activity as an enjoyable behavior and perceived broad benefits (e.g., impacts in the self-belief system, improvements in flexibility, joint mobility, coordination, and vitality).Change in quality of lifeQuality of life scoreThematic analysis of BCS perceived benefits“In an emotional level I really liked it because it helped me stay in a more optimistic state of mind (...) here I feel identified with everyone. I worked hard, I was very happy, and I felt that I forgot all my problems” (Intervention group, interview #3).We did not find a significant change in QoL, but through the interviews women reported broad perceived benefits contributing to well-being enhancement. It is necessary to evaluate a larger sample size and longer intervention to capture changes in QoL. Additionally, relevant health outcomes for BCS that should be reported include sleep dysfunction, joint pain, specific self-efficacy measures for dancing (or corresponding type of physical activity), coordination, time management, self-care, and social networking. Considerations for future implementationPerceived acceptability of My Body programThematic analysis of aspects to maintain“For me it was very gratifying [sharing with BCS] not because it happened to them but because I didn’t feel so alone. It’s not the same to talk to or be with someone who hasn’t been through what I’ve been through than to have a fellow fighting companion that knows what it’s like.” ( Intervention group, interview #6)Women underscored as successful aspects of the physical activity program: [1] the professionals implementing it, [2] the enjoyable physical activity sessions, [3] practicing physical activity with peers, family members, and friends, and [4] the communication channels. Thematic analysis of aspects to strengthen or include“These programs truly help and should be growing even more because if you tell us where to go, that’s where we’ll go to promote it further (…) tell them ‘look, there’s a program for this, to help us with our self-esteem because it goes so low’. I’d love to spread the word because I don’t want others to go through what I went through. On the contrary, I want to walk into those chemo rooms were people are pitying themselves and tell them, ‘no, we’re going to get through this and get pretty’.” ( Intervention group, interview #10)BCS suggested [1] increasing sites and hours for the physical activity sessions, [2] including nutrition workshops for preparation of recipes, and [3] installing capacity among BCS to become agents of physical activity promotion.
## Discussion
My Body, a theory-driven and dance-based physical activity program for BCS, has the potential of being effective for increasing physical activity and improving quality of life by using a community setting approach. The intervention group was able to add ~10 min of MVPA per day (i.e., ~70 min/week) in response to the intervention. Physical activity has been linked with a variety of positive health outcomes in adults, including women who are survivors of breast cancer [10, 46]. Importantly, we observed an increase in intrinsic regulation for physical activity, reflecting the fact that women became increasingly motivated to participate in regular physical activity based on the benefits that it could bring to them personally. Regarding QoL, we observed improvements in the scores for physical functioning and insomnia symptoms—two key outcomes for BCS. Additionally, through the post-intervention interviews, women reported broad perceived benefits contributing to well-being enhancement. The cancer-specific focus of the program, their enjoyment of the reasonably moderate physical activity intensity, and the encouraging program environment are positive features that could be maintained in the My Body program. Lastly, adherence to the program was high. To the best of our knowledge, this is the first study in Latin America to engage a transdisciplinary community-setting approach to implement a safe, accessible, and culturally appropriate program to affect the general wellness of BCS. While the study was conducted prior to the onset of the COVID-19 pandemic, participants also appreciated the availability of recorded sessions, with remote platforms such as Facebook and YouTube serving as additional methods for delivering portions of the program during times when in-person gatherings are limited.
In the last few years, several studies in high-income countries (HIC) have reported the effects of dance-based physical activity interventions on BCS, reporting promising improvements in physical, mental, and social health [47, 48]. Given this, this research area can benefit at this juncture from the delivery and evaluation of effective intervention protocols at a community level [49]. My Body was effectively delivered as part of a publicly available municipal physical activity program offered by the city of Bogotá. Future larger-scale studies involving randomization or crossover designs with larger periods of intervention and evaluation could provide evidence of program effects on additional important outcomes for this population. Furthermore, follow-up of a longer duration could address the question of sustainability.
Across different cultures, dancing has been indicated as providing an optimal balance between an effective as well as engaging training protocol for women, including those with a breast cancer diagnosis [48]. This was reflected in the self-regulation for physical activity increases, particularly, significant increases for intrinsic motivation and identified regulation which, according to self-determination theory, are associated with adherence to regular active behaviors [42, 50]. These results suggest that participants were no longer exercising simply for extrinsic reasons but developed a more personal interest in physical activity, finding it enjoyable and beneficial [41]. Women expressed interest in replicating active behaviors by dancing along with videos at home and noting interest in becoming champions for physical activity among BCS more generally. Additionally, BCS underscored the peer care network as a facilitator for engaging in the intervention, which generated feelings of comfort and confidence, and enabled emotional support and role modeling to support one’s own goals and dissipate personal fears. Overall, participants expressed that group-based cancer-specific physical activity encouraged a sense of belonging, worth, mutual support, and emotional bonding [51].
In terms of cancer survivors’ QoL, a growing body of literature has indicated that tumor-related peer support groups enhance a long-term development of positive QoL and coping [51–53]. Future studies should involve larger samples and longer evaluation periods to potentially capture additional changes in other symptoms and dimensions of QoL.
Analyzing participant narratives allowed for a qualitative process evaluation of changes in women’s perceptions about physical activity. Women reported that they gained confidence in their competence to perform physical activity as a possible additional activity to daily tasks and found it to be increasingly relevant to personal enjoyment, self-care, and well-being enhancement. Such improvements in participant attitudes indicate the potential of My Body to improve perceived behavioral capabilities to be active.
Overall, participants were satisfied with the program while identifying facilitators and barriers that align with findings obtained in similar studies [52, 53]. Facilitators of program attendance included enjoyment of dance sessions, increased social support, and motivated and knowledgeable staff. The main reported barriers to regular attendance were medical schedules (as patients and caregivers) and limited social support among family. Some participants missed sessions due to medical appointments, underscoring that the lack of physical activity programs offered within the health care system is a community-level barrier. If physical activity promotion was part of their oncological treatment, then the overlap between medical appointments and activity sessions could potentially be more readily diminished.
Future studies should include virtual sessions that could improve adherence. Women stated having improved skills for time management and negotiation among their social networks related to distributing home tasks and scheduling appointments. Indeed, previous studies have indicated that not only women need to be encouraged to see beyond the myths and barriers associated with physical activity in BCS, but family members and even physicians could benefit from these insights, as well [41, 54]. Our findings support this idea, as the underlying fears related to BCS performing physical activity could be seen not only during the intervention but especially before the program even started. Such barriers included several difficulties in accessing information from the health care setting relating to potential participants, and some ethics committee fears concerning approving the intervention due to perceived risks to the participants, which were not justified given the substantial evidence base supporting the benefits of such programs for BCS.
## Strengths and limitations
In relation to study strengths and limitations, our multidisciplinary mixed-methods approach allowed the development of an evidence-supported, culturally relevant, and attractive behavior change intervention (i.e., a dancing protocol). However, participants noted that they also would have appreciated receiving complementary workshops regarding a healthy diet. Second, recruitment was found to be challenging in this first-generation study, particularly given some hesitation among healthcare providers to recommend physical activity to BCS, even though evidence strongly supports its benefits for this population. Nevertheless, partnerships with multisectoral institutions and the sports physician evaluation were crucial for women feeling supported in enrolment. We subsequently reported these results to clinicians and the ministry of health, creating trust and feasibility for future studies with BCS. Additionally, we learned that relevant health and behavioral outcomes for BCS that should be reported include sleep dysfunction, joint pain, specific self-efficacy measures for dancing (or corresponding types of physical activity), time management, self-care, and social networking. Likewise, for future studies involving larger samples, the analysis could benefit from being stratified by time since diagnosis, as this can be an important modifier of the intervention effects [22]. Although we confirmed the relevance of including certain portions of the health sector, the sports/recreation sector, and academia in this research, further dissemination of the program should consider the direct participation of local oncologists to recommend the community-based program.
## Conclusion
My *Body is* a theory-driven community-based physical activity program for BCS implemented through the engagement of multi-sectoral stakeholders. It has the potential of generating behavioral changes while making use of the benefits of dance sessions as a catalyst for engaging in health-enhancing physical activity. It was tailored specifically to a real-world community setting and to meet BCS needs regarding physical, mental, and social health. We believe that the promising results found in this first-generation study in Latin America merit additional investigation in relation to this important and growing population segment in Colombia.
## Supplementary Information
Additional file 1. Components of My Body behavioral intervention. Additional file 2. Physical activity intensity during My Body intervention dancing-based sessions.
## References
1. Roth GA, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N. **Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017**. *Lancet* (2018.0) **392** 1736-1788. DOI: 10.1016/S0140-6736(18)32203-7
2. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. **Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA Cancer J Clin* (2018.0) **68** 394-424. DOI: 10.3322/caac.21492
3. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. **Global cancer statistics, 2012**. *CA Cancer J Clin* (2015.0) **65** 87-108. DOI: 10.3322/caac.21262
4. Pardo Ramos C, Cendales Duarte R. *Incidencia, mortalidad y prevalencia de Cáncer en Colombia 2007–2011* (2011.0)
5. 5.Calderón MI, Ospino MCO, Mendoza GMH, Molina CÁ. Boletín legislativo & político: Salud Pública del Instituto Nacional de Cancerologia; 2018. https://www.cancer.gov.co/conozca-sobre-cancer-1/publicaciones/2018-2-boletin-legislativo-politico.
6. Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M. **Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries**. *Lancet* (2018.0) **391** 1023-1075. DOI: 10.1016/S0140-6736(17)33326-3
7. Battaglini CL. **Twenty-five years of research on the effects of exercise training in breast cancer survivors: a systematic review of the literature**. *World J Clin Oncol* (2014.0) **5** 177. DOI: 10.5306/wjco.v5.i2.177
8. Basen-Engquist K, Alfano CM, Maitin-Shepard M, Thomson CA, Schmitz KH, Pinto BM. **Agenda for translating physical activity, nutrition, and weight management interventions for cancer survivors into clinical and community practice**. *Obesity* (2017.0) **25** S9-22. PMID: 29086526
9. 9.American College of Sports MedicineACSM’s guidelines for exercise testing and prescription201810PhiladelphiaWolters Kluwer. *ACSM’s guidelines for exercise testing and prescription* (2018.0)
10. 10.Department of Health & Human Services2018 physical activity guidelines advisory committee scientific report2018. *2018 physical activity guidelines advisory committee scientific report* (2018.0)
11. Mason C, Alfano CM, Smith AW, Wang CY, Neuhouser ML, Duggan C. **Long-term physical activity trends in breast cancer survivors**. *Cancer Epidemiol Biomarkers Prev* (2013.0) **22** 1153-1161. DOI: 10.1158/1055-9965.EPI-13-0141
12. Phillips SM, Dodd KW, Steeves J, McClain J, Alfano CM, McAuley E. **Physical activity and sedentary behavior in breast cancer survivors: new insight into activity patterns and potential intervention targets**. *Gynecol Oncol* (2015.0) **138** 398-404. DOI: 10.1016/j.ygyno.2015.05.026
13. Kim RB, Phillips A, Herrick K, Helou M, Rafie C, Anscher MS. **Physical activity and sedentary behavior of cancer survivors and non-cancer individuals: results from a national survey**. *PLoS One* (2013.0) **8** e57598. DOI: 10.1371/journal.pone.0057598
14. Ortiz A, Tirado M, Hughes DC, Gonzalez V, Song J, Mama SK. **Relationship between physical activity, disability, and physical fitness profile in sedentary Latina breast cancer survivors**. *Physiother Theory Pract* (2018.0) **34** 783-794. DOI: 10.1080/09593985.2018.1424978
15. 15.Ministerio de SaludRESUMEN EJECUTIVO Primeros resultados de la Encuesta Nacional de la Situación Nutricional en Colombia ENSIN—2015201564. *RESUMEN EJECUTIVO Primeros resultados de la Encuesta Nacional de la Situación Nutricional en Colombia ENSIN—2015* (2015.0) 64
16. Rubio MA, Guevara-Aladino P, Urbano M, Cabas S, Mejia-Arbelaez C, Rodriguez Espinosa P. **Innovative participatory evaluation methodologies to assess and sustain multilevel impacts of two community-based physical activity programs for women in Colombia**. *BMC Public Health* (2022.0) **22** 771. DOI: 10.1186/s12889-022-13180-2
17. Keller C, Fleury J. **Factors related to physical activity in Hispanic women**. *J Cardiovasc Nurs* (2006.0) **21** 142-145. DOI: 10.1097/00005082-200603000-00012
18. Finne E, Glausch M, Exner AK, Sauzet O, Stölzel F, Seidel N. **Behavior change techniques for increasing physical activity in cancer survivors: a systematic review and meta-analysis of randomized controlled trials**. *Cancer Manag Res* (2018.0) **10** 5125-5143. DOI: 10.2147/CMAR.S170064
19. Guthold R, Stevens GA, Riley LM, Bull FC. **Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1.9 million participants**. *Lancet Glob Heal* (2018.0) **6** e1077-e1086. DOI: 10.1016/S2214-109X(18)30357-7
20. Pinto BM, Ciccolo JT. **Physical activity motivation and cancer survivorship**. *Recent results in cancer research* (2011.0)
21. Short CE, James EL, Stacey F, Plotnikoff RC. **A qualitative synthesis of trials promoting physical activity behaviour change among post-treatment breast cancer survivors**. *J Cancer Surviv* (2013.0) **7** 570-581. DOI: 10.1007/s11764-013-0296-4
22. Grimmett C, Corbett T, Brunet J, Shepherd J, Pinto BM, May CR. **Systematic review and meta-analysis of maintenance of physical activity behaviour change in cancer survivors**. *Int J Behav Nutr Phys Act* (2019.0) **16** 1-20. DOI: 10.1186/s12966-019-0787-4
23. Emslie C, Whyte F, Campbell A, Mutrie N, Lee L, Ritchie D. **“I wouldn’t have been interested in just sitting round a table talking about cancer”; exploring the experiences of women with breast cancer in a group exercise trial**. *Health Educ Res* (2007.0) **22** 827-838. DOI: 10.1093/her/cyl159
24. Midtgaard J, Røssell K, Christensen JF, Uth J, Adamsen L, Rørth M. **Demonstration and manifestation of self-determination and illness resistance: a qualitative study of long-term maintenance of physical activity in posttreatment cancer survivors**. *Support Care Cancer* (2012.0) **20** 1999-2008. DOI: 10.1007/s00520-011-1304-8
25. Errico KM, Rowden D. **Experiences of breast cancer survivor-advocates and advocates in countries with limited resources: a shared journey in breast cancer advocacy**. *Breast J* (2006.0) **12** S111-S1S6. DOI: 10.1111/j.1075-122X.2006.00208.x
26. Perez LG, Kerr J, Sallis JF, Slymen D, McKenzie TL, Elder JP. **Perceived neighborhood environmental factors that maximize the effectiveness of a multilevel intervention promoting physical activity among Latinas**. *Am J Heal Promot* (2018.0) **32** 334-343. DOI: 10.1177/0890117117742999
27. Larsen BA, Pekmezi D, Marquez B, Benitez TJ, Marcus BH. **Physical activity in Latinas: social and environmental influences**. *Womens Health (Lond Engl)* (2013.0) **9** 201-210. DOI: 10.2217/WHE.13.9
28. Díaz del Castillo A, González SA, Ríos AP, Páez DC, Torres A, Díaz MP. **Start small, dream big: experiences of physical activity in public spaces in Colombia**. *Prev Med (Baltim)* (2017.0) **103** S41-S50. DOI: 10.1016/j.ypmed.2016.08.028
29. 29.Torres A, Díaz MP, Hayat MJ, Lyn R, Pratt M, Salvo D, et al. Assessing the effect of physical activity classes in public spaces on leisure-time physical activity: “Al Ritmo de las Comunidades” a natural experiment in Bogota, Colombia. Prev Med (Baltim). 2017;103:S51–8. 10.1016/j.ypmed.2016.11.005.
30. Beune E, Muilwijk M, Jelsma JGM, van Valkengoed I, Teitsma-Jansen AM, Kumar B. **The acceptability and effect of a culturally-tailored dance intervention to promote physical activity in women of South Asian origin at risk of diabetes in the Netherlands—a mixed-methods feasibility study**. *PLoS One* (2022.0) **17** e0264191. DOI: 10.1371/journal.pone.0264191
31. Jain S, Brown DR. **Cultural dance: an opportunity to encourage physical activity and health in communities**. *Am J Heal Educ* (2001.0) **32** 216-222. DOI: 10.1080/19325037.2001.10603469
32. 32.World Economic ForumThe inclusive development index 2018 summary and data highlights. Report2018. *The inclusive development index 2018 summary and data highlights. Report* (2018.0)
33. 33.Gaffney LK, Lozano OD, Almanza A, Ruiz N, Mantero A, Stoutenberg M. The implementation of a national physical activity intervention in Colombia. J Phys Act Health. 2019;16(6):430–6. 10.1123/jpah.2018-0183. Epub 2019 May 19.
34. Pluye P, Hong QN. **Combining the power of stories and the power of numbers: mixed methods research and mixed studies reviews**. *Annu Rev Public Health* (2014.0) **35** 29-45. DOI: 10.1146/annurev-publhealth-032013-182440
35. Rubio MA, Mosquera D, Blanco M, Montes F, Finck C, Duval M. **Cross-sector co-creation of a community-based physical activity program for breast cancer survivors in Colombia**. *Health Promot Int* (2022.0) **37** daac073. DOI: 10.1093/heapro/daac073
36. McEwan D, Rhodes RE, Beauchamp MR. **What happens when the party is over?: sustaining physical activity behaviors after intervention cessation**. *Behav Med* (2020.0) **0** 1-9. DOI: 10.1080/08964289.2020.1750335
37. Pudkasam S, Polman R, Pitcher M, Fisher M, Chinlumprasert N, Stojanovska L. **Physical activity and breast cancer survivors: importance of adherence, motivational interviewing and psychological health**. *Maturitas* (2018.0) **116** 66-72. DOI: 10.1016/j.maturitas.2018.07.010
38. Karageorghis CI, Jones L, Priest DL, Akers RI, Clarke A, Perry JM. **Revisiting the relationship between exercise heart rate and music tempo preference**. *Res Q Exerc Sport* (2011.0) **82** 274-284. DOI: 10.1080/02701367.2011.10599755
39. Akimoto T, Sugawara J. *Article in the journal of sports medicine and physical fitness* (2006.0)
40. Freedson PS, Melanson E, Sirard J. **Calibration of the Computer Science and Applications, Inc. accelerometer**. *Med Sci Sport Exerc* (1998.0) **30** 777-781. DOI: 10.1097/00005768-199805000-00021
41. Brunet J, Burke SM, Sabiston CM. **The benefits of being self-determined in promoting physical activity and affective well-being among women recently treated for breast cancer**. *Psychooncology* (2013.0) **22** 2245-2252. DOI: 10.1002/pon.3287
42. 42.Teixeira PJ, Carraça EV, Markland D, Silva MN, Ryan RM. Exercise, physical activity, and self-determination theory: a systematic review. Int J Behav Nutr Phys Act. 2012;9(78) Available from: http://www.ijbnpa.org/content/9/1/78.
43. González-cutre D, Sicilia Á. **Hacia una mayor comprensión de la motivación en el ejercicio físico : medición de la regulación integrada en el contexto español [Towards a better understanding of motivation in physical activity: measuring integrated regulation in the Spanish context ]**. *Psichothema* (2010.0) **22** 841-847
44. Finck C, Barradas S, Singer S, Zenger M, Hinz A. **Health-related quality of life in Colombia: reference values of the EORTC QLQ-C30**. *Eur J Cancer Care (Engl)* (2012.0) **21** 829-836. DOI: 10.1111/ecc.12000
45. Sallis JF, Cervero RB, Ascher W, Henderson KA, Kraft MK, Kerr J. **An ecological approach to creating active living communities**. *Annu Rev Public Health* (2006.0) **27** 297-322. DOI: 10.1146/annurev.publhealth.27.021405.102100
46. Wirtz P, Baumann FT. **Physical activity, exercise and breast cancer—what is the evidence for rehabilitation, aftercare, and survival? A review**. *Breast Care* (2018.0) **13** 93-101. DOI: 10.1159/000488717
47. Rogers LQ, Vicari S, Courneya KS. **Lessons learned in the trenches: facilitating exercise adherence among breast cancer survivors in a group setting**. *Cancer Nurs* (2010.0) **33** 1-12. DOI: 10.1097/NCC.0b013e3181db699d
48. Cerulli C, Parisi A, Sacchetti M, Tranchita E, Murri A, Minganti C. **Dancing with health: a new dance protocol to improve the quality of life of breast cancer survivors**. *Med dello Sport* (2019.0) **72** 295-304
49. Leach HJ, Danyluk JM, Culos-Reed SN. **Design and implementation of a community-based exercise program for breast cancer patients**. *Curr Oncol* (2014.0) **21** 267-271. DOI: 10.3747/co.21.2079
50. Caperchione CM, Sabiston CM, Stolp S, Bottorff JL, Campbell KL, Eves ND. **A preliminary trial examining a “real world” approach for increasing physical activity among breast cancer survivors: findings from project MOVE**. *BMC Cancer* (2019.0) **19** 1-12. DOI: 10.1186/s12885-019-5470-2
51. 51.Szalai M, Szirmai A, Füge K, Makai A, Erdélyi G, Prémusz V, et al. Special aspects of social support: qualitative analysis of oncologic rehabilitation through a belly dancing peer support group. Eur J Cancer Care (Engl). 2017;26(6). 10.1111/ecc.12656.
52. Balneaves LG, Van Patten C, Truant TLO, Kelly MT, Neil SE, Campbell KL. **Breast cancer survivors’ perspectives on a weight loss and physical activity lifestyle intervention**. *Support Care Cancer* (2014.0) **22** 2057-2065. PMID: 24633590
53. Wurz A, St-Aubin A, Brunet J. **Breast cancer survivors’ barriers and motives for participating in a group-based physical activity program offered in the community**. *Support Care Cancer* (2015.0) **23** 2407-2416. DOI: 10.1007/s00520-014-2596-2
54. Loh SY, Chew SL, Lee SY. **Physical activity and women with breast cancer: insights from expert patients**. *Asian Pacific J Cancer Prev* (2011.0) **12** 87-94
|
---
title: In silico Identification of Hypoxic Signature followed by reverse transcription-quantitative
PCR Validation in Cancer Cell Lines
authors:
- Sara Shayan
- Golnaz Bahramali
- Arash Arashkia
- Kayhan Azadmanesh
journal: Iranian Biomedical Journal
year: 2022
pmcid: PMC9971715
doi: 10.52547/ibj.3803
license: CC BY 3.0
---
# In silico Identification of Hypoxic Signature followed by reverse transcription-quantitative PCR Validation in Cancer Cell Lines
## Abstract
### Background:
Hypoxic tumor microenvironment is one of the important impediments for conventional cancer therapy. This study aimed to computationally identify hypoxia-related mRNA signatures in nine hypoxic-conditioned cancer cell lines and investigate their role during hypoxia.
### Methods:
Nine RNA-Seq expression data sets were retrieved from the Gene Expression Omnibus database. DEGs were identified in each cancer cell line. Then 23 common DEGs were selected by comparing the gene lists across the nine cancer cell lines. qRT-PCR was performed to validate the identified DEGs.
### Results:
By comparing the data sets, GAPDH, LRP1, ALDOA, EFEMP2, PLOD2, CA9, EGLN3, HK, PDK1, KDM3A, UBC, and P4HA1 were identified as hub genes. In addition, miR-335-5p, miR-122-5p, miR-6807-5p, miR-1915-3p, miR-6764-5p, miR-92-3p, miR-23b-3p, miR-615-3p, miR-124-3p, miR-484, and miR-455-3p were determined as common miRNAs. Four DEGs were selected for mRNA expression validation in cancer cells under normoxic and hypoxic conditions with qRT-PCR. The results also showed that the expression levels determined by qRT-PCR were consistent with RNA-Seq data.
### Conclusion:
The identified PPI network of common DEGs could serve as potential hypoxia biomarkers and might be helpful for improving therapeutic strategies.
## INTRODUCTION
A gene expression signature is a single or combined group of genes whose expression responds to a particular signal or changes in cellular status in a predictable way. Gene signatures are frequently extracted from a set of DEGs by comparing two groups, such as cell lines under different treatment conditions. Gene expression signatures can therefore be used as surrogate markers to comprehend the complexity of pathway activation.
Oxygen deprivation occurs in almost all solid tumors. A shortage of oxygen is the consequence of inadequate oxygen delivery via inefficient tumor vasculature[1]. Hypoxia affects tumor behavior and facilitates tumor progression and metastasis, leading to resistance to conventional chemo- and radiotherapy[2]. Therefore, identifying the key genes regulating cancer cell behavior during hypoxia is essential for developing anticancer agents that efficiently kill tumor cells under hypoxic conditions.
A growing number of studies have identified DEGs during hypoxia in different cancer cell types using RNA-Seq analysis[3-5]. However, their findings only represent the genetic characteristics of specific tumor cells during hypoxia. In this study, we used RNA-Seq datasets of nine different hypoxic-conditioned cancer cell lines to find hypoxia-related mRNA signatures. Since human cancer cell lines are widely used for better understanding of cancer biology, cancer cell characterization, and anticancer drug discovery[6], we selected the available RNA-Seq datasets of cell lines to explore the effect of hypoxia on gene expression profiles.
MiRNAs play a central role in regulating gene expression[7]. Kulshreshtha and colleagues[8] described a functional link between hypoxia and miRNA expression. They indicated that miRNAs profile are regulated by hypoxia in a variety of cell types, and their dysregulation is associated with many cancers, making their signature a potential prognostic biomarker[9]. In the present study, common DEGs along with their hub genes among the nine different cancer cell lines were screened during hypoxia. Then we investigated a PPI network and predicted a miRNA-targeted gene network, which might provide a basis for further studies. Our aim was to discover the molecular mechanism underlying the effect of hypoxia and provide potential prognostic markers.
## MATERIALS AND METHODS
Raw biological data and differential RNA expression analysis Raw RNA-*Seq data* of nine hypoxia-conditioned cancer cell lines were retrieved from the Sequence Read Archive (www.ncbi.nlm.nih.gov/geo). Among these datasets, GSE131378 contained four samples of hypoxic-conditioned and four samples of normoxic-conditioned A549 cells, while GSE72437 consisted of five samples of hypoxic-conditioned and five samples of normoxic-conditioned BeWo cells. Moreover, GSE78025, GSE81513, GSE84167, GSE13967, GSE149132, and GSE160491 contained three samples of hypoxic-conditioned and also three samples of normoxic-conditioned U78-MG, HCT116, MCF-7, ASPC-1, T47D, and BCPAP, respectively. GSE131379 also comprised of two samples of hypoxic-conditioned and three samples of normoxic-conditioned Hela cells. SAMtools was used to extract raw sequencing reads. The read quality was examined using FastQC version 0.11.2, and low quality bases and adaptor sequences were removed using Trimmomatic version 0.32; the expression level of each transcript was then quantified in transcripts per million using Kallisto[10]. The counts were imported into software R v. 3.4.0 using the tximport R package v. 1.4.0, and the DEGs were identified with a | log2 fold change | ≥1 and a false discovery rate <0.05 using the DESeq2 package in R v. 3.2.3. The UpSetR package in R was employed to find common genes between different datasets[11]. The default values were employed for all the packages.
Function enrichment analysis We used Database for Annotation, Visualization, and Integrated discovery (DAVID) (https://david.ncifcrf. gov/; version 6.8) for GO functional analysis and KEGG pathway analysis of DEGs[12-14]. The Evolutionary Relationships (PANTHER) was also used to determine protein class over-representation[15], and $p \leq 0.05$ represented statistical significance.
Construction of a PPI network Interactions between the common DEGs and other proteins would be useful to fully understand their biological roles. In this study, 23 common DEG PPI network were constructed by Retrieval of Interacting Genes (STRING; https://string-db.org/). Moreover, 23 common DEGs were integrated into the International Molecular Exchange Consortium database (https:// www.imexconsortium.org/) to identify the hub genes information in PPI network[16]. The protein interaction network was visualized using NetworkAnalyst (https://www.networkanalyst.ca) and Cytoscape (3.9.1)[16]. To evaluate the nodes in the PPI network, we adopted several topological measures, including degree (k), MCC, BC, and CC. Since degree (k), BC, and MCC are often used for detecting the hub in a network[17-19], we determined hub genes based on connectivity degree (number of interactions) >10, MCC, and BC using Cytohubba on Cytoscape.
MiRNA interactions analysis To identify the miRNA-mRNA target interactions, miRTarBase[20] and TarBase[21] (both version 8.0) were employed to collect the miRNA-gene interaction data. Topological analysis based on degree and betweenness centrality as key topological parameters was performed utilizing NetworkAnalyst.
Cell culture for qRT-PCR validation To validate our findings, we selected four hub genes, including GAPDH, LRP1, ALDOA, and PLOD2 to determine their expression in cancer cell lines (A549, U78-MG, HCT116, Hela, and MCF-7) under hypoxic or normoxic conditions. Cells were purchased from the National Cell Bank of Iran (Pasteur Institute, Tehran, Iran). Cells used in the experiment were cultured in DMEM supplemented with $10\%$ FBS and incubated in a humidified incubator with $5\%$ CO2 at 37 °C.
Cancer cell adaptation to hypoxia Cells were seeded in a T25 flask and cultured in DMEM medium supplemented with $10\%$ FBS. The cells were repeatedly incubated in hypoxic conditions in an Anoxomat chamber (Mart Microbiology, Lichtenvoorde, The Netherland; $1\%$ O2) for 4 h and then incubated in a standard culture environment ($5\%$ CO2 and $95\%$ air) at 37 °C for 48-72 h. Cells were treated twice weekly, and hypoxic-conditioned cell lines were generated after 20 exposures to hypoxia[22].
RNA isolation and qRT-PCR Trizol reagent (TaKara, Kusatsu, Shiga, Japan) was used for RNA isolation from the cells during normoxia and hypoxia. RNA samples were reversely transcribed to complementary DNA by the QIAGEN Reverse Transcription Kit (Qiagen, Germany). Subsequently, the quantification of cDNA was performed by the qRT-PCR method using SYBR Green Master Mix (Amplicon). The reaction conditions were conducted at 95 °C for 10 min, 40 cycles of 95 °C for 10 s, 60 °C for 30 s, and 72 °C for 30 s. The RPLP0 was used as an internal reference control[23]. Gene expression levels were calculated based on the Delta-Delta Ct relative quantification.
Statistical analysis Statistical analyses were performed using the student’s t-test with GraphPad Prism 8 software (GraphPad Prism, San Diego, CA, USA). The p value was considered statistically significant when it was less than 0.05.
## RESULTS
Differential RNA expression analysis RNA sequencing data from the nine different hypoxic-conditioned cancer cell lines (A549, BeWo, U78-MG, HCT116, Hela, MCF-7, ASPC-1, T47D, and BCPAP) were analyzed, and 23 common DEGs were identified (Fig. 1), including EGLN3, ANGPTL4, GPR146, C4orf47, KCTD11, CA9, PPFIA4, PLOD2, HK2, and TMEM. Interestingly, all of these genes were upregulated in the hypoxic-conditioned cancer cell lines.
Functional categories and pathway analysis The PANTHER protein classification revealed that the common DEGs were classified into nine groups according to their function: protein modifying enzyme (PPFIA4, PDK1, and PLOD2), scaffold/adaptor protein (KCTD11), transfer/carrier protein (LRP1), transmembrane signal receptor (GPR146), cytoskeletal protein (HK2), extracellular matrix protein (EFEMP2), intercellular signal molecule (ANGPTL4), metabolite interconversion enzyme (FUT11, GAPDH, QSOX1, PFKFB4, ALDOA, and HK2), and regulatory protein (KDM3A). GO analysis, which covered the three GO categories (i.e. CC, BP, and MF), was performed using DAVID. DEGS were enriched significantly in different GO terms, including hexose metabolic process (ontology: BP), monosaccharide binding (ontology: MF), and mitochondrial pyruvate dehydrogenase complex (ontology: CCO); the results are summarized in Table 1. The significance threshold of $p \leq 0.05$ was selected. Moreover, seven pathways were significantly enriched based on KEGG pathway analysis, including HIF-1 signaling pathway, fructose and mannose metabolism, glycolysis/gluconeogenesis, carbon metabolism, cholesterol metabolism, central carbon metabolism in cancer, and biosynthesis of amino acids (Table 2).
**Fig. 1:** *UpSet plot of DEGs. (A) Total number of DEGs during hypoxia; (B) intersection of gene sets in hypoxic conditions. Black circles indicate the total number of DEGs with differences in log2 fold change expression in each dataset, and connecting bars show the overlapping DEGs*
PPI network construction and hub gene selection Using the STRING database, a PPI network obtained from 23 common DEGs, which was composed of 22 nodes and 25 edges, was constructed and visualized in Cytoscape (Supplementary Fig. 1). In order to screen the PPI network’s interactions with other proteins, which provide important clues about their functions, the PPI network was integrated into the International Molecular Exchange Consortium database. A PPI network composed of 448 nodes and 531 edges was obtained (Fig. 2). Twelve hub proteins, including GAPDH, LRP1, ALDOA, EFEMP2, PLOD2, CA9, EGLN3, HK, PDK1, KDM3A, UBC, and P4HA1, were identified in this network based on degrees (>10), MCC, and BC (Fig. 3 and Table 3).
Gene regulatory network analysis The key miRNAs (miR-335-5p, miR-122-5p, miRr-6807-5p, miR-1915-3p, miR-6764-5p, mirR-92-3p, miR-23b-3p, miR-615-3p, miR-124-3p, miR-484, and miR-455-3p) were identified based on network topological properties (degree and betweenness centrality). Additionally, our results indicate miR-92-3p can regulate a large number of mRNA targets ($$n = 88$$), as shown by the PPI network (Fig. 4).
Quantitative real-time PCR for DEGs In order to validate the DEGs identified by RNA-seq analysis, four hub genes, including GAPDH, LRP1, ALDOA, and PLOD2, were selected for analysis via qRT-PCR under normoxic and hypoxic conditions. Primers were designed based on available sequences to amplify the specific altered genes. Primer sequences are shown in Table 4. Based on the qRT-PCR results, the candidate genes were upregulated in A549, U78-MG, HCT116, Hela, and MCF-7 cells under hypoxic conditions (Fig. 5). The expression profiles of four genes confirmed the original transcriptome data obtained by RNA-Seq.
## DISCUSSION
Because hypoxic cells are likely to be resistant to chemo- and radiotherapy, it is of high importance to identify the key hypoxia-inducible genes and resistance mechanisms for efficient therapeutic intervention. Moreover, it is well established that miRNA plays a central role in regulating the various biological pathways[24]. Therefore, exploring the role and impact of mRNA and miRNA in cancer cells, especially during hypoxia, could be helpful in cancer diagnosis and treatment.
In the current study, we conducted bioinformatics analysis to identify the candidate key genes and biological pathways among nine different cancer cell lines exposed to hypoxic conditions. Data was extracted from GSE131378, GSE72437, GSE78025, GSE81513, GSE131379, GSE84167, GSE13967, GSE149132, and GSE160491 datasets, among which 23 common DEGs were screened. To our surprise, all the common DEGs were upregulated in all the nine hypoxic-conditioned cancer cell lines. In order to gain some insight into how hypoxia affects the expression of genes at the molecular level, GO and KEGG pathway enrichment analyses were carried out[13,14]. Functional enrichment analysis revealed that the hexose metabolic process, response to hypoxia, and glucose metabolic process were significantly changed. According to KEGG enrichment analysis, 23 common genes were enriched in the HIF-1 signaling pathway, including fructose and mannose metabolism, glycolysis/gluconeogenesis, carbon metabolism, cholesterol metabolism, central carbon metabolism in cancer, and biosynthesis of amino acids. Since it is believed that proteins with more interactions have higher chances of being involved in the essential PPI[25], the PPI network was constructed and GAPDH, LRP1, ALDOA, EFEMP2, PLOD2, CA9, EGLN3, HK, and PDK1 were identified as the hub genes.
To support our findings, we selected four hub genes (GAPDH, LRP1, ALDOA, and PLOD2) for qRT-PCR validation in A549, U78-MG, HCT116, Hela, and MCF-7 cells under normoxic and hypoxic conditions. Expression patterns of four genes generated by qRT-PCR were consistent with RNA-seq data. Consistently, several studies have found that hypoxia-related genes such as GAPDH, LRP1, ALDOA, EFEMP2, PLOD2, CA9, EGLN3, HK, and PDK1 are upregulated during hypoxia[26-28].
**Fig. 2:** *PPI network of common genes among nine different cell lines during hypoxia by mapping DEGs into the NetworkAnalyst database. Purple nodes represent the 23 common DEGs, and the area of each circle demonstrates the degree of the node in the network. The color of nodes is proportional to their BC values* **Fig. 3:** *Results of algorithms from the Cytohubba. Hub genes were screened by degree, MCC, and BC according to the Cytohubba plug-in. Centrality in the network was measured by CC. The more forward ranking is represented by a redder color* TABLE_PLACEHOLDER:Table 3 **Fig. 4:** *Network analysis of DEG-miRNA interactions. NetworkAnalyst was used to visualize data obtained from the miRTarBase and TarBase databases. Blue squares represent microRNAs, and red circles represent genes. The area of each circle demonstrates the degree of the node in the network. The color of nodes is proportional to their BC values*
GAPDH and ALDOA are involved in glycolysis. It is widely believed that the overexpression of glycolytic enzymes in a large number of tumors compensates for the increased energy demands and supports rapid tumor growth[29]. However, many glycolytic enzymes have non-glycolytic functions, as well[30]. For instance, overexpressed GAPDH could inhibit caspase-independent cell death by inducing Bcl-xL upregulation, leading to cancer cell survival and resistance to chemotherapeutic agents[31,32]. Moreover, GAPDH protects cancer cells against chemotherapy by directly binding to the telomeric DNA and prevents the rapid degradation of telomeres[33]. More importantly, GAPDH, which is perceived as a common reference gene, is upregulates under hypoxic conditions. Therefore, using GAPDH as a housekeeping gene should be avoided due to its unstable expression level during hypoxia.
ALDOA and PDK1 are glycolytic enzymes that contribute to the progress of cancer and metastasis[34-37]. ALDOA overexpression could suppress the expression of proteins responsible for cell-cell adhesion and induce the expression of epithelial-mesenchymal transition[34]. Chang et al.[34] have demonstrated a feedback loop between ALDOA and HIF-1, by which ALDOA activates HIF-1α/MMP9 and promotes cancer cell invasion. Under hypoxic conditions, PDK1 attenuates mitochondrial respiration and ROS production by inactivating the pyruvate dehydrogenase[38]. Additionally, Gibadulinova et al.[39] have indicated that carbonic anhydrase IX promotes metabolic adaptation to hypoxia through the regulation of PDK1. A number of studies have also revealed that PDK1 overexpression promotes cancer cell metastasis, but the molecular mechanism is unclear[36,37]. Siu et al.[37] have explained that PDK1 expression is associated with ovarian cancer metastasis through the activation of JNK/IL-8 signaling. It has also been displayed that procollagen-lysine, 2-oxoglutarate, PLOD2 promotes migration and invasion of cancer cells during hypoxia. PLOD2, a regulator of collagen cross-linking, is located in the upstream of HK2 and can regulate HK2 expression through the activation of signal transducer and activator of transcription 3 (STAT3)[40].
To predict the correlation of common DEGs with miRNA, a DEG-miRNA network was constructed (Fig. 3). These miRNAs have been reported in some cancer types. We also identified miR-335-5p, miR-122-5p, miR-6807-5p, miR-1915-3p, miR-6764-5p, miR-92-3p, miR-23b-3p, miR-615-3p, miR-124-3p, miR-484, and miR-455-3p as the key interacting miRNAs in hypoxia in different cancer cell lines. The miR-335-5p has been exhibited to have ability to regulate cancer cell metastasis. Zhang et al.[41] showed that miR-335-5p can promote apoptosis in prostate cancer cells and may be used as a biomarker in the treatment of this disease[41,42]. Upregulation of miR-6807-5p was reported in glioma specimens[43]. Dysregulation of miR-6764-5p was also identified in pituitary adenomas[44]. MiR-92-3p and miR-122-5p have been identified as the markers of hypoxic environments. MiR-92-3p can be used as a potential therapeutic target in patients with metastatic colorectal cancer[45,46]. MiR-455-5p is dysregulated in many tumor cells[47,48], while miR-1915-3p and miR-124-3p could inhibit apoptosis, resulting in cancer progression. It has been exhibited that miR-1915-3p may play a role in the progression of gastric cancer and may have a potential therapeutic application in gastric cancer[49,50]. Contradictorily, miR-484 could promote apoptosis by targeting Apaf-1[51], and miR-23b-3p and miR-615-3p could act as either tumor suppressors or oncogenes, which mainly depends on their context[52,53].
In summary, the present study identified hypoxia-related gene signatures among the hypoxia-conditioned cancer cell lines using RNA-Seq. Our analysis revealed the common hub genes and key pathways in cancer cells under hypoxic conditions. Moreover, we predicted a miRNA signature, among which miR-335-5p had the highest betweenness centrality during hypoxia. To our knowledge, for the first time, our results demonstrate that miR-6807-5p and miR-6764-5p are dysregulated under hypoxic conditions. However, further molecular biological experiments are required to confirm the function of the identified miRNA associated with hypoxia. The results of the present study may provide future directions in identifying the presence of cancer and determining the characteristics of cancer. For instance, hypoxia is a characteristic feature of cancer, and the hypoxia signature identified in this study, as well as predicted miRNAs might be helpful to detect the hypoxic state of cancer cells. Hypoxia is common in majority of malignant tumors and an attractive therapeutic target. As hypoxia targeted treatment are effective in patients with the most hypoxic tumors, hypoxic signature might be useful for developing proper treatment, such as engineered oncolytic viruses that could be utilized to control or regulate the biological interactions responsible for the functioning or malfunctioning of cancer cells during hypoxia[22].
## Ethical statement
Not applicable.
## Data availability
The raw data supporting the conclusions of this article are available from the authors upon request.
## Author contributions
SS: conceived, designed the analysis and performed the analysis; GB: performed bioinformatics analyses; AA: drafted or provided critical revision of the article; KA: conceived and designed the study, supervised the data analysis and interpretation. All authors have read and approved the final manuscript.
## Conflict of interest
None declared.
## Funding/support
This study was funded as Ph.D. student project by Pasteur Institute of Iran, Tehran.
## References
1. Jing X, Yang F, Shao C, Wei K, Xie M, Shen H, Shu Y. **Role of hypoxia in cancer therapy by regulating the tumor microenvironment**. *Molecular cancer* (2019) **18** 157. PMID: 31711497
2. Challapalli A, Carroll L, Aboagye EO. **Molecular mechanisms of hypoxia in cancer**. *Clinical and translational imaging* (2017) **5** 225-253. PMID: 28596947
3. Makhijani RK, Raut SA, Purohit HJ. **Identification of common key genes in breast, lung and prostate cancer and exploration of their heterogeneous expression**. *Oncology letters* (2018) **15** 1680-1690. PMID: 29434863
4. Kulshrestha A, Suman S, Ranjan R. **Network analysis reveals potential markers for pediatric adrenocortical carcinoma**. *Oncotargets and therapy* (2016) **9** 4569-4581. PMID: 27555782
5. Mao Y, Nie Q, Yang Y, Mao G. **Identification of co‑expression modules and hub genes of retinoblastoma via co‑expression analysis and protein‑protein interaction networks**. *Molecular medicine reports* (2020) **22** 1155-1168. PMID: 32468072
6. Mirabelli P, Coppola L, Salvatore M. **Cancer cell lines are useful model systems for medical rsearch**. *Cancers (Basel).* (2019) **11** 1098. PMID: 31374935
7. Oliveto S, Mancino M, Manfrini N, Biffo S. **Role of microRNAs in translation regulation and cancer**. *World journal of biological chemistry* (2017) **8** 45-56. PMID: 28289518
8. Kulshreshtha R, Ferracin M, Wojcik SE, Garzon R, Alder H, Agosto-Perez FJ, Davuluri R, Liu CG, Croce CM, Negrini M, Calin GA, Ivan M. **A microRNA signature of hypoxia**. *Molecular and cellular biology* (2007) **27** 1859-1867. PMID: 17194750
9. Lan H, Lu H, Wang X, Jin H. **MicroRNAs as Potential Biomarkers in Cancer: Opportunities and challenges**. *BioMed research international* (2015) **2015** 125094. PMID: 25874201
10. Soneson C, Love MI, Robinson MD. **Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences**. *F1000research* (2015) **4** 1521. PMID: 26925227
11. Conway JR, Lex A, Gehlenborg N. **UpSetR: an R package for the visualization of intersecting sets and their properties**. *Bioinformatics* (2017) **33** 2938-2940. PMID: 28645171
12. Li Z, Zhao K, Tian H. **Integrated analysis of differential expression and alternative splicing of non-small cell lung cancer based on RNA sequencing**. *Oncology letters* (2017) **14** 1519-1525. PMID: 28789374
13. Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S, Hub W. **AmiGO: online access to ontology and annotation data**. *Bioinformatics* (2009) **25** 288-289. PMID: 19033274
14. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y. **KEGG for linking genomes to life and the environment**. *Nucleic acids research* (2008) **36** D480-484. PMID: 18077471
15. Mi H, Muruganujan A, Thomas PD. **PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees**. *Nucleic acids research* (2013) **41** D377-386. PMID: 23193289
16. Xia J, Gill EE, Hancock REW. **NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data**. *Nature protocols* (2015) **10** 823-44. PMID: 25950236
17. Chen SJ, Liao DL, Chen CH, Wang TY, Chen KC. **Construction and analysis of protein-protein interaction network of heroin use disorder**. *Scientific reports* (2019) **9** 4980. PMID: 30899073
18. Soofi A, Taghizadeh M, Tabatabaei SM, Rezaei Tavirani M, Shakib H, Namaki S, Safari Alighiarloo N. **Centrality analysis of protein-protein interaction networks and molecular docking prioritize potential drug-targets in type 1 diabetes**. *Iranian journal of pharmaceutical research* (2020) **19** 121-134. PMID: 33841528
19. Li CY, Cai JH, Tsai JJP, Wang CCN. **Identification of hub genes associated with development of head and neck squamous cell carcinoma by integrated bioinformatics analysis**. *Frontiers in oncology* (2020) **10** 681. PMID: 32528874
20. Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, Tsai WT, Chen GZ, Lee CJ, Chiu CM, Chien CH, Wu NH, Huang CY, Tsou AP, Huang HD. **miRTarBase: a database curates experimentally validated microRNA-target interactions**. *Nucleic acids research* (2011) **39(Database issue)** D163-169. PMID: 21071411
21. Sethupathy P, Corda B, Hatzigeorgiou AG. **TarBase: A comprehensive database of experimentally supported animal microRNA targets**. *RNA* (2006) **12** 192-197. PMID: 16373484
22. Shayan S, Arashkia A, Bahramali G, Abdoli A, Nosrati MSS, Azadmanesh K. **Cell type-specific response of colon cancer tumor cell lines to oncolytic HSV-1 virotherapy in hypoxia**. *Cancer cell international* (2022) **22** 164. PMID: 35477503
23. Bakhashab S, Lary S, Ahmed F, Schulten HJ, Bashir A, Ahmed FW, Al-Malki AL, Jamal HS, Gari MA, Weaver JU. **Reference genes for expression studies in hypoxia and hyperglycemia models in human umbilical vein endothelial cells**. *G3 (Bethesda, Md)* (2014) **4** 2159-2165. PMID: 25193495
24. Si W, Shen J, Zheng H, Fan W. **The role and mechanisms of action of microRNAs in cancer drug resistance**. *Clinical epigenetics* (2019) **11** 25. PMID: 30744689
25. He X, Zhang J. **Why Do Hubs Tend to Be Essential in Protein Networks?**. *PLOS genetics* (2006) **2** e88. PMID: 16751849
26. Ardila DC, Aggarwal V, Singh M, Chattopadhyay A, Chaparala S, Sant S. **Identifying molecular signatures of distinct modes of collective migration in response to the microenvironment using three-dimensional breast cancer models**. *Cancers (Basel)* (2021) **13** 6
27. Cal R, Castellano J, Revuelta-López E, Aledo R, Barriga M, Farré J, Vilahur G, Nasarre L, Hove-Madsen L, Badimon L, Llorente-Cortés V. **Low-density lipoprotein receptor-related protein 1 mediates hypoxia-induced very low density lipoprotein-cholesteryl ester uptake and accumulation in cardiomyocytes**. *Cardiovascular research* (2012) **94** 469-479. PMID: 22454363
28. Koslowski M, Luxemburger U, Türeci Ö, Sahin U. **Tumor-associated CpG demethylation augments hypoxia-induced effects by positive autoregulation of HIF-1α**. *Oncogene* (2011) **30** 876-882. PMID: 21042279
29. Liberti MV, Locasale JW. **The warburg effect: how does it benefit cancer cells?**. *Trends in biochemical sciences* (2016) **41** 211-218. PMID: 26778478
30. Seki SM, Gaultier A. **Exploring non-metabolic functions of glycolytic enzymes in immunity**. *Frontiers in immunology* (2017) **8** 1549. PMID: 29213268
31. Jacquin MA, Chiche J, Zunino B, Bénéteau M, Meynet O, Pradelli LA, Marchetti S, Cornille A, Carles M, Ricci JE. **GAPDH binds to active Akt, leading to Bcl-xL increase and escape from caspase-independent cell death**. *Cell death and differentiation* (2013) **20** 1043-1054. PMID: 23645209
32. Chiche J, Pommier S, Beneteau M, Mondragón L, Meynet O, Zunino B, Mouchotte A, Verhoeyen E, Guyot M, Pagès G, Mounier N, Imbert V, Colosetti P, Goncalvès D, Marchetti S, Brière J, Carles M, Thieblemont C, Ricci JE. **GAPDH enhances the aggressiveness and the vascularization of non-Hodgkin’s B lymphomas via NF-κB-dependent induction of HIF-1α**. *Leukemia* (2015) **29** 1163-1176. PMID: 25394713
33. Demarse NA, Ponnusamy S, Spicer EK, Apohan E, Baatz JE, Ogretmen B, Davies C. **Direct binding of glyceraldehyde 3-phosphate dehydrogenase to telomeric DNA protects telomeres against chemotherapy-induced rapid degradation**. *Journal of molecular biology* (2009) **394** 789-803. PMID: 19800890
34. Chang YC, Chan YC, Chang WM, Lin YF, Yang CJ, Su CY, Huang MS, T H Wu A, Hsiao M. **Feedback regulation of ALDOA activates the HIF-1α/MMP9 axis to promote lung cancer progression**. *Cancer letters* (2017) **403** 28-36. PMID: 28610954
35. Saito Y, Takasawa A, Takasawa K, Aoyama T, Akimoto T, Ota M, Magara K, Murata M, Hirohashi Y, Hasegawa T, Sawada N, Saito T, Osanai M. **Aldolase A promotes epithelial-mesenchymal transition to increase malignant potentials of cervical adenocarcinoma**. *Cancer science* (2020) **111** 3071-3081. PMID: 32530543
36. Du J, Yang M, Chen S, Li D, Chang Z, Dong Z. **PDK1 promotes tumor growth and metastasis in a spontaneous breast cancer model**. *Oncogene* (2016) **35** 3314-3323. PMID: 26455327
37. Siu MKY, Jiang Y-x, Wang J-j, Leung THY, Ngu SF, Cheung ANY, Ngan HYS, Chan KKL. **PDK1 promotes ovarian cancer metastasis by modulating tumor-mesothelial adhesion, invasion, and angiogenesis via α5β1 integrin and JNK/IL-8 signaling**. *Oncogenesis* (2020) **9**
38. Kim JW, Tchernyshyov I, Semenza GL, Dang CV. **HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia**. *Cell metabolism* (2006) **3** 177-185. PMID: 16517405
39. Gibadulinova A, Bullova P, Strnad H, Pohlodek K, Jurkovicova D, Takacova M, Pastorekova S, Svastova E. **CAIX-Mediated control of LIN28/let-7 axis contributes to metabolic adaptation of breast cancer cells to hypoxia**. *International journal of molecularsciences* (2020) **21** 4299
40. Du W, Liu N, Zhang Y, Liu X, Yang Y, Chen W, He Y. **PLOD2 promotes aerobic glycolysis and cell progression in colorectal cancer by upregulating HK2**. *Biochemistry and cell biology* (2020) **98** 386-395. PMID: 31742425
41. Zhang P, Yang X, Wang L, Zhang D, Luo Q, Wang B. **Overexpressing miR‑335 inhibits DU145 cell proliferation by targeting early growth response 3 in prostate cancer**. *International journal of oncology* (2019) **54** 1981-1994. PMID: 31081063
42. Wang X, Wu G, Cao G, Chen X, Huang J, Jiang X, Hou J. **MicroRNA‑335 inhibits bladder cancer cell growth and migration by targeting mitogen‑activated protein kinase 1**. *Molecular medicine reports* (2016) **14** 1765-1770. PMID: 27356628
43. Wang K, Yang S, Gao Y, Zhang C, Sui Q. **MicroRNA‑769‑3p inhibits tumor progression in glioma by suppressing ZEB2 and inhibiting the Wnt/β‑catenin signaling pathwayCorrigendum in /10**. *3892/ol.2021.12489. Oncology letters* (2020) **19** 992-1000
44. Németh K, Darvasi O, Likó I, Szücs N, Czirják S, Reiniger L, Szabó B, Krokker L, Pállinger E, Igaz P, Patócs A, Butz H. **Comprehensive analysis of circulating microRNAs in plasma of patients with pituitary adenomas**. *Journal of clinical endocrinology and metabolism* (2019) **2018** 02479
45. Kiss I, Mlčochová J, Součková K, Fabian P, Poprach A, Halamkova J, Svoboda M, Vyzula V, Slaby O. **MicroRNAs as outcome predictors in patients with metastatic colorectal cancer treated with bevacizumab in combination with FOLFOX**. *Oncollogy letters* (2017) **14** 743-750
46. Zeng Y, Lv Y, Tao L, Ma J, Zhang H, Xu H, Xiao B, Shi Q, Ma K, Chen L. **G6PC3, ALDOA and CS induction accompanies mir-122 down-regulation in the mechanical asphyxia and can serve as hypoxia biomarkers**. *Oncotarget* (2016) **7** 74526-74536. PMID: 27793029
47. Zhao Y, Yan M, Yun Y, Zhang J, Zhang R, Li Y, Wu X, Liu Q, Miao W, Jiang H. **MicroRNA-455-3p functions as a tumor suppressor by targeting eIF4E in prostate cancer**. *Oncology reports* (2017) **37** 2449-2458. PMID: 28350134
48. Li Z, Meng Q, Pan A, Wu X, Cui J, Wang Y, Li L. **MicroRNA-455-3p promotes invasion and migration in triple negative breast cancer by targeting tumor suppressor EI24**. *Oncotarget* (2017) **8** 19455-1966. PMID: 28038450
49. Cui HW, Han WY, Hou LN, Yang L, Li X, Su XL. **miR-1915-3p inhibits Bcl-2 expression in the development of gastric cancer**. *Bioscience reports* (2019) **39** BSR20182321. PMID: 31036603
50. Pu J, Long Y, Zhou J, Zhan Y, Qin X. **MiR-124 regulates apoptosis in hypoxia-induced human brain microvessel endothelial cells through targeting Bim**. *Applied biological chemistry* (2018) **61** 689-696
51. Ahn YH, Ko YH. **Diagnostic and therapeutic implications of microRNAs in non-Small cell lung cancer**. *International journal of molecular sciences* (2020) **21** 8782. PMID: 33233641
52. Hannafon BN, Cai A, Calloway CL, Xu Y-F, Zhang R, Fung K-M, Ding WQ. **miR-23b and miR-27b are oncogenic microRNAs in breast cancer: evidence from a CRISPR/Cas9 deletion study**. *BMC cance* (2019) **19** 642
53. Godínez-Rubí M, Ortuño-Sahagún D. **miR-615 fine-tunes growth and development and has a Role in cancer and in neural repair**. *Cells* (2020) **9** 1566. PMID: 32605009
|
---
title: Dietary supplementation of Acanthopanax senticosus extract alleviates motor
deficits in MPTP-induced Parkinson’s disease mice and its underlying mechanism
authors:
- Jingbin Li
- Yang He
- Jia Fu
- Yimin Wang
- Xing Fan
- Tian Zhong
- Hui Zhou
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9971719
doi: 10.3389/fnut.2023.1121789
license: CC BY 4.0
---
# Dietary supplementation of Acanthopanax senticosus extract alleviates motor deficits in MPTP-induced Parkinson’s disease mice and its underlying mechanism
## Abstract
Acanthopanax senticosus extract (ASE), a dietary supplement with antifatigue, neuroprotective, and immunomodulatory properties, has been widely used due to its high polyphenol content. Our previous study showed that ASE could be used to treat Parkinson’s disease (PD) as it contains multiple monoamine oxidase B inhibitors prescribed in early PD. However, its mechanism remains ambiguous. In this study, we investigated the protective effects of ASE on MPTP-induced PD in mice and explored the underlying mechanisms of action. We found that the administration of ASE significantly improved motor coordination in mice with MPTP-induced PD. As shown by quantitative proteomic analysis, 128 proteins’ expression significantly changed in response to ASE administration, most of which were involved with Fcγ receptor-mediated phagocytosis in macrophages and monocytes signaling pathway, PI3K/AKT signaling pathway, and insulin receptor signaling pathway. Furthermore, the network analysis results showed that ASE modulates protein networks involved in regulating cellular assembly, lipid metabolism, and morphogenesis, all of which have implications for treating PD. Overall, ASE served as a potential therapeutic because it regulated multiple targets to improve motor deficits, which could lay the strong foundation for developing anti-PD dietary supplements.
## 1. Introduction
Parkinson’s disease (PD) ranks second among all neurodegenerative disorders in terms of its morbidity, and its prevalence is rising [1, 2]. It has been reported that PD affects more than 6 million people worldwide, and that number may rise to 10 million by 2030 [3, 4]. Its early clinical symptoms include constipation, hyposmia, cogwheel rigidity, bradykinesia, and tremors (typical triad in diagnosing PD clinically). Eventually, it leads to postural instability, freezing of gait (FOG), and ataxia during the late stages [5]. Dysphonia, dysphagia, and emotionless facial expressions are also clinical features. Recent research has linked the pathogenic mechanism of PD to the degeneration of dopaminergic neurons as well as dysfunctions of astrocytes and microglia [6, 7]. Nevertheless, the mechanism of PD’s pathogenesis is still only partially understood. Thus, to reduce its prevalence, new biomarkers need to be identified.
Historically used to treat a wide variety of illnesses, traditional Chinese medicine (TCM) has recently gained widespread acceptance due to its efficacy in treating a variety of conditions in clinical settings across Eastern Asian countries, especially in China (8–11). According to TCM, *Acanthopanax senticosus* extract (ASE) was used to nourish qi, fortify the spleen, tonifying the kidney, and smooth the mind [12, 13]. Our previous study showed that ASE extracts could inhibit monoamine oxidase B (MAO-B) [14], making them useful for treating several disorders like Alzheimer’s disease (AD) [15], Parkinson’s disease [16], stroke [17, 18], and depression [19]. Recent research indicates that ASE exerts anti-fatigue [20], neuroprotective [21], and immunomodulatory activities [22]. In addition, ASE protects mice from both MPTP- and structure-mediated dysfunction as well as structural damage of mitochondria [23]. However, how exactly ASE treats PD is unknown, which is why this study used proteomics technologies to try to figure it out.
Proteomics is a high-throughput and powerful approach to study the protein changes in the process of disease and it have been widely applied for the discovery of biomarkers in the pharmacology research of many major diseases, such as Human colorectal cancer (CRC) [24], hepatocellular carcinoma (HCC) [25], breast cancer (BC) [26], and Alzheimer’s disease (AD) [27]. The isobaric Target for Relative and Absolute Quantification (iTRAQ)-based quantitative proteomics is the second generation of gel-free proteomic analysis that allows for the identification and quantification of proteins with a high degree of precision and consistency (28–30). It can simultaneously detect eight different samples and achieve a detailed insight into the global proteomic changes and their integrated meaning [31]. This is consistent with the integrity of TCM theory and reflects the overall state of the human body under the interaction of TCM-based dietary supplements. Therefore, iTRAQ technology presents as a more appropriate research strategy to investigate the TCM’s therapeutic mechanisms.
In this study, we developed a mouse PD model by intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). We used iTRAQ-LC-MS/MS to discover biomarkers within mouse brain tissues and investigated the changes following ASE treatment. Furthermore, Ingenuity Pathway Analysis (IPA) predicted the signaling pathway and protein network regulated by ASE in treating PD, which provided novel insights into PD-related molecular alterations and assisted in identifying promising therapeutic targets for prevention, intervention, and the identification of novel potential PD biomarkers. In addition, the present work helps to understand PD pathogenesis and ASE’s therapeutic mechanism of action.
## 2.1. Materials and chemicals
Acanthopanax senticosus root was purchased from Beijing Tongrentang Pharmacy and authenticated by Professor Zhiqiang Liu, Changchun Institute of Applied Chemistry (CIAC), Chinese Academy of Sciences, Changchun, China. High-performance liquid chromatography (HPLC)-grade methanol, acetonitrile, and formic acid were purchased from Fisher Scientific Corporation (Loughborough, UK). Other analytical-grade chemicals were supplied by Beijing Chemical Works (Beijing, China). Ultrapure water was made using the Milli-Q purification system (Billerica, MA, USA). Dimethyl sulfoxide (DMSO), Fetal bovine serum (FBS), penicillin, streptomycin, Dulbecco’s modified Eagle’s medium (DMEM), [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2 Htetrazolium bromide (MTT), and Trypsin were supplied by Beijing Dingguo Changsheng Biotechnology Co., Ltd. (Beijing, China). MPTP was obtained from Shanghai Demo Pharmaceutical Technology Co., LTD. ( Shanghai, China). AKT and JAK1 antibodies were purchased from Abcam (Cambridge, MA).
## 2.2. Preparation of ASE
The ASE solution was prepared by first soaking 5 kg of dried *Acanthopanax senticosus* roots in distilled water (10-fold volumes) for 0.5 hours, then heating the resultant mixture for one hour, and finally filtering the supernatants. The roots were extracted several times with water (10-fold volume). The combined extracts were then concentrated into an aqueous ASE. Following overnight precipitation with $70\%$ ethanol, the extract was passed through the AB-8 macroporous resin column to separate the supernatant. Elution was later carried out with $30\%$ ethanol at a flow rate of 4 BV/h and 5-fold volume. The ASE extract powder was obtained after the rotary evaporation and lyophilization of $30\%$ ethanol eluate.
## 2.3. UPLC-MS analysis of ASE
The liquid chromatographic separation was performed with a Waters Acquity UPLC system equipped with a Waters Acquity BEH C18 1.7 μm, 2.1 (i.d.) × 50 mm column (Waters Corp., MA, USA). The sample injection volume was 5 μL and the column temperature was kept at 35°C. The gradient elution mobile phases contained $0.1\%$ formic acid in water (phase A) and acetonitrile (phase B). The gradient elution was performed with 0.3 mL/min of flow rate as follows: 5-$25\%$ B, 0-8 min; 25-$55\%$ B, 8-14 min; 55-$80\%$ B, 14-17 min; 80-$95\%$ B, 17-18 min; 95-$100\%$ B. Tandem mass spectra were measured on a SYNAPT G2 Q-TOF HDMS (Waters MS Technologies, Manchester, UK). The mass spectrometer parameters were set as follows: ion source temperature: 100°C; m/z scan range: 100 to 1,500 Da; desolvation temperature, 250°C; cone gas (N2): 50L/h; desolvation gas (N2): 600L/h; capillary voltage: 2.5 kV; Leucine encephalin reference ions with m/z of 556.2771 (for ESI +) or 554.2615 (for ESI-) were infused during data acquisition for online calibration. MSE was applied for MS/MS analysis with a low collision energy of 6 eV and a high collision energy of 20-45 eV. LC-MS/MS data were acquired and analyzed using MassLynx 4.1 software (Waters). The compound structural elucidation was performed in reference to MassBank and ChemSpider.
## 2.4. Animal grouping and model preparation
A total of 60 C57/BL6 mice (male, 8 weeks old) weighting 200-220g were purchased from Liaoning Changsheng Biotechnology (Liaoning, China). The mice were housed in a temperature-controlled (22 ± 2°C) room with a 12 h light/dark cycle and free access to food and water. After 1 week of adaptive feeding, The mice were randomly split into 4 groups ($$n = 15$$) for this study: controls, models, Madopar-treated, and ASE-treated. Madopar-treated and ASE-treated mice were administrated with Madopar (100 mg kg–1d–1) and ASE (100 mL kg–1d–1, which is equivalent to 4.5 g crude drug kg–1d–1) once a day for 15 days, respectively. Mice in model and control groups were administrated with an equal amount of distilled water. The mice in all groups were administered daily for 15 days via oral gavage. The mice in the model, Madopar-treated, and ASE-treated groups received 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) (30 mg/kg/d) via intraperitoneal injection for 7 days starting on day 8, while the mice in the control group received saline via intraperitoneal injection. MPTP is a well-known dopaminergic neuron toxin, which causes dopaminergic neuronal loss and degeneration through enhancing oxidative stress (OS) while suppressing mitochondrial respiratory-chain complex I [32, 33]. At the end of the experiment, mice were subjected to behavioral tests. Every experiment was carried out following the Guide for Care and Use of Laboratory Animals.
## 2.5. Locomotor function assessment
Four motor behavior tests were used to evaluate the locomotor function after ASE administration: the spontaneous activity test, the rotarod test, the pole-climbing test, and the hanging test.
## 2.5.1. Spontaneous activity test
Using an open field test (OFT), the amount of spontaneous locomotory movement in mice was measured. In brief, each mouse was adapted to the environment for a 2-h period before OFT and put separately to face an identical wall of a white square box (dimension, 50 × 50 × 25 cm) in the dark for a 5-min period in a quiet environment. The number of spontaneous activities of mice in each group was recorded.
## 2.5.2. Rotarod test
On the seventh day after the last MPTP injection, the accelerating rotarod apparatus was utilized to assess rotarod performance using a suspended rod (3 cm in diameter) at the constant accelerating rate of 20 rpm/s. After 300 s or after the mice fell off the rotarod, the test was ended.
## 2.5.3. Pole-climbing test
Each mouse was put on top of the wooden pole (height, 50 cm; diameter, 8 mm) with a rough surface. This work later recorded the overall time needed for each animal to descend the pole (until the mouse reached the floor) and turn. Bradykinesia was reflected by the delayed or extended time necessary for completing the test.
## 2.5.4. Hanging test
Every mouse was put onto the horizontal wire (diameter, 1.5 mm), followed by suspension 30 cm away from the ground. After that, the limb coordination of mice was detected by recording the hanging time.
## 2.6.1. Protein sampling
After the motor behavior tests, all the mice were sacrificed by cervical dislocation. The total protein from each cerebral tissue sample was homogenized on ice. After centrifugation of cell lysate for 30 min at 14,000 rpm, the supernatant was collected, and protein concentration was determined using the BCA assay.
## 2.6.2. iTRAQ labeling
In order to reduce variation between biological samples, equal amounts of protein from each of the 7 brain tissues were mixed to generate one normalization pool. In total, 8 mixing sample pools were obtained: control group 1, control group 2, model group 1, model group 2, Madopar-treated group 1, Madopar-treated group 2, ASE-treated group 1 and ASE-treated group 2. Proteins (100 μg) from each pool were denatured, alkylated, and subjected to overnight trypsin digestion at a 1:20 enzyme/protein ratio at 37°C, followed by iTRAQ reagent tag labeling with the 8-plex iTRAQ kit according to manufacturer’s protocol. Tryptic peptides derived from the control group 1, control group 2, model group 1, model group 2, Madopar-treated group 1, Madopar-treated group 2, ASE-treated group 1 and ASE-treated group 2 were labeled as 114, 115, 116, 117, 118, 119, and 121 respectively. This labeling reaction was kept at room temperature for 1 h before mixing the labeled samples in the same ratio. The technical repeats were used to adjust experimental randomness, and proteins were screened twice (biological repeats) using the iTRAQ procedure.
## 2.6.3. Ultra-performance liquid chromatography (UPLC) fractionation as well as LC–MS/MS analysis
The Sep-Pak Vac C18 (Waters, MA, USA) was used to process the labeled samples to remove salts. Meanwhile, peptides were fractionated using UPLC (Waters, Milford, MA, USA) (34–36). Following that, the bridged ethylene hybrid C18 column (2.1 × 50 mm, 1.7 μm) was used for analysis. Later, elution was done with a linear gradient of 2 mobile phases (solvent A consisted of ammonium formate (20 mM), pH 10, and solvent B consisted of acetonitrile, which began with $5\%$ solvent B then increased to $35\%$ within 16 min) at a flow rate of 600 μL/min. Absorbance (A) was measured at 214 nm, and 10 fractions were harvested.
The nano-HPLC (Eksigent Technologies) was used to separate fractions using a secondary reversed-phase analytical column (Eksigent, C18, 3 μm, 150 mm × 75 μm). After peptide separation, they were eluted from the analytical column at a flow rate of 300 nL/min using two mobile phases (solvent A: $2\%$ acetonitrile with $0.1\%$ FA; solvent B: $98\%$ acetonitrile with $0.1\%$ FA), beginning with $5\%$ solvent B and holding for 5-min, then 5–$40\%$ B gradient within 65 min, ramping to $80\%$ B within 1 min, 5-min holding with $90\%$ B, ramping to $5\%$ B within 1 min, 18-min holding with $5\%$ B before subsequent sample loading. The eluted sample was simultaneously passed through a mass spectrometer at the 2.5 kV electrospray voltage.
For the automatic shift between MS/MS and MS acquisition, this work used the information-dependent data acquisition mode of the Q-Exactive mass spectrometer. We acquired the full-scan MS spectra at 350–1250 m/z. Altogether 25 of the high-intensity precursors were chosen in each cycle’s fragmentation, and the dynamic exclusion time was 25 s.
## 2.7. Identification, quantitation, and bioinformatics analyses
Each MS/MS sample was searched against the UniProt-mouse database with the MIS search type using the Mascot search engine (Matrix Science, London, UK; version 2.3.02). Carbamidomethyl (C) and iTRAQ 8 plex (K) were selected as fixed modifications. Later, resultant peptide hits with the maximal $5\%$ false discovery rate (FDR) were selected. iTRAQ8plex quantification analysis was used to determine reporter ratios, with fragment and peptide mass tolerances of ± 0.1 Da and 20.0 PPM, respectively.
Biological functions together with protein interaction pathways with obvious changes were identified using IPA software (IPA software v7.1, Ingenuity System Inc., Redwood City, CA, USA; www.ingenuity.com). We used cutoffs of a fold change (FC) of 1.2 and a t-test significance level of p 0.05 from two independent replicates to classify differentially expressed proteins as either up- or down-regulated. Simultaneously, the right-tailed Fisher’s exact test was used to calculate p-values. Later, protein scores based on p-values were calculated, representing the clustering possibility of proteins identified in the network. Typically, networks with scores greater than 2 were clustered.
Statistical analysis: *All data* were expressed as mean ± SD and analyzed using GraphPad Prism software. The Student’s t-test was used to analyze the statistical significance of the data between two groups. A probability of $p \leq 0.05$ was considered to reflect statistical significance.
## 3.1. Composition analysis of ASE
UPLC-Q-TOF ESI MS/MS was used to interpret a mass spectral fragmentation pattern to determine ASE’s chemical makeup. Before analysis, we optimized ESI-MS parameters, like electrospray voltage, capillary temperature, and capillary voltage. Negative ion mass spectrometry provided more structural information for ASE than positive ion mass spectrometry. The ASE was well separated and detected within 16 min (Figure 1). During this study, molecular weight and retention time obtained by Q-TOF-MS, along with fragment ion data obtained by MS/MS, were used primarily in the identification process. Eighteen chemical components were identified including 5 organic acid compounds (vanillic acid, chlorogenic acid, p-coumaroyl quinic acid, dicaffeoylquinic acid, 3-O-caffeoyl feruloylquinic acid) and 13 phenolic glycosides (glucosyringic acid, ferulyl quinic acid glucoside, isofraxidin, dimethoxyl lariciresinol glucoside, pinoresinol diglucoside, pinoresinol, guaicylglycerol hydroconiferol glucoside, medioresinol glucoside, syringaresinol-4-O-β-D-glucoside, eletutheroside E, ciwujiatong glucoside, syringaresinol, trihydroxy-octadecenoic acid). According to relevant literature reports, triterpenoid saponins, lignans, coumarins and flavonoids constitute the majority of compounds found in ASE. Lignans and coumarins are also considered phenolic compounds by some researchers. Thus, phenolic compounds are the main active constituents of ASE.
**FIGURE 1:** *(A) The chemical composition of ASE identified by UPLC-ESI-Q-TOF-MS/MS analysis in negative ion mode. (B) MS/MS spectrum of Syringaresinol-4-O-β-D-glucoside.*
## 3.2. ASE alleviates motor deficits in mice with MPTP-induced Parkinson’s disease
Behavioral tests, such as spontaneous activity, rotarod, and suspension pole-climbing tests, were used to determine how ASE suppression affected MPTP-mediated behavioral deficits (Figure 2). In the spontaneous activity test, the spontaneous activity score of the model group was significantly lower than the control group ($p \leq 0.01$). compared with the model group, madopa-treated and ASE-treated mice showed a significant increase in spontaneous activity. The latency to fall in the rotarod test and the hang time in the suspension test decreased in the model group compared with those in the control group. In contrast, Madopa-treated and ASE-treated mice showed a significant increase in the rotarod test and the suspension test. Similarly, the Madopa-treated and ASE-treated groups showed a significantly shorter time to descend on the pole-climbing test compared to MPTP-treated mice. These results suggest that ASE may play an anti-PD role by protecting the neurocytotoxicity of MPTP in mice, relieving some external behavioral ability, and restoring the dysregulated motor coordination seen in mice with Parkinson-like symptoms. It was further confirmed that ASE helps in both the prevention and treatment of PD.
**FIGURE 2:** *ASE treatment alleviates motor deficits in MPTP-induced Parkinson’s disease mice. (A) the spontaneous activity test; (B) the rotarod test; (C) the pole-climbing test; (D) the hanging test. The data are presented as the means ± SDs of 15 biological replicates (###p < 0.01, compared with the control group; ***p < 0.05, compared with the model group).*
## 3.3. ASE modulates MPTP-induced protein expression
Protein extracts from the brains of control, model, and ASE-treated mice were analyzed to learn more about the role of ASE in MPTP-induced PD. ITRAQ was used to label fractions recovered after high-abundance protein was removed, and LC–MS/MS was utilized for detection. The proteomic analysis uncovered the changes in 5,306 different proteins. 128 proteins in the ASE-treated group were significantly different from those in the model group (fold change > 1.2, $P \leq 0.05$), with 76 showing up-regulation and 52 showing down-regulation. Figure 3 displays a volcano plot for differential protein screening and the heatmap analysis of the differentially expressed proteins between the ASE treatment and the model group. GO analysis of the differential proteins between the model group and the ASE-treated group is shown in Figure 4. In the biological process, the differentially expressed proteins (DEPs) are mainly concentrated in peptidyl-serine phosphorylation, negative regulation of apoptotic process, and cell differentiation. Cellular component analysis shows that among the differentially regulated proteins in the ASE-treated group, cytoplasmic proteins accounted for $20.1\%$, nuclear proteins for $13.6\%$, and extracellular alien proteins for $13.2\%$. Molecular function (MF) analysis shows that $8.2\%$ of proteins have homodimerization activity, $1.5\%$ have transport activity, and $5.3\%$ have serine/threonine kinase activity. DNA-based transcription ($20.0\%$), protein transport ($12.4\%$), and DNA-based transcriptional regulation ($12.3\%$) rank first, second, and third, respectively, in the list of biological processes in which proteins are involved. The KEGG analysis results of differential proteins showed that the typical pathway corresponding to DEPs between ASE-treated and model groups was associated with Fcγ R-mediated phagocytosis, Ras signaling pathway, PI3K-AKT signaling pathway.
**FIGURE 3:** *(A) Volcano plot analysis of the differentially expressed proteins between the ASE treatment and the PD group. Proteins that have a difference of fold change > 1.2 or < −1.2 and a P < 0.05 are defined as significantly different. The red points indicate proteins with a significant upregulation, while the green points indicate proteins with a significant downregulation. (B) Heatmap analysis of the differentially expressed proteins between the ASE treatment and the PD group.* **FIGURE 4:** *Gene Ontology functional annotations and KEGG pathway analysis of ASE in treating PD. The radius of the circle indicates the number of genes enriched, while the color indicates the number of p-values.*
## 3.4. Role of ASE in canonical pathways and networks
Functional annotation and pathway analysis of DEPs were performed to delve deeper into the ASE-related mechanism in the treatment of PD. According to IPA annotation, 128 DEPs responding to ASE treatment play critical roles in organismal injury and abnormalities ($$n = 117$$) and neurological disease ($$n = 38$$), different cell activities like cellular development ($$n = 40$$), cellular assembly and organization ($$n = 37$$), cellular function and maintenance ($$n = 36$$), and cellular growth and proliferation ($$n = 36$$). Protein-protein interaction (PPI) network analysis was performed using IPA, and the results suggested that Fcγ receptor-mediated phagocytosis in macrophages and monocytes, PI3K/AKT signaling, and insulin receptor signaling pathway were the most significantly enriched pathways by ASE treatment (Figure 5A). Ten proteins were found to be up-regulated (red), and sixteen proteins were found to be down-regulated (blue) among the 128 proteins identified as responsive to ASE treatment (green). Thus, cellular morphology, lipid metabolism, and cellular assembly and organization were heavily represented in the most significant PPI network (score = 47) (Figure 5B). 23 key ASE targets for PD therapy were preliminarily identified as ALB, CCDC22, COMMD9, DCN, FABP7, GTF3C2, HERC2, HPX, JAK1, KCND2, KIT, MPDZ, NUCB2, OCRL, PHKB, RAB34, RAB4A, RUFY1, SEMA6A, SLC4A7, SMG1, SPHK2, and SYVN1.
**FIGURE 5:** *(A) The highest ranked canonical IPA pathways of the differentially expressed proteins. The horizontal axis shows the pathway name and the vertical axis is the p-value of each pathway. (B) The top protein-protein interaction network responding to ASE treatment for PD was predicted by IPA. Twenty-six proteins are involved in this network. The symbols labeled in red and green represent up-regulation and down-regulation, respectively.*
## 4. Discussion
Acanthopanax senticosus is a well known traditional Chinese medicine and has been widely used as popular functional food for maintaining and promoting the health. In our previous study on the extract of *Acanthopanax senticosus* (ASE), we found that stress-induced oxidative damage was effectively protected by ASE in vitro. And we screened and identified a variety of monoamine oxidase B inhibitors from ASE that may offer therapeutic potential in neurodegenerative diseases [14]. To examine whether ASE administration improved locomotor function in MPTP-induced PD mice, we conducted motor behavioral tests including spontaneous activity test, rotarod test, pole-climbing test, and the hanging test. A pre-test was conducted prior to ASE administration to determine the dosage. A low, median, and high dose were set as representatives of 2.25, 4.5, and 13.5 g of crude drug per kg/day. The results showed that the mice in the high-dose administration group showed significant weight loss after ASE administration and almost all died by the end of the 15th day. The behavioral results in the low-dose administration group was not significantly different from that in the control group, indicating that the low-dose group had no obvious effect on anti-PD. In contrast, the mice in the median-dose group showed improved behavior as compared to those in the model group, and their weight and mental state did not differ from those in the model group. Therefore, based on the studies evaluated above, the medium dose (4.50 g crude drug kg–1d–1) was selected as the administration concentration. Behavioral evaluation of this study indicates that dietary ASE mitigate behavioral deficits in MPTP-induced PD mice. These results confirm and extend our earlier of screening multiple monoamine oxidase inhibitors from ASE.
Furthermore, by using iTRAQ-based proteomics and bioinformatics, we identified 128 proteins that were differentially expressed after ASE administration and predicted multiple signaling pathways. our findings suggest that most of the ASE modulates proteins were involved with Fcγ receptor-mediated phagocytosis in macrophages and monocytes signaling pathway, PI3K/AKT signaling pathway, and insulin receptor signaling pathway. In macrophages and monocytes, the Fcγ receptor controls the expression of PLA2G6, RPS6KB1, AKT1, and PLD1. Fcγ receptors (FcγRs) for IgG are surface glycoproteins that mediate the interaction between antibodies and effector cells, thereby connecting the cellular and humoral arms of the immune system. Some immune system cells, known as effector cells, express FcγRs and are responsible for processes like phagocytosis, inflammatory cell activation, and antibody-dependent cell-mediated cytotoxicity (ADCC) [37]. So far, there are 4 distinct FcγRs classes (FcγRI, FcγRIIB, FcγRIII, and FcγRIV) being identified in mice [38, 39]. Since ASE appears to regulate Fc receptors, we hypothesize that it can dampen the inflammatory response brought on by PD. In future studies, more research on ASE inhibitors that block the Fcγ receptor-mediated phagocytosis in macrophages and monocytes signaling pathway is needed. Additionally, ASE activates the insulin receptor pathway, which is closely related to insulin resistance. Insulin is not only responsible for glucose balance and energy metabolism as a peripheral hormone. It can also pass through the blood-brain barrier (BBB) and affect many life processes in the brain including regulating the survival and growth of neurons, maintaining synaptic stability and so on [40]. A growing number of studies have shown that insulin resistance occurs in the brains of PD patients and animal models. When insulin resistance occurs in the brain, it will cause abnormal aggregation of α-synuclein, mitochondrial dysfunction, neuroinflammation, and cognitive impairments, all of which play a key role in the pathogenesis of PD. Moreover, the aggregation of alpha-synuclein can also inhibit the insulin signaling pathway, further aggravating the disease [41]. As a result, insulin-sensitizing drugs such as thiazolidinediones, GLP-1 analogs, and DPP-IV inhibitors may delay PD pathogenesis. In this study, ASE was shown to regulate insulin receptor signaling related proteins. Therefore, we proposed that ASE might increase insulin sensitivity by directly or indirectly increasing insulin synthesis and secretion, which would also contribute to the treatment of PD. Meanwhile, we believe that active ingredients related to insulin receptor signaling pathways can be used as a reference for drug development against PD. Simultaneously, PI3K and AKT are the downstream effector in the insulin receptor substrate (IRS), and when insulin binds to insulin receptors, IRS phosphorylation is induced, and the signaling of the PI3K/AKT pathway is activated. The PI3K/AKT signaling pathways control phagocytosis by regulating RPS6KB1, JAK1, AKT1, and OCRL and thus have important effects on anti-apoptosis and nerve regeneration. Notably, PI3K/AKT pathway activation in PD is found to significantly affect axonal regrowth in adult nigrostriatal projection. A high level of PI3K/Akt pathway activity is associated with neuro-defense, which shows a neuroprotective effect by preventing neuroinflammation and apoptosis [42]. It is therefore possible that ASE may exert its neuroprotective effect by activating PI3K and Akt signaling pathways. According to our findings, ASE can simultaneously regulate PI3K/AKT signaling and insulin receptor signaling pathways, both of which are linked to diabetes, suggesting that ASE may be effective in treating PD complicated by diabetes. The findings of our study support the use of ASE as a dietary supplement for chronic disease prevention and management.
## 5. Conclusion
In this study, we used iTRAQ-based quantitative proteomics to look into the effects of ASE on mice with PD and reveal new mechanistic insights of the therapeutic effect of ASE in PD. Our results shed novel light on several protein targets for botanical molecules to alleviate motor deficits. According to our identified protein levels, several target-related pathways were predicted. Our research indicates that the therapeutic mechanism by which ASE fosters resilience in neurological disorders involves its action on multiple targets. It may be possible to better understand the therapeutic mechanisms that promote resilience to neurological diseases by analyzing the multi-target action of ASE.
## Data availability statement
The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
## Ethics statement
The animal study was reviewed and approved by Institutional Animal Ethics Committee (IEC) of Jilin University.
## Author contributions
HZ and TZ conceived the idea and designed the experiments. JL, YH, JF, YW, and XF performed the experiments or performed data analysis. JL and YH wrote the manuscript. HZ and TZ revised and edited the manuscript. All authors commented on and approved the manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. Klemann C, Martens G, Sharma M, Martens M, Isacson O, Gasser T. **Integrated molecular landscape of Parkinson’s disease.**. (2017) **3**. DOI: 10.1038/s41531-017-0015-3
2. Xi X, Han L. **Exploring the relationship between novel Coronavirus pneumonia and Parkinson’s disease.**. (2022) **101**. DOI: 10.1097/MD.0000000000031813
3. Feigin V, Nichols E, Alam T, Bannick M, Beghi E, Blake N. **Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.**. (2019) **18** 459-80. DOI: 10.1016/s1474-4422(18)30499-x
4. Marini K, Seppi K, Tschiderer L, Kiechl S, Stockner H, Willeit P. **Application of the updated movement disorder society criteria for prodromal Parkinson’s disease to a population-based 10-year study.**. (2021) **36** 1464-6. DOI: 10.1002/mds.28570
5. Jankovic J. **Parkinson’s disease: clinical features and diagnosis.**. (2008) **79** 368-76. DOI: 10.1136/jnnp.2007.131045
6. Legarda S, Michas-Martin P, McDermott D. **Managing intractable symptoms of Parkinson’s disease: a nonsurgical approach employing infralow frequency neuromodulation.**. (2022) **16**. DOI: 10.3389/fnhum.2022.894781
7. Navarro J, Hessner S, Yenisetti S, Bayersdorfer F, Zhang L, Voigt A. **Analysis of dopaminergic neuronal dysfunction in genetic and toxin-induced models of Parkinson’s disease in drosophila.**. (2014) **131** 369-82. DOI: 10.1111/jnc.12818
8. Rong N, Zhao Y, Rong P, Zhang H, Huang L. **Cherish civilization roots, draw on other civilizations, be forward-thinking: globalization of Chinese medicine.**. (2022) **64** 2672-6. DOI: 10.1360/tb-2019-0001
9. Law S, Lo C, Han J, Leung A, Xu C. **Is the traditional Chinese herb “Bombax Malabaricum” a natural anticancer medicine?**. (2022) **25** 145-7. DOI: 10.3831/KPI.2022.25.2.145
10. Zhan M, Huo R, Zhu B, Zhi Q. **Annual advances of traditional Chinese medicine on tumor immunity regulation in 2021.**. (2022) **7**. DOI: 10.53388/tmr20220615001
11. Zhou S, Li K, Ogihara A, Wang X. **Perceptions of traditional Chinese medicine doctors about using wearable devices and traditional Chinese medicine diagnostic instruments: a mixed-methodology study.**. (2022) **8**. DOI: 10.1177/20552076221102246
12. Zhou H, Xing J, Liu S, Song F, Cai Z, Pi Z. **Screening and determination for potential a-glucosidase inhibitors from leaves of**. (2012) **23** 315-23. DOI: 10.1002/pca.1360
13. Lin B, Chen R, Wang Q, Li Z, Yang S, Feng Y. **Transcriptomic and metabolomic profiling reveals the protective effect of**. (2021) **12**. DOI: 10.3389/fphar.2021.619076
14. He Y, Wang Y, Zhang X, Zheng Z, Liu S, Xing J. **Chemical characterization of small-molecule inhibitors of monoamine oxidase B synthesized from the**. (2020) **34**. DOI: 10.1002/rcm.8694
15. Chan H, Tse M, Kumar S, Zhuo L. **A novel selective MAO-B inhibitor with neuroprotective and anti-Parkinsonian properties.**. (2018) **818** 254-62. DOI: 10.1016/j.ejphar.2017.10.023
16. Yang X, Qiang X, Li Y, Luo L, Xu R, Zheng Y. **Pyridoxine-resveratrol hybrids mannich base derivatives as novel dual inhibitors of AChE and MAO-B with antioxidant and metal-chelating properties for the treatment of Alzheimer’s disease.**. (2017) **71** 305-14. DOI: 10.1016/j.bioorg.2017.02.016
17. Wang R, Liu S, Liu T, Wu J, Zhang H, Sun Z. **Mass spectrometry-based serum lipidomics strategy to explore the mechanism of**. (2021) **12** 4519-34. DOI: 10.1039/d0fo02845b
18. Wang R, Shi L, Liu S, Liu Z, Song F, Sun Z. **Mass spectrometry-based urinary metabolomics for the investigation on the mechanism of action of**. (2019) **241**. DOI: 10.1016/j.jep.2019.111969
19. Berlowitz I, Egger K, Cumming P. **Monoamine oxidase inhibition by plant-derived beta-carbolines; implications for the psychopharmacology of tobacco and ayahuasca.**. (2022) **13**. DOI: 10.3389/fphar.2022.886408
20. Zhang X, Ren F, Huang W, Ding R, Zhou Q, Liu X. **Anti-fatigue activity of extracts of stem bark from**. (2010) **16** 28-37. PMID: 21187815
21. Lee D, Park J, Yoon J, Kim M, Choi H, Kim H. **Neuroprotective effects of**. (2012) **139** 6-11. DOI: 10.1016/j.jep.2011.05.024
22. Kimura Y, Sumiyoshi M. **Effects of various**. (2004) **95** 447-53. DOI: 10.1016/j.jep.2004.08.027
23. Liu S, Li X, Zhang S, Yang Z, Wang K, Lu F. (2018) **24** 835-43. DOI: 10.1007/s11655-018-2935-5
24. Peng F, Huang Y, Li M, Li G, Huang H, Guan R. **Dissecting characteristics and dynamics of differentially expressed proteins during multistage carcinogenesis of human colorectal cancer.**. (2016) **22** 4515-28. DOI: 10.3748/wjg.v22.i18.4515
25. Sun W, Xing B, Guo L, Liu Z, Mu J, Sun L. **Quantitative proteomics analysis of tissue interstitial fluid for identification of novel serum candidate diagnostic marker for hepatocellular carcinoma.**. (2016) **6**. DOI: 10.1038/srep26499
26. Mertins P, Mani D, Ruggles K, Gillette M, Clauser K, Wang P. **Proteogenomics connects somatic mutations to signalling in breast cancer.**. (2016) **534** 55-62. DOI: 10.1038/nature18003
27. Chen J, Zhan L, Lu X, Xiao C, Sun N. **The Alteration of ZiBuPiYin recipe on proteomic profiling of forebrain postsynaptic density of**. (2017) **56** 471-89. DOI: 10.3233/JAD-160691
28. Mertins P, Udeshi N, Clauser K, Mani D, Patel J, Ong S. **iTRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics.**. (2012) **11**. DOI: 10.1074/mcp.M111.014423
29. Yin L, Qi Y, Xu Y, Xu L, Han X, Tao X. **Corrigendum: dioscin inhibits HSC-T6 cell migration via adjusting SDC-4 expression: insights from iTRAQ-based quantitative proteomics.**. (2019) **10**. DOI: 10.3389/fphar.2019.01036
30. Ye H, Hill J, Kauffman J, Han X. **Qualitative and quantitative comparison of brand name and generic protein pharmaceuticals using isotope tags for relative and absolute quantification and matrix-assisted laser desorption/ionization tandem time-of-flight mass spectrometry.**. (2010) **400** 46-55. DOI: 10.1016/j.ab.2010.01.012
31. Craft G, Chen A, Nairn A. **Recent advances in quantitative neuroproteomics.**. (2013) **61** 186-218. DOI: 10.1016/j.ymeth.2013.04.008
32. Xie L, Hu L. **Research progress in the early diagnosis of Parkinson’s disease.**. (2022) **43** 6225-31. DOI: 10.1007/s10072-022-06316-0
33. Zhang L, Karsten P, Hamm S, Pogson J, Muller-Rischart A, Exner N. **TRAP1 rescues PINK1 loss-of-function phenotypes.**. (2013) **22** 2829-41. DOI: 10.1093/hmg/ddt132
34. Xu Z, Han S, Chen H, Zhu Z, Han L, Dong X. **Characterization of chelation and absorption of calcium by a**. (2022) **9**. DOI: 10.3389/fnut.2022.840638
35. Pekov S, Indeykina M, Popov I, Kononikhin A, Bocharov K, Kozin S. **Application of MALDI-TOF/TOF-MS for relative quantitation of alpha- and beta-Asp7 isoforms of amyloid-beta peptide.**. (2018) **24** 141-4. DOI: 10.1177/1469066717730544
36. Xu Z, Zhu Z, Chen H, Han L, Shi P, Dong X. **Application of a**. (2022) **282**. DOI: 10.1016/j.biomaterials.2022.121390
37. Park H, Kim H, Chung D. **Fcgamma receptors modulate pulmonary inflammation by activating innate immune cells in murine hypersensitivity pneumonitis.**. (2010) **10** 26-34. DOI: 10.4110/in.2010.10.1.26
38. Nimmerjahn F, Ravetch J. **Divergent immunoglobulin g subclass activity through selective Fc receptor binding.**. (2005) **310** 1510-2. DOI: 10.1126/science.1118948
39. Nimmerjahn F. **Activating and inhibitory FcgammaRs in autoimmune disorders.**. (2006) **28** 305-19. DOI: 10.1007/s00281-006-0052-1
40. Duarte A, Moreira P, Oliveira C. **Insulin in central nervous system: more than just a peripheral hormone.**. (2012) **2012**. DOI: 10.1155/2012/384017
41. Khang R, Park C, Shin J. **Dysregulation of parkin in the Substantia nigra of**. (2015) **294** 182-92. DOI: 10.1016/j.neuroscience.2015.03.017
42. Nakano N, Matsuda S, Ichimura M, Minami A, Ogino M, Murai T. **PI3K/AKT signaling mediated by G protein-coupled receptors is involved in neurodegenerative Parkinson’s disease (Review).**. (2017) **39** 253-60. DOI: 10.3892/ijmm.2016.2833
|
---
title: 'Association between bilirubin levels with incidence and prognosis of stroke:
A meta-analysis'
authors:
- Kun Zhao
- Rui Wang
- Rongrong Chen
- Jialei Liu
- Qing Ye
- Kai Wang
- Juebao Li
journal: Frontiers in Neuroscience
year: 2023
pmcid: PMC9971723
doi: 10.3389/fnins.2023.1122235
license: CC BY 4.0
---
# Association between bilirubin levels with incidence and prognosis of stroke: A meta-analysis
## Abstract
### Objective
Bilirubin has anti-inflammatory, antioxidant, and neuroprotective properties, but the association between bilirubin and stroke remains contentious. A meta-analysis of extensive observational studies on the relationship was conducted.
### Methods
Studies published before August 2022 were searched in PubMed, EMBASE, and Cochrane Library. Cohort, cross-sectional and case-control studies that examined the association between circulating bilirubin and stroke were included. The primary outcome included the incidence of stroke and bilirubin quantitative expression level between stroke and control, and the secondary outcome was stroke severity. All pooled outcome measures were determined using random-effects models. The meta-analysis, subgroup analysis, and sensitivity analysis were performed using Stata 17.
### Results
A total of 17 studies were included. Patients with stroke had a lower total bilirubin level (mean difference = −1.33 μmol/L, $95\%$ CI: −2.12 to −0.53, $P \leq 0.001$). Compared with the lowest bilirubin level, total odds ratio (OR) of the highest bilirubin for the occurrence of stroke was 0.71 ($95\%$ CI: 0.61–0.82) and ischemic stroke was 0.72 ($95\%$ CI: 0.57–0.91), especially in cohort studies with accepted heterogeneity (I2 = 0). Serum total and direct bilirubin levels were significantly and positively associated with stroke severity. A stratified analysis based on gender showed that the total bilirubin level in males correlated with ischemic stroke or stroke, which was not noted in females.
### Conclusion
While our findings suggest associations between bilirubin levels and stroke risk, existing evidence is insufficient to establish a definitive association. Better-designed prospective cohort studies should further clarify pertinent questions (PROSPERO registration number: CRD42022374893).
## Introduction
Stroke is a clinical syndrome of brain injury due to blood vessel blockage or blood vessel rupture and bleeding classified as ischemic and hemorrhagic stroke (Campbell and Khatri, 2020). Intravenous plasminogen activator is currently the gold standard treatment for patients with acute ischemic stroke. However, it is prone to cause adverse reactions such as cerebral hemorrhage, and most patients arriving at the stroke center exceed the optimal treatment window of 4.5 h (Thakkar et al., 2019). Therefore, early prediction of stroke occurrence and identification of stroke prognostic factors are important for stroke management.
Bilirubin, an end-product of heme catabolic pathway, has long been regarded as a potentially toxic substance whose elevated levels can cause irreversible damage to the brain and nervous system. However, there is strong evidence that bilirubin has anti-inflammatory, antioxidant, and neuroprotective properties (Thakkar et al., 2019). Previous meta-analyses suggested that a higher total level of bilirubin was related to a lower stroke prevalence and had a significant association with stroke severity, indicating that bilirubin might be involved in the progression of stroke (Zhong et al., 2019; Song et al., 2022). Recently, however, there has been no definitive consensus on the relationship of bilirubin levels with stroke risk (Page et al., 2021). In addition, few studies have concentrated on direct or indirect bilirubin, limiting the explanation of the relation between stroke and bilirubin of various types. Therefore, this study aimed to systematically update the meta-analysis to investigate the connection between the different bilirubin subtypes and the risk as well as the prognostic outcome of stroke.
## Materials and methods
This study was registered on the PROSPERO (register number: CRD42022374893) and was conducted following the Preferred Reporting Items for Systematic review and Meta-analyses (PRISMA) guidelines (Page et al., 2021).
## Search strategy
The electronic databases of PubMed, EMBASE, and Cochrane Central were searched till 25 August 2022, using a string of keywords that are related to bilirubin (such as “bilirubin” or “BIL”) and stroke (such as “stroke,” “cerebral infarction”). No language restrictions have been established within the research strategy. The search strategy in detail was shown in Supplementary Table 1. To avoid the absence of documentation, manual searches were also carried out from the reference lists of all included articles and previous meta-analyses.
## Selection criteria
Inclusion criteria for included studies are as follow: [1] observational studies, including cohort studies, case-control studies, and cross-sectional studies; [2] the relationship between circulating total bilirubin level, direct bilirubin level, or indirect bilirubin level and stroke risk was investigated; and [3] reporting hazard ratio (HR), relative risk (RR), or odds ratio (OR) with the corresponding confidence intervals (CI) for stroke risk or other poor clinical outcomes, or reporting mean differences of concentrations for various bilirubin subtype between stroke patients and control group at discharge or follow-up. Exclusion criteria are as follows: [1] the study did not provide complete effect estimates or data were not available, including conference abstract, data cannot be transformed to standard format or blending stroke data with other cardiovascular diseases, and [2] articles not published in English. Screening of relevant articles to identify eligible studies for inclusion was performed separately by two investigators, and disagreements were resolved through discussion.
## Data extraction
The primary outcome was the occurrence risk of ischemic stroke or all stroke subtypes. The secondary outcome was the occurrence of poor outcomes [modified Rankin score (mRS) >2, National Institutes of Health Stroke Scale (NIHSS) ≥8] and the mean difference in bilirubin levels between patients with or without stroke. Two researchers independently reviewed the full text of potential included studies to extract pertinent information as follow: first author, location, age, gender, sample size, cases size and definition criteria, study design, sample nature, source, category of exposure (total, direct, or indirect bilirubin) and determination method, adjusted confounding factors, and outcome with summary statistics. The disagreements in this process were reviewed by discussion.
## Quality assessment
The Newcastle-Ottawa Scale (NOS) criteria was used to perform quality assessment for cohort studies and case-control studies (Wells et al., 2022). For cross-sectional studies, quality assessment using the Agency for Healthcare Research and Quality (AHRQ) criteria (Rostom et al., 2004). Disagreements about methodological quality were addressed through discussion and mutual consultation. Overall, scores of six or more were rated as being of good quality.
## Statistical analysis
The impact of bilirubin subtypes levels on all stroke or ischemic stroke risk was assessed using OR with corresponding $95\%$ CI, and HR or RR was directly converted into OR. Because statistical results were reported in distinct ways among different studies (OR per quartile, per quintile or per 1-unit increment in the continuous bilirubin traits), the results were first transformed into the OR between the highest and lowest levels (reference group) for each study. Individual adjusted OR and $95\%$ CI were preferentially estimated for pooling. For studies that investigated the difference in bilirubin subtype levels between stroke patients and the control group, we pooled the mean differences by meta-analysis. All units were converted to μmol/L and 1 mg/L total bilirubin equal to 17.1 μmol/L if not consistent.
Heterogeneity between the studies was estimated using the Cochran Q test and statistical method I2, and low, moderate and high levels of heterogeneity were cut-off with the values of 25, 50, and $75\%$, respectively (Higgins et al., 2003). Due to the variation in study characteristics, we assumed that the actual effect size may vary from study to study as the existence of clinical heterogeneity, random-effects model of DerSimonian and Laird method were determined to perform meta-analysis (DerSimonian and Laird, 1986). Pre-established subgroup analyses of study design, gender, and bilirubin subtypes were conducted. Sensitivity analysis was conducted by omitting one study by turns to examine the robustness of pooled risk estimates. If the number of studies included for the outcome indicators exceeds 10, the funnel plot and egger test are used for qualitative and quantitative testing of publication bias. Statistical analyses were performed using STATA version 17.0. Two-sided P-values below 0.05 were considered statistically significant.
## Certainty of evidence assessment
The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach which graded the evidence as “high,” “moderate,” “low,” or “very low” was used to assess the certainty of the evidence (Guyatt et al., 2011).
## Study identification and selection
The searching and screening process of the included studies are shown in Figure 1. The initial search included 5,030 potential studies from databases (PubMed, EMBASE, and Cochrane Library). After the exclusion of duplicates and irrelevant studies, 33 reports were left for retrieval. After the full-text search, 17 studies were included in the final meta-analysis (Perlstein et al., 2008; Pineda et al., 2008; Kimm et al., 2009; Ekblom et al., 2010; Luo et al., 2012; Oda and Kawai, 2012; Xu et al., 2013; Jørgensen et al., 2014; Li et al., 2014, 2020; Mahabadi et al., 2014; Zhou et al., 2014; Kunutsor et al., 2015; Lee et al., 2017; Marconi et al., 2018; Liu et al., 2020; Peng et al., 2022; Figure 1).
**FIGURE 1:** *PRISMA Flow chart of the study collection for the present review and meta-analysis.*
## Study characteristics
Table 1 lists study-level characteristics and shows the quality assessment score of included studies. Seventeen studies between 2008 and 2022 were included, including 7 prospective cohort studies (Kimm et al., 2009; Ekblom et al., 2010; Luo et al., 2012; Mahabadi et al., 2014; Kunutsor et al., 2015; Lee et al., 2017; Marconi et al., 2018), and 10 cross-sectional studies (Perlstein et al., 2008; Pineda et al., 2008; Oda and Kawai, 2012; Xu et al., 2013; Jørgensen et al., 2014; Li et al., 2014, 2020; Zhou et al., 2014; Liu et al., 2020; Peng et al., 2022). The sample size varied between 316 and 96,381, involving a total of 12,081 stroke patients. Three studies presented their respective findings for men and women (Kimm et al., 2009; Ekblom et al., 2010; Oda and Kawai, 2012). Based on the NOS criteria, all studies are of good quality.
**TABLE 1**
| References | Country | Design | Sample size | Age (years) | Male (%) | Cases | Category of exposure | Exposure level | Adjustment | Outcome | Study quality (NOS/AHRQ) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Perlstein et al., 2008 | USA | Cross-sectional | 13214 | ≥20 | 48.1 | 453, AS | Total bilirubin | Highest: 0.8–12.9 Lowest: 0.1–0.5 | Age, sex, race/ethnicity, smoking, hypertension, total to HDL cholesterol ratio, and diabetes | Stroke prevalence and adverse stroke outcomes | 7 |
| Pineda et al., 2008 | USA | Cross-sectional | 743 | 67.5 ± 16.6 | 47.6 | 743 | Direct (Dbil) bilirubin | Highest: ≤0.1 mg/dl Lowest: ≥0.4 mg/dl | Sex, serum glucose, prior antithrombotic use, hypertension, atrial fibrillation, and mRS scores | Adverse stroke outcomes (NIHSS >12) | 9 |
| Kimm et al., 2009 | Korea | Prospective cohort | 78724 | 30–89 | 52.15 | 1,964 1,189 473 | Total bilirubin | Highest: 22.2–34.2 Lowest: 0–10.2 | Age, smoking (nonsmoker, ex-smoker, and current smoker), alcohol (yes or no), exercise (yes or no), ALT, GGT, total cholesterol, type 2 diabetes, and hypertension | Stroke prevalence | 9 |
| Ekblom et al., 2010 | Sweden | Prospective cohort | 693 | 25–74 | 55.0 | 231 | Total bilirubin | Unclear | Age, BMI, systolic blood pressure, smoking, apolipoprotein B/A1, diabetes and hsCRP | Stroke prevalence | 7 |
| Luo et al., 2012 | China | Prospective cohort | 531 | 67.00 ± 12.9 | 63.5 | 531 | Total bilirubin Direct (Dbil) bilirubin | Highest: ≥22.2 Lowest: 0–10.2 Highest: ≥6.84 Lowest: 0–3.42 | BG, TC, HDL-C, hypertension, AF, sex, and age | Adverse stroke outcomes (NIHSS ≥8) | 9 |
| Oda and Kawai, 2012 | Japan | Cross-sectional | 5444 | >18 | 62.0 | 92 | Total bilirubin | Highest: 17.9–71.8 Lowest: 2.6–9.3 | Age, aspartate aminotransferase, alanine aminotransferase, γ-glutamyl transferase, current smoking, physical activity, and everyday drinking | Stroke prevalence | 6 |
| Xu et al., 2013 | China | Cross-sectional | 2361 | NS | 63.2 | 2361 | Total bilirubin Direct bilirubin | Highest: 18.0–88.0 Lowest: 1.0–10.0 Highest: 4.2–37 Lowest: 0.4–2.0 | Age, sex, alcohol consumption, cigarette smoking, blood levels of glucose and lipids, admission SBP and DBP, blood urea nitrogen, serum creatinine, sodium, hematocrit, history of stroke, hypertension, diabetes, coronary heart disease, rheumatic heart disease, and atrial fibrillation, family history of stroke, hypertension, and diabetes | Adverse stroke outcomes (NIHSS ≥10) | 8 |
| Jørgensen et al., 2014 | 16 countries | Cross-sectional | 9742 | ≥55 | 57.4 | 221 | Total bilirubin | Highest: >13 Lowest: ≤8 | Sex, age, and sibutramine/placebo | Stroke prevalence | 8 |
| Li et al., 2014 | China | Cross-sectional | 2856 | 30–69 | 63.9 | 343 | Total bilirubin | Highest: >13.9 Lowest: ≤7.8 | Sex, BMI, smoking status, DBP, LDL-C, FPG, eGFR, TB, DM, and baPWV | Stroke prevalence | 8 |
| Mahabadi et al., 2014 | Germany | Prospective cohort | 3553 | 45–75 | 44.0 | 95 | Total bilirubin | / | Age, gender, BMI, systolic blood pressure, LDL, HDL, antihypertensive medication, lipid-lowering medication, diabetes, smoking status, and CAC score | Stroke prevalence | 9 |
| Zhou et al., 2014 | China | Cross-sectional | 1098 | >18 | 45.7 | 733 | Total bilirubin | Highest: >12.99 Lowest: ≤9.58 | Age, sex, and vascular risk factors | Stroke prevalence | 8 |
| Kunutsor et al., 2015 | Netherlands | Prospective cohort | 7222 | 28–75 | 48.5 | 159 | Total bilirubin | Per 1-SD higher | Age and sex, smoking status, history of diabetes, systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, and BMI, alcohol consumption, glucose, and triglycerides, γ-glutamyl transferase, and alanine aminotransferase | Stroke prevalence | 9 |
| Lee et al., 2017 | Korea | Prospective cohort | 5599 | >18 | 66.9 | 806 | Total bilirubin | | Age, sex, systolic blood pressure, fasting serum glucose, total cholesterol, high-density lipoprotein-cholesterol, and smoking status | Stroke prevalence | 9 |
| Marconi et al., 2018 | USA | Prospective cohort | 96381 | 48 (mean) | 97.0 | 2112 | Total bilirubin | Highest: ≥15.39 Lowest: ≤6.84 | Age, sex, race-ethnicity, systolic blood pressure, smoking, diabetes mellitus, total cholesterol, high-density lipoprotein cholesterol, HIV, hepatitis C, liver fibrosis measured by FIB-4, alcohol abuse/dependence, cocaine, and obesity | Stroke prevalence | 9 |
| Li et al., 2020 | China | Cross-sectional | 610 | 66.7 (mean) | 63.11 | 610 | Total bilirubin | / | High density lipoprotein cholesterol, and triglyceride | Adverse stroke outcomes | 9 |
| Liu et al., 2020 | China | Cross-sectional | 316 | 70.36 ± 10.06 | 42.72 | 42 | Total bilirubin | Highest: 34.15 ± 9.78 Lowest: ≤8.04 ± 2.03 | Age, sex, body mass index, systolic blood pressure, the congestive heart failure, hypertension, age >75 years, diabetes, and previous stroke/transient ischemic attack score, left ventricular ejection fraction, left atrial diameter, alanine aminotransferase, aspartate aminotransferase, uric acid, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglycerides, estimated glomerular filtration rate, heart failure, coronary artery disease, hypertension, diabetes, drinking, smoking, international standardized ratio value, and taking oral anticoagulant and antiplatelet drugs | Stroke prevalence | 9 |
| Peng et al., 2022 | China | Cross-sectional | 585 | 64.9 ± 12.2 | 66.5 | 585 | Total bilirubin Direct bilirubin Indirect bilirubin | | Age, sex, onset-time to treatment, admission glucose, admission ALT, admission AST, current smoking, alcohol drinking, history of stroke, cerebral hemorrhage, hypertension, diabetes mellitus, and hyperlipidemia, admission NIHSS score | Adverse stroke outcomes | 8 |
## Primary outcome
Twelve included studies examined the association of total bilirubin level with stroke, Figure 2 shows the adjusted ORs for each study and the pooled OR with the highest vs. lowest bilirubin level groups. Heterogeneity (I2 = $80.56\%$, $P \leq 0.01$) was observed and compared to the group at the lowest bilirubin level group, the risk of stroke was significantly lower among participants at the highest bilirubin level (OR = 0.71, $95\%$ CI: 0.61–0.82, $P \leq 0.01$). Similarly, the random-effect model analysis comparing ischemic risk and total bilirubin level quartiles showed a significant inverse association, with a pooled effect OR of 0.72 ($95\%$ CI: 0.57–0.91, $$P \leq 0.01$$, Figure 3) and a high degree of heterogeneity (I2 = $87.17\%$, $P \leq 0.01$). Sensitivity analysis revealed no apparent influence of an individual study on the results of meta-analysis for all types of stroke (Supplementary Figure 1). However, when the Marconi et al. [ 2018] study was excluded, the findings did not reveal any significant association with the risk of ischemic stroke (OR = 0.73, $95\%$ CI: 0.51–1.03, $$P \leq 0.07$$), implying that the pooled risk estimates were not robust (Supplementary Figure 2). No studies were conducted on the relationship between direct or indirect bilirubin levels and the risk of stroke or ischemic stroke.
**FIGURE 2:** *Forest plot of the association between total bilirubin level and stroke prevalence.* **FIGURE 3:** *Forest plot of the association between total bilirubin level and ischemic stroke prevalence.*
## Secondary outcome
Nine studies reported the differences in different bilirubin types level between stroke patients and the control group. Six studies showed that the group of stroke patients had a significantly lower total bilirubin level (mean difference = −1.33 μmol/L, $95\%$ CI: −2.12 to −0.53, $P \leq 0.01$, Supplementary Figure 3). Sensitivity analysis by omitting one study each time also revealed no evident influence on the pooled results (Supplementary Figure 4). Direct bilirubin and indirect bilirubin level did not show a significant difference due to the limited included studies (Supplementary Figure 3).
All included studies concentrated on associating bilirubin subtypes with ischemic stroke severity. Five data groups were obtained from four studies examining total bilirubin levels and the severity of ischemic stroke. [ Three studies (Luo et al., 2012; Xu et al., 2013; Li et al., 2020) defined severe strokes as NIHSS score ≥8 and one study defined them as 3–6 in mRS (Peng et al., 2022).] The meta-analysis results showed a positive correlation between total bilirubin level and stroke poor outcome (OR: 1.13, $95\%$ CI: 1.03–1.24) with a high degree of heterogeneity (I2 = $86.45\%$) (shown in Figure 4). Six datasets of direct bilirubin from five studies were pooled, and the findings indicated that direct bilirubin level was also positively associated with the poor outcome of ischemic stroke (OR: 1.93, $95\%$ CI: 1.43–2.61, and I2 = $88.59\%$) (shown in Figure 5). Additionally, when we performed sensitivity analyses, the meta-analyses results were robust (Supplementary Figures 5, 6). Not enough data was collected because we could not pool the correlation between indirect bilirubin level and ischemic stroke severity.
**FIGURE 4:** *Forest plot of the association between total bilirubin level and ischemic stroke severity.* **FIGURE 5:** *Forest plot of the association between direct bilirubin level and ischemic stroke severity.*
## Subgroup analysis
For the primary outcome, prespecified subgroup analyses by study design, gender, and location were performed (Table 2). The results showed less heterogeneity after being grouped by study design. No substantial heterogeneity was found among prospective cohorts (I2 = $0.00\%$) and pooled results for a significant relationship between stroke (OR: 0.75, $95\%$ CI: 0.69–0.82) and ischemic stroke patients (OR: 0.72, $95\%$ CI: 0.64–0.81). Three studies separately reviewed the relationship between total bilirubin level and stroke risk in males and females, respectively. Two of them examined the correlation between total bilirubin level and the risk of ischemic stroke. Interestingly, we observed a statistical association between a low risk of stroke or ischemic stroke with higher total bilirubin levels in the male population, but this statistical difference disappeared in females.
**TABLE 2**
| Subgroups | Stroke risk | Stroke risk.1 | Stroke risk.2 | Ischemic stroke risk | Ischemic stroke risk.1 | Ischemic stroke risk.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | Number of groups of data | Pooled OR [95% CI] | Heterogeneity | Number of groups of data | Pooled OR [95% CI] | Heterogeneity |
| Study design | Study design | Study design | Study design | Study design | Study design | Study design |
| Cohort | 7 | 0.75 [0.69, 0.82] | I2 = 0%, P = 0.49 | 4 | 0.72 [0.64, 0.81] | I2 = 0%, P = 0.74 |
| Cross-sectional | 7 | 0.63 [0.43, 0.91] | I2 = 86.74%, P < 0.01 | 3 | 0.74 [0.37, 1.49] | I2 = 93.27%, P < 0.01 |
| Gender | Gender | Gender | Gender | Gender | Gender | Gender |
| Male | 3 | 0.71 [0.55, 0.91] | I2 = 6.34%, P = 0.34 | 2 | 0.68 [0.52, 0.90] | I2 = 0%, P = 0.61 |
| Female | 3 | 0.55 [0.25, 1.18] | I2 = 65.87%, P = 0.05 | 2 | 0.64 [0.26, 1.54] | I2 = 61.61%, P = 0.11 |
## Publication bias
We tested the publication bias for the outcome of the correlation between total bilirubin level and stroke risk, and the P-values of the egger test were 0.08, indicating no inflation of effect sizes due to selective publication. The funnel plot was performed in Supplementary Figure 7.
## GRADE assessment
The Supplementary Table 2 shows that the level of the evidence was graded as “low” owing to upgrade for dose-response and downgrades for inconsistency.
## Discussion
To more comprehensively and accurately assess the influence of bilirubin subtype levels on stroke outcomes, we performed this comprehensive updated meta-analysis. The pooled results showed that bilirubin level was negatively associated with ischemic stroke and stroke risk after adjustment. Furthermore, a highly significant relationship was detected between total bilirubin levels or direct bilirubin levels and ischemic stroke severity. The sensitivity analysis provided additional insight into the robustness of the pooled risk estimates. Moreover, subgroup analyses by gender showed a statistical association between a low risk of stroke or ischemic stroke with higher total bilirubin levels in the male population, but this statistical difference disappeared in females. As a result, this meta-analysis confirms that bilirubin levels at the onset are a biomarker for improved diagnosis and prognosis in stroke patients.
The relationship between total bilirubin level and all types of stroke or ischemic stroke risk was consistent with previous studies (Zhong et al., 2019). Unlike it, we excluded Ren et al. ’s [2016] study, because its outcome was cardiovascular disease, including prior coronary heart disease or peripheral arterial disease with stroke. However, it did not provide data on stroke separately, which will lead to biased results. In addition, we have added two new studies (Marconi et al., 2018; Liu et al., 2020) to confirm the link between bilirubin levels and stroke more comprehensively and accurately. It was worth mentioning that Liu’s study reported that each 1 mmol/L increase in total bilirubin would increase the risk of first ischemic stroke in patients with non-valvular atrial fibrillation. The sensitivity analysis showed that this did not affect the robustness of the conclusion. Our meta-analysis observed a negative statistical association between ischemic stroke/stroke and total bilirubin level in the male population, but this statistical difference disappeared in females. These results were consistent with the study of Zhong and his colleagues (Zhong et al., 2019). The difference in bilirubin levels between gender might be attributed to differences in heme oxygenase, serum oestrogen, iron storage, and the influence of lifestyle (Zhong et al., 2019). Our subgroup analysis of prospective cohort studies indicated that total bilirubin level was inversely associated with the incidence of all stroke or ischemic stroke with low heterogeneity. This result again emphasized that bilirubin level was essential in developing stroke risk. However, the causal relationship needs to be confirmed by further cohort studies, as genetic evidence according to Mendelian randomization approaches did not suggest any causal effect of bilirubin levels on developing stroke in Koreans (Lee et al., 2017). Therefore, Mendelian randomization studies based on different races should also be carried out in the future to avoid ignoring some important information or leading to erroneous conclusions. In addition to diagnostic value, we investigated the prognostic value of bilirubin subtypes in ischemic stroke. Our results determined that the direct and total bilirubin levels were positively correlated with the ischemic stroke severity, consistent with the systematic review of Ghojazadeh et al. ’s [2020] study and Song et al. ’s [2022] study. Song et al. also established that bilirubin is linear with ischemic stroke severity and trial sequential analysis found that the sample size for this study is sufficient. A cross-sectional descriptive analysis conducted by Sagheb Asl et al. [ 2018] also showed that total, direct and indirect bilirubin levels was significantly associated with mortality in ischemic stroke patients. Total bilirubin and direct bilirubin may be critical endogenous antioxidants and their levels can reflect the stroke severity and can be used as an auxiliary indicator. Because of the limited number of studies, future investigations may explore the association between indirect bilirubin levels and stroke severity.
Ischemic stroke is characterized by a sudden loss of blood circulation in a focal area of the brain, preventing the proper delivery of glucose, oxygen, and nutrients, causing chemokines, cytokines, and reactive oxygen species (ROS) trigger inflammatory responses (Maida et al., 2020). ROS can stimulate many signal transduction pathways important for maintaining neuronal homeostasis, but the overproduction of ROS can induce structural and functional damage of neurons throughout the whole process of acute ischemic stroke, leading to brain injury (Niizuma et al., 2009). Bilirubin is an endogenous antioxidant that protects against the oxidation of low-density lipoprotein cholesterol, scavenging oxygen-free radicals boosting heme oxygenase activity and helping serum cholesterol to dissolve (Schwertner et al., 1994). Bilirubin contains an extended conjugated double bond system and a reactive hydrogen atom, which has strong antioxidant properties, thus preventing the generation of cellular ROS and the formation of atherosclerotic plaques (Vogel et al., 2017), thereby avoiding the onset of stroke; at the same time bilirubin can affect the inflammatory pathways by preventing the connection between C1q and immunoglobulins, significantly reduces the ability of complement to initiate through the traditional pathway, controlling the proliferation of T-regulatory cells (Tregs), modifies the activity of cytotoxic T-lymphocytes, blocking the production of pro-inflammatory cytokines, as the recruitment of pro-inflammatory cytokines is one of important factors in the formation of stroke (Thakkar et al., 2019); it can also resist myeloperoxidase-induced protein or lipid oxidation, scavenges hypochlorous acid, and prevents stroke (Boon et al., 2015). The exact mechanism linking direct bilirubin level to the high incidence of ischemic stroke remains unclear, and some possible explanations can be suggested. Direct bilirubin is more soluble in serum than the lipophilic indirect bilirubin, thus making direct bilirubin an active form that is more readily available than indirect bilirubin (Hansen et al., 2020). Additionally, as a systemic disease, an elevated level of direct bilirubin may indicate the injury of hepatocytes (Sharma et al., 2021); therefore, the positive association of direct bilirubin levels with poor clinical outcomes might be due to the hepatic dysfunction. Future studies are required to demonstrate the specific differences in various bilirubin subtypes concerning their molecular mechanisms of action.
In summary, the relationship between bilirubin levels and stroke is complex. On the one hand, the production of bilirubin was physiologically enhanced in response to oxidative stress. When bilirubin level was upregulated through the stroke, we suppose it would play an important role. Additionally, strokes with higher severity are accompanied by higher levels of oxidative stress, which may also induce elevated anti-oxidative power reflected by the level of bilirubin (Domínguez et al., 2010). On the other hand, upregulated bilirubin can protect neurons against oxidation between a specific concentration range. At pathologic levels, bilirubin has been considered as a neurotoxic agent. Therefore, future studies can explore the impact of high-concentration bilirubin levels on the nervous system of stroke, so as to provide more decision-making suggestions for drug use.
This study is a meta-analysis of observational studies investigating the association between bilirubin levels of various subtypes and strokes from the most comprehensive literature research for now. The enrolled studies were analyzed according to the adjusted results. The limitations of this study also need to be recognized. Firstly, the substantial heterogeneity of included studies suggests that applicability of the results should be interpreted with caution. Secondly, the sample nature and the laboratory measurement method of bilirubin level were inconsistent, which may lead to some bias into the results. Thirdly, only a few studies in this meta-analysis have taken gender into consideration. As previous studies have revealed, the association between the bilirubin level and stroke risk was only confirmed in the males population using a gender stratified analysis, there is a lot of space remained to be explored in these results (Zhong et al., 2019). Fourthly, due to the limited number of studies, some outcomes have not enough power to be addressed in the clinical practice. More research is needed to investigate this relationship in the future. Lastly, hemorrhagic stroke is also a subtype of stroke, but only one showed that total bilirubin levels were not significantly associated with hemorrhagic stroke risk (Kimm et al., 2009). The relationship between bilirubin level and hemorrhagic stroke needs further investigation.
## Conclusion
Although our findings suggested a negative association between total bilirubin levels and all stroke or ischemic stroke risk and a highly significant relationship between total/direct bilirubin levels and the severity of ischemic stroke, the existing evidence is inadequate to establish a definitive conclusion. More sophisticated prospective cohort studies and further analyses are required to further explain relevant issues.
## Data availability statement
The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
KZ and JBL contributed to the study design and data research. RW, RC, and JLL contributed to study selection and quality evaluation. KZ, RW, and KW contributed to statistical analysis. KZ, RC, JLL, QY, KW, and JBL contributed to drafting of the manuscript and language modification. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2023.1122235/full#supplementary-material
## References
1. Boon A., Hawkins C., Coombes J., Wagner K., Bulmer A.. **Bilirubin scavenges chloramines and inhibits myeloperoxidase-induced protein/lipid oxidation in physiologically relevant hyperbilirubinemic serum.**. (2015) **86** 259-268. DOI: 10.1016/j.freeradbiomed.2015.05.031
2. Campbell B., Khatri P.. **Stroke.**. (2020) **396** 129-142. DOI: 10.1016/S0140-6736(20)31179-X
3. DerSimonian R., Laird N.. **Meta-analysis in clinical trials.**. (1986) **7** 177-188. DOI: 10.1016/0197-2456(86)90046-2
4. Domínguez C., Delgado P., Vilches A., Martín-Gallán P., Ribó M., Santamarina E.. **Oxidative stress after thrombolysis-induced reperfusion in human stroke.**. (2010) **41** 653-660. DOI: 10.1161/STROKEAHA.109.571935
5. Ekblom K., Marklund S., Johansson L., Osterman P., Hallmans G., Weinehall L.. **Bilirubin and UGT1A1*28 are not associated with lower risk for ischemic stroke in a prospective nested case-referent setting.**. (2010) **30** 590-596. DOI: 10.1159/000319778
6. Ghojazadeh M., Asl E., Vahed N., Hassanpour R., Sanaie S., Mahmoodpoor A.. **Determination of the predictive value of serum bilirubin in patients with ischemic stroke: A systematic review.**. (2020) **7** e99302. DOI: 10.1186/s13054-016-1208-6
7. Guyatt G., Oxman A., Akl E., Kunz R., Vist G., Brozek J.. **GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables.**. (2011) **64** 383-394. DOI: 10.1016/j.jclinepi.2010.04.026
8. Hansen T., Wong R., Stevenson D.. **molecular physiology and pathophysiology of bilirubin handling by the blood, liver, intestine, and brain in the Newborn.**. (2020) **100** 1291-1346. DOI: 10.1152/physrev.00004.2019
9. Higgins J., Thompson S., Deeks J., Altman D.. **Measuring inconsistency in meta-analyses.**. (2003) **327** 557-560. DOI: 10.1136/bmj.327.7414.557
10. Jørgensen M., Torp-Pedersen C., Finer N., Caterson I., James W., Legler U.. **Association between serum bilirubin and cardiovascular disease in an overweight high risk population from the SCOUT trial.**. (2014) **24** 656-662. DOI: 10.1016/j.numecd.2013.12.009
11. Kimm H., Yun J., Jo J., Jee S.. **Low serum bilirubin level as an independent predictor of stroke incidence: A prospective study in Korean men and women.**. (2009) **40** 3422-3427. DOI: 10.1161/STROKEAHA.109.560649
12. Kunutsor S., Bakker S., Gansevoort R., Chowdhury R., Dullaart R.. **Circulating total bilirubin and risk of incident cardiovascular disease in the general population.**. (2015) **35** 716-724. DOI: 10.1161/ATVBAHA.114.304929
13. Lee S., Jee Y., Jung K., Hong S., Shin E., Jee S.. **Bilirubin and stroke risk using a mendelian randomization design.**. (2017) **48** 1154-1160. DOI: 10.1161/STROKEAHA.116.015083
14. Li R., Cao Z., Zhang J., Li Y., Wang R.. **Decreased serum bilirubin is associated with silent cerebral infarction.**. (2014) **34** 946-951. DOI: 10.1161/ATVBAHA.113.303003
15. Li Z., Zhang J., Luo Y.. **Impact of triglyceride playing on stroke severity correlated to bilirubin.**. (2020) **99** e21792. DOI: 10.1097/MD.0000000000021792
16. Liu Y., Wang J., Zeng W., Lyu Q.. **Nonlinear relationship between serum total bilirubin levels and initial ischemic stroke in patients with non-valvular atrial fibrillation.**. (2020) **48** 300060520962347. DOI: 10.1177/0300060520962347
17. Luo Y., Li J., Lu Z., Wang C., Guan D., Xu Y.. **Serum bilirubin after acute ischemic stroke is associated with stroke severity.**. (2012) **9** 128-132. DOI: 10.2174/156720212800410876
18. Mahabadi A., Lehmann N., Möhlenkamp S., Kälsch H., Bauer M., Schulz R.. **Association of bilirubin with coronary artery calcification and cardiovascular events in the general population without known liver disease: The Heinz Nixdorf Recall study.**. (2014) **103** 647-653. DOI: 10.1007/s00392-014-0697-z
19. Maida C., Norrito R., Daidone M., Tuttolomondo A., Pinto A.. **Neuroinflammatory mechanisms in ischemic stroke: Focus on cardioembolic stroke, background, and therapeutic approaches.**. (2020) **21** 6454. DOI: 10.3390/ijms21186454
20. Marconi V., Duncan M., So-Armah K., Re V., Lim J., Butt A.. **Bilirubin is inversely associated with cardiovascular disease among HIV-positive and HIV-negative individuals in VACS (Veterans Aging Cohort Study).**. (2018) **7** e007792. DOI: 10.1161/JAHA.117.007792
21. Niizuma K., Endo H., Chan P.. **Oxidative stress and mitochondrial dysfunction as determinants of ischemic neuronal death and survival.**. (2009) **109** 133-138. PMID: 19393019
22. Oda E., Kawai R.. **A possible cross-sectional association of serum total bilirubin with coronary heart disease and stroke in a Japanese health screening population.**. (2012) **27** 29-36. DOI: 10.1007/s00380-011-0123-7
23. Page M., McKenzie J., Bossuyt P., Boutron I., Hoffmann T., Mulrow C.. **The PRISMA 2020 statement: An updated guideline for reporting systematic reviews.**. (2021) **372** n71. PMID: 33782057
24. Peng Q., Bi R., Chen S., Chen J., Li Z., Li J.. **Predictive value of different bilirubin subtypes for clinical outcomes in patients with acute ischemic stroke receiving thrombolysis therapy.**. (2022) **28** 226-236. DOI: 10.1111/cns.13759
25. Perlstein T., Pande R., Creager M., Weuve J., Beckman J.. **Serum total bilirubin level, prevalent stroke, and stroke outcomes: NHANES 1999-2004.**. (2008) **121** 781.e-788.e. DOI: 10.1016/j.amjmed.2008.03.045
26. Pineda S., Bang O., Saver J., Starkman S., Yun S., Liebeskind D.. **Association of serum bilirubin with ischemic stroke outcomes.**. (2008) **17** 147-152. DOI: 10.1016/j.jstrokecerebrovasdis.2008.01.009
27. Ren Y., Jin N., Hong T., Mu Y., Guo L., Ji Q.. **Interactive effect of serum uric acid and total bilirubin for cardiovascular disease in Chinese patients with type 2 diabetes.**. (2016) **6** 36437. PMID: 27805038
28. Rostom A., Dubé C., Cranney A., Saloojee N., Sy R., Garritty C.. (2004)
29. Sagheb Asl E., Taheraghdam A., Rahmani F., Javadrashid R., Golzari S., Ghaemian N.. **Determination of the predictive value of serum bilirubin in patients with ischemic stroke: A prospective descriptive analytical study.**. (2018) **8** 715-719. DOI: 10.15171/apb.2018.080
30. Schwertner H., Jackson W., Tolan G.. **Association of low serum concentration of bilirubin with increased risk of coronary artery disease.**. (1994) **40** 18-23. PMID: 8287538
31. Sharma K., Zajc I., Žiberna L.. **Dietary vitamin D equilibrium in serum ameliorates direct bilirubin associated diabetes mellitus.**. (2021) **337** 109399. DOI: 10.1016/j.cbi.2021.109399
32. Song Y., Zhang X., Li C., Xu S., Zhou B., Wu X.. **Is bilirubin associated with the severity of ischemic stroke? A dose response meta-analysis.**. (2022) **11** 3262. PMID: 35743332
33. Thakkar M., Edelenbos J., Doré S.. **Bilirubin and ischemic stroke: Rendering the current paradigm to better understand the protective effects of bilirubin.**. (2019) **56** 5483-5496. DOI: 10.1007/s12035-018-1440-y
34. Vogel M., Idelman G., Konaniah E., Zucker S.. **Bilirubin prevents atherosclerotic lesion formation in low-density lipoprotein receptor-deficient mice by inhibiting endothelial VCAM-1 and ICAM-1 signaling.**. (2017) **6** e004820. DOI: 10.1161/JAHA.116.004820
35. Wells G., Shea B., O’Connell D., Peterson J., Welch V., Losos M.. (2022)
36. Xu T., Zhang J., Xu T., Liu W., Kong Y., Zhang Y.. **Association of serum bilirubin with stroke severity and clinical outcomes.**. (2013) **40** 80-84. PMID: 23250132
37. Zhong P., Wu D., Ye X., Wang X., Zhou Y., Zhu X.. **Association of circulating total bilirubin level with ischemic stroke: A systemic review and meta-analysis of observational evidence.**. (2019) **7** 335. DOI: 10.21037/atm.2019.06.71
38. Zhou X., Wang L., Liu H., Zhang C., Zhu M., Zhu X.. **Serum antioxidant levels associated with subcortical ischemic vascular disease.**. (2014) **41** 375-381. DOI: 10.1017/S0317167100017339
|
---
title: High-mobility group box 1 protein, receptor for advanced glycation end products
and nucleosomes increases after marathon
authors:
- Julia Schoenfeld
- Astrid Roeh
- Stefan Holdenrieder
- Pia von Korn
- Bernhard Haller
- Kimberly Krueger
- Peter Falkai
- Martin Halle
- Alkomiet Hasan
- Johannes Scherr
journal: Frontiers in Physiology
year: 2023
pmcid: PMC9971726
doi: 10.3389/fphys.2023.1118127
license: CC BY 4.0
---
# High-mobility group box 1 protein, receptor for advanced glycation end products and nucleosomes increases after marathon
## Abstract
Background: Prolonged and strenuous exercise has been linked to potential exercise-induced myocardial damages. One potential key to unmask the discussed underlying mechanisms of this subclinical cardiac damage could be markers of immunogenic cell damage (ICD). We investigated the kinetics of high-mobility group box 1 protein (HMGB1), soluble receptor for advanced glycation end products (sRAGE), nucleosomes, high sensitive troponin T (hs-TnT) and high sensitive C-reactive protein (hs-CRP) before and up to 12 weeks post-race and described associations with routine laboratory markers and physiological covariates.
Methods: In our prospective longitudinal study, 51 adults ($82\%$ males; 43 ± 9 years) were included. All participants underwent a cardiopulmonary evaluation 10–12 weeks pre-race. HMGB1, sRAGE, nucleosomes, hs-TnT and, hs-CRP were analysed 10–12 weeks prior, 1–2 weeks before, immediately, 24 h, 72 h, and 12 weeks post-race.
Results: HMGB1, sRAGE, nucleosomes and hs-TnT increased significantly from pre- to immediately post-race (0.82–2.79 ng/mL; 1132–1388 pg/mL; 9.24–56.65 ng/mL; 6–27 ng/L; $p \leq 0.001$) and returned to baseline within 24–72 h. Hs-CRP increased significantly 24 h post-race (0.88–11.5 mg/L; $p \leq 0.001$). Change in sRAGE was positively associated with change in hs-TnT (rs = 0.352, $$p \leq 0.011$$). Longer marathon finishing time was significantly associated with decreased levels of sRAGE [−9.2 pg/mL (β = −9.2, SE = 2.2, $p \leq 0.001$)].
Conclusion: Prolonged and strenuous exercise increases markers of ICD immediately post-race, followed by a decrease within 72 h. An acute marathon event results in transient alterations of ICD, we assume that this is not solely driven by myocyte damages.
## 1 Introduction
The beneficial effects of regular moderate exercise on the cardiovascular system are well established. Regular exercise improves oxidative capacity (Irving et al., 2015), microvascular collateral formation (Möbius-Winkler et al., 2016) and contractile myocardial function (Nystoriak and Bhatnagar, 2018). Among endurance athletes, there is evidence that prolonged and strenuous exercise is associated with oxidative stress, increased cardiac volume load (Małek Ł et al., 2019) and oxidative DNA damage (Borghini et al., 2015) and may exert exercise-induced myocardial damage and even injury in terms of reversible fatigue or irreversible heart failure (Douglas et al., 1990). Furthermore, an increased risk of exercise-related sudden cardiac death (SCD) has been reported among endurance runners with pre-existing cardiac abnormalities, most likely caused by prolonged strenuous exercise (Albert et al., 2000).
This assumption is supported by studies from the 1990s which reported increased markers of cardiac and muscular damage like troponins (Tn) and creatine kinase (CK) in otherwise healthy individuals after heavy exercise and extreme endurance events such as the Berlin Marathon (Artner-Dworzak et al., 1990; Koller et al., 1995). Additionally, systematic reviews and meta-analyses within the last decade (Regwan et al., 2010; Sedaghat-Hamedani et al., 2015) showed increased Tn values above the 99th percentile (14 ng/L) in $83\%$ of endurance athletes immediately after prolonged and strenuous exercise (Sedaghat-Hamedani et al., 2015). However, our group was able to demonstrate a return of Tn levels to baseline levels within 72 h after a marathon race (Scherr et al., 2011). The time of appearance of the absolute peak value and downslope differ from e.g. myocardial infarction with much faster kinetics pointing to a revesible effect, which leads to the conclusion that these are benign phenomenon. However, recent imaging studies suggest a compromised cardiomyocyte integrity (Aengevaeren et al., 2020).
One potential key to unmask the discussed underlying mechanisms of this subclinical cardiac damage could be markers of immunogenic cell damage (ICD) such as circulating nucleosomes, high-mobility group box 1 protein (HMGB1), and soluble receptor for advanced glycation end-products (sRAGE). Nucleosomes are basic components of nuclear chromatin and are formed by a complex of core histone proteins and DNA that is twisted around them. Linker DNA connects them to a nucleosomal chain (Koyama and Kurumizaka, 2017). After cleavage by specific endonucleases, they are released by dying and stressed cells into the blood circulation. HMGB1 is a nuclear non-histone-DNA-binding protein that stabilizes the nucleosome structure, enables the bending of DNA and facilitates transcription (Balliano et al., 2017). Upon cell stress, HMGB1 can be released in association with nucleosomes or dissociated from them (Bell et al., 2006). RAGE is an important cellular receptor and binding partner for HMGB1, S100 and other danger associated molecular pattern (DAMP) markers transmitting proinflammatory signals on the surface of antigen presenting cells, dendritic cells and macrophages (Pilzweger and Holdenrieder, 2015). However, RAGE can also be shed from these cells and act as a soluble anti-inflammatory decoy receptor by catching HMGB1 and other DAMPs (Bell et al., 2006). These immunogenic markers are released by stressed/damaged cells like muscle, immune, endothelial or liver cells by necrotic, apoptotic or NETosis pathways and therefore, are associated with cardiac cell stress, acute and chronic diseases like diabetes (Volz et al., 2010), arteriosclerosis (Andrassy et al., 2012), cancer (Stoetzer et al., 2012), trauma (Stahl et al., 2016), sepsis (Matsumoto et al., 2015), myocardial infarction (MI) (Sorensen et al., 2011), and heart failure (Volz et al., 2010; Pilzweger and Holdenrieder, 2015).
A recent study conducted by Beko and colleagues including 70 non-professional half marathon and marathon runners reported increased levels of HMGB1 and sRAGE immediately after the race. Both, HMGB1 and sRAGE returned to baseline values after two to 7 days of recovery (Bekos et al., 2016). Beko et al. were not able to detect an increase in sRAGE after a marathon race and they related this to the different training habits. However, they did not further evaluate the association with performance. Consistent with these observations of HMGB1 and sRAGE, circulating cell-free DNA (cfDNA), paralleled that of plasma HMGB1, increased in half marathon runners immediately after the race and decreased to baseline values within 2 h post-race (Atamaniuk et al., 2004). Although, the studies showed mostly an increase of these markers following exercise, many questions remain. To date, no study has evaluated the predictive factors e.g., performance, and laboratory markers associated with the increases of ICD and illuminated the sources of ICD. Therefore, we aimed to evaluate the kinetics of HMGB1, sRAGE, nucleosomes, hs-TnT and hs-CRP before, immediately after, and up to 12 weeks post-race after a marathon race. Furthermore, we identified routine markers of clinical chemistry (e.g., Creatinine and CK) and physiological covariates (exercise capacity, age, BMI and marathon finishing time) associated with post-race concentration of these biomarkers.
## 2.1 Study participants
We included 51 marathon runners (of the initial 100 participants) of the longitudinal observational ReCaP-Study [Running effects on cognition and plasticity; details published previously (Roeh et al., 2019)] in our analyses. Participants were aged between 18 to 60 years, successfully registered for the Munich Marathon 2017, completed at least one half-marathon prior to the event, had sufficient German language skills and provided written informed consent. Participants with relevant neurological, cardiac or psychiatric diseases, pregnancy, cannabis abuse, and BMI >30 were excluded.
## 2.2 Ethical approval
The study has been approved by two local ethic committees: Ludwig-Maximilians University Munich (reference number 17-148) and the University Hospital Klinikum rechts der Isar, Technical University of Munich (reference number $\frac{218}{17}$S). The trial was registered at DRKS-German Clinical Trials Register (DRKS-ID: DRKS00012496).
## 2.3 Study design
A total of six visits were performed. The first (baseline) visit took place 10–12 weeks (V-1) and the second visit 1–2 weeks (V0) prior to the marathon. The third, fourth, fifth, and sixth were immediately (V1), 24 h (V2.1), 72 h (V2.2) and 12 weeks (V3) after the marathon.
## 2.4 Examinations
All participants underwent a standardized physical examination and a full cardiac checkup at study site by trained medical staff. Body height and weight were measured via seca scale (Seca, 764, seca GmbH, Hamburg, Germany). Blood pressure (mmHg) was measured after resting in supine position for 5 min. Body fat was measured via calipometry and calculated according to the 7-folder formula of Jackson and Pollock [1978]. A standardized 12-lead resting electrocardiography (ECG) was recorded (1 min duration, speed of 50 mm·s−1 and a voltage scale equivalent of 10 mm·mV−1) using Custo cardio 200 (custo diagnostics 3.8; custo med GmbH, Ottobrunn, Germany) in supine position. Additionally, physical fitness and chronotropic competence of all participants were measured via cardiopulmonary exercise test (CPET) including a 12-lead ECG on a treadmill by ramp protocol until voluntary exhaustion. Blood pressure was measured before the test and at maximum load, as well as one and 3 min after the test. Furthermore, a transthoracic echocardiography (IE33, Philips, Amsterdam, Netherlands; standard 2D parasternal short- and long-axis images and apical 2-, 3-, and 4-chamber views) was performed.
## 2.5 Blood sampling
At all six visits, blood samples were taken from the antecubital vein using two 10 mL lithium-heparin plasma tube, a 5 mL K2-EDTA plasma tube and a 10 mL gel serum tube (Sarstedt, Nuermbrecht, Germany). Samples were transported (<20 min) to the certified central laboratory of the German Heart Centre Munich where they were centrifuged and analyzed for routine markers of clinical chemistry such as creatinine and creatine kinase (CK) according to national quality standards. Residual samples of heparin, EDTA-plasma and serum were subsequently aliquoted into 2 mL cryotubes and stored at −80°C until further analyses.
## 2.5.1 High-mobility group box 1 protein (HMGB1) and receptor for advanced glycation end products (Soluble RAGE)
HMGB1 and sRAGE concentration was assessed from serum tube by a sandwich enzyme linked immunosorbent assay (ELISA) according to the instructions of the manufacturer (HMGB1 ELISA, ST51011, TECAN IBL International GmbH, Hamburg, Germany; Quantikine® Human RAGE ELISA, R&D Diagnostics, Minneapolis, MN) that was applied on a DS2 automated ELISA processing system (Dynex Technologies, Chantilly, VA, United States). The quantification of the results was done by use of a calibration curve with a measuring range of 0.625–80 ng/mL for HMGB1 and 78–5,000 pg/mL for RAGE.
## 2.5.2 Nucleosomes
Nucleosomes were measured from serum tube by the Cell Death Detection ELISAPLUS according to the instructions of the manufacturer (Cat. No. 11774425001, Version 15, Roche Diagnostics, Mannheim, Germany). The quantification of the nucleosomes results was done by use of a calibration curve with a measuring range of 2.83–241.5 ng/mL.
Methods for HMGB1, sRAGE, and nucleosomes were research-use-only (RUO) ELISA assays that were thoroughly validated for their analytical performance and for preanalytically influencing factors before use (Holdenrieder et al., 2001; Lehner et al., 2012; Wittwer et al., 2012). Measurements were done as single determinations. Standard curves and quality controls were included in every run. Serial samples of individuals were run in the same assays. Inter-assay variabilities of the different plates were checked by artificial and serum control materials. Thereby coefficients of variations (CVs) of HMGB1 were between $7.5\%$ and $11.7\%$, of sRAGE between $5.4\%$ and $8.1\%$ and for nucleosomes at $6.4\%$.
## 2.5.3 High sensitive Troponin T (hs-TnT)
Hs-TnT was measured from serum tube quantitatively by the highly sensitive electro-chemiluminescence-immunoassay (ECLIA) technology on a Cobas E411 analyser platform (Roche Diagnostics Deutschland GmbH, Mannheim, Germany). The limit of detection (LoD) of the method was 5 ng/L and the limit of quantification (LoQ) 13 ng/L. The reference limit of Hs-TnT in healthy volunteers (99th percentile) was 14 ng/L (Saenger et al., 2011).
## 2.5.4 High sensitive C-reactive protein (hs-CRP)
Hs-CRP was measured from serum tube by a latex-particle amplified turbidimetry immunoassay method using a Cobas c 501 analyzer (Roche Diagnostics Deutschland GmbH, Mannheim, Germany) with a measurement range of 0.3–200.0 mg/L. The reference limit for adults was <3.0 mg/L (Ridker, 2003).
Methods for hs-cTnT and hs-CRP were IVD-CE labelled methods used in routine diagnostics of the Institute of Laboratory Medicine of the German Heart Centre Munich according the quality control system defined by the guidelines of the German Federal Medical Council (Rili-BÄK).
## 2.6 Statistical analysis
The statistical analysis was performed with IBM SPSS Statistics for Windows version 25 (IBM Corp., Armonk, NY, United States). The Gaussian distribution was tested using the Kolmogorov–Smirnov test. Normally distributed variables are presented as mean ± standard deviation (SD), non-normally distributed data as median and interquartile range (IQR), categorical variables as absolute (n) and relative (%) frequencies. Distributions of immunogenic markers at different visits were compared using Friedman’s test and between two visits by Wilcoxon signed-rank test; for categorical variables Cochran’s Q-test. Correlations between the variables were assessed using Pearson’s correlation or Spearman’s correlation coefficient. We analyzed the influence of the covariates physical performance, age, BMI and marathon finishing time on the increase of the biomarker with a linear regression model. All tests were performed two-sided with a significance level of α = 0.05.
## 3 Results
Out of the initial 100 participants, 51 were included in the present analysis. Reasons for exclusion from final analysis (exclusion reasons during the study period) were: time constraints ($$n = 18$$), internal sicknesses ($$n = 6$$), orthopedic disease ($$n = 7$$), intermediate visit missing for time reasons ($$n = 10$$), marathon not finished ($$n = 4$$), termination of the study due to personal reasons ($$n = 1$$), exclusion criterion for marathon at V-1 ($$n = 1$$) and missing blood samples ($$n = 2$$). Baseline characteristics of the study population [$$n = 51$$, aged 43 ± 9 years ($82\%$ male)] are shown in Table 1. A total of 27 ($52\%$) of the included participants had a positive family history of cardiovascular diseases.
**TABLE 1**
| Age [years] | 43 ± 9 |
| --- | --- |
| Sex Male n [%] | 42 [82.4] |
| Weight [kg] | 75.2 ± 12.6 |
| Height [cm] | 178.2 ± 9.1 |
| BMI [kg/m2] | 23.5 ± 2.5 |
| Body fat [%] | 16.6 ± 5.5 |
| Female | 22.2 ± 5.3 |
| Male | 15.4 ± 4.8 |
| SBP [mmHg] | 122.8 ± 11.3 |
| DBP [mmHg] | 80.7 ± 6.4 |
| Comorbid disease | Comorbid disease |
| Hypertension n [%] | 2 [3.9] |
| Diabetes mellitus n [%] | — |
| Smoker n [%] | 1 [2] |
| Ex-Smoker n [%] | 10 [19.6] |
| Dyslipidemia n [%] | — |
| Obesity (BMI>25 kg/m2) n [%] | 8 [16] |
| Family history of CVD n [%] | 27 [52.9] |
| Performance, running time, and training distance | Performance, running time, and training distance |
| RERmax | 1.1 ± 0.7 |
| HRmax [1*min−1] | 173.7 ± 16.3 |
| Lactatemax [mmol* L−1] | 7.5 ± 2.2 |
| VO2peak [ml*kg−1*min−1] | 46.9 ± 6.5 |
| Female | 41.7 ± 7.7 |
| Male | 48.0 ± 5.7 |
| Marathon time [min] | 237.1 ± 36.7 |
| Female | 264.6 ± 48.0 |
| Male | 231.1 ± 31.4 |
| Weekly training history [km* week−1] | 42.3 ± 21.6 |
| Annual training history [km* year−1] | 1,862.8 ± 917.9 |
## 3.1 Kinetics of the biomarkers
Figure 1 shows the kinetics of HMGB1, sRAGE, nucleosomes, hs-TnT and hs-CRP over the different measurement points. Comparison of the data at baseline to immediately after the marathon showed significant increases in sRAGE with medians 1,132 pg/mL (IQR 960–1,430 pg/mL) and 1,388 pg/mL (1,148–1,686 pg/mL, $p \leq 0.001$), in HMGB1 with medians of 0.82 ng/mL (IQR 0.62–1.27 ng/mL) and 2.79 ng/mL (1.37–3.87 ng/mL, $p \leq 0.001$), in nucleosomes with medians of 9.24 ng/mL (IQR 4.34–15.96 ng/mL) and 56.65 ng/mL (35.12–87.5 ng/mL, $p \leq 0.001$), and hs-TnT with medians of 6 ng/L (IQR 5–8 ng/L) and 27 ng/L (19–45 ng/L, $p \leq 0.001$). Hs-CRP increased with a peak at 24 h after the marathon (medians 0.88 mg/L (IQR 0.64–1.35 mg/L) and 11.5 mg/L (7.21–16.70 mg/L, $p \leq 0.001$) and remained elevated until 72 h 3.8 mg/L (2.34–5.13 mg/L, $p \leq 0.001$) after the marathon (Table2). Most remarkably, some runners had very high levels (>100 ng/L) of hs-TnT and/or some also very high levels of sRAGE, HMGB1 and nucleosomes 24 h post-race. In total, HMGB1 and hs-TnT were still slightly elevated after 24 h and returned to baseline 72 h after the marathon while sRAGE and nucleosomes reached baseline levels already after 24 h post-race.
**FIGURE 1:** *HMGB1 (A), sRAGE (B), nucleoseome (C), hs-TnT (D), and hs-CPR (E) concentration 10–12 weeks, 1–2 weeks before the marathon, immediately after the marathon, 24 h, 72 h after the marathon and 12 weeks after the marathon. Boxplots show median and the interquartile range (IQR); *$p \leq 0.05$ compared to the baseline values.* TABLE_PLACEHOLDER:TABLE 2 HMGB1, sRAGE, nucleosomes, hs-TnT, and hs-CPR concentrations over the study period are summarized in Table 2.
One of the runners had a pre‐race (V-1) and 43 runners a post-race (V1) hs-TnT level above the upper reference limit of 14 ng/L ($p \leq 0.001$). In one runner (♂, 58 years, BMI 25.5 kg/m2, blood pressure $\frac{130}{80}$ mmHg), hs-TnT was elevated at baseline, increased immediately and remained elevated until 72 h after the marathon (Baseline: 24 ng/L; immediately after the marathon: 58 ng/L; 24 h after the marathon: 205 ng/L; 72 h after the marathon: 148 ng/L; 12 weeks after the marathon: 7 ng/L). He revealed neither further cardiovascular risk factors nor symptoms in the course of the study, both initial examinations with cardiopulmonary exercise testing and subsequent examinations were unremarkable.
Overall, none of the runners reported symptoms suggestive of acute myocardial infarction during and/or after the race. Following a comprehensive clinical evaluation at baseline of all included individuals, there was no evidence for any clinically relevant cardiac abnormalities and pathologies.
## 3.2 Correlations of the biomarkers
Regarding the acute effects of marathon running, the following correlations were found: sRAGE immediately after the marathon correlated negatively with the finishing time (r = −0.541; $p \leq 0.001$; $$n = 48$$, Supplementary Figure S1). Changes in hs-TnT and hs-CRP or CK did not correlate with an increase in HMGB1 or nucleosomes (all $p \leq 0.05$). Only, the increase in sRAGE correlated positively with changes in hs-TnT (rs = 0.352, $$p \leq 0.011$$, $$n = 51$$), HMGB1 correlated positive with an increase in nucleosomes ($r = 0.377$, $$p \leq 0.006$$, $$n = 48$$), and a hs-TnT increase correlated significantly with an increase in hs-CRP (rs = 0.322, $$p \leq 0.021$$, $$n = 51$$).
## 3.3 Effects of covariates on the biomarkers
Linear regression models including age, BMI, marathon finishing time and VO2peak (R 2 = 0.271, F [4, 44] = 5.45, $p \leq 0.001$) showed that every 1-min increase in marathon finishing time was associated with a sRAGE decrease at visit 1 of −9.2 pg/mL (regression coefficient b = −9.2, standard error = 2.2, $p \leq 0.001$), other variables were not significantly associated (BMI: b = −3.0 se = 27.2, $$p \leq 0.911$$; VO2peak: b = −19.72, se = 12.1, $$p \leq 0.110$$; age: $b = 1.32$, se = 6.6, $$p \leq 0.843$$).
## 4 Discussion
In this prospective observational study, we observed that sRAGE, HMGB1, nucleosomes and hs-TnT increased significantly directly after the marathon. Only hs-CRP increased- as expected- with a delay of 24 h after the marathon. Our findings concerning the kinetics of those markers after marathon complement and extend the work by Beiter et al. [ 2011]; Scherr et al. [ 2011]; Bekos et al. [ 2016]. The three studies showed an increase of HMGB1, nucleosomes or TnT immediately after exhausting exercise in marathon or treadmill runners. Contrary to our results, Bekos et al. did not demonstrate an increase of RAGE immediately after a marathon (Bekos et al., 2016), but only after a half-marathon The author hypothesized that the missing increase of RAGE immediately after the marathon could be the result of training habits or exercise intensity. In this regard, we tested the hypothesis, that training had an influence on the expression of ICD markers, in our analysis. The fact that we found an association between RAGE and marathon finishing time—slower runners had a higher decrease in the RAGE units—suggests that training conditions influence expression of RAGE markers after a marathon. We could not demonstrate further associations between the expression of RAGE and performance parameters. When comparing studies described in the literature regarding training influences on RAGE expression, it shows inhomogeneous results, with studies indicating an inducible decrease of RAGE after training interventions (Kotani et al., 2011; Drosatos et al., 2021). Nevertheless, two studies indicated an increase of RAGE values within training interventions in cardiovascular risk patients (Choi et al., 2012; Sponder et al., 2018). In one randomized controlled trial with type 2 diabetes patients, RAGE levels increased after a 12-week aerobic exercise intervention (60 min at moderate intensity, 5 times/week) (Choi et al., 2012). In a second prospective study, the influence of long-term physical activity (8 months) on serum sRAGE levels in 98 participants was investigated. RAGE levels increased up to $22\%$ in participants with a long-term performance gain of >$5\%$ compared to <$4.9\%$ (Sponder et al., 2018). Authors of the mentioned studies hypothesized that this variating pattern of decrease and increase in RAGE levels depending on exercise intensity, points towards the capacity of sRAGE to act as a marker for increased or decreased AGEs production (Choi et al., 2012; Sponder et al., 2018). In the present study, we could not demonstrate any association between HMGB1 or Nucleosomes and training intensity. However, previous studies reported that regular training could decrease HMGB1 levels (Gmiat et al., 2017) and that HMGB1 reacts depending on the intensity of the load. This can rather be attributed to an anti-inflammatory process and thus to the chronic long-term effects regarding training. In this case, HMGB1 and nucleosomes are low and sRAGE is rather elevated (as described above). This should be investigated in further studies.
Baseline values such as age, sex or body compositions may contribute to changes in biomarkers released into the circulation. Contrary to the literature, in which more studies indicate an inducible increase in RAGE and nucleosomes in older participants (Pinti et al., 2014; Recabarren-Leiva et al., 2021), we did not find an association between the kinetics of ICD markers and age or sex in our cohort. Only body fat correlated negatively with the increase in post-race sRAGE levels, consistent with existing literature (Dozio et al., 2017; Miranda et al., 2018). A possible explanation for this could be the homogeneous age level (43 ± 9 years) of the participants included in this study. Furthermore, it should be considered that our sample size consists only of 9 females and 42 males, which limits the statistical validity.
It is still unclear whether marathon running can lead to an irreversible cardiac damage or just a reversible cardiac fatigue. Comparing the current results with previous studies assessing the kinetics of TnT and ICM markers in patients, the patterns are significantly different (Apple et al., 1998). In patients after myocardial infarction, TnT and ICM kinetics are characterized by a steep peak increase and followed by prolonged elevation for at least 4–7 days (Apple et al., 1998; Kohno et al., 2009). Furthermore, we found a positive correlation between the increase in sRAGE and hs-TnT (rs = 0.352, $$p \leq 0.011$$), which is a different pattern to that reported in a previous study in patients with non-ST-segment elevation myocardial infarction. This observational study indicated that low levels of sRAGE are associated with high serum levels of cTnI and therefore, suggested that the severity of cardiac damage varies inversely with the levels of sRAGE (McNair et al., 2011). Finally, we plotted the increase in ICD markers from pre to post marathon in relation to the established cardiovascular risk factors (hypertension). We found a significant difference of 0.7 ± 1.0 vs. 2.0 ± 2.0, $$p \leq 0.031$$ in the change of HGMB1 in patients with hypertension compared to without hypertension. However, it should be noted that only two subjects were diagnosed with hypertension.
Since we could not detect any clear cardiac damage, the increases in the markers tend to originate from cells that play a greater role in terms of quantity (muscle cells, endothelia, leukocytes, CRP from liver cells, etc.) and less from perishing cardiac cells. The TnT increase can be explained by a release from muscle cells and the cytosol of the cardiomyocytes ($10\%$ of the TnT) (Bleier et al., 1998). The structurally bound fraction, however, is only released after the cardiomyocytes have perished, which can explain the days-long increases in TnT levels after an MI (Pilzweger and Holdenrieder, 2015). Regarding the ICM markers, a distinction must be made with regard to the reaction of the markers to acute and chronic effects. The acute effects only occur immediately after the marathon and are quickly compensated in a good training situation. In the acute phase, all three markers, nucleosomes, HMGB1 and sRAGE, are elevated. After the impact is absent again, the markers will decrease. At least for HMGB1 it is known that–after cessation of the damaging stimulus–it may contribute to regeneration of the tissue and preservation of the organ function on a local level. Therefore, HMGB1 after an acute marathon is hypothesized to be released from inflammatory cells and exerts protective functions for the heart preventing pathological remodeling (Germani et al., 2007). Recent studies suggest that the release of nucleosomes attributes either a rapid, active release mechanism from immune- or endothelial cells, or a passive shear stress-induced detachment mechanism from the cell surface area (Neuberger and Simon, 2022). The typical cell death mechanisms such as necrosis, apoptosis, and “suicidal NETosis (Brinkmann et al., 2004)”, in which chromatin is released from dying neutrophils after chromatin de-condensation and subsequent rupture of the nuclear and cell membrane, requires hours to complete (Neuberger and Simon, 2022). In contrast, the “vital” NETosis, in which detachment occurs passively from the cell surface can happen very rapidly (Pfister, 2022). As described in Pfister, in the vital NETosis, vesicles, containing nuclear DNA from neutrophils, merge with plasma membrane eventually resulting in an alive anuclear cytoplast (Pfister, 2022). Therefore, it seems to be well imaginable that “vital” NETosis is a major mechanism for the release of nucleosomes after marathon. A prolonged increase of nucleosomes following marathon however could originate either from reduced elimination and/or subsequent cell death originating from damaged tissues. In conclusion, the transient increase in these parameters may not reflect a cardiovascular strain. Further studies should investigate the mentioned other originating cell forms.
## 4.1 Limitations
There are some limitations that should be considered. The sample size of 51 marathon runners is relatively small. However, we investigated our cohort in the context of the training as well as in the recovery period over a after over a 6-month time period. In addition, the recovery period was determined to be 12 weeks after the marathon, which makes it unlikely to detect chronic pathological findings, especially as severe events in a young and healthy study population are rare. Furthermore, cut off values could be addressed only for hs-TnT and CRP. Values for sRAGE, HMGB1 and nucleosome still need to be explored. Future studies should therefore investigate the individual differences over time more closely and examine the cut off values for sRAGE and nucleosomes in order to support clinicians interpreting potential pathological alterations. To evaluate the pathomechanisms of the investigated biomarkers, additional clinical routine markers were taken in account. Nevertheless, specific and unspecific cardiac and pro inflammation pathways with more allocation to a specific organ tend to be more insightful. For example, inclusion of additional biomarkers such as CK-MB, interleukins, myeloperoxidase (MPO), active neutrophil elastase (NE), and proteinase 3 (PR3) should be considered.
## 5 Conclusion
In our study, markers of immunogenic cellular damage increased significantly in the early period after prolonged and strenuous exercise and returned to baseline values after a recovery phase of 24–72 h. Based on our results, it seems as if a marathon event does not lead to necrotic alterations but rather points to transient ischemic alterations instead. Therefore, there seems to be no increased risk for persistent cardiac necrosis if no underlying pathology is present. Furthermore, the training condition of active patients should be observed, as more intensive training or a regular training rhythm can influence the expression of the evaluated biomarkers.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committees of both the Ludwig-Maximilians University Munich (approval number 17-148) and the Technical University Munich (approval number $\frac{218}{17}$S). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AR, JS, AH, MH, and PF contributed to the study design. JScho wrote the draft of the manuscript. AR, JS, and SH supervised and manuscript drafting. AR, JS, AH, MH, PF, PK, and BH revised it critically for important intellectual content. KK and SH, contributed to the data acquisition and performed the analysis of the blood samples. JScho, PK, and BH make the statistical analysis for this manuscript. All authors read and gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2023.1118127/full#supplementary-material
## References
1. Aengevaeren V. L., Froeling M., Hooijmans M. T., Monte J. R., van den Berg-Faay S., Hopman M. T. E.. **Myocardial injury and compromised cardiomyocyte integrity following a marathon run**. *JACC Cardiovasc. imaging* (2020) **13** 1445-1447. DOI: 10.1016/j.jcmg.2019.12.020
2. Albert C. M., Mittleman M. A., Chae C. U., Lee I. M., Hennekens C. H., Manson J. E.. **Triggering of sudden death from cardiac causes by vigorous exertion**. *N. Engl. J. Med.* (2000) **343** 1355-1361. DOI: 10.1056/NEJM200011093431902
3. Andrassy M., Volz H. C., Maack B., Schuessler A., Gitsioudis G., Hofmann N.. **HMGB1 is associated with atherosclerotic plaque composition and burden in patients with stable coronary artery disease**. *PloS one* (2012) **7** e52081. DOI: 10.1371/journal.pone.0052081
4. Apple F. S., Sharkey S. W., Falahati A., Murakami M., Mitha N., Christensen D.. **Assessment of left ventricular function using serum cardiac troponin I measurements following myocardial infarction**. *Clin. chimica acta; Int. J. Clin. Chem.* (1998) **272** 59-67. DOI: 10.1016/s0009-8981(97)00252-0
5. Artner-Dworzak E., Mair J., Seibt I., Koller A., Haid C., Puschendorf B.. **Cardiac troponin T identifies unspecific increases of CKMB after physical exercise**. *Clin. Chem.* (1990) **36** 1853. DOI: 10.1093/clinchem/36.10.1853a
6. Atamaniuk J., Vidotto C., Tschan H., Bachl N., Stuhlmeier K. M., Müller M. M.. **Increased concentrations of cell-free plasma DNA after exhaustive exercise**. *Clin. Chem.* (2004) **50** 1668-1670. DOI: 10.1373/clinchem.2004.034553
7. Balliano A., Hao F., Njeri C., Balakrishnan L., Hayes J. J.. **HMGB1 stimulates activity of polymerase β on nucleosome substrates**. *Biochemistry* (2017) **56** 647-656. DOI: 10.1021/acs.biochem.6b00569
8. Beiter T., Fragasso A., Hudemann J., Niess A. M., Simon P.. **Short-term treadmill running as a model for studying cell-free DNA kinetics**. *Clin. Chem.* (2011) **57** 633-636. DOI: 10.1373/clinchem.2010.158030
9. Bekos C., Zimmermann M., Unger L., Janik S., Hacker P., Mitterbauer A.. **Non-professional marathon running: RAGE axis and ST2 family changes in relation to open-window effect, inflammation and renal function**. *Sci. Rep.* (2016) **6** 32315. DOI: 10.1038/srep32315
10. Bell C. W., Jiang W., Reich C. F., Pisetsky D. S.. **The extracellular release of HMGB1 during apoptotic cell death**. *Am. J. physiology Cell physiology* (2006) **291** C1318-C1325. DOI: 10.1152/ajpcell.00616.2005
11. Bleier J., Vorderwinkler K. P., Falkensammer J., Mair P., Dapunt O., Puschendorf B.. **Different intracellular compartmentations of cardiac troponins and myosin heavy chains: A causal connection to their different early release after myocardial damage**. *Clin. Chem.* (1998) **44** 1912-1918. DOI: 10.1093/clinchem/44.9.1912
12. Borghini A., Giardini G., Tonacci A., Mastorci F., Mercuri A., Mrakic-Sposta S.. **Chronic and acute effects of endurance training on telomere length**. *Mutagenesis* (2015) **30** 711-716. DOI: 10.1093/mutage/gev038
13. Brinkmann V., Reichard U., Goosmann C., Fauler B., Uhlemann Y., Weiss D. S.. **Neutrophil extracellular traps kill bacteria**. *Sci. (New York, NY)* (2004) **303** 1532-1535. DOI: 10.1126/science.1092385
14. Choi K. M., Han K. A., Ahn H. J., Hwang S. Y., Hong H. C., Choi H. Y.. **Effects of exercise on sRAGE levels and cardiometabolic risk factors in patients with type 2 diabetes: A randomized controlled trial**. *J. Clin. Endocrinol. Metabolism* (2012) **97** 3751-3758. DOI: 10.1210/jc.2012-1951
15. Douglas P. S., O'Toole M. L., Woolard J.. **Regional wall motion abnormalities after prolonged exercise in the normal left ventricle**. *Circulation* (1990) **82** 2108-2114. DOI: 10.1161/01.cir.82.6.2108
16. Dozio E., Briganti S., Delnevo A., Vianello E., Ermetici F., Secchi F.. **Relationship between soluble receptor for advanced glycation end products (sRAGE), body composition and fat distribution in healthy women**. *Eur. J. Nutr.* (2017) **56** 2557-2564. DOI: 10.1007/s00394-016-1291-0
17. Drosatos I. A., Tsoporis J. N., Izhar S., Gupta S., Tsirebolos G., Sakadakis E.. **Differential regulation of circulating soluble receptor for advanced glycation end products (sRAGEs) and its ligands S100a8/A9 four weeks post an exercise intervention in a cohort of young army recruits**. *Biomolecules* (2021) **11** 1354. DOI: 10.3390/biom11091354
18. Germani A., Limana F., Capogrossi M. C.. **Pivotal advances: High-mobility group box 1 protein--a cytokine with a role in cardiac repair**. *J. Leukoc. Biol.* (2007) **81** 41-45. DOI: 10.1189/jlb.0306165
19. Gmiat A., Mieszkowski J., Prusik K., Prusik K., Kortas J., Kochanowicz A.. **Changes in pro-inflammatory markers and leucine concentrations in response to Nordic Walking training combined with vitamin D supplementation in elderly women**. *Biogerontology* (2017) **18** 535-548. DOI: 10.1007/s10522-017-9694-8
20. Holdenrieder S., Stieber P., Bodenmuller H., Fertig G., Furst H., Schmeller N.. **Nucleosomes in serum as a marker for cell death**. *Clin. Chem. Lab. Med.* (2001) **39** 596-605. DOI: 10.1515/CCLM.2001.095
21. Irving B. A., Lanza I. R., Henderson G. C., Rao R. R., Spiegelman B. M., Nair K. S.. **Combined training enhances skeletal muscle mitochondrial oxidative capacity independent of age**. *J. Clin. Endocrinol. metabolism* (2015) **100** 1654-1663. DOI: 10.1210/jc.2014-3081
22. Jackson A. S., Pollock M. L.. **Generalized equations for predicting body density of men**. *Br. J. Nutr.* (1978) **40** 497-504. DOI: 10.1079/bjn19780152
23. Kohno T., Anzai T., Naito K., Miyasho T., Okamoto M., Yokota H.. **Role of high-mobility group box 1 protein in post-infarction healing process and left ventricular remodelling**. *Cardiovasc Res.* (2009) **81** 565-573. DOI: 10.1093/cvr/cvn291
24. Koller A., Mair J., Mayr M., Calzolari C., Larue C., Puschendorf B.. **Diagnosis of myocardial injury in marathon runners**. *Ann. N. Y. Acad. Sci.* (1995) **752** 234-235. DOI: 10.1111/j.1749-6632.1995.tb17431.x
25. Kotani K., Caccavello R., Sakane N., Yamada T., Taniguchi N., Gugliucci A.. **Influence of physical activity intervention on circulating soluble receptor for advanced glycation end products in elderly subjects**. *J. Clin. Med. Res.* (2011) **3** 252-257. DOI: 10.4021/jocmr704w
26. Koyama M., Kurumizaka H.. **Structural diversity of the nucleosome**. *J. Biochem.* (2017) **163** 85-95. DOI: 10.1093/jb/mvx081
27. Lehner J., Wittwer C., Fersching D., Siegele B., Holdenrieder S., Stoetzer O. J.. **Methodological and preanalytical evaluation of an HMGB1 immunoassay**. *Anticancer Res.* (2012) **32** 2059-2062. PMID: 22593488
28. Małek Ł A., Barczuk-Falęcka M., Werys K., Czajkowska A., Mróz A., Witek K.. **Cardiovascular magnetic resonance with parametric mapping in long-term ultra-marathon runners**. *Eur. J. radiology* (2019) **117** 89-94. DOI: 10.1016/j.ejrad.2019.06.001
29. Matsumoto H., Matsumoto N., Ogura H., Shimazaki J., Yamakawa K., Yamamoto K.. **The clinical significance of circulating soluble RAGE in patients with severe sepsis**. *J. Trauma Acute Care Surg.* (2015) **78** 1086-1093. DOI: 10.1097/TA.0000000000000651
30. McNair E. D., Wells C. R., Qureshi A. M., Pearce C., Caspar-Bell G., Prasad K.. **Inverse association between cardiac troponin-I and soluble receptor for advanced glycation end products in patients with non-ST-segment elevation myocardial infarction**. *Int. J. angiology official Publ. Int. Coll. Angiology, Inc.* (2011) **20** 49-54. DOI: 10.1055/s-0031-1272552
31. Miranda E. R., Fuller K. N. Z., Perkins R. K., Kroeger C. M., Trepanowski J. F., Varady K. A.. **Endogenous secretory RAGE increases with improvements in body composition and is associated with markers of adipocyte health**. *Nutr. Metabolism Cardiovasc. Dis.* (2018) **28** 1155-1165. DOI: 10.1016/j.numecd.2018.07.009
32. Möbius-Winkler S., Uhlemann M., Adams V., Sandri M., Erbs S., Lenk K.. **Coronary collateral growth induced by physical exercise: Results of the impact of intensive exercise training on coronary collateral circulation in patients with stable coronary artery disease (EXCITE) trial**. *Circulation* (2016) **133** 1438-1448. DOI: 10.1161/CIRCULATIONAHA.115.016442
33. Neuberger E. W. I., Simon P.. **Cell-free DNA in sports medicine: Implications for clinical laboratory medicine**. *LaboratoriumsMedizin* (2022) **46** 295-300. DOI: 10.1515/labmed-2022-0027
34. Nystoriak M. A., Bhatnagar A.. **Cardiovascular effects and benefits of exercise**. *Front. cardiovasscular Med.* (2018) **5** 135. DOI: 10.3389/fcvm.2018.00135
35. Pfister H.. **Neutrophil extracellular traps and neutrophil-derived extracellular vesicles: Common players in neutrophil effector functions**. *Diagn. (Basel, Switz.* (2022) **12** 1715. DOI: 10.3390/diagnostics12071715
36. Pilzweger C., Holdenrieder S.. **Circulating HMGB1 and RAGE as clinical biomarkers in malignant and autoimmune diseases**. *Diagnostics* (2015) **5** 219-253. DOI: 10.3390/diagnostics5020219
37. Pinti M., Cevenini E., Nasi M., De Biasi S., Salvioli S., Monti D.. **Circulating mitochondrial DNA increases with age and is a familiar trait: Implications for “inflamm-aging”**. *Eur. J. Immunol.* (2014) **44** 1552-1562. DOI: 10.1002/eji.201343921
38. Recabarren-Leiva D., Burgos C. F., Hernández B., Garcïa-García F. J., Castro R. I., Guzman L.. **Effects of the age/rage axis in the platelet activation**. *Int. J. Biol. Macromol.* (2021) **166** 1149-1161. DOI: 10.1016/j.ijbiomac.2020.10.270
39. Regwan S., Hulten E. A., Martinho S., Slim J., Villines T. C., Mitchell J.. **Marathon running as a cause of troponin elevation: A systematic review and meta-analysis**. *J. interventional Cardiol.* (2010) **23** 443-450. DOI: 10.1111/j.1540-8183.2010.00575.x
40. Ridker P. M.. **Clinical application of C-reactive protein for cardiovascular disease detection and prevention**. *Circulation* (2003) **107** 363-369. DOI: 10.1161/01.cir.0000053730.47739.3c
41. Roeh A., Bunse T., Lembeck M., Handrack M., Pross B., Schoenfeld J.. **Running effects on cognition and plasticity (ReCaP): Study protocol of a longitudinal examination of multimodal adaptations of marathon running**. *Res. sports Med. (Print)* (2019) **28** 241-255. DOI: 10.1080/15438627.2019.1647205
42. Saenger A. K., Beyrau R., Braun S., Cooray R., Dolci A., Freidank H.. **Multicenter analytical evaluation of a high-sensitivity troponin T assay**. *Clin. chimica acta; Int. J. Clin. Chem.* (2011) **412** 748-754. DOI: 10.1016/j.cca.2010.12.034
43. Scherr J., Braun S., Schuster T., Hartmann C., Moehlenkamp S., Wolfarth B.. **72-h kinetics of high-sensitive troponin T and inflammatory markers after marathon**. *Med. Sci. sports Exerc.* (2011) **43** 1819-1827. DOI: 10.1249/MSS.0b013e31821b12eb
44. Sedaghat-Hamedani F., Kayvanpour E., Frankenstein L., Mereles D., Amr A., Buss S.. **Biomarker changes after strenuous exercise can mimic pulmonary embolism and cardiac injury--a metaanalysis of 45 studies**. *Clin. Chem.* (2015) **61** 1246-1255. DOI: 10.1373/clinchem.2015.240796
45. Sorensen M. V., Pedersen S., Mogelvang R., Skov-Jensen J., Flyvbjerg A.. **Plasma high-mobility group box 1 levels predict mortality after ST-segment elevation myocardial infarction**. *JACC Cardiovasc Interv.* (2011) **4** 281-286. DOI: 10.1016/j.jcin.2010.10.015
46. Sponder M., Campean I. A., Emich M., Fritzer-Szekeres M., Litschauer B., Graf S.. **Long-term physical activity leads to a significant increase in serum sRAGE levels: A sign of decreased AGE-mediated inflammation due to physical activity?**. *Heart vessels* (2018) **33** 893-900. DOI: 10.1007/s00380-018-1125-5
47. Stahl J. B., Hoecherl E. F., Durner J., Nagel D., Wolf K., Holdenrieder S.. **HMGB1, nucleosomes and sRAGE as new prognostic serum markers after multiple trauma**. *LaboratoriumsMedizin* (2016) **40** 165-173. DOI: 10.1515/labmed-2016-0004
48. Stoetzer O. J., Wittwer C., Lehner J., Fahmueller Y. N., Kohles N., Fersching D. M. I.. **Circulating nucleosomes and biomarkers of immunogenic cell death as predictive and prognostic markers in cancer patients undergoing cytotoxic therapy**. *Expert Opin. Biol. Ther.* (2012) **12** S217-S224. DOI: 10.1517/14712598.2012.689280
49. Volz H. C., Seidel C., Laohachewin D., Kaya Z., Müller O. J., Pleger S. T.. **HMGB1: The missing link between diabetes mellitus and heart failure**. *Basic Res. Cardiol.* (2010) **105** 805-820. DOI: 10.1007/s00395-010-0114-3
50. Wittwer C., Lehner J., Fersching D., Siegele B., Stoetzer O. J., Holdenrieder S.. **Methodological and preanalytical evaluation of a RAGE immunoassay**. *Anticancer Res.* (2012) **32** 2075-2078. PMID: 22593491
|
---
title: 'Iron status and obesity-related traits: A two-sample bidirectional Mendelian
randomization study'
authors:
- Zengyuan Zhou
- Hanyu Zhang
- Ke Chen
- Changqi Liu
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9971727
doi: 10.3389/fendo.2023.985338
license: CC BY 4.0
---
# Iron status and obesity-related traits: A two-sample bidirectional Mendelian randomization study
## Abstract
### Background
The association between iron status and obesity-related traits is well established by observational studies, but the causality is uncertain. In this study, we performed a two-sample bidirectional Mendelian randomization analysis to investigate the causal link between iron status and obesity-related traits.
### Methods
*The* genetic instruments strongly associated with body mass index (BMI), waist-hip ratio (WHR), serum ferritin, serum iron, transferrin saturation (TSAT), and total iron-binding capacity (TIBC) were obtained through a series of screening processes from summary data of genome-wide association studies (GWAS) of European individuals. We used numerous MR analytical methods, such as inverse-variance weighting (IVW), MR-Egger, weighted median, and maximum likelihood to make the conclusions more robust and credible, and alternate methods, including the MR-Egger intercept test, Cochran’s Q test, and leave-one-out analysis to evaluate the horizontal pleiotropy and heterogeneities. In addition, the MR-PRESSO and RadialMR methods were utilized to identify and remove outliers, eventually achieving reduced heterogeneity and horizontal pleiotropy.
### Results
The results of IVW analysis indicated that genetically predicted BMI was associated with increased levels of serum ferritin (β: 0.077, $95\%$ CI: 0.038, 0.116, $$P \leq 1.18$$E-04) and decreased levels of serum iron (β: -0.066, $95\%$ CI: -0.106, -0.026, $$P \leq 0.001$$) and TSAT (β: -0.080, $95\%$ CI: -0.124, -0.037, $$P \leq 3.08$$E-04), but not associated with the levels of TIBC. However, the genetically predicted WHR was not associated with iron status. Genetically predicted iron status were not associated with BMI and WHR.
### Conclusions
In European individuals, BMI may be the causative factor of serum ferritin, serum iron, and TSAT, but the iron status does not cause changes in BMI or WHR.
## Introduction
Obesity and iron deficiency (ID) are the most common nutritional diseases worldwide and have attracted great interest currently [1]. Over the decades, the prevalence of obesity has substantially increased and reached pandemic levels [2], and ID anemia has become one of the leading causes of years lived with disability [3]. The high prevalence of obesity and ID can lead to substantial health and economic burdens [4, 5].
As a nutritionally essential trace element, iron can affect the physical performance in hematopoiesis, transport oxygen, and various metabolic pathways [6]. Given that, accumulating evidence has suggested the correlation between iron status and obesity since 1962 [7]. Observational studies have found that obesity can predict lower iron absorption and affect iron metabolism, and body fat distribution (central obesity) is more likely to cause abnormal iron metabolism in women [8, 9]. Additionally, previous studies suggested that inflammatory response characterized by obesity is involved in the regulation of iron metabolism [10, 11]. A cross-sectional study found that serum ferritin was positively associated with lipid metabolism and abdominal obesity [12]. Some studies have shown that iron homeostasis plays a crucial role in lipid accumulation and is a manifestation of obesity [13]. Recently, studies indicated that the association between obesity and iron metabolism is reciprocal [14]. Although observational evidence has recognized the association between obesity and iron status, it is difficult to assess the causal association.
The causal estimate of a modifiable phenotype or exposure to a disease is often of public health interest, but it is difficult or impractical to investigate the causality through randomized controlled trials (RCT). Mendelian randomization (MR) is an analytic approach using genetic variants as instrumental variables (IVs) for exposure, which can reduce the influence of confounders due to the premise that genotype only indirectly affects the disease state and is allocated during meiosis [15]. It can provide a novel way to assess the causal association between potential factors and disease and solve existing reverse causation or confounding factors in observational epidemiology [16]. To obtain causal estimates, MR analysis needs to fulfill the following three key assumptions: [1] genetic variants associated with the phenotype or exposure; [2] genetic variants have no association with confounding factors, and [3] genetic variants have no association with the disease or outcome except through the exposure [17]. Thus, this method could provide new opportunities to elucidate the causal relationship between iron status and obesity-related traits.
In this study, we used body mass index (BMI) and waist and hip ratio (WHR) as obesity-related traits and four iron-related biomarkers for clinical evaluation of iron status to explore the causal relationship between iron status and obesity-related traits in European individual. We aimed to use the bidirectional MR analysis to supplement the findings of observational studies on whether iron status has an effect on obesity and vice versa.
## Study design
This study described the bidirectional causality assessment of the relationships between iron status and obesity-related traits. Genetic instruments are required for both iron status-related biomarkers and obesity-related traits, and MR analysis was performed in both directions. The data of the instrumental variable are publicly available from the summary statistics among European populations, and no ethical approval was required. The overall design of our study is shown in Figure 1.
**Figure 1:** *A flow diagram of the process in the current Mendelian randomization analysis.*
## Iron status
We obtained the genetic instruments based on the latest genome-wide association study (GWAS) data on iron status, which performed a meta-analysis combining GWAS results from Iceland (deCODE genetics), United Kingdom (INTERVAL study) and Denmark (Danish Blood Donor Study) for four iron-related biomarkers: serum ferritin (up to $$n = 246$$,139 individuals), serum iron (up to $$n = 163$$,511 individuals), TIBC (up to $$n = 135$$,430 individuals), and TSAT (up to $$n = 131$$,471 individuals) [18]. For the iron status, serum ferritin reflects stored iron, serum iron reflects circulating iron, and transferrin saturation reflects iron availability, which is derived as serum iron divided by the total iron-binding capacity (TIBC) from Genetics of Iron Status (GIS), and the TIBC as measured directly.
## Obesity-related traits
Genetic instruments with obesity-related traits were obtained from the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. The BMI data were derived from a two-stage meta-analysis of 322,154 individuals of European descent [19], and WHR data were obtained from a meta-analysis of GWAS in 212,244 European individuals [20]. Supplementary Table 1 summarizes the data sources used in this study.
Genetic variants were extracted from each published GWAS at the genome-wide significance level ($P \leq 5$×10−8) for each exposure. To ensure that each single nucleotide polymorphism (SNP) was independent of the other, we assessed the linkage disequilibrium (LD) between the SNPs, satisfying the criteria of r2 = 0.001 and kb=5,000. If an exposure-associated index SNP was absent from the outcome data set, a proxy SNP was not used. In addition, we performed variant harmonization of the genetic variants by combining two or more independently generated data. It is necessary to ensure that genetic variants from all publicly available datasets are consistent and allele mismatches are avoided, which can lead to bias in the causal effect estimate [21]. Palindromic and ambiguous SNPs were removed from the analysis. The R2 and F values for each trait were calculated based on the derived summary statistics (Supplementary Tables 2-7). The F statistic can reflect the strength of instrumental variants (IVs), and a threshold of F < 10 has been used to define a weak IV, which is well-accepted in the field. Thus, we screened the F statistic of IVs > 10 in our analysis, ensuring that the relative bias in effect estimations caused by weak IVs was < $10\%$ [22]. This was calculated using the following formula: R2, which is as an indicator of power for MR studies, is defined as the proportion of variability in the exposure explained by genetic variants; k is the number of instruments used in the model; and n is the sample size.
## Mendelian randomization
In this study, we consider several methods, including Inverse variance weighted (IVW), MR-Egger regression, weighted median, and maximum likelihood, to weigh the estimated impact between exposure and outcome [23]. The IVW was used as the most common method to assess the MR estimates for the causal effect under the assumption of balanced pleiotropy [24]. The multiplicative random-effects model IVW was used as the primary analysis to avoid heterogeneity bias [25]. Meanwhile, other MR methods have also been used to provide more accurate estimates, one of which is the MR-Egger regression, which differs from the IVW method by allowing a non-zero intercept. Additionally, the intercept term can be used to predict the directional pleiotropic effects [26]. The weighted median estimator method was consistent even if $50\%$ of the genetic variants were invalid [27]. The maximum likelihood method estimates the probability distribution parameters by maximizing the likelihood function, and the bias is small [28]. Indeed, these methods have relatively low statistical power compared to the IVW method, which was mainly designed to confirm consistent effects estimates seen in the main IVW estimate to determine the reliability of the results.
## Sensitivity analysis
After the causal effect was detected using the above methods, we performed sensitivity analyses to assess the robustness of these findings to the assumption of balanced pleiotropy, including heterogeneity and the pleiotropy effect. Cochran’s Q was used in our study to identify heterogeneity in MR analysis [29]. Additionally, we focused on methods that can test the bias from the horizontal pleiotropic effect. The MR-Egger intercept represents the average pleiotropic effect across genetic variants. If the intercept differs from zero (MR-Egger test), there is directional pleiotropy evidence [30]. Then, the MR pleiotropy residual sum and outlier (MR-PRESSO) test removed the variant in question and refused IVW regression to identify horizontal pleiotropic outliers in the MR context [31]. To improve the visualization of the IVW, we performed radial variants of the IVW instead of a scatter plot. The RadialMR imaging was used to complete the automated detection of outliers [32]. The leave-one-out analysis was performed to assess whether the potentially pleiotropic SNPs affected causal estimates. In addition, we used MR Steiger to infer the direction of causality in our hypothesis. The threshold of the P-value was < 0.05, which is likely to be correct about the causal direction [33].
## Statistical analyses
The TwoSampleMR package [34] and the RadialMR package [32] were used to perform the analysis in R (version 4.0.3). A $P \leq 0.05$ was defined as a threshold for statistical significance. The Bonferroni correction redefined the threshold for statistical significance in multiple testing ($P \leq 0.05$/n), where n refers to the number of MR tests [35]. The adjusted P-values were 0.025 ($\frac{0.05}{2}$) for the forward MR analysis and 0.0125 ($\frac{0.05}{4}$) for the reverse MR analysis.
## Iron status and obesity-related traits
In this study, the four biomarkers of iron status constituted the exposure and obesity-related traits were set as the outcomes. A total of 23 SNPs of serum ferritin, 6 SNPs of serum iron, 8 SNPs of TIBC, and 6 SNPs of TSAT remained (Supplementary Tables 2-7) after filtering and variant harmonization. The F statistics for the four biomarkers of iron status ranged from 10 to 86. The detailed MR estimates from different methods of assessing the causal effect are available in Supplementary Tables 8-13, which are presented as forest plots (Figures 2, 3).
**Figure 2:** *Forest plots of Mendelian randomization analyses of the association between genetically predicted iron status and obesity-related traits. (A) serum ferritin – BMI and WHR; (B) serum iron – BMI and WHR; (C) TIBC – BMI and WHR; (D) TSAT - BMI and WHR. Data are expressed as raw β with 95% CI. IVW, inverse variance–weighted method; fe, fixed effects model; re, multiplicative random effects model; BMI, body mass index; WHR, waist-hip ratio; TIBC, total iron-binding capacity; TSAT, transferrin saturation.* **Figure 3:** *Forest plots of Mendelian randomization analyses of the association between genetically predicted iron status and obesity-related traits. (A) BMI - serum ferritin, serum iron, TIBC and TSAT; (B) WHR - serum ferritin, serum iron, TIBC and TSAT. Data are expressed as raw β with 95% CI.*
With the IVW method as the primary analysis, there was no association between the genetically predicted iron status biomarkers and the risk of obesity, including serum ferritin for BMI (β = 0.016, $95\%$ CI: -0.030, 0.063, $$P \leq 0.490$$) and WHR (β = 0.001, $95\%$ CI: -0.060, 0.061, $$P \leq 0.985$$); serum iron for BMI (β = -0.055, $95\%$ CI: -0.137, 0.027, $$P \leq 0.190$$) and WHR (β = -0.070, $95\%$ CI: -0.148, 0.009, $$P \leq 0.081$$); TIBC for BMI (β = -0.031, $95\%$ CI: -0.107, 0.045, $$P \leq 0.424$$) and WHR (β = -0.009, $95\%$ CI: -0.094, 0.077, $$P \leq 0.838$$); TSAT for BMI (β = 0, $95\%$ CI: -0.091, 0.091, $$P \leq 0.993$$) and WHR (β = 0.020, $95\%$ CI: -0.041, 0.081, $$P \leq 0.526$$). The MR Egger, weighted median, and maximum likelihood had the same effects as the IVW estimates. Moreover, the sensitivity analyses suggested no evidence of pleiotropy or heterogeneity (Table 1 and Supplementary Tables 8-11). The MR-PRESSO and RadialMR outliers were removed to obtain the robust results (Supplementary Tables 2-5 and Supplementary Figure 1). The result of the leave-one-out plot indicated that excluding any single SNP of the genetic variants hardly biased the outcome (Supplementary Figure 3). The results of the MR Steiger directionality test illustrated the accuracy of our estimate of causal direction (Supplemental Tables 2-5).
**Table 1**
| OutcomeExposure | Unnamed: 1 | BMI | BMI.1 | BMI.2 | WHR | WHR.1 | WHR.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| OutcomeExposure | method | SNPs | β (95% CI) | P | SNPs | β (95% CI) | P |
| Ferritin | IVW (fe) | 19 | 0.016(-0.039, 0.072) | 0.561 | 20 | 0.001(-0.058, 0.060) | 0.984 |
| | IVW (re) | 19 | 0.016(-0.030, 0.063) | 0.490 | 20 | 0.001(-0.060, 0.061) | 0.985 |
| | MR Egger | 19 | 0.018(-0.103, 0.140) | 0.774 | 20 | 0.036(-0.100, 0.172) | 0.608 |
| | Weighted median | 19 | 0.039(-0.034, 0.113) | 0.296 | 20 | 0.03(-0.055, 0.114) | 0.492 |
| | Maximum likelihood | 19 | 0.017(-0.039, 0.072) | 0.559 | 20 | 0.001(-0.059, 0.060) | 0.984 |
| Iron | IVW (fe) | 5 | -0.055(-0.143, 0.033) | 0.221 | 5 | -0.07(-0.164, 0.025) | 0.149 |
| | IVW (re) | 5 | -0.055(-0.137, 0.027) | 0.190 | 5 | -0.07(-0.148, 0.009) | 0.081 |
| | MR Egger | 5 | 0.056(-0.187, 0.299) | 0.682 | 5 | -0.112(-0.385, 0.160) | 0.479 |
| | Weighted median | 5 | -0.045(-0.148, 0.058) | 0.390 | 5 | -0.034(-0.149, 0.082) | 0.566 |
| | Maximum likelihood | 5 | -0.055(-0.144, 0.033) | 0.220 | 5 | -0.07(-0.165, 0.025) | 0.150 |
| TIBC | IVW (fe) | 8 | -0.031(-0.100, 0.038) | 0.382 | 8 | -0.009(-0.085, 0.067) | 0.818 |
| | IVW (re) | 8 | -0.031(-0.107, 0.045) | 0.424 | 8 | -0.009(-0.094, 0.077) | 0.838 |
| | MR Egger | 8 | -0.055(-0.295, 0.185) | 0.670 | 8 | 0.12(-0.131, 0.371) | 0.384 |
| | Weighted median | 8 | -0.032(-0.126, 0.063) | 0.511 | 8 | 0.028(-0.068, 0.124) | 0.564 |
| | Maximum likelihood | 8 | -0.031(-0.101, 0.039) | 0.379 | 8 | -0.009(-0.086, 0.068) | 0.818 |
| TSAT | IVW (fe) | 5 | 0(-0.069, 0.070) | 0.991 | 5 | 0.02(-0.055, 0.094) | 0.602 |
| | IVW (re) | 5 | 0(-0.091, 0.091) | 0.993 | 5 | 0.02(-0.041, 0.081) | 0.526 |
| | MR Egger | 5 | 0.059(-0.271, 0.389) | 0.749 | 5 | 0.075(-0.164, 0.315) | 0.581 |
| | Weighted median | 5 | 0.029(-0.060, 0.118) | 0.521 | 5 | 0.048(-0.044, 0.139) | 0.309 |
| | Maximum likelihood | 5 | 0(-0.070, 0.071) | 0.991 | 5 | 0.02(-0.055, 0.094) | 0.601 |
## Obesity-related traits and iron status
For the reverse MR, the obesity-related traits constituted the exposure and the biomarkers of iron status were the outcomes. We screened 58 and 24 SNPs associated with BMI and WHR (Supplementary Tables 6-7). The F statistics for the obesity-related traits ranged from 10 to 97. The primary analysis revealed that obesity-related traits and iron status had a significant causal association after excluding the outlier SNPs. Specifically, the BMI for serum ferritin is β = 0.077, $95\%$ CI: 0.038,0.116, $$P \leq 1.18$$E-04; for serum iron β = -0.066, $95\%$ CI: -0.106, -0.026, $$P \leq 0.001$$ and for TSAT β = -0.080, $95\%$ CI: -0.124, -0.037, $$P \leq 3.08$$E-04. However, we found that genetically predicted BMI was not associated with TIBC. The robustness of these results has been validated with additional methods (Table 2 and Supplementary Table 12). Cochran’s Q and MR Egger intercept tests showed no evidence of instrumental heterogeneity and horizontal pleiotropy for serum ferritin, serum iron and TSAT ($P \leq 0.05$). Leave-one-out analysis indicated that the causality was not driven by any SNP (Supplemental Figure 4).
**Table 2**
| OutcomeExposure | Unnamed: 1 | Ferritin | Ferritin.1 | Ferritin.2 | Iron | Iron.1 | Iron.2 | TIBC | TIBC.1 | TIBC.2 | TSAT | TSAT.1 | TSAT.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| OutcomeExposure | method | SNPs | β (95% CI) | P | SNPs | β (95% CI) | P | SNPs | β (95% CI) | P | SNPs | β (95% CI) | P |
| BMI | IVW (fe) | 48 | 0.077 (0.037, 0.117) | 1.46E-04 | 48 | -0.066 (-0.112, -0.021) | 0.004 | 53 | 0.048 (-0.004, 0.100) | 0.068 | 55 | -0.08 (-0.129, -0.031) | 0.001 |
| | IVW (re) | 48 | 0.077 (0.038, 0.116) | 1.18E-04 | 48 | -0.066 (-0.106, -0.026) | 0.001 | 53 | 0.048 (0, 0.096) | 0.052 | 55 | -0.08 (-0.124, -0.037) | 3.08E-04 |
| | MR Egger | 48 | 0.128 (0.013, 0.242) | 0.035 | 48 | -0.116 (-0.248, 0.016) | 0.091 | 53 | 0.051 (-0.098, 0.201) | 0.505 | 55 | -0.161 (-0.303, -0.019) | 0.031 |
| | Weighted median | 48 | 0.086 (0.027, 0.144) | 0.004 | 48 | -0.056 (-0.122, 0.009) | 0.089 | 53 | 0.080 (-0.001, 0.161) | 0.052 | 55 | -0.086 (-0.160, -0.013) | 0.021 |
| | Maximum likelihood | 48 | 0.078 (0.038, 0.118) | 1.35E-04 | 48 | -0.065 (-0.111, -0.020) | 0.005 | 53 | 0.049 (-0.004, 0.101) | 0.068 | 55 | -0.08 (-0.129, -0.030) | 0.002 |
| WHR | IVW (fe) | 20 | 0.082(0.018, 0.146) | 0.012 | 20 | -0.05(-0.125, 0.025) | 0.188 | 21 | 0.097(0.013, 0.182) | 0.024 | 18 | -0.035(-0.118, 0.047) | 0.401 |
| | IVW (re) | 20 | 0.082(0.023, 0.141) | 0.006 | 20 | -0.05(-0.122, 0.022) | 0.172 | 21 | 0.097(-0.002, 0.196) | 0.055 | 18 | -0.035(-0.124, 0.053) | 0.431 |
| | MR Egger | 20 | 0.051(-0.227, 0.330) | 0.722 | 20 | -0.145(-0.479, 0.189) | 0.407 | 21 | -0.04(-0.492, 0.411) | 0.863 | 18 | 0.036(-0.313, 0.386) | 0.841 |
| | Weighted median | 20 | 0.063(-0.028, 0.153) | 0.174 | 20 | -0.04(-0.150, 0.069) | 0.47 | 21 | 0.109(-0.018, 0.236) | 0.092 | 18 | -0.01(-0.133, 0.114) | 0.875 |
| | Maximum likelihood | 20 | 0.085(0.020, 0.149) | 0.01 | 20 | -0.049(-0.124, 0.027) | 0.205 | 21 | 0.101(0.015, 0.186) | 0.022 | 18 | -0.035(-0.118, 0.049) | 0.416 |
In addition, WHR was associated with the serum ferritin levels (β = 0.082, $95\%$CI: 0.023, 0.141, $$P \leq 0.006$$). However, MR Egger and weighted median did not provide consistent results (Table 2 and Supplemental Tables 13). The results of the MR Steiger directionality test illustrated the accuracy of the causal direction of the current MR analysis. The forest plots and scatter plots of MR analysis were used to present the data (Figures 2 – 5).
**Figure 4:** *Scatter plots of Mendelian randomization analyses of the association between genetically predicted obesity-related traits and iron status. (A) BMI - serum ferritin, serum iron, TIBC and TSAT; (B) WHR - serum ferritin, serum iron, TIBC and TSAT.* **Figure 5:** *Scatter plots of Mendelian randomization analyses of the association between genetically predicted iron status and obesity-related traits. (A) serum ferritin – BMI and WHR; (B) serum iron – BMI and WHR; (C) TIBC – BMI and WHR; (D) TSAT - BMI and WHR.*
## Discussion
In this two-sample bidirectional MR study, we used the summary GWAS data from the European population to investigate the causal association between iron status and obesity-related traits. Our findings showed that genetically predicted iron status biomarkers were not associated with a higher risk of obesity. For the reverse analysis, genetically predicted BMI was associated with decreased serum iron and TSAT levels and increased serum ferritin levels. Moreover, the inference of causality between WHR and serum ferritin levels cannot be confirmed using additional methods, suggesting that the evidence is insufficient and conclusion should be drawn carefully. To the best of our knowledge, this is the first study on causality between iron status and obesity-related traits using MR approach. The stability of our results lies in the following: We identified and removed the outliers using multiple methods and obtained a stable positive result. In addition, the same results can be obtained using various MR methods, which can increase the stability of the results.
A previous study indicated that iron content in the adipose tissue of obese patients appears to be increased [36]. A cross-sectional study showed a high burden of obesity in women of reproductive age with iron deficiency anemia (IDA) [37]. In addition, animal studies revealed that dietary iron deficiency could modulate diet-induced weight gain in mice [38]. However, numerous other factors contribute to obesity, including total energy intake. Thus, the previous observational studies might influence by unmeasured confounders. Our findings suggest no causal association between iron status biomarkers and the risk of obesity. Further investigation is still needed into the effect of iron status on obesity and the potential mechanism.
We found evidence that BMI was associated with decreased serum iron and TSAT levels and increased serum ferritin levels. Our results are in line with previous clinical studies that the serum iron reserves in obese participants are consistently higher, and the decrease in BMI improves the iron state and absorption by reducing hepcidin levels [39, 40]. In addition, epidemiological studies showed that BMI was negatively associated with iron, TIBC, and TSAT levels and positively associated with serum ferritin levels [41, 42]. Potential mechanism might be proposed to explain this association. The association between BMI and iron status could be attributed to inflammation and hepcidin. In the set of ongoing inflammation, the serum ferritin levels increase while the serum iron levels decrease, which impacts iron homeostasis [43]. Some researchers suggest that hepcidin may involve in mediating the pathways between inflammation and iron status [44]. Hepcidin is regarded as a central regulatory peptide of intestinal iron absorption and iron recycling and is regulated by interleukin-6 (IL-6) secreted by adipose tissues [45]. Therefore, we speculated that obesity might reduce iron absorption and storage by mediating inflammation and decreasing hepcidin levels. In addition, Failla et al. observed lower concentrations of iron in obese mice, which may be due to an adaptive response to expanded blood volume [46]. Our results provide a theoretical basis for further research on lowering BMI to the prevention of abnormal iron metabolism. Further studies should provide insights into the pathogenesis of BMI in ID.
In addition, the causal estimate of WHR and serum ferritin did not yield stable results in our analysis. Studies on the impact of WHR on the iron status are still limited. Waist circumference, as the main indicator of concentric obesity, is suggested to be positively but insignificantly associated with plasma iron, TSAT, and ferritin concentrations [47]. The potential mechanism may be that the massive release of leptin from the adipose tissue causes increased hepcidin release, resulting in iron depletion or low circulating iron concentrations [47]. Nevertheless, more studies are needed to confirm this result.
The main strength of our study is the use of inborn genetic instruments to proxy the exposure trait, which could be less influenced by confounding or reverse causation. However, our study has several limitations. First, the data of genetic variants relied primarily on the GWAS of iron status and obesity-related traits from European descent, which may limit our finding from being fully representative of the whole population. However, restricting participants’ descent can minimize the risk of confounding by population admixture. Second, the use of summary-level data limits the range of analyses that can be performed, including the nonlinear relationship between exposure and outcomes and the stratified analysis of either age or sex. Last, we are not able to estimate the degree of overlap of participants between the datasets in the current study. However, the genetic instrument used in the current study was judged by F-statistics, with a strong instrument defined as an F > 10, which could minimize the bias from sample overlap [48].
## Conclusion
In summary, we found that the genetically predicted BMI may contribute to the decreased serum ferritin levels and increased serum iron and TSAT levels. However, there was no evidence to support the causality of iron status on the risk of obesity. This may help inform clinicians that early intervention to improve iron status should involve appropriate weight control, which may be an effective measure and potential strategy.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Author contributions
ZZ, HZ conceived and designed the study. ZZ, HZ, KC, and CL performed experiments and wrote the manuscript. ZZ interpreted the data and prepared the manuscript. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.985338/full#supplementary-material
## References
1. Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, Lee A. **Health effects of overweight and obesity in 195 countries over 25 years**. *New Engl J Med* (2017) **377** 13-27. DOI: 10.1056/NEJMoa1614362
2. Blüher M. **Obesity: Global epidemiology and pathogenesis**. *Nat Rev Endocrinol* (2019) **15**. DOI: 10.1038/s41574-019-0176-8
3. Disease GBD, Injury I, Prevalence C. **Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: A systematic analysis for the global burden of disease study 2016**. *Lancet* (2017) **390**. DOI: 10.1016/S0140-6736(17)32154-2
4. Zimmermann MB, Hurrell RF. **Nutritional iron deficiency**. *Lancet* (2007) **370**. DOI: 10.1016/s0140-6736(07)61235-5
5. Dai H, Alsalhe TA, Chalghaf N, Riccò M, Bragazzi NL, Wu J. **The global burden of disease attributable to high body mass index in 195 countries and territories, 1990-2017: An analysis of the global burden of disease study**. *PLoS Med* (2020) **17**. DOI: 10.1371/journal.pmed.1003198
6. McClung JP. **Iron, zinc, and physical performance**. *Biol Trace Element Res* (2019) **188**. DOI: 10.1007/s12011-018-1479-7
7. Wenzel BJ, Stults HB, Mayer J. **Hypoferraemia in obese adolescents**. *Lancet* (1962) **2**. DOI: 10.1016/s0140-6736(62)90110-1
8. Stoffel NU, El-Mallah C, Herter-Aeberli I, Bissani N, Wehbe N, Obeid O. **The effect of central obesity on inflammation, hepcidin, and iron metabolism in young women**. *Int J Obes (2005)* (2020) **44**. DOI: 10.1038/s41366-020-0522-x
9. Zimmermann MB, Zeder C, Muthayya S, Winichagoon P, Chaouki N, Aeberli I. **Adiposity in women and children from transition countries predicts decreased iron absorption, iron deficiency and a reduced response to iron fortification**. *Int J Obes (2005)* (2008) **32**. DOI: 10.1038/ijo.2008.43
10. Ellulu MS, Patimah I, Khaza'ai H, Rahmat A, Abed Y. **Obesity and inflammation: The linking mechanism and the complications**. *Arch Med Sci AMS* (2017) **13**. DOI: 10.5114/aoms.2016.58928
11. Coimbra `S, Catarino C, Santos-Silva A. **The role of adipocytes in the modulation of iron metabolism in obesity**. *Obes Rev an Off J Int Assoc Stud Obes* (2013) **14**. DOI: 10.1111/obr.12057
12. Jehn M, Clark JM, Guallar E. **Serum ferritin and risk of the metabolic syndrome in U.S. adults**. *Diabetes Care* (2004) **27**. DOI: 10.2337/diacare.27.10.2422
13. Ma W, Jia L, Xiong Q, Feng Y, Du H. **The role of iron homeostasis in adipocyte metabolism**. *Food Funct* (2021) **12**. DOI: 10.1039/d0fo03442h
14. El-Mallah CA, Beyh YS, Obeid OA. **Iron fortification and supplementation: Fighting anemia of chronic diseases or fueling obesity**. *Curr Dev Nutr* (2021) **5**. DOI: 10.1093/cdn/nzab032
15. Didelez V, Sheehan N. **Mendelian randomization as an instrumental variable approach to causal inference**. *Stat Methods Med Res* (2007) **16**. DOI: 10.1177/0962280206077743
16. Smith GD, Ebrahim S. **'Mendelian randomization': Can genetic epidemiology contribute to understanding environmental determinants of disease**. *Int J Epidemiol* (2003) **32** 1-22. DOI: 10.1093/ije/dyg070
17. Davey Smith G, Hemani G. **Mendelian randomization: Genetic anchors for causal inference in epidemiological studies**. *Hum Mol Genet* (2014) **23**. DOI: 10.1093/hmg/ddu328
18. Bell S, Rigas AS, Magnusson MK, Ferkingstad E, Allara E, Bjornsdottir G. **A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis**. *Commun Biol* (2021) **4** 156. DOI: 10.1038/s42003-020-01575-z
19. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR. **Genetic studies of body mass index yield new insights for obesity biology**. *Nature* (2015) **518** 197-206. DOI: 10.1038/nature14177
20. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R. **New genetic loci link adipose and insulin biology to body fat distribution**. *Nature* (2015) **518**. DOI: 10.1038/nature14132
21. Hartwig FP, Davies NM, Hemani G, Davey Smith G. **Two-sample mendelian randomization: Avoiding the downsides of a powerful, widely applicable but potentially fallible technique**. *Int J Epidemiol* (2016) **45**. DOI: 10.1093/ije/dyx028
22. Pierce BL, Ahsan H, Vanderweele TJ. **Power and instrument strength requirements for mendelian randomization studies using multiple genetic variants**. *Int J Epidemiol* (2011) **40**. DOI: 10.1093/ije/dyq151
23. Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG, Consortium E-I. **Using published data in mendelian randomization: A blueprint for efficient identification of causal risk factors**. *Eur J Epidemiol* (2015) **30**. DOI: 10.1007/s10654-015-0011-z
24. Burgess S, Butterworth A, Thompson SG. **Mendelian randomization analysis with multiple genetic variants using summarized data**. *Genet Epidemiol* (2013) **37**. DOI: 10.1002/gepi.21758
25. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM. **Guidelines for performing mendelian randomization investigations**. *Wellcome Open Res* (2019) **4** 186. DOI: 10.12688/wellcomeopenres.15555.2
26. Hemani G, Bowden J, Davey Smith G. **Evaluating the potential role of pleiotropy in mendelian randomization studies**. *Hum Mol Genet* (2018) **27**. DOI: 10.1093/hmg/ddy163
27. Bowden J, Davey Smith G, Haycock PC, Burgess S. **Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator**. *Genet Epidemiol* (2016) **40**. DOI: 10.1002/gepi.21965
28. Milligan BG. **Maximum-likelihood estimation of relatedness**. *Genetics* (2003) **163**. DOI: 10.1093/genetics/163.3.1153
29. Greco MF, Minelli C, Sheehan NA, Thompson JR. **Detecting pleiotropy in mendelian randomisation studies with summary data and a continuous outcome**. *Stat Med* (2015) **34**. DOI: 10.1002/sim.6522
30. Bowden J, Davey Smith G, Burgess S. **Mendelian randomization with invalid instruments: Effect estimation and bias detection through egger regression**. *Int J Epidemiol* (2015) **44**. DOI: 10.1093/ije/dyv080
31. Verbanck M, Chen CY, Neale B, Do R. **Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases**. *Nat Genet* (2018) **50**. DOI: 10.1038/s41588-018-0099-7
32. Bowden J, Spiller W, Del Greco MF, Sheehan N, Thompson J, Minelli C. **Improving the visualization, interpretation and analysis of two-sample summary data mendelian randomization**. *Int J Epidemiol* (2018) **47**. DOI: 10.1093/ije/dyy101
33. Hemani G, Tilling K, Davey Smith G. **Orienting the causal relationship between imprecisely measured traits using gwas summary data**. *PLoS Genet* (2017) **13** e1007081. DOI: 10.1371/journal.pgen.1007081
34. Hemani G, Zheng J, Elsworth B, Wade KH, Haberland V, Baird D. **The Mr-base platform supports systematic causal inference across the human phenome**. *eLife* (2018) **7**. DOI: 10.7554/eLife.34408
35. Wang X, Gao X, Han Y, Zhang F, Lin Z, Wang H. **Causal association between serum thyrotropin and obesity: A bidirectional, mendelian randomization study**. *J Clin Endocrinol Metab* (2021) **106**. DOI: 10.1210/clinem/dgab183
36. Pihan-Le Bars F, Bonnet F, Loréal O, Le Loupp AG, Ropert M, Letessier E. **Indicators of iron status are correlated with adiponectin expression in adipose tissue of patients with morbid obesity**. *Diabetes Metab* (2016) **42**. DOI: 10.1016/j.diabet.2015.10.007
37. Ali NB, Dibley MJ, Islam S, Rahman MM, Raihana S, Bhuiyan SET. **Overweight and obesity among urban women with iron deficiency anaemia in Bangladesh**. *Matern Child Nutr* (2021) **17**. DOI: 10.1111/mcn.13102
38. Yook J-S, Thomas SS, Toney AM, You M, Kim Y-C, Liu Z. **Dietary iron deficiency modulates adipocyte iron homeostasis, adaptive thermogenesis, and obesity in C57bl/6 mice**. *J Nutr* (2021) **151**. DOI: 10.1093/jn/nxab222
39. Moreno-Navarrete JM, Moreno M, Puig J, Blasco G, Ortega F, Xifra G. **Hepatic iron content is independently associated with serum hepcidin levels in subjects with obesity**. *Clin Nutr (Edinburgh Scotland)* (2017) **36**. DOI: 10.1016/j.clnu.2016.09.022
40. Amato A, Santoro N, Calabrò P, Grandone A, Swinkels DW, Perrone L. **Effect of body mass index reduction on serum hepcidin levels and iron status in obese children**. *Int J Obes* (2010) **34**. DOI: 10.1038/ijo.2010.204
41. Hamza RT, Hamed AI, Kharshoum RR. **Iron homeostasis and serum hepcidin-25 levels in obese children and adolescents: Relation to body mass index**. *Horm Res Paediatr* (2013) **80**. DOI: 10.1159/000351941
42. Aka S, Kilercik M, Arapoglu M, Semiz S. **The hepcidin and 25-Oh-Vitamin d levels in obese children as a potential mediator of the iron status**. *Clin Lab* (2021) **67**. DOI: 10.7754/Clin.Lab.2020.200813
43. Kernan KF, Carcillo JA. **Hyperferritinemia and inflammation**. *Int Immunol* (2017) **29**. DOI: 10.1093/intimm/dxx031
44. McClung JP, Karl JP. **Iron deficiency and obesity: The contribution of inflammation and diminished iron absorption**. *Nutr Rev* (2009) **67**. DOI: 10.1111/j.1753-4887.2008.00145.x
45. Nemeth E, Valore EV, Territo M, Schiller G, Lichtenstein A, Ganz T. **Hepcidin, a putative mediator of anemia of inflammation, is a type ii acute-phase protein**. *Blood* (2003) **101**. DOI: 10.1182/blood-2002-10-3235
46. Failla ML, Kennedy ML, Chen ML. **Iron metabolism in genetically obese (Ob/Ob) mice**. *J Nutr* (1988) **118** 46-51. DOI: 10.1093/jn/118.1.46
47. Choma SS, Alberts M, Modjadji SE. **Conflicting effects of bmi and waist circumference on iron status**. *J Trace Elements Med Biol Organ Soc Minerals Trace Elements (GMS)* (2015) **32**. DOI: 10.1016/j.jtemb.2015.06.003
48. Pierce BL, Burgess S. **Efficient design for mendelian randomization studies: Subsample and 2-sample instrumental variable estimators**. *Am J Epidemiol* (2013) **178**. DOI: 10.1093/aje/kwt084
|
---
title: Network pharmacology-based analysis of Resinacein S against non-alcoholic fatty
liver disease by modulating lipid metabolism
authors:
- Fei-Fei Mao
- Shan-Shan Gao
- Yan-Jie Huang
- Nian Zhou
- Jin-Kai Feng
- Zong-Han Liu
- Yu-Qing Zhang
- Lu-Yun Yuan
- Gang Wei
- Shu-Qun Cheng
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9971728
doi: 10.3389/fnut.2023.1076569
license: CC BY 4.0
---
# Network pharmacology-based analysis of Resinacein S against non-alcoholic fatty liver disease by modulating lipid metabolism
## Abstract
### Background
Ganoderma lucidum is reportedly the best source of traditional natural bioactive constituents. Ganoderma triterpenoids (GTs) have been verified as an alternative adjuvant for treating leukemia, cancer, hepatitis and diabetes. One of the major triterpenoids, Resinacein S, has been found to regulate lipid metabolism and mitochondrial biogenesis. Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disease that has become a major public health problem. Given the regulatory effects on lipid metabolism of Resinacein S, we sought to explore potential protective effects against NAFLD.
### Methods
Resinacein S was extracted and isolated from G. lucidum. And mice were fed with high fat diet with or without Resinacein S to detect hepatic steatosis. According to Network Pharmacology and RNA-seq, we analyzed the hub genes of Resinacein S against NAFLD disease.
### Results
Our results can be summarized as follows: [1] The structure of Resinacein S was elucidated using NMR and MS methods. [ 2] Resinacein S treatment could significantly attenuate high-fat diet (HFD)-induced hepatic steatosis and hepatic lipid accumulation in mouse. [ 3] GO terms, KEGG pathways and the PPI network of Resinacein S induced Differentially Expressed Genes (DEGs) demonstrated the key target genes of Resinacein S against NAFLD. [ 4] The hub proteins in PPI network analysis could be used for NAFLD diagnosis and treatment as drug targets.
### Conclusion
Resinacein S can significantly change the lipid metabolism in liver cells and yield a protective effect against steatosis and liver injury. Intersected proteins between NAFLD related genes and Resinacein S-induced DEGs, especially the hub protein in PPI network analysis, can be used to characterize targets of Resinacein S against NAFLD.
## Introduction
Ganoderma lucidum is a medicinal mushroom that can prolong life and promote health and has a long history in traditional Chinese medicine. Given its growing consumption, it has been intensively planted and sold since the 1970s. It is widely thought to be effective in preventing and treating many diseases and has anti-cancer properties [1]. G. lucidum is considered the best source of traditional natural bioactive components. It contains various compounds, including polyphenols, polysaccharides, steroids, triterpenes, nucleotides, amino acids, trace elements, and vitamins. Over the past few years, G. lucidum extract has been used as a dietary addition to treating various diseases [2]. Among various active components of G. lucidum, polysaccharides (GL-PS) and terpenoids (GL-T) predominantly exert physiological activities. They can inhibit the cell cycle and yield cytotoxicity and anti-metastasis, immunomodulation, antioxidant, antibacterial, anti-inflammatory and other effects [3]. Moreover, G. lucidum has been recognized as an alternative adjuvant for treating leukemia, cancer, hepatitis and diabetes [4]. Current evidence suggests that Ganoderma triterpenes represent one of the main active ingredients of mushrooms and yield an inhibitory effect on adipogenesis, leading to decreased lipid synthesis and accumulation. Other studies revealed that G. lucidum extracts and ethanol extracts of chigger mites rich in triterpenes contribute to adipogenesis and adipocyte differentiation. Ganoderma resinaceum is generally utilized for treating hepatitis, hyperglycemia, and dysimmunity in China and Nigeria [5].
Previous researchers had separated four new triterpenoids and four identified triterpenoids with anti-obesity effects from G. resinaceum. What’s more, one of the triterpenoids, Resinacein S has been found to induce beige and brown phenotypes, which may be relevant to the activation of AMPK/PGC1α signaling pathway to inhibit and treat obesity and relevant diseases. At the molecular level, Resinacein S treatment could significantly induce the expression of genes and/or proteins related to thermogenesis, fatty acid oxidation and lipolysis [6]. Resinacein S, as one of the major triterpenoids from G. resinaceum, provides a therapeutic strategy for lipid metabolic diseases such as NAFLD, but whether Resinacein S treatment could provide protective aspects against NAFLD remains unknown.
Nonalcoholic fatty liver disease (NAFLD) is currently recognized as the most common liver disease in the world, affecting about $25\%$ of adults worldwide. It encompasses steatosis simplex to nonalcoholic steatohepatitis, fibrosis, cirrhosis, and hepatocellular carcinoma. The clinical manifestation of nonalcoholic steatohepatitis (NASH) is a serious form of nonalcoholic fatty liver disease (NAFLD) characterized by the accumulation (steatosis) of triglycerides in liver cells, inflammation, injury and apoptosis, which may lead to cirrhosis and liver cancer in extreme cases (7–9). According to the regional epidemiological model of NAFLD, economy, environment and lifestyle are the key factors of disease progression [10]. Hepatic steatosis or fatty liver refers to the increase of lipid accumulation in liver cells caused by increased production or decreased clearance of hepatic triglycerides or fatty acids [11]. It has been established that Resinacein S can reduce fat and triglyceride accumulation by inducing the expression of genes and/or proteins related to thermogenesis, fatty acid oxidation and lipolysis at the molecular level. Nevertheless, the protective aspects and molecular mechanisms underlying the ability of Resinacein S to target NAFLD development remain to be illustrated. Therefore, this study aimed to explore the protective effects of Resinacein S against NAFLD.
## General experimental procedures
The NMR spectra were recorded on Bruker AV500 and AVIII600 instruments, with TMS used as an internal benchmark. Optical rotations were measured on an Autopol VI-91058 digital polarimeter. The UV spectra were detected by a UV-2700 spectrophotometer, while IR spectra were measured on a Nicolet iS10 spectrometer. HR-TOF ESI/MS was conducted on an Agilent 6,200 Q-TOF MS system spectrometer. Column chromatography was carried out with silica gel, reversed-phase C18 silica gel and Sephadex LH-20 as packing materials. Fractions were quantitated by thin-layer chromatography after spraying with sulphuric acid and heating. TLC was carried out on silica gel GF254. HPLC was performed on an Agilent 1,100 liquid chromatography system coupled with a diode-array detector and an Agilent Zorbax SB-C18 column (5 μm, 9.4 × 250 mm).
## Fungal material
Fruiting bodies of G. resinaceum were collected from Vientiane, Laos, identified by Peigui Liu and a voucher specimen (accession number: KGR-201606) was deposited at the State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, China [6].
Extraction and isolation procedures have been described before [6].
## Animal treatments and histological evaluation
Four-week-old male C57BL/6 mice were kept under the standard environment. Mice were fed with high fat diet with or without Resinacein S (intraperitoneal injection, interval for 48 h) at a dose of 10 mg/kg/day for 15 weeks [9]. Then mice were sacrificed and the liver were fixed in $4\%$ formaldehyde for HE staining and Oil Red O (G1262, Solarbio, China) staining (abs7049, Absin Bioscience, China). Images were acquired with a light microscope (Zeiss, Germany). Triglycerides (TG) were detected by a commercial kit (E1025-105, Applygen, Beijing, China) according to manufacturer’s instructions. All the experiments were approved by the ethics committee of Shanghai tenth People’s Hospital, Shanghai, China.
## Collection and sorting of nonalcoholic fatty liver disease disease-related genes
Genes related to NAFLD were acquired from DisGeNET,1 GeneCards,2 and OMIM3 databases. The keyword “nonalcoholic fatty liver disease” was entered into the above three databases to search for genes related to NAFLD. Then, the intersection was obtained using the Venny Venn diagram tool (version 2.1.0).4
## Determination of relevant target of Resinacein S
First, the canonical SMILES structure of Resinacein S was obtained through PubChem.5 Later, canonical SMILES information of Resinacein S was input into the SwissTargetPrediction6 database. Finally, the Uniprot ID obtained from the TargetNet database was transformed into Gene ID in UniProt7 database, yielding a total of 124 target genes associated with Resinacein S.
## Protein–protein interactions network construction
The above Resinacein S targets of NAFLD were imported in String,8 with the organization parameter and threshold of combination score being set as Homo sapiens and 0.7 for obtaining protein–protein interactions (PPI). Then, the protein interaction network data from the STRING database was incorporated into Cytoscape 3.9.0 for classification and mapping according to the degree value.
## RNA-seq analysis
TRIzol reagent was utilized for extracting total cellular RNA, which was later subject to spectrophotometry and agarose gel electrophoresis (AGE) with the NanoDrop ND-1000 instrument to analyze RNA integrity. Using a KAPA Stranded RNA-seq Library Prep Kit, this study built an RNA library, while a 2100 Bioanalyzer was employed for library quality assessment. Quantification of the library was performed by qRT-PCR. NCBI Gene Expression Omnibus was utilized to import raw RNA-seq information.
## Functional analysis of identified genes
FastQC v0.11.8 was applied to analyze the raw RNA-seq data. Fragments per kilobase of gene/transcript model per million mapped fragments (FPKM) values of diverse genes and transcripts were determined by the Ballgown package of R v2.10.0 software. Additionally, R was used to generate volcano plots and heatmaps to further analyze the gene expression profiles.
Gene Ontology (GO) enrichment of Differentially Expressed Genes (DEGs) was conducted to identify significantly enriched GO-biological processes (BP), GO-molecular functions (MF) and GO-cellular components (CC). Meanwhile, DEG-related pathways were identified based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [12].
## Statistical analysis
All data were expressed as mean ± standard deviation (SD). Experiments were repeated at least three times. The statistical significance of differences was evaluated using either the Student’s unpaired t-test, One-way analysis of variance (ANOVA), or two-way ANOVA as indicated. A p-value <0.05 was statistically significant.
## Structure elucidation of Resinacein S
Resinacein S is a natural compound that has only been documented in G. resinaceum. We obtained Resinacein S white powder from the ethanol extracts of G. resinaceum through a series partition and column chromatography as previously described (Figure 1A) [13]. Resinacein S was established as C30H44O8 based on the HRESIMS (Supplementary Figure S6), and the X-ray crystallographic structure of Resinacein S was obtained (Figure 1B). The 1H NMR and 13C NMR data of Resinacein S are shown in Table 1. The 1H NMR, 13C NMR and DEPT, HSQC, HMBC, 1H-1H COSY, and ROESY spectra are displayed in Figures 1B,C and Supplementary Figures S1–S6. Resinacein S was elucidated as (6R)-6-((3S,5R,7S,10S,13R,14R,17R)-3,7-dihydroxy-4,4,10,13,14-pentamethyl-11,15-dioxo-,3,4,5,6,7,10,11,12,13,14,15,16,17-tetradecahydro-1H-cyclopenta[a]phenanthren-17-yl)-3-hydroxy-2-methyl-4-oxo heptanoic acid.
**Figure 1:** *Resinacein S was obtained from *Ganoderma resinaceum*
(A), and 1H–1H COSY correlations, key HMBC, ROESY correlations for Resinacein S (B), and 1H NMR spectrum (600 MHz) of Resinacein S in CD3OD (C).* TABLE_PLACEHOLDER:Table 1
## Resinacein S alleviated liver damage and hepatic lipid accumulation
To investigate the benefits of Resinacein S against the HFD-induced extraphysiological process of NAFLD, we employed Resinacein S to treat HFD-diet mice and found that Resinacein S could effectively reduce liver damage and hepatic steatosis in HFD-fed mice (Figures 2A,B). In addition, Oil Red O staining of livers in Resinacein S-treated HFD-fed mice yielded few lipid droplets of small size. Compared to the control, the livers of Resinacein S-treated mice displayed an almost normal phenotype (Figures 2C–E). Consistently, significantly lower TG levels were found in the livers of the Resinacein S-treated group (Figure 2F).
**Figure 2:** *Resinacein S reduced liver damage and hepatic steatosis in HFD-fed mice. (A,B) H&E staining of livers in HFD-fed mice (A) and Resinacein S treated HFD-fed mice (B). (C–E) Oil Red O staining of livers in HFD-fed mice (C) and Resinacein S treated HFD-fed mice (D). (E) is the quantitative analysis of Oil Red O staining. (F) TG levels in livers of Resinacein S treated or not HFD-fed mice were detected. Values were means ± SD, and for statistical analysis, one-way ANOVA were performed between indicated groups.*
## Target recognition results of Resinacein S and nonalcoholic fatty liver disease
Twenty and 101 gene targets in human related to Resinacein S were obtained from SwissTargetPrediction and TargetNet databases, respectively. Moreover, 61, 395 and 183 gene targets related to NAFLD disease were obtained from DisGeNET, GeneCards and OMIM, respectively. The Venny 2.1.0 online system was used for target recognition (Figure 3A). One hundred nineteen action targets of Resinacein S were acquired, along with 576 NAFLD related genes. Finally, 20 action targets of Resinacein S against NAFLD come to light. Detailed information on the action targets is provided in Table 2.
**Figure 3:** *(A) 119 drug treatment targets based on the examination and combination of Swiss Target Prediction and TargetNet databases. Five hundred and seventy six genes related to NAFLD based on the examination and combination of DisGeNET, GeneCards and OMIM databases. There are 20 intersection targets between NAFLD and Resinacein S. (B) PPI protein interaction network diagram, in which the innermost circle degree value is 0–6, and the outer circle degree value is 7–16.* TABLE_PLACEHOLDER:Table 2
## Protein–protein interactions network results
The top 20 action targets of Resinacein S for NAFLD were put into the STRING database. Moreover, the PPI network was obtained according to the method described in 2.5. Data from the STRING database was put into Cytoscape 3.9.0 for beautification and classification according to the degree value. The degree values were classified into 0–6 and 7–16, and the protein–protein interaction network was generated (Figure 3B).
## Resinacein S induced gene expression profiles in human liver cells
In order to further investigate the hub regulated genes of Resinacein S against NAFLD in human liver cells, we treated the human normal liver cell line L02 with Resinacein S and detected the gene expression profiles by RNA-Seq. According to log2FC > 0.585 or < −0.585 and FDR < 0.05, we obtained 172 DEGs (111 up-regulated and 61 down-regulated) in Resinacein S treated group compared to DMSO group (Figure 4, Supplementary Table 1).
**Figure 4:** *(A) The heatmap of the DEGs between Resinacein S treated group and DMSO group. (B) The volcano plot of the DEGs between Resinacein S treated group and DMSO group.*
## Functional enrichment analysis of Resinacein S induced genes in human liver cells
To study the biological function of Resinacein S induced genes in human liver cells, GO annotation was conducted. The significantly enriched GO terms in cellular component (CC), molecular function (MF) and biological process (BP) are shown in Figure 5A and Table 3. Significantly enriched GO terms associated with cellular components included mitochondrial oxoglutarate dehydrogenase complex, tRNA methyltransferase complex, mitochondrial oxoglutarate dehydrogenase complex, nuclear lumen, nuclear outer membrane, cytoplasmic microtubule, microtubule-associated complex, intermediate filament cytoskeleton, cytoskeleton, and nucleus. Significantly enriched GO terms associated with molecular function (MF) included N-acylglucosamine 2-epimerase activity, glycogenin glucosyltransferase activity, kinase activator activity, glucokinase activity, mannokinase activity, fructokinase activity, hexokinase activity, glucose binding, MAP kinase kinase activity, and MAP kinase kinase kinase activity. Significantly enriched GO terms associated with biological process (BP) consisted of fatty acid omega-oxidation, extracellular matrix constituent secretion, inflammatory cell apoptotic process, miRNA catabolic process, regulation of fatty acid oxidation, steroid catabolic process, reactive oxygen species metabolic process, oligosaccharide metabolic process, dolichol-linked oligosaccharide biosynthetic process, and positive regulation of the apoptotic process.
**Figure 5:** *GO and KEGG pathway enrichment analysis was conducted with Resinacein S induced genes in human liver cells. (A) The categories in GO terms included cellular components (CC), molecular function (MF), and biological process (BP) were analyzed. (B) The top 20 significant GO terms of the overlapped DEGs were chosen based on the order of p value from small to large. (C) KEGG pathway enrichment analysis was performed with Resinacein S-induced genes. The top 20 significant KEGG pathways of the DEGs were chosen based on the order of p value from small to large.* TABLE_PLACEHOLDER:Table 3 The top 20 GO terms associated with Resinacein S induced genes in human liver cells were selected according to the order of value of p in ascending order (Figure 5B; Table 3). It was found that the DEGs were mainly enriched in the fatty acid omega-oxidation, dolichol-linked oligosaccharide biosynthetic process, glucose binding, inflammatory cell apoptotic process, glycogenin glucosyltransferase activity, and fatty acid omega-oxidation, etc. These functions were closely related to the physiological regulation of liver metabolism, especially the metabolic homeostasis of lipids and cholesterol.
KEGG enrichment analysis was carried out to explore the biological pathways of the Resinacein S-induced DEGs (Figure 5C; Table 4). We found that the DEGs were significantly enriched in KEGG pathways, including T cell receptor signaling pathway, N-Glycan biosynthesis, Amino sugar and nucleotide sugar metabolism, Glycosphingolipid biosynthesis-globo series, MAPK signaling pathway, and TNF signaling pathway, etc.
**Table 4**
| Category | Pathway ID | p value | Genes |
| --- | --- | --- | --- |
| Fc gamma R-mediated phagocytosis | PATH:04666 | 0.028775 | VAV3/SCIN/LIMK2 |
| Butirosin and neomycin biosynthesis | PATH:00524 | 0.035794 | HKDC1 |
| T cell receptor signaling pathway | PATH:04660 | 0.044183 | MAP3K8/VAV3/MALT1 |
| TNF signaling pathway | PATH:04668 | 0.045195 | MAP3K8/FAS/MAP2K6 |
| Amino sugar and nucleotide sugar metabolism | PATH:00520 | 0.047149 | RENBP/HKDC1 |
| MAPK signaling pathway | PATH:04010 | 0.047367 | MAP3K8/FAS/MAP2K5 MAP2K6/CACNA1H |
| N-Glycan biosynthesis | PATH:00510 | 0.048932 | ALG9/DOLPP1 |
| Caffeine metabolism | PATH:00232 | 0.049758 | NAT1 |
| Axon guidance | PATH:04360 | 0.069072 | LIMK2/ABLIM3/NTN4 |
| Taurine and hypotaurine metabolism | PATH:00430 | 0.070332 | CSAD |
| Regulation of actin cytoskeleton | PATH:04810 | 0.071639 | VAV3/SCIN/LIMK2 IQGAP2 |
| Natural killer cell mediated cytotoxicity | PATH:04650 | 0.076769 | FAS/VAV3/ULBP3 |
| Non-homologous end-joining | PATH:03450 | 0.09047 | POLM |
| Glycosphingolipid biosynthesis - globo series | PATH:00603 | 0.097087 | ST3GAL1 |
| Glycosaminoglycan biosynthesis - keratan sulfate | PATH:00533 | 0.103657 | ST3GAL1 |
| Glycosphingolipid biosynthesis - ganglio series | PATH:00604 | 0.103657 | ST3GAL1 |
| B cell receptor signaling pathway | PATH:04662 | 0.104971 | VAV3/MALT1 |
| Fc epsilon RI signaling pathway | PATH:04664 | 0.121474 | VAV3/MAP2K6 |
| Influenza A | PATH:05164 | 0.133377 | FAS/RAE1/MAP2K6 |
| Nitrogen metabolism | PATH:00910 | 0.154559 | CA5B |
Overall, these metabolic pathways are closely related to liver metabolic balance, which substantiates the protective effect of Resinacein S against NAFLD.
## The hub proteins of Resinacein S regulated against nonalcoholic fatty liver disease
To determine the hub proteins that mediate the protective role of Resinacein S against NAFLD in human liver cells, we analyzed the interaction relationships of 172 DEGs of Resinacein S regulated in human normal liver cell line L02 according to RNA-Seq (Figure 3), and 20 target genes of Resinacein S against NAFLD from public database (Figure 4). The relationships were obtained by the STRING database, including evidence from experiments, databases, and co-expression data, and binding scores greater than 0.7 (high confidence). Therefore, according to the STRING analysis, the Resinacein S-mediated junction proteins against NAFLD were TNF, PIK3CA, AKT1, AKT2, ESR1, CYP3A4, CYP17A1, and PTPN11 (Figure 6). Among them, AKT1 and AKT2 play an important role in the interaction relationships which indicated the regulation of AKT pathway by Resinacein S against NAFLD.
**Figure 6:** *The hub proteins of Resinacein S regulated against NAFLD. The interaction relationship analysis was performed with the DEGs of Resinacein S regulated in human liver cells and the target genes of Resinacein S against NAFLD from public database. DEGs of Resinacein S regulated in human normal liver cells were shown in red circle, and the 20 target genes of Resinacein S against NAFLD from public database were shown in green. The analysis was generated by Cytoscape 3.9.0.*
## Discussion
NAFLD is widely thought to be the most common chronic liver disease, concomitant with the global obesity epidemic. During the development and progress of NAFLD, liver steatosis is considered a benign state, while NASH exhibits chronic progressive liver damage. Fatty degeneration is related to inflammation and fibrosis and gradually develops into cirrhosis. The pathogenesis of NAFLD is closely related to dysregulated metabolism in obese patients, excess free fatty acid (FFA) accumulation and the excessive secretion of several cellular inflammatory mediators [14]. There is an increasing consensus that NAFLD is a heterogeneous disease associated with multiple-hit pathogenesis yielding different phenotypes. Although the clinical presentation of NAFLD can be heterogeneous, insulin resistance plays a leading role in NAFLD [15].
In this study, we focused on the role of the Resinacein S compound extracted from G. resinaceum in treating NAFLD patients. First, we identified the extracted triterpenoid compounds and obtained the molecular structure of Resinacein S through mass spectrometry analysis. By interaction relationship analysis of the DEGs of Resinacein S regulated in human liver cells and the target genes of Resinacein S against NAFLD from public database, we found 8 proteins TNF, PIK3CA, AKT1, AKT2, ESR1, CYP3A4, CYP17A1, and PTPN11 may be the hub proteins of Resinacein S regulated against NAFLD.
According to former studies, Resinacein S treatment can dramatically induce the expression of thermogenesis related genes such as Ucp1 and Pgc1α, fatty acid oxidation related genes such as Pparα and Cpt1α, lipolysis related genes such as Hsl and Atgl, and mitochondriogenesis-related genes. Resinacein S may be involved in activation of AMPK/PGC1α signaling pathway [6]. Now we obtained eight Resinacein S targeting genes with potential therapeutic effects against NAFLD through RNA seq analysis and PPI network analysis.
Obesity is related to the increase of circulating Tumor Necrosis Factor (TNF, TNF-α), a pro-inflammatory cytokine that induces the death of liver cells [16]. In the process of inflammation, TNF is one of the main pro-inflammatory cytokines, which regulates innate immunity and adaptive immune response [17]. Clinical evidence shows that the level of circulating TNF-α in patients with nonalcoholic steatohepatitis (NASH) is highly correlated with the degree of liver fibrosis [18]. AKT Serine/Threonine Kinase 1 (AKT1) is a serine/threonine protein kinase, which is called an important downstream target signal pathway of insulin and has anti-apoptosis and peripheral metabolic effects. Studies have shown that the inhibition of ROS production mediated by AKT1 inhibits the fibrosis transformation from NAFLD to liver [19]. AKT1 deficiency led to the inhibition of AKT/mTOR/S6K signaling pathway in hepatocytes, which was crucial for the development of hepatic steatosis and provided a new scheme for the development of NAFLD therapy [20]. Studies have shown that AKT Serine/Threonine Kinase 2 (AKT2) is essential for lipid synthesis in the liver [21]. It was found that hepatocyte-specific Phosphatase and Tensin Homolog (PTEN) deficiency in mice showed liver steatosis due to over-activation of AKT2 [22]. As the precursor of NAFLD, deleting AKT2, the downstream target of PTEN signal, can block the development of NASH and reduce the development of liver fibrosis, which also reduces the occurrence of NAFLD from another development process [23]. Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha (PIK3CA) is an important component of PI3K/AKT/mTOR pathway. PIK3CA mutated cells showed inflated induction of the de novo lipogenesis transcriptional regulator SREBP1 and elevated exogenous FA uptake capacity both of which can lead to lipid-enriched phenotype [24, 25]. ESR1 is a nuclear and membrane hormone receptor. Previous studies showed that ESR1 could negatively regulate hepatocyte pyroptosis by directly interacting with gasdermin D (GSDMD). ESR1 deficiency could induce pyroptosis, impaired glucose tolerance, and reduce lipid accumulation in hepatocytes (26–28). Cytochrome P450 Family 3 Subfamily A Member 4 (CYP3A4) is involved in the metabolism of sterols, retinoids and fatty acids. The activity of CYP3A4 is usually reduced in mouse and cell models of NAFLD, also in human. In NAFLD-model mice, the luciferase activity of CYP3A4 from livers was about $38\%$ lower than the normal ones [29, 30]. Cytochrome P450 Family 17 Subfamily A Member 1 (CYP17A1) is an important enzyme for Dehydroepiandrosterone (DHEA) synthesis. Former evidences showed that DHEA can modulate oxidative stress, insulin resistance, and fibrosis observed in serious NAFLD [31, 32]. Protein Tyrosine Phosphatase Non-Receptor Type 11 (PTPN11) is the first identified oncogenic tyrosine phosphatase which can cooperate with PTEN to maintain the liver homeostasis and function. PTPN11 in hepatocytes could induce early-onset non-alcoholic steatohepatitis (NASH) [33].
Besides, according to our KEGG enrichment analysis, we revealed that the targets of Resinacein S mainly focused on T cell receptor signaling pathway, N-Glycan biosynthesis, Amino sugar and nucleotide sugar metabolism, Glycosphingolipid biosynthesis-globo series, MAPK signaling pathway, and TNF signaling pathway.
In conclusion, we have revealed the structure of Resinacein S and demonstrated that Resinacein S could significantly attenuate high-fat diet induced hepatic steatosis and hepatic lipid accumulation. We have also developed a new gene expression feature related to NAFLD by using Resinacein S-dependent DEGs, especially hub proteins in PPI network analysis, which can assist in diagnosing and treating NAFLD populations as well as drug discovery and development in the near future. Nevertheless, although the extracts of Ganoderma are usually safe and most of the side effects are very mild, the side effect of Resinacein S deserves further studies especially on human. Besides, the molecular mechanisms of Resinacein S against NAFLD and in vivo models for further proving the metabolic phenotypes of Resinacein S need to be further investigated.
## Conclusion
Overall, Resinacein S yields a protective effect against steatosis and liver injury and can significantly change the gene expression profile in fatty liver cells. Functional enrichment analysis showed significant enrichment in lipid and glucose metabolism. In addition, intersected genes between NAFLD and Resinacein S-induced DEGs, especially the hub protein in PPI network analysis, can be used to characterize NAFLD-related gene expression characteristics for NAFLD diagnosis, treatment and drug development.
## Data availability statement
The data presented in the study are deposited in the GEO repository, accession number GSE223990.
## Ethics statement
The animal study was reviewed and approved by Animal Care and Use Committee of Shanghai Tenth People’s Hospital of Tongji University.
## Author contributions
S-QC, GW, and F-FM conceived the project. The data analysis was done by FM, S-SG, and Y-JH. F-FM and S-SG wrote the drafts of the manuscript. Y-JH checked and revised the manuscript. NZ and J-KF participated in collection of the published datasets. Z-HL, Y-QZ, and L-YY gave many suggestions about the manuscript writing. All authors contributed to the article and approved the submitted version.
## Funding
This work was partially supported by grants from the National Natural Science Foundation of China (Nos. 82002480, 8200032).
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2023.1076569/full#supplementary-material
## References
1. Unlu A, Nayir E, Kirca O, Ozdogan M. *J BUON* (2016) **21** 792-8. PMID: 27685898
2. Ahmad R, Riaz M, Khan A, Aljamea A, Algheryafi M, Sewaket D. *Phytother Res* (2021) **35** 6030-62. DOI: 10.1002/ptr.7215
3. Cizmarikova M. **The efficacy and toxicity of using the**. *Int J Med Mushrooms* (2017) **19** 861-77. DOI: 10.1615/IntJMedMushrooms.2017024537
4. Sanodiya BS, Thakur GS, Baghel RK, Prasad GB, Bisen PS. *Curr Pharm Biotechnol* (2009) **10** 717-42. DOI: 10.2174/138920109789978757
5. Niu XM, Li SH, Xiao WL, Sun HD, Che CT. **Two new Lanostanoids from**. *J Asian Nat Prod Res* (2007) **9** 659-64. DOI: 10.1080/10286020600979910
6. Huang Y, Wei G, Peng X, Hu G, Su H, Liu J. **Triterpenoids from functional mushroom**. *Food Res Int* (2020) **136** 109303. DOI: 10.1016/j.foodres.2020.109303
7. Eslam M, Valenti L, Romeo S. **Genetics and epigenetics of NAFLD and NASH: clinical impact**. *J Hepatol* (2018) **68** 268-79. DOI: 10.1016/j.jhep.2017.09.003
8. Raza S, Rajak S, Upadhyay A, Tewari A, Anthony SR. **Current treatment paradigms and emerging therapies for NAFLD/NASH**. *Front Biosci* (2021) **26** 206-37. DOI: 10.2741/4892
9. Liao M, Zhang R, Wang Y, Mao Z, Wu J, Guo H. **Corilagin prevents non-alcoholic fatty liver disease via improving lipid metabolism and glucose homeostasis in high fat diet-fed mice**. *Front Nutr* (2022) **9** 983450. DOI: 10.3389/fnut.2022.983450
10. Zhou J, Zhou F, Wang W, Zhang XJ, Ji YX, Zhang P. **Epidemiological features of NAFLD from 1999 to 2018 in China**. *Hepatology* (2020) **71** 1851-64. DOI: 10.1002/hep.31150
11. Manne V, Handa P, Kowdley KV. **Pathophysiology of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis**. *Clin Liver Dis* (2018) **22** 23-37. DOI: 10.1016/j.cld.2017.08.007
12. Sui Y, Lu K, Fu L. **Prediction and analysis of novel key genes ITGAX, LAPTM5, SERPINE1 in clear cell renal cell carcinoma through bioinformatics analysis**. *PeerJ* (2021) **9** e11272. DOI: 10.7717/peerj.11272
13. Oyetayo OV. **Medicinal uses of mushrooms in Nigeria: towards full and sustainable exploitation**. *Afr J Tradit Complement Altern Med* (2011) **8** 267-74. DOI: 10.4314/ajtcam.v8i3.65289
14. Xu Y, Wang N, Tan HY, Li S, Zhang C, Feng Y. **Gut-liver Axis modulation of**. *Hepatol Int* (2021) **15** 350-65. DOI: 10.1007/s12072-021-10138-1
15. Goh GB, Pagadala MR, Dasarathy J, Unalp-Arida A, Sargent R, Hawkins C. **Clinical spectrum of non-alcoholic fatty liver disease in diabetic and non-diabetic patients**. *BBA Clin* (2015) **3** 141-5. DOI: 10.1016/j.bbacli.2014.09.001
16. Ezquerro S, Mocha F, Fruhbeck G, Guzman-Ruiz R, Valenti V, Mugueta C. **Ghrelin reduces TNF-alpha-induced human hepatocyte apoptosis, autophagy, and pyroptosis: role in obesity-associated NAFLD**. *J Clin Endocrinol Metab* (2019) **104** 21-37. DOI: 10.1210/jc.2018-01171
17. Jorge ASB, Andrade JMO, Paraiso AF, Jorge GCB, Silveira CM, de Souza LR. **Body mass index and the visceral adipose tissue expression of Il-6 and TNF-alpha are associated with the morphological severity of non-alcoholic fatty liver disease in individuals with class III obesity**. *Obes Res Clin Pract* (2018) **12** 1-8. DOI: 10.1016/j.orcp.2016.03.009
18. Ma R, Zhan Y, Zhang Y, Wu L, Wang X, Guo M. **Schisandrin B ameliorates non-alcoholic liver disease through anti-inflammation activation in diabetic mice**. *Drug Dev Res* (2022) **83** 735-44. DOI: 10.1002/ddr.21905
19. Hu Q, Wei S, Wen J, Zhang W, Jiang Y, Qu C. **Network pharmacology reveals the multiple mechanisms of Xiaochaihu decoction in the treatment of non-alcoholic fatty liver disease**. *BioData Min* (2020) **13** 11. DOI: 10.1186/s13040-020-00224-9
20. Li CH, Tang SC, Wong CH, Wang Y, Jiang JD, Chen Y. **Berberine induces miR-373 expression in hepatocytes to inactivate hepatic steatosis associated AKT-S6 kinase pathway**. *Eur J Pharmacol* (2018) **825** 107-18. DOI: 10.1016/j.ejphar.2018.02.035
21. Pan Q, Qin T, Gao Y, Li S, Li D, Peng M. **Hepatic mTOR-AKT2-Insig2 signaling pathway contributes to the improvement of hepatic steatosis after Roux-en-Y gastric bypass in mice**. *Biochim Biophys Acta Mol basis Dis* (2019) **1865** 525-34. DOI: 10.1016/j.bbadis.2018.12.014
22. Schultze SM, Dietrich M, Hynx D, Geier A, Niessen M, Spinas GA. **Reduced hepatic lipid content in Pten-Haplodeficient mice because of enhanced AKT2/PKBbeta activation in skeletal muscle**. *Liver Int* (2015) **35** 1354-66. DOI: 10.1111/liv.12600
23. He L, Gubbins J, Peng Z, Medina V, Fei F, Asahina K. **Activation of hepatic stellate cell in Pten null liver injury model**. *Fibrogenesis Tissue Repair* (2016) **9** 8. DOI: 10.1186/s13069-016-0045-1
24. Koundouros N, Karali E, Tripp A, Valle A, Inglese P, Perry NJS. **Metabolic fingerprinting links oncogenic Pik3ca with enhanced arachidonic acid-derived eicosanoids**. *Cells* (2020) **181** 1596-1611.e27. DOI: 10.1016/j.cell.2020.05.053
25. Ricoult SJH, Yecies JL, Ben-Sahra I, Manning BD. **Oncogenic PI3K and K-Ras stimulate de novo lipid synthesis through mTORC1 and SREBP**. *Oncogene* (2016) **35** 1250-60. DOI: 10.1038/onc.2015.179
26. Gao X, Liu S, Tan L, Ding C, Fan W, Gao Z. **Estrogen receptor alpha regulates metabolic-associated fatty liver disease by targeting NLRP3-GSDMD axis-mediated hepatocyte pyroptosis**. *J Agric Food Chem* (2021) **69** 14544-56. DOI: 10.1021/acs.jafc.1c05400
27. Palmisano BT, Zhu L, Stafford JM. **Role of estrogens in the regulation of liver lipid metabolism**. *Adv Exp Med Biol* (2017) **1043** 227-56. DOI: 10.1007/978-3-319-70178-3_12
28. Chen KL, Madak-Erdogan Z. **Estrogens and female liver health**. *Steroids* (2018) **133** 38-43. DOI: 10.1016/j.steroids.2017.10.015
29. Woolsey SJ, Mansell SE, Kim RB, Tirona RG, Beaton MD. **CYP3A activity and expression in nonalcoholic fatty liver disease**. *Drug Metab Dispos* (2015) **43** 1484-90. DOI: 10.1124/dmd.115.065979
30. Woolsey SJ, Beaton MD, Mansell SE, Leon-Ponte M, Yu J, Pin CL. **A fibroblast growth factor 21-Pregnane X receptor pathway downregulates hepatic CYP3A4 in nonalcoholic fatty liver disease**. *Mol Pharmacol* (2016) **90** 437-46. DOI: 10.1124/mol.116.104687
31. Charlton M, Angulo P, Chalasani N, Merriman R, Viker K, Charatcharoenwitthaya P. **Low circulating levels of Dehydroepiandrosterone in histologically advanced nonalcoholic fatty liver disease**. *Hepatology* (2008) **47** 484-92. DOI: 10.1002/hep.22063
32. Li L, Wang H, Yao Y, Cao J, Jiang Z, Yan W. **The sex steroid precursor Dehydroepiandrosterone prevents nonalcoholic steatohepatitis by activating the AMPK pathway mediated by GPR30**. *Redox Biol* (2021) **48** 102187. DOI: 10.1016/j.redox.2021.102187
33. Luo X, Liao R, Hanley KL, Zhu HH, Malo KN, Hernandez C. **Dual Shp2 and Pten deficiencies promote non-alcoholic steatohepatitis and genesis of liver tumor-initiating cells**. *Cell Rep* (2016) **17** 2979-93. DOI: 10.1016/j.celrep.2016.11.048
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title: Psychological stress induces an increase in cholinergic enteric neuromuscular
pathways mediated by glucocorticoid receptors
authors:
- Justine Blin
- Camille Gautier
- Philippe Aubert
- Tony Durand
- Thibauld Oullier
- Laetitia Aymeric
- Philippe Naveilhan
- Damien Masson
- Michel Neunlist
- Kalyane Bach-Ngohou
journal: Frontiers in Neuroscience
year: 2023
pmcid: PMC9971731
doi: 10.3389/fnins.2023.1100473
license: CC BY 4.0
---
# Psychological stress induces an increase in cholinergic enteric neuromuscular pathways mediated by glucocorticoid receptors
## Abstract
### Introduction
Repeated acute stress (RASt) is known to be associated with gastrointestinal dysfunctions. However, the mechanisms underlying these effects have not yet been fully understood. While glucocorticoids are clearly identified as stress hormones, their involvement in RASt-induced gut dysfunctions remains unclear, as does the function of glucocorticoid receptors (GR). The aim of our study was to evaluate the involvement of GR on RASt-induced changes in gut motility, particularly through the enteric nervous system (ENS).
### Methods
Using a murine water avoidance stress (WAS) model, we characterized the impact of RASt upon the ENS phenotype and colonic motility. We then evaluated the expression of glucocorticoid receptors in the ENS and their functional impact upon RASt-induced changes in ENS phenotype and motor response.
### Results
We showed that GR were expressed in myenteric neurons in the distal colon under basal conditions, and that RASt enhanced their nuclear translocation. RASt increased the proportion of ChAT-immunoreactive neurons, the tissue concentration of acetylcholine and enhanced cholinergic neuromuscular transmission as compared to controls. Finally, we showed that a GR-specific antagonist (CORT108297) prevented the increase of acetylcholine colonic tissue level and in vivo colonic motility.
### Discussion
Our study suggests that RASt-induced functional changes in motility are, at least partly, due to a GR-dependent enhanced cholinergic component in the ENS.
## Introduction
Repeated acute stress (RASt) is known to be associated with gastrointestinal (GI) disorders or dysfunctions such as visceral hypersensitivity and altered gastrointestinal motility, both of which lead to decreased gastric emptying and enhanced colonic transit time, or altered epithelial functions, such as increased epithelial secretion or permeability (Bhatia and Tandon, 2005; Reed et al., 2016; Meerveld and Johnson, 2018). However, the mechanisms underlying RASt-induced changes in gut functions have not yet been fully decrypted (Bonaz et al., 2002; Bhatia and Tandon, 2005; Meerveld and Johnson, 2018), especially the mediators and receptors involved.
Repeated acute stress is associated with the activation of every component of stress response. This response can be two-fold: on the one hand, stimulation of the sympathetic nervous system induces a rapid release of neuromediators such as catecholamines with adrenaline and noradrenaline, leading to rapid synaptic effects (Mittal et al., 2017). The activation of the hypothalamic-pituitary-adrenal (HPA) axis, on the other hand, induces slower effects and potentially longer-term changes (Datson, 2008). Activation of the HPA axis is characterized by secretion of corticotropin-releasing hormone (CRH) by the hypothalamus, which induces the release of adreno-corticotropic hormone (ACTH) by the pituitary gland, and finally the secretion of glucocorticoids (GC) by the adrenal glands (Larauche et al., 2009; Taché et al., 2009Gourcerol et al., 2011; Taché, 2015). CRH has been the most widely studied of the mediators released following HPA activation and involved in RASt effects upon GI functions. In particular, CRH modulates GI functions both through the central nervous system (CNS) (Martínez et al., 2004; Tsukamoto et al., 2006) and the autonomic nervous system, in which both the sympathetic nervous system and the enteric nervous system (ENS) may be involved (Taché and Bonaz, 2007; Taché et al., 2009; Gourcerol et al., 2011).
Corticotropin-releasing hormone, in particular, can directly act on the neurons of the ENS, an integrative neuronal network located all along the gastrointestinal tract, involved in the regulation of gastrointestinal functions such as motility and secretion (Neunlist et al., 2013; Furness et al., 2014; Spencer and Hu, 2020). CRH effects upon the ENS were shown to be mediated in a region and function-dependent manner by the activation of two receptors sub-types, corticotropin-releasing factor receptor 1 and 2 (CRF-R1 and CRF-R2). Numerous studies have tested the hypothesis that stress could impact digestive function by acting on these receptors (Rivier et al., 2003). Furthermore, the activation of each of these two receptors by CRH leads to functionally distinct responses: while activation of CRF-R1 increases colonic motor response, activation of CRF-R2 inhibits gastric emptying (Martínez et al., 2004). In addition, activation of CRF-R2 by CRH increases mucosal chloride secretion in the colon via enteric pathways (Liu et al., 2021).
When compared to CRH, the role of GC in RASt-mediated effects upon GI functions and its putative impact upon the ENS remains largely unknown. GC effects are mediated via two major nuclear receptors, i.e., the mineralocorticoid receptor (MR) and the glucocorticoid receptor (GR). GC have a high affinity for MR receptors, which are often activated under basal conditions. In contrast, GC have a lower affinity for GR receptors which are activated under “inducible” conditions, such as following HPA activation (Datson, 2008; Revollo and Cidlowski, 2009; Oakley and Cidlowski, 2013). GC activation of GR results in nuclear translocation of cytoplasmic GR and binding to the GR response element, leading to various transcriptional processes. The effects of corticosterone are limited by its degrading enzymes 11-β hydroxysteroid deshydrogenase type 2 (11βHSD-2) (Chapman et al., 2013).
Glucocorticoid receptor expression has been identified in other neuronal systems besides the ENS such as the sympathetic system, more specifically in dorsal root sensory neurons or in specific brain structures (Lerch et al., 2017). In CNS and in dorsal root sensory neurons, activation of GR by GC has been shown to regulate neuromediators synthesis or different neuronal processes, such as plasticity and sprouting (Lerch et al., 2017; Buurstede et al., 2022). In the ENS, corticosterone has been shown to modulate calcium concentration in enteric neurons and increase the proportion of choline acetyltransferase (ChAT) in rat primary ENS culture (Lowette et al., 2014; Aubert et al., 2019b). However, although RASt has been shown to increase colonic motility via CRF1 receptors, its ability to regulate ENS and colonic motility via GR remains unknown.
In this context, our study aimed to determine whether the ENS expresses GR receptors and whether RASt-induced effects on ENS phenotype and on functional remodeling are mediated, at least in part, via GR-dependent pathways.
## Animals
Our study was carried out in accordance with French standard ethical guidelines for laboratory animals (Agreement 02376.01) as well as with ARRIVE guidelines.1 All experiments involving mice were approved by the ethics committee for animal experimentation of Pays de la Loire (Apafis 6751) based at the Nantes University CHU, of which our INSERM unit is a part. Adult male C57BL/6 mice (5–6 weeks; Janvier Labs, Le Genest-Saint-Isle, France) weighing 25–30 g were used. Mice were housed in cages with free access to food and tap water. Animals were quarantined under controlled conditions in terms of illumination (12 h light/dark cycle), temperature, and humidity.
## Water avoidance stress (WAS) protocol
For stress exposure, a single 1 h WAS between 8 and 9 a.m. was performed daily when the mice were 5–6 weeks old for 4 consecutive days, corresponding to the repeated acute stress protocol (Figure 1A). Mice were placed on an elevated circular platform that was placed 1 cm above the water level (10 cm height; 4 cm diameter) and positioned at the center of a plastic tank (52 × 37 × 27 cm3) filled with water at room temperature. This protocol was adapted from a previous study (Reed et al., 2016).
**FIGURE 1:** *Experimental design. (A) Schematic timeline depicting the experimental design applied to control and water avoidance stress (WAS) mice for the assessment of in vivo and ex vivo colonic motility. FPO, fecal pellet output. (B) Schematic timeline depicting the experimental design applied to mice treated with CORT 108297 (85 mg/kg) or vehicle (sesame oil) for assessment of in vivo and ex vivo colonic motility.*
## Pharmacological treatment protocol
The specific GR antagonist, CORT108297 (kindly provided by Dr. Azel Hunt, Corcept, Drive Menlo Park, CA, USA) or the vehicle (sesame oil, Millipore Sigma, Burlington, MA, USA) were injected intraperitoneally at a dose of 85 mg/kg 1 h before the WAS procedure (Figure 1B). This dose was selected following a literature review (Zalachoras et al., 2013; Beaudry et al., 2014; Meyer et al., 2014; Pineau et al., 2016).
## In vivo measurement of colonic motility
Following the procedures described in previous studies (Tasselli et al., 2013; Rincel et al., 2019), to assess the fecal pellet output (FPO), control mice were placed individually in clean cages without bedding, food, or water for fecal pellet collection during 1 h. For mice subjected to WAS, the fecal pellet collection was performed for 1 h during the WAS time period while the mice were on the platform. Fecal pellets were collected and counted immediately after expulsion (Figure 1).
## Ex vivo measurement of colonic motility
As previously described (Aubert et al., 2019a), at the end of the fecal pellet output collection from both the control mice and the mice after the WAS treatment, on the fourth day, both the control and stressed animals were killed by cervical dislocation. Tissues were immediately transferred to cold HBSS (Eurobio, Courtaboeuf, France) after surgical resection and brought to the laboratory. Strips of longitudinal muscle were dissected and placed into an organ chamber (Radnoti, CA, USA) with 15 mL of Krebs solution at 37°C, continuously bubbled with $95\%$ O2 and $5\%$ CO2. The contractile response of muscle strip was continuously recorded using isometric force transducers (No. TRI202PAD, Panlab, Cornellã, Spain) coupled to a computer equipped with the PowerLab $\frac{8}{30}$ System and the *Labchart data* analysis software (AD Instruments, Spechbach, Germany). Strips were stretched with an initial tension of 1 g and reached a mean tension of 0.7 g (range 0.5–0.9) after an equilibrium period of 60 min. Next, strips were subjected to electrical field stimulation (EFS) using a STG 4008 MCS electrical stimulator (Multi Channel Systems, Reutlingen, Germany). EFS parameters were as follows: train duration, 10 s; pulse frequency, 20 Hz; pulse duration, 400 μs; pulse amplitude 11 V. This procedure was repeated 3 times with a space of 4 min between repetitions. The following drugs were then added sequentially to the baths after a 10-min incubation period: nitro-L-arginine methyl ester (L-Name, 50 μM; Sigma-Aldrich, Merck, Darmstadt, Germany), an inhibitor of the nitric oxide synthase (NOS); and then atropine (1 μM; Sigma-Aldrich, Merck, Darmstadt, Germany), a muscarinic receptor antagonist. The same EFS stimulation protocol was repeated. Next, tissues were washed 5 times over a 10-min period and then allowed to recover for an additional 20 min. At the end of each experiment, a dose-response curve was created by measuring the area under the curve of the bethanechol-induced contraction. Bethanechol is a selective muscarinic receptor agonist (Millipore Sigma, Burlington, MA, USA). The response was measured for 2 min after bethanechol (10–9–10–2 M) was added. All values were normalized to the tissue weight.
## Blood sampling and corticosterone assessment
Mice were bled from their tail, at day 0 (D0) and at day 4 (D4), at the end of the 1 h-long WAS test (as described in Figure 1). Samples were obtained by quickly clipping the distal tip of the tail with a razor blade and collecting ∼50 μL of blood into EDTA-containing tubes.
In order to minimize hormonal variability due to circadian fluctuations, all procedures were performed during the circadian cycle of the diurnal corticosterone rhythm, before 11:00 a.m. After collection, blood samples were centrifuged at 1007 g, for 15 min at 4°C and then plasma samples were kept at −20°C. Corticosterone plasma levels were measured by enzymatic immunoassay according to the manufacturer’s instructions (Laboratoire IDS, Pouilly-en-Auxois, France).
## Acetylcholine assay
After the mice were killed, portions of their distal colons were removed and lysed in a RIPA buffer (Merck Millipore, Fontenay-sous-Bois, France) containing phosphatase inhibitor cocktail III (Sigma-Aldrich, Merck, Darmstadt, Germany) and protease inhibitors cocktail (Roche, Neuilly-sur-Seine, France). Briefly, tissues were crushed in the RIPA buffer using a “Precellys 24” tissue homogenizer (Bertin technologies, St Quentin-en-Yvelines, France), followed by sonication with a “Vibra-Cell 75 186” device (Sonics, Newton, CT, USA). The amount of proteins was assessed with a Bradford reagent using a BioPhotometer D30 spectrophotometer (Eppendorf, Montesson, France). Acetylcholine (ACh) concentration was determined in tissue homogenates using Amplex Red, acetylcholine/acetylcholinesterase assay kit (Invitrogen Thermofisher Scientific, Waltham, MA, USA) and normalized to the protein amount.
## Western blot analysis
As previously described (Prigent et al., 2019), protein extraction from colonic tissues was performed with NucleoSpin RNA/Protein Kit (Macherey-Nagel, Hoerdt, France, Cat# 740966) according to the manufacturer instructions. Samples were further prepared for electrophoresis by diluting with a NuPAGE sample buffer (Life Technologies, Saint-Aubin, France, Cat# NP0008) then heated at 98°C for 5 min. Lysates were separated using the NuPAGE 4–$12\%$ Bis-Tris gels (Life Technologies, Cat# NP0336BOX) together with the 2-(N-morpholino)ethanesulfonic acid/sodium dodecyl sulfate running buffer (Life Technologies, Cat# IB23002) before electrophoretic transfer to nitrocellulose membranes (Life Technologies, Cat# NP0002) with the iBlot2 Dry Blotting System (Life Technologies, Cat# IB21001). Membranes were then blocked for 1 h at 21°C in Tris-buffered saline (Sigma, Cat# T5912) with $0.1\%$ (v/v) Tween-20 (Sigma, Cat# P1379) and $5\%$ (w/v) non-fat dry milk and incubated overnight at 4°C with the following primary antibodies: rabbit anti-GR (D8H2, 3660S 1:500, Cell Signaling, Danvers, MA, USA), mouse monoclonal anti-β-actin (1:10000; Sigma, Cat#A5441, RRID:AB_476744). Bound antibodies were detected with horseradish peroxidase-conjugated anti-rabbit (Life Technologies Cat# 31460, diluted 1:5000) or anti-mouse antibodies (Sigma, Cat# A9044, diluted 1:5000) and visualized by enhanced chemiluminescent detection (Biorad, Clarity ECL, Marnes-la-Coquette, France, Cat# 170-5061).
## Quantitative PCR analysis
As previously described (Schemann et al., 1993), 1 μg of purified mRNA by Nucleospin RNA/Protein kit was denatured and converted to cDNA using the SuperScript III Reverse Transcriptase (Life Technologies). qPCR was performed using a StepOnePlus RealTime PCR Instrument (Life Technologies) with a FastSYBR Green Master Mix kit (Applied Biosystems, Foster City, CA, USA). Ribosomal protein S6 (RPS6) transcript was used as a reference. The relative expression of the gene of interest was measured by the 2–ΔΔCt method. The following primers were used:
## Immunofluorescence staining
Distal colon segments were fixed in 0.1M PBS containing $4\%$ paraformaldehyde at room temperature for 3 h. Whole mounts of longitudinal muscle and myenteric plexus were obtained by microdissection and permeabilized with PBS containing $10\%$ horse serum (HS) and $4\%$ Triton X-100 for 2 h at room temperature. Tissues were then incubated with the following primary antibodies: rabbit anti-ChAT (1:1000, a gift from Professor M. Schemann, Hannover, Germany) (Schemann et al., 1993), mouse anti-neuronal NOS (nNOS; 610308, 1:500; BD Biosciences), rabbit anti-GR (D8H2, 3660S 1:500, Cell Signaling), human anti-Hu (Bodin et al., 2021) (gift from the CHU of Nantes; 1:5000) diluted in PBS containing $10\%$ horse serum and $0.5\%$ Triton X-100 for 24 or 48 h at room temperature. After washing, tissues were incubated for 2 h at room temperature with the appropriate secondary antibodies, respectively, anti-rabbit CY5 (1:500), anti-rabbit CY3 (1:500) and anti-human FP488 (1:200), and mounted Glycerol $60\%$ (vol/vol) (Thermo Fisher Scientific). Nuclei were stained with 4′ 6-Diamidino-2-phenylindole dihydrochloride (DAPI D9542; 1:10000; Sigma Aldrich, Paris, France).
## Image analysis and quantification
Images from immunostained tissues were acquired with a digital camera (Axiozoom, Carl Zeiss, Jena, Germany). The number of Hu-, ChAT-, nNOS- and GR-neurons was counted in 20 ganglia per animal. The data were expressed as the percentage of ChAT-, nNOS-, or GR-neurons normalized to the total number of Hu-neurons.
For GR immunostaining, confocal microscopy was performed using a Nikon A1R confocal inverted microscope (Nikon France SAS, Champigny sur Marne, France) with a Nikon X60 Plan-Apo numerical aperture (NA) 1.4 oil-immersion objective (MicroPICell core facility).
The intensity of cytoplasmic and nuclear expression of GR receptor was analyzed in 10 ganglia for all Hu identified neurons. Image analysis was performed with the ImageJ (ROI Manager) following immunohistochemical staining for GR, Hu, and DAPI. First, neurons were identified using Hu and its perimeter defined manually using ImageJ. Next, the neuronal nucleus (identified with DAPI staining) perimeter was defined using ImageJ. Then both neuronal perimeter and nucleus perimeter were overlaid on the GR staining. Finally, the intensity of GR staining in the neuronal nucleus and in the neuronal cytoplasm were calculated using ImageJ. The intensity of neuronal cytoplasmic GR abundance was determined by subtracting the intensity of the GR abundance in the nucleus from that of the whole neuron. Finally, the ratio of nuclear over cytoplasmic GR abundance was calculated. This analysis was blinded to the treatment group.
## Statistical analyses
Statistical analyses were performed using GraphPad Prism 5 (GraphPad Software, San Diego, CA, USA). Data were represented as means ± SD. Group comparisons were made using the Mann–Whitney U test or by 2-way ANOVA and Dunnett’s multiple comparisons test as indicated. Values of $p \leq 0.05$ were considered statistically significant. When necessary we tested our data with the Grubbs test to detect Outliers. Only one outlier has been identified in this study (see Figure 5C) and has been removed, leaving 15 points in the control group against 16 in the WAS group.
All data generated or analyzed during this study are included in this published article and its Supplementary material.
## Enteric neurons of mice distal colonic ENS express the GR glucocorticoid receptor
We first aimed to determine whether enteric neurons express GR receptors. We therefore performed GR immunohistochemical labeling in whole mounts of longitudinal muscle-myenteric plexus of distal colon segments. Neurons were stained using antibodies directed against neuronal Hu-IR protein. Using high resolution confocal microscopy, we showed that GR is expressed in a large proportion of Hu-immunoreactive (IR) enteric neurons (Figures 2A–D). We focused on the Hu-IR cells and did not study other enteric cell types. For these Hu-IR cells, we observed that GR was preferentially located in the nucleus using the orthogonal viewing mode (Figure 2E). GR expression was also reported in other non-neuronal cells within and outside enteric ganglia.
**FIGURE 2:** *GR is expressed by neurons of enteric nervous system (ENS) in distal colon of mice. Confocal acquisition of double immunolabeling for Hu-IR (A) and GR (B) in the myenteric plexus of the distal colon in the mice. 4′,6-diamidino-2-phenylindole (DAPI) was used for immunolabelling of nuclei (C). The merged image (D) revealed the colocalization of GR with the DAPI labeling, suggesting a nuclear localization of GR. Scale bars of panels (A–D) represent 25 μm. Double arrows show GR expression by Hu-IR enteric neurons and simple arrows show GR expression by non-neuronal cells. (E) Z-reconstruction of confocal micrographs of myenteric plexus after double immunostaining with Hu and GR antibodies showing the preferential nuclear localization of GR in the neuron. Scale bars of panel (E) represent 10 μm.*
## Stress increases nuclear translocation of GR
We next aimed to determine whether RASt modulates GR expression in enteric neurons. We first showed that repeated acute WAS significantly increased serum corticosterone levels as compared to control mice (282.6 ± 113.9 ng/ml vs. 51.8 ± 19.2, respectively; $p \leq 0.0001$) (Supplementary Figure 1). Next, we showed that the intensity of GR expression was not modified by RASt (Supplementary Figure 2) in Hu-IR cells. Furthermore, western blot and PCR analysis of colonic tissue showed that RASt did not modify protein and mRNA expression of GR as compared to control (Supplementary Figure 3). However, using immunohistochemistry we were able to show that the ratio of the intensity of nuclear GR abundance over cytoplasmic GR abundance was significantly increased by RASt as compared to control (Figure 3).
**FIGURE 3:** *Stress increases the nuclear translocation of GR in neurons of the myenteric plexus. (A) Double immunolabeling for Hu (green; arrowheads indicate neuron markers) and GR (red; arrows indicate glucocorticoid receptor markers in neurons) in the distal colons of the myenteric plexus in control and WAS mice. A scale bar represents 25 μm. (B) Quantitative nucleo-cytoplasmic ratio of GR labeling intensity (IntDen) per ganglion. Data are represented as means ± SD (n = 6/group) *p < 0.05 (Mann–Whitney U test).*
## RASt-induced phenotypic modulation of ENS neurons is mediated by GR in mice distal colon
We next aimed to determine whether RASt modulates the expression of key enzymes such as ChAT and nNOS which are involved, respectively, in the synthesis of ACh and NO, two key neuromodulators involved in the regulation of gut motility (Figure 4A). We first showed that repeated acute WAS did not modify the number of neurons (identified by Hu-IR) as compared to control (Figure 4B). Interestingly, repeated acute WAS induced a significant $63\%$ increase in the proportion of ChAT-IR neurons as compared to control (Figure 4C). However, RASt did not modify the proportion of nNOS-IR neurons (Figure 4D). We next showed that the increase in the proportion of ChAT-IR neurons was associated with a significant increase in ACh concentration in colonic tissue as compared to control (Figure 4E). Interestingly, RASt-induced increase in ACh colonic tissue level was prevented by the pretreatment of mice with CORT108297 (85 mg/kg), the selective GR receptor antagonist, prior to RASt.
**FIGURE 4:** *Stress induces modifications of enteric neuronal phenotype. (A) Immunolabeling for Hu, ChAT, and nNOS in the myenteric plexus of the distal colon of mice in control or WAS conditions (representative images are shown). Scale bars represent 30 μm. Arrows point out ChAT-positive neurons. (B) Quantitative analysis for the neurons per ganglion (gg) in control and WAS groups. Data are represented as means ± SD (n = 14/group) p = 0.511 (Mann–Whitney U test). (C) Proportion of neurons expressing ChAT (ChAT/Hu) in control and WAS groups. Data are represented as means ± SD (n = 7 for control mice and n = 10 for WAS mice) **p < 0.01 (Mann–Whitney U test). (D) Proportion of neurons expressing nNOS (nNOS/Hu) in control and WAS groups. Data are represented as means ± SD (n = 8/group) p = 0.328 (Mann–Whitney U test). (E) Distal colonic acetylcholine tissue levels (ACh) in control and WAS conditions ± GR antagonist CORT108297 (μmol/μg of protein). Data are presented as means ± SD. One way ANOVA test and Dunnett’s multiple comparisons test was used for panel (E). *p < 0.05.*
## RASt increases colonic motility in a GR-dependent manner
Then, we asked whether RASt-induced changes in ENS phenotype were associated with changes in colonic motor functions in a GR-dependent manner.
We analyzed ex vivo, in an organ chamber, the neuromuscular response of segments of the distal colon to electrical field stimulation. First, we analyzed the spontaneous contractile motility pattern in the colon. We did not find any difference of mean basal tension or amplitude of contractions from control or WAS mice. In contrast, WAS significantly increased the basal area under the curve (AUC) of the spontaneous contractile activity (Supplementary Figure 4). We next analyzed the EFS-induced contractile responses were analyzed in the absence or presence of L-NAME and/or atropine (Figure 5A). The EFS-induced AUC was larger in WAS mice than in controls ($$n = 16$$; $p \leq 0.001$) (Figure 5B). In the presence of L-NAME, EFS-induced AUC was still significantly larger in WAS as compared to controls ($$n = 16$$; $$p \leq 0.026$$) (Figure 5B). Finally, in the presence of atropine, EFS-induced AUC were similar in both WAS and control mice ($$n = 16$$; $$p \leq 0.56$$) (Figure 5B). However, the amplitude of the atropine-sensitive AUC component was higher in the WAS group as compared to controls ($$n = 16$$; $$p \leq 0.005$$) (Figure 5C). Of interest, the amplitude of the atropine-sensitive AUC of the spontaneous contractile activity was significantly higher in the WAS group as compared to controls ($$n = 8$$; $p \leq 0.001$) (Supplementary Figure 4).
**FIGURE 5:** *WAS mice exhibit increased colonic motility. Distal colonic longitudinal muscle segments were stimulated by electrical field stimulation (EFS). (A) Representative traces of EFS contractile response in basal conditions, with L-Name and with L-Name + Atropine in control and WAS mice. (B) Mean EFS-induced area under curve (AUC) WAS of distal colon longitudinal muscle segment after a treatment with L-Name or L-Name + Atropine (n = 16/group). (C) Amplitude of atropine-sensitive EFS-induced AUC in control and in WAS mice (n = 16/group). (D) Representative trace responses to increasing doses of bethanechol of isolated colon from control and WAS mice. (E) Dose–response AUC contractions of distal colonic longitudinal muscular segments from control and WAS mice to increasing doses of bethanechol (n = 16/group). (F)
In vivo propulsive motor function of the colon assessed by measuring FPO of mice after 1 h of WAS treatment at day 0 (D0) before RASt and on day 4 (D4), after 4 days of repeated WAS (n = 16/group). (G) Comparison of the in vivo propulsive motor function for mice receiving the vehicle (sesame oil), between day 0 and day 4, after 4 days of repeated WAS (n = 5/group). (H) Comparison of the in vivo propulsive motor function for mice receiving the CORT108297, between day 0 and day 4, after 4 days of repeated WAS (n = 5/group). (I) Comparison of the in vivo propulsive motor function for mice receiving the CORT108297, between day 0 and day 4, without any WAS stimulation (n = 5/group) (p-value = 0.07). Data are represented using bars as means ± SD *p < 0.05, **p < 0.01, and ***p < 0.001 Mann–Whitney U test were used for all panels, except panel (E) where one-way ANOVA was used.*
In order to exclude the possibility that the differential response to atropine between WAS and control was reflecting a differential sensitivity of cholinergic receptors on muscles, we measured the muscle response to cholinergic muscarinic agonist (bethanechol) stimulation. We showed that bethanechol induced a similar dose-dependent increase in contractile response in tissues of both control and WAS mice (Figures 5D, E).
Finally, we aimed to determine whether changes in neuromuscular transmission induced by WAS in the distal colon were associated with functional changes in vivo. We showed that FPO were significantly higher in WAS mice as compared to control, with, respectively, 5.8 ± 4.3 in control mice vs. 12.3 ± 3.5 in WAS mice ($p \leq 0.001$; $$n = 16$$) (Figure 5F). Interestingly, we showed that pretreatment of WAS mice with CORT108297 (85 mg/kg) prevented WAS increase in FPO (Figures 5G, H). Basal administration of CORT108297 did not modify FPO (Figure 5I).
## Discussion
Our study demonstrates that myenteric neurons express the glucocorticoid receptor GR in the distal colon. We further showed that RASt enhanced the nuclear translocation of GR in myenteric neurons and induced an increase in the following: the number of ChAT-IR neurons, the ACh tissue level in the distal colon, and cholinergic neuromuscular transmission. We also showed that RASt caused an increase in motility and in the distal colonic ACh level, both of which were prevented by the GR specific antagonist CORT108297. Altogether, our study suggests that RASt-induced functional changes in colonic motility are, at least partly, due to GR-dependent ENS cholinergic up-regulation. Our findings could, therefore, open novel therapeutic perspectives by targeting GR expressed by colonic neurons in gut functional disorders associated with an altered HPA axis.
A first major finding of our study is to consider corticosterone as an important regulator of ENS and gut functions in response to RASt. Indeed, until recently, a RASt-induced increase in CRH was considered to mediate the majority of stress-induced effects upon gut functions (Rivier et al., 2003; Taché et al., 2005; Fukudo, 2007), in part via its activation of CRH receptors expressed by enteric neurons. However, an increasing amount of evidence suggests that corticosterone could directly modulate neuronal functions in the ENS. First, corticosterone can be produced in the ENS micro-environment. Indeed, 11-β hydroxy steroid deshydrogenase type 1 (11-βHSD1), the bidirectional enzyme converting inactive 11-dehydrocorticosterone to active corticosterone, has been identified in populations of enteric neurons (Lowette et al., 2014). However, in the ENS, the role of 11-βHSD1 remains unknown. Secondly, concerning the direct effects of corticosterone on enteric neurons, acute corticosterone application onto myenteric neurons has been shown to increase intracellular calcium while long term (20 h) incubation of ENS culture with corticosterone reduced EFS induced Ca2+ response in neurons (Lowette et al., 2014). In addition, the incubation of primary culture of ENS with corticosterone increased the proportion of ChAT-IR neurons (Aubert et al., 2019b), echoing the findings we obtained in vivo following RASt. Interestingly, increased enteric cholinergic activity was also reported in a porcine model of early weaning stress which represents a psychological model of HPA axis activation (Medland et al., 2016).
Another important finding of our study is the identification of GR in RASt-induced effects upon ENS and GI functions. Expression of GR in the gut has been previously reported in various cells of the GI tract, i.e., epithelial and immune cells of the lamina propria (Zheng et al., 2013; Zong et al., 2019). GR is also expressed in neuronal cell types of the peripheral nervous system such as neurons of the dorsal root ganglia and also in region-specific parts of the brain (Lerch et al., 2017). In the enteric neurons, GR expression has been recently revealed by single-cell sequencing by Zeisel et al. [ 2018]. We have now demonstrated its expression in myenteric neurons by immunohistochemistry. In neurons of the CNS, GC/GR interactions are associated with morphological plasticity and alterations of neuronal functions (Madalena and Lerch, 2016). However, GR-induced neuronal changes vary with stress and cell type (Madalena and Lerch, 2017). For instance, acute exposure of dorsal root sensory neurons to GC were associated with increased synaptic plasticity while more chronic exposure is associated with dendritic atrophy (Madalena and Lerch, 2016, 2017). Whether region-specific expression of GR and/or cell-type specific expression of GR also exist in the ENS remains currently unknown. Following RASt we observed an increase in the ratio of nuclear/cytoplasmic abundance of GR in enteric neurons. This finding is consistent with the nuclear translocation of GR upon its binding with its ligand, leading to activation of GR-dependent transcriptomic response (Meijer et al., 2019). As a consequence of GR activation by RASt, our study demonstrated an increase in the proportion of ChAT-IR neurons associated with an increase in ACh tissue level. In addition, we showed that RASt increased both cholinergic spontaneous contractile response and enhanced EFS-induced contractile responses, as assessed by the sensitivity of these response to atropine, supporting the functional impact of the increased cholinergic phenotype induced by RASt. In contrast, RASt did not modify NO dependent neuromuscular responses consistent with no change in proportion of nNOS-IR neurons. However, one cannot exclude the contribution of an inducible NO (iNOS) component in RASt effects upon motility (Traini et al., 2021) have shown that WAS increased iNOS expression in rat myenteric neurons. Although it was well-known that acute stress could activate cholinergic myenteric neurons (Miampamba et al., 2007), no study had identified the ability of GC to modulate ENS functions and an associated impact upon colonic motility. While CRH-mediated effects can be considered a short-term response leading to acute activation of enteric neurons (Hanami and Wood, 1992; Tache et al., 2018), GC could preferentially induce long-term changes in neurons via GR (Datson, 2008).
The mechanisms underlying the increased proportion of cholinergic neurons and colonic tissue ACh content induced by RASt have not been directly addressed in our study. However, it is tempting to speculate that following WAS, activation of GR increases the level of ChAT mRNA which would result in an increased abundance of ChAT protein in enteric neurons. On the one hand, this would lead to an increased detection in the number of ChAT-IR neurons detected by immunohistochemical methods (as ChAT antibodies have a relatively low sensibility). This increase, in turn, would explain the higher proportion of ChAT-IR neurons following WAS as compared to control observed in our study (as no change in the total number of Hu-IR neurons was reported following WAS). On the other hand, increased ChAT protein abundance in enteric neurons would lead to an increased synthesis of ACh and contribute to the increased colonic tissue content of ACh we observed. The hypothesized increase level of ChAT mRNA could be due either to increased transcriptional activity of ChAT Promoter or the stabilization of ChAT mRNA. In the CNS, some studies suggest that GC could indeed indirectly activate ChAT expression by modulating activity of neurotrophic factors like nerve growth factor (Shi et al., 1998; Gonzalez et al., 1999; Borges et al., 2013). Another pathway potentially involved in GR-induced modulation of cholinergic phenotype is the ability of GC to down-regulate NF-KB expression in cellular models of neurons (Toliver-Kinsky et al., 2000). Since NF-KB acts as a repressor of ChAT transcription (Toliver-Kinsky et al., 2000), GR-induced NF-kB inhibition could ultimately lead to increased ChAT expression. Therefore, future specific studies would be warranted to explore mechanisms underlying GR-induced changes in cholinergic expression in the ENS.
Finally, our study demonstrated the efficacy of a GR-specific antagonist at preventing neuromediator and functional remodeling induced by RASt. Indeed, pretreatment with CORT108297 decreased FPO and ACh colonic tissue level in mice subjected to WAS. CORT108297 is a GR antagonist with a strong affinity (Meyer et al., 2014), and, unlike the classic GR antagonist RU486, has the advantage of being devoid of a progesterone receptor inhibitory effect (Clark, 2008; Sindelar et al., 2014). CORT108297 has been shown to be an antagonist specific to GR at the dose used in this study (Zalachoras et al., 2013; Beaudry et al., 2014; Meyer et al., 2014; Pineau et al., 2016), and to potently suppress corticosterone responses to a forced swim test (FST) and restraint stress (Solomon et al., 2014).
Altogether, our data suggest that corticosterone represents a novel actor involved in the regulation of ENS and gut functions. Better understating mechanisms involved in ENS regulation by corticosterone in health and disease might lead to the development of novel therapeutic approaches targeting GR or downstream targets to improve GI dysfunctions associated with HPA dysfunctions.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by French standard ethical guidelines for laboratory animals (Agreement 02376.01).
## Author contributions
MN and KB-N conceived and designed the study. JB, CG, PA, TO, and TD conceived and conducted the experiment(s). PN performed the immunohistochemical analysis. MN, KB-N, JB, and CG analyzed the results. JB, KB-N, PN, and MN wrote the manuscript. DM, PA, TD, and LA critically reviewed the manuscript. All authors reviewed the manuscript and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2023.1100473/full#supplementary-material
## References
1. Aubert P., Oleynikova E., Rizvi H., Ndjim M., Berre-Scoul C. L., Grohard P. A.. **Maternal protein restriction induces gastrointestinal dysfunction and enteric nervous system remodeling in rat offspring.**. (2019b) **33** 770-781. DOI: 10.1096/fj.201800079R
2. Aubert P., Guinobert I., Blondeau C., Bardot V., Ripoche I., Chalard P.. **Basal and spasmolytic effects of a hydroethanolic leaf extract of**. (2019a) **22** 653-662. DOI: 10.1089/jmf.2018.0154
3. Beaudry J. L., Dunford E., Teich T., Zaharieva D., Hunt H., Belanoff J.. **Effects of selective and non-selective glucocorticoid receptor II antagonists on rapid-onset diabetes in young rats.**. (2014) **9**. DOI: 10.1371/journal.pone.0091248
4. Bhatia V., Tandon R. K.. **Stress and the gastrointestinal tract.**. (2005) **20** 332-339. PMID: 15740474
5. Bodin R., Paill V., Oullier T., Durand T., Aubert P., Berre-Scoul C. L.. **The ephrin receptor EphB2 regulates the connectivity and activity of enteric neurons.**. (2021) **297**. DOI: 10.1016/j.jbc.2021.101300
6. Bonaz B., Baciu M., Papillon E., Bost R., Gueddah N., Bas J. F.. **Central processing of rectal pain in patients with irritable bowel syndrome: An fMRI study.**. (2002) **97** 654-661. PMID: 11926209
7. Borges S., Coimbra B., Soares-Cunha C., Ventura-Silva A., Pinto L., Carvalho M.. **Glucocorticoid programing of the mesopontine cholinergic system.**. (2013) **4**. DOI: 10.3389/fendo.2013.00190
8. Buurstede J. C., van Weert L., Colucci P., Gentenaar M., Viho E., Koorneef L.. **Hippocampal glucocorticoid target genes associated with enhancement of memory consolidation.**. (2022) **55** 2666-2683. DOI: 10.1111/ejn.15226
9. Chapman K., Holmes M., Seckl J.. **11β-hydroxysteroid dehydrogenases: Intracellular gate-keepers of tissue glucocorticoid action.**. (2013) **93** 1139-1206. DOI: 10.1152/physrev.00020.2012
10. Clark R. D.. **Glucocorticoid receptor antagonists.**. (2008) **8** 813-838. PMID: 18537690
11. Datson N. A.. **Central corticosteroid actions: Search for gene targets.**. (2008) **583** 272-289. DOI: 10.1016/j.ejphar.2007.11.070
12. Fukudo S.. **Role of corticotropin-releasing hormone in irritable bowel syndrome and intestinal inflammation.**. (2007) **42(Suppl. 17)** 48-51. PMID: 17238026
13. Furness J. B., Callaghan B. P., Rivera L. R., Cho H.-J.. **The enteric nervous system and gastrointestinal innervation: Integrated local and central control.**. (2014) **817** 39-71. PMID: 24997029
14. Gonzalez S. L., Saravia F., Gonzalez Deniselle M. C., Lima A. E., De Nicola A. F.. **Glucocorticoid regulation of motoneuronal parameters in rats with spinal cord injury.**. (1999) **19** 597-611. DOI: 10.1023/a:1006980301382
15. Gourcerol G., Wu S., Yuan P., Pham H., Miampamba M., Larauche M.. **Activation of corticotropin-releasing factor receptor 2 mediates the colonic motor coping response to acute stress in rodents.**. (2011) **140** 1586-1596.e6. DOI: 10.1053/j.gastro.2011.01.039
16. Hanami M., Wood J. D.. **Corticotropin-releasing hormone excites myenteric neurons in the guinea-pig small intestine.**. (1992) **211** 23-27. PMID: 1618264
17. Larauche M., Gourcerol G., Wang L., Pambukchian K., Brunnhuber S., Adelson D. W.. **Cortagine, a CRF1 agonist, induces stresslike alterations of colonic function and visceral hypersensitivity in rodents primarily through peripheral pathways.**. (2009) **297** G215-G227. DOI: 10.1152/ajpgi.00072.2009
18. Lerch J. K., Alexander J., Madalena K., Motti D., Quach T., Dhamija A.. **Stress increases peripheral axon growth and regeneration through glucocorticoid receptor-dependent transcriptional programs.**. (2017) **4**. DOI: 10.1523/ENEURO.0246-17.2017
19. Liu S., Karo A., Agterberg S., Hua H., Bhargava A.. **Effects of stress-related peptides on chloride secretion in the mouse proximal colon.**. (2021) **33**. DOI: 10.1111/nmo.14021
20. Lowette K., Tack J., Vanden Berghe P.. **Role of corticosterone in the murine enteric nervous system during fasting.**. (2014) **307** G905-G913. DOI: 10.1152/ajpgi.00233.2014
21. Madalena K. M., Lerch J. K.. **Glucocorticoids and nervous system plasticity.**. (2016) **11** 37-41. PMID: 26981074
22. Madalena K. M., Lerch J. K.. **The effect of glucocorticoid and glucocorticoid receptor interactions on brain, spinal cord, and glial cell plasticity.**. (2017) **2017**. DOI: 10.1155/2017/8640970
23. Martínez V., Wang L., Rivier J., Grigoriadis D., Taché Y.. **Central CRF, urocortins and stress increase colonic transit via CRF1 receptors while activation of CRF2 receptors delays gastric transit in mice.**. (2004) **556** 221-234. DOI: 10.1113/jphysiol.2003.059659
24. Medland J. E., Pohl C. S., Edwards L. L., Frandsen S., Bagley K., Li Y.. **Early life adversity in piglets induces long-term upregulation of the enteric cholinergic nervous system and heightened, sex-specific secretomotor neuron responses.**. (2016) **28** 1317-1329. DOI: 10.1111/nmo.12828
25. Meerveld B. G.-V., Johnson A. C.. **Mechanisms of stress-induced visceral pain.**. (2018) **24** 7-18. PMID: 29291604
26. Meijer O. C., Buurstede J. C., Schaaf M. J. M.. **Corticosteroid receptors in the brain: Transcriptional mechanisms for specificity and context-dependent effects.**. (2019) **39** 539-549. DOI: 10.1007/s10571-018-0625-2
27. Meyer M., Gonzalez Deniselle M. C., Hunt H., de Kloet E. R., De Nicola A. F.. **The selective glucocorticoid receptor modulator CORT108297 restores faulty hippocampal parameters in Wobbler and corticosterone-treated mice.**. (2014) **143** 40-48. DOI: 10.1016/j.jsbmb.2014.02.007
28. Miampamba M., Million M., Yuan P.-Q., Larauche M., Taché Y.. **Water avoidance stress activates colonic myenteric neurons in female rats.**. (2007) **18** 679-682. DOI: 10.1097/WNR.0b013e3280bef7f8
29. Mittal R., Debs L. H., Patel A. P., Nguyen D., Patel K., O’Connor G.. **Neurotransmitters: The critical modulators regulating gut-brain axis.**. (2017) **232** 2359-2372. PMID: 27512962
30. Neunlist M., Landeghem L. V., Mah M., Derkinderen P., Varannes S. B., Rolli-Derkinderen M.. **The digestive neuronal-glial-epithelial unit: A new actor in gut health and disease.**. (2013) **10** 90-100. DOI: 10.1038/nrgastro.2012.221
31. Oakley R. H., Cidlowski J. A.. **The biology of the glucocorticoid receptor: New signaling mechanisms in health and disease.**. (2013) **132** 1033-1044. DOI: 10.1016/j.jaci.2013.09.007
32. Pineau F., Canet G., Desrumaux C., Hunt H., Chevallier N., Ollivier M.. **New selective glucocorticoid receptor modulators reverse amyloid-β peptide-induced hippocampus toxicity.**. (2016) **45** 109-122. DOI: 10.1016/j.neurobiolaging.2016.05.018
33. Prigent A., Gonzales J., Durand T., Berre-Scoul C. L., Rolli-Derkinderen M., Neunlist M.. **Acute inflammation down-regulates alpha-synuclein expression in enteric neurons.**. (2019) **148** 746-760. DOI: 10.1111/jnc.14656
34. Reed D. E., Zhang Y., Beyak M. J., Lourenssen S., Blennerhassett M. G., Paterson W. G.. **Stress increases descending inhibition in mouse and human colon.**. (2016) **28** 569-580. DOI: 10.1111/nmo.12755
35. Revollo J. R., Cidlowski J. A.. **Mechanisms generating diversity in glucocorticoid receptor signaling.**. (2009) **1179** 167-178. PMID: 19906239
36. Rincel M., Aubert P., Chevalier J., Grohard P., Basso L., Oliveira C. M.. **Multi-hit early life adversity affects gut microbiota, brain and behavior in a sex-dependent manner.**. (2019) **80** 179-192. DOI: 10.1016/j.bbi.2019.03.006
37. Rivier C. L., Grigoriadis D. E., Rivier J. E.. **Role of corticotropin-releasing factor receptors type 1 and 2 in modulating the rat adrenocorticotropin response to stressors.**. (2003) **144** 2396-2403. DOI: 10.1210/en.2002-0117
38. Schemann M., Sann H., Schaaf C., Mäder M.. **Identification of cholinergic neurons in enteric nervous system by antibodies against choline acetyltransferase.**. (1993) **265** G1005-G1009. DOI: 10.1152/ajpgi.1993.265.5.G1005
39. Shi B., Rabin S. J., Brandoli C., Mocchetti I.. **Dexamethasone induces hypertrophy of developing medial septum cholinergic neurons: Potential role of nerve growth factor.**. (1998) **18** 9326-9334. DOI: 10.1523/JNEUROSCI.18-22-09326.1998
40. Sindelar D. K., Carson M. W., Morin M., Shaw J., Barr R. J., Need A.. **LLY-2707, a novel nonsteroidal glucocorticoid antagonist that reduces atypical antipsychotic-associated weight gain in rats.**. (2014) **348** 192-201. DOI: 10.1124/jpet.113.205864
41. Solomon M. B., Wulsin A. C., Rice T., Wick D., Myers B., McKlveen J.. **The selective glucocorticoid receptor antagonist CORT 108297 decreases neuroendocrine stress responses and immobility in the forced swim test.**. (2014) **65** 363-371. DOI: 10.1016/j.yhbeh.2014.02.002
42. Spencer N. J., Hu H.. **Enteric nervous system: Sensory transduction, neural circuits and gastrointestinal motility.**. (2020) **17** 338-351. PMID: 32152479
43. Taché Y.. **Corticotrophin-releasing factor 1 activation in the central amygdale and visceral hyperalgesia.**. (2015) **27** 1-6. DOI: 10.1111/nmo.12495
44. Taché Y., Bonaz B.. **Corticotropin-releasing factor receptors and stress-related alterations of gut motor function.**. (2007) **117** 33-40. PMID: 17200704
45. Taché Y., Kiank C., Stengel A.. **A role for corticotropin-releasing factor in functional gastrointestinal disorders.**. (2009) **11** 270-277. PMID: 19615302
46. Tache Y., Larauche M., Yuan P.-Q., Million M.. **Brain and gut CRF signaling: Biological actions and role in the gastrointestinal tract.**. (2018) **11** 51-71. DOI: 10.2174/1874467210666170224095741
47. Taché Y., Million M., Nelson A. G., Lamy C., Wang L.. **Role of corticotropin-releasing factor pathways in stress-related alterations of colonic motor function and viscerosensibility in female rodents.**. (2005) **2** 146-154. DOI: 10.1016/s1550-8579(05)80043-9
48. Tasselli M., Chaumette T., Paillusson S., Monnet Y., Lafoux A., Huchet-Cadiou C.. **Effects of oral administration of rotenone on gastrointestinal functions in mice.**. (2013) **25** e183-e193. PMID: 23281940
49. Toliver-Kinsky T., Wood T., Perez-Polo J. R.. **Nuclear factor kappaB/p49 is a negative regulatory factor in nerve growth factor-induced choline acetyltransferase promoter activity in PC12 cells.**. (2000) **75** 2241-2251. DOI: 10.1046/j.1471-4159.2000.0752241.x
50. Traini C., Idrizaj E., Garella R., Faussone-Pellegrini M., Baccari M. C., Vannucchi M. G.. **Otilonium bromide treatment prevents nitrergic functional and morphological changes caused by chronic stress in the distal colon of a rat IBS model.**. (2021) **25** 6988-7000. DOI: 10.1111/jcmm.16710
51. Tsukamoto K., Nakade Y., Mantyh C., Ludwig K., Pappas T. N., Takahashi T.. **Peripherally administered CRF stimulates colonic motility via central CRF receptors and vagal pathways in conscious rats.**. (2006) **290** R1537-R1541. DOI: 10.1152/ajpregu.00713.2005
52. Zalachoras I., Houtman R., Atucha E., Devos R., Tijssen A. M., Hu P.. **Differential targeting of brain stress circuits with a selective glucocorticoid receptor modulator.**. (2013) **110** 7910-7915. DOI: 10.1073/pnas.1219411110
53. Zeisel A., Hochgerner H., Lönnerberg P., Johnsson A., Memic F., van der Zwan J.. **Molecular architecture of the mouse nervous system.**. (2018) **174** 999-1014.e22. PMID: 30096314
54. Zheng G., Wu S., Hu Y., Smith D. E., Wiley J. W., Hong S.. **Corticosterone mediates stress-related increased intestinal permeability in a region-specific manner.**. (2013) **25** e127-e139. DOI: 10.1111/nmo.12066
55. Zong Y., Zhu S., Zhang S., Zheng G., Wiley J. W., Hong S.. **Chronic stress and intestinal permeability: Lubiprostone regulates glucocorticoid receptor-mediated changes in colon epithelial tight junction proteins, barrier function and visceral pain in the rodent and human.**. (2019) **31**. DOI: 10.1111/nmo.13477
|
---
title: Systematic investigation of the underlying mechanisms of GLP-1 receptor agonists
to prevent myocardial infarction in patients with type 2 diabetes mellitus using
network pharmacology
authors:
- Guorong Deng
- Jiajia Ren
- Ruohan Li
- Minjie Li
- Xuting Jin
- Jiamei Li
- Jueheng Liu
- Ya Gao
- Jingjing Zhang
- Xiaochuang Wang
- Gang Wang
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC9971732
doi: 10.3389/fphar.2023.1125753
license: CC BY 4.0
---
# Systematic investigation of the underlying mechanisms of GLP-1 receptor agonists to prevent myocardial infarction in patients with type 2 diabetes mellitus using network pharmacology
## Abstract
Background: Several clinical trials have demonstrated that glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1RAs) reduce the incidence of non-fatal myocardial infarction (MI) in patients with type 2 diabetes mellitus (T2DM). However, the underlying mechanism remains unclear. In this study, we applied a network pharmacology method to investigate the mechanisms by which GLP-1RAs reduce MI occurrence in patients with T2DM.
Methods: Targets of three GLP-1RAs (liraglutide, semaglutide, and albiglutide), T2DM, and MI were retrieved from online databases. The intersection process and associated targets retrieval were employed to obtain the related targets of GLP-1RAs against T2DM and MI. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genes (KEGG) enrichment analyses were performed. The STRING database was used to obtain the protein-protein interaction (PPI) network, and Cytoscape was used to identify core targets, transcription factors, and modules.
Results: A total of 198 targets were retrieved for the three drugs and 511 targets for T2DM with MI. Finally, 51 related targets, including 31 intersection targets and 20 associated targets, were predicted to interfere with the progression of T2DM and MI on using GLP-1RAs. The STRING database was used to establish a PPI network comprising 46 nodes and 175 edges. The PPI network was analyzed using Cytoscape, and seven core targets were screened: AGT, TGFB1, STAT3, TIMP1, MMP9, MMP1, and MMP2. The transcription factor MAFB regulates all seven core targets. The cluster analysis generated three modules. The GO analysis for 51 targets indicated that the terms were mainly enriched in the extracellular matrix, angiotensin, platelets, and endopeptidase. The results of KEGG analysis revealed that the 51 targets primarily participated in the renin-angiotensin system, complement and coagulation cascades, hypertrophic cardiomyopathy, and AGE-RAGE signaling pathway in diabetic complications.
Conclusion: GLP-1RAs exert multi-dimensional effects on reducing the occurrence of MI in T2DM patients by interfering with targets, biological processes, and cellular signaling pathways related to atheromatous plaque, myocardial remodeling, and thrombosis.
## Introduction
Over the past four decades, the number of people living with diabetes has increased from 108 million in 1980 to 537 million in 2021, of which the overwhelming majority (over $90\%$) were diagnosed as type 2 diabetes mellitus (T2DM). In 2021, 6.7 million deaths were caused by diabetes or its complications (International Diabetes Federation, 2022). Among the extensive T2DM-related complications, acute myocardial infarction is a life-threatening and severe complication (Rosenblit, 2019). More than one-third of T2DM patients with myocardial infarction (MI) die within 10 years, and long-term all-cause mortality and cardiovascular mortality are even higher in younger patients than in elderly patients (Singh et al., 2020; Zheng et al., 2021). Numerous studies have shown that strict glycemic control promotes a decrease in non-fatal MI (Rodriguez-Gutierrez et al., 2019). However, intensive controls are followed by severe side effects, such as hypoglycemia; therefore, effective and safe methods for controlling glycemic levels, while simultaneously reducing risk factors for MI, act as necessary interventions in treating patients with T2DM.
Glucagon-like peptide-1 receptor agonists (GLP-1RAs), such as liraglutide and dulaglutide, are widely used to treat patients with T2DM and obesity. They exert beneficial effects, including inhibition of glucagon secretion, delayed gastric emptying, decreased appetite, rare occurrence of hypoglycemia, and controlled weight gain (Helmstadter et al., 2022). In recent years, four clinical trials have shown that dulaglutide (REWIND trial) (Gerstein et al., 2019), albiglutide (HARMONY trial) (Hernandez et al., 2018), semaglutide (SUSTAIN-6 trial) (Marso et al., 2016a), and liraglutide (LEADER trial) (Marso et al., 2016b) have cardiovascular benefits in patients with T2DM, including reducing the occurrence of non-fatal MI. A meta-analysis reported that patients with T2DM benefited from different GLP-1RAs in terms of major adverse cardiac events, all-cause mortality, hospital admission for heart failure, and renal function (Sattar et al., 2021). However, although GLP-1RA therapies are approved and considered safe for treating patients with T2DM, the exerted cardiovascular protection mechanism is still not fully clear. An increasing number of studies have demonstrated that the GLP-1 receptor is expressed in numerous types of cells, including those in the cardiovascular tissues, such as endothelial cells of the left ventricle (GTExPortal, 2021). Theoretically, GLP-1 binds to its receptor, stimulating the adenylyl cyclase pathway, and leading to insulin synthesis and release. As the treatment for T2DM may not fully explain the cardiovascular protective effects of GLP-1RAs, these still must be comprehensively investigated.
Network pharmacology is a big data integration method based on numerous databases and statistical algorithms (Hong et al., 2021). It aims to investigate diseases at the systemic level and define the interaction between drugs and the body based on the equilibrium theory of biological networks (Zhang, 2016). Chronic diseases are generally caused by a complicated dysfunction of a related regulatory network instead of a single protein or gene (Nogales et al., 2022). Based on an integrated research strategy, the network pharmacology method provides a more efficient and convenient system for determining the relationship between drugs and diseases. In this study, we applied an integrated research strategy to investigate the mechanism of specific GLP-1RAs in T2DM and MI, which may provide a comprehensive interpretation of the cardiovascular protective effect of GLP-1RAs. A flow chart of the study process is shown in Figure 1.
**FIGURE 1:** *Flow chart for the process of the study. The flow chart shows the process of investigating the pharmacology mechanism of GLP-1RAs against T2DM and MI.*
## Target prediction for GLP-1 agonists
The chemical structures (mainly in SMILES format) of three GLP-1Ras (liraglutide, semaglutide, and albiglutide) were retrieved from PubChem, an open chemistry database at the National Institutes of Health (https://pubchem.ncbi.nlm.nih.gov). As dulaglutide does not have a defined chemical structure, it was excluded from our study. Next, the following four target prediction databases were selected to retrieve targets for the GLP-1RAs: [1] The Binding Database (http://www.bindingdb.org/bind/ByTargetNames.jsp), a public and web-accessible database containing 2,096,653 binding data points for 8,185 proteins and over 920,703 drug-like molecules (Gilson et al., 2016); [2] The SEA database (https://sea.bkslab.org/), which can be rapidly used to search large compounds and to build cross-target similarity maps (Keiser et al., 2007); [3] The Swiss Target Prediction (http://www.swisstargetprediction.ch/) that allows estimating the most probable protein targets of a small molecule (Daina et al., 2019), and [4] the TargetNet (http://targetnet.scbdd.com/home/index/), an open web server that can be used to predict the binding of multiple targets for any given molecule across 623 proteins by establishing a high-quality model for each human protein (Yao et al., 2016). All targets from the four databases were further standardized into official gene symbols using Universal Protein Resource (https://www.uniprot.org/) (Consortium, 2021) for subsequent analysis.
## Target collection for T2DM and MI
With the keywords “type 2 diabetes,” “type 2 diabetes mellitus,” “myocardial infarction,” “non-fatal myocardial infarction,” “acute myocardial infarction,” “ST-segment elevation myocardial infarction,” and “non-ST-segment elevation myocardial infarction,” the target genes associated with T2DM and MI were retrieved from the PharmGkb (https://www.pharmgkb.org/), TTD (http://db.idrblab.net/ttd/), GeneCards (https://www.genecards.org), DrugBank (https://go.drugbank.com/), and OMIM (https://www.omim.org) databases. The PharmGKB database is a pharmacogenomic knowledge resource containing clinical information (Whirl-Carrillo et al., 2021). The TTD database provides information about the known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information, and the corresponding drugs directed at each target (Wang et al., 2020). The GeneCards database integrates gene-centric data from more than 150 web sources, including genomic, transcriptomic, proteomic, genetic, clinical, and functional information (Stelzer et al., 2016). The DrugBank database contains information regarding drugs and drug targets (Wishart et al., 2018). The OMIM database is a comprehensive and authoritative compendium of human genes and genetic phenotypes. The five databases provided comprehensive and complementary resources for obtaining targets for the diseases. All T2DM and MI targets from the five databases were transformed into the official gene symbol format.
## Related targets
The targets of GLP-1RAs, T2DM, and MI were uploaded to an online Venn diagram tool (http://www.bioinformatics.com.cn/static/others/jvenn/example.html) to obtain a Venn diagram showing the intersection targets of GLP-1RAs against T2DM and MI. Then, the GeneMANIA database was applied to find extra targets, highly associated with the intersection targets, using a massive set of functional association data (Warde-Farley et al., 2010). Finally, these targets and intersecting targets were integrated into a set of related targets for further analysis.
## Construction of the drug-target-disease network
The relationship among the two diseases, three GLP-1RAs, and extra targets was established using Microsoft Excel and then input into Cytoscape (Version 3.8.2) to build and visualize a drug-target-disease network presented by nodes and edges. Nodes represent drugs, diseases, and target genes, whereas edges represent the existing correlations between any two nodes.
## Protein-protein interaction (PPI) network data
The related targets were uploaded to the STRING database (https://string-db.org/) and processed in a multiple protein analysis pattern to obtain the PPI network data. The STRING database focuses on researching the interactive relationships between proteins, which helps identify core regulatory genes (Szklarczyk et al., 2019). Some key parameters were also set. For example, the organism was chosen as Homo sapiens; the interaction score was selected as high confidence of 0.7, and disconnected nodes were hidden in the network. Finally, the network data were downloaded in a “TSV” format file for further analysis, and a visualized network image was obtained.
## Gene Ontology (GO) and Kyoto Encyclopedia of genes and genomes (KEGG) enrichment analysis
To further uncover the underlying biological process and involved signaling pathways in related targets, the KEGG pathway and GO enrichment analysis, including biological process (BP), cellular component (CC), and molecular function (MF), were conducted using Enrichr web tools (Kuleshov et al., 2016), and these enrichment results were presented in a scatter plot using the Appyters web application (Clarke et al., 2021). *Similar* gene sets were clustered in a scatter plot using the Leiden algorithm. According to the q value (adjusted p-value), the top five GO and KEGG analysis terms were listed and marked in the scatter plots.
## PPI network analysis
The “TSV” file of the PPI network data was input to the Cytoscape software to identify hub targets and clusters using the Cytohubba (Version 0.1) and MCODE (Version 2.0.0) plugins, respectively. The Cytohubba plugin provides 11 methods for exploring virtual nodes in biological networks (Chin et al., 2014). Referring to the method from Shen Jiayu et al., the maximal clique centrality (MCC), edge percolated component (EPC), maximum neighborhood component (MNC), and degree algorithms (Shen et al., 2019) were selected in this study to generate four values for each target, calculate a mean value for each algorithm, and finally select the targets for which the values were simultaneously higher than the mean values of each algorithm as the core targets. The Iregulon (Version 1.3) plugin was used to identify the direct transcription factor of core targets (Janky et al., 2014). The MCODE plugin can mine protein complexes or functional modules from complex protein networks (Bader and Hogue, 2003). All the processing parameters were set to the default values. Additionally, the most important node in the cluster, SEED node, may be the critical target of each cluster. Next, the targets in each cluster were subjected to KEGG pathway enrichment analysis using the Enrichr web tool.
## Potential targets of the agonists/diseases and their related targets
We collected the molecular structures of three GLP-1RAs (liraglutide, semaglutide, and albiglutide) from the PubChem database. The detailed information is listed in Table 1. In total, 210 targets were obtained from the Binding Database: 77 were for albiglutide, 77 for liraglutide, and 56 for semaglutide. A total of 370 targets were identified using the SEA database: 155 for albiglutide, 133 for liraglutide, and 82 for semaglutide. The Swiss database was used to obtain 158 potential targets: 62 for albiglutide, 60 for liraglutide, and 36 for semaglutide. From the TargetNet database, 167 targets were obtained: 61 for albiglutide, 56 for liraglutide, and 50 for semaglutide. Finally, 198 targets remained after the integration and elimination of duplicates.
**TABLE 1**
| Order | Name | PubChem ID | Molecular formula | Molecular weight |
| --- | --- | --- | --- | --- |
| 1 | Albiglutide | 145994868 | C148H224N40O45 | 3283.6 |
| 2 | Liraglutide | 16134956 | C172H265N43O51 | 3751.0 |
| 3 | Semaglutide | 56843331 | C187H291N45O59 | 4114.0 |
When collecting disease targets, T2DM-related keywords were input into the five databases, and a total of 5004 targets were obtained: 4486 from the GeneCards database, 282 from the OMIM database, 24 from the PharmGkb database, 109 from the TTD database, and 103 from the DrugBank database. Finally, 4623 targets remained after removing repetitions. Parallelly, we also retrieved the MI-related targets from the five databases, and 727 targets were identified, including 313 from the GeneCards database, 18 from the OMIM database, 121 from the PharmGkb database, 47 from the TTD database, and 133 from the DrugBank database. A total of 511 unique targets were identified after removing duplicates. Interestingly, 511 targets of MI were all covered into targets of T2DM. The online Venn diagram tool generated 31 intersection targets between drugs and diseases (Figure 2A).
**FIGURE 2:** *Intersection targets and associated targets constructed the protein-protein interaction (PPI) network. (A) The 31 intersection targets overlap between the targets of GLP-1RAs and the targets of T2DM with MI. (B) Twenty targets associated with 31 intersection targets were generated via the GeneMANIA database. (C) The 51 related targets constructed a PPI network containing 46 nodes and 175 edges. The interaction score was set at 0.7 (high confidence) and hid disconnected nodes in the network.*
The GeneMANIA database provided additional 20 targets (Figure 2B), highly associated with intersection targets. Finally, 51 related targets were identified, indicating potential mechanisms to understand how diabetic patients benefit from the three GLP-1RAs in preventing MI. All 51 related targets were uploaded to the STRING database, and a PPI diagram (Figure 2C) and a TSV format file were obtained. The PPI network included 46 nodes and 175 edges.
## Construction of the drugs-diseases-targets network
The three GLP-1RAs, T2DM and MI, and 51 related targets were input into Cytoscape to construct a drug-disease-target network (Figure 3A). The network contains 56 nodes and 114 edges. We found that T2DB was linked to 47 related targets and MI to 38 targets. Furthermore, 25 connections linked albiglutide to all targets, 21 to liraglutide, and 13 to semaglutide. In contrast, four nodes (NLRC4, ITGA11, ITGA9, and CTSZ) were not associated with any agonists or diseases. Although the three agonists had disparate targets, we intended to explore some shared targets for intervention in the progression of T2DM and MI, and 12 common targets were identified (Figure 3B), namely, AGTR1, AGTR2, CASP1, CCNA2, CCND1, CXCR4, EDNRA, F7, MME, REN, SCN5A, and SIRT1, suggesting that these 12 targets may contribute to the fundamental mechanisms of GLP-1RAs against T2DM and MI.
**FIGURE 3:** *The construction of a drug-target-disease network. (A) Network construction of drug-target-disease composed of three drugs (purple), 102 targets (cyan), and two diseases (orange) via Cytoscape software. (B) Venn diagram shows 12 common targets of three drugs against T2DM and MI, and these targets could be regarded as crucial factors mediating the effects of GLP-1RAs on T2DM and MI.*
## Analysis of the PPI network of the related targets
The TSV file of the PPI network was loaded into Cytoscape software, and the MMC, MNC, EPC, and Degree algorithms in the “Cytohubba” plugin were used to calculate the core targets. Seven core targets were identified, including AGT, TGFB1, STAT3, TIMP1, MMP9, MMP1, and MMP2 (Figure 4A), suggesting that they may play a pivotal role in the PPI network of GLP-1RAs interfering with T2DM and MI.
**FIGURE 4:** *Seven core targets and one important transcription factor. (A) The core targets are obtained by the intersection of the four algorithms’ results: AGT, TGFB1, STAT3, TIMP1, MMP9, MMP1, and MMP2. (B) The interaction between the transcription factor (octagon) and seven core targets (ellipse) was analyzed and constructed by the Iregulon plugin in Cytoscape software.*
Next, the Iregulon plugin was applied to find the direct transcription factors of seven core targets, and the normalized enrichment score (NES) was calculated and used for ranking purposes (Table 2). The higher the NES value, the better the confidence. The transcription factor MAFB had the highest transcription target number and NES value simultaneously (target number = 7; NES = 7.802) (Figure 4B). The Iregulon plugin detects transcription factors using more than one thousand ChIP-Seq tracks, providing highly credible results. Therefore, we suggest that MAFB may positively contribute to the beneficial effect of GLP-1RAs in reducing MI in T2DM patients.
**TABLE 2**
| Rank | Transcript factor | NES | Targets number | Motifs/Tracks |
| --- | --- | --- | --- | --- |
| 1 | MAFB | 7.802 | 7 | 11 |
| 2 | ATF6 | 7.084 | 5 | 10 |
| 3 | POU3F4 | 5.922 | 4 | 4 |
| 4 | CBFB | 5.803 | 3 | 3 |
| 5 | CEBPA | 5.719 | 4 | 10 |
| 6 | FOXO1 | 5.684 | 3 | 8 |
| 7 | FOXA1 | 5.561 | 6 | 14 |
| 8 | NKFB1 | 5.536 | 4 | 6 |
| 9 | SLC18A1 | 5.516 | 2 | 1 |
| 10 | POU4F3 | 5.373 | 3 | 8 |
The MCODE plugin was then used to predict the modules in the PPI network. The targets were clustered into three modules. Each module represents a densely connected region of the molecular interaction network (Bader and Hogue, 2003). Detailed characteristics of the modules are shown in Figure 5. Moreover, three seed nodes, TIMP1, AGT, and TFPI (marked by a rhombic shape), had the highest weights in the respective modules. KEGG analysis was performed using the Enrichr web tool. The three top-ranked terms in each module were the AGE-RAGE signaling pathway in diabetic complications (module 1), the renin-angiotensin system (RAS) (module 2), and complement and coagulation cascades (module 3). KEGG results are presented in Table 3.
**FIGURE 5:** *Three modules were clustered from 46 related targets of the PPI network and distinguished by different colors. The module-1 has top clustered strength according to the score. The size of each node is proportionate to the degree value of the node. The diamond-shaped nodes in each module represent the seed node with the highest weight.* TABLE_PLACEHOLDER:TABLE 3
## Global GO and KEGG enrichment analysis
The 51 related targets were uploaded to Enrichr for KEGG and GO enrichment analyses, and the top five terms ranked by q-value were selected for display. For the GO enrichment analysis, we identified the top five terms from BP, CC, and MF. The results are shown in detail in Figures 6A–C. First, in BP analysis, the top three terms were extracellular matrix (ECM) organization, angiotensin maturation, and regulation of angiotensin levels in the blood. The top three terms for CC were collagen-containing ECM, platelet-alpha granules, and platelet-alpha granule lumen. In the MF analysis, the top three terms were endopeptidase activity, serine-type endopeptidase activity, and serine-type peptidase activity.
**FIGURE 6:** *GO and KEGG enrichment analysis of 51 related targets via the Enrichr database. (A–C) Biological processes, cellular components, and molecular functions in GO biological annotation analysis. (D) KEGG pathway enrichment analysis entries. All results show the top five items according to the q value. The lower the q value, the higher the credibility.*
The top five terms for global KEGG enrichment analysis are shown in Figure 6D. The detailed terms were RAS, complement and coagulation cascades, pathways in cancer, hypertrophic cardiomyopathy, and the AGE-RAGE signaling pathway in diabetic complications.
## Discussion
Complications related to diabetes result in 1.5 million deaths per year, and cardiovascular events are the primary cause of death (Collaborators, 2018). Several trials have demonstrated that GLP-1RAs protect diabetic patients from the occurrence of MI, but the underlying mechanism remains unclear. The present study provides novel and systematic explanations for how GLP-1RAs decrease the occurrence of MI in T2DM patients. We applied network pharmacology to predict 51 related targets between GLP-1RAs and diseases and filtered out seven core targets: AGT, TGFB1, STAT3, TIMP1, MMP9, MMP1, and MMP2. MAFB is an essential transcription factor that regulates the expression of all seven core targets. The GO enrichment terms mainly involved angiotensin, ECM, and platelets. For KEGG enrichment analysis, the related targets were principally enriched in RAS, complement and coagulation cascades, and the AGE-RAGE signaling pathway in diabetic complications.
Researchers have proposed various hypotheses to elucidate the therapeutic mechanism of GLP-1RAs in cardiovascular diseases. Many studies have consistently reported that several GLP-1RAs exert inhibitory effects on plaque formation, development, and rupture (Tashiro et al., 2014; Sudo et al., 2017; Wu et al., 2019; Li et al., 2020). However, evidence is insufficient to comprehensively explain the signaling pathways involved in the prophylactic effects of GLP-1RAs on MI. Our results are aligned with previous studies and hypotheses while also unveiling novel perspectives. After applying the CytoHubba plugin using a multi-algorithm, we identified seven core targets. Four targets (MMP1, MMP2, MMP9, and TIMP1) are associated with metalloproteinases. MMP1, MMP2, and MMP9 participate in ECM proteolysis, whereas TIMP1 acts as a metalloproteinase inhibitor that inhibits the function of MMP 1, MMP2, and MMP9 (Moore et al., 2012). The dynamic balance between MMPs and TIMP1 maintains myocardial ECM stability. Several reports have demonstrated that diabetes disrupts the balance of MMPs/TIMPs in the serum and related tissues (Li et al., 2013; Bastos et al., 2017; Zhou et al., 2021), which may significantly enhance the activities of MMPs and pathological remodeling of the vessel wall (Wang and Khalil, 2018), subsequently resulting in obstruction and ischemia. Several reports have shown that GLP-1, exenatide, and semaglutide reduce MMP expression (such as MMP1, MMP2, MMP9, and MMP13), which maintains intact fibrous caps and protects atheromatous plaque from rupture (Burgmaier et al., 2013; Garczorz et al., 2018; Rakipovski et al., 2018). GLP-1RAs may contribute to the reduction of atherosclerotic plaque instability and cardiac ECM degradation by maintaining the balance between MMPs and TIMPs.
The other three core targets identified were AGT, STAT3, and TGFB1. AGT-encoding angiotensinogen is an essential component of the RAS and participates in the regulation of blood pressure, body fluids, and electrolyte balance. Angiotensinogen undergoes two cleavages to form angiotensin II (Ang II), which has well-known adverse effects on the myocardium. A recent study showed that the mRNA expression of GLP-1R was considerably associated with the components of the renin-angiotensin-aldosterone system (RAAS) detected in epicardial and pericardial fat in patients with severe coronary artery disease (Haberka et al., 2021). However, the interactive regulation between GLP-1R and the RAAS is still unclear. TGFB1 mediates Ang II-induced myocardial fibrosis (Frangogiannis, 2019). Limited data have shown an inconsistent relationship between GLP-1RAs and TGFB1. Long-acting semaglutide decreased hepatic TGFB1 expression (McLean et al., 2021), whereas exendin-4 and liraglutide did not reduce TGFB1 levels in adipose tissue (Pastel et al., 2016; Pastel et al., 2017). Although GLP-1 has a beneficial effect on myocardial ECM remodeling (Robinson et al., 2015), the relationship between GLP-1RAs and TGFB1 in the heart is unclear and requires further investigation.
STAT3 responds to cytokines and growth factors. Shiraishi et al. demonstrated that GLP-1 induces macrophage transformation into the M2 phenotype, contributing to the beneficial effects of GLP-1 against diabetes (Shiraishi et al., 2012). A later study also provided consistent evidence that in the process of myocardial repair, STAT3 activation was a prerequisite for macrophage transformation to the reparative M2 phenotype (Shirakawa et al., 2018). Thus, the changes induced by GLP-1RAs contribute to a reduction in the size and instability of atherosclerotic plaques (Vinué et al., 2017), which could, to some extent, explain the lower MI mortality and incidence in patients using GLP-1RA.
The Iregulon plugin used in this study showed that the transcription factor MAFB may regulate all seven core targets, suggesting that it exerts crucial effects on GLP-RAs by interfering with T2DM and MI. In patients with T2DM, MAFB expression is significantly reduced (Guo et al., 2013). In contrast, overexpressed MAFB can upregulate some cell cycle regulators and subsequently promote human β cell proliferation (Lu et al., 2012). Many previous studies have considered MAFA to be a characteristic of human β-cell function, whereas this view is increasingly being challenged (Velazco-Cruz et al., 2019). Recently, it was suggested that MAFB could be regarded as an essential regulator of the human β-cell signature (Russell et al., 2020). However, only a few studies have tried to reveal the potential relationship between GLP-1 and MAFB. A recent study showed that exendin [9-39] accelerated the transdifferentiation from α cells to β cells by reducing MAFB expression in α cells (Zhang et al., 2019). Therefore, the relationship between GLP-1 and MAFB in β-cells warrants further investigation.
The MCODE plugin provides a practical clustering algorithm to identify the potential functional modules behind these targets. Three modules were obtained in this study and were subsequently subjected to KEGG analysis. The enrichment results mainly focused on the AGE-RAGE signaling pathway, RAS, and complement and coagulation cascades. These biological processes have been demonstrated to have a significant pathogenic relationship with T2DM and MI (Beckman et al., 2002; Husain et al., 2015; Yamagishi, 2019). Some researchers have provided evidence regarding the association between GLP-1RAs and the abovementioned results. GLP-1RAs, such as liraglutide and exenatide, attenuate RAGE expression in several cell types, especially under diabetic conditions (Zhang et al., 2016; Zhang et al., 2017; Zhang et al., 2020), suggesting that the downregulation of RAGE represents a potential mechanism of GLP-1RAs against T2DM. In module two, RAS was a significant KEGG-enriched item. GLP-1RAs play a competitive role in regulating RAS by inhibiting renin synthesis and increasing the inactive form of renin in blood circulation (Puglisi et al., 2021). The final module included three coagulation-related targets. To date, there is rare information on the direct influence of GLP-1RAs on the processes of coagulation cascades. Furthermore, hyperglycemia facilitates coagulation activation and invalidation of fibrinolytic activity in diabetic patients (Sechterberger et al., 2015). Thus, we hypothesize that the effect of GLP-1RAs on blood coagulation is mediated primarily through controlling blood glucose levels.
Several GLP-1RAs trials have demonstrated that these agonists reduce cardiovascular risk factors, including glycated hemoglobin (HbAc1) values, systolic blood pressure, and body weight (Marso et al., 2016a; Marso et al., 2016b; Hernandez et al., 2018). The median duration of these trials ranged from 1.6 to 3.8 years. Cumulative beneficial changes induced by GLP-1RAs contributed to a decrease in MI prevalence. However, the exact mechanisms underlying the contribution of GLP-1RAs remain unclear, as no cardiac tissue was detected in the trials. The GO and KEGG analyses showed that the enrichment terms mainly focused on ECM, coagulation, RAS, and endopeptidase. As most matrix metalloproteinases are elastase-type endopeptidases, mainly MMP2 and MMP9 (Shapiro, 1998), the dynamic balance of MMPs/TIMPs maintains the stabilization of the ECM; however, diabetes disturbs this balance and causes atherosclerotic plaque disruption, myocardial fibrosis, and remodeling. Four of the seven core targets were involved in this balance, and AGT and TGFB1 directly influenced fibrosis and remodeling. These pathological processes considerably induce cardiac death in diabetic cardiomyopathy and acute MI. Therefore, our results suggest that GLP-1RAs play a crucial role in regulating plaque stability, myocardial fibrosis, and remodeling. Other significant enrichment sets included coagulation, complement, and platelets. The burden of thrombus formation induced by coagulation and the complement system in the coronary artery mainly determines the MI area and clinical outcomes (Sianos et al., 2007; Sianos et al., 2010). Thus, inhibition of the coagulation cascades is an effective measure when plaque ruptures occur. To a certain extent, the related targets and enrichment terms involved in coagulation and complement provide a plausible mechanistic explanation for the fact that some GLP-1RAs reduce non-fatal MI in patients with T2DM.
## Conclusion
Our study provides a comprehensive investigation and analysis of the multi-dimensional effects of GLP-1RAs on preventing MI in patients with T2DM, which may be mainly mediated by interfering with specific targets, biological processes, and cellular signaling pathways related to atheromatous plaque, myocardial remodeling, and thrombosis. However, this study has some limitations as it lacks a series of experiments to prove the proposed hypothesis. Accordingly, further experiments and multi-omics studies are warranted to understand the comprehensive mechanism of GLP-1RAs.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Author contributions
GW, XW, and GD generated the conception of the study; GD and JR analyzed the data and wrote the manuscript; XJ, ML, and RL helped to revise and improve the manuscript; JL, YG, and JZ, assisted in retrieving for databases.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. Bader G. D., Hogue C. W.. **An automated method for finding molecular complexes in large protein interaction networks**. *BMC Bioinforma.* (2003) **4** 2. DOI: 10.1186/1471-2105-4-2
2. Bastos M. F., Tucci M. A., de Siqueira A., de Faveri M., Figueiredo L. C., Vallim P. C.. **Diabetes may affect the expression of matrix metalloproteinases and their inhibitors more than smoking in chronic periodontitis**. *J. Periodontal Res.* (2017) **52** 292-299. DOI: 10.1111/jre.12394
3. Beckman J. A., Creager M. A., Libby P.. **Diabetes and atherosclerosis: Epidemiology, pathophysiology, and management**. *Jama* (2002) **287** 2570-2581. DOI: 10.1001/jama.287.19.2570
4. Burgmaier M., Liberman A., Möllmann J., Kahles F., Reith S., Lebherz C.. **Glucagon-like peptide-1 (GLP-1) and its split products GLP-1(9-37) and GLP-1(28-37) stabilize atherosclerotic lesions in apoe⁻/⁻ mice**. *Atherosclerosis* (2013) **231** 427-435. DOI: 10.1016/j.atherosclerosis.2013.08.033
5. Chin C. H., Chen S. H., Wu H. H., Ho C. W., Ko M. T., Lin C. Y.. **cytoHubba: identifying hub objects and sub-networks from complex interactome**. *BMC Syst. Biol.* (2014) **8** S11. DOI: 10.1186/1752-0509-8-S4-S11
6. Clarke D. J. B., Jeon M., Stein D. J., Moiseyev N., Kropiwnicki E., Dai C.. **Appyters: Turning jupyter notebooks into data-driven web apps**. *Patterns (N Y)* (2021) **2** 100213. DOI: 10.1016/j.patter.2021.100213
7. Collaborators G. D. a. I. I. a. P.. **Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: A systematic analysis for the global burden of disease study 2017**. *Lancet* (2018) **392** 1789-1858. DOI: 10.1016/S0140-6736(18)32279-7
8. Consortium U.. **UniProt: The universal protein knowledgebase in 2021**. *Nucleic Acids Res.* (2021) **49** D480-d489. DOI: 10.1093/nar/gkaa1100
9. Daina A., Michielin O., Zoete V.. **SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules**. *Nucleic Acids Res.* (2019) **47** W357-w364. DOI: 10.1093/nar/gkz382
10. Frangogiannis N. G.. **Cardiac fibrosis: Cell biological mechanisms, molecular pathways and therapeutic opportunities**. *Mol. Asp. Med.* (2019) **65** 70-99. DOI: 10.1016/j.mam.2018.07.001
11. Garczorz W., Gallego-Colon E., Kosowska A., Kłych-Ratuszny A., Woźniak M., Marcol W.. **Exenatide exhibits anti-inflammatory properties and modulates endothelial response to tumor necrosis factor α-mediated activation**. *Cardiovasc Ther.* (2018) **36** e12317. DOI: 10.1111/1755-5922.12317
12. Gerstein H. C., Colhoun H. M., Dagenais G. R., Diaz R., Lakshmanan M., Pais P.. **Dulaglutide and renal outcomes in type 2 diabetes: An exploratory analysis of the REWIND randomised, placebo-controlled trial**. *Lancet* (2019) **394** 131-138. DOI: 10.1016/S0140-6736(19)31150-X
13. Gilson M. K., Liu T., Baitaluk M., Nicola G., Hwang L., Chong J.. **BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology**. *Nucleic Acids Res.* (2016) **44** D1045-D1053. DOI: 10.1093/nar/gkv1072
14. **Transcripts per million (TPM) of the GLP1R gene [Online]**. (2021)
15. Guo S., Dai C., Guo M., Taylor B., Harmon J. S., Sander M.. **Inactivation of specific β cell transcription factors in type 2 diabetes**. *J. Clin. Invest* (2013) **123** 3305-3316. DOI: 10.1172/jci65390
16. Haberka M., Siniarski A., Gajos G., Machnik G., Kowalówka A., Deja M.. **Epicardial, pericardial fat and glucagon-like peptide-1 and -2 receptors expression in stable patients with multivessel coronary artery disease: An association with the renin-angiotensin-aldosterone system**. *Pol. Arch. Intern Med.* (2021) **131** 233-240. DOI: 10.20452/pamw.15797
17. Helmstadter J., Keppeler K., Kuster L., Munzel T., Daiber A., Steven S.. **Glucagon-like peptide-1 (GLP-1) receptor agonists and their cardiovascular benefits-The role of the GLP-1 receptor**. *Br. J. Pharmacol.* (2022) **179** 659-676. DOI: 10.1111/bph.15462
18. Hernandez A. F., Green J. B., Janmohamed S., D'Agostino R. B., Sr C. B.. **Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (harmony outcomes): A double-blind, randomised placebo-controlled trial**. *Lancet* (2018) **392** 1519-1529. DOI: 10.1016/S0140-6736(18)32261-X
19. Hong Z., Zhang T., Zhang Y., Xie Z., Lu Y., Yao Y.. **Reveals of candidate active ingredients in Justicia and its anti-thrombotic action of mechanism based on network pharmacology approach and experimental validation**. *Sci. Rep.* (2021) **11** 17187. DOI: 10.1038/s41598-021-96683-z
20. Husain K., Hernandez W., Ansari R. A., Ferder L.. **Inflammation, oxidative stress and renin angiotensin system in atherosclerosis**. *World J. Biol. Chem.* (2015) **6** 209-217. DOI: 10.4331/wjbc.v6.i3.209
21. International Diabetes Federation (2022). IDF diabetes atlas. 10th ed[Online]. Brussels, Belgium. 2021Available: https://www.diabetesatlas.org/ (Accessed).. *IDF diabetes atlas* (2022)
22. Janky R., Verfaillie A., Imrichova H., Van de Sande B., Standaert L., Christiaens V.. **iRegulon: from a gene list to a gene regulatory network using large motif and track collections**. *PLoS Comput. Biol.* (2014) **10** e1003731. DOI: 10.1371/journal.pcbi.1003731
23. Keiser M. J., Roth B. L., Armbruster B. N., Ernsberger P., Irwin J. J., Shoichet B. K.. **Relating protein pharmacology by ligand chemistry**. *Nat. Biotechnol.* (2007) **25** 197-206. DOI: 10.1038/nbt1284
24. Kuleshov M. V., Jones M. R., Rouillard A. D., Fernandez N. F., Duan Q., Wang Z.. **Enrichr: A comprehensive gene set enrichment analysis web server 2016 update**. *Nucleic Acids Res.* (2016) **44** W90-W97. DOI: 10.1093/nar/gkw377
25. Li Q., Tuo X., Li B., Deng Z., Qiu Y., Xie H.. **Semaglutide attenuates excessive exercise-induced myocardial injury through inhibiting oxidative stress and inflammation in rats**. *Life Sci.* (2020) **250** 117531. DOI: 10.1016/j.lfs.2020.117531
26. Li Z., Guo S., Yao F., Zhang Y., Li T.. **Increased ratio of serum matrix metalloproteinase-9 against TIMP-1 predicts poor wound healing in diabetic foot ulcers**. *J. Diabetes Complicat.* (2013) **27** 380-382. DOI: 10.1016/j.jdiacomp.2012.12.007
27. Lu J., Hamze Z., Bonnavion R., Herath N., Pouponnot C., Assade F.. **Reexpression of oncoprotein MafB in proliferative β-cells and Men1 insulinomas in mouse**. *Oncogene* (2012) **31** 3647-3654. DOI: 10.1038/onc.2011.538
28. Marso S. P., Bain S. C., Consoli A., Eliaschewitz F. G., Jodar E., Leiter L. A.. **Semaglutide and cardiovascular outcomes in patients with type 2 diabetes**. *N. Engl. J. Med.* (2016a) **375** 1834-1844. DOI: 10.1056/NEJMoa1607141
29. Marso S. P., Daniels G. H., Brown-Frandsen K., Kristensen P., Mann J. F., Nauck M. A.. **Liraglutide and cardiovascular outcomes in type 2 diabetes**. *N. Engl. J. Med.* (2016b) **375** 311-322. DOI: 10.1056/NEJMoa1603827
30. McLean B. A., Wong C. K., Kaur K. D., Seeley R. J., Drucker D. J.. **Differential importance of endothelial and hematopoietic cell GLP-1Rs for cardiometabolic versus hepatic actions of semaglutide**. *JCI Insight* (2021) **6** e153732. DOI: 10.1172/jci.insight.153732
31. Moore L., Fan D., Basu R., Kandalam V., Kassiri Z.. **Tissue inhibitor of metalloproteinases (TIMPs) in heart failure**. *Heart Fail Rev.* (2012) **17** 693-706. DOI: 10.1007/s10741-011-9266-y
32. Nogales C., Mamdouh Z. M., List M., Kiel C., Casas A. I., Schmidt H.. **Network pharmacology: Curing causal mechanisms instead of treating symptoms**. *Trends Pharmacol. Sci.* (2022) **43** 136-150. DOI: 10.1016/j.tips.2021.11.004
33. Pastel E., Joshi S., Knight B., Liversedge N., Ward R., Kos K.. **Effects of Exendin-4 on human adipose tissue inflammation and ECM remodelling**. *Nutr. Diabetes* (2016) **6** e235. DOI: 10.1038/nutd.2016.44
34. Pastel E., McCulloch L. J., Ward R., Joshi S., Gooding K. M., Shore A. C.. **GLP-1 analogue-induced weight loss does not improve obesity-induced AT dysfunction**. *Clin. Sci. (Lond)* (2017) **131** 343-353. DOI: 10.1042/cs20160803
35. Puglisi S., Rossini A., Poli R., Dughera F., Pia A., Terzolo M.. **Effects of SGLT2 inhibitors and GLP-1 receptor agonists on renin-angiotensin-aldosterone system**. *Front. Endocrinol. (Lausanne)* (2021) **12** 738848. DOI: 10.3389/fendo.2021.738848
36. Rakipovski G., Rolin B., Nøhr J., Klewe I., Frederiksen K. S., Augustin R.. **The GLP-1 analogs liraglutide and semaglutide reduce atherosclerosis in ApoE(-/-) and LDLr(-/-) mice by a mechanism that includes inflammatory pathways**. *JACC Basic Transl. Sci.* (2018) **3** 844-857. DOI: 10.1016/j.jacbts.2018.09.004
37. Robinson E., Cassidy R. S., Tate M., Zhao Y., Lockhart S., Calderwood D.. **Exendin-4 protects against post-myocardial infarction remodelling via specific actions on inflammation and the extracellular matrix**. *Basic Res. Cardiol.* (2015) **110** 20. DOI: 10.1007/s00395-015-0476-7
38. Rodriguez-Gutierrez R., Gonzalez-Gonzalez J. G., Zuñiga-Hernandez J. A., McCoy R. G.. **Benefits and harms of intensive glycemic control in patients with type 2 diabetes**. *Bmj* (2019) **367** l5887. DOI: 10.1136/bmj.l5887
39. Rosenblit P. D.. **Extreme atherosclerotic cardiovascular disease (ASCVD) risk recognition**. *Curr. Diab Rep.* (2019) **19** 61. DOI: 10.1007/s11892-019-1178-6
40. Russell R., Carnese P. P., Hennings T. G., Walker E. M., Russ H. A., Liu J. S.. **Loss of the transcription factor MAFB limits β-cell derivation from human PSCs**. *Nat. Commun.* (2020) **11** 2742. DOI: 10.1038/s41467-020-16550-9
41. Sattar N., Lee M. M. Y., Kristensen S. L., Branch K. R. H., Del Prato S., Khurmi N. S.. **Cardiovascular, mortality, and kidney outcomes with GLP-1 receptor agonists in patients with type 2 diabetes: A systematic review and meta-analysis of randomised trials**. *Lancet Diabetes Endocrinol.* (2021) **9** 653-662. DOI: 10.1016/S2213-8587(21)00203-5
42. Sechterberger M. K., Hermanides J., Poolman R. W., Kal J. E., Meijers J. C., Hoekstra J. B.. **Lowering blood glucose during hip surgery does not influence coagulation activation**. *BBA Clin.* (2015) **3** 227-232. DOI: 10.1016/j.bbacli.2015.03.001
43. Shapiro S. D.. **Matrix metalloproteinase degradation of extracellular matrix: Biological consequences**. *Curr. Opin. Cell Biol.* (1998) **10** 602-608. DOI: 10.1016/s0955-0674(98)80035-5
44. Shen J., Yu S., Sun X., Yin M., Fei J., Zhou J.. **Identification of key biomarkers associated with development and prognosis in patients with ovarian carcinoma: Evidence from bioinformatic analysis**. *J. Ovarian Res.* (2019) **12** 110. DOI: 10.1186/s13048-019-0578-1
45. Shiraishi D., Fujiwara Y., Komohara Y., Mizuta H., Takeya M.. **Glucagon-like peptide-1 (GLP-1) induces M2 polarization of human macrophages via STAT3 activation**. *Biochem. Biophys. Res. Commun.* (2012) **425** 304-308. DOI: 10.1016/j.bbrc.2012.07.086
46. Shirakawa K., Endo J., Kataoka M., Katsumata Y., Yoshida N., Yamamoto T.. **IL (Interleukin)-10-STAT3-Galectin-3 Axis is essential for osteopontin-producing reparative macrophage polarization after myocardial infarction**. *Circulation* (2018) **138** 2021-2035. DOI: 10.1161/circulationaha.118.035047
47. Sianos G., Papafaklis M. I., Daemen J., Vaina S., van Mieghem C. A., van Domburg R. T.. **Angiographic stent thrombosis after routine use of drug-eluting stents in ST-segment elevation myocardial infarction: The importance of thrombus burden**. *J. Am. Coll. Cardiol.* (2007) **50** 573-583. DOI: 10.1016/j.jacc.2007.04.059
48. Sianos G., Papafaklis M. I., Serruys P. W.. **Angiographic thrombus burden classification in patients with ST-segment elevation myocardial infarction treated with percutaneous coronary intervention**. *J. Invasive Cardiol.* (2010) **22** 6B-14B. PMID: 20048390
49. Singh A., Gupta A., DeFilippis E. M., Qamar A., Biery D. W., Almarzooq Z.. **Cardiovascular mortality after type 1 and type 2 myocardial infarction in Young adults**. *J. Am. Coll. Cardiol.* (2020) **75** 1003-1013. DOI: 10.1016/j.jacc.2019.12.052
50. Stelzer G., Rosen N., Plaschkes I., Zimmerman S., Twik M., Fishilevich S.. **The GeneCards suite: From gene data mining to disease genome sequence analyses**. *Curr. Protoc. Bioinforma.* (2016) **54** 131-3031. DOI: 10.1002/cpbi.5
51. Sudo M., Li Y., Hiro T., Takayama T., Mitsumata M., Shiomi M.. **Inhibition of plaque progression and promotion of plaque stability by glucagon-like peptide-1 receptor agonist: Serial**. *Atherosclerosis* (2017) **265** 283-291. DOI: 10.1016/j.atherosclerosis.2017.06.920
52. Szklarczyk D., Gable A. L., Lyon D., Junge A., Wyder S., Huerta-Cepas J.. **STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets**. *Nucleic Acids Res.* (2019) **47** D607-D613. DOI: 10.1093/nar/gky1131
53. Tashiro Y., Sato K., Watanabe T., Nohtomi K., Terasaki M., Nagashima M.. **A glucagon-like peptide-1 analog liraglutide suppresses macrophage foam cell formation and atherosclerosis**. *Peptides* (2014) **54** 19-26. DOI: 10.1016/j.peptides.2013.12.015
54. Velazco-Cruz L., Song J., Maxwell K. G., Goedegebuure M. M., Augsornworawat P., Hogrebe N. J.. **Acquisition of dynamic function in human stem cell-derived β cells**. *Stem Cell Rep.* (2019) **12** 351-365. DOI: 10.1016/j.stemcr.2018.12.012
55. Vinué Á., Navarro J., Herrero-Cervera A., García-Cubas M., Andrés-Blasco I., Martínez-Hervás S.. **The GLP-1 analogue lixisenatide decreases atherosclerosis in insulin-resistant mice by modulating macrophage phenotype**. *Diabetologia* (2017) **60** 1801-1812. DOI: 10.1007/s00125-017-4330-3
56. Wang X., Khalil R. A.. **Matrix metalloproteinases, vascular remodeling, and vascular disease**. *Adv. Pharmacol.* (2018) **81** 241-330. DOI: 10.1016/bs.apha.2017.08.002
57. Wang Y., Zhang S., Li F., Zhou Y., Zhang Y., Wang Z.. **Therapeutic target database 2020: Enriched resource for facilitating research and early development of targeted therapeutics**. *Nucleic Acids Res.* (2020) **48** D1031-D1041. DOI: 10.1093/nar/gkz981
58. Warde-Farley D., Donaldson S. L., Comes O., Zuberi K., Badrawi R., Chao P.. **The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function**. *Nucleic Acids Res.* (2010) **38** W214-W220. DOI: 10.1093/nar/gkq537
59. Whirl-Carrillo M., Huddart R., Gong L., Sangkuhl K., Thorn C. F., Whaley R.. **An evidence-based framework for evaluating pharmacogenomics knowledge for personalized medicine**. *Clin. Pharmacol. Ther.* (2021) **110** 563-572. DOI: 10.1002/cpt.2350
60. Wishart D. S., Feunang Y. D., Guo A. C., Lo E. J., Marcu A., Grant J. R.. **DrugBank 5.0: A major update to the DrugBank database for 2018**. *Nucleic Acids Res.* (2018) **46** D1074-d1082. DOI: 10.1093/nar/gkx1037
61. Wu Y. C., Wang W. T., Lee S. S., Kuo Y. R., Wang Y. C., Yen S. J.. **Glucagon-like peptide-1 receptor agonist attenuates autophagy to ameliorate pulmonary arterial hypertension through Drp1/NOX- and atg-5/atg-7/beclin-1/lc3β pathways**. *Int. J. Mol. Sci.* (2019) **20** 3435. DOI: 10.3390/ijms20143435
62. Yamagishi S. I.. **Role of advanced glycation endproduct (AGE)-Receptor for advanced glycation endproduct (RAGE) Axis in cardiovascular disease and its therapeutic intervention**. *Circ. J.* (2019) **83** 1822-1828. DOI: 10.1253/circj.CJ-19-0618
63. Yao Z. J., Dong J., Che Y. J., Zhu M. F., Wen M., Wang N. N.. **TargetNet: A web service for predicting potential drug-target interaction profiling via multi-target SAR models**. *J. Comput. Aided Mol. Des.* (2016) **30** 413-424. DOI: 10.1007/s10822-016-9915-2
64. Zhang H., Chu Y., Zheng H., Wang J., Song B., Sun Y.. **Liraglutide improved the cognitive function of diabetic mice via the receptor of advanced glycation end products down-regulation**. *Aging (Albany NY)* (2020) **13** 525-536. DOI: 10.18632/aging.202162
65. Zhang S. S., Wu Z., Zhang Z., Xiong Z. Y., Chen H., Huang Q. B.. **Glucagon-like peptide-1 inhibits the receptor for advanced glycation endproducts to prevent podocyte apoptosis induced by advanced oxidative protein products**. *Biochem. Biophys. Res. Commun.* (2017) **482** 1413-1419. DOI: 10.1016/j.bbrc.2016.12.050
66. Zhang W.. **Network pharmacologya further description**. *Netw. Pharmacol.* (2016) **1** 1-14
67. Zhang Z., Hu Y., Xu N., Zhou W., Yang L., Chen R.. **A new way for beta cell neogenesis: Transdifferentiation from alpha cells induced by glucagon-like peptide 1**. *J. Diabetes Res.* (2019) **2019** 2583047. DOI: 10.1155/2019/2583047
68. Zhang Z., Yang L., Lei L., Chen R., Chen H., Zhang H.. **Glucagon-like peptide-1 attenuates advanced oxidation protein product-mediated damage in islet microvascular endothelial cells partly through the RAGE pathway**. *Int. J. Mol. Med.* (2016) **38** 1161-1169. DOI: 10.3892/ijmm.2016.2711
69. Zheng Y., Lang Y., Qi Z., Gao W., Hu X., Li T.. **PIK3R1, SPNB2, and CRYAB as potential biomarkers for patients with diabetes and developing acute myocardial infarction**. *Int. J. Endocrinol.* (2021) **2021** 2267736. DOI: 10.1155/2021/2267736
70. Zhou P., Yang C., Zhang S., Ke Z. X., Chen D. X., Li Y. Q.. **The imbalance of MMP-2/TIMP-2 and MMP-9/TIMP-1 contributes to collagen deposition disorder in diabetic non-injured skin**. *Front. Endocrinol. (Lausanne)* (2021) **12** 734485. DOI: 10.3389/fendo.2021.734485
|
---
title: 'Metabolic resuscitation therapy in critically ill patients with sepsis and
septic shock: A pilot prospective randomized controlled trial'
authors:
- Fang Feng
- Huyong Yang
- Weiwei Yang
- Yu Chen
journal: Open Medicine
year: 2023
pmcid: PMC9971735
doi: 10.1515/med-2023-0637
license: CC BY 4.0
---
# Metabolic resuscitation therapy in critically ill patients with sepsis and septic shock: A pilot prospective randomized controlled trial
## Abstract
The main purpose of our research was to further clarify the effectiveness and potential pathophysiological principles of metabolic resuscitation therapy in critically ill patients with sepsis and septic shock. We found that metabolic resuscitation therapy is beneficial for patients with sepsis and septic shock, shortening the length of intensive care unit (ICU) stay, reducing the duration of vasopressor use, and reducing the ICU mortality rate of patients with sepsis and septic shock, but it does not reduce the hospital mortality rate.
## Introduction
Sepsis is a common disease that is easily complicated by a variety of problems. Approximately 1.7 million cases occur annually in the United States, with more than 270,000 deaths [1]. Although intensive medical care has achieved substantial progress, sepsis and septic shock are still the most common causes of death in the intensive care unit. Even if patients with sepsis survive during hospitalization, residual organ dysfunction that requires continued treatment after discharge is common, such as sepsis leading to acute kidney injury followed by chronic renal failure [2].
Ascorbic acid (vitamin C) is a water-soluble vitamin that is essential for many bodily processes. Ascorbic acid, an antioxidant, is an electron donor that directly scavenges free radicals by inhibiting the NADPH oxidase pathway, preventing the formation of new free radicals and assisting with the circulation of other antioxidants [3,4,5]. However, another crucial question is when to administer the first dose of vitamin C. In previous studies, the time to the first dose of vitamin C following admission to hospital was unclear. In our study, as soon as a patient had a suspected infection and the sequential organ failure assessment (qSOFA) score was greater than 2, we administered vitamin C immediately. We are very aggressive in our use of vitamin C. Previous trials [6,7,8] have suggested that the administration of intravenous vitamin C in this setting may have beneficial effects, such as reducing the incidence of organ failure and improving survival.
Recently, Marik et al. [ 9] published papers in chest describing the application of metabolic resuscitation therapy in patients with sepsis and septic shock. This approach may represent a new approach for treating sepsis and septic shock. Because the trial had a retrospective before-and-after design, we conducted this prospective randomized controlled trial to further clarify the effectiveness, possible pathophysiological principles, and potential applications of metabolic resuscitation therapy in medical patients with sepsis and septic shock.
## Clinical data
We enrolled medical patients with sepsis and septic shock from September 2019 to March 2020. A tabular form was used to collect the following information: age, sex, use of mechanical ventilation, arterial lactate level at admission and 6 h after admission (GEM3000 blood gas analyser), procalcitonin level at admission and 72 h after admission (using the VIDAS BRAHMS procalcitonin assay, BioMerieux, Inc., Marcy l ‘Etoile, France), use of vasopressin, and acute physiology, age, and chronic health evaluation (APACHE) II score.
## Design
The study was a pilot prospective, randomized controlled trial. This trial was approved by the ethics committee of Lanzhou University Second Hospital. Randomization was performed with the use of a centralized computer-generated assignment sequence on the first day in the intensive care unit (ICU). Block randomization was used as a method of random grouping. Complete randomization was applied in our study. First, patients were ranked according to the order of enrolment. A set of random numbers [10] was then assigned to the patients in the same order. Then, the random number column was ranked from smallest to largest, with the first five for the experimental group and the last five for the control group. The intervention treatment was initiated on the first day. The control group received standard care only, and the experimental group received standard care and metabolic resuscitation, including vitamin C (1.5 g in an intravenous infusion q 6 h for 3 days), vitamin B1 (200 mg in an intramuscular injection q 12 h for 3 days), and hydrocortisone (50 mg in an intravenous infusion q 6 h for 7 days). If adequate fluid resuscitation and vasoactive agents failed to stabilize the haemodynamics, steroids were added.
## Sample size
According to the previous treatment of patients with sepsis in our hospital, it was estimated that the ICU mortality in the control group was $20\%$. The power for the primary endpoint was calculated based on a two-sided t test with a significance level of $5\%$ using PASS 11 software, with a sample size of 62 subjects treated with metabolic resuscitation therapy and 54 subjects treated with placebo. The trial had more than $80\%$ power to detect a difference between metabolic resuscitation therapy and placebo. If the rate of loss to follow-up was $10\%$, the sample size of the metabolic resuscitation therapy group was $\frac{62}{0.9}$ = 68 subjects and that of the control group was $\frac{54}{0.9}$ = 60 subjects.
## Inclusion criteria
[1] Consistent with the diagnostic criteria for sepsis 3.0, patients were diagnosed with sepsis when a definite or suspicious infective focus was present, the qSOFA score was greater than or equal to 2 (score standard: systolic pressure ≤100 mm Hg, 1 point; GCS ≤13, 1 point; and respiratory rate greater than 22 breaths per minute, 1 point), and there was evidence of organ failure.[2] Procalcitonin >2 ng/mL.
## Exclusion criteria
[1] Age <18 years;[2] Pregnant women;[3] End-stage liver disease; and[4] G6PD deficiency.
## Methods
According to the previous treatment of patients with sepsis in our hospital, it is estimated that the ICU mortality in the control group is $20\%$. We calculated that we would need to enrol 140 patients for the trial to have $80\%$ power to show ICU mortality at a two-sided alpha level of $5\%$. Patients with sepsis and septic shock who were admitted to the ICU from September 2019 to March 2020 were prospectively enrolled. According to the computerized random sequence table, the patients were randomly divided into the experimental group and the control group (the random sequence was placed in a sealed, numbered envelope with no light transmission). When patients meeting the criteria were included, the envelopes were selected, and the patients were randomly grouped. All patients with sepsis were included in the research cluster of initial therapy (measurement of arterial lactate level, adequate fluid resuscitation, empiric broad-spectrum antibiotics, vasoactive agents [noradrenaline was preferred; when the dosage of norepinephrine >20 μg/min, it was combined with 0.03 U/min vasopressin] and maintenance of mean arterial pressure [MAP] >65 mm Hg) (see Appendix 1).
## Outcomes
The primary outcomes were ICU mortality rate and hospital mortality rate.
The secondary outcomes included the duration of vasoactive drugs (time from the initial use of the vasoactive drugs to the time of withdrawal; the preferred injection of norepinephrine was used to maintain MAP >65 mm Hg, and when the dosage of norepinephrine was greater than 20 μg/min, then 0.03 U/min vasopressin was added); 72 h procalcitonin clearance rate (initial procalcitonin minus procalcitonin at 72 h, divided by the initial procalcitonin and multiplied by 100) at admission; 6 h arterial lactate clearance rate (initial arterial lactate level minus the arterial lactate level at 6 h after admission, divided by the initial arterial lactate value and multiplied by 100); and ICU length of stay (ICU LOS).
## Statistical analysis
SPSS 21.0 software (SPSS Inc., Chicago, IL, USA) was used for statistical analysis of the data. If the measured data displayed a normal distribution, the means ± standard deviations (x ± s) were reported. The t test was used to compare the data between the two groups, and the q–q normal probability graph was used for the normality test. If the measured data did not display a normal distribution, the median (M) and interquartile ranges (QL, Qu) were used to present the measured data. Count data were reported as n (%). Pearson’s test was used to compare the dichotomous data between the two groups, and the Mann–Whitney U test was used for the ordered classification of multiple groups. All tests were two-sided, and $P \leq 0.05$ was defined as a statistically significant difference.
## Study population
According to the inclusion and exclusion criteria, 140 patients were enrolled; among them, the length of the ICU stay of four patients was less than 24 h. There were 17 patients admitted from the wards (10 patients had primary infections that worsened and 7 patients had hospital-acquired secondary infections), and the others were from the emergency department. All patients were medical patients. Then, 136 patients with sepsis and septic shock were included in the study and randomly divided into two groups: 68 in the experimental group and 68 in the control group (Figure 1). Ninety-six percent of patients in the experimental group and $94\%$ in the control group completed standard care. In addition, $100\%$ of patients in the experimental group and $99\%$ in the control group received adequate initial antibiotic treatment (the type of antibiotics was chosen according to the location of infection and the infection indicators). The baseline data collected from the two groups were comparable. Participants in the study were $60\%$ male and $40\%$ female. The mean SOFA score was 8.7 ± 1.3, and the APACHE II score was 11.6 ± 3.2. renal replacement therapy (RRT) for acute kidney injury (AKI) was performed throughout the ICU course. The fluid balance at 6 h was 2,103 ± 140 and 2,009 ± 99 mL in the experimental group and the control group, respectively ($$P \leq 0.875$$). The serum creatinine (SCr) at discharge was 75.2 ± 1.7 and 76.1 ± 1.9 µmol/L in the experimental group and the control group, respectively ($$P \leq 0.349$$) (Table 1). Among the 136 patients with sepsis and septic shock included in this study, pneumonia was the main cause, as it occurred in 33 ($49\%$) and 41 ($60\%$) patients in the experimental group and the control group, respectively. Gram-negative bacilli were the main pathogenic bacteria ($\frac{112}{136}$, $82.4\%$), and there were two negative cultures in the experimental group and three negative cultures in the control group. We describe the bacterial resistance data in Appendix 2. The mean time to the first dose of vitamin C was 1.7 ± 0.2 h (as soon as a patient had a suspected infection and the qSOFA score was greater than 2). Seventeen patients received hydrocortisone in the control group (100 mg q 12 h).
**Figure 1:** *Flow diagram.* TABLE_PLACEHOLDER:Table 1
## Primary outcomes
The ICU mortality rates were $8.8\%$ ($\frac{6}{68}$) and $15\%$ ($\frac{15}{68}$) in the experimental group and the control group, respectively ($$P \leq 0.033$$). The hospital mortality rates were $16.2\%$ ($\frac{11}{68}$) and $25\%$ ($\frac{17}{68}$) in the experimental group and the control group, respectively ($$P \leq 0.071$$). The Cox regression is shown in Figure 2.
**Figure 2:** *Cox regression.*
## Secondary outcomes
The ICU length of stay for the experimental group and the control group was 9 (7–12) and 11 (9–14) days, respectively ($$P \leq 0.002$$). The duration of vasoactive drug use (h) was 20.8 ± 9.9 and 46.7 ± 12.8, respectively ($P \leq 0.001$). The 6 h lactate clearance rates were $66.2\%$ (55.5, 76.7) and $30.1\%$ (15.6, 50) in the experimental group and the control group, respectively ($P \leq 0.001$). The 72 h procalcitonin clearance rates were $70\%$ (57.5, 80.3) and $40.7\%$ (24.8, 52.2), respectively ($P \leq 0.001$). The full results are shown in Table 2.
**Table 2**
| Unnamed: 0 | Experimental group (n = 68) | Control group (n = 68) | P-value |
| --- | --- | --- | --- |
| ICU LOS (days) | 9 (7–12) | 11 (9–14) | 0.002 |
| Duration of vasoactive drug use (h) | 20.8 ± 9.9 | 46.7 ± 12.8 | <0.001 |
| Lactate clearance rate (6 h) | 66.2% (55.5, 76.7) | 30.1% (15.6, 50) | <0.001 |
| RRT for AKI | 1 | 2 | 0.075 |
| Procalcitonin clearance rate (72 h) | 70% (57.5, 80.3) | 40.7% (24.8, 52.2) | <0.001 |
| ICU mortality rate (%) | 8.8% (6/68) | 15% (15/68) | 0.033 |
| Hospital mortality rate (%) | 16.2% (11/68) | 25% (17/68) | 0.071 |
We also performed a subgroup analysis of patients with sepsis versus septic shock and patients who received vasoactive drugs versus patients who did not. The results showed that the mortality between sepsis and septic shock was significantly different, with a P-value of 0.04; however, no significant difference was observed between patients who received vasoactive drugs and those who did not (Figures 3 and 4).
**Figure 3:** *Subgroup analysis of patients with sepsis versus septic shock.* **Figure 4:** *Subgroup analysis of patients who received vasoactive drugs versus those who did not.*
## Discussion
The fundamental pathogenesis of sepsis is not yet understood and involves many aspects, such as complex systemic inflammatory network effects, gene polymorphisms, immune dysfunction, coagulation dysfunction, tissue damage, and the host’s abnormal response to different infectious pathogens and their toxins. Over the past 30 years, a substantial amount of research and improved clinical processes have increased the speed of recognition and treatment of sepsis. However, the results are still inconsistent. We noticed that the initial time to receive intervention was particularly important. Long MT [10] performed a retrospective cohort study of 208 patients with septic shock and showed that APACHE-adjusted ICU mortality was significantly reduced in patients who received vitamin C, thiamine and steroids in sepsis within 6 h compared with patients who received standard care (odds ratio 0.075 [0.0, 0.59], $P \leq 0.01$). Another retrospective study [11] found that delays in the administration of hydrocortisone, vitamin C, and thiamine beyond 12 h after the presentation of sepsis had no influence on patient outcomes, while early therapy was associated with substantial effects.
In our study, the early use of vitamin C in sepsis patients yielded satisfactory results, which further validates the advantage of vitamin C in reversing shock reported in previous studies.
Thiamine (vitamin B1) is a water-soluble vitamin that is a key component of cellular metabolism. Its phosphorylated form, thiamine pyrophosphate, functions as a cofactor of pyruvate dehydrogenase, converting pyruvate to acetyl-CoA and entering the tricarboxylic acid cycle. When the thiamine content is insufficient, pyruvate is not converted to acetyl-CoA, causing impairment in the aerobic chain and a shift to the anaerobic pathway, leading to elevated serum lactate levels [12,13,14]. A retrospective, single-centre, matched cohort study [15] showed that thiamine administration within 24 h of admission in patients presenting with septic shock was associated with improved lactate clearance and a reduction in 28 day mortality compared with matched controls. The research performed by Donnino and colleagues showed that in those with baseline thiamine deficiency, patients in the thiamine group had significantly lower lactate levels at 24 h and a possible decrease in mortality over time [16]. Therefore, the concentration of thiamine should be monitored in patients with sepsis and septic shock in future studies so that thiamine supplementation can be more effective.
Many large-scale randomized trials have evaluated the additional benefits of corticosteroids as part of the treatment for septic shock. According to these studies, corticosteroids generally improve a variety of clinical outcomes of septic shock (e.g., time to shock reversal and number of ventilation-free days) but have different outcomes for mortality [17,18,19]. The routine use of hydrocortisone in patients with septic shock remains a topic of debate [20,21]. However, the biological basis for the inclusion of hydrocortisone in drug combinations is based on the potential synergistic effects of ascorbic acid and hydrocortisone [22]. The absorption of ascorbic acid in cells is mediated by the sodium-vitamin C transporter (SVCT2), whose expression is downregulated in response to inflammation. The use of glucocorticoids has been shown to increase the expression of transporters [23,24]. Previous randomized controlled trials (RCTs) have been published regarding the same intervention in septic patients. Until now, the role of metabolic resuscitation in patients with sepsis and septic shock has remained controversial. Therefore, our trial was designed as a prospective randomized controlled trial to further clarify the role of metabolic resuscitation in patients with sepsis and septic shock. Our study showed that the mortality rate of both groups was lower than that in previous studies (the VITAMINS trial was $19.6\%$, the HYVCTTSSS trial was $27.5\%$, and the ORANGES trial was $9\%$). There may be several reasons. First, the included patients had an APACHE II score of less than 15, so it was possible to conclude that the mortality rate was lower than that in previous studies. Second, the 6 h lactate clearance rate and the 72 h procalcitonin clearance rate were significantly higher than those of the control group, so the timely initiation of antibiotic therapy and fluid resuscitation may also have contributed to the lower mortality rate compared with previous studies. Finally, the prognosis was better than that of conventional RCTs, probably because the intervention was performed early, especially the first dose of vitamin C administration. In the original plan, vitamin B1 was injected intravenously, but because the intravenous route was not mentioned in the specification for vitamin B1 in China, intramuscular injection was used instead.
Another finding in our study is unchanged hospital mortality with improved ICU mortality; this is not the first publication on vitamin C that has seen this result. A retrospective study [11] showed the same results. The application of HAT may improve early shock symptoms, but some patients eventually die from a variety of complications. In this case, there was an improvement in ICU mortality but no difference in hospital mortality.
## Limitations
Our study still has several limitations. First, it is a single-centre study that included a homogeneous patient population. Second, we did not measure the concentration of vitamin C. Finally, our study was designed solely according to the protocol reported by Marik. Although the results showed that metabolic resuscitation may reduce the mortality associated with sepsis and septic shock in patients, the results should be interpreted with caution, and the mechanism by which metabolic resuscitation therapy treats sepsis requires further study in the future.
## Conclusions
Metabolic resuscitation therapy is beneficial for patients with sepsis and septic shock, shortening the length of ICU stay, reducing the duration of vasopressor use, and reducing the ICU mortality rate, but it does not reduce the hospital mortality rate.
## Appendix 1
Standard care:Measure lactate level. Re-measure if initial lactate is > 2 mmol/L;Obtain blood cultures prior to administration of antibiotics;Administer broad-spectrum antibiotics;(One or more antimicrobial agents are administered intravenously in order to cover all possible pathogens. Once the results of pathogen culture and drug sensitivity tests are obtained or the patient is confirmed to be free of infection, empirical anti-infective therapy should be restricted or discontinued immediately. *Third* generation cephalosporin is preferred, and then, according to the infection indicators, e.g.: WBC, PCT and so on. If necessary, we change appropriate antibiotic according to clinical pharmacist's suggestion.)Rapidly administer 30 mL/kg crystalloid for hypotension or lactate ≥4 mmol/L;Apply vasopressors if patient is hypotensive during or after fluid resuscitation to maintainMAP ≥65 mm Hg.
## Appendix 2
A total of 112 cases of 136 patients were cultured with Gram-negative bacilli, detailed information is shown in the table below. Escherichia coli 7414 (ESBL) *Pseudomonas aeruginosa* 21 *Klebsiella pneumoniae* 102 (ESBL) *Acinetobacter baumannii* 72 (XDR), 1 (PDR) The initial antibiotic therapy was guided by clinical pharmacists.
## References
1. Rhee C, Dantes R, Epstein L, Murphy DJ, Seymour CW, Iwashyna TJ. **Incidence and trends of sepsis in US hospitals using clinical vs claims data, 2009–2014**. *Jama* (2017) **318** 1241-9. DOI: 10.1001/jama.2017.13836
2. Rhodes A, Evans LE, Alhazzani W, Levy MM, Dellinger RP. **Surviving sepsis campaign: International guidelines for management of sepsis and septic shock**. *Crit care Med* (2017) **45** 486-552. DOI: 10.1097/CCM.0000000000002255
3. Mortensen A, Lykkesfeldt J. **Does vitamin C enhance nitric oxide bioavailability in a tetrahydrobiopterin-dependent manner?**. *Nitric oxide Biol Chem* (2014) **36** 51-7. DOI: 10.1016/j.niox.2013.12.001
4. Oudemans-van Straaten HM, Spoelstra-de Man AM, de Waard MC. **Vitamin C revisited**. *Crit care* (2014) **18** 460. DOI: 10.1186/s13054-014-0460-x
5. Wu F, Tyml K, Wilson JX. **iNOS expression requires NADPH oxidase-dependent redox signaling in microvascular endothelial cells**. *J Cell Physiol* (2008) **217** 207-14. DOI: 10.1002/jcp.21495
6. Kahn SA, Beers RJ, Lentz CW. **Resuscitation after severe burn injury using high-dose ascorbic acid: A retrospective review**. *J Burn care Res* (2011) **32** 110-7. DOI: 10.1097/BCR.0b013e318204b336
7. Anaka H, Matsuda T, Miyagantani Y. **Reduction of resuscitation fluid volumes in severely burned patients using ascorbic acid administration**. *Arch Surg* (2000 Mar) **135** 326-31. DOI: 10.1001/archsurg.135.3.326
8. Fowler AA, Syed AA, Knowlson S, Sculthorpe R, Farthing D, Dewilde C. **Phase I safety trial of intravenous ascorbic acid in patients with severe sepsis**. *J Transl Med* (2014) **12** 32. DOI: 10.1186/1479-5876-12-32
9. Marik PE, Khangoora V, Rivera R, Hooper MH, Catravas J. **Hydrocortisone, vitamin C, and thiamine for the treatment of severe sepsis and septic shock: A retrospective before-after study**. *Chest* (2017) **151** 1229-38. DOI: 10.1016/j.chest.2016.11.036
10. Frommelt MA, Kory P, Long MT. **Letter on update to the vitamin C, thiamine, and steroids in sepsis (VICTAS) protocol**. *Trials* (2020) **21** 350. DOI: 10.1186/s13063-020-04289-z
11. Long MT, Frommelt MA, Ries MP, Murray M, Kory P. **Early hydrocortisone, ascorbate and thiamine therapy for severe septic shock**. *Crit Care Shock* (2020) **23** 23-34
12. Mallat J, Lemyze M, Thevenin D. **Do not forget to give thiamine to your septic shock patient**. *J Thorac Dis* (2016) **8** 1062-6. DOI: 10.21037/jtd.2016.04.32
13. Gioda CR, de Oliveira Barreto T, Primola-Gomes TN, de Lima DC, Campos PP, Capettini Ldos S. **Cardiac oxidative stress is involved in heart failure induced by thiamine deprivation in rats**. *Am J Physiol Heart Circ Physiol* (2010) **298** H2039-45. DOI: 10.1152/ajpheart.00820.2009
14. Pacal L, Tomandl J, Svojanovsky J, Krusova D, Stepankova S, Rehorova J. **Role of thiamine status and genetic variability in transketolase and other pentose phosphate cycle enzymes in the progression of diabetic nephropathy**. *Nephrol Dial Transplant* (2011) **26** 1229-36. DOI: 10.1093/ndt/gfq550
15. Woolum JA, Abner EL, Andrew K, Thompson B, Morris PE, Flannery AH. **Effect of Thiamine Administration on Lactate Clearance and Mortality in Patients With Septic Shock**. *Crit care Med* (2018) **46** 1747-52. DOI: 10.1097/CCM.0000000000003311
16. Donnino MW, Carney E, Cocchi MN, Barbash I, Chase M, Joyce N. **Thiamine deficiency in critically ill patients with sepsis**. *J Crit Care* (2010) **25** 576-81. DOI: 10.1016/j.jcrc.2010.03.003
17. Venkatesh B, Finfer S, Cohen J, Rajbhandari D, Arabi Y, Bellomo R. **Adjunctive glucocorticoid therapy in patients with septic shock**. *N Engl J Med* (2018) **378** 797-808. DOI: 10.1056/NEJMoa1705835
18. Annane D, Renault A, Brun-Buisson C, Megarbane B, Quenot JP, Siami S. **Hydrocortisone plus fludrocortisone for adults with septic shock**. *N Engl J Med* (2018) **378** 809-18. DOI: 10.1056/NEJMoa1705716
19. Sprung CL, Annane D, Keh D, Moreno R, Singer M, Freivogel K. **Hydrocortisone therapy for patients with septic shock**. *N Engl J Med* (2008) **358** 111-24. DOI: 10.1056/NEJMoa071366
20. Rochwerg B, Oczkowski SJ, Siemieniuk RAC, Agoritsas T, Belley-Cote E, D’Aragon F. **Corticosteroids in sepsis: An updated systematic review and meta-analysis**. *Crit care Med* (2018) **46** 1411-20. DOI: 10.1097/CCM.0000000000003262
21. Rygard SL, Butler E, Granholm A, Moller MH, Cohen J, Finfer S. **Low-dose corticosteroids for adult patients with septic shock: A systematic review with meta-analysis and trial sequential analysis**. *Intensive care Med* (2018) **44** 1003-16. DOI: 10.1007/s00134-018-5197-6
22. Okamoto K, Tanaka H, Makino Y, Makino I. **Restoration of the glucocorticoid receptor function by the phosphodiester compound of vitamins C and E, EPC-K1 (L-ascorbic acid 2-[3,4-dihydro-2,5,7,8-tetramethyl-2-(4,8,12-trimethyltridecyl)-2H-1-benzopyran-6 -yl hydrogen phosphate] potassium salt), via a redox-dependent mechanism**. *Biochem Pharmacol* (1998) **56** 79-86. DOI: 10.1016/s0006-2952(98)00121-x
23. Fujita I, Hirano J, Itoh N, Nakanishi T, Tanaka K. **Dexamethasone induces sodium-dependant vitamin C transporter in a mouse osteoblastic cell line MC3T3-E1**. *Br J Nutr* (2001) **86** 145-9. DOI: 10.1079/bjn2001406
24. Barabutis N, Khangoora V, Marik PE, Catravas JD. **Hydrocortisone and ascorbic acid synergistically prevent and repair lipopolysaccharide-induced pulmonary endothelial barrier Dysfunction**. *Chest* (2017) **152** 954-62. DOI: 10.1016/j.chest.2017.07.014
|
---
title: 'The stereotype content model and mental disorders: Distinct perceptions of
warmth and competence'
authors:
- Ramona C. Allstadt Torras
- Corinna Scheel
- Angela R. Dorrough
journal: Frontiers in Psychology
year: 2023
pmcid: PMC9971816
doi: 10.3389/fpsyg.2023.1069226
license: CC BY 4.0
---
# The stereotype content model and mental disorders: Distinct perceptions of warmth and competence
## Abstract
This work investigates the perception of eight different mental disorders within the Stereotype Content Model (SCM). The presented study ($$n = 297$$) includes a sample representative for the German population in terms of age and gender. Results reveal distinct warmth and competence evaluations for people with different mental disorders, e.g., people with alcohol dependence were seen as less warm and less competent than people with depression or phobia. Future directions and practical implications are discussed.
## Introduction
More than one third of the northeastern German population is affected by mental disorders (12-month prevalence of $36.3\%$; Asselmann et al., 2019), which are associated with far reaching restrictions on daily life such as negative impacts on personal well-being, social life, and work productivity as well as a significant decreased quality of life and increased impairment (Alonso et al., 2004). In addition to these direct negative impacts, people with mental disorders are often stigmatized by their environment. Angermeyer et al. [ 2013] showed that the acceptance of treatment offered by mental health professionals has increased in the period from 1990 to 2011 in Germany. However, public attitudes (which were conceptualized as a more or less positive emotional reaction and the desire for social distance) toward people with major depression and alcohol dependency did not increase significantly and attitudes toward people with schizophrenia have even worsened. Similar studies conducted in the US (e.g., Pescosolido et al., 2010), Australia (Reavley and Jorm, 2011), Austria (Grausgruber et al., 2009), England, and Scotland (Mehta et al., 2009) also failed to find a decrease in stigmatization toward people suffering from mental disorders (for a meta-analysis, see Schomerus et al., 2012). Schmitt et al. [ 2014] demonstrated in two meta-analyzes that perceived discrimination has subsequent negative effects on several aspects of psychological well-being and even hinders people with mental health problems from seeking help (Clement et al., 2015; Henderson et al., 2017), thus impeding recovery. Stigmatization can also lead to additional restrictions for people with mental disorders with regard to obtaining or maintaining employment in accordance with their education or abilities (Corrigan, 2004). Furthermore, research indicates that people with mental disorders experience work-related discrimination (Yoshimura et al., 2018) and are systematically disadvantaged in comparison to people with physical disorders in the labor market (Hipes et al., 2016). Although the causes of discrimination are manifold, generalized beliefs regarding a group of people based on their group membership (i.e., stereotypes) are seen as one cognitive component (e.g., Ashmore and Del Boca, 1981; Fiske, 1999; Kanahara, 2006). Stereotypes are often automatically activated when encountering a person belonging to a specific group (e.g., Macrae and Bodenhausen, 2000) and can subsequently shape behavior toward that person (Cuddy et al., 2007). Because of the wide-ranging negative consequences resulting from stereotypes, especially for people with mental health problems, understanding and counteracting stereotypes toward this minority is crucial.
According to the Stereotype Content Model (SCM), which has been validated in a wide range of cultures (Cuddy et al., 2009; Durante et al., 2013, 2017; Fiske and Durante, 2016), stereotypes about a social group follow two fundamental dimensions of social judgment, namely warmth and competence (Fiske et al., 2002; Cuddy et al., 2008). Research has shown that the social perception of various groups (such as people with different nationalities, professions, social backgrounds, and religions, and so on; for a summary see Fiske, 2018) varies along these two dimensions. Apart from different social and cultural groups, some studies have investigated stereotypes and stigmas for people with mental disorders. Most of this research focused on mental disorders in general (Phelan et al., 2000; Corrigan, 2004; Hinshaw and Stier, 2008; Mehta et al., 2009; Asbrock, 2010) or investigated only one or a small number of specific categories of mental disorders such as depression (Griffiths et al., 2008), anxiety (Curcio and Corboy, 2020), and schizophrenia (e.g., Link et al., 2004; Rüsch et al., 2005). A study including the general category “mentally ill“, found that participants attribute a moderate value on the warmth dimension and almost no competence to this group (Cuddy et al., 2009). Asbrock [2010] shows that people with mental and physical disorders are both located in the same cluster together with social categories such as people living in homelessness, welfare recipients, and people receiving unemployment. People with mental disorders are perceived to be lower in warmth and competence than people with physical disorders. Angermeyer and Dietrich [2006] criticized the use of broad categories or selected disorders, as this may possibly lead to incorrect conclusions by neglecting the diverse nature of different mental disorders. Furthermore, the perception of mental disorders has seldom been systematically investigated within the scope of the most prominent model of stereotype perception, the SCM: Fiske [2012] reported that mental health issues such as eating disorders, depression, and autism were located in the left lower corner (i.e., neither warm nor competent) of the coordinate system spanning the two dimensions. Sadler et al. [ 2012] found that, analogous to Cuddy et al. [ 2009], the overarching category of mentally ill people was perceived to be moderate in warmth and relatively low in competence. Furthermore, specific stereotypes about different disorders revealed four distinct clusters distributed across the two-dimensional coordinate system: [1] low warmth/low competence (e.g., addictions, and schizophrenia), [2] moderate warmth/moderate competence (e.g., anxiety, depression, eating disorders, and obsessive–compulsive disorder), [3] low warmth/moderate competence (sociopathy and violent criminals), [4] high warmth/low competence (e.g., neurocognitive disorders and mental retardation). Sönmez and Karaoğlu [2022] had a variety of mental disorders rated on the dimensions of the SCM by Turkish psychology students. Boysen [2017] used the SCM to measure the perception of 17 different mental disorders and focused on aggregated perceptions of typically masculine and feminine disorders and Gärtner et al. [ 2022] have shown that the SCM is also applicable to the self-stigmatization of people with various mental disorders.
The studies described above that have assessed the public perception of several mental disorders on the warmth and competence dimensions (Sadler et al., 2012; Boysen, 2017) included random samples from the Mechanical Turk panel provider in the United States of America or a sample of Turkish undergraduates in psychology (Sönmez and Karaoğlu, 2022). However, the role of the respondents’ demographic characteristics on stereotypical perceptions of people with mental disorders has not been fully determined. According to a review article by Angermeyer and Dietrich [2006] 11 studies reported more negative attitudes of male compared to female participants toward people with mental disorders, whereas 6 studies showed opposite results. In addition, 18 studies did not report any effects of participant gender on how different mental disorders were perceived. Furthermore, in 32 out of 33 studies they found a positive association between participants’ age and the stereotypical perception. Holzinger et al. [ 2012] summarized that emotional reactions toward people with mental disorders differed between the genders in that women expressed more positive reactions and less anger, but showed more anxiety than men. A nation-wide cross-sectional survey on mental health literacy in Singapore with 3,006 participants revealed that attitudes towards people with mental disorders were more negative amongst older participants and participants with male gender (Yuan et al., 2016). In sum, the results regarding the impact of age and gender on the stereotype perception of people with mental disorders are mixed. However, since the previous findings also do not indicate that these variables are irrelevant in this context, we select a sample that is representative of the German population in terms of age and gender. With regard to cultural differences, Cuddy et al. [ 2009] indicated with their studies that there can be similarities but also differences with regard to how various social categories are perceived on the warmth and competence dimensions. Therefore, we consider it reasonable to extend the existing data of mapped stereotypes towards people with specific mental disorders with a sample from Germany.
The present study shall provide a general picture of warmth and competence stereotypes for different mental disorders in Germany. In order to address the most relevant and prominent mental disorders, we chose eight mental disorders from the ICD-10 (World Health Organization [WHO], 1993) and included highly prevalent affective and anxiety disorders (major depressive disorder (MDD; F32), specific phobia (F40.29), obsessive compulsive disorder (OCD; F42)), one personality disorder, additional societally relevant disorders [emotional-unstable personality disorder, borderline type (BPD; F60.31), alcohol dependence disorder (ADD; F10.2); anorexia nervosa (F50.0)], and also other less prevalent but commonly known disorders [schizophrenia (F20), pathological stealing/kleptomania (F63.2)]. The remaining chapter V disorders about organic (including symptomatic) mental disorders (F0), mental retardation (F7), and disorders of psychological development (F8) as well as behavioral and emotional disorders with onset typically occurring in childhood and adolescence (F9) describe mental disorders in a broader sense and were therefore not included.
The materials and data can be found at https://osf.io/ukzn$\frac{2}{.}$ We did not pre-register the hypotheses and did not estimate power beforehand, as the data was collected in 2016 and both practices had not yet been added to our research repertoire at that time. A post-hoc sensitivity analysis assuming Chi square test as our main test using G*Power (Faul et al., 2007) indicated that we can detect small to medium effects (Cohen’s ω =0.22; α = 0.05, χ2-test) with the available sample and a power of 1−β = 0.80.
## Hypotheses
The main hypothesis predicts distinct patterns for different mental disorders in terms of warmth and competence (H1). The following disorder-specific hypotheses are based on previous research concerning stereotypes about different mental disorders.
## Schizophrenia
In studies on social perception, participants stated a high desire to maintain social distance from people with schizophrenia and associated such individuals with dangerousness (e.g., Link et al., 1999; Thompson et al., 2002; Read et al., 2006; Ahmed et al., 2020), rejection (Angermeyer and Matschinger, 2004, 2005), anger (Angermeyer and Matschinger, 2003; Ahmed et al., 2020), and perceived dependency (Angermeyer and Matschinger, 2003) as well as fear and unpredictability (Levey et al., 1995; Angermeyer and Matschinger, 2003; Read et al., 2006). These findings suggest that within the SCM, people with schizophrenia might be perceived to be low in warmth. Pescosolido et al. [ 1999] found that only $25.7\%$ of a representative sample rated people with schizophrenia as very or somewhat able to manage treatment decisions, which might result in low ratings of competence. In line with the results by Sadler et al. [ 2012], who located this group in the low warmth/low competence cluster, we hypothesize that people with schizophrenia are perceived to be low in warmth and competence within the SCM (H2a).
## Alcohol dependence disorder
Schomerus et al. [ 2012] concluded that people with ADD are perceived to be equally or more dangerous and more unpredictable than people with other mental disorders; in addition, participants reported that their desire for social distance toward this group is even greater. Furthermore, people with ADD are seen to be much more responsible for their own disorder as compared to non-substance abuse disorders. Both the high degree of perceived dangerousness and unpredictability as well as the ascribed responsibility led to a desire for social distance and a low level of sympathy (Feldman and Crandall, 2007), which might be associated with low warmth ratings within the SCM. In Pescosolido et al. [ 1999], $35.5\%$ of participants rated individuals with drug dependence as “not at all” able to make adequate decisions, suggesting that affected people might be perceived as low in competence. In line with the described findings and the results by Sadler et al. [ 2012], who located this group in the low warmth/low competence cluster, we hypothesize that people with ADD will be rated as low in warmth and competence within the SCM (H2b).
## Major depression disorder
Pescosolido et al. [ 1999] showed that almost two-thirds of their participants rated people with MDD as being able to make proper medical treatment decisions. Thus, moderate to low competence ratings in the SCM are possible. Regarding the warmth dimension, a study by Link et al. [ 1999] reveals that, in comparison to other disorders, people suffering from MDD are perceived to be less likely to become violent. In Sadler et al. [ 2012], people with MDD fell into the “internal” cluster (moderate warmth $M = 2.86$/moderate competence $M = 3.08$). We, therefore, assume that people with MDD are perceived to be moderate in warmth and competence (H2c).
## Borderline personality disorder
Several studies indicate that healthcare professionals, in particular, have negative attitudes toward people with BPD. They predominantly perceive them to be manipulative and difficult and report that they make them angry or annoyed (Deans and Meocevic, 2006; Ross and Goldner, 2009). This suggests that people with BPD may also be perceived to be low in warmth by the general public. The literature provides only little evidence regarding the perceived competence of people with BPD, which leads us to the hypothesis that they are perceived to be low in warmth and moderate in competence (H2d).
## Anorexia nervosa
People with eating disorders (i.e., bulimia and anorexia) were rated as being more fragile than people with MDD (Roehrig and McLean, 2010). As for ADD, people with eating disorders, including anorexia, are perceived to be responsible for their own condition (Feldman and Crandall, 2007; Crisafulli et al., 2008; Roehrig and McLean, 2010; Ebneter et al., 2011). This potentially leads to a desire for social distance and a low level of sympathy and, thus, social perception ratings of low warmth. According to Sadler et al. [ 2012], eating disorders are part of the moderate warmth/moderate competence cluster (“internal cluster” in Sadler et al., 2012). Thus, we hypothesize that people with anorexia nervosa are perceived as being low in warmth and moderate in competence (H2e).
## Specific phobia
Little has been published on the stereotypical perception of people with phobias to date. In Sadler et al. [ 2012], people with anxiety (in general) were located in the medium warmth/medium competence cluster. Angermeyer and Dietrich [2006] showed that negative attitudes toward people suffering from anxiety disorders (in general) are not as pronounced as toward disorders such as ADD and schizophrenia. This appears consistent with the tendency of people with anxiety and phobic disorders to generally show more avoidance tendencies and inwardly directed coping rather than, for example, outwardly aggressive behavior. This would suggest no danger for other people and therefore comparably high warmth ratings. Concerning competence, there are no findings that would suggest high or low ratings of competence. We therefore hypothesize that people with phobic disorders are perceived to be high in warmth and moderate in competence (H2f).
## Obsessive–compulsive disorder
Sadler et al. [ 2012] found that people with OCD are perceived to be moderate in warmth and competence. As we have no additional information on social judgments of people with OCD, we hypothesize that they are perceived to be moderate in warmth and competence within the SCM (H2g).
## Kleptomania
Little is known about the perception of patients with kleptomania. Regarding the disorders included in the current study, the desire for social distance ranked the highest among people with kleptomania in a study by Feldman and Crandall [2007], which indicates that people with this disorder might be perceived to be low in warmth within the SCM. There are no findings that would provide an indication for suggesting high or low ratings on the competence dimension. We, therefore, hypothesize that kleptomania is perceived as low in warmth and moderate in competence within the SCM (H2h).
## Participants and design
We conducted an online study using a panel provider1 that recruited a sample representative for the German population in terms of age and gender ($$n = 297$$; 18–83 years of age, $52\%$ female) for this study. An open-text item was used to assess participants’ current employment. The answers reflect a high degree of occupational and educational diversity of the sample: Retired $29.05\%$, in school, training, or university $7.09\%$, blue-collar worker $6.42\%$, white-collar worker $6.42\%$, without work and on parental leave $5.74\%$, freelancer $3.72\%$, specialized worker $3.38\%$, salesperson/distributor $3.38\%$, administration $2.70\%$, technician $2.36\%$, teacher $2.03\%$, nursing/medical staff $2.03\%$, consultant $1.69\%$, pedagogue $1.69\%$, CEO/owner $1.35\%$, engineer $1.35\%$, and many more. $3.7\%$ of the sample (11 participants) stated that they themselves were affected by the disorder they assessed (5 with MDD, 2 with ADD and 1 with phobia, anorexia and kleptomania respectively). $42.74\%$ of the sample (127 participants) stated that they know someone with the respective disorder in their social environment. $53.54\%$ of the sample (159 participants) stated that neither themselves nor someone in their social environment was affected by the disorder. The participants reported an average personal contact with people with the respective disorders of 2.17 [SD = 1.11; “How do you rate your personal experience with people with (mental disorder)?”; 5-point Likert scale ranging from 1: very little to 5: a lot of contact] and an average expert knowledge about the disorders of 2.16 [SD = 1.08; “How do you rate your expert knowledge of people with (mental disorder)?”; 5-point Likert scale ranging from 1: very little to 5: high degree of expert knowledge]. The study was conceptualized as a between-subject design, in that participants answered questions concerning one of eight disorders (MDD, phobia, OCD, BPD, ADD, anorexia, schizophrenia and kleptomania). Following previous studies applying the SCM (e.g., Cuddy et al., 2004; Caprariello et al., 2009), we chose a between-subject design to avoid that participants make their judgments in comparison to the other included disorders (sensitivity effects). Furthermore, the between-subject design was chosen to minimize consistency effects (striving for a contradiction-free response), practice and fatigue effects, as well as demand characteristics (guessing the hypotheses) that are common to within-designs. Participants were randomly assigned to one condition/mental disorder, resulting in the following distribution: Schizophrenia $11\%$, MDD $16\%$, ADD $11\%$, OCD $16\%$, BPD $10\%$, phobia $11\%$, anorexia $12\%$, and kleptomania $13\%$.
## Materials and procedure
After providing informed consent, participants indicated their gender, nationality, and age as well as whether they were from a city with a population of more than 100,000 inhabitants. This was followed by the warmth and competence ratings with regard to a randomly selected mental disorder. In line with Sadler et al. [ 2012], we presented groups as individuals with a certain disorder. We used a German short version (Asbrock, 2010) of the original warmth and competence measure by Fiske et al. [ 1999], which included three items for each dimension (warmth: likable, warm, good-natured; competence: competent, competitive, independent using a five-point Likert scale from 1: not at all to 5: completely). Asbrock [2010] reported excellent reliability scores for the short measure with Cronbach’s α of 0.86 for warmth and 0.98 for competence. The aggregated reliability scores in our sample are 0.72 for warmth and 0.74 for competence. For the different disorders, the scores for warmth vary between 0.52 (anorexia) and 0.78 (BPD), whereas the scores for the competence dimension vary between 0.51 (BDP) and 0.82 (schizophrenia). After the warmth and competence ratings, personal contact and expert knowledge were assessed as described above. At the end of the study and for exploratory purposes, participants answered questions about their training and occupation, as well as one question concerning data quality. The respective results are not reported here but can be provided upon request. Participants received a fixed payment based on the regulations of the online panel provider.
To test hypothesis 1, which claims distinct patterns for different mental disorders for warmth and competence, we conducted a Kruskal-Wallis Test and a hierarchical cluster analysis (Ward’s method). In order to test the disorder specific hypotheses H2a-h, we conducted one t-test for each dimension against the test value 3 (middle of the used Likert scale). In cases where we hypothesized high or low ratings, we ran one-tailed t-tests due to directional hypotheses, when we hypothesized moderate ratings, we ran two-tailed t-tests. We partly hypothesized moderate ratings on warmth and competence dimensions, which statistically equal null hypotheses. Therefore, we additionally run Bayesian t-tests (Rouder et al., 2009) for all non-significant t-tests as described in the “Bayes Factor” package (Morey et al., 2015) in R. Within this method, the Bayes Factor will indicate the probability of the null (moderate values, BF > 1) or alternative hypothesis (high/low values, BF < 1) given the observational data. Bayesian t-tests were calculated online2. We also report warmth and competence scores for the entire sample and overall disorders to assess whether the overarching category of people with mental disorders is consistent with the results of previous studies.
## Results
Descriptively, the different mental disorders appear to vary on the two dimensions (see Figure 1; see also Table 1 for mean values and standard deviations).
**Figure 1:** *Two cluster-solution of different mental disorders within the SCM of the general public.* TABLE_PLACEHOLDER:Table 1 *This is* statistically confirmed by a Kruskal-Wallis Test, indicating that individuals have distinct stereotypes for different mental disorders with regard to warmth (χ2[7] = 66.02, $p \leq 0.001$) and competence χ2[7] = 45.08, $p \leq 0.001$). The hierarchical cluster analysis (Ward’s method, minimizing within-cluster variance and maximizing between-cluster variance), reveals a two-cluster solution (see Figure 1). K-means cluster analysis with parallel threshold method assigned each group to a cluster. The first cluster is named the moderate warmth and moderate competence cluster (MW/MC) and includes the disorders MDD, BPD, phobia, and OCD with cluster means of 3.22 for warmth and 2.93 for competence. The second cluster is named the low warmth and low competence cluster (LW/LC), which includes the disorders anorexia, ADD, kleptomania, and schizophrenia with cluster means of 2.69 for warmth and 2.48 for competence. The cluster means differ significantly regarding warmth (t = −7.93, $p \leq 0.001$) and competence (t = −5.31, $p \leq 0.001$). One-sample t-tests reveal mixed results concerning hypotheses 2a-h, with three fully supported hypotheses, four partly supported hypotheses, and one rejected hypothesis (see Table 2). We also calculated the averages for the SCM dimensions across all mental disorders, revealing moderate ratings for warmth ($M = 2.97$, SD = 0.65) and slightly lower ratings for competence ($M = 2.72$, SD = 0.76).
**Table 2**
| Disorder | Warmth t | Competence t | Warmth Bayes factor | Competence Bayes factor | Hypotheses |
| --- | --- | --- | --- | --- | --- |
| Schizophrenia (N = 32) | −0.65 | −2.14* | 4.37 | | 2a: partly supported |
| ADD (N = 35) | −4.49*** | −8.44*** | | | 2b: supported |
| MDD (N = 49) | 4.39*** | −1.36 | | 2.71 | 2c: partly supported |
| BPD (N = 30) | 1.69 | −2.25* | 1.44 | | 2d: not supported |
| Anorexia (N = 34) | −2.56** | −5.36*** | | | 2e: partly supported |
| Specific phobia (N = 34) | 2.89** | 1.13 | | 3.02 | 2f: supported |
| OCD (N = 44) | 1.11 | −0.31 | 3.46 | 5.85 | 2 g: supported |
| Kleptomania (N = 39) | −4.22*** | −2.07* | | | 2 h: partly supported |
Exploratory multiple regression analyses predicting warmth or competence by participant gender, age, and personal contact reveal that women gave significantly higher warmth ratings ($b = 0.250$, $$p \leq 0.001$$) but do not differ from male participants concerning competence ratings ($b = 0.092$, $$p \leq 0.325$$). Age is a significant predictor for perceived competence (b = −0.006, $$p \leq 0.042$$), but not for warmth (b = −0.003, $$p \leq 0.209$$). Furthermore, the degree of personal contact in our overall sample is a predictor for warmth ($b = 0.129$, $p \leq 0.001$) but not competence ratings ($b = 0.008$, $$p \leq 0.847$$). Based on these results, we rerun the main analyses separately for male vs. female participants, older vs. younger participants, and participants with high vs. low personal contact (the last two via median split): All conclusions from the above-mentioned Kruskal-Wallis Tests remain unchanged when doing so. The results listed in Table 2 partly deviate for different subgroups (e.g., one-sample t-tests regarding warmth are no longer significant within the subgroups MDD and specific phobia in male and anorexia in female participants) but the findings should not be overinterpreted since these subgroup analyses reduce statistical power.
## Discussion
Distinct patterns of warmth and competence stereotypes for eight different mental disorders were observed (whereas some disorders are located in different clusters than expected). Thus, in line with Sadler et al. [ 2012] and the more recent contribution of Sönmez and Karaoğlu [2022] including Turkish undergraduates, results indicate that people with mental disorders are not perceived as one indefinable social group but rather that the perception does differentiate between different disorders. *The* general public reported negative stereotypes for people with ADD, anorexia, and kleptomania with regard to warmth and competence, whereas people with schizophrenia and BDP are only negatively rated with regard to competence. The stereotypical perception of people with MDD and phobia is positive on the warmth dimension. Averaging all groups revealed moderate ratings for warmth and somewhat lower ratings for competence in a German sample, which is in line with previous results based on stereotypical perceptions of the overarching category of “people with mental disorders/illness” (Asbrock, 2010; Sadler et al., 2012).
Based on theoretical accounts and previous empirical results, disorder specific hypotheses regarding the perceived warmth and competence of different mental disorders were derived for this project. These predictions were supported by four out of eight disorders (ADD, MDD, specific Phobia, and OCD). For the other disorders, hypotheses were not (BPD) or only partially (schizophrenia, anorexia, kleptomania) supported, indicating a potential involvement of other factors such as cultural differences (since most studies have been conducted in the US) or other unknown determinants.
Some of the disorder specific results can be related to other research. For example, according to MDD, the findings are mixed: Görzig et al. [ 2019] as well as Follmer and Jones [2017] reported that people with depression were rated low in warmth and competence, whereas in Sadler et al. [ 2015] they were rated high in warmth and low in competence. In our study in turn they were rated as high in warmth and moderate in competence. Schizophrenia was located in the low warmth/low competence cluster and the competence rating of this disorder significantly deviated from the midpoint of the scale. However, regarding perceived warmth, we did not find a significant deviation from the scale mean. This finding somewhat contradicts other results showing that people with schizophrenia were rated as being low in both dimensions (Sadler et al., 2012; Sönmez and Karaoğlu, 2022). Also, other findings deviate from what has been found by other researchers: For example, the competence scores for anorexia and BPD have been low relative to the other disorders, which deviates from the findings of Sadler et al. [ 2012] and Sönmez and Karaoğlu [2022]. However, it should be noted that these studies assessed perceptions of “eating disorders” in general and not of “anorexia” specifically.
Other studies on stereotypic perceptions of people with mental disorders differ from our study with regard to several aspects that could potentially account for the divergent results: Our study was conducted in Germany. Therefore, cultural differences might contribute to different results in other countries such as the US (Sadler et al., 2012) and Turkey (Sönmez and Karaoğlu, 2022). We used a sample representative of the German population in terms of age and gender and with a higher diversity than other studies, which could also lead to different results. Also, deviating from other studies, we asked about personal perceptions rather than assumed opinions in society, possibly resulting in a higher degree of social desirability and therefore in more positive ratings (Kotzur et al., 2020). Given the mixed findings, future studies could readdress the perception of people with mental disorders using different measures and contexts.
Boysen [2017] included the concept of internalizing vs. externalizing mental disorders in his work about the stereotypical perception of people with mental disorders within the SCM. Internalizing disorders share the common feature that distress is processed more inwardly on a social, behavioral, and emotional level (Krueger et al., 2001; Achenbach et al., 2016). Externalizing disorders, on the other hand, tend to transfer distress outwards, which manifests as observable behaviors, such as impulsive, aggressive, disruptive, or addiction-related behaviors. Similar to Boysen [2017], we found that more externalizing disorders (i.e., ADD, kleptomania, and schizophrenia) are associated with less perceived competence and warmth, whereas more internalizing disorders (i.e., MDD, specific Phobia, OCD) are perceived more positively on both dimensions. It appears plausible that people with more externalizing as compared to internalizing disorders are located in the LW/LC cluster, because they might appear impulsive, unpredictable, and potentially dangerous (i.e., lack of perceived warmth). The clusters retrieved in our studies are also comparable to those reported in Sadler et al. [ 2012], in that five out of six (except anorexia) included similar disorders and were located at similar positions in the two-dimensional space.
In line with previous research, women gave more positive ratings (Yuan et al., 2016). Specifically, they perceived people with mental disorders as higher in warmth. No gender differences were found for perceived competence. Again, in line with Yuan et al. [ 2016] we found a negative relationship between age and the stereotypical perception, here regarding competence ratings (but see also Angermeyer and Dietrich, 2006). Thus, future research should include demographically diverse samples considering potential influences of demographic variables such as participant age and gender.
In sum, our data indicates that the stereotypical perception of eight different mental disorders [major depressive disorder (MDD), specific phobia, obsessive compulsive disorder (OCD), emotional-unstable personality disorder, borderline type (BPD), alcohol dependence disorder (ADD), anorexia nervosa, schizophrenia, and kleptomania] can be displayed on the two dimensions of warmth and competence of the Stereotype Content Model (SCM) in Germany. Aggregating the values of all disorders resulted in moderate warmth ratings and lower competence ratings, which is consistent with prior research examining the category “people with mental disorders” within the SCM (Sadler et al., 2012).
## Limitations
As a limitation of the study, it must be mentioned that some of the reliability scores for the warmth and competence ratings were questionable (below 0.70), indicating that the participants rated the same mental disorder very differently on the two dimensions. Low reliabilities for some groups have been also reported in other studies (e.g., Nett et al., 2020; Chen et al., 2021). Thus, it is possible that some groups cannot be exclusively or sufficiently described by two dimensions. Future research on the stereotypical perception of mental disorders might thus consider alternative measures (e.g., the ABC-Model by Koch et al., 2016). Deviating from other research in this research area (e.g., Sadler et al., 2012), we asked for participants’ personal perceptions of the different groups [“How likeable do you think people with (mental illness) are?”], rather than the assumed perceptions of the general population [“As viewed by society, how likeable are people with (mental illness)?”]. Doing so allows us to compare the perception across different samples. However, this decision might have implications for the interpretation of the data. Kotzur et al. [ 2020] for example have shown that questions about personal perceptions within SCM result in more positive ratings than questions about the society’s perceptions, presumably for reasons of social desirability. Thus, providing our participants with the alternative measure might have resulted in even more negative ratings. Another limitation of the study is that the data collection already took place in 2016. However, since prejudices and social stigmas change only slowly (Möller-Leimkühler, 2004), we assume that the data is still relevant. In addition, this is a study with an explicit measurement of stereotypes that may cause effects of social desirability. Gonzalez-Sanguino et al. [ 2019] indicated that the use of implicit measurement methods of attitudes may be advantageous in future studies. With regard to the here applied between-subject design, one has to consider a recently published comprehensive reanalysis of published studies including the warmth and competence dimensions that has shown that studies using a within-subject design overall reached a better model fit compared to studies with a between-subject design (Friehs et al., 2022), leading us the conclusion that we would use within-subject designs in future SCM studies.
## Conclusion and future directions
It is important to understand how people with mental disorders are perceived by the general public – because these perceptions can lead to predictions of specific emotional and behavioral tendencies (Cuddy et al., 2007). Although there already exists a considerable amount of research concerning discrimination of people with mental disorders in social or occupational life, many lack a proper theoretical framework or use an experimental design with insufficient internal validity. The SCM can offer an adequate theoretical framework to investigate such stereotypes toward people with different mental disorders. Future research can complement our research by employing the data driven ABC-Model by Koch et al. [ 2016], where the dimensions A – agency and C – communion are related to competence and warmth and are complemented by a third dimension B – belief, emphasizing the role of traditional vs. progressive tendencies for social judgments. The present research provides data on mapped stereotypes toward people with specific mental disorders from a representative sample in Germany in addition to the existing literature. A future cross-national or cross-cultural perspective on stereotypes of people with mental disorders within the SCM (and ABC) could be investigated, as existing research suggests that there are, e.g., differences between Western and Non-western cultures with regard to affective, cognitive, and behavioral dimensions of stigma (Ahmed et al., 2020). Future research could also investigate further predictors of stereotypical perceptions. Our explorative analyses suggest that the frequency of personal contact with people with mental disorders might have a positive influence on the stereotypical perception of warmth. However, previous research results in this respect are mixed: Pettigrew and Tropp [2000] found more positive stereotypical perceptions of people with mental disorders with increasing contact. Other studies found that healthcare professionals, who typically have frequent personal contact, perceived people with mental disorders more negatively than the general public (for a review, see Alshahrani, 2018). The findings by Kotzur and Wagner [2021] and Kotzur et al. [ 2019] suggest that the frequency of positive or negative experiences determines stereotype perceptions. Thus, it would be of interest to investigate the extent to which stereotypical perceptions (within the SCM framework) of people with mental disorders are influenced by positive and negative personal contact. Future studies could also take an intersectional perspective and consider some of the multiple variables already identified as relevant to stereotype perception (Fiske, 2010), such as gender, in assessing perceptions of people with mental disorders. For example, one could examine whether stereotype perceptions are different for men or women with certain mental disorders since there is evidence that certain disorders are associated with masculinity or femininity, affecting social perception (Boysen, 2017). From a practical perspective, our results suggest that specific therapy and awareness programs are crucial, since people with different disorders are perceived differently by the general public. In line with previous findings (Sadler et al., 2012), our results for example show that people with ADD are perceived more negatively than people with MDD or phobia. Based on these results, one can recommend developing specific awareness programs for the particularly negatively perceived disorders (e.g., ADD, schizophrenia, kleptomania), which might influence their recovery and reintegration process into society.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number (s) can be found at: The materials and data can be found at https://osf.io/ukzn$\frac{2}{.}$
## Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AD and CS developed the study design and collected the data. RA conducted the literature research, analyzed data, and documented the results together with AD. RA wrote the article which was revised by AD. All authors contributed to the article and approved the submitted version.
## Funding
Funding for data collection via the panel provider https://www.consumerfieldwork.com/ was provided by resources of the University of Siegen.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. Achenbach T. M., Ivanova M. Y., Rescorla L. A., Turner L. V., Althoff R. R.. **Internalizing/externalizing problems: review and recommendations for clinical and research applications**. *J. Am. Acad. Child Adolesc. Psychiatry* (2016) **55** 647-656. DOI: 10.1016/j.jaac.2016.05.012
2. Ahmed S., Birtel M. D., Pyle M., Morrison A. P.. **Stigma towards psychosis: cross-cultural differences in prejudice, stereotypes, and discrimination in White British and south Asians**. *J. Community Appl. Soc. Psychol.* (2020) **30** 199-213. DOI: 10.1002/casp.2437
3. Alonso J., Angermeyer M. C., Bernert S., Bruffaerts R., Brugha T. S., Bryson H.. **Disability and quality of life impact of mental disorders in Europe: results from the European study of the epidemiology of mental disorders (ESEMeD) project**. *Acta Psychiatr. Scand.* (2004) **109** 38-46. DOI: 10.1111/j.1600-0047.2004.00329.x
4. Alshahrani W.. **A literature review of healthcare professionals’ attitudes towards patients with mental illness**. *J. Med. Res. Health Educ.* (2018) **2** 1-5
5. Angermeyer M. C., Dietrich S.. **Public beliefs about and attitudes towards people with mental illness: a review of population studies**. *Acta Psychiatr. Scand.* (2006) **113** 163-179. DOI: 10.1111/j.1600-0447.2005.00699.x
6. Angermeyer M. C., Matschinger H.. **Public beliefs about schizophrenia and depression: similarities and differences**. *Soc. Psychiatry Psychiatr. Epidemiol.* (2003) **38** 526-534. DOI: 10.1007/s00127-003-0676-6
7. Angermeyer M. C., Matschinger H.. **The stereotype of schizophrenia and its impact on discrimination against people with schizophrenia: results from a representative survey in Germany**. *Schizophr. Bull.* (2004) **30** 1049-1061. DOI: 10.1093/oxfordjournals.schbul.a007120
8. Angermeyer M. C., Matschinger H.. **Causal beliefs and attitudes to people with schizophrenia. Trend analysis based on data from two population surveys in Germany**. *Br. J. Psychiatry* (2005) **186** 331-334. DOI: 10.1192/bjp.186.4.331
9. Angermeyer M. C., Matschinger H., Schomerus G.. **Attitudes towards psychiatric treatment and people with mental illness: changes over two decades**. *Br. J. Psychiatry* (2013) **203** 146-151. DOI: 10.1192/bjp.bp.112.122978
10. Asbrock F.. **Stereotypes of social groups in Germany in terms of warmth and competence**. *Soc. Psychol.* (2010) **41** 76-81. DOI: 10.1027/1864-9335/a000011
11. Ashmore R. D., Del Boca E. K., Hamilton D. L.. **Conceptual approaches to stereotypes and stereotyping**. *Cognitive Processes in Stereotyping and Intergroup Behavior* (1981) 1-35
12. Asselmann E., Beesdo-Baum K., Hamm A., Schmidt C. O., Hertel J., Grabe H. J.. **Lifetime and 12-month prevalence estimates for mental disorders in northeastern Germany: findings from the study of health in Pomerania**. *Eur. Arch. Psychiatry Clin. Neurosci.* (2019) **269** 341-350. DOI: 10.1007/s00406-018-0911-5
13. Boysen G. A.. **Exploring the relation between masculinity and mental illness stigma using the stereotype content model and BIAS map**. *J. Soc. Psychol.* (2017) **157** 98-113. DOI: 10.1080/00224545.2016.1181600
14. Caprariello P. A., Cuddy A. J., Fiske S. T.. **Social structure shapes cultural stereotypes and emotions: a causal test of the stereotype content model**. *Group Process. Intergroup Relat.* (2009) **12** 147-155. DOI: 10.1177/1368430208101053
15. Chen V. H. H., Ahmed S., Chib A.. **The role of social media behaviors and structural intergroup relations on immigrant stereotypes**. *Int. J. Commun.* (2021) **15** 4085-4109
16. Clement S., Schauman O., Graham T., Maggioni F., Evans-Lacko S., Bezborodovs N.. **What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies**. *Psychol. Med.* (2015) **45** 11-27. DOI: 10.1017/S0033291714000129
17. Corrigan P.. **How stigma interferes with mental health care**. *Am. Psychol.* (2004) **59** 614-625. DOI: 10.1037/0003-066X.59.7.614
18. Crisafulli M. A., Von Holle A., Bulik C. M.. **Attitudes towards anorexia nervosa: the impact of framing on blame and stigma**. *Int. J. Eat. Disord.* (2008) **41** 333-339. DOI: 10.1002/eat.20507
19. Cuddy A. J., Fiske S. T., Glick P.. **When professionals become mothers, warmth doesn't cut the ice**. *J. Soc. Issues* (2004) **60** 701-718. DOI: 10.1111/j.0022-4537.2004.00381.x
20. Cuddy A. J., Fiske S. T., Glick P.. **The BIAS map: behaviors from intergroup affect and stereotypes**. *J. Pers. Soc. Psychol.* (2007) **92** 631-648. DOI: 10.1037/0022-3514.92.4.631
21. Cuddy A. J., Fiske S. T., Glick P.. **Warmth and competence as universal dimensions of social perception: the stereotype content model and the BIAS map**. *Adv. Exp. Soc. Psychol.* (2008) **40** 61-149. DOI: 10.1016/S0065-2601(07)00002-0
22. Cuddy A. J., Fiske S. T., Kwan V. S., Glick P., Demoulin S., Leyens J. P.. **Stereotype content model across cultures: towards universal similarities and some differences**. *Br. J. Soc. Psychol.* (2009) **48** 1-33. DOI: 10.1348/014466608X314935
23. Curcio C., Corboy D.. **Stigma and anxiety disorders: a systematic review**. *Stigma Health* (2020) **5** 125-137. DOI: 10.1037/sah0000183
24. Deans C., Meocevic E.. **Attitudes of registered psychiatric nurses towards patients diagnosed with borderline personality disorder**. *Contemp. Nurse* (2006) **21** 43-49. DOI: 10.5172/conu.2006.21.1.43
25. Durante F., Fiske S. T., Gelfand M. J., Crippa F., Suttora C., Stillwell A.. **Ambivalent stereotypes link to peace, conflict, and inequality across 38 nations**. *Proc. Natl. Acad. Sci.* (2017) **114** 669-674. DOI: 10.1073/pnas.1611874114
26. Durante F., Fiske S. T., Kervyn N., Cuddy A. J., Akande A., Adetoun B. E.. **Nations' income inequality predicts ambivalence in stereotype content: how societies mind the gap**. *Br. J. Soc. Psychol.* (2013) **52** 726-746. DOI: 10.1073/pnas.161187411
27. Ebneter D. S., Latner J. D., O’Brien K. S.. **Just world beliefs, causal beliefs, and acquaintance: associations with stigma toward eating disorders and obesity**. *Personal. Individ. Differ.* (2011) **51** 618-622. DOI: 10.1016/j.paid.2011.05.029
28. Faul F., Erdfelder E., Lang A.-G., Buchner A.. **G*power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences**. *Behav. Res. Methods* (2007) **39** 175-191. DOI: 10.3758/BF03193146
29. Feldman D. B., Crandall C. S.. **Dimensions of mental illness stigma: what about mental illness causes social rejection?**. *J. Soc. Clin. Psychol.* (2007) **26** 137-154. DOI: 10.1521/jscp.2007.26.2.137
30. Fiske S. T., Gilbert D. T., Fiske S. T., Lindzey G.. **Stereotyping, prejudice, and discrimination**. *Handbook of Social Psychology* (1999) **55** 473-489
31. Fiske S. T.. **Venus and Mars or down to earth: stereotypes and realities of gender differences**. *Perspect. Psychol. Sci.* (2010) **5** 688-692. DOI: 10.1177/1745691610388768
32. Fiske S. T.. **Warmth and competence: stereotype content issues for clinicians and researchers**. *Can. Psychol.* (2012) **53** 14-20. DOI: 10.1037/a0026054
33. Fiske S. T.. **Stereotype content: warmth and competence endure**. *Curr. Dir. Psychol. Sci.* (2018) **27** 67-73. DOI: 10.1177/0963721417738825
34. Fiske S. T., Cuddy A. J., Glick P., Xu J.. **A model of (often mixed) stereotype content: competence and warmth respectively follow from perceived status and competition**. *J. Pers. Soc. Psychol.* (2002) **82** 878-902. DOI: 10.1037/0022-3514.82.6.878
35. Fiske S. T., Durante F., Gelfand M. J., Chiu C.-Y., Hong Y.-Y.. **Stereotype content across cultures: variations on a few themes**. *Handbook of Advances in Culture and Psychology* (2016) 209-258
36. Fiske S. T., Xu J., Cuddy A. C., Glick P.. **(dis) respecting versus (dis) liking: status and interdependence predict ambivalent stereotypes of competence and warmth**. *J. Soc. Issues* (1999) **55** 473-489. DOI: 10.1111/0022-4537.00128
37. Follmer K. B., Jones K. S.. **Stereotype content and social distancing from employees with mental illness: the moderating roles of gender and social dominance orientation**. *J. Appl. Soc. Psychol.* (2017) **47** 492-504. DOI: 10.1111/jasp.12455
38. Friehs M.-T., Kotzur P. F., Böttcher J., Zöller A.-K. C., Lüttmer T., Wagner U.. **Examining the structural validity of stereotype content scales – a preregistered re-analysis of published data and discussion of possible future directions**. *Int. Rev. Soc. Psychol.* (2022) **35** 1-18. DOI: 10.5334/irsp.613
39. Gärtner L., Asbrock F., Euteneuer F., Rief W., Salzmann S.. **Self-stigma among people with mental health problems in terms of warmth and competence**. *Front. Psychol.* (2022) **13** 877491. DOI: 10.3389/fpsyg.2022.877491
40. Gonzalez-Sanguino C., Munoz M., Castellanos M. A., Perez-Santos E., Orihuela-Villameriel T.. **Study of the relationship between implicit and explicit stigmas associated with mental illness**. *Psychiatry Res.* (2019) **272** 663-668. DOI: 10.1016/j.psychres.2018.12.172
41. Görzig A., Bedrosova M., Machackova H.. **Do stereotypes of mental and developmental disorders predict bystander intentions in cyberbullying? An application of the stereotype content model**. *Int. J. Dev. Sci.* (2019) **13** 83-95. DOI: 10.3233/DEV-190270
42. Grausgruber A., Schöny W., Grausgruber-Berner R., Koren G., Apor B. F., Wancata J.. **Schizophrenia has many faces - evaluation of the Austrian anti-stigma-campaign 2000-2002**. *Psychiatr. Prax.* (2009) **36** 327-333. DOI: 10.1055/s-0029-1220386
43. Griffiths K. M., Christensen H., Jorm A. F.. **Predictors of depression stigma**. *BMC Psychiatry* (2008) **8** 1-12. DOI: 10.1186/1471-244X-8-25
44. Henderson C., Robinson E., Evans-Lacko S., Thornicroft G.. **Relationships between anti-stigma programme awareness, disclosure comfort and intended help-seeking regarding a mental health problem**. *Br. J. Psychiatry* (2017) **211** 316-322. DOI: 10.1192/bjp.bp.116.195867
45. Hinshaw S. P., Stier A.. **Stigma as related to mental disorders**. *Annu. Rev. Clin. Psychol.* (2008) **4** 367-393. DOI: 10.1146/annurev.clinpsy.4.022007.141245
46. Hipes C., Lucas J., Phelan J. C., White R. C.. **The stigma of mental illness in the labor market**. *Soc. Sci. Res.* (2016) **56** 16-25. DOI: 10.1016/j.ssresearch.2015.12.001
47. Holzinger A., Floris F., Schomerus G., Carta M. G., Angermeyer M. C.. **Gender differences in public beliefs and attitudes about mental disorder in western countries: a systematic review of population studies**. *Epidemiol. Psychiatr. Sci.* (2012) **21** 73-85. DOI: 10.1017/S2045796011000552
48. Kanahara S.. **A review of the definitions of stereotype and a proposal for a progressional model**. *Individ. Differ. Res.* (2006) **4** 306-321
49. Koch A., Imhoff R., Dotsch R., Alves H., Unkelbach C.. **The ABC of stereotypes about groups: agency/socioeconomic success, conservative-progressive beliefs, and communion**. *J. Pers. Soc. Psychol.* (2016) **110** 675-709. DOI: 10.1037/pspa0000046
50. Kotzur P. F., Schäfer S. J., Wagner U.. **Meeting a nice asylum seeker: intergroup contact changes stereotype content perceptions and associated emotional prejudices, and encourages solidarity-based collective action intentions**. *Br. J. Soc. Psychol.* (2019) **58** 668-690. DOI: 10.1111/bjso.12304
51. Kotzur P. F., Veit S., Namyslo A., Holthausen M. A., Wagner U., Yemane R.. **‘Society thinks they are cold and/or incompetent, but I do not’: stereotype content ratings depend on instructions and the social group's location in the stereotype content space**. *Br. J. Soc. Psychol.* (2020) **59** 1018-1042. DOI: 10.1111/bjso.12375
52. Kotzur P. F., Wagner U.. **The dynamic relationship between contact opportunities, positive and negative intergroup contact, and prejudice: a longitudinal investigation**. *J. Pers. Soc. Psychol.* (2021) **120** 418-442. DOI: 10.1037/pspi0000258
53. Krueger R. F., McGue M., Iacono W. G.. **The higher-order structure of common DSM mental disorders: internalization, externalization, and their connections to personality**. *Personal. Individ. Differ.* (2001) **30** 1245-1259. DOI: 10.1016/S0191-8869(00)00106-9
54. Levey S., Howells K., Levey S.. **Dangerousness, unpredictability and the fear of people with schizophrenia**. *J. Forensic Psychiatry* (1995) **6** 19-39. DOI: 10.1080/09585189508409874
55. Link B. G., Phelan J. C., Bresnahan M., Stueve A., Pescosolido B. A.. **Public conceptions of mental illness: labels, causes, dangerousness, and social distance**. *Am. J. Public Health* (1999) **89** 1328-1333. DOI: 10.2105/ajph.89.9.1328
56. Link B. G., Yang L. H., Phelan J. C., Collins P. Y.. **Measuring mental illness stigma**. *Schizophr. Bull.* (2004) **30** 511-541. DOI: 10.1093/oxfordjournals.schbul.a007098
57. Macrae C. N., Bodenhausen G. V.. **Social cognition: thinking categorically about others**. *Annu. Rev. Psychol.* (2000) **51** 93-120. DOI: 10.1146/annurev.psych.51.1.93
58. Mehta N., Kassam A., Leese M., Butler G., Thornicroft G.. **Public attitudes towards people with mental illness in England and Scotland, 1994–2003**. *Br. J. Psychiatry* (2009) **194** 278-284. DOI: 10.1192/bjp.bp.108.052654
59. Möller-Leimkühler A. M.. **Stigmatisierung psychisch Kranker aus der Perspektive sozialpsychologischer Stereotypenforschung [why are stereotypes about mentally ill so resistant? Lessons from social psychology]**. *Fortschr. Neurol. Psychiatr.* (2004) **72** 36-44. DOI: 10.1055/s-2003-812456
60. Morey R. D., Rouder J. N., Jamil T., Morey M. R. D.. (2015). PMID: 17695343
61. Nett T., Dorrough A., Jekel M., Glöckner A.. **Perceived biological and social characteristics of a representative set of German first names**. *Soc. Psychol.* (2020) **51** 17-34. DOI: 10.1027/1864-9335/a000383
62. Norouzian R., de Miranda M., Plonsky L.. **The Bayesian revolution in second language research: An applied approach**. *Language Learning.* (2018) **68** 1032-1075. DOI: 10.1111/lang.12310
63. Pescosolido B. A., Martin J. K., Long J. S., Medina T. R., Phelan J. C., Link B. G.. **“A disease like any other”? A decade of change in public reactions to schizophrenia, depression, and alcohol dependence**. *Am. J. Psychiatr.* (2010) **167** 1321-1330. DOI: 10.1176/appi.ajp.2010.09121743
64. Pescosolido B. A., Monahan J., Link B. G., Stueve A., Kikuzawa S.. **The public's view of the competence, dangerousness, and need for legal coercion of persons with mental health problems**. *Am. J. Public Health* (1999) **89** 1339-1345. DOI: 10.2105/ajph.89.9.1339
65. Pettigrew T. F., Tropp L. R., Oskamp S.. **Does intergroup contact reduce prejudice: recent meta-analytic findings**. *Reducing Prejudice and Discrimination* (2000) 93-114
66. Phelan J. C., Link B. G., Stueve A., Pescosolido B. A.. **Public conceptions of mental illness in 1950 and 1996: what is mental illness and is it to be feared?**. *J. Health Soc. Behav.* (2000) **41** 188-207. DOI: 10.2307/2676305
67. Read J., Haslam N., Sayce L., Davies E.. **Prejudice and schizophrenia: a review of the ‘mental illness is an illness like any other’ approach**. *Acta Psychiatr. Scand.* (2006) **114** 303-318. DOI: 10.1111/j.1600-0447.2006.00824.x
68. Reavley N. J., Jorm A. F.. **Stigmatizing attitudes towards people with mental disorders: findings from an Australian National Survey of mental health literacy and stigma**. *Aust. N. Z. J. Psychiatry* (2011) **45** 1086-1093. DOI: 10.3109/00048674.2011.621061
69. Roehrig J. P., McLean C. P.. **A comparison of stigma toward eating disorders versus depression**. *Int. J. Eat. Disord.* (2010) **43** 671-674. DOI: 10.1002/eat.20760
70. Ross C. A., Goldner E. M.. **Stigma, negative attitudes and discrimination towards mental illness within the nursing profession: a review of the literature**. *J. Psychiatr. Ment. Health Nurs.* (2009) **16** 558-567. DOI: 10.1111/j.1365-2850.2009.01399.x
71. Rouder J. N., Speckman P. L., Sun D., Morey R. D., Iverson G.. **Bayesian t tests for accepting and rejecting the null hypothesis**. *Psychon. Bull. Rev.* (2009) **16** 225-237. DOI: 10.3758/PBR.16.2.225
72. Rüsch N., Angermeyer M. C., Corrigan P. W.. **Mental illness stigma: concepts, consequences, and initiatives to reduce stigma**. *Eur. Psychiatry* (2005) **20** 529-539. DOI: 10.1016/j.eurpsy.2005.04.004
73. Sadler M. S., Kaye K. E., Vaughn A. A.. **Competence and warmth stereotypes prompt mental illness stigma through emotions**. *J. Appl. Soc. Psychol.* (2015) **45** 602-612. DOI: 10.1111/jasp.12323
74. Sadler M. S., Meagor E. L., Kaye K. E.. **Stereotypes of mental disorders differ in competence and warmth**. *Soc. Sci. Med.* (2012) **74** 915-922. DOI: 10.1016/j.socscimed.2011.12.019
75. Schmitt M. T., Branscombe N. R., Postmes T., Garcia A.. **The consequences of perceived discrimination for psychological well-being: a meta-analytic review**. *Psychol. Bull.* (2014) **140** 921-948. DOI: 10.1037/a0035754
76. Schomerus G., Schwahn C., Holzinger A., Corrigan P. W., Grabe H. J., Carta M. G.. **Evolution of public attitudes about mental illness: a systematic review and meta-analysis**. *Acta Psychiatr. Scand.* (2012) **125** 440-452. DOI: 10.1111/j.1600-0447.2012.01826.x
77. Sönmez B., Karaoğlu K. M.. **Contents of stereotypes toward mental illness**. *Curr. Psychol.* (2022) **1-10** 693. DOI: 10.1007/s12144-022-03693-9
78. Thompson A. H., Stuart H., Bland R. C., Arboleda-Florez J., Warner R., Dickson R. A.. **Attitudes about schizophrenia from the pilot site of the WPA worldwide campaign against the stigma of schizophrenia**. *Soc. Psychiatry Psychiatr. Epidemiol.* (2002) **37** 475-482. DOI: 10.1007/s00127-002-0583-2
79. World Health Organization [WHO]. (1993). The ICD-10 Classification of Mental and Behavioral Disorders. Geneva: World Health Organization.. *The ICD-10 Classification of Mental and Behavioral Disorders* (1993)
80. Yoshimura Y., Bakolis I., Henderson C.. **Psychiatric diagnosis and other predictors of experienced and anticipated workplace discrimination and concealment of mental illness among mental health service users in England**. *Soc. Psychiatry Psychiatr. Epidemiol.* (2018) **53** 1099-1109. DOI: 10.1007/s00127-018-1561-7
81. Yuan Q., Abdin E., Picco L., Vaingankar J. A., Shahwan S., Jeyagurunathan A.. **Attitudes to mental illness and its demographic correlates among general population in Singapore**. *PLoS One* (2016) **11** e0167297. DOI: 10.1371/journal.pone.0167297
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---
title: 'Schizophrenia as a risk factor for cardiovascular and metabolic health outcomes:
a comparative risk assessment'
authors:
- S. Ali
- D. Santomauro
- A. J Ferrari
- F. Charlson
journal: Epidemiology and Psychiatric Sciences
year: 2023
pmcid: PMC9971851
doi: 10.1017/S2045796023000045
license: CC BY 4.0
---
# Schizophrenia as a risk factor for cardiovascular and metabolic health outcomes: a comparative risk assessment
## Abstract
### Aims
Cardiometabolic diseases are responsible for the majority of premature deaths in people with schizophrenia. This study aimed to quantify the fatal burden of ischaemic heart disease (IHD), stroke and diabetes attributable to schizophrenia.
### Methods
Comparative Risk Assessment methodology from the Global Burden of Disease (GBD) study was used to calculate attributable burden; pooled relative risks (RRs) for IHD, stroke and diabetes were estimated via meta-regression, which were combined with GBD schizophrenia prevalence estimates to calculate the deaths and years of life lost (YLLs) caused by these health outcomes that were attributable to schizophrenia. The proportion of explained all-cause fatal burden and corresponding unexplained burden was also calculated.
### Results
The pooled RRs for IHD, stroke and diabetes mortality were 2.36 [$95\%$ uncertainty interval (UI) 1.77 to 3.14], 1.86 ($95\%$ UI 1.36 to 2.54) and 4.08 ($95\%$ UI 3.80 to 4.38) respectively. Schizophrenia was responsible for around 50 000 deaths and almost 1.5 million YLLs globally in 2019 from these health outcomes combined. IHD, stroke and diabetes together explained around $13\%$ of all deaths and almost $11\%$ of all YLLs attributable to schizophrenia, resulting in 320 660 ($95\%$ UI 288 299 to 356 517) unexplained deaths and 12 258 690 ($95\%$ UI 10 925 426 to 13 713 646) unexplained YLLs.
### Conclusions
Quantifying the physical disease burden attributable to schizophrenia provides a means of capturing the substantial excess mortality associated with this disorder within the GBD framework, contributing to an important evidence base for healthcare planning and practice.
## Introduction
People with schizophrenia have a decreased life expectancy of 13 to 15 years (Hjorthøj et al., 2017). While this population experiences higher rates of deaths from unnatural causes compared to the general population, most premature deaths are attributable to natural causes (Lawrence et al., 2010). The most common cause of death among people with schizophrenia in high-income countries (HICs) is cardiovascular disease (CVD), accounting for approximately one-quarter of male deaths and one-third of female deaths, with limited data available from low- and middle-income countries (LMICs) (Lawrence et al., 2013; Olfson et al., 2015; Westman et al., 2018; Laursen et al., 2019; Pan et al., 2020; Ali et al., 2022). The leading cause of CVD deaths worldwide are ischaemic heart disease (IHD) and stroke, although mortality rates have declined dramatically in HICs over the past 50 years due to a reduction in risk factors and improved medical care (Lopez and Adair, 2019; GBD Collaborative Network, 2020b). However, people with schizophrenia have not benefitted from these improvements and experience higher mortality following CVD diagnoses compared to people without schizophrenia (Kugathasan et al., 2018; Yung et al., 2019). Diabetes mellitus is an established risk factor for CVD and associated with greater severity and higher fatality (The Emerging Risk Factors Collaboration, 2010; Leon and Maddox, 2015; Zheng et al., 2018). Diabetes is highly prevalent in people with schizophrenia, affecting around 1 in 10 people, with elevated diabetes-related mortality compared to people with diabetes only (Vancampfort et al., 2016; Toender et al., 2020). The pervasive mortality gap experienced by people with schizophrenia in regard to these preventable and manageable cardiometabolic diseases is not currently reflected in global health estimates, including the Global Burden of Disease (GBD) study.
GBD measures the disability and death caused by diseases, injuries and risk factors, which is critical for informed policy-making and shaping health systems to meet the needs of the populations they serve. Mortality, or fatal burden, is not only measured through number of deaths but also years of life lost (YLLs), which is calculated by subtracting the age at death from the longest possible life expectancy for a person at that age. GBD adheres to the International Classification of Diseases (ICD-10) death-coding system, which attributes death to a single underlying cause; mental disorders are rarely listed as the underlying cause of death on death certificates and premature deaths are captured under other causes (Whiteford et al., 2013; Vigo et al., 2016). For example, the death of someone with schizophrenia who dies from IHD will be attributed entirely to IHD, regardless of the contribution of schizophrenia to the premature death. Subsequently, there are very few deaths attributed to mental disorders in GBD (GBD 2019 Mental Disorders Collaborators, 2022). GBD's Comparative Risk Assessment (CRA) methodology offers a means of investigating the contribution of other underlying causes of death while circumventing death coding practices (GBD 2019 Risk Factors Collaborators, 2020). This methodology is used to quantify and compare the contribution of risk factors to disease burden by estimating attributable burden – the difference between the burden currently observed and the burden that would have been observed under a counterfactual level of risk factor exposure (Ezzati et al., 2002). Framing schizophrenia as a risk factor for other health outcomes, such as CVD and diabetes, allows for the contribution of the mental disorder to the burden of these diseases to be quantified.
Unlike more proximal risk factors where reducing the distribution of the risk factor itself will improve population health, the disease burden attributable to schizophrenia can be prevented by addressing modifiable factors in health-related behaviours and health services (Firth et al., 2019). These estimates can therefore play an important role in healthcare policy and service planning, providing evidence to integrate agendas on mental health and non-communicable diseases, as well as for coordinated care and primary prevention. We will also be examining how much of the overall fatal burden attributable to schizophrenia (i.e., all-cause mortality) is accounted for by cardiometabolic diseases, in order to quantify how much burden remains to be explained.
This study aims to estimate the mortality risk of IHD, stroke and diabetes in people with schizophrenia and quantify the fatal burden of these physical health outcomes attributable to schizophrenia, as well as the proportion of explained all-cause burden and corresponding unexplained burden.
## Overview
We used CRA methodology to estimate the burden attributable to schizophrenia as a risk factor for IHD, stroke and diabetes (GBD 2019 Risk Factors Collaborators, 2020). This process consisted of five key steps: Establishing that there is sufficient evidence for causal relationships between the risk factor and outcomes; a number of comprehensive review articles have been published outlining the mechanisms, including biological pathways, linking schizophrenia to CVD (Ringen et al., 2014; Nielsen et al., 2020; Lemogne et al., 2021), and diabetes (Ward and Druss, 2015; Mamakou et al., 2018; Mizuki et al., 2020).Estimating the relative risk (RR) of each outcome due to the risk factor; we compiled RRs of IHD, stroke and diabetes mortality for persons with schizophrenia and pooled these estimates using meta-regression. Estimating exposure levels of the risk factor; we compiled prevalence estimates of schizophrenia from the GBD 2019 study. Determining the counterfactual level of exposure, known as the theoretical minimum risk exposure level (the distribution of a risk that would lead to the greatest improvement in population health); we defined this as the absence of schizophrenia within the population. Calculating population attributable fractions (PAFs) and attributable burden: we combined the pooled RR estimates with the prevalence estimates to generate PAFs, which were then multiplied by the underlying fatal burden (deaths and YLLs) of each health outcome to estimate attributable burden.
Additionally, the RR of all-cause mortality for schizophrenia was used to calculate the proportion of explained fatal burden and corresponding unexplained burden attributable to schizophrenia.
## Case definitions
Schizophrenia was defined according to ICD or the Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnostic criteria: ICD-10 code F20 and DSM-IV code 295.90, to align with the GBD prevalence data. However, we also explored the utility of including studies with a wider case definition that included schizoaffective disorder (ICD-10 code F25, DSM-IV code 295.70) via a covariate in the meta-regression detailed below due to its very low prevalence and likely minimal impact on the overall estimates (Laursen et al., 2007).
The health outcomes were also defined according to ICD coding; IHD (which is used interchangeably with coronary heart disease): ICD-10 codes I20–25 or ICD-9 codes 410–414; stroke: ICD-10 codes I60–69 or ICD-9 codes 430–438; and diabetes: ICD-10 codes E10–E14 or ICD-9 code 250.
## Prevalence of exposure
Prevalence data for schizophrenia was obtained from GBD 2019, with detailed methods available elsewhere (GBD 2019 Mental Disorders Collaborators, 2022). Briefly, these estimates are based on a systematic literature review, which included surveys with representative samples of the general population reporting past-year schizophrenia prevalence or less. DisMod-MR 2.1, a Bayesian meta-regression tool, was used to produce pooled prevalence estimates by age and sex for 204 countries and territories. Global age- and sex-specific data for 2019 was used in this study. All GBD 2019 analyses complied with the Guidelines for Accurate and Transparent Health Estimates Reporting statement (GBD 2019 Diseases and Injuries Collaborators, 2020; GBD 2019 Mental Disorders Collaborators, 2022).
## Relative risk estimates
Studies containing estimates of IHD, stroke and diabetes mortality in people with schizophrenia were identified from a previous systematic review detailed elsewhere (Ali et al., 2022), which adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. In brief, the online databases PubMed, EMBASE and PsycINFO were searched from $\frac{1}{1}$/1980 to $\frac{31}{12}$/20 for studies examining excess mortality in people with severe mental disorders (SMD). Studies were eligible if they were longitudinal; the study population was diagnosed according to established criteria, not restricted to subgroups and the disorder was primary and not acute or transient; and mortality was reported in comparison to the general population or a control group without SMD. Details of the data extraction process are provided in the online Supplementary material (page 1). The following effect measures were included and treated as equivalent measures of mortality risk; standardised mortality ratio (SMR), hazard ratio (HR) and relative risk (RR) (including mortality rate ratios); HRs and SMRs were converted to RRs where possible as detailed in (Shor et al., 2017) and (Jones and Swerdlow, 1998) respectively. Risk of bias was assessed using an adaptation of the Newcastle-Ottawa scale (Wells et al., 2019). A summary of the included studies can be found in Table 1. Table 1.Summary of included studiesStudyCountryDiagnosesSample sizeObservation periodPopulation typeOutcomesCastagnini et al. [ 2013]DenmarkSZ45761995–2008Inpatient and outpatientStrokeCrump et al. [ 2013]SwedenSZ82772003–2009Inpatient and outpatientIHD, stroke, diabetesKo et al. [ 2018]TaiwanSZ42981998–2010InpatientStroke, diabetesLahti et al. [ 2012]FinlandSZ + SA2041969–2003InpatientIHD, strokeLaursen et al. [ 2007]DenmarkSZNot reported1973–2001InpatientStrokeLaursen et al. [ 2013]Denmark, Finland, SwedenSZ20430, 20835, 248232000–2007InpatientIHD, strokeLaursen et al. [ 2019]DenmarkSZ 47 5541995–2015Inpatient and outpatientDiabetesLawrence et al. [ 2003]AustraliaSZNot reported1980–1998Inpatient and outpatientIHDOlfson et al. [ 2015]USASZ + SA 1 138 8532001–2007Inpatient and outpatientIHD, stroke, diabetesOsborn et al. [ 2007]UKSZNot reported1987–2002OutpatientIHD, strokeOsby et al. [ 2000]SwedenSZ77841973–1995InpatientStrokePan et al. [ 2020]TaiwanSZ + SA95632, 1045612005–2008, 2010–2013Inpatient and outpatientDiabetesWestman et al. [ 2018]SwedenSZ + SA 46 9111987–2010InpatientIHD, strokeYung et al. [ 2021]ChinaSZ + SA 46 8962006–2016Inpatient and outpatientIHD, strokeSZ, schizophrenia; SA, schizoaffective disorder; IHD, ischaemic heart disease.
Multi-level meta-regression (with estimates nested within each study) was used to pool mortality estimates for each health outcome separately using the metafor package (Viechtbauer, 2010) in R (version 4.1.2). The following covariates were tested as potential sources of heterogeneity as guided by the findings of Ali et al., 2022: population type (inpatient and outpatient combined vs inpatient only), case definition (schizophrenia vs schizophrenia and schizoaffective disorder combined), sex, age and age-sex interaction. Backward elimination using the Akaike information criterion was used to develop the final model for each health outcome. Further details of the analysis methods are provided in the online Supplementary material (page 1). In terms of all-cause mortality, the meta-regression model for schizophrenia in Ali et al., 2022 (see page 1 of the online Supplementary material for details on covariates) was used to derive age- and sex-specific RRs for the calculation of unexplained burden described below; as a summary estimate, the adjusted RR for both sexes was 2.89 ($95\%$ UI 2.50 to 3.34) based on 23 studies and 70 estimates.
## Attributable burden
Using R, PAFs were calculated by age and sex using the following formula (GBD 2019 Risk Factors Collaborators, 2020):Where P is the global prevalence of schizophrenia and RR is the adjusted relative risk of each health outcome. Age-sex-specific PAFs were multiplied by the corresponding GBD 2019 deaths and YLLs for each health outcome (GBD 2019 Diseases and Injuries Collaborators, 2020) to calculate age-sex-specific attributable burden. All-age and both-sex data was population weighted using GBD 2019 population estimates (GBD Collaborative Network, 2020a). The proportion of deaths and YLLs attributable to schizophrenia was calculated by dividing the attributable burden by the total burden for each health outcome. To estimate the unexplained fatal burden and proportion of explained fatal burden for schizophrenia, attributable burden was also calculated for all causes. Unexplained burden was calculated by subtracting the attributable burden for each health outcome from the all-cause attributable burden. A Markov chain Monte Carlo simulation was conducted and 1000 samples from the probability distributions of the RRs, prevalence, deaths, YLLs and population estimates were pulled in order to propagate all of these sources of uncertainty into the final estimates. Prevalence and deaths were logit-transformed to ensure all samples remained between 0 and 1, and YLLs were log-transformed to ensure all samples were above 0. The reported estimates and $95\%$ uncertainty intervals (UI) correspond to the mean and 2.5th and 97.5th quantiles of the samples.
Schizophrenia was responsible for $0.25\%$ ($95\%$ UI $0.21\%$ to $0.29\%$) and $0.42\%$ ($95\%$ UI $0.37\%$ to $0.48\%$) of total IHD deaths and YLLs respectively, amounting to 22 603 ($95\%$ UI 19 475 to 25 998) deaths and 742 715 ($95\%$ UI 645 855 to 849 705) YLLs (Table 3). Greater attributable burden was observed in males compared to females, and burden increased with age, peaking at 50 to 54 years (online Supplementary Fig. S2; note that deaths and YLLs showed the same age pattern). Table 3.Estimated burden attributable to schizophreniaHealth outcomeProportion of deaths, %a ($95\%$ UI)Proportion of YLLs, %a ($95\%$ UI)Attributable deaths ($95\%$ UI)Attributable YLLs ($95\%$ UI)IHDBoth sexes0.25 (0.21–0.29)0.42 (0.37–0.48) 22 603 (19 475–25 998) 742 715 (645 855–849 705)Female0.22 (0.18–0.27)0.44 (0.37–0.51)9282 (7696–11 110) 304 406 (256 848–356 052)Male0.27 (0.21–0.33)0.41 (0.33–0.49) 13 320 (10 598–16 219) 438 310 (353 239–530 715)StrokeBoth sexes0.20 (0.17–0.23)0.28 (0.24–0.33) 12 998 (10 914–15 409) 351 820 (295 765–411 293)Female0.16 (0.12–0.20)0.24 (0.19–0.30)5156 (3923–6545) 134 403 (105 013–168 347)Male0.24 (0.18–0.29)0.31 (0.24–0.39)7842 (6014–9818) 217 415 (168 908–269 829)DiabetesBoth sexes0.81 (0.77–0.85)1.08 (1.03–1.13) 12 623 (11 986–13 314) 369 356 (350 940–389 901)Female0.92 (0.86–0.98)1.23 (1.16–1.32)7317 (6804–7859) 205 387 (191 000–220 766)Male0.70 (0.66–0.76)0.93 (0.87–1.00)5306 (4927–5738) 163 969 (152 410–176 591)All-causeBoth sexes0.65 (0.59–0.72)0.82 (0.73–0.91) 368 883 (332 468–408 595) 13 722 580 (12 255 974–15 300 532)Female0.53 (0.46–0.62)0.67 (0.58–0.77) 137 703 (119 024–159 068) 4 816 726 (4 161 168–5 535 587)Male0.75 (0.65–0.86)0.92 (0.79–1.07) 231 180 (199 214–265 150) 8 905 854 (7 657 443–10 251 196)YLLs, years of life lost; UI, uncertainty interval; IHD, ischaemic heart disease.aProportions correspond to attributable burden divided by total burden for each health outcome, converted to percentages.
There were almost half as much stroke deaths and YLLs attributable to schizophrenia compared to IHD; 12 998 ($95\%$ UI 10 914 to 15 409) and 351 820 ($95\%$ UI 295 765 to 411 293) respectively, which corresponded to $0.20\%$ ($95\%$ UI $0.17\%$ to $0.23\%$) and $0.28\%$ ($95\%$ UI $0.24\%$ to $0.33\%$) of total stroke deaths and YLLs (Table 3). The same sex and age patterns as IHD were observed, with a slightly later peak at age 60 to 64 (online Supplementary Fig. S2).
The largest proportion of disease burden attributable to schizophrenia was for diabetes: $0.81\%$ ($95\%$ UI $0.77\%$ to $0.85\%$) of deaths and $1.08\%$ ($95\%$ UI $1.03\%$ to $1.13\%$) of YLLs (Table 3). This amounted to 12 623 ($95\%$ UI 11 986 to 13 314) deaths and 369 356 ($95\%$ UI 350 940 to 389 901) YLLs. While the age pattern was the same as IHD and stroke, the sex pattern was reversed, with more attributable burden and proportions of total burden observed in females (online Supplementary Fig. S2).
In terms of all-cause mortality, schizophrenia was responsible for $0.65\%$ ($95\%$ UI $0.59\%$ to $0.72\%$) and $0.82\%$ ($95\%$ UI $0.73\%$ to $0.91\%$) of deaths and YLLs, which corresponded to 368 883 ($95\%$ UI 332 468 to 408 595) deaths and 13 722 580 ($95\%$ UI 12 255 974 to 15 300 532) YLLs.
IHD, stroke and diabetes together explained $13.08\%$ ($95\%$ UI $12.43\%$ to $13.82\%$) of all deaths and $10.68\%$ ($95\%$ UI $10.04\%$ to $11.32\%$) of all YLLs attributable to schizophrenia, which amounted to a total of 48 223 ($95\%$ UI 43 573 to 53 227) deaths and 1 463 891 ($95\%$ UI 1 327 260 to 1 607 376) YLLs. This resulted in 320 660 ($95\%$ UI 288 299 to 356 517) unexplained deaths and 12 258 690 ($95\%$ UI 10 925 426 to 13 713 646) unexplained YLLs (Table 4). Table 4.Unexplained and proportion of explained burden attributable to schizophreniaProportion of explained deaths, % ($95\%$ UI)Proportion of explained YLLs, % ($95\%$ UI)*Unexplained deathsa* ($95\%$ UI)Unexplained YLLsa ($95\%$ UI)Both sexes13.08 (12.43–13.82)10.68 (10.04–11.32) 320 660 (288 299–356 517) 12 258 690 (10 925 426–13 713 646)Female15.81 (15.03–16.65)13.39 (12.59–14.20) 115 947 [996 36 134 437] 4 172 529 (3 580 561–4 821 672)Male11.46 (10.61–12.45)9.22 (8.49–10.09) 204 713 (175 509–235 797) 8 086 160 (6 928 283–9 323 921)YLLs, years of life lost; UI, uncertainty interval.aUnexplained burden corresponds to the attributable burden for each health outcome subtracted from all-cause attributable burden.
## Pooled relative risks
A total of 14 studies covering 8 countries were included in the analyses for IHD, stroke and diabetes mortality (see pages 2–3 of the online Supplementary material for the results of the search and selection process). The final meta-regression model coefficients and pooled RRs can be found in Table 2. For IHD, 8 studies provided 49 estimates resulting in an RR of 2.36 ($95\%$ UI 1.77 to 3.14) for both sexes. Sex, age, age-sex interaction and case definition were included in the final model, the latter of which was not significant. The RR for stroke was 1.86 ($95\%$ UI 1.36 to 2.54) for both sexes based on 11 studies and 56 estimates, with significant age effects. Population type was also included as a covariate in the final model but was not statistically significant. Diabetes had the largest RR, 4.08 ($95\%$ UI 3.80 to 4.38), from 5 studies and 20 estimates, with significant effects of sex, age and population type (inpatients only had a larger RR compared to inpatients and outpatients). RRs were larger in females for IHD: 2.73 ($95\%$ UI 2.04 to 3.64) compared to 2.04 ($95\%$ UI 1.53 to 2.72) for males; and diabetes: 4.84 ($95\%$ UI 4.48 to 5.24) compared to 3.43 ($95\%$ UI 3.17 to 3.72). RRs decreased with age for all three health outcomes. Table 2.Final meta-regression model coefficients and relative risks by health outcomeCovariateβ ($95\%$ UI)pRR ($95\%$ UI)IHD ($$n = 8$$ studies, 49 estimatesa)Intercept0.86 (0.57–1.15)<0.0012.36 (1.77–3.14)Sex0.29 (0.25–0.33)<0.001Age−0.02 (−0.02 to −0.02)<0.001Age-sex−0.01 (−0.02 to −0.01)<0.001Case definitionSZ ($$n = 5$$)ReferenceSZ + SA ($$n = 4$$)0.35 (−0.09 to 0.79)0.116Stroke ($$n = 11$$ studies, 56 estimatesa)Intercept0.62 (0.31–0.93)<0.0011.86 (1.36–2.54)Age−0.01 (−0.01 to <−0.01)<0.001Age-sex<−0.01 (−0.01 to <−0.01)0.039Population typeInpatient + outpatient ($$n = 4$$)ReferenceInpatient ($$n = 7$$)0.32 (−0.09 to 0.73)0.124Diabetes ($$n = 5$$ studies, 20 estimatesa)Intercept1.41 (1.33–1.48)<0.0014.08 (3.80–4.38)Sex0.34 (0.28–0.41)<0.001Age−0.01 (−0.02 to −0.01)<0.001Population typeInpatient + outpatient ($$n = 4$$)ReferenceInpatient ($$n = 1$$)0.97 (0.41–1.52)<0.001UI, uncertainty interval; RR, relative risk; IHD, ischaemic heart disease; SZ, schizophrenia; SA, schizoaffective disorder. Note: RRs are for both sexes and correspond to the exponentiated intercept of each multivariate meta-regression model; sex covariate corresponds to per cent female and age corresponds to mid-point of age-range centred at mean age.aEach study could contribute more than one estimate if different age and sex stratifications or non-overlapping time periods were reported.
## Discussion
This is the first study to quantify the physical health burden attributable to schizophrenia as a risk factor. This study goes beyond estimates of elevated mortality risk from previous studies (Correll et al., 2022; Lambert et al., 2022) to describe specifically how many deaths and YLLs due to cardiometabolic diseases are driven by having schizophrenia. The proportion of attributable burden ranged between 0.16 to $1.23\%$ of the total burden of each health outcome, driven by the low prevalence of schizophrenia which was $0.32\%$ ($95\%$ UI 0.27 to $0.37\%$) globally for both sexes, all ages in 2019 (GBD Collaborative Network, 2020b). For IHD, stroke and diabetes combined, this amounted to around 50 000 deaths and almost 1.5 million YLLs, which is a considerable amount of fatal burden and critically, potentially preventable.
In terms of sex patterns, males had greater attributable burden for IHD and stroke than females, likely due to the overall fatal burden of these diseases being larger in males (GBD Collaborative Network, 2020b). Looking at diabetes mortality in females, the larger RR alongside a higher overall death rate (GBD Collaborative Network, 2020b) resulted in greater attributable burden compared to males. Females also had larger IHD RRs, which is important to consider in light of the greater proportion of CVD deaths noted in the introduction. This may relate to sex and gender-related healthcare disparities, which are apparent for a range of chronic diseases; for example, women are less likely to receive evidence-based treatment for IHD and CVD risk factors including diabetes, which is also a stronger risk factor for vascular disease onset and mortality in women (Prospective Studies Collaboration and Asia Pacific Cohort Studies Collaboration, 2018; Mauvais-Jarvis et al., 2020). A study looking at the quality of clinical management of cardiometabolic risk factors in patients with SMD found that women with obesity were less likely than men to receive dietary advice (Ringen et al., 2022). Sex differences have also been found in regard to the adverse metabolic risks associated with antipsychotic medication, with greater metabolic disturbances observed in females (Kraal et al., 2017). Sex-specific risks and disparities do not appear to be recognised in key recommendations and guidelines for managing cardiometabolic risk factors and physical health conditions in people with SMD (De Hert et al., 2009; Galderisi et al., 2021; Gronholm et al., 2021). Our results warrant this consideration, particularly for the treatment of diabetes and other CVD risk factors in women with schizophrenia.
Around $87\%$ of deaths and $89\%$ of YLLs attributable to schizophrenia could not be explained by the three health outcomes included in the study. This large proportion of unexplained burden points to the contribution of other causes of death. Respiratory diseases are highly prevalent in people with schizophrenia; a recent meta-analysis reported an adjusted prevalence of almost $20\%$ for chronic obstructive pulmonary disease (COPD), the third leading cause of death in the world in 2019 (GBD Collaborative Network, 2020b; Suetani et al., 2021). People with schizophrenia are over four times as likely to die of respiratory diseases as defined by ICD-10, which includes both infections and chronic diseases (Ali et al., 2022). GBD examines these categories separately, which alongside limited data on more specific causes like COPD, prevented us from including respiratory diseases in the present study. With both the large RR and overall fatal burden, these diseases are likely to explain a significant proportion of the unexplained burden attributable to schizophrenia. While cancer mortality is also consistently elevated, albeit less so with a pooled RR of 1.76, there is a weak or absent link between schizophrenia and cancer incidence (Nordentoft et al., 2021; Ali et al., 2022). As explored in a recent review, this discrepancy may be due to people with schizophrenia being less likely to receive a cancer diagnosis or effective treatment (Nordentoft et al., 2021). The authors also point out the need to investigate mortality from organ-specific cancers due to differing mechanisms and preventative measures, and as with specific respiratory diseases, there is limited data to pool.
Unnatural causes of death may also be responsible for some of the unexplained burden. While natural causes are responsible for the majority of excess deaths in schizophrenia, unnatural causes are associated with the highest mortality risks, with a pooled RR of around 20 for suicide (Ali et al., 2022). Even though the amount of underlying burden due to suicide is considerably less than CVD, due to this large RR, there would be a significant proportion of suicide burden attributable to schizophrenia.
This study has several limitations. Firstly, the schizophrenia prevalence data is based on global estimates, which may not correspond exactly with the eight countries included in the analysis. These countries were primarily high-income, which hinders generalisability to LMICs. It is possible that RRs vary by location due to different risk factor profiles and trends in disease burden, however, there is limited data to pool to reliably test for differences (Ali et al., 2022). The limited studies also restricted the number of covariates that could be tested and the statistical power to pick up on differences between subgroups. For diabetes, even though there was only one inpatient study, we included population type as a covariate to control for the bias from this study and avoid overestimating the RR, however the magnitude of the covariate should be interpreted with caution. It should also be noted that the single underlying cause of death which the mortality estimates are based on cannot capture the contribution of multiple diseases. This is important to consider in light of people with psychotic disorders being at an increased risk of multimorbidity (Rodrigues et al., 2021). Finally, the theoretical framework of the CRA methodology is based on a hierarchical model of causation, which does not take into account the underlying complexity of schizophrenia as a distal risk factor, encompassing a range of interacting causal factors. This extends to the counterfactual risk exposure, the absence of schizophrenia, which is useful for modelling but less applicable to real-world actions.
In terms of future directions, the inherent complexity to this problem calls for further research using approaches designed specifically to address complex causes, such as systems thinking. This methodology can address causes that encompass numerous factors at different levels of influence, while taking into account dynamic and reciprocal relationships (Galea et al., 2010). Additionally, more data on specific causes of death in people with schizophrenia is required, in order to create a detailed picture of disease risks, and the targeted preventive measures and treatments that are required.
Highlighting the potentially avoidable disease burden attributable to schizophrenia provides an important evidence base for healthcare planning and practice. In particular, these findings underscore the need for integrated care of mental and physical health. As outlined in the Lancet Psychiatry Commission on physical health in people with mental illness, providing holistic care enables the common risk factors, bidirectional interactions and treatments for mental disorders and physical diseases to be addressed together (Firth et al., 2019). The authors highlight the need to protect cardiometabolic health from the earliest stages of mental health treatment. Additionally, the substantial amount of attributable burden not accounted for by cardiometabolic diseases underscores the need to quantify other potentially avertable health outcomes for schizophrenia.
In conclusion, this study has produced estimates of the under-recognised burden of schizophrenia as a risk factor for physical health outcomes, providing a means of capturing the excess mortality associated with mental disorders within the GBD framework. The ongoing issue of excess mortality in people with schizophrenia is a matter of health equity and our results demonstrate how much disease burden could be avoided by reducing disparities in physical health, which needs to occur at all stages of the care pathway. Having a mental illness should not be a barrier to leading a healthy life.
## Financial support
This research received no specific grant from any funding agency, commercial or not-for-profit sectors. SA is supported by an Australian Government Research Training Program Scholarship. AJF is supported by a National Health and Medical Research Council (NHMRC) Early Career Fellowship Grant APP1121516. FC is supported by a NHMRC Early Career Fellowship Grant APP1138488. AJF, FC and DS are employed by the Queensland Centre for Mental Health Research which receives core funding from the Queensland Department of Health.
## Conflict of interest
None.
## Availability of data and materials
The GBD prevalence and burden data used in this study is publicly available at https://vizhub.healthdata.org/gbd-results/. The data used for the relative risk estimates is available at https://osf.io/tvkfj/.
## References
1. Ali S, Santomauro D, Ferrari AJ, Charlson F. **Excess mortality in severe mental disorders: a systematic review and meta-regression**. *Journal of Psychiatric Research* (2022) **149** 97-105. PMID: 35259666
2. Castagnini A, Foldager L, Bertelsen A. **Excess mortality of acute and transient psychotic disorders: Comparison with bipolar affective disorder and schizophrenia**. (2013) **128** 370-375. PMID: 23331302
3. Correll CU, Solmi M, Croatto G, Schneider LK, Rohani-Montez SC, Fairley L, Smith N, Bitter I, Gorwood P, Taipale H, Tiihonen J. **Mortality in people with schizophrenia: a systematic review and meta-analysis of relative risk and aggravating or attenuating factors**. *World Psychiatry* (2022) **21** 248-271. PMID: 35524619
4. Crump C, Winkleby MA, Sundquist K, Sundquist J. **Comorbidities and mortality in persons with schizophrenia: A Swedish national cohort study**. (2013) **170** 324-333. PMID: 23318474
5. De Hert M, Dekker JM, Wood D, Kahl KG, Holt RI, Moller HJ. **Cardiovascular disease and diabetes in people with severe mental illness position statement from the European Psychiatric Association (EPA), supported by the European Association for the Study of Diabetes (EASD) and the European Society of Cardiology (ESC)**. *European Psychiatry* (2009) **24** 412-424. PMID: 19682863
6. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJL. **Selected major risk factors and global and regional burden of disease**. *The Lancet* (2002) **360** 1347-1360
7. Firth J, Siddiqi N, Koyanagi A, Siskind D, Rosenbaum S, Galletly C, Allan S, Caneo C, Carney R, Carvalho AF, Chatterton ML, Correll CU, Curtis J, Gaughran F, Heald A, Hoare E, Jackson SE, Kisely S, Lovell K, Maj M, Mcgorry PD, Mihalopoulos C, Myles H, O'donoghue B, Pillinger T, Sarris J, Schuch FB, Shiers D, Smith L, Solmi M, Suetani S, Taylor J, Teasdale SB, Thornicroft G, Torous J, Usherwood T, Vancampfort D, Veronese N, Ward PB, Yung AR, Killackey E, Stubbs B. **The lancet psychiatry commission: a blueprint for protecting physical health in people with mental illness**. *The Lancet Psychiatry* (2019) **6** 675-712. PMID: 31324560
8. Galderisi S, De Hert M, Del Prato S, Fagiolini A, Gorwood P, Leucht S, Maggioni AP, Mucci A, Arango C. **Identification and management of cardiometabolic risk in subjects with schizophrenia spectrum disorders: a Delphi expert consensus study**. *European Psychiatry* (2021) **64** e7. PMID: 33413701
9. Galea S, Riddle M, Kaplan GA. **Causal thinking and complex system approaches in epidemiology**. *International Journal of Epidemiology* (2010) **39** 97-106. PMID: 19820105
10. **Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019**. *The Lancet* (2020) **396** 1204-1222
11. **Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019**. *The Lancet Psychiatry* (2022) **9** 137-150. PMID: 35026139
12. **Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019**. *The Lancet* (2020) **396** 1223-1249
13. GBD Collaborative Network (2020a) Global Burden of Disease Study 2019 (GBD 2019) Population Estimates 1950–2019. Seattle, USA: Institute for Health Metrics and Evaluation (IHME).. *Global Burden of Disease Study 2019 (GBD 2019) Population Estimates 1950–2019* (2020a)
14. GBD Collaborative Network (2020b) Global Burden of Disease Study 2019 (GBD 2019) Results. Available at https://vizhub.healthdata.org/gbd-results/ (Accessed 08/03/2022).. (2020b)
15. Gronholm PC, Chowdhary N, Barbui C, Das-Munshi J, Kolappa K, Thornicroft G, Semrau M, Dua T. **Prevention and management of physical health conditions in adults with severe mental disorders: WHO recommendations**. *International Journal of Mental Health Systems* (2021) **15** 22. PMID: 33658061
16. Hjorthøj C, Stürup AE, Mcgrath JJ, Nordentoft M. **Years of potential life lost and life expectancy in schizophrenia: a systematic review and meta-analysis**. *The Lancet Psychiatry* (2017) **4** 295-301. PMID: 28237639
17. Jones ME, Swerdlow AJ. **Bias in the standardized mortality ratio when using general population rates to estimate expected number of deaths**. *American Journal of Epidemiology* (1998) **148** 1012-1017. PMID: 9829874
18. Ko YS, Tsai HC, Chi MH, Su CC, Lee IH, Chen PS, Chen KC, Yang YK. **Higher mortality and years of potential life lost of suicide in patients with schizophrenia**. *Psychiatry Research* (2018) **270** 531-537. PMID: 30342411
19. Kraal AZ, Ward KM, Ellingrod VL. **Sex differences in antipsychotic related metabolic functioning in schizophrenia spectrum disorders**. *Psychopharmacology Bulletin* (2017) **47** 8-21
20. Kugathasan P, Laursen TM, Grontved S, Jensen SE, Aagaard J, Nielsen RE. **Increased long-term mortality after myocardial infarction in patients with schizophrenia**. *Schizophrenia Research* (2018) **199** 103-108. PMID: 29555214
21. Lahti M, Tiihonen J, Wildgust H, Beary M, Hodgson R, Kajantie E, Osmond C, Räikkönen K, Eriksson J. **Cardiovascular morbidity, mortality and pharmacotherapy in patients with schizophrenia**. *Psychological Medicine* (2012) **42** 2275-2285. PMID: 22405504
22. Lambert AM, Parretti HM, Pearce E, Price MJ, Riley M, Ryan R, Tyldesley-Marshall N, Avsar TS, Matthewman G, Lee A, Ahmed K, Odland ML, Correll CU, Solmi M, Marshall T. **Temporal trends in associations between severe mental illness and risk of cardiovascular disease: a systematic review and meta-analysis**. *PLoS Medicine* (2022) **19** e1003960. PMID: 35439243
23. Laursen TM, Munk-Olsen T, Nordentoft M, Mortensen PB. **Increased mortality among patients admitted with major psychiatric disorders: a register-based study comparing mortality in unipolar depressive disorder, bipolar affective disorder, schizoaffective disorder, and schizophrenia**. *The Journal of Clinical Psychiatry* (2007) **68** 899-907. PMID: 17592915
24. Laursen TM, Wahlbeck K, Hällgren J, Westman J, Ösby U, Alinaghizadeh H, Gissler M, Nordentoft M, Mazza M. **Life expectancy and death by diseases of the circulatory system in patients with bipolar disorder or schizophrenia in the Nordic countries**. *PLoS ONE* (2013) **8** e67133. PMID: 23826212
25. Laursen TM, Plana-Ripoll O, Andersen PK, Mcgrath JJ, Toender A, Nordentoft M, Canudas-Romo V, Erlangsen A. **Cause-specific life years lost among persons diagnosed with schizophrenia: is it getting better or worse?**. *Schizophrenia Research* (2019) **206** 284-290. PMID: 30446270
26. Lawrence DM, D'Arcy C, Holman J, Jablensky AV, Hobbs MST. **Death rate from ischaemic heart disease in Western Australian psychiatric patients 1980-1998**. *British Journal of Psychiatry* (2003) **182** 31-36
27. Lawrence D, Kisely S, Pais J. **The epidemiology of excess mortality in people with mental illness**. *The Canadian Journal of Psychiatry* (2010) **55** 752-760. PMID: 21172095
28. Lawrence D, Hancock KJ, Kisely S. **The gap in life expectancy from preventable physical illness in psychiatric patients in Western Australia: retrospective analysis of population based registers**. *British Medical Journal* (2013) **346** f2539. PMID: 23694688
29. Lemogne C, Blacher J, Airagnes G, Hoertel N, Czernichow S, Danchin N, Meneton P, Limosin F, Fiedorowicz JG. **Management of cardiovascular health in people with severe mental disorders**. *Current Cardiology Reports* (2021) **23** 7. PMID: 33409804
30. Leon BM, Maddox TM. **Diabetes and cardiovascular disease: epidemiology, biological mechanisms, treatment recommendations and future research**. *World Journal of Diabetes* (2015) **6** 1246-1258. PMID: 26468341
31. Lopez AD, Adair T. **Is the long-term decline in cardiovascular-disease mortality in high-income countries over? Evidence from national vital statistics**. *International Journal of Epidemiology* (2019) **48** 1815-1823. PMID: 31378814
32. Mamakou V, Thanopoulou A, Gonidakis F, Tentolouris N, Kontaxakis V. **Schizophrenia and type 2 diabetes mellitus**. *Psychiatrike = Psychiatriki* (2018) **29** 64-73. PMID: 29754122
33. Mauvais-Jarvis F, Bairey Merz N, Barnes PJ, Brinton RD, Carrero J-J, Demeo DL, De Vries GJ, Epperson CN, Govindan R, Klein SL, Lonardo A, Maki PM, Mccullough LD, Regitz-Zagrosek V, Regensteiner JG, Rubin JB, Sandberg K, Suzuki A. **Sex and gender: modifiers of health, disease, and medicine**. *The Lancet* (2020) **396** 565-582
34. Mizuki Y, Sakamoto S, Okahisa Y, Yada Y, Hashimoto N, Takaki M, Yamada N. **Mechanisms underlying the comorbidity of schizophrenia and type 2 diabetes mellitus**. *International Journal of Neuropsychopharmacology* (2020) **24** 367-382
35. Nielsen RE, Banner J, Jensen SE. **Cardiovascular disease in patients with severe mental illness**. *Nature Reviews: Cardiology* (2020) **18** 136-145. PMID: 33128044
36. Nordentoft M, Plana-Ripoll O, Laursen TM. **Cancer and schizophrenia**. *Current Opinion in Psychiatry* (2021) **34** 260-265. PMID: 33560020
37. Olfson M, Gerhard T, Huang C, Crystal S, Stroup TS. **Premature mortality among adults with schizophrenia in the United States**. *JAMA Psychiatry* (2015) **72** 1172-1181. PMID: 26509694
38. Osborn DP, Levy G, Nazareth I, Petersen I, Islam A, King MB. **Relative risk of cardiovascular and cancer mortality in people with severe mental illness from the United Kingdom's General Practice Rsearch Database**. *Arch Gen Psychiatry* (2007) **64** 242-249. PMID: 17283292
39. Osby U, Correia N, Brandt L, Ekbom A, Sparen P. **Mortality and causes of death in schizophrenia in Stockholm County, Sweden**. (2000) **45** 21-28. PMID: 10978869
40. Pan YJ, Yeh LL, Chan HY, Chang CK. **Excess mortality and shortened life expectancy in people with major mental illnesses in Taiwan**. *Epidemiology and Psychiatric Sciences* (2020) **29** e156. PMID: 32792024
41. **Sex-specific relevance of diabetes to occlusive vascular and other mortality: a collaborative meta-analysis of individual data from 980 793 adults from 68 prospective studies**. *The Lancet Diabetes & Endocrinology* (2018) **6** 538-546. PMID: 29752194
42. Ringen PA, Engh JA, Birkenaes AB, Dieset I, Andreassen OA. **Increased mortality in schizophrenia due to cardiovascular disease – a non-systematic review of epidemiology, possible causes and interventions**. *Frontiers in Psychiatry* (2014) **5** 137. PMID: 25309466
43. Ringen PA, Lund-Stenvold E, Andreassen OA, Gaarden TL, Hartberg CB, Johnsen E, Myklatun S, Osnes K, Sorensen K, Sorensen K, Vaaler A, Tonstad S, Engh JA, Hoye A. **Quality of clinical management of cardiometabolic risk factors in patients with severe mental illness in a specialist mental health care setting**. *Nordic Journal of Psychiatry* (2022) **76** 602-609. PMID: 35200088
44. Rodrigues M, Wiener JC, Stranges S, Ryan BL, Anderson KK. **The risk of physical multimorbidity in people with psychotic disorders: a systematic review and meta-analysis**. *Journal of Psychosomatic Research* (2021) **140** 110315. PMID: 33307516
45. Shor E, Roelfs D, Vang ZM. **The “Hispanic mortality paradox” revisited: meta-analysis and meta-regression of life-course differentials in Latin American and Caribbean immigrants’ mortality**. *Social Science and Medicine* (2017) **186** 20-33. PMID: 28577458
46. Suetani S, Honarparvar F, Siskind D, Hindley G, Veronese N, Vancampfort D, Allen L, Solmi M, Lally J, Gaughran F, Stubbs B, Pillinger T. **Increased rates of respiratory disease in schizophrenia: a systematic review and meta-analysis including 619,214 individuals with schizophrenia and 52,159,551 controls**. *Schizophrenia Research* (2021) **237** 131-140. PMID: 34521040
47. **Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies**. *The Lancet* (2010) **375** 2215-2222
48. Toender A, Vestergaard M, Munk-Olsen T, Larsen JT, Kristensen JK, Laursen TM. **Risk of diabetic complications and subsequent mortality among individuals with schizophrenia and diabetes - a population-based register study**. *Schizophrenia Research* (2020) **218** 99-106. PMID: 32029352
49. Vancampfort D, Correll CU, Galling B, Probst M, De Hert M, Ward PB, Rosenbaum S, Gaughran F, Lally J, Stubbs B. **Diabetes mellitus in people with schizophrenia, bipolar disorder and major depressive disorder: a systematic review and large scale meta-analysis**. *World Psychiatry* (2016) **15** 166-174. PMID: 27265707
50. Viechtbauer W. **Conducting meta-analyses in R with the metafor package**. *Journal of Statistical Software* (2010) **36** 1-48
51. Vigo D, Thornicroft G, Atun R. **Estimating the true global burden of mental illness**. *The Lancet Psychiatry* (2016) **3** 171-178. PMID: 26851330
52. Ward M, Druss B. **The epidemiology of diabetes in psychotic disorders**. *The Lancet Psychiatry* (2015) **2** 431-451. PMID: 26360287
53. Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M, Tugwell P. (2019)
54. Westman J, Eriksson SV, Gissler M, Hallgren J, Prieto ML, Bobo WV, Frye MA, Erlinge D, Alfredsson L, Osby U. **Increased cardiovascular mortality in people with schizophrenia: a 24-year national register study**. *Epidemiology and Psychiatric Sciences* (2018) **27** 519-527. PMID: 28580898
55. Whiteford HA, Degenhardt L, Rehm J, Baxter AJ, Ferrari AJ, Erskine HE, Charlson FJ, Norman RE, Flaxman AD, Johns N, Burstein R, Murray CJL, Vos T. **Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010**. *The Lancet* (2013) **382** 1575-1586
56. Yung NCL, Wong CSM, Chan JKN, Or PCF, Chen EYH, Chang WC. **Mortality in patients with schizophrenia admitted for incident ischemic stroke: a population-based cohort study**. *European Neuropsychopharmacology* (2019) **31** 152-157. PMID: 31883653
57. Yung NCL, Wong CSM, Chan JKN, Chen EYH, Chang WC. **Excess Mortality and Life-Years Lost in People With Schizophrenia and Other Non-affective Psychoses: An 11-Year Population-Based Cohort Study**. *Schizophrenia Bulletin* (2021) **47** 474-484. PMID: 33009566
58. Zheng Y, Ley SH, Hu FB. **Global aetiology and epidemiology of type 2 diabetes mellitus and its complications**. *Nature Reviews: Endocrinology* (2018) **14** 88-98
|
---
title: Six weeks of N-acetylcysteine antioxidant in drinking water decreases pathological
fiber branching in MDX mouse dystrophic fast-twitch skeletal muscle
authors:
- Asma Redwan
- Leonit Kiriaev
- Sindy Kueh
- John W. Morley
- Peter Houweling
- Ben D. Perry
- Stewart I. Head
journal: Frontiers in Physiology
year: 2023
pmcid: PMC9971923
doi: 10.3389/fphys.2023.1109587
license: CC BY 4.0
---
# Six weeks of N-acetylcysteine antioxidant in drinking water decreases pathological fiber branching in MDX mouse dystrophic fast-twitch skeletal muscle
## Abstract
Introduction: It has been proposed that an increased susceptivity to oxidative stress caused by the absence of the protein dystrophin from the inner surface of the sarcolemma is a trigger of skeletal muscle necrosis in the destructive dystrophin deficient muscular dystrophies. Here we use the mdx mouse model of human Duchenne Muscular Dystrophy to test the hypothesis that adding the antioxidant NAC at $2\%$ to drinking water for six weeks will treat the inflammatory phase of the dystrophic process and reduce pathological muscle fiber branching and splitting resulting in a reduction of mass in mdx fast-twitch EDL muscles.
Methods: Animal weight and water intake was recorded during the six weeks when $2\%$ NAC was added to the drinking water. Post NAC treatment animals were euthanised and the EDL muscles dissected out and placed in an organ bath where the muscle was attached to a force transducer to measure contractile properties and susceptibility to force loss from eccentric contractions. After the contractile measurements had been made the EDL muscle was blotted and weighed. In order to assess the degree of pathological fiber branching mdx EDL muscles were treated with collagenase to release single fibers. For counting and morphological analysis single EDL mdx skeletal muscle fibers were viewed under high magnification on an inverted microscope.
Results: During the six-week treatment phase NAC reduced body weight gain in three- to nine-week-old mdx and littermate control mice without effecting fluid intake. NAC treatment also significantly reduced the mdx EDL muscle mass and abnormal fiber branching and splitting.
Discussion: We propose chronic NAC treatment reduces the inflammatory response and degenerative cycles in the mdx dystrophic EDL muscles resulting in a reduction in the number of complexed branched fibers reported to be responsible for the dystrophic EDL muscle hypertrophy.
## Introduction
It has been proposed that oxidative stress plays a role in the pathophysiology of Duchenne Muscular Dystrophy (DMD), despite this there is currently no effective antioxidant treatment for DMD (Mosca et al., 2021; Lian et al., 2022). N-acetylcysteine (NAC) is an antioxidant that is approved for use in humans, making it attractive as a potential therapeutic treatment for inflammatory conditions in humans (Mokhtari et al., 2017). In skeletal muscle NAC acts as a scavenger of free radicals and contributes to increasing levels of the powerful endogenous intracellular antioxidant glutathione (Kerksick and Willoughby, 2005). Oral dosing with NAC counteracts oxidative stress in skeletal muscles of humans and mice (Medved et al., 2004; Matuszczak et al., 2005; Terrill et al., 2012; Michelucci et al., 2017) and has been shown to ameliorate respiratory muscle dysfunction in animal models of hypoxic disease (O'Halloran and Lewis, 2017).
Several studies have used the mdx dystrophin deficient mouse, the most used animal model of the destructive human dystrophin deficient muscular dystrophy DMD, to study the chronic effects of oral administration of NAC in drinking water or by intraperitoneal injection on skeletal muscle pathology (Terrill et al., 2012; Pinto et al., 2013; Pinniger et al., 2017). These mdx mouse studies show NAC lowers markers of inflammation and oxidative stress in diaphragm and limb muscles and reduces necrotic damage.
There have been reports of a reduction in body weight gain in mice chronically dosed with NAC in their drinking water (Flurkey et al., 2010; Kim et al., 2013; Pinniger et al., 2017). However, rather than this being an adverse effect of NAC dosing it is commonly accepted that oral NAC supplementation reduces body mass gain by decreasing fat levels (Kim et al., 2013; Cao and Picklo, 2014; Ma et al., 2016) and this effect drops off with continued treatment with NAC (Cao and Picklo, 2014).
Previous studies from our laboratory and others have demonstrated a characteristic feature of the dystrophic pathology in mdx mice and DMD boys are the presence of abnormally regenerated branched limb skeletal muscle fibers which increase in number and complexity with age (Bell and Conen, 1968; Schmalbruch, 1976; Head et al., 1992; Chan and Head, 2011; Massopust et al., 2020; Kiriaev et al., 2021b). We and others have demonstrated the increase of these branched fibers mechanically weakens the muscle in the later stages of the dystrophic disease (Head et al., 1992; Chan and Head, 2011; Head, 2012; Pichavant et al., 2016; Kiriaev et al., 2018).
Since the first reports of the mdx mouse it has been noted that the fast-twitch muscles are more susceptible to eccentric (lengthening) contraction (EC) damage (Head et al., 1992; Moens et al., 1993). This susceptibility to EC damage increases as the dystrophic animal ages (Chan et al., 2007; Chan and Head, 2011; Head, 2012; Kiriaev et al., 2018; Kiriaev et al., 2021a). The reason for this increased susceptibility to EC damage is not clear and there have been several hypotheses put forward to explain this effect (Allen et al., 2016); 1) The absence of dystrophin mechanically weakens the sarcolemma making it more susceptible to rupture during EC, 2) Absence of dystrophin is responsible for cycles of necrosis/regeneration resulting in the increase of abnormally regenerated branched fibers structurally compromising the dystrophic membranes ability to resist EC damage 3) The absence of dystrophin results in an increased susceptibility to oxidative damage triggered by EC or 4) The absence of dystrophin sensitizes the ionic mechanisms of the electrical pathways that trigger muscle contraction to damage from EC. It is likely that the increased susceptibility of dystrophic fast-twitch muscle to EC damage involves some combination of these factors.
To test the efficacy of NAC as a possible treatment for DMD we gave it orally for 6 weeks to growing mdx and control mice and measured its effect on body weight gain, EDL muscle weight, fiber branching morphology, contractile function and response to EC. Its main efficacy was in reducing the number of abnormally branched fibers and reversing the increased muscle mass which results from the proliferation of branched fibers as the dystrophic phenotype progresses.
## Ethics approval
Animal use was approved by the Western Sydney University Animal Care and Ethics Committee (A14350). Experiments were conducted in compliance with the animal ethics checklist and ethical principles under which the journal operates.
## Animals
Many previous studies on mdx mice have used a separate in-bred colony of wild-type (WT) mice to act as controls. These WT colonies have been separately in-bred since the discovery of the mdx mouse over 25 years ago. This raises the possibility of novel mutations in the WT control group confounding the interpretation of the data from the mdx dystrophic mouse. In our study littermates are bred to act as control animals for mdx mice. These are the gold standard controls for mdx dystrophic studies as both dystrophin negative and dystrophin positive animals are on identical genetic backgrounds, with the only difference being the mutation in the dystrophin gene on the X chromosome at locus Xp21. Mice were obtained from the Western Sydney University animal facility. Male mice were used in the present study to reflect the sex linked DMD condition in boys. The colony of dystrophic mice and littermate controls (LC) used in this study were second generation offspring of C57BL/10ScSn DMD (mdx) mice. The LC were distinguished from dystrophic mice by genotyping. NAC treatment began at 3 weeks of age, and all mice were sampled at 9 weeks of age. The age of mice and duration of treatment were chosen as they reflect a period when the mdx mice have undergone at least one cycle of necrosis and regeneration. The three-to-nine-week age range in mice can be compared to the growth phase of adolescent in humans (Grounds et al., 2008). Mice were housed individually (1 animal per cage) in an environmentally controlled room with a 12 h light/dark cycle at 20°C–25°C and had access to food and water ad libitum.
## NAC treatment
NAC (Sigma-Aldrich, Australia) treatment in drinking water was commenced post weaning at 3 weeks of age. NAC was dissolved in RO water and administered as $2\%$ NAC in drinking water for 6 weeks. To overcome any NAC taste avoidance issues drinking water was flavored with banana and caramel (Australian Food Ingredient Suppliers, AFIS); diluted according to manufacturer’s recommendation ($0.1\%$ v/v); and sweetened using $0.1\%$ sucralose (Splenda®). Untreated animals received same flavored drinking water without NAC. The four experimental groups ($$n = 6$$ mice per group) were: 1) mdx NAC treated; 2) mdx untreated; 3) LC NAC treated; and 4) LC untreated. Each mouse was housed individually in its own cage. The cage water dispenser was weighed to determine each individual animals water consumption. The mice were weighed every 3 days.
## Muscle preparation
After the 6 weeks NAC treatment, a mouse was placed in a transparent induction chamber and overdosed with isoflurane delivered at $4\%$ in oxygen from a precision vaporizer. The mouse was removed from the chamber and a cervical dislocation carried out. The left and right fast-twitch EDL muscles were dissected from the hind limbs. Once isolated, the EDL muscle was suspended in an organ bath filled with $100\%$ O2 bubbled Tyrode and tied by its tendons from one end to a dual force transducer/linear tissue puller (300 Muscle Lever; Aurora Scientific Instruments, Canada) and secured to a base at the other end using 6–0 silk sutures (Pearsalls Ltd., UK). The EDL data from each animal was averaged to give $$n = 6$$ (number of mice used). At all times during the dissection and prior to use the hind limb and muscles were kept under $100\%$ oxygenated Tyrode solution to minimize any post-mortem deficits in initial maximum force (Po). Composition of Tyrode solution in mM (also used as dissection solution): 4 KCl, 135 NaCl, 0.33 NaH2PO4, 1 MgCl2, 10 HEPES, 2.5 CaCl2 and 11 glucose, $0.1\%$ fetal calf serum (FCS).
## Muscle force recordings
The muscle was stimulated to contract isometrically by applying a supramaximal voltage across parallel platinum electrodes (701C stimulator; Aurora Scientific Instruments) running the full length of the EDL muscle. Isometric force recordings and eccentric (lengthening) contractions (EC) were made on a 300C Muscle Lever (Aurora Scientific Instruments, Canada). Force responses were analyzed using the 615 A Dynamic Muscle Control and Analysis software (Aurora Scientific Instruments). At the start of each experiment, the muscle was set to optimal length (Lo) which produces maximal twitch force and maintained at this length throughput the experiment. All procedures were performed at a room temperature (20°C–22°C).
## Initial maximum force (Po) and Pmax
At the start of the contractile experiments a supramaximal stimulus was given at 125 Hz (1 ms pulses) for 1 s and the maximum force produced during the tetanic plateau was recorded as Po, the maximum force output of the muscle at optimal length Lo. For the force frequency Pmax was obtained from the curve fitted to the sigmoidal equation given below. For the EC studies Pmax was recorded from the isometric plateau of the first EC.
## Force frequency curve
Force-frequency curves were generated at frequencies 2, 15, 25, 37.5, 50, 75, 100, 125 and 150 Hz. A 30s rest was given between each frequency to minimize the effects of fatigue. A sigmoid curve relating muscle force (P) to stimulation frequency (f) was fitted by using a sigmoidal equation. The curve had the equation.
From the fitted parameters of the curve, the following contractile properties were obtained: Half-frequency (Kf) where the force developed is half the sum of (Pmin) and (Pmax). The Hill coefficient (h) which quantifies the slope of the muscle force frequency sigmoidal curve. These were used for population statistics.
## Eccentric contractions and recovery
A series of eccentric (lengthening) contractions (EC) were then performed on each EDL where the contracted muscle was stretched $20\%$ from Lo. At $t = 0$s, the muscle was stimulated via supramaximal pulses of 1 ms duration and 125 Hz frequency. At $t = 0.9$s, after maximal isometric force was attained, each muscle was stretched $20\%$ longer than their optimal length at a velocity of 2.4 mm/s then held at this length for 2s before returning to Lo. Electrical stimulus was stopped at $t = 5$s. The EC procedure was repeated 6 times with 3-min rest intervals in between each EC. On completion of the EC protocol, recovery force was measured via an isometric contraction given for 1s at 125 Hz (1 ms pulses) at the following time points; 0, 20, 40 and 60 min.
## Muscle mass
After the contractile procedures were completed, the muscle was removed from the transducer, blotted on filter paper, and weighed.
## Skeletal muscle single fiber enzymatic isolation and morphology
EDL muscles were digested in Tyrode solution (without FCS) containing 3 mg/mL collagenase type IVA (Sigma Aldrich,United States), gently bubbled with oxygen, and maintained at 37°C. After 25 min the muscle was removed from solution, rinsed in Tyrode solution containing $0.1\%$ FCS and placed in a relaxing solution with the following composition (mM): 117 KCl, 36 NaCl, 1 MgSO4, 60 HEPES, 8 ATP, 50 EGTA). Each muscle was then gently agitated using pipette suction, releasing individual fibers from the muscle mass. Using a pipette 0.5 mL of solution was drawn and placed on a glass slide for counting. A total of 2025 fibers from 12 EDL muscles were counted: mdx untreated ($$n = 1186$$ fibers from 6 EDL) vs. mdx NAC treated ($$n = 839$$ fibers from 6 EDL). Only intact fibers with no evidence of digestion damage were selected for counting. All muscle fibers from LCs showed no branching and were not counted (Kiriaev et al., 2021a).
## Statistical analyses
Data are presented as means ± SD. Differences occurring between genotypes and treatment groups were assessed by two-way ANOVA. Post hoc analysis was performed using Sidak’s multiple comparisons test. Where indicated an unpaired t-test was used and significance was accepted at $p \leq 0.05.$ All statistical tests and curve fitting were performed using a statistical software package Prism Version 10 (GraphPad, CA,United States).
## NAC treatment and water intake, body mass and EDL muscle mass
Figure 1 shows the effect on body weight gain, fluid intake and EDL muscle mass of 6 weeks $2\%$ NAC in drinking water. The animals were given the NAC over 3–9 weeks of age which is the period of active growth for mice. Figures 1A, B shows NAC significantly reduced the rate of growth in LC from days 19–42 of treatment ($$p \leq 0.0015$$) and mdx animals from days 23–42 of treatment ($$p \leq 0.0008$$). Figure 1C shows that the application of NAC to the flavored drinking water did not markedly affect mean fluid intake during the period of the study (for clarity the SD bars have been omitted). Figure 1D shows in LC NAC did not affect the EDL mass, but in mdx NAC treated EDL muscles were significantly lower ($$p \leq 0.0034$$) in weight compared to untreated ones. Dystrophic EDL mass was heavier compared to LC ($$p \leq 0.0062$$).
**FIGURE 1:** *Effect of NAC on body weight (A) LC (B) mdx (C) water intake of animals throughout treatment period and (D) muscle mass (A) shows significant differences in weight gain from day 19 onward for LCs untreated vs. NAC treated (p = 0.0015). (B) shows significant differences in weight gain from day 23 onward for mdx mice untreated vs. NAC treated (p = 0.0008). (C) Line graph showing water intake of all animals throughout the treatment period (error bars omitted for clarity) (n = 6) LC untreated/treated and n = 6 mdx untreated/treated) (D) Scatterplots of EDL muscle mass for LC and mdx untreated and treated groups (n = 6 EDL from each group). Dystrophic EDL muscles from mice not treated with NAC were heavier compared to the treated mdx group (p = 0.0034).). Genotype differences between untreated mice show increased muscle mass in mdx EDL compared to LCs (p = 0.0062). Data set represent mean value ±SD. Statistical differences displayed within graphs are differences between genotypes and treatment groups assessed by two-way ANOVA, post hoc analysis using Sidak’s multiple comparisons test with significance established at p < 0.05.*
## Force-frequency curves and specific Po
The aggregate force–frequency curves for NAC treated and non-treated LC and mdx EDL muscles are shown in Figure 2. These curves are fitted with the sigmoidal equation given in the methods, the following best-fit parameters defining these curves were obtained from the group data, half frequency which produced $50\%$ force and the Hill coefficient which is the slope of the rising portion of the sigmoidal curve Figures 2B, C. In Figure 2A forces are expressed as a percentage of the pre-EC Pmax. There were no significant differences in half frequency and Hill coefficient with respect to genotype or treatment (Figures 2B, C). Figure 2D shows the maximum specific force (Po) produced by the EDL muscles, there was no effect of NAC treatment, however, as has been reported previously the dystrophin deficient mdx muscles produced significantly less force than LCs, both in NAC treated ($$p \leq 0.0035$$) and untreated (0.0023) conditions.
**FIGURE 2:** *Force frequency curves and fitted parameters and specific force. (A) Force frequency data from individual EDL muscles were aggregated to produce a single curve for LC and mdx mice to visualize differences between treatment groups and genotypes (n = 6 EDL for each group). Forces are normalized to Pmax. In graph (A) SD error bars are omitted for clarity. (B,C) are scatterplots of half-frequency and Hill coefficient obtained from force frequency curve shown in (A). (D) Shows the maximum specific force (Po) produced by the EDL muscles, there was no effect of NAC treatment, however, as has been reported previously the dystrophin deficient mdx muscles produced significantly less force than LCs, both in NAC treated (p = 0.0035) and untreated (0.0023) conditions. Data set represent mean value ±SD.*
## Percentage force loss resulting from a series of six ECs at 20% stretch from Lo
In Figure 3 force was normalized to the isometric plateau of the first EC, prior to stretching to Lo+$20\%$ for $$n = 6$$ EDL muscles of each group. Figure 3A shows that there was a graded reduction of force with each EC for both NAC treated and untreated muscles. As has been previously reported mdx muscle were significantly more susceptible to EC induced force loss when compared to age matched LC (Kiriaev et al., 2018; Kiriaev et al., 2021a) and this was not altered by treatment with NAC. Figure 3B shows the rate of recovery of force over 60 min, mdx muscles recovered ∼$20\%$ and this recovery was not different in EDL muscles from NAC treated mdx. In contrast Figure 3B shows EDL muscle from both NAC treated and non-treated LC animals recovered ∼$100\%$ force over 60 min.
**FIGURE 3:** *Percentage force loss and force recovery from EC protocol (A) Percentage force loss resulting from a series of six ECs at 20% stretch at 2.4 mm/s from Lo (n = 6). Force was normalized to the isometric plateau of the first EC in each group (B) Percentage force recovery recorded at 0, 20, 40, 60 -minute intervals post EC (n = 6). Data shown in both graphs are mean ± SD.*
## Fiber branching morphology in mdx
Figure 4A compares complex fiber branching (3 + branches) between mdx untreated and mdx NAC treated (P= <0.0001). Figure 4B includes photomicrographs showing an example of a non-branched fiber Figure 4Bi and examples of fiber branching of differing complexity Figures 4Bii–viii.
**FIGURE 4:** *Enzymatically digested mdx EDL muscle fibers. (A) Scatterplots of complex fiber branching (3 +) between mdx untreated and mdx NAC treated (P= <0.0001). Fibers from LC were omitted due to no presence of fiber branching. Data shown in both graphs are mean ± SD. Statistical differences displayed in graph are assessed by unpaired t-test with significance established at p < 0.05 (B) Photomicrographs (X200) of enzymatically digested EDL muscle fibers (note in some cases the whole fiber is not shown.) a selection of NAC treated and untreated mdx are shown in (Bi-viii) (Bi) a straight mdx fiber with no branching. (Bii): is a fiber with 3 branches (Biii,v,vii): fibers with one branch (Biv,vi) fibers with 2 small branches (Bviii): fiber has a split that is connected to the main (i,iii,iv,v,vii) are from mdx NAC; (ii,vi,viii) are from mdx untreated mice.*
## The effect of 6 weeks of 2% NAC in drinking water on body weight, muscle mass, and water intake
There have been findings of reduced body mass gain in NAC-supplemented rodents (Kim et al., 2006; Kondratov et al., 2009; Kim et al., 2013; Cao and Picklo, 2014). In particular (Pinniger et al., 2017) using $2\%$ NAC in drinking water for 6 weeks concluded that the reduction of body weight gain in mdx mice was a red flag for considering the use of NAC as a treatment for boys with DMD. Commenting on Pinniger et al., 2017 study (O'Halloran et al., 2018), suggested that some of the reduced weight gain may be attributed to a decrease in body fat and not because of a reduction in lean mass and also that the acidification of the drinking water resulting from the addition of NAC may reduce fluid intake, accounting for some of the weight loss. To address this latter concern, we measured the fluid intake in NAC treated animals compared with no NAC and found no significant effect of NAC on fluid consumption. Confirming Pinniger et al., 2017 finding we also reported that 6 weeks of $2\%$ NAC drinking water caused significant reduction in EDL muscle mass. It is unlikely that this reduction in muscle weight is entirely due to reduced lean body mass, as NAC treatment in LC mice did not cause a significant reduction in the weight of EDL muscles (Figure 1D). As mentioned above a component of the reduction in body weight gain with NAC supplementation has been shown to be due to NAC reducing fat (Kim et al., 2006) and increasing energy expenditure (Ma et al., 2016). It has been shown that fiber branching is responsible for the hypertrophy in the mdx mouse (Faber et al., 2014), so it is reasonable to hypothesize that a proportion of the reduction in EDL muscle mass we show here (Figure 1D) could be a consequence of the reduction in complex 3 + fiber branching we report (Figure 4A).
NAC does not reduce the susceptibility to EC force loss seen in dystrophin deficient fast-twitch muscles, nor does it improve recovery post EC force loss.
The fast twitch EDL muscles of mdx mice have long been known to be more susceptible to damage from EC compared to age match WT controls, as measured by a reduction in Po post EC (Head et al., 1992) and this was confirmed in later studies which utilized LCs for the mdx mice (Kiriaev et al., 2018). In the present study we use a strong eccentric contraction protocol that produced a ∼$90\%$ Po force loss in mdx which only recovered to ∼$20\%$ of Po after 60 min demonstrating that it was likely the EC had caused significant membrane damage (Olthoff et al., 2018). In contrast, the same EC protocol applied to age matched LC EDL muscle resulted in only a ∼$40\%$ force loss which recovered to ∼$100\%$ max Po after 60 min, suggesting that no significant membrane damage had occurred in LC as a result of the EC protocol. In the LC the rapidly reversible component of EC force loss fits well with a fatigue induced and/or a reversible ROS-mediated inhibition of contractile force and we conclude there is no muscle damage connected with insults to the sarcolemma integrity. In contrast, Post EC in the mdx muscle there was only $10\%$ Po remaining which recovered to only $20\%$ after 60 min, leading us to surmise that only $10\%$ of the $90\%$ EC induced force loss in the mdx was fatigue induced and/or a reversible ROS-mediated inhibition of contractile force, while the remaining $80\%$ Po deficit is likely muscle damage connected with insults to the sarcolemma integrity disrupting its electrical and ionic control pathways. Six weeks treatment with $2\%$ NAC did not ameliorate the EC induced force loss or improve recovery post EC either in LCs or mdx, in keeping with (Whitehead et al., 2008) who showed 6 weeks of treatment with NAC in drinking water did not protect from eccentric damage at 35°C and there was only a small ∼$7\%$ significant improvement at room temperature. We hypothesize that the reduction in complex branching resulting from NAC treatment we report here was not sufficient to reduce the dystrophic muscle below the branched fiber “tipping point” (Chan and Head, 2011; Head, 2012; Kiriaev et al., 2021a). Supplementary Table S2 shows the absolute tetanic force, absolute twitch force, specific twitch force of NAC treated and untreated animals with respect to genotype before EC, after EC and recovery 60 min post EC. The twitch kinetics; time to peak (TTP) and half relaxation time (HRT) for NAC treated and untreated animals with respect to genotype for each of the experimental timepoints are also given in Supplementary Table S2. There was no effect of NAC treatment on any of the parameters.
## Mdx EDL fiber branching in singly housed mice
When compared to our earlier studies (Kiriaev et al., 2018; Kiriaev et al. 2021a; Kiriaev et al. 2021b) the amount of complex 3 + fiber branching we report here from 9-week-old mdx is similar to the mdx EDL muscles from >4 months mdx, see Supplementary Figure S1 for graphical comparison. These current mdx EDL muscles from 9-week-old mice show a force deficit comparable with the mdx EDL muscles from mice aged >4 months. Additionally, 60 min after the EC protocol these current mdx EDL muscles from 9-week-old mice regained a similar amount of force to the mdx EDL muscles from mice aged >4 months. A major difference with our earlier study, where mice were housed ∼ six per cage, is that in this current study mice were housed, one animal per cage. Here this single housing is associated with an accelerate dystrophic branching phenotype. It is possible that single housing may predispose the male mdx mice to greater locomotory activity, possibly associated with the increased space available per animal (Krohn et al., 2006), which would accelerate the appearance of the branching phenotype. It has long been reported that contractile activity is associated with muscle damage in the dystrophinopathies, in fact studies have demonstrated that if you immobilize dystrophic muscles, they do not undergo pathological changes (Allen et al., 2016). While we cannot rule out the possibility that the reduction in branching we see in mdx NAC treatment is due to NAC acting to reduce motor activity by reducing anxiety and stress, it would appear unlikely as a systematic review of clinical trials involving NAC reported that NAC does not reduce anxiety (Deepmala et al., 2015). Additionally, it has been demonstrated that single housing of male mice causes less stress when compared with group-housing of male mice (Kamakura et al., 2016). It is important to note that the force loss and recovery after EC is related to the degree of branching and not the chronological age of the mdx mouse, which supports our long-standing hypothesis that it is the degree of branching which is responsible for the increased EC damage with age (Chan and Head, 2011; Head, 2012; Kiriaev et al., 2018). Supplementary Figure S1 and Supplementary Table S1 uses data from our earlier studies (Kiriaev et al., 2018; Kiriaev et al. 2021a; Kiriaev et al. 2021b) to illustrate the significant increase in 3 + complex branching that occurs from 3 weeks to >112 weeks of age. It is clear that while the degree of motor activity may have a small effect on complex 3 + branching between 9 and 16 weeks, the major phenotype seen across the lifespan of the mdx mouse is the transition from non-branched muscle fibers to the point where >$80\%$ of fast-twitch fibers have 3 + complexed branched fibers (Supplementary Figure S1 and Supplementary Table S1), with many fibers containing 10 or more branches (Kiriaev et al., 2021a; Kiriaev et al., 2021b).
Fiber branching, a marker of pathology in mdx muscle, is reduced in fast-twitch EDL muscles from NAC treated mdx.
We and others have shown that a striking pathological feature of regenerated skeletal muscle in the dystrophinopathies is the presence of abnormally branched fibers which dramatically increase in both number and complexity with age (Bell and Conen, 1968; Schmalbruch, 1976; Head et al., 1992; Chan and Head, 2011; Head, 2012; Massopust et al., 2020; Kiriaev et al., 2021a). The number and complexity of these fibers can be used as a marker of the progression of the dystrophinopathies (Bell and Conen, 1968; Kiriaev et al., 2021a). The presence of centrally nucleated (CN) fibers is commonly used as a measure of regeneration, however, it is accepted that by > 8 weeks of age over $90\%$ of mdx mouse skeletal muscle fibers will have regenerated. From 24 weeks to >104 weeks mdx skeletal muscle fibers are $100\%$ centrally nucleated (Massopust et al., 2020), and thus unlike fiber branching, which continues to increase in complexity (Kiriaev et al., 2021a) CN is not reliable indicator of the number of cycles of degeneration/regeneration. Here we show NAC treatment reduces the complexity of branched fibers in the fast-twitch mdx EDL muscle, demonstrating that NAC treatment improves mdx fast-twitch muscle pathology. In the dystrophinopahties it has been proposed that the absence of the protein, dystrophin, from the inner surface of the sarcolemma predispose the skeletal muscle to increased oxidative stress and free radical triggered necrosis, which has been identified as a major cause of muscle injury (Disatnik et al., 2000; Mosca et al., 2021; Lian et al., 2022). The reduction in fiber branching we report here is likely due to NAC reducing (Rando, 2002) the level of free radical triggered necrosis in the active phase of the disease. The reduction in branching we report here was not sufficient to confer a protective effect from the force deficit caused by EC (Chan and Head, 2011). As the mdx mouse model is characterized by a period of florid necrosis 3–20 weeks of age (Duddy et al., 2015) many biochemical changes which are indicative of oxidative damage could be a secondary result of the necrosis, inflammatory response, and subsequent regeneration (Rando, 2002). However, the fact that acute NAC application to the organ bath in in vitro experiments has shown that NAC can prevent eccentric contraction force loss in fast twitch mdx muscles (Whitehead et al., 2008; Olthoff et al., 2018; Lindsay et al., 2020) supports the hypothesis that the absence of dystrophin directly increases the susceptibility the muscle to oxidative triggered damage (Mosca et al., 2021; Lian et al., 2022). The present study provides further support for this in finding that $2\%$ NAC in drinking water for 6 weeks prevents the increase in muscle mass characteristic of the dystrophinopathies and reduces the number of complex 3 + fiber branching. These are effects which could be predicted to occur if NAC does indeed reduce myonecrosis (Head, 2012).
## Summary
In the current study we confirmed that 6 weeks of $2\%$ NAC in drinking water significantly reduces body weight gain in both mdx and LC mice. This was not the result of a reduced fluid intake, as was reported in (Kondratov et al., 2009), because we monitored water intake and after some initial fluctuation it was the same for NAC treated mdx and LCs compared to non-treated mdx and LCs. We showed that NAC prevents the increase in skeletal muscle mass characteristic of the dystrophinopathies and reduces the number and complexity of branched fibers which have been shown to be responsible for this increase in mass (Faber et al., 2014; Froehner et al., 2015). This reduction in fiber branching suggests that the antioxidant action of NAC may have reduced the myonecrosis involved in the degeneration/regeneration cycles characteristic of the mdx skeletal muscle pathology. We have previously shown as the number of these cycles increases with age so does the number and complexity of branched fibers in the mdx EDL (Chan and Head, 2011; Kiriaev et al., 2018). However, in our present study NAC does not ameliorate the sensitivity of mdx dystrophic fast-twitch EDL muscle to damage caused by EC (Head et al., 1992) possibly because the reduction in 3 + complex branched fibers is not of sufficient magnitude to be protective.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Ethics statement
The animal study was reviewed and approved by the animal study was reviewed and approved by Western Sydney University Animal Care and Ethics Committee.
## Author contributions
AR and SK carried out the experiments. AR, LK, SK, and SH analyzed the data and interpreted results of experiments. AR and SH drafted the manuscript. AR, LK, SK, JM, PH, BP, and SH edited and revised the manuscript. All authors approved the final version of manuscript and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All persons designated as authors quality for authorship, and all those who qualify for authorship are listed.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2023.1109587/full#supplementary-material
## Abbreviations
ANOVA, Analysis of Variance; CN, Centrally nucleated; DMD, Duchenne Muscular Dystrophy; EC, Eccentric Contraction; EDL, Extensor Digitorum Longus; f, Stimulation frequency; FCS, Fetal Calf Serum; h, Hill coefficient; HRT, Half relaxation time; Kf, Half frequency; LC, Littermate controls, Lo, Optimal length; NAC, N-acetylcysteine; P, Force developed, Po, Initial Maximal Force from a single 2 s tetani at 125 Hz; Pmax, Maximal tetanic force; Pmin/Pmax, Force developed at minimum/Force developed at maximum; SD, Standard Deviation; TTP, Time to peak.
## References
1. Allen D. G., Whitehead N. P., Froehner S. C.. **'Absence of dystrophin disrupts skeletal muscle signaling: Roles of Ca2+, reactive oxygen species, and nitric oxide in the development of muscular dystrophy**. *Physiol. Rev.* (2016) **96** 253-305. DOI: 10.1152/physrev.00007.2015
2. Bell C. D., Conen P. E.. **'Histopathological changes in Duchenne muscular dystrophy**. *J. Neurol. Sci.* (1968) **7** 529-544. DOI: 10.1016/0022-510x(68)90058-0
3. Cao J. J., Picklo M. J.. **'N-acetylcysteine supplementation decreases osteoclast differentiation and increases bone mass in mice fed a high-fat diet**. *J. Nutr.* (2014) **144** 289-296. DOI: 10.3945/jn.113.185397
4. Chan S., Head S. I., Morley J. W.. **Branched fibers in dystrophic mdx muscle are associated with a loss of force following lengthening contractions**. *Am. J. Physiology-Cell Physiology* (2007) **293** C985-C992. DOI: 10.1152/ajpcell.00128.2007
5. Chan S., Head S. I.. **The role of branched fibres in the pathogenesis of Duchenne muscular dystrophy**. *Exp. Physiol.* (2011) **96** 564-571. DOI: 10.1113/expphysiol.2010.056713
6. Deepmala J. S., Kumar N., Delhey L., Berk M., Dean O., Spielholz C.. **Clinical trials of N-acetylcysteine in psychiatry and neurology: A systematic review**. *Neurosci. Biobehav Rev.* (2015) **55** 294-321. DOI: 10.1016/j.neubiorev.2015.04.015
7. Disatnik M. H., Chamberlain J. S., Rando T. A.. **Dystrophin mutations predict cellular susceptibility to oxidative stress**. *Muscle Nerve* (2000) **23** 784-792. DOI: 10.1002/(sici)1097-4598(200005)23:5<784::aid-mus17>3.0.co;2-y
8. Duddy W., Duguez S., Johnston H., Cohen T. V., Phadke A., Gordish-Dressman H.. **Muscular dystrophy in the mdx mouse is a severe myopathy compounded by hypotrophy, hypertrophy and hyperplasia**. *Skelet. muscle* (2015) **5**
9. Faber R. M., Hall J. K., Chamberlain J. S., Banks G. B.. **'Myofiber branching rather than myofiber hyperplasia contributes to muscle hypertrophy in mdx mice**. *Skelet. Muscle* (2014) **4** 10. DOI: 10.1186/2044-5040-4-10
10. Flurkey K., Astle C. M., Harrison D. E.. **Life extension by diet restriction and N-acetyl-L-cysteine in genetically heterogeneous mice**. *J. Gerontol. A Biol. Sci. Med. Sci.* (2010) **65** 1275-1284. DOI: 10.1093/gerona/glq155
11. Froehner S. C., Reed S. M., Anderson K. N., Huang P. L., Percival J. M.. **'Loss of nNOS inhibits compensatory muscle hypertrophy and exacerbates inflammation and eccentric contraction-induced damage in mdx mice**. *Hum. Mol. Genet.* (2015) **24** 492-505. DOI: 10.1093/hmg/ddu469
12. Grounds M. D., Radley H. G., Lynch G. S., Nagaraju K., De Luca A.. **'Towards developing standard operating procedures for pre-clinical testing in the mdx mouse model of Duchenne muscular dystrophy**. *Neurobiol. Dis.* (2008) **31** 1-19. DOI: 10.1016/j.nbd.2008.03.008
13. Head S., Madhuri Hegde Dr. **A two stage model of skeletal muscle necrosis in muscular dystrophy-the role of fiber branching in the terminal stage**. *Muscular dystrophy* (2012)
14. Head S. I., Williams D. A., Stephenson D. G.. **'Abnormalities in structure and function of limb skeletal muscle fibres of dystrophic mdx mice**. *Proc. Biol. Sci.* (1992) **248** 163-169. DOI: 10.1098/rspb.1992.0058
15. Kamakura R., Kovalainen M., Leppaluoto J., Herzig K. H., Makela K. A.. **The effects of group and single housing and automated animal monitoring on urinary corticosterone levels in male C57BL/6 mice**. *Physiol. Rep.* (2016) **4** e12703. DOI: 10.14814/phy2.12703
16. Kerksick C., Willoughby D.. **'The antioxidant role of glutathione and N-acetyl-cysteine supplements and exercise-induced oxidative stress**. *J. Int. Soc. Sports Nutr.* (2005) **2** 38-44. DOI: 10.1186/1550-2783-2-2-38
17. Kim J. H., Kwak H. B., Thompson L. V., Lawler J. M.. **'Contribution of oxidative stress to pathology in diaphragm and limb muscles with Duchenne muscular dystrophy**. *J. Muscle Res. Cell Motil.* (2013) **34** 1-13. DOI: 10.1007/s10974-012-9330-9
18. Kim J. R., Ryu H. H., Chung H. J., Lee J. H., Kim S. W., Kwun W. H.. **'Association of anti-obesity activity of N-acetylcysteine with metallothionein-II down-regulation**. *Exp. Mol. Med.* (2006) **38** 162-172. DOI: 10.1038/emm.2006.20
19. Kiriaev L., Kueh S., Morley J. W., North K. N., Peter J. H., Head S. I.. **Branched fibers from old fast-twitch dystrophic muscles are the sites of terminal damage in muscular dystrophy**. *Am. J. Physiology-Cell Physiology* (2018) **314** C662-C674. DOI: 10.1152/ajpcell.00161.2017
20. Kiriaev L., Kueh S., Morley J. W., Houweling P. J., Chan S., North K. N.. **'Dystrophin-negative slow-twitch soleus muscles are not susceptible to eccentric contraction induced injury over the lifespan of the mdx mouse**. *Am. J. Physiology Cell Physiology* (2021b) **321** C704-C720. DOI: 10.1152/ajpcell.00122.2021
21. Kiriaev L., Kueh S., Morley J. W., North K. N., Houweling P. J., Head S. I.. **'Lifespan analysis of dystrophic mdx fast-twitch muscle morphology and its impact on contractile function**. *Front. Physiol.* (2021a) **12** 771499. DOI: 10.3389/fphys.2021.771499
22. Kondratov R. V., Vykhovanets O., Kondratova A. A., Antoch M. P.. **'Antioxidant N-acetyl-L-cysteine ameliorates symptoms of premature aging associated with the deficiency of the circadian protein BMAL1**. *Aging (Albany NY)* (2009) **1** 979-987. DOI: 10.18632/aging.100113
23. Krohn T. C., Sørensen D. B., Ottesen J. L., Hansen A. K.. **The effects of individual housing on mice and rats: A review**. *Anim. Welf.* (2006) **15** 343-352. DOI: 10.1017/s0962728600030669
24. Lian D., Chen M. M., Wu H., Deng S., Hu X.. **'The role of oxidative stress in skeletal muscle myogenesis and muscle disease**. *Antioxidants (Basel)* (2022) **11** 755. DOI: 10.3390/antiox11040755
25. Lindsay A., Baumann C. W., Rebbeck R. T., Yuen S. L., Southern W. M., Hodges J. S.. **'Mechanical factors tune the sensitivity of mdx muscle to eccentric strength loss and its protection by antioxidant and calcium modulators**. *Skelet. Muscle* (2020) **10** 3. DOI: 10.1186/s13395-020-0221-2
26. Ma Y., Gao M., Liu D.. **N-acetylcysteine protects mice from high fat diet-induced metabolic disorders**. *Pharm. Res.* (2016) **33** 2033-2042. DOI: 10.1007/s11095-016-1941-1
27. Massopust R. T., Thompson W. J., Lee Y. I., Pritchard A. L., Nguyen V-K. M., McCreedy D. A.. **Lifetime analysis of mdx skeletal muscle reveals a progressive pathology that leads to myofiber loss**. *Sci. Rep.* (2020) **10** 17248-48. DOI: 10.1038/s41598-020-74192-9
28. Matuszczak Y., Farid M., Jones J., Lansdowne S., Smith M. A., Taylor A. A.. **'Effects of N-acetylcysteine on glutathione oxidation and fatigue during handgrip exercise**. *Muscle Nerve* (2005) **32** 633-638. DOI: 10.1002/mus.20385
29. Medved I., Brown M. J., Bjorksten A. R., Murphy K. T., Petersen A. C., Sostaric S.. **'N-acetylcysteine enhances muscle cysteine and glutathione availability and attenuates fatigue during prolonged exercise in endurance-trained individuals**. *J. Appl. Physiol.* (2004) **97** 1477-1485. DOI: 10.1152/japplphysiol.00371.2004
30. Michelucci A., De Marco A., Guarnier F. A., Protasi F., Boncompagni S.. **'Antioxidant treatment reduces formation of structural cores and improves muscle function in RYR1(Y522S/WT) mice**. *Oxid. Med. Cell Longev.* (2017) **2017** 6792694. PMID: 29062463
31. Moens P., Baatsen P. H., Marechal G.. **'Increased susceptibility of EDL muscles from mdx mice to damage induced by contractions with stretch**. *J. Muscle Res. Cell Motil.* (1993) **14** 446-451. DOI: 10.1007/BF00121296
32. Mokhtari V., Afsharian P., Shahhoseini M., Kalantar S. M., Moini A.. **A review on various uses of N-acetyl cysteine**. *Cell J.* (2017) **19** 11-17. DOI: 10.22074/cellj.2016.4872
33. Mosca N., Petrillo S., Bortolani S., Monforte M., Ricci E., Piemonte F.. **'Redox homeostasis in muscular dystrophies**. *Cells* (2021) **10** 1364. DOI: 10.3390/cells10061364
34. O'Halloran K. D., Lewis P.. **'Respiratory muscle dysfunction in animal models of hypoxic disease: Antioxidant therapy goes from strength to strength**. *Hypoxia (Auckl)* (2017) **5** 75-84. DOI: 10.2147/HP.S141283
35. O'Halloran K. D., Murphy K. H., Burns D. P.. **Antioxidant therapy for muscular dystrophy: Caveat lector**. *J. Physiol.* (2018) **596** 737-738. DOI: 10.1113/JP275598
36. Olthoff J. T., Lindsay A., Abo-Zahrah R., Baltgalvis K. A., Patrinostro X., Belanto J. J.. **'Loss of peroxiredoxin-2 exacerbates eccentric contraction-induced force loss in dystrophin-deficient muscle**. *Nat. Commun.* (2018) **9** 5104. DOI: 10.1038/s41467-018-07639-3
37. Pichavant C., Burkholder T. J., Pavlath G. K.. **'Decrease of myofiber branching via muscle-specific expression of the olfactory receptor mOR23 in dystrophic muscle leads to protection against mechanical stress**. *Skelet. Muscle* (2016) **6** 2. DOI: 10.1186/s13395-016-0077-7
38. Pinniger G. J., Jessica R. T., Assan E. B., MirandaGrounds D., Arthur P. G.. **'Pre‐clinical evaluation of N‐acetylcysteine reveals side effects in the mdx mouse model of Duchenne muscular dystrophy**. *J. physiology* (2017) **595** 7093-7107. DOI: 10.1113/JP274229
39. Pinto F., Moraes N., Marques M., Neto H. S.. **N-acetylcysteine treatment reduces TNF-α levels and myonecrosis in diaphragm muscle of mdx mice**. *Clin. Nutr.* (2013) **32** 472-475. DOI: 10.1016/j.clnu.2012.06.001
40. Rando T. A.. **'Oxidative stress and the pathogenesis of muscular dystrophies**. *Am. J. Phys. Med. Rehabil.* (2002) **81** S175-S186. DOI: 10.1097/00002060-200211001-00018
41. Schmalbruch H.. **'Muscle fibre splitting and regeneration in diseased human muscle**. *Neuropathology Appl. Neurobiol.* (1976) **2** 3-19. DOI: 10.1111/j.1365-2990.1976.tb00558.x
42. Terrill J. R., Radley-Crabb H. G., Grounds M. D., Arthur P. G.. **'N-Acetylcysteine treatment of dystrophic mdx mice results in protein thiol modifications and inhibition of exercise induced myofibre necrosis**. *Neuromuscul. Disord.* (2012) **22** 427-434. DOI: 10.1016/j.nmd.2011.11.007
43. Whitehead N. P., Pham C., Gervasio O. L., Allen D. G.. **'N‐Acetylcysteine ameliorates skeletal muscle pathophysiology in mdx mice**. *J. physiology* (2008) **586** 2003-2014. DOI: 10.1113/jphysiol.2007.148338
|
---
title: 'Clinical aspects of binge eating disorder: A cross-sectional mixed-methods
study of binge eating disorder experts'' perspectives'
authors:
- Brenna Bray
- Adam Sadowski
- Chris Bray
- Ryan Bradley
- Heather Zwickey
journal: Frontiers in Psychiatry
year: 2023
pmcid: PMC9971930
doi: 10.3389/fpsyt.2022.1087165
license: CC BY 4.0
---
# Clinical aspects of binge eating disorder: A cross-sectional mixed-methods study of binge eating disorder experts' perspectives
## Abstract
### Introduction
Research on binge eating disorder continues to evolve and advance our understanding of recurrent binge eating.
### Methods
This mixed-methods, cross-sectional survey aimed to collect information from experts in the field about clinical aspects of adult binge eating disorder pathology. Fourteen experts in binge eating disorder research and clinical care were identified based on receipt of relevant federal funding, PubMed-indexed publications, active practice in the field, leadership in relevant societies, and/or clinical and popular press distinction. Anonymously recorded semi-structured interviews were analyzed by ≥2 investigators using reflexive thematic analysis and quantification.
### Results
Identified themes included: [1] obesity ($100\%$); [2] intentional/voluntary or unintentional/involuntary food/eating restriction ($100\%$); [3] negative affect, emotional dysregulation, and negative urgency ($100\%$); [4] diagnostic heterogeneity and validity ($71\%$); [5] paradigm shifts in understanding binge eating disorder ($29\%$); and [6] research gaps/future directives ($29\%$).
### Discussion
Overall, experts call for a better understanding of the relationship between binge eating disorder and obesity, including a need for clarification around the extent to which the two health issues are separate vs. related/overlapping. Experts also commonly endorse food/eating restriction and emotion dysregulation as important components of binge eating disorder pathology, which aligns with two common models of binge eating disorder conceptualization (e.g., dietary restraint theory and emotion/affect regulation theory). A few experts spontaneously identified several paradigm shifts in our understanding of who can have an eating disorder (beyond the anorexi-centric “thin, White, affluent, cis-gendered neurotypical female” stereotype), and the various factors that can drive binge eating. Experts also identified several areas where classification issues may warrant future research. Overall, these results highlight the continual advancement of the field to better understand adult binge eating disorder as an autonomous eating disorder diagnosis.
## Introduction
Binge eating disorder (discrete rapid consumption of objectively large amounts of food associated with loss of control and distress without compensatory behaviors) became a formally recognized autonomous eating disorder diagnosis with the publication of the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V) in 2013 [1]. It was previously classified in the DSM-IV as eating disorder not otherwise specified (ED-NOS) [2]. While research and literature on binge eating disorder have been growing, historically there has been greater understanding and awareness of anorexia nervosa and bulimia nervosa, and less so of binge eating disorder. However, the literature continues to evolve and advance our understanding of recurrent binge eating.
Historically, there is a tendency to view binge eating disorder as resulting from overevaluation of body weight/shape/size leading to food/eating restriction and subsequent binge eating (e.g., transdiagnostic-, dietary restraint-, and dual pathway models) (3–7). However, several alternative conceptualizations of binge eating disorder have gained attention in recent years [4].
Emotion/affect regulation models are perhaps the most widely supported and accepted in the field, along with dietary restraint models [4]. These models center around the view that negative emotions, moods, or affective experiences can prompt binge eating, which can become negatively reinforced by providing temporary relief from the associated discomfort [4]. In this way, it is believed binge eating can become a maladaptive emotion regulation/coping strategy resulting from lack of more adaptative tools. In these models, the aversive experiences that drive binge eating often include distress (unhappiness, pain, and/or suffering affecting the mind or body) and negative affect (the subjective experience of a cluster of negative emotional states that include anxiety, depression, stress, sadness, worry, guilt, shame, anger, and envy), which can result in negative urgency (an impulsive inclination to engage in risky or unhealthy behaviors when in a state of poor emotion regulation) (8–12). These models are strongly supported in the literature (4, 8–10) and—along with dietary restraint—represent commonly overlapping concepts across various conceptualizations of binge eating disorder (e.g., dual pathway models, escape/disassociation models, ICAT models, interpersonal models, and transdiagnostic models) [4].
The issue of obesity also remains a point of contention in the field. Literature demonstrates binge eating disorder has a 40–$70\%$ incidence of lifetime obesity (13–15) and obesity has a ≤ $47\%$ prevalence of binge eating disorder [16]. However, there remains a need for updated information on the extent to which binge eating disorder and weight issues are separate/related/overlapping.
Negative health implications associated with obesity (e.g., cardiometabolic syndrome) highlight another important question of the extent to which binge eating disorder should be considered a purely mental health disorder vs. a physiological/biological one. Weight regulation models of eating disorders are under development that propose weight and weight history are causal variables that have clinically significant impacts on eating disorder psychopathology and perpetuation [17]. However, these models remain to be tested.
Here, we present findings from a mixed-methods, cross-sectional survey aimed to collect information from experts in the field about clinical aspects of adult binge eating disorder pathology.
## Participants and recruitment
This study recruited expert researchers, clinicians, and healthcare administrators in the field of adult binge eating disorder. Eligibility criteria is previously published in Bray et al. [ 18, 19] and is shown in Table 1.
**Table 1**
| I. Eligibility criteria for researchers (18 recruited, 7 enrolled) |
| --- |
| Eligibility criteria for researchers required meeting one of the following four criteria: |
| 1. ≥1 active R01, T32, or P grant on binge eating or food addiction as identified on NIH RePORTER (https://report.nih.gov) |
| 2. Last author of ≥10 PubMed-indexed publications published 2010–2020 on adult binge eating disorder AND ≥5 PubMed-indexed publications in 2015–2020 on the same topic |
| 3. Last author of ≥5 PubMed publications published in 2015–2020 relevant to food addictiona |
| 4. Referral from someone who meets one of the qualifications above (I.1–3) |
| II. Eligibility criteria for clinicians and healthcare |
| administrators (18 recruited, 6 enrolled) |
| Eligibility for clinicians and healthcare administrators required meeting ≥3 of the following criteria: |
| 1. Award Winner or Honoree of the Association of Eating Disorders (AED, 2010–2020) or the Castle Connolly Top Doctors Distinction in Psychiatry—Eating Disorders (2020/21) (20, 21) |
| 2. Executive position/board member for one of ten relevant societies: Academy of Nutrition & Dietetics, Academy of Eating Disorders (AED, FAED), American Society for Metabolic and Bariatric Surgery (ASMBS), Behavioral Health Nutrition Society, Eating Disorder Research Society (EDRS), International Association of Eating Disorder Providers (IAEDP), Johns Hopkins 2020 Eating Disorders Conference, National Center of Excellence for Eating Disorders (NCEED), National Eating Disorder Association (NEDA), Obesity Society (22–32) |
| 3. Adult binge eating disorder provider listed in the National Eating Disorder Association (NEDA)– or Alliance for Eating Disorders Awareness Provider Directories (33, 34) or associated with an eating disorder program or treatment center with ≥5 locations listed in the NEDA directory (33) |
| 4. Popular press distinction (35, 36) |
| 5. Referral from an individual meeting ≥2 qualifications above |
| 6. Registered Dietician (RD) meeting ≥2 criteria above |
| III. Additional Eligibility Criteria (2 recruited, 2 enrolled b ) |
| Individuals who met ≥1 academic/research criterion (I) and ≥1 clinical criterion (II) were also eligible. |
## Procedure
The procedure is described in Bray et al. [ 18, 19]. With approval from the National University of Natural Medicine (NUNM) IRB (# HZ12120), BB sent eligible participants a scripted email study invitation. Consenting respondents were interviewed anonymously on Zoom (Zoom.com, last accessed May 19, 2022), with verbal consent obtained at the start of each interview. Interviews were recorded with participant consent. Recordings began after introductions, to protect participant anonymity. Most interviews were scheduled for 2 h, with abbreviated 30–60-min interviews conducted as needed. Interview questions pertaining to binge eating disorder pathology are shown in Table 2. Demographic information was collected at the end of each interview verbally or through follow-up email survey.
**Table 2**
| Question | n asked (n/14) |
| --- | --- |
| 1. Please describe your perspective on (or knowledge of) literature and research findings, current clinical guidelines, and your own personal experiences that relate to binge eating disorder pathology and treatment. | 14 (100%) |
| 2. How do you view the disorder in relation to the following possible aspects, and how important is it for treatment interventions to address these aspects (if at all)? | 2. How do you view the disorder in relation to the following possible aspects, and how important is it for treatment interventions to address these aspects (if at all)? |
| a) Physical/Biological b) Cognitive/mental c) Emotional d) Spiritual e) Economic f) Social g) Cultural h) Other | 14 (100%) 14 (100%) 14 (100%) 14 (100%) 11 (79%) 12 (86%) 12 (86%) 14 (100%) |
| 3. Please describe your view on the following health factors as they relate to adult binge eating disorder pathology and treatment: | 3. Please describe your view on the following health factors as they relate to adult binge eating disorder pathology and treatment: |
| a) Metabolic Disorder b) Obesity | 12 (86%) 12 (86%) |
| 4. Are there any other aspects of binge eating disorder pathology that you feel are important to address or discuss (that have not been addressed above)? | 12 (86%) |
| 5. Please describe your perspective on current research gaps that exist in the field of binge eating disorder. | 14 (100%) |
## Data analysis
Interview recordings were transcribed. Transcripts were de-identified and then reviewed and qualitatively analyzed by BB and HZ (separately) for common themes using a reflexive thematic analysis approach [37]. BB and HZ independently coded each interview. Themes were identified independently then discussed and finalized through reflexive engagement with the data [37]. BB also analyzed transcripts quantitatively to identify the number of participants who expressed positive/supportive, negative/skeptical, or neutral perspectives on each identified theme. HZ and CB were consulted when quantitative analysis questions arose and for tiebreakers.
## Participant response rates and characteristics
Thirty-eight experts met enrollment criteria and fourteen consented, enrolled, and participated in the study (Figure 1). Fourteen experts consented, enrolled, and participated in the study, including six individuals who met the academic/research criteria ($\frac{6}{14}$, $43\%$), five who met the clinical criteria ($\frac{5}{14}$, $36\%$), one who met both the academic/research and clinical criteria ($\frac{1}{14}$, $7\%$), and two who met some criteria from the academic- and clinical categories to qualify for inclusion in a mixed option ($\frac{2}{14}$, $14\%$) (Table 1). Table 3 shows characteristics for the $\frac{13}{14}$ participants who provided demographic information.
**Figure 1:** *Diagram of study flow, from participant identification to enrollment and follow-up. Thirty-eight experts met enrollment criteria and were invited to participate in the study. This included 18 experts who met the academic/research criteria ($\frac{18}{38}$, $47\%$), 18 experts who met the clinical criteria ($\frac{18}{38}$, $47\%$), and two who met the dual criteria ($\frac{2}{38}$, $5\%$; Table 1). Fourteen eligible experts consented, enrolled, and participated in the study ($\frac{14}{38}$, $37\%$), including six individuals who met the academic/research criteria ($\frac{6}{14}$, $43\%$), five who met the clinical criteria ($\frac{5}{14}$, $36\%$), one who met both the academic/research and clinical criteria ($\frac{1}{14}$, $7\%$), and two who met the dual criteria option ($\frac{2}{14}$, $14\%$) (Table 3). Thirteen participants ($\frac{13}{14}$, $93\%$) provided demographic information and were included in demographic analysis (Table 3). All 14 participant interviews were included in thematic analysis. Reproduced with permission from Bray et. al., (18).* TABLE_PLACEHOLDER:Table 3
## Theme 1: obesity domain (100%)
All 14 participants ($\frac{14}{14}$, $100\%$) addressed the domain of obesity as relevant to binge eating disorder. Subthemes included: (i) the relationship between obesity and binge eating disorder ($\frac{13}{14}$, $93\%$); (ii) possible underlying mechanisms that link obesity to binge eating disorder ($\frac{9}{14}$, $64\%$); and (iii) validity of links to negative health consequences in binge eating disorder ($\frac{4}{14}$, $29\%$) (Table 4).
**Table 4**
| Subtheme (i) relationship between obesity and BED | 13 (93%) |
| --- | --- |
| (a) Common link between obesity and BED | 8 (57%) |
| (b) Many with BED struggle with obesity | 5 (36%) |
| (c) Not everyone with BED has a larger body or obesity | 4 (29%) |
| (d) Need for clarifying extent to which obesity and BED are separate vs. related/overlapping | 4 (29%) |
| (e) Obesity can motivate BED treatment | 3 (21%) |
| (f) Obesity as negative consequence of BED | 2 (14%) |
| (g–j) Possible relationships identified by 1 participant each are included in Supplementary Table 1 | (g–j) Possible relationships identified by 1 participant each are included in Supplementary Table 1 |
| Subtheme (ii) possible relationship mechanisms | 11 (79%) |
| (a) Contributions to weight stigma and resulting traumaa | 6 (43%)a |
| (b) Inflammatory processes related to food choices and mood | 4 (29%) |
| (c) Sleep disturbances | 3 (21%) |
| (d) Links between obesity and the gut microbiome | 2 (14%) |
| (e) Obesity impacting relationships and interpersonal factors that mediate/moderate binge eating | 2 (14%) |
| (f–h) Possible mechanisms identified by 1 participant each are included in Supplementary Table 4 | (f–h) Possible mechanisms identified by 1 participant each are included in Supplementary Table 4 |
| Subtheme (iii) validity of links to negative health consequences | 4 (29%) |
| (a) Obesity can increase risk for medical complications | 2 (14%) |
| (b) Not everyone with obesity has negative health consequencesb | 2 (14%) |
| Additional participant statements related to subtheme i, “relationship between obesity and BED” | Additional participant statements related to subtheme i, “relationship between obesity and BED” |
| “Well clearly lots of research has been done on relationships of weight and high weight and binge eating disorder… that's where epidemiologists have done a lot” (P16) | “Well clearly lots of research has been done on relationships of weight and high weight and binge eating disorder… that's where epidemiologists have done a lot” (P16) |
| Additional participant statements related to subtheme iii, “validity of negative health consequences” | Additional participant statements related to subtheme iii, “validity of negative health consequences” |
| “I see metabolic disorder as being one of the … scare tactics used when [addressing] an obesity epidemic: ‘look, [obesity is] associated with higher rates of metabolic disorder and high blood pressure,' and … [there are] certain things that are associated with, but there are definitely people with binge eating disorder, or people with obesity, who don't have any of those problems. … In other words, they don't have metabolic syndrome. They don't have high blood pressure, they don't have diabetes, and yet …obesity has been declared a disease like a disorder” (P38) | “I see metabolic disorder as being one of the … scare tactics used when [addressing] an obesity epidemic: ‘look, [obesity is] associated with higher rates of metabolic disorder and high blood pressure,' and … [there are] certain things that are associated with, but there are definitely people with binge eating disorder, or people with obesity, who don't have any of those problems. … In other words, they don't have metabolic syndrome. They don't have high blood pressure, they don't have diabetes, and yet …obesity has been declared a disease like a disorder” (P38) |
| “We do have this really strong assumption, and I think this weight stigma as well, that [is] shared by [some] physicians [but not all, and represents a] view in the general population that all overweight is unhealthy and that any degree of overweight must be bad for your physical health, and you must improve your physical health with any degree of weight loss. And that's just simply not true. I think that's weight stigma as well. …We need to address that” (P93) | “We do have this really strong assumption, and I think this weight stigma as well, that [is] shared by [some] physicians [but not all, and represents a] view in the general population that all overweight is unhealthy and that any degree of overweight must be bad for your physical health, and you must improve your physical health with any degree of weight loss. And that's just simply not true. I think that's weight stigma as well. …We need to address that” (P93) |
## Subtheme i: Relationship between obesity and binge eating disorder (93%)
Thirteen participants made statements expressing views on the nature of the relationship between obesity and binge eating disorder pathology, including the possible directionality or statistical nature of the relationship ($\frac{11}{14}$, $79\%$). Eight participants endorsed a common link between obesity and binge eating disorder ($\frac{8}{14}$, $57\%$). Five participants ($\frac{5}{14}$, $36\%$) described obesity as a condition that many with binge eating disorder struggle with. Four participants ($\frac{4}{14}$, $29\%$) noted that not everyone with binge eating disorder has a larger body or obesity. Four participants endorsed a need for clarification on the extent to which obesity and binge eating disorder are separate vs. related/overlapping ($\frac{4}{14}$, $29\%$). Three participants expressed views that obesity can motivate treatment for binge eating ($\frac{3}{14}$, $21\%$). Two participants described obesity as a negative consequence of binge eating disorder ($\frac{2}{14}$, $14\%$). Additional possible relationships that were addressed by only one participant each are included in Supplementary material S1.1.
## Subtheme ii: Possible relationship mechanisms (79%)
Eleven participants ($\frac{11}{14}$, $79\%$) spontaneously described eight possible mechanisms by which obesity may be related to binge eating disorder. These included: (a) obesity contributing to weight stigma and resulting trauma that can contribute to and/or exacerbate binge eating disorder symptoms ($\frac{6}{14}$, $43\%$); (b) links to inflammatory processes related to food choices and mood (e.g., depression; $\frac{4}{14}$, $29\%$); (c) obesity contributing to sleep disturbances that can exacerbate binge eating ($\frac{3}{14}$, $21\%$); (d) links between obesity and the gut microbiome ($\frac{2}{14}$, $14\%$); (e) obesity impacting relationships and interpersonal factors (e.g., isolation, social support, social anxiety) that mediate/moderate binge eating ($\frac{2}{14}$, $14\%$). Additional possible mechanisms addressed by one participant each are included in Supplementary material S1.2.
## Subtheme iii: Validity of links to negative health consequences (29%)
Two participants ($\frac{2}{14}$, $14\%$) expressed views that obesity can increase risk for medical complications and two participants ($\frac{2}{14}$, $14\%$) stated that not everyone with obesity has negative health consequences (e.g., metabolic disorder, diabetes, hypertension, hypercholesterolemia).
## Theme 2: Intentional/voluntary or unintentional/involuntary restriction (100%)
All 14 participants spontaneously identified a relationship between binge eating disorder and food/eating restriction, whether voluntary (e.g., self-elected dieting) or involuntary (e.g., imposed by a parent, medical provider, or economic conditions) ($\frac{14}{14}$, $100\%$) (Table 5). Three subthemes were identified: (i) the relationship between restriction and binge eating ($100\%$); (ii) spontaneously identified forms of restriction ($43\%$); and (iii) factors contributing to restriction ($43\%$).
**Table 5**
| Subtheme (i) relationship between restriction and BED | 14 (100%) |
| --- | --- |
| Can or does lead to binge eating | 13 (93%) |
| Expressed a personal view that restriction can/does lead to binge eating | 11 (79%) |
| Described a perspective that restriction can/does lead to binge eating as being endorsed in the field, but did not endorse or negate the view personally | 2 (14%) |
| Predominant phenotype of BED | 3 (21%) |
| Perceive high prevalence of restriction among individuals with BED | 2 (14%) |
| Restriction may index distress about weight or pre-existing BEDa | 2 (14%) |
| Additional participant statements related to subtheme i, “relationship between restriction & BED” | Additional participant statements related to subtheme i, “relationship between restriction & BED” |
| “There is a group without question that is just hardwired to be higher weighted, and they are big time restrictors” (P72) | “There is a group without question that is just hardwired to be higher weighted, and they are big time restrictors” (P72) |
| “About a third [of my clients] described dieting and want to stop that cycle, but it actually is binge eating disorder” (P37) | “About a third [of my clients] described dieting and want to stop that cycle, but it actually is binge eating disorder” (P37) |
The experts' general views that food restriction can or does lead to binge eating (theme 2, subtheme i) aligns with that of cognitive behavioral therapy (CBT), the first-line therapeutic intervention for any eating disorder, including binge eating disorder [3, 5, 6], which posits that dieting behavior drives binge eating and results from overevaluation of eating and body weight/shape/size [3]. However, CBT fails to produce longstanding remission in ~$50\%$ of individuals with binge eating disorder [49], suggesting possible limitations in this view that are supported in the literature (7, 50–52).
The relationship between dietary restraint and economic precarity has recently gained recognition in the field [18]. Here, food scarcity was recognized as a common form of food restraint by most experts, second to dieting (theme 3, subtheme ii). This recognition aligns with findings from several studies conducted at a food pantry in San Antonio, TX between 2015 and 2016 (53–55). These studies found $90\%$ of respondents had a clinically significant eating disorder [55], with eating disorder pathology severity significantly correlating with deliberately trying to limit food consumption or going >8 h without food consumption ($r = 0.25$, $$p \leq 0.0001$$), which $52\%$ of respondents reported [53]. Reasons for food/eating restraint included lack of resources, SNAP/food stamps being insufficient, and emphasizing other family members receive access to food [53]. More recent findings suggest binge eating disorder is 1.65 times more common in indivdiuals with food insecurity ($8.6\%$ prevalence vs. $5.2\%$ prevlance in food-secure indivdiuals; $$p \leq 0.02$$) [56] and both food insecurity and/or receiving government assistance before age 18 are both associated with increased odds of having binge eating disorder [57, 58].
## Subtheme i: Relationship between restriction and binge eating (100%)
All participant statements addressed the existence of a possible relationship between restriction and binge eating ($\frac{14}{14}$, $100\%$) (Table 5). Eleven participants expressed views that restriction can or does lead to binge eating ($\frac{11}{14}$, $79\%$). Two additional participants described this view as being endorsed by cognitive behavioral therapy and in the field but did not endorse or negate the view personally ($\frac{2}{14}$, $14\%$). Three participants described food restriction as a predominant phenotype of binge eating disorder ($\frac{3}{14}$, $21\%$). Two participants expressed perceptions that a high prevalence of restriction exists among individuals with binge eating disorder, whether the individuals themselves realize it or not ($\frac{2}{14}$, $14\%$). Two participants also expressed views that restriction may index distress about weight or pre-existing binge eating (rather than directly causing binge eating) ($\frac{2}{14}$, $14\%$). Select quotes from participants regarding the relationship between restriction and binge eating disorder are shown below and in Table 5.
## Subtheme ii: Spontaneously identified forms of restriction (100%)
All participants ($\frac{14}{14}$, $100\%$) spontaneously identified different types of restriction, including (a) self-imposed dieting ($\frac{11}{14}$, $79\%$); (b) restriction coinciding with food scarcity or economic insecurity ($\frac{9}{14}$, $64\%$); (c) externally mandated (by a medical doctor or parents, often linked to weight) ($\frac{2}{14}$, $14\%$); and (d) restricting certain types of foods, food enjoyment, or calories ($\frac{2}{14}$, $14\%$) (Table 6). Select quotes from participants spontaneously identifying forms of restriction are shown below and in Table 6.
**Table 6**
| Subtheme (ii) forms of restriction identified by participants | 14 (100%) |
| --- | --- |
| (a) Dieting | 11 (79%) |
| (b) Restricted food access resulting from economic precarity | 9 (64%) |
| (c) Externally mandated (by medical doctor or parents)a | 2 (14%) |
| (d) Restricting certain of foods, food enjoyment, or calories | 2 (14%) |
| Additional participant statements relating to subtheme ii, “forms of binge eating” | Additional participant statements relating to subtheme ii, “forms of binge eating” |
| “About a third [of my clients] described dieting and want to stop that cycle, but it actually is binge eating disorder” (P37) | “About a third [of my clients] described dieting and want to stop that cycle, but it actually is binge eating disorder” (P37) |
| “I work with patients who have said, ‘well yeah, I have binge eating.' I binge eat the first two weeks of the month ‘cause that's when we have food in the house and then there's no food in the house the last two weeks of the month.' That's a systemic issue that I think needs to be addressed and needs to be talked about in terms of people's vulnerability to eating disorders” (P75) | “I work with patients who have said, ‘well yeah, I have binge eating.' I binge eat the first two weeks of the month ‘cause that's when we have food in the house and then there's no food in the house the last two weeks of the month.' That's a systemic issue that I think needs to be addressed and needs to be talked about in terms of people's vulnerability to eating disorders” (P75) |
| “When you put somebody on a diet, it's a medical intervention, … you're doing something physically to their body, and to their mind, so that's under the realm of …biological [interventions] because restriction and cutting someone's calories or cutting food groups or telling them to …count carbs, or keep points, or count calories, or whatever …that's really external regulation” (P7) | “When you put somebody on a diet, it's a medical intervention, … you're doing something physically to their body, and to their mind, so that's under the realm of …biological [interventions] because restriction and cutting someone's calories or cutting food groups or telling them to …count carbs, or keep points, or count calories, or whatever …that's really external regulation” (P7) |
## Foci a: Specific/micro factors (50%)
Seven participants spontaneously identified eight different factors contributing to restriction ($\frac{7}{14}$, $50\%$; Table 7), including: (a) body weight/shape/size (especially in naturally higher-weighted individuals, $\frac{4}{14}$, $29\%$); (b) restricting to soothe or cope ($\frac{2}{14}$, $14\%$); and (c) shame around eating ($\frac{2}{14}$, $14\%$). Additional specific factors identified by one participant ($\frac{1}{14}$, $7\%$) each are included in Supplementary material S2.1.
**Table 7**
| Foci (a) specific/micro factors | 7 (50%) |
| --- | --- |
| (a) Body weight/shape/sizea | 4 (29%) |
| (b) Restricting to soothe or cope | 2 (14%) |
| (c) Shame around eating | 2 (14%) |
| (d–h) Additional specific/micro factors identified by only 1 participant (7%) each are included in Supplementary Table 2 | (d–h) Additional specific/micro factors identified by only 1 participant (7%) each are included in Supplementary Table 2 |
| Foci (b) contextual/macro factors | 5 (36%) |
| (a) Culturally driven (linked to weight) | 3 (21%) |
| (b) Socially reinforced | 2 (14%) |
| (c) Biologically reinforcing | 1 (7%) |
## Foci b: Contextual/macro factors (36%)
At a macro level, restriction was described as often being culturally driven (linked to weight) ($\frac{3}{14}$, $21\%$), but also socially reinforced ($\frac{2}{14}$, $14\%$) and biologically reinforcing.
## Foci a: Negative affect addressed verbatim (50%)
Negative affect was addressed verbatim by seven participants ($\frac{7}{14}$, $50\%$; Table 8). Six participants described negative affect as driving binge eating ($\frac{6}{14}$, $43\%$). Three participants ($\frac{3}{14}$, $21\%$) described a mechanism by which binge eating is used to reduce or alleviate negative affect; two participants referenced literature supporting this possibility ($\frac{2}{14}$, $14\%$). Two participants suggested negative affect makes binge eating disorder and its associated symptoms more difficult to manage, in part through the added burden of managing binge eating disorder with a concurrent mood disorder. One participant ($\frac{1}{14}$, $7\%$) suggested negative affect can contribute to increased risk for binge eating disorder (and referenced work supporting this possibility).
**Table 8**
| Foci (a) negative affect when addressed verbatim | 7 (50%) |
| --- | --- |
| (a) Drives binge eating | 6 (43%) |
| (b) Reduced or alleviated by binge eating | 3 (21%) |
| Literature cited | 2 (14%) |
| (c) Makes binge eating disorder and symptoms more difficult to managea | 2 (14%) |
| (d) Contributes to increased risk for binge eating disorder (work cited) | 1 (7%) |
| Foci (b) negative affective states | 10 (71%) b |
| (a) Guilt | 6 (43%) |
| (b) Shame | 6 (43%) |
| (c) Poor self-esteem | 5 (36%) |
| (d) Self-hate | 2 (14%) |
| Foci (c) mechanisms relating negative affective states to BED | 5 (36%) |
| (a) Negative affect states linked to eating behaviorc | 5 (36%) |
| Guilt | 4 (29%) |
| Shame | 3 (21%) |
| Self-esteem | 1 (7%) |
| (b) Negative affect states linked to body image | 2 (14%) |
| Shame | 1 (7%) |
| Low self-esteem | 1 (7%) |
| (c–f) Additional possible mechanisms that were identified by 1 participant (7%) each are included in Supplementary Table S3.1 | (c–f) Additional possible mechanisms that were identified by 1 participant (7%) each are included in Supplementary Table S3.1 |
| Additional participant statements related to foci b, “negative affective states” | Additional participant statements related to foci b, “negative affective states” |
| “Negatively-associated valence mood[s]—things like anger, things like anxiety, things like shame [are very important aspects of binge eating disorder]” (P84) | “Negatively-associated valence mood[s]—things like anger, things like anxiety, things like shame [are very important aspects of binge eating disorder]” (P84) |
| “[Speaking to] the guilt of [the] eating disorder behavior: [an individual with binge eating disorder may] feel like it's all their fault [and have] … cognitive thoughts like, ‘…I'll never be able to do this. This is all my fault. I have no willpower. I'm a terrible person. How come I can't do this,'…. [these] cognitive aspects [of binge eating disorder] are of course, influenced by environmental aspects. People think things in part because they hear them in the environment and believe that they should think them, so the thoughts about ‘I should be a certain way,' or, ‘I shouldn't eat this certain way,' or, ‘I should have control,' or, ‘I should have willpower,' or, ‘that person is judging me for ABC and I'm judging myself for XYZ,' that cognitive thought pattern is so debilitating for so many people who then [think], ‘…I should be able to do something with that, with my eating or my whatever.' Not to mention the [deeper] thoughts that some of those [initial negative thoughts] connect to, of, ‘I can't do anything,' you know, the pieces that we target with cognitive restructuring, that all-or-nothing thinking, the … disaster-framing, [the] sort of mystical … crystal ball thinking—that they know what's happening… [Those cognitive processes are] in large part based on environmental input and this sort of biological mismatch of, ‘I feel like I should be able to control this, but my body is so excited about that food that I can't, and now I feel like I failed.' So that piece, I think is critical to help people to understand how their biology influences how they hear those messages, which influence how they think and how they feel and how they behave” (P60) | “[Speaking to] the guilt of [the] eating disorder behavior: [an individual with binge eating disorder may] feel like it's all their fault [and have] … cognitive thoughts like, ‘…I'll never be able to do this. This is all my fault. I have no willpower. I'm a terrible person. How come I can't do this,'…. [these] cognitive aspects [of binge eating disorder] are of course, influenced by environmental aspects. People think things in part because they hear them in the environment and believe that they should think them, so the thoughts about ‘I should be a certain way,' or, ‘I shouldn't eat this certain way,' or, ‘I should have control,' or, ‘I should have willpower,' or, ‘that person is judging me for ABC and I'm judging myself for XYZ,' that cognitive thought pattern is so debilitating for so many people who then [think], ‘…I should be able to do something with that, with my eating or my whatever.' Not to mention the [deeper] thoughts that some of those [initial negative thoughts] connect to, of, ‘I can't do anything,' you know, the pieces that we target with cognitive restructuring, that all-or-nothing thinking, the … disaster-framing, [the] sort of mystical … crystal ball thinking—that they know what's happening… [Those cognitive processes are] in large part based on environmental input and this sort of biological mismatch of, ‘I feel like I should be able to control this, but my body is so excited about that food that I can't, and now I feel like I failed.' So that piece, I think is critical to help people to understand how their biology influences how they hear those messages, which influence how they think and how they feel and how they behave” (P60) |
| Additional participant statements related to foci c, “underlying mechanisms” | Additional participant statements related to foci c, “underlying mechanisms” |
| “We always think purely about what you feel”—sad, depressed, irritable, fill in the blank, some sort of negative affect—and thus you binge [eat or] overeat these ultra-processed foods. But I actually think there's another half of the loop that's been under-explored. So … when I think of tobacco, … it's like, ‘oh, you're stressed, you're irritable, you're anxious, [so] you smoked a cigarette'. But when you smoke a cigarette, you're going to have a crash, and it's going to put you into an irritable withdrawn state a couple hours after it, which is going to make you more prone to negative affect, which is going to make you more likely to want another cigarette, and it's this very dangerous, cyclical process. So, the cigarette itself drives forward a tendency to experience negative affect. I think with food, we're in the zone of just [thinking that] negative affect triggers overeating of food, but [we're not yet asking] ‘do these foods then lead …a couple hours later… to [feeling] more prone to experiencing negative affect?”' (P19) | “We always think purely about what you feel”—sad, depressed, irritable, fill in the blank, some sort of negative affect—and thus you binge [eat or] overeat these ultra-processed foods. But I actually think there's another half of the loop that's been under-explored. So … when I think of tobacco, … it's like, ‘oh, you're stressed, you're irritable, you're anxious, [so] you smoked a cigarette'. But when you smoke a cigarette, you're going to have a crash, and it's going to put you into an irritable withdrawn state a couple hours after it, which is going to make you more prone to negative affect, which is going to make you more likely to want another cigarette, and it's this very dangerous, cyclical process. So, the cigarette itself drives forward a tendency to experience negative affect. I think with food, we're in the zone of just [thinking that] negative affect triggers overeating of food, but [we're not yet asking] ‘do these foods then lead …a couple hours later… to [feeling] more prone to experiencing negative affect?”' (P19) |
| “I think … the conceptualization [that difficulties with negative affect play a role in risk for binge eating] would still sort of cycle back to … the question[s] of ‘why does this person have high levels of negative affect?' Or ‘why does this person have difficulty—when encountering high levels of negative affect—with managing that?' And it's very tempting … to draw lines back to environmental and genetic factors to help explain that” (P5) | “I think … the conceptualization [that difficulties with negative affect play a role in risk for binge eating] would still sort of cycle back to … the question[s] of ‘why does this person have high levels of negative affect?' Or ‘why does this person have difficulty—when encountering high levels of negative affect—with managing that?' And it's very tempting … to draw lines back to environmental and genetic factors to help explain that” (P5) |
## Foci b: negative affective states (71%)
Ten ($\frac{10}{14}$, $71\%$) provided descriptions of negative affective states ($\frac{10}{14}$, $71\%$; Table 8). These included: (a) guilt ($\frac{6}{14}$, $43\%$), (b) shame ($\frac{6}{14}$, $43\%$), (c) poor self-esteem ($\frac{5}{14}$, $36\%$), and (d) self-hate ($\frac{2}{14}$, $14\%$).
## Foci c: Underlying mechanisms (36%)
Five participants ($\frac{5}{14}$, $36\%$) identified mechanisms relating negative affective states to binge eating disorder (Table 8). These included: (a) binge eating behavior being linked to negative affective states ($\frac{5}{14}$, $36\%$), including guilt ($\frac{4}{14}$, $29\%$), shame ($\frac{3}{14}$, $21\%$), and self-esteem ($\frac{1}{14}$, $7\%$); (b) binge eating behavior being linked to body image ($\frac{2}{14}$, $14\%$), including shame and low self-esteem ($\frac{1}{14}$, $7\%$ each); and (c) binge eating driving negative affect through induction of subsequent withdrawal [e.g., opponent-process theory [38, 39]]. Additional possible underlying mechanisms spontaneously identified by one participant each ($\frac{1}{14}$, $7\%$ each) are included in Supplementary material S3.1.1 and Supplementary Table S3.1.
## Subtheme ii: Distress (64%)
Distress was addressed by nine participants ($\frac{9}{14}$, $64\%$) (Table 9). Five participants described distress as central to binge eating disorder pathology ($\frac{5}{14}$, $36\%$) and four described it as impacting binge eating disorder development ($\frac{4}{14}$, $29\%$). Three participants identified distress as central to self-identification and treatment-seeking for binge eating disorder ($\frac{3}{14}$, $21\%$). Three participants recognized distress a central DSM diagnostic construct [1] and three described distress as a key criterion that differentiates individuals with binge eating disorder from those with overweight, obesity, or loss of control eating ($\frac{3}{14}$, $21\%$ each).
**Table 9**
| Subtheme (ii) distress | 9 (64%) |
| --- | --- |
| (a) Central to binge eating disorder pathology | 5 (36%) |
| (b) Impacting BED development | 4 (29%) |
| (c) Central to BED self-identification and treatment-seeking | 3 (21%) |
| (d) Central DSM diagnostic constructa | 3 (21%) |
| (e) Key criterion differentiating BED from overweight, obesity, or loss of control eating | 3 (21%) |
| Additional participant statements related to theme 3, subtheme ii, “distress” | Additional participant statements related to theme 3, subtheme ii, “distress” |
| “There's distress around the binge eating that can be emotional but I think [it is] really core…” (P93) | “There's distress around the binge eating that can be emotional but I think [it is] really core…” (P93) |
| “People who self-identify as having binge eating disorder are—in my mind—a distinct subgroup from people with obesity in that they experienced that sense of loss of control, they're often more distressed about their eating patterns. They have more comorbidity. And there's certainly a lot of data that their healthcare utilization costs are higher. That may be psychiatric. I don't I don't think that's clear” (P72) | “People who self-identify as having binge eating disorder are—in my mind—a distinct subgroup from people with obesity in that they experienced that sense of loss of control, they're often more distressed about their eating patterns. They have more comorbidity. And there's certainly a lot of data that their healthcare utilization costs are higher. That may be psychiatric. I don't I don't think that's clear” (P72) |
| “Binge eating disorder, … is defined really solely on the behavior of binge eating and the second criteria of diagnostic specifiers, and the third criteria is marked distress regarding binge eating” (P93) | “Binge eating disorder, … is defined really solely on the behavior of binge eating and the second criteria of diagnostic specifiers, and the third criteria is marked distress regarding binge eating” (P93) |
## Theme iii: Emotion regulation and negative urgency (64%)
Nine participants identified emotional regulation or negative urgency as being central to binge eating disorder pathology ($\frac{9}{14}$, $57\%$; Table 10). Two participants referenced empirical support ($\frac{2}{14}$, $14\%$). Six participants described a paradigm in which binge eating is used as a strategy for regulating, stabilizing, or coping with emotions or negative affect ($\frac{6}{14}$, $43\%$). One participant discussed emotion regulation as being related to food- and serotonin dysregulation ($\frac{1}{14}$, $7\%$). One participant questioned the impact of emotion regulation on binge eating disorder pathology, stating emotion regulation interventions have not been found to differ in their effectiveness from guided self-help cognitive behavioral therapy, suggesting the pathology may be equal parts emotional and cognitive behavioral. Additional findings are described in the Supplementary material S3.2 and Supplementary Table S3.2.
**Table 10**
| Subtheme (iii) Emotional regulation & negative urgency | 8 (57%) |
| --- | --- |
| (a) Central to BED pathology | 8 (57%) |
| (b) Described paradigm in which binge eating is used as a strategy for regulating, stabilizing, or coping with emotions or negative affect | 6 (43%) |
| (b.1) Referenced empirical support | 2 (14%) |
| (c–d) Additional statements related to subtheme iii, “emotion regulation and negative urgency,” that were identified by 1 participant (1/14, 7%) each are included in Supplementary Table S3.2 | (c–d) Additional statements related to subtheme iii, “emotion regulation and negative urgency,” that were identified by 1 participant (1/14, 7%) each are included in Supplementary Table S3.2 |
| Additional participant statements related to subtheme iii, “emotion regulation & negative urgency” | Additional participant statements related to subtheme iii, “emotion regulation & negative urgency” |
| “The main problem is this distress regulation in the first place” (P53) | “The main problem is this distress regulation in the first place” (P53) |
| “There are also issues around serotonin levels that can have impacts on cognitive abilities and emotional abilities. …I think [this is] a very strong biological thing that needs understanding. … [emotional aspects of binge eating disorder are] very important indeed; partly because the emotions go haywire when serotonin levels [get] low, so … when somebody tries to clean carbohydrates out of their diet, they're already in trouble, but also partly because of the mood-stabilization effects [of binge-eating]—people learn that binge [eating works] as a way of stabilizing mood against a background of invalidating environments, uncontrollable mood states, etc. [especially] negatively-associated valence mood, things like anger, anxiety, shame… ” (P84) | “There are also issues around serotonin levels that can have impacts on cognitive abilities and emotional abilities. …I think [this is] a very strong biological thing that needs understanding. … [emotional aspects of binge eating disorder are] very important indeed; partly because the emotions go haywire when serotonin levels [get] low, so … when somebody tries to clean carbohydrates out of their diet, they're already in trouble, but also partly because of the mood-stabilization effects [of binge-eating]—people learn that binge [eating works] as a way of stabilizing mood against a background of invalidating environments, uncontrollable mood states, etc. [especially] negatively-associated valence mood, things like anger, anxiety, shame… ” (P84) |
## Theme 4: Diagnostic heterogeneity and validity (71%)
Ten participants ($\frac{10}{14}$, $71\%$) expressed views related to the diagnostic validity and/or heterogeneity of binge eating disorder (Table 11).
**Table 11**
| Subtheme (i) Diagnostic heterogeneity | 10 (71%) |
| --- | --- |
| a) Possible binge eating disorder subsets or phenotypes spontaneously identified or referenced by more than one participant | 9 (64%) |
| 1. “Food/eating addiction” or reward-based phenotypea | 4 (29%) |
| 2. Trauma, adversity, or PTSD-like factors present and predominant | 4 (29%) |
| 3. ADD/ADHD-like presentationsb | 3 (21%) |
| 4. Chronic dieting/restriction-mediated | 3 (21%) |
| 5. Obsession and/or compulsion around food and/or eatingc | 3 (21%) |
| 6. Hyper-sensitivity (to taste, facial cues, or social threat) | 2 (14%) |
| 7. Mood or emotion dysregulation-driven | 2 (14%) |
| 8–19. Additional possible subsets of phenotypes that were spontaneously identified or referenced by only one participant (1/14, 7%) each are included in Supplementary Table S4.1 | 8–19. Additional possible subsets of phenotypes that were spontaneously identified or referenced by only one participant (1/14, 7%) each are included in Supplementary Table S4.1 |
| (b) Proposed AN, BN, and BED each contain the same three subgroupsd | 1 (7%) |
| Subtheme (ii) diagnostic validity | 5 (36%) |
| (a) Skepticism of the current diagnostic criteria | 5 (36%) |
| (b) “Debate about how to measure binge size” | 3 (21%) |
| (c) Questioned validity of binge eating disorder as a psychiatric disorder | 2 (14%) |
| (d) Proposed re-classification of eating disorder diagnoses with recurrent binge eating (e.g., BED, BN)e | 1 (7%) |
| Additional participant statements related to subtheme ii, “diagnostic validity” | Additional participant statements related to subtheme ii, “diagnostic validity” |
| “I wonder if people could binge eat without visible consequences, if it would be a problem at all” (P38) | “I wonder if people could binge eat without visible consequences, if it would be a problem at all” (P38) |
| “And there's reasons to raise questions, to what degree is eating behavior of folks with binge eating disorder really different from the eating behavior of folks without binge eating disorder in the real world? Until somebody comes up with a way of assessing objectively [not] self-report, … eating behavior in the real world, we're not going to be able to sort that out” (P46) | “And there's reasons to raise questions, to what degree is eating behavior of folks with binge eating disorder really different from the eating behavior of folks without binge eating disorder in the real world? Until somebody comes up with a way of assessing objectively [not] self-report, … eating behavior in the real world, we're not going to be able to sort that out” (P46) |
| “There's a lot of debate about how you measure binge size. [Binges] are discrete episodes that, … definitely involve sort of large amount of food that are unusual, as the DSM-5 would define it. But then you also see episodes in which there's a loss of control where it wouldn't necessarily be clinically large, but a lot of those episodes are still linked with people's perception of psychopathology” (P72) | “There's a lot of debate about how you measure binge size. [Binges] are discrete episodes that, … definitely involve sort of large amount of food that are unusual, as the DSM-5 would define it. But then you also see episodes in which there's a loss of control where it wouldn't necessarily be clinically large, but a lot of those episodes are still linked with people's perception of psychopathology” (P72) |
| “… I think going forward, we'll need to re-think, at some point, this dichotomy we've got between the two forms of recurrent binge eating [e.g., bulimia nervosa and binge eating disorder] and [have] some reconciliation of them…” “We need to have [a] much better understanding of how we classify and … diagnose … the relationship between binge | “… I think going forward, we'll need to re-think, at some point, this dichotomy we've got between the two forms of recurrent binge eating [e.g., bulimia nervosa and binge eating disorder] and [have] some reconciliation of them…” “We need to have [a] much better understanding of how we classify and … diagnose … the relationship between binge |
| eating disorder and other disorders of recurrent binge eating in terms of how we conceptualize and classify the disorders, and is binge eating… really so distinct from bulimia nervosa and other eating disorders or not? [And what are] other ways of conceptualizing eating disorders …maybe more based on … the psychological understanding of disordered eating behaviors, such as degree of over-obsessionality, over-control or impulsiveness and under control.” (P93) | eating disorder and other disorders of recurrent binge eating in terms of how we conceptualize and classify the disorders, and is binge eating… really so distinct from bulimia nervosa and other eating disorders or not? [And what are] other ways of conceptualizing eating disorders …maybe more based on … the psychological understanding of disordered eating behaviors, such as degree of over-obsessionality, over-control or impulsiveness and under control.” (P93) |
| “[In a 2009 publication on] the validity of binge eating disorder,[(40)] …comparing people who have binge eating disorder to weight-matched individuals without binge eating disorder… there were a very limited number of differences between those two groups. …And so, for me, we need to own the empirical support for the idea of binge eating disorder and what in fact it really is, and how does it differ from normality and how does it differ from overweight and obesity” (P33) | “[In a 2009 publication on] the validity of binge eating disorder,[(40)] …comparing people who have binge eating disorder to weight-matched individuals without binge eating disorder… there were a very limited number of differences between those two groups. …And so, for me, we need to own the empirical support for the idea of binge eating disorder and what in fact it really is, and how does it differ from normality and how does it differ from overweight and obesity” (P33) |
The experts' recognition of heterogeneity in binge eating disorder aligns with the literature, in which upwards of seven different models of binge eating disorder conceptualization have empirical support [4]. Interestingly, the possible binge eating disorder subsets or phenotypes spontaneously identified or referenced by the experts tend to align with various models/conceptualizations of binge eating disorder (Table 14).
**Table 14**
| BED model/theory/ conceptualization supported in the literature (4) | Brief explanation of the model/theory | Identified as a theme among expert responses? | Expert recognition of BED phenotype(s) that align with this model (theme 4, subtheme i, S4.1, and Table 11) | Other expert views that align with this model/theory | Other expert views that align with this model/theory.1 |
| --- | --- | --- | --- | --- | --- |
| Dietary restraint models | Dietary restraint viewed as a prospective risk factor for binge eating (66) | Yes (theme 2, theme 4, subtheme i) | “Chronic dieting-restriction-mediated subtype” identified by 3 experts (21%; theme 4, subtheme i, Supplementary material S4.1 and Table S4.1) | 93% of experts expressed views that restriction can or does lead to binge eating (theme 2, Table 5) | 93% of experts expressed views that restriction can or does lead to binge eating (theme 2, Table 5) |
| Dual pathway models | Body dissatisfaction viewed as leading to binge eating through restrained eating and negative affect (7) | Not directly, though see columns 5–6 | | 29% of experts identified body weight/shape/size as a factor contributing to restriction and 14% of experts described restricting to soothe or cope (theme 2, subtheme iii, Table 7) | 43% of experts described a paradigm in which binge eating is used as a strategy for regulating, stabilizing, or coping with emotions or negative affect, though not necessarily linked to body dissatisfaction (theme 3, subtheme iii, Table 10) |
| | | | | | 14% of experts described an old focus on body weight/shape/size overvaluation/dissatisfaction and restriction as driving binge eating, and a new understanding of the role of emotion regulation as a driving factor (theme 5, Table 12) |
| Emotion/affect regulation models | View that negative emotions, moods, or affective experiences can create discomfort that is alleviated by binge eating, thus negatively reinforcing the behavior (4) | Yes (theme 3, theme 4, subtheme i) | “Mood or emotion dysregulation-driven” subtype identified by 2 experts (14%; theme 4, subtheme i, Supplementary material S4.1 and Table S4.1) | All experts identified associations between negative affect and binge eating (100% endorsement: theme 3, Table 8) | 43% of experts described a paradigm in which binge eating is used as a strategy for regulating, stabilizing, or coping with emotions or negative affect, though not necessarily linked to body dissatisfaction (theme 3, subtheme iii, Table 10) |
| | | | | | 14% of experts described an old focus on body weight/shape/size overvaluation/dissatisfaction and restriction as driving binge eating, and a new understanding of the role of emotion regulation as a driving factor (theme 5, Table 12) |
| Escape (disassociation) models | View that binge eating is used to alleviate negative affect associated with high self-awareness (related to pressures, threats, long-term concerns, and lasting consequences of experiences) and hyper-awareness of failings to meet high internal and external expectations [(67) as cited in (4)] | No, though see columns 5–6 | | Escape (disassociation) models were not identified as a theme across expert interviews. However, several experts' descriptions of negative affect, negative urgency, and emotion regulation align with the view that individuals with BED have high awareness of their failings to meet the expectations set for them by themselves or others (theme 3, subthemes i, iii; Tables 8, 10) | Escape (disassociation) models were not identified as a theme across expert interviews. However, several experts' descriptions of negative affect, negative urgency, and emotion regulation align with the view that individuals with BED have high awareness of their failings to meet the expectations set for them by themselves or others (theme 3, subthemes i, iii; Tables 8, 10) |
| Food addiction models | View that binge eating can result from the same psychopathology and behavior that occurs in substance-related and addictive disorders, but in relation to certain foods, food aspects, or eating behaviors (e.g., highly palatable, processed, or caloric foods; sugar; binge eating behavior) | This was identified as a theme that is addressed in a separate manuscript about expert perspectives on food/eating addiction | “Food/eating addiction' or reward-based phenotype” identified by 4 experts (29%; theme 4, subtheme i, Supplementary material S4.1 and Table S4.1) | Expert opinions on the concept of food/eating addition and reward-based phenotypes are addressed elsewhere | Expert opinions on the concept of food/eating addition and reward-based phenotypes are addressed elsewhere |
| Integrative cognitive-affective theory (ICAT) models | View that self-discrepancy (disparity between how an individual views the self vs. comparisons to standards related to an “ideal/ought” self) can lead to negative affect, which can precipitate and negatively reinforce binge eating (68) | No, though see columns 5–6 | | 43% of experts expressed views that negative affect drives binge eating (theme 3, subtheme i). One expert linked negative affect and binge eating to discrepancy between self-views in comparison to self-standards (P60, Table 8) | 43% of experts expressed views that negative affect drives binge eating (theme 3, subtheme i). One expert linked negative affect and binge eating to discrepancy between self-views in comparison to self-standards (P60, Table 8) |
| Interpersonal models | View that relationships can crucially impact self-esteem, anxiety, and psychopathology either positively or negatively, with interpersonal stressors promoting negative affect and low self-esteem that binge eating is used to alleviate | This was identified as a theme that is addressed in Bray et al. (18) | “Social-anxiety-driven” subtype identified by 1 expert (7%; theme 4, subtheme i, Supplementary material S4.1 and Table S4.1) | See findings reported in Bray et al. (18) | See findings reported in Bray et al. (18) |
| Neurocognitive/neurobiological models | Emphasizes the role of neurocognitive factors (e.g., executive functioning, inhibitory control, set shifting, and reward processing) in increasing risk for loss of control and binge eating | No However, many of these concepts are addressed in a separate manuscript about expert perspectives on food/eating addiction | “General cognitive deficits/sequential issues” subtype identified by 1 expert (7%; theme 4, subtheme i, Supplementary material S4.1 and Table S4.1) | Many of these concepts are addressed in two separate manuscripts about expert mental health aspects of BED (19) and perspectives on food/eating addiction (forthcoming) | Many of these concepts are addressed in two separate manuscripts about expert mental health aspects of BED (19) and perspectives on food/eating addiction (forthcoming) |
| Perfectionism models | View that socially prescribed perfectionism—or the perception of it—promotes vulnerability to binge eating by increasing interpersonal discrepancies and decreasing interpersonal esteem (4) | No | | One participant expressed a view that AN, BN, and BED each contain the same three subgroups of: (a) control-driven individuals with high levels of perfectionism and high obsessive-compulsive rates; (b) individuals with low ED psychopathology but disordered eating; and (c) individuals with higher impulsivity (theme 4, subtheme i, P72) | One participant expressed a view that AN, BN, and BED each contain the same three subgroups of: (a) control-driven individuals with high levels of perfectionism and high obsessive-compulsive rates; (b) individuals with low ED psychopathology but disordered eating; and (c) individuals with higher impulsivity (theme 4, subtheme i, P72) |
| Schema models | View that unmet emotional needs can result in long-standing patterns of maladaptive thinking, feeling, behaving, and coping that can maintain eating disorder pathology (69) | No, though see columns 4–5 | “Trauma, adversity, or PTSD-like subtype” (endorsed by 29% of experts) “Learned emotional invalidation” subtype (endorsed by 7% of experts) “Invalidating environments” subtype (endorsed by 7% of experts) | Expert opinions on trauma, adversity, and PTSD are addressed in Bray et al. (18, 19) | Expert opinions on trauma, adversity, and PTSD are addressed in Bray et al. (18, 19) |
| Transdiagnostic model | Expanded conceptualization based on the original cognitive-behavioral theory of bulimia nervosa (5, 70, 71) that suggests a dysfunctional scheme for evaluating the self—including overvaluation of body weight/shape/size and eating behavior and perfectionistic tendencies—result in low self-esteem that promote extreme and maladaptive weight control behaviors that prompt a cycle of dieting/weight loss and refractory binge eating (4, 5) | This was not directly identified as a theme, though see columns 4–5 | “Chronic Dieting/Restriction-mediated” subtype (endorsed by 21% of experts) partly aligns with this model | Participant descriptions of negative affective states (theme 3, subtheme i, foci b) and possible mechanisms relating negative affective states to BED (theme 3, subtheme i, foci c) support a view that overvaluation of eating behavior contributes to poor self-esteem, which perpetuate binge eating behavior and psychopathology | Participant descriptions of negative affective states (theme 3, subtheme i, foci b) and possible mechanisms relating negative affective states to BED (theme 3, subtheme i, foci c) support a view that overvaluation of eating behavior contributes to poor self-esteem, which perpetuate binge eating behavior and psychopathology |
| Weight regulation models | View weight and weight history as causal variables with clinically significant impacts on ED psychopathology and perpetuation (17) | Though expert statements aligning with weight regulation models was not identified as a theme, obesity was (theme 1) | | Although multiple links were identified between obesity and BED in theme 1, none of the experts in this study identified obesity or weight history as impacting or perpetuating BED psychopathology when specifically addressing the topic of obesity (theme 1) | 93% of experts expressed views that restriction can or does lead to binge eating (theme 2, Table 5) |
| | | | | 29% of experts identified body weight/shape/size as a factor contributing to restriction (theme 2, subtheme iii, Table 7) | In the theme of restriction (theme 2 subtheme ii), 2 experts (P37, P60) described a pathology in which a natural predisposition for being higher-weighted results in internally- or externally imposed food/eating restriction, which induces or perpetuates a binge-restriction cycle or ED psychopathology |
| Two participants also expressed views that (1) AN, BN, and BED each contain the same three subgroups of: (a) control-driven individuals with high obsessive-compulsive rates and high levels of perfectionism; (b) individuals with low ED psychopathology but disordered eating; and (c) individuals with higher impulsivity (P72) and (2) re-classification of eating disorder diagnoses with recurrent binge eating (e.g., BED, BN) should be considered based on similar subsets observed within the different diagnoses [e.g., “the degree of over-obsessionality [and] over-control, or impulsiveness and under-control” that underpin the ED behaviors (P93) (theme 4, subthemes i and ii)]. | Two participants also expressed views that (1) AN, BN, and BED each contain the same three subgroups of: (a) control-driven individuals with high obsessive-compulsive rates and high levels of perfectionism; (b) individuals with low ED psychopathology but disordered eating; and (c) individuals with higher impulsivity (P72) and (2) re-classification of eating disorder diagnoses with recurrent binge eating (e.g., BED, BN) should be considered based on similar subsets observed within the different diagnoses [e.g., “the degree of over-obsessionality [and] over-control, or impulsiveness and under-control” that underpin the ED behaviors (P93) (theme 4, subthemes i and ii)]. | Two participants also expressed views that (1) AN, BN, and BED each contain the same three subgroups of: (a) control-driven individuals with high obsessive-compulsive rates and high levels of perfectionism; (b) individuals with low ED psychopathology but disordered eating; and (c) individuals with higher impulsivity (P72) and (2) re-classification of eating disorder diagnoses with recurrent binge eating (e.g., BED, BN) should be considered based on similar subsets observed within the different diagnoses [e.g., “the degree of over-obsessionality [and] over-control, or impulsiveness and under-control” that underpin the ED behaviors (P93) (theme 4, subthemes i and ii)]. | Two participants also expressed views that (1) AN, BN, and BED each contain the same three subgroups of: (a) control-driven individuals with high obsessive-compulsive rates and high levels of perfectionism; (b) individuals with low ED psychopathology but disordered eating; and (c) individuals with higher impulsivity (P72) and (2) re-classification of eating disorder diagnoses with recurrent binge eating (e.g., BED, BN) should be considered based on similar subsets observed within the different diagnoses [e.g., “the degree of over-obsessionality [and] over-control, or impulsiveness and under-control” that underpin the ED behaviors (P93) (theme 4, subthemes i and ii)]. | Two participants also expressed views that (1) AN, BN, and BED each contain the same three subgroups of: (a) control-driven individuals with high obsessive-compulsive rates and high levels of perfectionism; (b) individuals with low ED psychopathology but disordered eating; and (c) individuals with higher impulsivity (P72) and (2) re-classification of eating disorder diagnoses with recurrent binge eating (e.g., BED, BN) should be considered based on similar subsets observed within the different diagnoses [e.g., “the degree of over-obsessionality [and] over-control, or impulsiveness and under-control” that underpin the ED behaviors (P93) (theme 4, subthemes i and ii)]. | Two participants also expressed views that (1) AN, BN, and BED each contain the same three subgroups of: (a) control-driven individuals with high obsessive-compulsive rates and high levels of perfectionism; (b) individuals with low ED psychopathology but disordered eating; and (c) individuals with higher impulsivity (P72) and (2) re-classification of eating disorder diagnoses with recurrent binge eating (e.g., BED, BN) should be considered based on similar subsets observed within the different diagnoses [e.g., “the degree of over-obsessionality [and] over-control, or impulsiveness and under-control” that underpin the ED behaviors (P93) (theme 4, subthemes i and ii)]. |
Recognizing, accepting, identifying, and classifying heterogeneity in binge eating disorder is an important step toward matching client heterogeneity to treatment modality, as has been done successfully in other disorders [72]. Future research needs to address concerns quantifying binge episodes and confirm whether additional objective criteria for “binge size” aids diagnostic validity.
Fortunately, progress to this end is underway. For example, a 2020 latent class analysis investigating potential sources of heterogeneity among 775 treatment-seeking adults with overweight or obesity and binge eating disorder identified two classes of individuals with binge eating disorder who differed most distinctly across differences in body image concerns, distress about binge eating, and depressive symptomatology [42]. The findings led the authors to critique the way we currently define binge eating disorder diagnostically, suggesting “many features currently used to define binge eating disorder (e.g., binge-eating frequency) are not helpful in explaining heterogeneity among individuals with [the] disorder. Instead, body image disturbances, which are not currently included as a part of the diagnostic classification system, appear to differentiate distinct subgroups of [these] individuals… Future research examining subgroups based on body image could be integral to resolving ongoing conflicting evidence related to the etiology and maintenance of binge eating disorder,” [42]. These important findings represent the ongoing evolution in our understanding of heterogeneity in binge eating disorder, and our ongoing evolution in refining binge eating disorder as a diagnosis.
## Subtheme i: Diagnostic heterogeneity (71%)
Ten participants made statements related to heterogeneity within binge eating disorder ($\frac{10}{14}$, $71\%$). Eight participants ($\frac{8}{14}$, $57\%$) expressed views of binge eating disorder as a heterogenous diagnosis that may encompass several different subsets or phenotypes.
Nine participants ($\frac{9}{14}$, $64\%$) spontaneously identified or referenced a total of nineteen possible phenotypes or subsets of binge eating disorder (Table 11; Supplementary Table S4.1). The five most commonly spontaneously endorsed phenotypes/subsets included individuals with: [1] hedonic/reward-based symptoms or driven by mechanisms implicit in substance-related and addictive disorders (SRADs) (e.g., a “food/eating addiction” phenotype) ($\frac{4}{14}$, $29\%$); [2] trauma, adversity, or post-traumatic stress disorder-like factors ($\frac{4}{14}$, $29\%$); [3] attention deficit disorder (ADD)/attention deficit hyperactive disorder (ADHD)-like presentations (having issues with “inhibitory control,” “impulsivity,” and “craving” or ”reward responsivity” ($\frac{3}{14}$, $21\%$); [4] chronic dieting or restricting ($\frac{3}{14}$, $21\%$); and [5] obsession and/or compulsion around food and/or eating [e.g., “obsessively thinking about food” or compulsivity around eating food ($\frac{3}{14}$, $21\%$)].
One participant ($\frac{1}{14}$, $7\%$) identified three possible phenotypes or subgroups that cut across all eating disorders (one group with high levels of perfectionism, control, and obsessive-compulsive tendencies, one group with disordered eating but low psychopathology, and one group with higher impulsivity).
## Subtheme ii: Diagnostic validity (36%)
Five participants ($\frac{5}{14}$, $36\%$) expressed skepticism of- or limitations in the current diagnostic criteria for binge eating disorder. Three participants ($\frac{3}{14}$, $21\%$) addressed a “debate about how [to] measure binge size”. Two participants ($\frac{2}{14}$, $14\%$) questioned the validity of binge eating disorder as a psychiatric disorder, one referencing a publication that found “a very limited number of differences” between individuals who have binge eating disorder and weight-matched individuals with overweight and obesity but not binge eating disorder [40], and emphasizing the need for “[continued help in separating] binge eating disorder as an entity from overweight and obesity”. One participant suggested the need to consider diagnostic reconfiguration in light of possible subsets of underlying psychopathology that are shared across a variety of eating disorders ($\frac{1}{14}$, $7\%$).
## Subtheme i: Anorexi-centric paradigm for understanding binge eating disorder (36%)
Five participants described an “anorexic-centric” paradigm that has historically been used for understanding binge eating disorder pathology, epidemiology, and treatment ($\frac{5}{14}$, $36\%$; Table 12).
**Table 12**
| Subtheme (i) “Anorexia-centric” paradigm for understanding BED | 5 (36%) |
| --- | --- |
| (a) “Anorexia-centric” understanding of who can have an ED | 4 (29%) |
| (b) Historical research focus on anorexia and bulimia nervosa | 3 (21%) |
| Subtheme (ii) paradigm shift in understanding drivers for BED | 4 (29%) |
| (a) Old focus on voluntary intentional food/eating restriction | 2 (14%) |
| (c) Old focus on body weight/shape/size over-valuation and dissatisfaction | 2 (14%) |
| (d) Newly included understanding of the role of emotion regulation | 2 (14%) |
| (e–g) See Supplementary Table S5.1 for additional comments and foci identified by only one participant (7%) each. | (e–g) See Supplementary Table S5.1 for additional comments and foci identified by only one participant (7%) each. |
## Foci a: Historical research focus on anorexia and bulimia nervosa (29%)
Four participants expressed views that eating disorder research has historically focused more on anorexia nervosa and bulimia nervosa vs. binge eating disorder ($\frac{4}{14}$, $29\%$; Table 12).
## Foci b: “Anorexi-centric” understanding of who can have an eating disorder (21%)
Three participants described a historically “anorexi-centric” understanding of who can have an eating disorder (e.g., ascribing eating disorders to thin, white, affluent, cis-gendered neurotypical females) ($\frac{2}{14}$, $14\%$; Table 12).
See statement from Participant 5 in Section 3.5.1.1 above.
## Subtheme ii: Paradigm shift in understanding drivers for binge eating disorder (29%)
Four participants described a shift in our understanding—as a field—of the mechanisms that can drive binge eating disorder ($\frac{4}{14}$, $29\%$; Table 12). Participants described old paradigms as focusing on voluntary intentional food/eating restraint (e.g., intentional fasting, see Theme 5) (endorsed by $\frac{2}{14}$, $14\%$) and body weight/shape/size over-valuation and dissatisfaction (endorsed by $\frac{2}{14}$, $14\%$) as driving binge eating. Participants described new paradigms as focusing on the roles of emotion regulation ($\frac{2}{14}$, $14\%$), inhibitory control ($\frac{1}{14}$, $7\%$), interpersonal factors ($\frac{1}{14}$, $7\%$), mood ($\frac{1}{14}$, $7\%$), and reward ($\frac{1}{14}$, $7\%$). Additional findings within this subtheme are described in Supplementary material S5 and Supplementary Table S5.1.
## Theme 6: Research gaps and future research directives (50%)
Two subthemes were identified regarding gaps in the literature and future research the experts would like to see closed related to the above topics: (i) Seven experts ($\frac{7}{14}$, $50\%$) identified a need for a better understanding of the relationship between binge eating disorder and overweight and/or obesity, including: (a) a need for clarification around the extent to which binge eating disorder and obesity are separate vs. related/overlapping ($\frac{4}{14}$, $29\%$); (b) greater clarification and understanding of how binge eating disorder differs from overweight and/or obesity ($\frac{3}{14}$, $21\%$); (c) what health risks and metabolic implications are associated with binge eating ($\frac{2}{14}$, $14\%$); and (d) prevalence of binge eating disorder in large and small body sizes ($\frac{1}{14}$, $7\%$) (Table 13). ( ii) Three experts ($\frac{3}{14}$, $21\%$), identified classification issues as an area warranting further research, including: (a) whether binge eating disorder is a viable disorder ($\frac{1}{14}$, $7\%$); (b) understanding the eating behavior of individuals with binge eating disorder as it occurs in the real world ($\frac{1}{14}$, $7\%$); and (c) consideration of reclassification of binge eating disorder with other eating disorders of recurrent binge eating (e.g., bulimia nervosa and binge-purge-type anorexia nervosa) ($\frac{1}{14}$, $7\%$).
**Table 13**
| Subtheme (i) better understanding of the relationship between weight and BED | 7 (50%) |
| --- | --- |
| (a) Need for clarification around extent to which BED and obesity are separate vs. related/overlapping | 4 (29%) |
| (b) Greater clarification and understanding of how BED differs from overweight/obesity | 4 (29%) |
| (c) What health risks and metabolic implications are associated with BED? | 2 (14%) |
| (d) Prevalence of BED in large and small body sizes | 1 (7%) |
| Subtheme (ii) classification issues | 3 (21%) |
| (a) Whether BED is a viable disorder | 1 (7%) |
| (b) Understanding the eating behavior of individuals with BED as it occurs in the real world | 1 (7%) |
| (c) Consideration of reclassification of BED with other EDs of recurrent binge eating (e.g., BN, B-P-type AN) | 1 (7%) |
## Novelty and innovation
To the authors' knowledge, our study is among the first to synthesize expert opinion on clinical factors pertaining to adult binge eating disorder pathology (and among the first to synthesize expert opinion on aspects of adult binge eating disorder in general). Synthesizing expert opinion isn't common in the binge-eating field. As such, this novel study that describes clinical factors pertaining to binge eating provides insights and expands upon several themes influencing the recognition of binge eating disorder, highlighting its heterogenous presentation and challenges in its clinical diagnosis, ultimately impacting management strategies. Exploring several themes and identifying novel viewpoints enables hypothesis-generating questions previously unexplored, or only explored within a limited capacity.
Most recently, a 2020 latent class analysis investigating potential sources of heterogeneity among 775 treatment-seeking adults with overweight or obesity and binge eating disorder identified two classes of individuals with binge eating disorder who differed in body image concerns, distress about binge eating, and depressive symptomatology [42]. The number of binge eating episodes was also significantly different between the two classes; whereas body mass index (BMI) was not a significant covariate in most models. The findings led the authors to critique the way we currently define binge eating disorder diagnostically, as current features used for diagnosis fail to adequately explain presenting heterogeneity. The study suggests there appear to be distinct subgroups within binge eating disorder, which was exposed by at least one of the experts interviewed here.
The important findings of our study in addition to the existing literature highlight the ongoing evolution in our understanding of heterogeneity in binge eating disorder, refining its diagnostic criteria, and pursuit for suitable management strategies outside of the constructs already dominated by anorexia nervosa and bulimia.
## Theme 1: Obesity domain (100%)
The experts' general recognition that obesity and binge eating disorder are commonly—but not always—linked (theme 1, subtheme i) aligns with current incidence and prevalence estimates (13–16), however, the nature of the relationship is less clear amongst interviewed experts. The experts' emphasis on the role of body/weight/shape stigmatization (theme 1, subtheme ii) seems to align with psychological contributions to intense concerns about body weight/shape/size overvaluation and heightened incentive for change [17]. Evidence suggests comorbid obesity and binge eating disorder is associated with more severe and prevalent levels of mental health disorders and negative affect than those observed in individuals with obesity or binge eating disorder alone (43–46).
Meanwhile, findings on physical health outcomes associated with comorbid obesity and binge eating disorder seem less clear, as recognized by the experts (theme 1, subtheme iii). A small observational study published in 2009 found $66\%$ of treatment-seeking individuals with binge eating disorder and obesity had metabolic syndrome, with men and whites having significantly higher rates than women and African Americans, respectively [47]. However, in this study, neither self-reported frequency of binge eating, nor severity of eating disorder psychopathology significantly differed among individuals with- vs. without metabolic syndrome. More recently, a 2014 factor structure analysis of metabolic syndrome in 347 individuals with obesity and binge eating disorder found metabolic syndrome factors (e.g., obesity, glucose regulation, blood pressure, and lipids) do not significantly differ in individuals with binge eating disorder and obesity vs. those found in normative population studies [48]. However, authors suggested “moderate attempts to regulate food intake may reduce the negative impact of obesity and binge eating pathology on metabolic function”, [48]. Furthermore, a 2008 review questions the validity of using obesity as a diagnostic criterion for binge eating disorder as the distress and psychopathology associated with binge eating disorder are not primarily due to obesity [19].
## Theme 3: Negative affect, distress, and emotion regulation (100%)
In line with general expert recognition of the links between negative affect, distress, emotion regulation, negative urgency, and binge eating (theme 3), literature supports adult binge eating disorder linked to psychosocial dysfunction across a wide range of domains, including affect and emotion regulation [59]. The majority experts' identification of negative affect, emotion dysregulation, and negative urgency as driving binge eating (theme 3) aligns with emotion/affect regulation models, which are well-supported in the literature [4]. This recognition also aligns with a paradigm shift in the field from a historical tendency to attribute all eating disorders to overvaluation of eating behavior and/or body weight/shape size and resulting dietary restraint (e.g., dietary restraint and dual pathway models) [3, 4, 7] to a more encompassing view of binge eating disorder as a heterogenous disorder with multiple possible underlying mechanisms and room to accommodate multiple conceptual models [4, 18]. This trend was also recognized by several experts (theme 5). Experts also reflect a belief that research investigating directionality of the associations between binge eating and negative affect, emotion dysregulation, and negative urgency is needed, as is reflected in the literature [59].
The concept of alexithymia is also one that warrants discussion alongside the topic of emotion regulation processes. Alexithymia is a subclinical phenomenon involving a lack of emotional awareness thought to result from difficulty in identifying and describing one's feelings and in distinguishing feelings from bodily sensations of emotional arousal [[60] as cited in [61]]. The involvement of alexithymia in anorexia nervosa and bulimia nervosa has been demonstrated in the literature [62]. The involvement of alexithymia has also been documented in individuals with obesity (without eating disorder diagnoses), both by self-report [63] and implicit measure [64]. Several evidence are also available in the literature indicating the role of alexithymia in binge eating disorder [62, 65]. Interestingly, the concept of alexithymia was only addressed specifically by one participant in this study (P72, Table 10). This participant's statement captures the intertwined relationship between alexithymia and emotion regulation. Future research investigating possible relationships between alexithymia, emotion regulation, and negative affect and urgency in binge eating disorder would be both interesting and impactful.
## Theme 5: Paradigm shifts in understanding binge eating disorder (43%)
Despite advances in the field of binge eating disorder, over one-third of interviewed participants continue to ascribe to an “anorexi-centric” understanding of binge eating disorder pathology, epidemiology, and treatment (theme 5, subtheme i). As described previously by Bray et al. [ 18], an overwhelming majority of individuals satisfying DSM criteria for binge eating disorder fail to achieve an accurate diagnosis and/or receive adequate treatment [18]. Furthermore, several minority and/or marginalized populations have a greater prevalence of binge eating disorder than the predominating white, cis-gendered, and heterosexual female described within the context of the “anorexi-centric” paradigm (18, 73–78). This phenomenon may result from reduced recognition and screening for binge eating disorder in minority and marginalized populations, which may result in turn from an “anorexi-centric” understanding of binge eating disorder and can further reinforce that understanding.
Overall, experts' recognition of our growing awareness of who can have an eating disorder (theme 5, subtheme I, foci b), the ways binge eating disorder differs distinctly from anorexia nervosa and bulimia nervosa (theme 5, subtheme i), and the heterogeneity in binge eating disorder factors and psychopathology that exists beyond dieting attempts are reflective of this recognition in the literature. These paradigm shifts offer hope for greater diagnostic specificity and treatment outcomes for this significant national and global health problem.
## Clinical implications
Our results call to light the need for a better understanding of the relationship between binge eating disorder and overweight and/or obesity, including a need for clarification around: [1] the extent to which the two health issues are separate vs. related/overlapping; [2] the validity of alleged health risks and metabolic implications associated with binge eating; and [3] the prevalence of binge eating disorder among individuals in large and small body sizes (theme 1, theme 6).
Further, while most experts expressed views about binge eating disorder psychopathology that align with dietary restraint models and emotion dysregulation models, a minority of experts recognize a historic trend in the field to view binge eating disorder as an extension of anorexia nervosa and bulimia nervosa. The experts also recognize a shift in these old paradigms toward greater recognition of who can have an eating disorder and around the heterogeneity that exists within the binge eating disorder diagnosis. It is important for clinicians to remember that the anorexi-centric stereotype of who can have an eating disorder (e.g., thin, White, affluent, cis-gendered, neurotypical females with anorexia nervosa) is outdated, and that binge eating disorder has a uniquely higher prevalence among racial, ethnic, sexual, gender-, and socioeconomic minorities [as also identified in [18]]. Thus, our findings also underscore the importance of equal and adequate screening for binge eating disorder across race, ethnicity, sexual and gender orientation, body weight/shape/size, and socioeconomic status. It is also important to identify ways to include marginalized individuals who do not have access to adequate information, screening, or treatment in binge eating research and help find treatment interventions accessible to them [see [18, 79]].
Lastly, our findings underscore the need for ongoing research on heterogeneity among binge eating disorder and for ongoing discussion and investigation of the way in which we diagnose and classify binge eating disorder. Improving diagnostic accuracy and specificity can help improve treatment specificity and outcome measures in turn.
## Study limitations and strengths
Although it would have been interesting to analyze interview transcripts with the specific question of which theoretical and conceptual models of binge eating disorder were spontaneously endorsed by experts [such as those identified by [4]], to do so would contradict the open-ended methodology of reflexive thematic analysis. Thus, the authors did not analyze transcripts with any conceptual models in mind and were blind to any information on various conceptual models prior to their analyses of the interview transcripts. We feel that overall, this nuance makes the analysis both unique, innovative, and more accurate and informative in its findings.
The qualitative and reflexive nature of this analysis limit its reproducibility, as the themes identified by the researchers are subjective based on their independent and joint analyses. Furthermore, the qualitative analysis of expert interviews was conducted by two individuals (BB and HZ with aid of CB). Thus, we cannot assess how accurately the themes identified here represent the true themes valued by expert binge eating disorder researchers (including those in this study and those at large). However, limitations are standard in the field of reflexive thematic qualitative analysis and are not generally viewed as discounting the methodology as a whole [37].
As is also standard for most qualitative research, it is important to note the study's sample size (which is appropriate for a mixed methods analyses of this nature) limits the generalizability of the data's themes and conclusions to the field of binge eating disorder researchers and clinicians at large. Additionally, as has been pointed out in previous publications [18, 19], one of the study's four possible eligibility criteria for researchers were NIH grant funding (Table 1), which presents a bias for including participants from the U.S. There were three other nationally independent eligibility criteria researchers could meet to be included in this study and the final study sample included participants from the UK, AU, and CA as well as from the US. Nevertheless, $50\%$ of participants were from the US and including criteria for funding form other federal agencies could have improved the population representation of the study overall. Additionally, this study collected demographic data on sex assigned at birth but not on gender. This unfortunate oversight follows an old convention (asking for sex assigned at birth rather than gender) that fails to account for equity and diversity inclusion and collects information that is not demographically relevant (sex assigned at birth) but misses information that is more demographically relevant (gender).
This study utilizes several methodological strengths that counterbalance the limitations identified above. The study's systematic inclusion criteria (Table 1) provides strong population representation of academic and clinical experts who lead the field. This includes researchers with the greatest recent and historic funding and publication records and clinicians with high clinical and academic engagement and access potential (e.g., those most likely to be identified through google searchers by individuals with binge eating disorder). Second, the study sample includes a well-rounded balance of binge eating disorder experts, including PhD/SciD researchers, medical doctors (MDs), licensed therapists and dieticians (LPs, RDs, LDs), and intuitive eating specialists, healthcare administrators, and public health advocates (MPHs) (Table 3).
## Conclusions
Overall, our understanding of adult binge eating disorder as an autonomous eating disorder diagnosis continues to grow and expand. Experts most commonly endorse food/eating restriction and emotion dysregulation as important components of binge eating disorder psychopathology, which aligns with two common historically supported models of binge eating disorder conceptualization (dietary restraint theory and emotion/affect regulation theory). At the same time, some experts recognize a historical oversight of viewing binge eating disorder through a limited “anorexi-centric lens”, particularly in relation to who is at risk for having an eating disorder and factors that drive binge eating. The experts identify several areas of binge eating disorder that continue to warrant further investigation. These include the extent to which binge eating disorder and obesity are separate vs. related/overlapping and improving our understanding of the heterogeneity that exists within the diagnosis. Overall, these results highlight the continual advancement of the field to better understand adult binge eating disorder as an autonomous eating disorder diagnosis.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by National University of Natural Medicine (NUNM) IRB (# HZ12120). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Informed consent was obtained verbally without use of participant names, to protect participant anonymity.
## Author contributions
BB and HZ: conceptualization, methodology, formal analysis, investigation, and project administration. BB: resources, data curation, and writing—original draft preparation. BB, AS, CB, HZ, and RB: writing—review and editing. HZ and RB: supervision. All authors have read and agreed to the published version of the manuscript. All authors agree to be accountable for the content of the work.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2022.1087165/full#supplementary-material
## References
1. 1.APA. Diagnostic and Statistical Manual of Mental Disorders: Diagnostic and Statistical Manual of Mental Disorders. 5th ed. Arlington, VA: American Psychiatric Association (2013).. *Diagnostic and Statistical Manual of Mental Disorders: Diagnostic and Statistical Manual of Mental Disorders.* (2013.0)
2. Hoek HW, van Elburg AA. **Feeding and eating disorders in the Dsm-5]**. *Tijdschr Psychiatr.* (2014.0) **56** 187-91. PMID: 24643829
3. Wiss D, Avena N, Frank GKW, Berner LA. **Food Addiction, binge eating, and the role of dietary restraint: converging evidence from animal and human studies**. *Binge Eating: A Transdiagnostic Psychopathology* (2021.0) 193-209
4. Higgins Neyland MK, Shank LM, Lavender JM, Frank GKW, Berner LA. **Theoretical development and maintenance models of binge eating**. *Binge Eating: A Transdiagnostic Psychopathology* (2021.0) 69-82
5. Fairburn CG, Cooper Z, Shafran R. **Cognitive behaviour therapy for eating disorders: a ”transdiagnostic“ theory and treatment**. *Behav Res Ther.* (2003.0) **41** 509-28. DOI: 10.1016/S0005-7967(02)00088-8
6. Hilbert A, Hoek HW, Schmidt R. **Evidence-based clinical guidelines for eating disorders: international comparison**. *Curr Opin Psychiatry.* (2017.0) **30** 423-37. DOI: 10.1097/YCO.0000000000000360
7. Sehm M, Warschburger P. **The dual-pathway model of binge eating: is there a need for modification?**. *Appetite.* (2017.0) **114** 137-45. DOI: 10.1016/j.appet.2017.03.028
8. Cyders MA, Smith GT. **Mood-based rash action and its components: positive and negative urgency**. *Pers Individ Dif.* (2007.0) **43** 839-50. DOI: 10.1016/j.paid.2007.02.008
9. Cyders MA, Smith GT. **Emotion-based dispositions to rash action: positive and negative urgency**. *Psychol Bull.* (2008.0) **134** 807-28. DOI: 10.1037/a0013341
10. Bardo MT, Weiss VG, Rebec GV, Sangha S, Foti D. **1 - Using preclinical models to understand the neural basis of negative urgency**. *Neurobiology of Abnormal Emotion and Motivated Behaviors* (2018.0) 2-20
11. **Distress**. *Merriam-Webstercom Dictionary*
12. Leung S, Lee A, Michalos AC. **Negative affect**. *Encyclopedia of Quality of Life and Well-Being Research* (2014.0) 4302-5
13. Hudson JI, Hiripi E, Pope HG Jr. *Biol Psychiatry.* (2007.0) **61** 348-58. DOI: 10.1016/j.biopsych.2006.03.040
14. da Luz FQ, Sainsbury A, Mannan H, Touyz S, Mitchison D, Girosi F. **an investigation of relationships between disordered eating behaviors, weight/shape overvaluation and mood in the general population**. *Appetite.* (2018.0) **129** 19-24. DOI: 10.1016/j.appet.2018.06.029
15. da Luz FQ, Hay P, Touyz S, Sainsbury A. **Obesity with comorbid eating disorders: associated health risks and treatment approaches**. *Nutrients.* (2018.0) **10** 829-37. DOI: 10.3390/nu10070829
16. Adami GF, Gandolfo P, Bauer B, Scopinaro N. **Binge eating in massively obese patients undergoing bariatric surgery**. *Int J Eating Disord.* (1995.0) **17** 45-50. DOI: 10.1002/1098-108X(199501)17:1<45::AID-EAT2260170106>3.0.CO;2-S
17. Lowe MR, Haller LL, Singh S, Chen JY. **Weight Dysregulation, Positive Energy Balance, and Binge Eating in Eating Disorders**. *Binge Eating: A Transdiagnostic Psychopathology* (2021.0) 59-67
18. Bray B, Bray C, Bradley R, Zwickey H. **Binge eating disorder is a social justice issue: a cross-sectional mixed-methods study of binge eating disorder experts' opinions**. *Int J Environ Res Public Health.* (2022.0) **19** 6243-80. DOI: 10.3390/ijerph19106243
19. Bray B, Bray C, Bradley R, Zwickey H. **Mental health aspects of binge eating disorder: a cross-sectional mixed-methods study of binge eating disorder experts' perspectives**. *Front Psychiatry.* (2022.0) **13** 953203. DOI: 10.3389/fpsyt.2022.953203
20. **Castle Connolly**. (2021.0)
21. 21.AED. About Aed: Honors & Awards. Academy for Eating Disorders (2020). Available online at: https://www.aedweb.org/about-aed/honors-awards; https://www.aedweb.org/about-aed/honors-awards (accessed August 16, 2021).. (2020.0)
22. 22.AND. Academy of Nutrition and Dietetics Leadership. (2022). Available online at: https://www.eatrightpro.org/leadership (accessed May 18, 2022).. (2022.0)
23. 23.AED. Academy for Eating Disorders Leadership. (2022). Available online at: https://www.aedweb.org/aedold/about/leadership (accessed May 18, 2022).. (2022.0)
24. 24.FAED. Academy for Eating Disorder Fellows. Academy for Eating Disorders (FAED) (2020). Available online at: https://www.aedweb.org/aedold/about/fellows (accessed August 16, 2021).. (2020.0)
25. 25.ASMBS. American Society for Metabolic and Bariatric Surgery Executive Council. (2022). Available online at: https://asmbs.org/about/executive-council (accessed May 18, 2022).. (2022.0)
26. 26.BHN. Behavioral Health Nutrition Executive Committee 2021-2022. (2022). Available online at: https://www.bhndpg.org/about/executive-committee (accessed May 18, 2022).. (2022.0)
27. 27.EDRS. Eating Disorder Research Society Board of Directors. (2022). Available online at: https://edresearchsociety.org/2020/board.php (accessed May 18, 2022).. (2022.0)
28. 28.IAEDP. International Association of Eating Disorders Providers (Iaedp) Foundation Membership. (2022). Available online at: https://iaedp.site-ym.com (accessed May 18, 2022).. (2022.0)
29. 29.Johns Hopkins 2020 Eating Disorders Conference Faculty (2022). Available online at: https://hopkinscme.cloud-cme.com/course/courseoverview?P=0&EID=18566 (accessed May 18, 2022).. (2022.0)
30. 30.NCEED. National Center of Excellence for Eating Disorders (Nceed) Leadership Team. (2022). Available online at: https://www.nceedus.org/about/ (accessed May 18, 2022).. (2022.0)
31. 31.NEDA. National Eating Disorder Association (Neda) Advisors. (2022). Available online at: https://www.nationaleatingdisorders.org/advisors (accessed May 18, 2022).. (2022.0)
32. 32.TOS. The Obesity Society Governing Board (20220). Available online at: https://www.obesity.org/the-obesity-society-governing-board/ (accessed May 18, 2022).. (20220.0)
33. 33.NEDA. Neda Treatment Providers. National Eating Disorders Association (2018). Available online at: https://map.nationaleatingdisorders.org; https://map.nationaleatingdisorders.org/virtual (accessed March 02, 2022).. (2018.0)
34. 34.Disorders NAfE,. Treatment Center & Practitioner Directory. National Alliance for Eating Disorders (2022). Available online at: https://findedhelp.com (accessed March 02, 2022).. (2022.0)
35. Goode E. **Centers to treat eating disorders are growing, and raising concerns**. *New York Times.* (2016.0) **14** 2016
36. 36.Linardon J,. 15 Best Eating Disorder Books of All-Time to Improve Your Eating Behaviors. BreakBingeEatingcom. (2021). p. 8–15. Available online at: https://breakbingeeating.com/eating-disorder-books/. *p. 8–15* (2021.0)
37. Braun V, Clarke V. **One Size Fits All? What counts as quality practice in (reflexive) thematic analysis?**. *Qual Res Psychol.* (2021.0) **18** 328-52. DOI: 10.1080/14780887.2020.1769238
38. Koob GF, Le Moal M. **Review. Neurobiological mechanisms for opponent motivational processes in addiction**. *Philos Transact R Soc London Ser B Biol Sci.* (2008.0) **363** 3113-23. DOI: 10.1098/rstb.2008.0094
39. Solomon RL, Corbit JD. **An opponent-process theory of motivation. I temporal dynamics of affect**. *Psychol Rev.* (1974.0) **81** 119-45. DOI: 10.1037/h0036128
40. Wonderlich SA, Gordon KH, Mitchell JE, Crosby RD, Engel SG. **The validity and clinical utility of binge eating disorder**. *Int J Eat Disord.* (2009.0) **42** 687-705. DOI: 10.1002/eat.20719
41. Stunkard AJ. **Eating patterns and obesity**. *Psychiatr Q.* (1959.0) **33** 284-95. DOI: 10.1007/BF01575455
42. Carr MM, Grilo CM. **Examining heterogeneity of binge-eating disorder using latent class analysis**. *J Psychiatr Res.* (2020.0) **130** 194-200. DOI: 10.1016/j.jpsychires.2020.07.032
43. McCuen-Wurst C, Ruggieri M, Allison KC. **Disordered eating and obesity: associations between binge-eating disorder, night-eating syndrome, and weight-related comorbidities**. *Ann N Y Acad Sci.* (2018.0) **1411** 96-105. DOI: 10.1111/nyas.13467
44. Kessler RC, Berglund PA, Chiu WT, Deitz AC, Hudson JI, Shahly V. **The prevalence and correlates of binge eating disorder in the World Health Organization world mental health surveys**. *Biol Psychiatry.* (2013.0) **73** 904-14. DOI: 10.1016/j.biopsych.2012.11.020
45. Poli R, Maninetti L, Bodini P, Agrimi E. **Obesity, binge eating, obstruction sleep apnea and psychopathological features**. *Clinical Neuropsychiatry.* (2012.0) **9** 166-70
46. Jones-Corneille LR, Wadden TA, Sarwer DB, Faulconbridge LF, Fabricatore AN, Stack RM. **Axis I psychopathology in bariatric surgery candidates with and without binge eating disorder: results of structured clinical interviews**. *Obes Surg.* (2012.0) **22** 389-97. DOI: 10.1007/s11695-010-0322-9
47. Roehrig M, Masheb RM, White MA, Grilo CM. **The metabolic syndrome and behavioral correlates in obese patients with binge eating disorder**. *Obesity.* (2009.0) **17** 481-6. DOI: 10.1038/oby.2008.560
48. Udo T, McKee SA, White MA, Masheb RM, Barnes RD, Grilo CM. **The factor structure of the metabolic syndrome in obese individuals with binge eating disorder**. *J Psychosom Res.* (2014.0) **76** 152-7. DOI: 10.1016/j.jpsychores.2013.10.007
49. Grilo CM, Masheb RM, Wilson GT, Gueorguieva R, White MA. **Cognitive-behavioral therapy, behavioral weight loss, and sequential treatment for obese patients with binge-eating disorder: a randomized controlled trial**. *J Consult Clin Psychol.* (2011.0) **79** 675-85. DOI: 10.1037/a0025049
50. Brewerton TD, Dansky BS, Kilpatrick DG, O'Neil PM. **Which comes first in the pathogenesis of bulimia nervosa: dieting or bingeing?**. *Int J Eat Disord.* (2000.0) **28** 259-64. DOI: 10.1002/1098-108X(200011)28:3<259::AID-EAT2>3.0.CO;2-D
51. Fairburn CG, Beglin SJ. **Assessment of eating disorders: interview or self-report questionnaire?**. *Int J Eating Disord.* (1994.0) **16** 363-70. DOI: 10.1002/1098-108X(199412)16:4<363::AID-EAT2260160405>3.0.CO;2-#
52. Masheb RM, Grilo CM. **Binge eating disorder: a need for additional diagnostic criteria**. *Compr Psychiatry.* (2000.0) **41** 159-62. DOI: 10.1016/S0010-440X(00)90041-5
53. Middlemass KM, Cruz J, Gamboa A, Johnson C, Taylor B, Gomez F. **Food insecurity & dietary restraint in a diverse urban population**. *Eat Disord.* (2020.0) **22** 1-14
54. Becker CB, Middlemass K, Taylor B, Johnson C, Gomez F. **Food insecurity and eating disorder pathology**. *Int J Eat Disord.* (2017.0) **50** 1031-40. DOI: 10.1002/eat.22735
55. Becker CB, Middlemass KM, Gomez F, Martinez-Abrego A. **Eating disorder pathology among individuals living with food insecurity: a replication study**. *Clin Psychol Sci.* (2019.0) **7** 1144-58. DOI: 10.1177/2167702619851811
56. Hazzard VM, Loth KA, Hooper L, Becker CB. **Food insecurity and eating disorders: a review of emerging evidence**. *Curr Psychiatry Rep.* (2020.0) **22** 74. DOI: 10.1007/s11920-020-01200-0
57. Coffino JA, Grilo CM, Udo T. **Childhood food neglect and adverse experiences associated with dsm-5 eating disorders in US national sample**. *J Psychiatr Res.* (2020.0) **127** 75-9. DOI: 10.1016/j.jpsychires.2020.05.011
58. Rasmusson G, Lydecker JA, Coffino JA, White MA, Grilo CM. **Household food insecurity is associated with binge-eating disorder and obesity**. *Int J Eat Disord.* (2018.0) **52** 28-35. DOI: 10.1002/eat.22990
59. Egbert AH, Smith K, Goldschmidt AB, Frank GKW, Berner LA. **Psychosocial correlates of binge eating**. *Binge Eating: A Transdiagnostic Psychopathology* (2021.0) 41-57
60. Nemiah JC. **Alexithymia: a view of the psychosomatic process**. *Modern Trends Psychosomat Med.* (1976.0) **3** 430-9
61. Singer T, Tusche A, Glimcher PW, Fehr E. **Chapter 27 - understanding others: brain mechanisms of theory of mind and empathy**. *Neuroeconomics.* (2014.0) 513-32
62. Nowakowski ME, McFarlane T, Cassin S. **Alexithymia and eating disorders: a critical review of the literature**. *J Eat Disord.* (2013.0) **1** 21. DOI: 10.1186/2050-2974-1-21
63. Da Ros A, Vinai P, Gentile N, Forza G, Cardetti S. **Evaluation of alexithymia and depression in severe obese patients not affected by eating disorders**. *Eat Weight Disord.* (2011.0) **16** 24-9. DOI: 10.1007/BF03327517
64. Scarpina F, Varallo G, Castelnuovo G, Capodaglio P, Molinari E, Mauro A. **Implicit facial emotion recognition of fear and anger in obesity**. *Eat Weight Disord.* (2021.0) **26** 1243-51. DOI: 10.1007/s40519-020-01010-6
65. Pinaquy S, Chabrol H, Simon C, Louvet J-P, Barbe P. **Emotional eating, alexithymia, and binge-eating disorder in obese women**. *Obes Res.* (2003.0) **11** 195-201. DOI: 10.1038/oby.2003.31
66. Racine SE, Burt SA, Iacono WG, McGue M, Klump KL. **Dietary restraint moderates genetic risk for binge eating**. *J Abnorm Psychol.* (2011.0) **120** 119-28. DOI: 10.1037/a0020895
67. Heatherton TF, Baumeister RF. **Binge eating as escape from self-awareness**. *Psychol Bull.* (1991.0) **110** 86-108. DOI: 10.1037/0033-2909.110.1.86
68. Wonderlich SA, Engel SG, Peterson CB, Robinson MD, Crosby RD, Mitchell JE. **Examining the conceptual model of integrative cognitive-affective therapy for bn: two assessment studies**. *Int J Eat Disord.* (2008.0) **41** 748-54. DOI: 10.1002/eat.20551
69. Talbot D, Smith E, Tomkins A, Brockman R, Simpson S. **Schema modes in eating disorders compared to a community sample**. *J Eat Disord.* (2015.0) **3** 41. DOI: 10.1186/s40337-015-0082-y
70. Fairburn CG, Wilson GT. *Binge Eating: Nature, Assessment, and Treatment* (1993.0)
71. Fairburn CG, Marcus MD, Wilson GT. *Cognitive-Behavioral Therapy for Binge Eating and Bulimia Nervosa: A Comprehensive Treatment Manual. Binge Eating: Nature, Assessment, and Treatment* (1993.0) 361-404
72. Group PMR. **Matching alcoholism treatments to client heterogeneity: project match posttreatment drinking outcomes**. *J Stud Alcohol.* (1997.0) **58** 7-29. DOI: 10.15288/jsa.1997.58.7
73. Striegel-Moore RH, Rosselli F, Holtzman N, Dierker L, Becker AE, Swaney G. **Behavioral symptoms of eating disorders in native americans: results from the add health survey Wave III**. *Int J Eat Disord.* (2011.0) **44** 561-6. DOI: 10.1002/eat.20894
74. Perez M, Ramirez AL, Trujillo-ChiVacuán E. **An introduction to the special issue on the state of the art research on treatment and prevention of eating disorders on ethnic minorities**. *Eat Disord.* (2019.0) **27** 101-9. DOI: 10.1080/10640266.2019.1583966
75. Goode RW, Cowell MM, Mazzeo SE, Cooper-Lewter C, Forte A, Olayia OI. **Binge eating and binge-eating disorder in black women: a systematic review**. *Int J Eat Disord.* (2020.0) **53** 491-507. DOI: 10.1002/eat.23217
76. Kamody RC, Grilo CM, Udo T. **Disparities in Dsm-5 defined eating disorders by sexual orientation among US Adults**. *Int J Eat Disord.* (2020.0) **53** 278-87. DOI: 10.1002/eat.23193
77. Watson RJ, Veale JF, Saewyc EM. **Disordered eating behaviors among transgender youth: probability profiles from risk and protective factors**. *Int J Eat Disord.* (2017.0) **50** 515-22. DOI: 10.1002/eat.22627
78. Linsenmeyer WR, Katz IM, Reed JL, Giedinghagen AM, Lewis CB, Garwood SK. **Disordered eating, food insecurity, and weight status among transgender and gender nonbinary youth and young adults: a cross-sectional study using a nutrition screening protocol**. *LGBT Health.* (2021.0) **8** 359-66. DOI: 10.1089/lgbt.2020.0308
79. Bray B, Rodríguez-Martín BC, Wiss DA, Bray CE, Zwickey H. **Overeaters anonymous: an overlooked intervention for binge eating disorder**. *Int J Environ Res Public Health.* (2021.0) **18** 7303-19. DOI: 10.3390/ijerph18147303
|
---
title: Expanding outpatient benefits package can reduce diabetes-related avoidable
hospitalizations
authors:
- Hao-Ran Liu
- Si-Yuan Chen
- Lan-Yue Zhang
- Hong-Qiao Fu
- Wei-Yan Jian
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9971935
doi: 10.3389/fpubh.2023.964789
license: CC BY 4.0
---
# Expanding outpatient benefits package can reduce diabetes-related avoidable hospitalizations
## Abstract
### Objective
To evaluate the policy effect of replacing hospitalization service with outpatient service and reducing diabetes-related avoidable hospitalizations by improving outpatient benefits package.
### Methods
A database of hospital discharge from 2015 to 2017 in City Z was used. All diabetic inpatient cases enrolled in Urban Employee Basic Medical Insurance were selected as the intervention group, and diabetic inpatient cases enrolled in Urban and Rural Resident Basic Medical Insurance were selected as the control group. The Difference-in-Difference model was used to analyze the effect of improving outpatient benefits package level of diabetes from 1800 yuan (about $252.82) to 2400 yuan (about $337.09) per capita per year on avoidable hospitalization rate, average hospitalization cost and average length of stay.
### Results
The avoidable hospitalization rate of diabetes mellitus decreased by 0.21 percentage points ($P \leq 0.01$), the average total cost of hospitalization increased by $7.89\%$ ($P \leq 0.01$), and the average length of stay per hospitalization increased by $5.63\%$ ($P \leq 0.01$).
### Conclusions
Improving the outpatient benefits package of diabetes can play a role in replacing hospitalization service with outpatient service, reducing diabetes-related avoidable hospitalizations, and reducing the disease burden and financial burden.
## 1. Introduction
Diabetes mellitus is one of the leading non-communicable diseases causing death in people aged 30 to 70 years worldwide [1]. Diabetes and its complications have been imposing substantial financial burden of health systems and disease burden for patients and their families. According to the 10th edition of the IDF Diabetes Atlas 2021, the estimated global direct health expenditure on diabetes is $966 billion in 2021 and could reach $1.05 trillion by 2045 [2]. Reducing the burden of diabetes is a common goal for all countries around the world. China has the largest number of diabetic patients in the world [2]. The total number of adults with diabetes in mainland *China is* estimated to be 140.9 million, with an estimated 174.4 million in 2045 and an adult prevalence of 13.0 % [2]. In 2021, diabetes-related health costs in China are as high as $165.3 billion, and the prevalence of diabetes in China continues to grow (2–5).
Avoidable hospitalizations are those that can be avoided through timely, effective, and adequate primary health care services [6]. If patients have access to timely and effective ambulatory care, it is possible to reduce hospitalizations for these conditions by preventing the occurrence of diseases or managing chronic conditions in an outpatient setting [7]. Diabetes mellitus has now been included in many index systems of avoidable hospitalizations diseases across the world (8–11). If the incidence of avoidable diabetes hospitalizations and their associated complications can be reduced, it will not only save medical costs, but also help improve the quality of primary health care service and slow down the disease process, thus reducing the disease burden of diabetes.
In many countries, great efforts have been made to reduce diabetes-related avoidable hospitalizations (DRAHs) through the substitution effect of outpatient services. There are three main mechanisms to replace inpatient services with primary care [12]. First, increasing outpatient services to avoid the need for inpatient treatment. For example, Hungary has increased outpatient costs to reduce inpatient costs [13]. Second, managing the health conditions with chronic diseases, such as the teach-back methods for outpatients in the United States [14], home healthcare within 14 days after hospital discharges [15], and the patient-centered Care Coordination Home Telehealth [16]. Third, utilizing the role of general practitioners (GP) as the gatekeepers to reduce referrals, such as Italy increasing the income of GPs in outpatient clinics to reduce the referral rate of outpatients [17], and the United States strengthening the level of communication between primary care and specialists physicians in outpatient visits [18]. All these measures are beneficial in reducing DRAHs.
In mainland China, the concept of avoidable hospitalization was introduced late and has attracted few attentions in the last 5 years. Most of the current literature focuses on the measurement of avoidable hospitalizations (19–22), with little assessment of the impact of existing policies on avoidable hospitalization. Therefore, using data from City Z (which we refer to “City Z” because the data provider does not want to disclose the name of the city), we empirically studied the impact of improved the outpatient benefits package on DRAHs. As we used the natural experiment in City Z and employed the Difference-in-Difference (DID) model, we could observe the causality between outpatient benefits package expansion and avoidable hospitalization.
In the City Z, the maximum annual payment limit of outpatient benefits package for diabetes who enrolled in Urban Employee Basic Medical Insurance (UEBMI) has been raised from 1,800 yuan (about $252.82) to 2,400 yuan (about $337.09) since January 1, 2016. If patients can take full advantages of health-care services to delay the progression of diabetes, then diabetes hospitalizations can be reduced. Therefore, our study aims to analyze the effect of improving outpatient reimbursement level from 1,800 yuan (about $252.82) to 2,400 yuan (about $337.09) per capita per year on DRAHs. In the context of today's lack of response to avoidable hospitalizations, evaluating the outpatient substitution effect and determining whether better health care provided by outpatient services can reduce avoidable hospitalizations will benefit not only China, but also other low-and middle-income countries (LMICs).
## 2.1. Study design
Since 2007, City Z has implemented a medical insurance policy to include diabetes in the priority treatment of outpatient chronic disease policy. The medical insurance fund specifies the maximum amount of payment for specialized medicines in the outpatient service of patients. The reimbursement level of basic medical insurance for diabetic inpatients who enrolled in UEBMI is 1,800 yuan (about $252.82) per person per year, while that for diabetic inpatients who enrolled in Urban and Rural Resident Basic Medical Insurance (URRBMI) is 1,200 yuan (about $168.55) per person per year. Since January 1, 2016, the level of reimbursement has changed with the implementation of the new policy. The outpatient benefits package for urban employee with diabetes increased by 600 yuan (about $84.28) per year, while that for urban and rural residents remained unchanged at 1,200 yuan (about $168.55) per year. Therefore, in order to evaluate the impact of expanded outpatient benefits package on DRAHs, we chose diabetic inpatients who enrolled in UEBMI as the intervention group and diabetic inpatients who enrolled in URRBMI as the control group. We use the DID model to examine the impact of improving outpatient benefits package level of diabetes from 1,800 yuan (about $252.82) to 2,400 yuan (about $337.09) per capita per year on DRAHs.
## 2.2. Data sources
In this study, the database of inpatient discharges from 2015 to 2017 was obtained from the health administration department of city Z, a developed city in eastern China. The data included patients' basic information, hospitalization information, and cost information. Basic information included patient ID, current address, age, gender, marital status, and nationality. Hospitalization information includes admission time, discharge time, visit time, length of stays, primary diagnosis and International Classification of Diseases (ICD) code, secondary diagnosis and ICD code, primary surgical operation and ICD code, and secondary surgical operation and ICD code. Cost information includes total hospitalization expenses, out-of-pocket hospitalization expenses, drug expenses, and consumable expenses.
The database contains discharge data of inpatients from all medical institutions in the city. After screening the avoidable hospitalizations and addressing the outliers and missing values, all data were included in the analysis and patient information has been desensitized. Comparable price adjustments were made to the relevant cost data using the China Consumer Price Index. The definition of avoidable hospitalization from “Health Care Quality and Outcomes (HCQO) 2020–2021 Indicator Definitions” was published by the Organization for Economic Co-operation and Development (OECD) in 2020. The inclusion and exclusion criteria for DRAHs were defined in detail by OECD [10]. OECD indicators provide guidance for comparing the performance of health systems among member countries, making avoidable hospitalizations comparable across countries and facilitating cross-country comparisons of avoidable hospitalizations [23, 24]. The single DRAHs indicator of the OECD is created by combining three widely used avoidable hospitalization indicators for people with diabetes as follows: uncontrolled diabetes without complications, diabetes with short-term complications, and diabetes with long-term complications. The specific inclusion and exclusion criteria are as follows.
Inclusion criteria: [1] age ≥15 years. All acute care hospitals patients, including public and private hospitals that provide inpatient care. [ 2] The primary discharge diagnosis was coded as diabetes. The first three ICD-10 codes were “E10”, “E11”, “E13” and “E14”.
Exclusion criteria: [1] Cases where the patient died in hospital during the admission. [ 2] Cases resulting from a transfer from another acute care institution (transfers-in). [ 3] Cases with MDC 14 or specified pregnancy, childbirth, and puerperium codes in any field. [ 4] Cases that are same day/day only admissions.
## 2.3. Indicators
The primary variables in this study were avoidable hospitalization rate, average hospitalization cost and average length of stay. Of all hospitalized patients during the year, those who belongs to avoided hospitalization were assigned a value of 1; otherwise, it equaled 0. The total cost per hospitalization and the average length of stay were transformed by logarithm.
The study controlled for the effects of three variables. We included age (15–40, 41–60, 61–80, >80), gender (female, male), and CCI (0, 1, ≥2). In order to analyze the basic information of avoidable hospitalization cases, we used the Charlson Comorbidity Index (CCI), which evaluates the severity of the damage and the abnormalities of the patient's other organs or tissues other than the underlying disease, allowing the patients to be classified according to the severity of comorbidities. The CCI is based on the ICD-10 and its calculation based on all secondary diagnoses of the patients. The higher the score, the more severe the disease comorbidity. CCI was divided into three groups [25, 26]. Age was grouped according to the quintile method. Because the inclusion criteria for the population were age ≥15 years, age was divided into four groups.
To validate the increase in the average severity of hospitalized patients with diabetes after policy changes, we defined the CCI group variable. CCI group =0 if CCI =0 or 1 and CCI group =1 if CCI ≥2.
## 2.4. Statistical analysis
The DID model was used to analyze the effect of improving outpatient benefit package level of diabetes from 1,800 yuan (about $252.82) to 2,400 yuan (about $337.09) per capita per year on avoidable hospitalization rate, average total cost per hospitalization and average length of stay. The model is as follows: The avoidable hospitalization rate in this model was defined as the proportion of avoidable hospitalizations in all hospitalizations during the current year, thus, in the regression model, hospitalization cases were represented by 1 and other cases were by 0. The two models of average total cost and the average length of stay corresponded to the average total cost, and the average length of stay after the logarithmic conversion, respectively. Reformi was a dummy variable of time points before and after the implementation of the policy. Reformi=1 if year = 2016 and later, Reformi=0 if year = 2015. β1 reflected the difference before and after the implementation of the policy. Interventioni was a dummy variable for grouping, where the intervention group was 1 and the control group was 0. β2 reflected the difference between the intervention group and the control group. The coefficient β3 of the interaction term Reformi*Interventioni estimated by the model was the actual impact of the policy. xji was a confounding factor that needs to be controlled, including age, gender, and CCI. εih was the residual value. A parallel trend test was conducted by using data from the year of 2015 to test whether the trends in the intervention group and the control group were parallel before the policy change. T1 = 0 when year = 2015 and the month = from January to April. T1 = 1 when year = 2015 and the month = from May to December. If δ = 0, the parallel trend hypothesis of the control group and the intervention group before the policy change is established. The coefficient β3 of the interaction term Reformi*Interventioni reflected the actual impact of the policy. If δ ≠ 0, the net effect of the policy is β3 minus δ. The DID model was also performed on the CCI group to verify whether policy changes could increase the average severity of diabetic inpatients. The robust test was used in parameter estimation.
All data analysis was conducted using Stata 16.0 software.
## 3.1. Descriptive data
According to the inclusion and exclusion criteria, a total of 73,750 cases of avoidable hospitalization were included from 2015 to 2017. There were 55,528 cases in the intervention group and 18,222 cases in the control group. We described the baseline of avoidable hospitalizations before and after the policy change (2015 before the change, 2016–2017 after the change) in the control and intervention groups by age, sex and CCI (Table 1). The 61–80 age group had the highest number of people, while the 15–40 age group had the lowest. Both before and after the policy change, the control group had slightly more females than males, while the intervention group had slightly more males than females. The distribution of CCI in the intervention group and the control group was approximately the same, with the highest proportion in the group 0 (about $50\%$), and a similar number in group 1 as in group 2 and above, the proportion in group 2 and above in the intervention group was slightly higher than that in the control group.
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Before the policy | Before the policy.1 | After the policy |
| --- | --- | --- | --- | --- |
| | Control group | Intervention group | Control group | Intervention group |
| N = | 4113 | 14735 | 14109 | 40793 |
| Age | Age | Age | Age | Age |
| 15–40 (N, %) | 280 (6.801) | 854 (5.796) | 839 (5.947) | 2493 (6.111) |
| 41–60 (N, %) | 1,530 (37.20) | 5,368 (36.43) | 4,316 (30.59) | 13,198 (32.35) |
| 61–80 (N, %) | 2,074 (50.43) | 6,859 (46.55) | 7,455 (52.84) | 18,659 (45.74) |
| >80 (N, %) | 229 (5.568) | 1,654 (11.22) | 1,499 (10.62) | 6,443 (15.79) |
| Gender | Gender | Gender | Gender | Gender |
| Female (N, %) | 2,168 (52.71) | 7,265 (49.30) | 7,171 (50.83) | 19,825 (48.60) |
| Male (N, %) | 1,945 (47.29) | 7,470 (50.70) | 6,938 (49.17) | 20,968 (51.40) |
| CCI | CCI | CCI | CCI | CCI |
| 0 (N, %) | 2,107 (51.23) | 7,292 (49.49) | 7,195 (51.00) | 19,741 (48.39) |
| 1 (N, %) | 1,137 (27.64) | 4,093 (27.78) | 3,800 (26.93) | 10,780 (26.43) |
| ≥2 (N, %) | 869 (21.13) | 3,350 (22.73) | 3,114 (22.07) | 10,272 (25.18) |
## 3.2. Change of trends
Table 2 showed the absolute changes in the avoidable hospitalization rate, average hospitalization cost per admission, the length of stay, age, gender and CCI for both intervention and control groups before and after the policy change. Compared with the control group, the avoidable hospitalization rate decreased in intervention group, while the average hospitalization cost and the absolute length of stay per hospitalization increased after the policy. Specifically, the total rate of avoidable hospitalizations decreased by $0.109\%$, the average hospitalization cost increased by 495 yuan (about $69.53), and the average hospitalization duration increased by 0.48 days. Compared with the control group, the group 2 and above of CCI increased in the intervention group, while the proportion of the group 0 and group 1 decreased after the policy implementation. Specifically, the percentage of patients in group 0 decreased by $0.87\%$, while the percentage in group 1 decreased by $0.64\%$, and the percentage in group 2 and above increased by $1.51\%$.
**Table 2**
| Indicators | Before the policy | Before the policy.1 | Before the policy.2 | After the policy | After the policy.1 | After the policy.2 | Difference-in-difference |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Indicators | Intervention group | Control group | Difference | Intervention group | Control group | Difference | |
| Avoidable hospitalization rate (%) | 2.512 | 1.791 | 0.721 | 2.72 | 2.108 | 0.612 | −0.109 |
| Average hospitalization cost (yuan) | 11,226 ($1,576.75) | 9,912 ($1,392.20) | 1,314 ($184.55) | 11,004 ($1,545.57) | 9,195 ($1,291.49) | 1,809 ($254.08) | 495 ($69.53) |
| Average hospitalization duration (day) | 10.39 | 9.841 | 0.549 | 10.48 | 9.451 | 1.029 | 0.48 |
| Age | Age | Age | Age | Age | Age | Age | Age |
| 15–40 (%) | 5.796 | 6.801 | −1.005 | 6.111 | 5.947 | 0.164 | 1.169 |
| 41–60 (%) | 36.43 | 37.20 | −0.77 | 32.35 | 30.59 | 1.76 | 2.53 |
| 61–80 (%) | 46.55 | 50.43 | −3.88 | 45.74 | 52.84 | −7.10 | −3.22 |
| >80 (%) | 11.22 | 5.568 | 5.652 | 15.79 | 10.62 | 5.17 | −0.482 |
| Gender | Gender | Gender | Gender | Gender | Gender | Gender | Gender |
| Female (%) | 49.30 | 52.71 | −3.41 | 48.60 | 50.83 | −2.23 | 1.18 |
| Male (%) | 50.70 | 47.29 | 3.41 | 51.40 | 49.17 | 2.23 | −1.18 |
| CCI | CCI | CCI | CCI | CCI | CCI | CCI | CCI |
| 0 (%) | 49.49 | 51.23 | −1.74 | 48.39 | 51.00 | −2.61 | −0.87 |
| 1 (%) | 27.78 | 27.64 | 0.14 | 26.43 | 26.93 | −0.50 | −0.64 |
| ≥2 (%) | 22.73 | 21.13 | 1.60 | 25.18 | 22.07 | 3.11 | 1.51 |
## 3.3. Policy effects
The DID model shown in Table 3 was applied to analyze the impact of the medical insurance policy reform on diabetes. Since 2016, the increase in medical insurance reimbursement had a significant effect on the avoidable hospitalization rate, the average hospitalization cost, and the average length of hospitalization. The avoidable hospitalization rate of diabetes mellitus decreased by 0.21 percentage points ($P \leq 0.01$), the total cost of hospitalization increased by $7.89\%$ ($P \leq 0.01$), and the length of hospitalization increased by $5.63\%$ ($P \leq 0.01$), with all the differences statistically significant. The CCI group of diabetes mellitus increased by $2.21\%$ ($P \leq 0.01$). As the avoidable hospitalization rate was ~$2.5\%$ in the intervention group during the study period, the decrease of 0.21 percentage points in avoidable hospitalization rate due to expansion of outpatient benefits package suggested that the volume of avoidable hospitalization fell by $8.4\%$ (=$\frac{0.21}{2.5}$).
**Table 3**
| Indicators | Unnamed: 1 | Avoidable hospitalization rate | Average hospitalization cost | Average hospitalization duration | CCI group |
| --- | --- | --- | --- | --- | --- |
| δ | | −0.0007 | 0.0125 | 0.0214 | 0.0083 |
| Net effect of policy | | −0.0021***(0.000) | 0.0789***(0.013) | 0.0563***(0.012) | 0.0221***(0.008) |
| Before and after the policy | | 0.0044***(0.000) | −0.0829***(0.011) | −0.0592***(0.011) | −0.0065(0.007) |
| Intervention group | | 0.0037***(0.000) | 0.1392***(0.011) | 0.0812***(0.010) | 0.0063(0.007) |
| Age | 15–40 | 0.0072***(0.000) | | | |
| Age | 41–60 | 0.0287***(0.000) | 0.1053***(0.010) | 0.0634***(0.010) | 0.1021***(0.004) |
| Age | 61–80 | 0.0286***(0.000) | 0.1796***(0.010) | 0.1691***(0.010) | 0.2345***(0.004) |
| Age | >80 | 0.0195***(0.000) | 0.2255***(0.011) | 0.2864***(0.011) | 0.3192***(0.006) |
| Gender(Female = Reference group) | Male | 0.0044***(0.000) | −0.0052(0.004) | −0.0129***(0.004) | 0.0473***(0.003) |
| CCI(0= Reference group) | 1 | 0.0194***(0.000) | 0.1219***(0.005) | 0.1098***(0.005) | |
| CCI(0= Reference group) | ≥2 | 0.0079***(0.000) | 0.2431***(0.006) | 0.1289***(0.005) | |
| Constant | | −0.0073***(0.000) | 8.7372***(0.014) | 1.9179***(0.013) | 0.0149**(0.007) |
| N = | | 2985434 | 73748 | 73750 | 73750 |
| R-squared | | 0.009 | 0.067 | 0.048 | 0.044 |
The parallel trend test results are shown in Table 4. We suppose the policy changes happened on May 1, 2015 and used the data in 2015 to conduct the DID analysis. The results show that the coefficient on variables of interest are all statistically insignificant, suggesting that the heterogeneous trends in the intervention group and the control group before the policy change could not drive the findings in our study.
**Table 4**
| Indicators | Unnamed: 1 | Avoidable hospitalization rate | Average hospitalization cost | Average hospitalization duration |
| --- | --- | --- | --- | --- |
| Effect of policy | | −0.0007(0.001) | 0.0125(0.024) | 0.0214(0.023) |
| Before and after the policy | | 0.0004(0.001) | −0.0322(0.022) | −0.0243(0.021) |
| Intervention group | | 0.0046***(0.001) | 0.1297***(0.020) | 0.0658***(0.019) |
| Age | 15–40 | 0.0061***(0.000) | | |
| Age | 41–60 | 0.0273***(0.000) | 0.0961***(0.021) | 0.0190(0.020) |
| Age | 61–80 | 0.0247***(0.000) | 0.1605***(0.021) | 0.1181***(0.0020) |
| Age | >80 | 0.0116***(0.000) | 0.2278***(0.026) | 0.2533***(0.023) |
| Gender(Female=Reference group) | Male | 0.0028***(0.000) | −0.0106(0.009) | −0.0089(0.008) |
| CCI(0= Reference group) | 1 | 0.0235***(0.001) | 0.1241***(0.011) | 0.1214***(0.010) |
| CCI(0= Reference group) | ≥2 | 0.0048***(0.000) | 0.2348***(0.012) | 0.1152***(0.011) |
| Constant | | −0.0043***(0.000) | 8.7762***(0.027) | 1.9768***(0.026) |
| N = | | 816287 | 18848 | 18848 |
| R-squared | | 0.009 | 0.048 | 0.039 |
## 4. Discussion
The study found that the avoidable hospitalization rate of diabetes mellitus decreased, the total cost of hospitalization and the length of hospitalization increased after the new policy was implemented in city Z. There are some similar studies. Following the introduction of financial incentives for the Emilia-Romagna in Italy, Elisa Iezzi et al. used a model to measure the impact of different levels of incentives for GPs on DRAHs in each region [17]. The model showed that for every 100 euros (~$17\%$ of the annual income of the GP diabetes program) increase in financial incentives paid to GPs, there was an average $1\%$ reduction in avoidable diabetes hospitalizations and an average reduction of ~100 cases across the region. Meanwhile, uninsured or underinsured groups had a higher rate of avoidable hospitalizations than the insured groups [27]. After the implementation of a health transformation program in a hospital in western Iran, the average cost of hospitalization for diabetes increased from $372.55 to $1,119.77, and the average length of stay increased from 5.6 days to 7.57 days [28]. Tables 2, 3 showed that compared with control group, the group 2 and above of CCI increased in the intervention group, while the proportion of the group 0 and group 1 decreased after the policy implementation. There was an increased proportion of avoidable diabetes hospitalizations with severe comorbidities.
This study found that expanding outpatient benefits package for diabetes can contribute to reduce avoidable hospitalizations, which is consistent with the results of Meng-Han Shen [29], Yang Fan [30] and Feng-Mei Zhu [31], which concluded that outpatient services have a substitution effect on inpatient services (13, 32–35). Previous studies have found that increasing the motivation of primary health care providers and strengthening the management of patients with chronic diseases can help reduce avoidable hospitalizations [17]. The increased use of outpatient services and outpatient reimbursement insurance can significantly reduce the cost of inpatient services in China's new rural cooperative medical system [34]. Poor outpatient benefits package may bring some adverse impacts. First, patients cannot receive the expected utilization of outpatient service and will have a heavy burden of outpatient medical costs. Thus, patients prefer to reduce the utilization of medical services and the usage of inpatient services may increase by the disease progression. Second, some patients with mild illness are dissatisfied with the outpatient benefits package so they prefer hospitalization treatment to receive higher reimbursement and alleviate the burden of personal expenses. As the outpatient benefits package expands, patients have an increased incentive to use outpatient services, thereby controlling the disease at an early stage and reducing hospitalization [31]. If the substitution effect of outpatient service can be exerted, the average hospitalization cost and average length of stay of inpatients will increase with the decrease of avoidable hospitalization rate. This is due to an increase in the average severity of diabetes inpatients following a reduction in avoidable hospitalizations.
This study was based on China, one of the LMICs, to examine the impact of policy effects on avoidable hospitalization. First, we describe the absolute changes in avoidable hospitalizations before and after policy changes. Second, our results show that expanding the outpatient benefits package for diabetes can reduce DRAHs, and explain its underlying causes. This has implications for the further improvement of primary health care in LMICs. At the beginning of the establishment of medical insurance system in China, the main purpose was to guarantee hospitalization service. The intention of the medical insurance system was to help the insured cope with the high risk of disease and improve the efficiency of the use of medical insurance funds in the face of limited financing capacity (36–38). However, the side-effect of the operation of the system is the overutilization of inpatient services, which undermines the efficiency of the utilization of the medical insurance fund (38–40). The policy change of city Z deserves further replication. Furthermore, in the light of international experience, there is a need for in-depth reform to improve the quality of primary healthcare services, including management of diabetic patients and improve the effectiveness of disease prevention and control. DRAHs can further be reduced.
Our study has some limitations. First, we were not able to analyze the influence of the policy on the outpatient service utilization because of the lack of the outpatient service utilization data in city Z. Second, expanding the outpatient benefits package leads to higher costs of primary care, while less DRAHs leads to lower costs for in-patients. This study did not further examine the overall economic benefits of policy. Third, the policy was implemented on January 1, 2016. The study only included data for 2 years after the policy change and did not observe long-term effects of policy implementation.
## 5. Conclusion
Expanding the outpatient benefits package for diabetes can reduce avoidable hospitalizations, and allow limited resources for inpatient services to be reserved for the treatment of serious cases that requires inpatient care, which assists in improving the overall efficiency of the health system. More importantly, patients would benefit from more timely and effective diagnosis and treatment, rather than delayed treatment. LMICs, including China, need to invest more in primary healthcare services and increase the capacity of primary healthcare services to make the overall health system more organized and efficient.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
W-YJ and H-QF led the design of the study and contributed to the interpretation of the results. H-RL conducted statistical analysis and drafted the manuscript. S-YC participated in paper writing and statistical analysis. L-YZ participated in paper writing. H-QF organized the database. All authors have read and approved the final draft.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. 1.WHO. World Health Statistics 2021: Monitoring health for the SDGs. (2021). Geneva: WHO. *World Health Statistics 2021: Monitoring health for the SDGs* (2021)
2. 2.International Diabetes Federation. IDF Diabetes Atlas, 10th ed. Brussels, Belgium: International Diabetes Federation (2021). Available online at: https://www.diabetesatlas.org.. *IDF Diabetes Atlas, 10th ed* (2021)
3. Wang L, Peng W, Zhao Z, Zhang M, Shi Z, Song Z. **Prevalence and Treatment of Diabetes in China, 2013-2018**. *JAMA.* (2021) **326** 2498-506. DOI: 10.1001/jama.2021.22208
4. Williams R, Karuranga S, Malanda B, Saeedi P, Basit A, Besançon S. **Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas, 9th edition**. *Diabetes Res Clin Pract.* (2020) **162** 108072. DOI: 10.1016/j.diabres.2020.108072
5. Yang W, Lu J, Weng J, Jia W, Ji L, Xiao J. **Prevalence of diabetes among men and women in China**. *N Engl J Med.* (2010) **362** 1090-101. DOI: 10.1056/NEJMoa0908292
6. Freund T, Campbell SM, Geissler S, Kunz CU, Mahler C, Peters-Klimm F. **Strategies for reducing potentially avoidable hospitalizations for ambulatory care-sensitive conditions**. *Ann Fam Med.* (2013) **11** 363-70. DOI: 10.1370/afm.1498
7. Pongiglione B, Torbica A, Gusmano MK. **Inequalities in avoidable hospitalisation in large urban areas: retrospective observational study in the metropolitan area of Milan**. *BMJ Open.* (2020) **10** e042424. DOI: 10.1136/bmjopen-2020-042424
8. 8.Agency for Healthcare Research and Quality. Guide to prevention quality indicators:hospital admission for ambulatory care sensitive conditions [EB/OL]. Rockville, MD: AHRQ (2001). Available online at: https://qualityindicators.ahrq.gov/ (accessed December 01, 2008).. *Guide to prevention quality indicators:hospital admission for ambulatory care sensitive conditions [EB/OL]* (2001)
9. Information CIfH. *Health Indicators 2013:Definitions, Data Sources and Rationale [EB/OL]* (2013)
10. 10.OECD. Health Care Quality and Outcomes (HCQO) 2020-21 Indicator Definitions. (2021). Available online at: https://www.oecd.org/els/health-systems/Definitions-of-Health-Care-Quality-Outcomes.pdf. *Health Care Quality and Outcomes (HCQO) 2020-21 Indicator Definitions.* (2021)
11. Ansari Z, Carson N, Serraglio A, Barbetti T, Cicuttini F. **The Victorian Ambulatory Care Sensitive Conditions study: reducing demand on hospital services in Victoria**. *Aust Health Rev.* (2002) **25** 71-7. DOI: 10.1071/AH020071
12. Fortney JC, Steffick DE, Burgess JF. **Jr**. *Health Serv Res.* (2005) **40** 1422-42. DOI: 10.1111/j.1475-6773.2005.00424.x
13. Elek P, Molnár T, Váradi B. **The closer the better: does better access to outpatient care prevent hospitalization?**. *Eur J Health Econ.* (2019) **20** 801-17. DOI: 10.1007/s10198-019-01043-4
14. Hong YR, Cardel M, Suk R, Vaughn IA, Deshmukh AA, Fisher CL. **Teach-back experience and hospitalization risk among patients with ambulatory care sensitive conditions: a matched cohort study**. *J Gen Intern Med.* (2019) **34** 2176-84. DOI: 10.1007/s11606-019-05135-y
15. Chen HF, Popoola T, Radhakrishnan K, Suzuki S, Homan S. **Improving diabetic patient transition to home healthcare: leading risk factors for 30-day readmission**. *Am J Manag Care.* (2015) **21** 440-50. PMID: 26168064
16. Jia H, Chuang HC, Wu SS, Wang X, Chumbler NR. **Long-term effect of home telehealth services on preventable hospitalization use**. *J Rehabil Res Dev.* (2009) **46** 557-66. DOI: 10.1682/JRRD.2008.09.0133
17. Iezzi E, Lippi Bruni M, Ugolini C. **The role of GP's compensation schemes in diabetes care: evidence from panel data**. *J Health Econ.* (2014) **34** 104-20. DOI: 10.1016/j.jhealeco.2014.01.002
18. O'Malley AS, Reschovsky JD, Saiontz-Martinez C. **Interspecialty communication supported by health information technology associated with lower hospitalization rates for ambulatory care-sensitive conditions**. *J Am Board Fam Med.* (2015) **28** 404-17. DOI: 10.3122/jabfm.2015.03.130325
19. Cheng-xin J, Qin J, Hai-long Z, Zhen-zhong Z. **Analysis on Avoidable Admission of Diabetes in China**. *Chin Health Econ* (2018) **37** 50-3. DOI: 10.7664/CHE20180513
20. Feicheng L, Weiyan J, Meiping S. **Avoidable Hospitalizations for Diabetes in Rural Residents**. *Chin Gener Pract.* (2019) **22** 2735-8. DOI: 10.12114/j.issn.1007-9572.2019.00.437
21. Siyuan C, Wuping Z, Weiyan J. **Avoidable Hospitalization for Diabetes and Hypertention in Rural Residents in Central China**. *Chin Health Qual Manage.* (2021) **28** 95-7. DOI: 10.13912/j.cnki.chqm.2021.28.5.26
22. Ying H. *Study on Potentially Preventable Hospitalization for Diabetic Based on the System of Health Account in Hebei Province.* (2021)
23. Kim H, Cheng SH. **Assessing quality of primary diabetes care in South Korea and Taiwan using avoidable hospitalizations**. *Health Policy.* (2018) **122** 1222-31. DOI: 10.1016/j.healthpol.2018.09.009
24. Youn HM, Choi DW, Jang SI, Park EC. **Disparities in diabetes-related avoidable hospitalization among diabetes patients with disability using a nationwide cohort study**. *Sci Rep.* (2022) **12** 1794. DOI: 10.1038/s41598-022-05557-5
25. Sundararajan V, Henderson T, Perry C, Muggivan A, Quan H, Ghali WA. **New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality**. *J Clin Epidemiol.* (2004) **57** 1288-94. DOI: 10.1016/j.jclinepi.2004.03.012
26. Kornelius E, Huang CN, Yang YS, Lu YL, Peng CH, Chiou JY. **Diabetes-related avoidable hospitalizations in Taiwan**. *Prim Care Diabetes.* (2014) **8** 330-7. DOI: 10.1016/j.pcd.2014.02.001
27. Weissman JS, Gatsonis C, Epstein AM. **Rates of avoidable hospitalization by insurance status in Massachusetts and Maryland**. *JAMA.* (1992) **268** 2388-94. DOI: 10.1001/jama.268.17.2388
28. Piroozi B, Amerzadeh M, Safari H, Mohamadi-Bolbanabad A, Afkhamzadeh A, Zarezadeh Y. **The burden of preventable hospitalizations before and after implementation of the health transformation plan in a hospital in west of Iran**. *Prim Health Care Res Dev.* (2019) **20** e87. DOI: 10.1017/S1463423618000841
29. Shen M, He W, Yeoh EK, Wu Y. **The association between an increased reimbursement cap for chronic disease coverage and healthcare utilization in China: an interrupted time series study**. *Health Policy Plan.* (2020) **35** 1029-38. DOI: 10.1093/heapol/czaa087
30. Fan Y. **Influence of the improvement of outpatient security level of employees on the utilization of medical services and the expenditure of medical insurance funds: Based on the case analysis of Sanming City, Fujian Province**. *Chin J Health Policy.* (2021) **14** 8-13. DOI: 10.3969/j.issn.1674-2982.2021.09.002
31. Feng-mei Z, Xiao-juan Z, Chun-peng H. **The Reform of Outpatient Security System:Policy Effect Analysis of “Replacing Outpatient Service with Hospitalization”-The empirical test based on the sample data of CHIRA**. *Insurance Stud* (2021) 73-90. DOI: 10.13497/j.cnki.is.2021.01.005
32. Kao YH, Lin WT, Chen WH, Wu SC, Tseng TS. **Continuity of outpatient care and avoidable hospitalization: a systematic review**. *Am J Manag Care.* (2019) **25** e126-e34. PMID: 30986022
33. Kiadaliri A, Englund M. **Osteoarthritis and risk of hospitalization for ambulatory care-sensitive conditions: a general population-based cohort study**. *Rheumatology (Oxford).* (2021) **60** 4340-7. DOI: 10.1093/rheumatology/keab161
34. Wei-yan J, Hai F. **An empirical analysis on the substitution effect of outpatient services on inpatient services**. *J Peking Univ (Health Sci)* (2015) **47** 459-63. DOI: 10.3969/j.issn.1671-167X.2015.03.017
35. Hu-yang Z, Wei-yan J, Hai F. **Analysis of substitutional effect of outpatient expenditure on inpatient expenditure in hypertensive patients with rural new cooperative medical scheme**. *J Peking Univ (Health Sci)* (2016) **48** 472-7. DOI: 10.3969/j.issn.1671-167X.2016.03.017
36. Dou G, Wang Q, Ying X. **Reducing the medical economic burden of health insurance in China: Achievements and challenges**. *Biosci Trends.* (2018) **12** 215-9. DOI: 10.5582/bst.2018.01054
37. Xin H. **Experiences and Lessons from Urban Health Insurance Reform in China**. *Popul Health Manag.* (2016) **19** 291-7. DOI: 10.1089/pop.2015.0086
38. Yang H. *Evaluation of the Impact of Basic Medical Insurance on Medical Services in China.* (2019)
39. Zhao C, Wang C, Shen C, Wang Q. **China's achievements and challenges in improving health insurance coverage**. *Drug Discov Ther.* (2018) **12** 1-6. DOI: 10.5582/ddt.2017.01064
40. Yu J, Qiu Y, He Z. **Is universal and uniform health insurance better for China? Evidence from the perspective of supply-induced demand**. *Health Econ Policy Law.* (2020) **15** 56-71. DOI: 10.1017/S1744133118000385
|
---
title: 'Sodium-glucose co-transporter-2 inhibitor (SGLT2i) treatment and risk of osteomyelitis:
A pharmacovigilance study of the FAERS database'
authors:
- Hui Zhao
- Zi-Ran Li
- Qian Zhang
- Ming-Kang Zhong
- Ming-Ming Yan
- Xiao-Yan Qiu
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC9971937
doi: 10.3389/fphar.2023.1110575
license: CC BY 4.0
---
# Sodium-glucose co-transporter-2 inhibitor (SGLT2i) treatment and risk of osteomyelitis: A pharmacovigilance study of the FAERS database
## Abstract
Background and purpose: Several clinical trials have indicated that the use of canagliflozin increases the risk of lower extremity amputation. Although the US Food and Drug Administration (FDA) has withdrawn its black box warning about amputation risk for canagliflozin, the risk still exists. We sought to estimate the association between hypoglycemic medications, especially sodium-glucose co-transporter-2 inhibitors (SGLT2is), and adverse events (AEs) before the irreversible outcome of amputation as a promising early warning, based on the FDA Adverse Event Reporting System (FAERS) data.
Methods: Publicly available FAERS data were analyzed using a reporting odds ratio (ROR) method and validated by a Bayesian confidence propagation neural network (BCPNN) method. The developing trend of the ROR was investigated by a series of calculations based on the accumulation of data in the FAERS database quarter by quarter.
Results: Ketoacidosis, infection, peripheral ischemia, renal impairment, and inflammation including osteomyelitis might be more likely to occur among users of SGLT2is, especially canagliflozin. Osteomyelitis and cellulitis are AEs unique to canagliflozin. Among 2,888 osteomyelitis-related reports referring to hypoglycemic medications, 2,333 cases were associated with SGLT2is, with canagliflozin accounting for 2,283 of these cases and generating an ROR value of 360.89 and a lower limit of information component (IC025) of 7.79. No BCPNN-positive signal could be generated for drugs other than insulin and canagliflozin. Reports suggesting that insulin could generate BCPNN-positive signals span from 2004 to 2021, whereas reports with BCPNN-positive signals emerged only since the second quarter (Q2) of 2017, 4 years since the approval of SGLT2is in Q2 of 2013, for canagliflozin and drug groups containing canagliflozin.
Conclusion: This data-mining investigation revealed a strong association between canagliflozin treatment and developing osteomyelitis that might be a crucial forewarning to lower extremity amputation. Further studies with updated data are needed to better characterize the risk of osteomyelitis associated with SGLT2is.
## 1 Introduction
Sodium-glucose co-transporter-2 inhibitors (SGLT2is) are a class of oral hypoglycemic agents that exert their glucose-lowering effect by lowering the renal threshold for glucose reabsorption in the proximal renal tubule, causing glycosuria, and increasing renal excretion of glucose. In patients with type 2 diabetes (T2D), SGLT2is are effective in controlling glycemia, blood pressure, and body weight (Zaccardi et al., 2016). Since the approval of canagliflozin in 2013, this drug has been reported to demonstrate a protective effect against renal and cardiovascular disease (CVD) (Perkovic et al., 2019; Zelniker et al., 2019; Bauersachs et al., 2022), thus preventing hospitalization for heart failure (HF) in patients with T2D with or without a prior history of HF or CVD at baseline (Mahaffey et al., 2018), and significantly improving outcomes for patients with HF and reduced ejection fraction, including $42\%$–$50\%$ of patients with T2D (Mahaffey et al., 2019).
However, in 2017, the US Food and Drug Administration (FDA) issued a drug safety communication regarding a boxed warning about foot and leg amputations with the use of canagliflozin and removed it in 2020, reconsidering its additional benefits. The amputation risk with canagliflozin remains and is still described in the warnings and precautions section of the prescribing information. Healthcare professionals and patients should continue to recognize the importance of preventative foot care and monitor for new pain, tenderness, sores, ulcers, and infections in the legs and feet. Risk factors that may predispose patients to the need for amputation should be considered when choosing antidiabetic medicines. This warning is based on evidence from two clinical trials. The Canagliflozin Cardiovascular Assessment Study (CANVAS) program used data from two trials and showed that there was a statistically significantly higher risk of amputation with canagliflozin than with placebo (6.3 vs. 3.4 participants with amputations per 1,000 patient-years) (Neal et al., 2015; Fulcher et al., 2016). A retrospective cohort study raised concerns in relation to the increased risk of lower extremity amputation with canagliflozin, and it remains unclear whether and to what extent this side effect could also occur with other SGLT2is, which are also reported to have the risk of osteomyelitis (Chang et al., 2018).
To the best of our knowledge, there is no grand assessment of the association between all classes of hypoglycemic drugs and adverse events (AEs), which might be precursors to lower extremity amputation, especially osteomyelitis, based on real-world data. Osteomyelitis is an inflammatory bone disease that is caused by an infectious microorganism and leads to progressive bone destruction and loss (Prokesch et al., 2016; Kavanagh et al., 2018), which complicates approximately $10\%$–$20\%$ of foot ulcers in individuals with diabetes attending specialist clinics (Shone et al., 2006), although a frequency as high as $68\%$ has been reported in a study (Newman et al., 1991; Schwegler et al., 2008). This complication greatly increases the risk of lower extremity amputation (Lavery et al., 2006; Game, 2010). Although both the CANVAS and Canagliflozin Cardiovascular Assessment Study–Renal (CANVAS-R) trials suggested an increased risk for lower limb amputations, they underestimated the risk of osteomyelitis, since its treatment might greatly reduce the risk of amputation. In this study, based on the US FDA Adverse Event Reporting System (FAERS), we investigated the association between treatment with hypoglycemic drugs and the AEs mentioned, as well as the association between diabetes and AEs. Some drug–AE pairs could generate stronger signals than pairs of the same AEs and diabetes, especially osteonecrosis-related AEs. These stronger signals could be used as a warning for the prognosis of lower extremity amputation, whereas minor signals of drug–AE pairs compared with those of diabetes–AE pairs could be considered to demonstrate curative effects.
## 2.1 Data source
Publicly available FAERS data from 1 January 2004 to 30 September 2021 were downloaded from the FDA website as raw data. Hypoglycemic medications were drugs mapped to the Anatomic Therapeutic Chemical Classification (ATC) as A10 class (antidiabetic drugs, ATCA10), including insulin (including insulin and its analogs discussed in this paper) and SGLT2is, as well as biguanides, dipeptidyl peptidase-4 (DPP4), glinides (GLN), glucagon-like peptide 1 (GLP-1), sulfonylureas (SUs), and thiazolidinediones (TZDs). Osteomyelitis was defined as all of the AEs containing the keyword “osteomyelitis,” which were determined by using the Standardized MedDRA Query (SMQ, version 23.0) terms (Katsuhara and Ikeda, 2021), within “Osteonecrosis (SMQ).” The dataset of “Diabetes” was composed of all reports in FAERS containing the keyword “diabetes.” The following criteria of exclusion (Figure 1) were applied: each potential case was subjected to a data-cleaning procedure to remove reports that were officially deleted by FDA authority, that were duplicated, with missing case ID and date, or with inaccurate data for gender and age. The obtained reports were then filtered with the targeted drug as the primary suspected (PS) drug. All the reports containing hypoglycemic medications other than the targeted medication were removed, to minimize the possibility of interfering effects.
**FIGURE 1:** *Scheme of the study. Publicly available FAERS data from 1 January 2004 to 30 September 2021 were filtered using the exclusion criteria, and all included reports were categorized into subgroups and analyzed for association with osteomyelitis in duplicate with or without filtering diabetes as an indication. N: number of cases of each drug or drug group; insulin: insulin and its analogs; SGLT2is: sodium-glucose co-transporter-2 inhibitors.*
## 2.2 Statistical analyses and signal detection
A data-mining procedure using a reporting odds ratio (ROR) method (Min et al., 2018; Moreland-Head et al., 2021) was introduced to investigate the disproportionality in reporting ratio caused by interested drug–AE pairs compared with a random drug–AE pair, which were then evaluated in tandem with a Bayesian confidence propagation neural network (BCPNN) method (Bate et al., 1998), thereby deducing the association between the target drug and event by a prior possibility. The association between diabetes and AEs was also investigated. Drug–AE pairs that could generate stronger signals than the same AEs paired with diabetes were screened out and demonstrated with a heatmap. Osteomyelitis was picked as the major candidate before lower extremity amputation. Data processing was conducted with RStudio 4.1.2, using a logistic regression model. For ROR, a signal was determined as the count of drug–AE pairs greater than 3, plus the value of the ROR higher than 1, and the lower limit of the $95\%$ confidence interval ($95\%$ CI) exceeding 1. For BCPNN, a signal was defined as the value of the lower limit of information component (IC025) exceeding 0; specifically, an IC025 value between 0 and 1.5 was defined as a weak signal, an IC025 value between 1.5 and 3 was considered as a medium signal, and an IC025 value > 3 was considered as a strong signal.
## 2.3 Data mining for osteomyelitis-related cases
All aforementioned drugs and drug groups were subjected to descriptive analysis for demographics, including gender, age category, annual report counts, occupation of the reporter, role of the targeted drug, and outcomes. Because hypoglycemic medications may sometimes be used by non-diabetic individuals or for non-diabetic purposes (Zhu et al., 2020; Bonora et al., 2021), many reports present no specific indications or missing information, and all interested drugs or drug groups were analyzed in duplicate with or without filtering diabetes as an indication (Figure 1). Reports referring to competing interfering indications such as from drugs known for causing osteomyelitis, including zoledronic acid and alendronate sodium, were excluded, as well as reports listing osteology conditions as indications and AEs, because osteomyelitis may occur preferentially in patients with diabetic ulcers, lower extremity amputation, and metatarsal excision (Game, 2010). Because osteomyelitis might occur preferentially in patients with known infections (Lavery et al., 2006), we excluded reports containing competing indications and AEs that are typically reported preferentially among users of SGLT2is, to minimize the bias due to dilution or competition (Davies et al., 2018; Pasquel et al., 2021), such as diabetic foot ulcers (Ramsey et al., 1999) and infections (Eckman et al., 1995), especially genital, genitourinary tract, and urinary tract infections, diabetic ketoacidosis, and Fournier’s gangrene, as well as reports listing all antibiotics or becaplermin (Kobayashi et al., 2022). Furthermore, because the use of insulin and its analogs is typically considered a proxy of disease severity or advanced disease stage (Davies et al., 2018; Pasquel et al., 2021), we categorized reports referring to insulin as a control group. In addition, the gender bias in the osteomyelitis reports was investigated. Reports referring to testosterone and estrogen were extracted and filtered as described earlier.
The developing trend of RORs on a quarterly basis was investigated. We designed a procedure to mimic the accumulation of FAERS data in real world by adding up every quarter of data into the dataset. A series of quarterly ROR (q-ROR) values was generated for interested drug/drug group–osteomyelitis pairs. Chi-square (Chi2) tests were performed to compare the changing tendencies of q-ROR values of given pairs, as well as the tendencies before and after SGLT2is were approved by the FDA, to eliminate the interfering effect caused by comorbidities or concomitants.
## 3.1 Heatmap of IC025 generated by hypoglycemic medications and diabetes paired with AEs
As shown in Figure 2 and Supplementary Table S1, compared with the risk factor diabetes, most of the hypoglycemic drugs demonstrated curative effects for patients with diabetes, whereas SGLT2is might increase the risk of ketoacidosis (IC025 of diabetes: 3.57 vs. IC025 of empagliflozin: 6.99), lower limb extremity amputation such as toe amputation (IC025 of diabetes: 3.38 vs. IC025 of canagliflozin: 5.35), gangrene such as Fournier’s gangrene (IC025 of diabetes: 3.75 vs. IC025 of empagliflozin: 6.76), infection such as urinary tract infection (IC025 of diabetes: 0.10 vs. IC025 of canagliflozin: 1.84), ulcer such as skin ulcer (IC025 of diabetes: 0.79 vs. IC025 of canagliflozin: 2.42), peripheral ischemia (IC025 of diabetes: 0.39 vs. IC025 of canagliflozin: 2.23), kidney injury such as acute kidney injury (IC025 of diabetes: 1.56 vs. IC025 of dapagliflozin: 2.27), and various inflammations including osteomyelitis, fasciitis, and cellulitis, especially for canagliflozin. Osteomyelitis (IC025 of diabetes: 1.80 vs. IC025 of canagliflozin: 4.17) and cellulitis (IC025 of diabetes: 0.56 vs. IC025 of canagliflozin: 2.16) were AEs unique to canagliflozin.
**FIGURE 2:** *Heatmap of IC025 values of blood glucose-lowering drugs and associated risk AEs. x-axis: diabetes and blood glucose-lowering drugs; y-axis: risk AEs with IC025 values higher than that of diabetes–AE pairs. IC025: lower limit of the information component.*
## 3.2 Demography of osteomyelitis-related cases
The FAERS database is composed of a total of 14,073,327 AE reports from 1 January 2004 to 30 September 2021. After applying the data-cleansing procedure described earlier, there were 2,888 osteomyelitis-related reports referring to hypoglycemic drugs, among which 2,333 reports were associated with SGLT2is, especially canagliflozin (2,283 reports; Table 1). Among reports referring to both canagliflozin and osteomyelitis, $73.50\%$ of patients are male, whereas the gross gender ratio for each category of hypoglycemic drugs is relatively balanced. Among all osteomyelitis-related patients treated with canagliflozin, $23.74\%$ of patients are 18–29 years old, $59.22\%$ are 30–49 years old, and $14.76\%$ are 50–64 years old, adding up to $97.72\%$ of patients aged from 18 to 64 years old compared with $79.07\%$ of cases categorized in the same age group for patients receiving hypoglycemic treatment. For exposure to canagliflozin, $99.82\%$ of reports classified the targeted drug as the PS drug. The most common reporting source is consumers, representing $91.90\%$ for canagliflozin-related cases associated with osteomyelitis. By contrast, approximately $50\%$ of reports referring to hypoglycemic drugs are filed by consumers.
**TABLE 1**
| Unnamed: 0 | all blood glucose lowering drugs | all blood glucose lowering drugs.1 | all blood glucose lowering drugs.2 | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| | (N = 486,927) | (N = 486,927) | (N = 486,927) | |
| | Osteomyelitis | Osteomyelitis | Osteomyelitis | |
| | (N = 2,888) | (N = 2,888) | (N = 2,888) | |
| | non-insulin | non-insulin | Insulin | |
| | (N = 2,634) | (N = 2,634) | (N = 405) | |
| | sglt2i | non-sglt2i | | |
| | (N = 2,333) | (N = 484) | | |
| | Canagliflozin | | | |
| | (N = 2,283) | | | |
| Gender: | Gender: | Gender: | Gender: | Gender: |
| Male | 1678 (73.50%) | 286 (59.09%) | 233 (57.53%) | 228600 (46.95%) |
| Female | 605 (26.50%) | 198 (40.91%) | 172 (42.47%) | 258080 (53.00%) |
| Age: | Age: | Age: | Age: | Age: |
| 0−9 yo | 1 (0.04%) | 0 (0.00%) | 1 (0.25%) | 2352 (0.48%) |
| 10−17 yo | 5 (0.22%) | 2 (0.41%) | 3 (0.74%) | 8611 (1.77%) |
| 18−29 yo | 542 (23.74%) | 66 (13.64%) | 59 (14.57%) | 63919 (13.13%) |
| 30−49 yo | 1352 (59.22%) | 252 (52.07%) | 193 (47.65%) | 175913 (36.13%) |
| 50−64 yo | 337 (14.76%) | 119 (24.59%) | 102 (25.19%) | 145152 (29.81%) |
| 65−75 yo | 44 (1.93%) | 35 (7.23%) | 36 (8.89%) | 66503 (13.66%) |
| 76−85 yo | 2 (0.09%) | 3 (0.62%) | 1 (0.25%) | 13499 (2.77%) |
| >85 yo | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 3356 (0.69%) |
| Yearly report: | Yearly report: | Yearly report: | Yearly report: | Yearly report: |
| 2004 | 0 (0.00%) | 5 (1.03%) | 5 (1.23%) | 3992 (0.82%) |
| 2005 | 0 (0.00%) | 5 (1.03%) | 10 (2.47%) | 5842 (1.20%) |
| 2006 | 0 (0.00%) | 11 (2.27%) | 11 (2.72%) | 15549 (3.19%) |
| 2007 | 0 (0.00%) | 3 (0.62%) | 6 (1.48%) | 16443 (3.38%) |
| 2008 | 0 (0.00%) | 5 (1.03%) | 5 (1.23%) | 11679 (2.40%) |
| 2009 | 0 (0.00%) | 15 (3.10%) | 10 (2.47%) | 13286 (2.73%) |
| 2010 | 0 (0.00%) | 10 (2.07%) | 17 (4.20%) | 16731 (3.44%) |
| 2011 | 0 (0.00%) | 17 (3.51%) | 10 (2.47%) | 21412 (4.40%) |
| 2012 | 0 (0.00%) | 19 (3.93%) | 17 (4.20%) | 19683 (4.04%) |
| 2013 | 0 (0.00%) | 12 (2.48%) | 13 (3.21%) | 21786 (4.47%) |
| 2014 | 0 (0.00%) | 19 (3.93%) | 20 (4.94%) | 31441 (6.46%) |
| 2015 | 1 (0.04%) | 28 (5.79%) | 33 (8.15%) | 59232 (12.16%) |
| 2016 | 6 (0.26%) | 23 (4.75%) | 29 (7.16%) | 41878 (8.60%) |
| 2017 | 25 (1.10%) | 37 (7.64%) | 36 (8.89%) | 38099 (7.82%) |
| 2018 | 1402 (61.41%) | 163 (33.68%) | 86 (21.23%) | 51442 (10.56%) |
| 2019 | 635 (27.81%) | 59 (12.19%) | 42 (10.37%) | 44095 (9.06%) |
| 2020 | 207 (9.07%) | 37 (7.64%) | 35 (8.64%) | 42959 (8.82%) |
| 2021 | 7 (0.31%) | 16 (3.31%) | 20 (4.94%) | 31373 (6.44%) |
| Time to onset: | Time to onset: | Time to onset: | Time to onset: | Time to onset: |
| <1 day | 16 (0.70%) | 3 (0.62%) | 5 (1.23%) | 31500 (6.47%) |
| 1−2 days | 1 (0.04%) | 1 (0.21%) | 0 (0.00%) | 4102 (0.84%) |
| 2−3 days | 3 (0.13%) | 0 (0.00%) | 1 (0.25%) | 2295 (0.47%) |
| 3−7 days | 7 (0.31%) | 1 (0.21%) | 0 (0.00%) | 5247 (1.08%) |
| 1−2 weeks | 3 (0.13%) | 2 (0.41%) | 2 (0.49%) | 5775 (1.19%) |
| 2−4 weeks | 24 (1.05%) | 1 (0.21%) | 1 (0.25%) | 7751 (1.59%) |
| 1−3 months | 59 (2.58%) | 7 (1.45%) | 4 (0.99%) | 13061 (2.68%) |
| 3−6 months | 92 (4.03%) | 5 (1.03%) | 4 (0.99%) | 9203 (1.89%) |
| 0.5−1 year | 130 (5.69%) | 7 (1.45%) | 1 (0.25%) | 9962 (2.05%) |
| >1 year | 255 (11.17%) | 31 (6.40%) | 22 (5.43%) | 26770 (5.50%) |
| Role of drug: | Role of drug: | Role of drug: | Role of drug: | Role of drug: |
| primary suspect | 2279 (99.82%) | 75 (15.50%) | 129 (31.85%) | 305376 (62.71%) |
| secondary suspect | 223 (9.77%) | 143 (29.55%) | 91 (22.47%) | 72440 (14.88%) |
| concomitant | 3 (0.13%) | 295 (60.95%) | 248 (61.23%) | 200501 (41.18%) |
| interacting | 0 (0.00%) | 0 (0.00%) | 1 (0.25%) | 1 (0.00%) |
| Filed by: | Filed by: | Filed by: | Filed by: | Filed by: |
| consumer | 2098 (91.90%) | 252 (52.07%) | 221 (54.57%) | 242021 (49.70%) |
| other health-professional | 110 (4.82%) | 69 (14.26%) | 47 (11.60%) | 58259 (11.96%) |
| health professional | 41 (1.80%) | 19 (3.93%) | 13 (3.21%) | 16056 (3.30%) |
| physician | 15 (0.66%) | 103 (21.28%) | 87 (21.48%) | 114613 (23.54%) |
| lawyer | 12 (0.53%) | 4 (0.83%) | 1 (0.25%) | 5856 (1.20%) |
| pharmacist | 7 (0.31%) | 16 (3.31%) | 9 (2.22%) | 27861 (5.72%) |
| Outcome: | Outcome: | Outcome: | Outcome: | Outcome: |
| hospitalization | 1923 (84.23%) | 372 (76.86%) | 317 (78.27%) | 194683 (39.98%) |
| disability | 415 (18.18%) | 67 (13.84%) | 46 (11.36%) | 11350 (2.33%) |
| death | 18 (0.79%) | 12 (2.48%) | 25 (6.17%) | 39769 (8.17%) |
| life-threatening | 7 (0.31%) | 15 (3.10%) | 16 (3.95%) | 24368 (5.00%) |
| required intervention | 0 (0.00%) | 3 (0.62%) | 0 (0.00%) | 2564 (0.53%) |
| congenital anomaly | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 576 (0.12%) |
## 3.3 Disproportionality analyses and signal detection
All interested drug–osteomyelitis pairs were subjected to disproportionality analysis and BCPNN in duplicate, with filtering for the diabetes indication. Results are shown in Figure 3. In total, 1,451 osteomyelitis-related AEs were generated out of a total of 1,438 reports listing canagliflozin. The ROR value is 360.89 ($95\%$ CI 340.58–382.41) coupled with an IC025 value of 7.79. For each osteomyelitis-related AE individually, the number of reports with the canagliflozin–osteomyelitis pair was 1,214, generating an ROR of 315.60 ($95\%$ CI 296.51–335.93) and an IC025 of 7.62; the number or reports with the canagliflozin–osteomyelitis acute pair was 157, generating an ROR of 1,391.14 ($95\%$ CI 1134.55–1705.76) and an IC025 of 6.48; and the number of reports with the canagliflozin–osteomyelitis chronic pair was 72, generating an ROR of 716.11 ($95\%$ CI 546.95–937.60) and an IC025 of 5.03. All of the aforementioned signals are classed as strong signals. For the canagliflozin–staphylococcal osteomyelitis pair, a high ROR value was generated (168.49; $95\%$ CI 81.87–346.74), but it was coupled with an IC025 of −1.21; therefore, it was cast out as a negative signal by BCPNN, as a false positive. For osteomyelitis associated with empagliflozin, the ROR value is 2.72 ($95\%$ CI 1.22–6.06) and the IC025 is −0.19 (Figure 3), whereas other SGLT2is as well as other hypoglycemic drugs, except for insulin, did not generate valid ROR values. Non-canagliflozin SGLT2is as a group could generate an ROR of 1.99 ($95\%$ CI 0.95–4.17) and an IC025 of −0.33, whereas other hypoglycemic drug groups including biguanides, DPP4, GLP1, and TZD did not generate valid ROR signals. Among all reports referring to osteomyelitis, 405 cases referred to insulin and its analogs and generated an ROR of 1.32 ($95\%$ CI 1.08–1.62) and an IC025 of 0.09, which could be considered as a weak signal, and 484 cases referred to non-insulin hypoglycemic drugs other than SGLT2is and could not generate valid ROR (0.28) and IC025 (−2.25) values, which meant no signal. With gender as a filter, a significant difference in the ROR of osteomyelitis associated with canagliflozin between male (ROR 453.79, $95\%$ CI 424.51–485.10, IC025 7.96) and female patients (ROR 190.38, $95\%$ CI 171.07–211.87, IC025 6.69) was observed. For the insulin–osteomyelitis pair, only male patients generated a weak signal (ROR 2.00, $95\%$ CI 1.53–2.63, IC025 0.57) (Figure 4).
**FIGURE 3:** *ROR and IC025 values of blood glucose-lowering drugs-associated osteomyelitis events, filtering diabetes as an indication. (A) Number of reports referring to both the targeted drug and the interested AE (targeted drug–osteomyelitis pair); (B) number of reports referring to the targeted drug paired with all reported AEs other than osteomyelitis; (C) number of reports referring to osteomyelitis concerning all drugs other than the targeted drug; (D) number of reports referring to all reported drug–AE pairs other than the targeted drug–osteomyelitis pair. IC025: lower limit of the information component of the Bayesian confidence propagation neural network.* **FIGURE 4:** *ROR and IC025 values of the blood glucose-lowering drug-associated osteomyelitis events, filtering gender. (A) Number of reports referring to both the targeted drug and the interested AE (targeted drug–osteomyelitis pair); (B) number of reports referring to the targeted drug paired with all reported AEs other than osteomyelitis; (C) number of reports referring to osteomyelitis concerning all drugs other than the targeted drug; (D) number of reports referring to all reported drug–AE pairs other than the targeted drug–osteomyelitis pair. IC025: lower limit of the information component of the Bayesian confidence propagation neural network; male: reports of male patients; female: reports of female patients.*
## 3.4 Quarterly trend of ROR
To demonstrate the changing pattern of q-ROR (Supplementary Table S2 and Figure 5), the natural logarithm value of ROR (Ln ROR) was used as vertical coordinates and plotted against quarters of the year as horizontal coordinates. As shown in Figure 5, although reporting counts of the canagliflozin–osteomyelitis pair diminishes considerably with filtering diabetes as an indication compared with without the filtering, the curve of canagliflozin is almost overlapping with the curve of canagliflozin (wo), i.e., the curve without filtering diabetes as an indication. When hypoglycemic medication drugs excluding SGLT2is and insulin are investigated as the drug group, all the q-ROR values are below the ROR threshold value of 1 (Ln ROR 0), whereas the Ln ROR–time curve of insulin yielded a generally horizontal line, with the ROR value consistently within the range from 1 to 2 (Ln ROR range 0–1), since the second quarter (Q2) of 2013. As shown in Supplementary Table S2, during 18 years from 1 January 2004 to 30 September 2021, the q-ROR value of insulin is always above the recognition threshold of 1 and fluctuates consistently around a median of 1.57 (mean 1.71 ± 0.44 SD), and a valid ROR could be identified since the third quarter (Q3) of 2004. The curves of canagliflozin and SGLT2is start to generate valid ROR signals since as early as 2017 in the fourth quarter (Q4), whereas for any drug group including SGLT2is, the first valid ROR emerges in 2018 in Q1. For any drug group excluding canagliflozin, such as non-canagliflozin ATCA10 and non-SGLT2i ATCA10, no valid ROR signal is generated (Supplementary Table S2).
**FIGURE 5:** *Trend of Ln ROR between all antidiabetic drugs, SGLT2is, insulin, non-insulin, canagliflozin (wo), canagliflozin, and osteomyelitis from the first quarter (Q1) up to the given quarter. x-axis: time in quarterly order; y-axis: Ln ROR; SGLT2is: sodium-glucose co-transporter-2 inhibitors; insulin: insulin and its analogs; non-insulin: antidiabetic drugs excluding insulin; canagliflozin (wo): canagliflozin without filtering diabetes as an indication.*
Chi2 tests were then applied to investigate the correlation between the series of q-RORs, using a null hypothesis claiming the prevalence of any two given series of q-RORs was the same. Among all series, canagliflozin, canagliflozin (wo), and SGLT2is shared the same pattern, although the scales of RORs were considerably different. Changing patterns of canagliflozin and SGLT2is demonstrate differences from insulin ($$p \leq 0.00$$) and other hypoglycemic drugs or drug groups (Supplementary Table S3). Another Chi2 test was introduced to determine the prevalence of q-ROR of the ATCA10–osteomyelitis pair before and after the approval of SGLT2is during the same time span, which is from Q2 of 2004 to Q4 of 2012 as serial A and from Q2 of 2013 to Q3 of 2021 as serial B. A p-value of 0.00924 was generated, and the null hypothesis was rejected.
## 4.1 Panorama of AEs associated with hypoglycemic treatment
As shown in Figure 2, ketoacidosis, various infections, peripheral ischemia, renal impairment, and inflammation including osteomyelitis might be more likely to occur among SGLT2i users, especially for canagliflozin. Our findings suggest that SGLT2is increased the risk of these issues or were less effective on them. SGLT2i treatment for patients who suffered from ketoacidosis, cardiovascular issues, renal problems, and inflammation was, therefore, not recommended. Osteomyelitis and cellulitis are AEs unique to canagliflozin. Osteomyelitis is considered to greatly increase the risk of lower extremity amputation (Lavery et al., 2006; Game, 2010), and our findings indicated that exposure to canagliflozin could notably increase the risk of developing osteomyelitis, whereas other hypoglycemic drugs could reduce such risk. These events could be monitored as a critical warning before lower limb extremity amputation, especially due to osteomyelitis. By contrast, according to the FAERS data, hypoglycemic medications, except SGLT2is, showed encouraging curative effects on ketoacidosis, various infections, peripheral ischemia, renal impairment, and inflammation, which could be considered as complications of diabetes mellitus. Further studies should be undertaken to evaluate the risks vs. benefits of SGLT2is, and SGLT2is might not be recommended for patients who have suffered from such issues.
## 4.2 Osteomyelitis and canagliflozin
Most osteomyelitis-related cases were referred to canagliflozin, indicating that there might be a strong correlation between SGLT2i exposure, especially canagliflozin, and developing osteomyelitis according to the FAERS data. In this study, ROR and BCPNN methods were applied to investigate the association between hypoglycemic drugs and osteomyelitis. Signals with a high ROR value indicated strong disproportionality and a strong association between the targeted drug and AEs. Because the value of the ROR did not directly indicate the significance of a signal, all positive signals were validated by the BCPNN method. Strong signals associated with osteomyelitis were generated for canagliflozin or any drug groups containing canagliflozin, whereas weak signals were generated for insulin–osteomyelitis pairs, and no signal was generated for other hypoglycemic drugs or drug groups excluding canagliflozin and insulin. Therefore, these findings indicated that an association between canagliflozin treatment and osteomyelitis was convincing. The weak signal generated by the insulin–osteomyelitis pair might be explained by insulin exposure as well as the morbidity of diabetes since insulin treatment, indicating a proxy of disease severity or advanced disease stage (Davies et al., 2018; Pasquel et al., 2021), but the morbidity of diabetes might neither be a sufficient condition nor a necessary condition for a patient with diabetes to develop osteomyelitis. The total number of reports on targeted drugs presented notable differences with or without filtering diabetes as an indication, and the IC025 value of canagliflozin–osteomyelitis pairs with the filtering was lower than that without it, suggesting that excluding cases without a specific indication as diabetes resulted in diminishing the intensity of the BCPNN signal.
## 4.3 Gender differences
Among patients who developed osteomyelitis, $73.50\%$ were male patients, whereas the gross gender ratio for each category of hypoglycemic drugs was relatively more balanced (Table 1). Moreover, disproportionality analysis was performed with gender as the filtering criterion, and the results suggested that there was a significant difference in the ROR of canagliflozin–osteomyelitis pairs between the two genders. Since filtering according to the aforementioned exclusion criteria had excluded all reports with infection known as a competing indication and reaction (Eckman et al., 1995), the gender ratio and differences in ROR and IC025 values between male and female patients were probably due to gender differences, and a negative correlation might have existed between glycosylated hemoglobin (HbA1c) and serum testosterone levels (Zhang et al., 2021). As displayed by the insulin–osteomyelitis pair, only the dataset of male patients could generate a valid ROR and a weak signal of BCPNN; thus, these findings support the hypothesis that male patients might be more likely to develop osteomyelitis. When exposure to the SGLT2i, canalization was the major factor for causing disproportionality, the dataset of male patients generated an ROR value three times higher than that of female patients. When the signals were validated by the BCPNN method, both genders generated strong signals (IC025 of 7.96 for male patients vs. IC025 of 6.69 for female patients), indicating that for reports of each gender, regardless of their differences in ROR values, canagliflozin presented a strong correlation with developing osteomyelitis (Figure 4).
## 4.4 Quarterly trend of ROR
A new approach, q-ROR, was introduced to demonstrate the developing trend of ROR, and the series of q-ROR values generated by different drugs or drug groups was subjected to Chi2 tests to determine their correlations statistically. Canagliflozin, SGLT2is, and ATCA10 demonstrated no prevalence difference, although there might be a gap in the scale of ROR values (Supplementary Table S2 and Figure 5), supporting the aforementioned speculation that the disproportionality of osteomyelitis-related reports was generated by canagliflozin. For hypoglycemic drugs other than canagliflozin and drug groups excluding SGLT2is, no positive signal was generated when paired with osteomyelitis-related AEs. These findings strongly indicated that the developing pattern of these drugs or drug groups was synchronized by the presence of canagliflozin. In a pharmacovigilance study, disproportionality emerges when a specific AE is associated with a given drug (Almenoff et al., 2007; Hou et al., 2014; Ang et al., 2016). In this study, we used q-ROR with the FAERS quarterly data and mimicked the accumulation of reports to the database in the real world. Starting from the setting date, a slice of data was added to the dataset in chronological order on a quarterly basis, and an ROR value from the setting date up to that quarter was calculated. A series of RORs was generated for any given interested drug/drug group–AE pair. Finally, the q-ROR value achieved equilibrium and approached its theoretical true value. For recently approved drugs with limited reports but with analogs that had been long approved, the q-ROR curve might be used to predict their association with interested AEs according to their precursors or as a drug group, such as ertugliflozin, luseogliflozin, remogliflozin, and other newly approved SGLT2is that fail to generate any positive signal. The q-ROR value of the insulin–osteomyelitis pair was always above the recognition threshold and fluctuated consistently around 1.5, and a positive signal could be identified since Q1 of 2005, whereas a series of q-RORs referring to any drug group excluding canagliflozin and insulin was below the threshold of 1 since 2005. Coupled with the dramatically increasing number of reports of canagliflozin-related osteomyelitis in the FAERS (25 cases in 2017 vs. 1,402 cases in 2018) (Table 1), the q-ROR pattern of canagliflozin ranges from 3.24 in Q4 of 2017 to 79.54 in Q4 of 2018 (Supplementary Table S2). This finding indicates that q-ROR could be used to monitor drug-induced ADRs unknown to premarketing trials as pharmacovigilance, when a dramatic rise in the ROR value is spotted for given drug–AE pairs and needs to be further verified by the BCPNN method.
## 4.5 Interfering caused by morbidities
Morbidity of diabetes mellitus is a risk factor for developing osteomyelitis, which occurs in approximately $10\%$–$20\%$ of patients with diabetes-related foot ulcers (Game, 2010), and osteomyelitis of the lower extremity is a commonly encountered problem in patients with diabetes (Butalia et al., 2008). In this study, such a dataset in the FAERS database was also equivalent to considering all hypoglycemic drugs as a drug group and filtering data with diabetes as an indication, which generated a signal considered to be caused by both the treatment and the morbidity. This dataset was also examined by the q-ROR method. The Chi2 test was used to compare a series of Ln ROR values before and after canagliflozin was approved in Q1 of 2013, and a p-value of 0.00924 ($p \leq 0.05$) indicated that a significant change in disproportionality of the diabetes–osteomyelitis combination, which was probably due to the exposure to SGLT2is, especially canagliflozin, because before the approval of SGLT2is, diabetes as a risk factor generated no positive signal when paired with osteomyelitis-related AEs.
Previous publications suggested that osteomyelitis of the lower extremity is a commonly encountered problem in patients with diabetes (Butalia et al., 2008) and occurred in approximately $10\%$–$20\%$ of patients with diabetes-related foot ulcers (Game, 2010). However, based on the FAERS data, drug or drug groups excluding canagliflozin and insulin generated no positive signal (Supplementary Table S2 and Figure 5) with osteomyelitis-related AEs. For insulin, the Ln ROR value was 1.71 ± 0.44, with a median of 1.57, before the approval of canagliflozin, and 1.53 ± 0.25, with a median of 1.45, since Q2 of 2013, when canagliflozin was approved as the first SGLT2i. This finding indicated that the morbidity of diabetes mellitus, even as a proxy of disease severity or advanced disease stage (Davies et al., 2018; Pasquel et al., 2021), might not be considered a significant interfering factor for drug-associated osteomyelitis based on the FAERS database. Therefore, this strengthened the results of the Chi2 tests between the q-ROR series that canagliflozin exposure might be the predominant cause of developing osteomyelitis for patients with diabetes, based on the FAERS database. By contrast, other widely used SGLT2is, such as dapagliflozin and empagliflozin, might not be associated with developing osteomyelitis. For recently approved SGLT2is that have not accumulated enough ADR reports for disproportionality analysis, predictions could be made based on the q-ROR pattern as pharmacovigilance on a quarterly basis.
## 4.6 Limitations
There are certain limitations that might undermine this study. Spontaneous reporting systems including the FAERS database were exposed to the biases inherent to pharmacovigilance studies. To the best of our knowledge, in 2018, Chang et al. [ 2018] mentioned the risk of osteomyelitis when discussing the association between SGLT2i treatment and lower extremity amputation among patients with T2D, and osteomyelitis of the lower extremity is a commonly encountered problem in patients with diabetes (Butalia et al., 2008) and occurs in approximately $10\%$–$20\%$ of patients with diabetes-related foot ulcers (Game, 2010). These publications coincidently matched with the outflow of osteomyelitis-related ADR reports and a surge in the ROR for the canagliflozin–osteomyelitis pair.
## 5 Conclusion
In conclusion, according to the FAERS data, most of the hypoglycemic agents demonstrated curative effects on preventing lower extremity amputation and osteomyelitis before such irreversible outcome, whereas SGLT2is were less effective on this issue. In this study, we investigated all hypoglycemic agents mapped in class A10 of the Anatomic Therapeutic Chemical Classification to provide insight into their association with referring AEs. Ketoacidosis, infection, peripheral ischemia, renal impairment, and inflammation might be more likely to occur among SGLT2i, especially canagliflozin, users. Osteomyelitis and cellulitis are AEs unique to canagliflozin and are, therefore, intensively discussed. ROR, IC025, and q-ROR tendencies of the canagliflozin–osteomyelitis pair were significantly different from those generated by the insulin–osteomyelitis pair, and there was no positive signal for hypoglycemic drugs paired with osteomyelitis other than canagliflozin and insulin. Our findings strongly indicated that canagliflozin treatment increases the risk of developing osteomyelitis from the very early stage of diabetes mellitus, before the advanced stage when insulin is prescribed. It is worth investigating whether SGLT2is can also result in the development of osteomyelitis in patients without diabetes, and the association between osteomyelitis and recently approved SGLT2is, when enough reports become available. Further studies are needed for a better understanding of the association between SGLT2i treatment and the risk of osteomyelitis.
## Data availability statement
Publicly available datasets were analyzed in this study. These data can be found at: https://fis.fda.gov/extensions/FPD-QDE-FAERS/FPD-QDE-FAERS.html.
## Author contributions
X-YQ and M-KZ designed the study; M-MY and HZ conducted the study; and Z-RL and QZ contributed to the creation of figures. X-YQ is the lead contact author.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1110575/full#supplementary-material
## References
1. Almenoff J., Pattishall E., Gibbs T., Dumouchel W., Evans S., Yuen N.. **Novel statistical tools for monitoring the safety of marketed drugs**. *Clin. Pharmacol. Ther.* (2007) **82** 157-166. DOI: 10.1038/sj.clpt.6100258
2. Ang P. S., Chen Z., Chan C. L., Tai B. C.. **Data mining spontaneous adverse drug event reports for safety signals in Singapore - a comparison of three different disproportionality measures**. *Expert Opin. Drug Saf.* (2016) **15** 583-590. DOI: 10.1517/14740338.2016.1167184
3. Bate A., Lindquist M., Edwards I., Olsson S., Orre R., Lansner A.. **A Bayesian neural network method for adverse drug reaction signal generation**. *Eur. J. Clin. Pharmacol.* (1998) **54** 315-321. DOI: 10.1007/s002280050466
4. Bauersachs J., De Boer R., Lindenfeld J., Bozkurt B.. **The year in cardiovascular medicine 2021: Heart failure and cardiomyopathies**. *Eur. Heart J.* (2022) **43** 367-376. DOI: 10.1093/eurheartj/ehab887
5. Bonora B., Raschi E., Avogaro A., Fadini G.. **SGLT-2 inhibitors and atrial fibrillation in the Food and Drug Administration adverse event reporting system**. *Cardiovasc. Diabetol.* (2021) **20** 39. DOI: 10.1186/s12933-021-01243-4
6. Butalia S., Palda V., Sargeant R., Detsky A., Mourad O.. **Does this patient with diabetes have osteomyelitis of the lower extremity?**. *JAMA* (2008) **299** 806-813. DOI: 10.1001/jama.299.7.806
7. Chang H. Y., Singh S., Mansour O., Baksh S., Alexander G. C.. **Association between sodium-glucose cotransporter 2 inhibitors and lower extremity amputation among patients with type 2 diabetes**. *JAMA Intern Med.* (2018) **178** 1190-1198. DOI: 10.1001/jamainternmed.2018.3034
8. Davies M., D'alessio D., Fradkin J., Kernan W., Mathieu C., Mingrone G.. **Management of hyperglycaemia in type 2 diabetes, 2018. A consensus report by the American diabetes association (ada) and the European association for the study of diabetes (easd)**. *Diabetologia* (2018) **61** 2461-2498. DOI: 10.1007/s00125-018-4729-5
9. Eckman M., Greenfield S., Mackey W., Wong J., Kaplan S., Sullivan L.. **Foot infections in diabetic patients. Decision and cost-effectiveness analyses**. *JAMA* (1995) **273** 712-720. DOI: 10.1001/jama.273.9.712
10. Fulcher G., Matthews D., Perkovic V., De Zeeuw D., Mahaffey K., Mathieu C.. **Efficacy and safety of canagliflozin when used in conjunction with incretin-mimetic therapy in patients with type 2 diabetes**. *Diabetes, Obes. metabolism* (2016) **18** 82-91. DOI: 10.1111/dom.12589
11. Game F.. **Management of osteomyelitis of the foot in diabetes mellitus**. *Nat. Rev. Endocrinol.* (2010) **6** 43-47. DOI: 10.1038/nrendo.2009.243
12. Hou Y., Ye X., Wu G., Cheng G., Du X., He J.. **A comparison of disproportionality analysis methods in national adverse drug reaction databases of China**. *Expert Opin. Drug Saf.* (2014) **13** 853-857. DOI: 10.1517/14740338.2014.915938
13. Katsuhara Y., Ikeda S.. **Correlations between SGLT-2 inhibitors and acute renal failure by signal detection using FAERS: Stratified analysis for reporting country and concomitant drugs**. *Clin. Drug Investig.* (2021) **41** 235-243. DOI: 10.1007/s40261-021-01006-9
14. Kavanagh N., Ryan E., Widaa A., Sexton G., Fennell J., O'rourke S.. **Staphylococcal osteomyelitis: Disease progression, treatment challenges, and future directions**. *Clin. Microbiol. Rev.* (2018) **31** e00084. DOI: 10.1128/cmr.00084-17
15. Kobayashi H., Fujita R., Hiratsuka S., Shimizu T., Sato D., Hamano H.. **Differential effects of anti-RANKL monoclonal antibody and zoledronic acid on necrotic bone in a murine model of Staphylococcus aureus-induced osteomyelitis**. *J. Orthop. Res. official Publ. Orthop. Res. Soc.* (2022) **40** 614-623. DOI: 10.1002/jor.25102
16. Lavery L., Armstrong D., Wunderlich R., Mohler M., Wendel C., Lipsky B.. **Risk factors for foot infections in individuals with diabetes**. *Diabetes care* (2006) **29** 1288-1293. DOI: 10.2337/dc05-2425
17. Mahaffey K., Jardine M., Bompoint S., Cannon C., Neal B., Heerspink H.. **Canagliflozin and cardiovascular and renal outcomes in type 2 diabetes mellitus and chronic kidney disease in primary and secondary cardiovascular prevention groups**. *Circulation* (2019) **140** 739-750. DOI: 10.1161/circulationaha.119.042007
18. Mahaffey K., Neal B., Perkovic V., De Zeeuw D., Fulcher G., Erondu N.. **Canagliflozin for primary and secondary prevention of cardiovascular events: Results from the CANVAS program (canagliflozin cardiovascular assessment study)**. *Circulation* (2018) **137** 323-334. DOI: 10.1161/circulationaha.117.032038
19. Min J., Osborne V., Kowalski A., Prosperi M.. **Reported adverse events with painkillers: Data mining of the US Food and drug administration adverse events reporting system**. *Drug Saf.* (2018) **41** 313-320. DOI: 10.1007/s40264-017-0611-5
20. Moreland-Head L. N., Coons J. C., Seybert A. L., Gray M. P., Kane-Gill S. L.. **Use of disproportionality analysis to identify previously unknown drug-associated causes of cardiac arrhythmias using the Food and drug administration adverse event reporting system (FAERS) database**. *J. Cardiovasc Pharmacol. Ther.* (2021) **26** 341-348. DOI: 10.1177/1074248420984082
21. Neal B., Perkovic V., De Zeeuw D., Mahaffey K., Fulcher G., Ways K.. **Efficacy and safety of canagliflozin, an inhibitor of sodium-glucose cotransporter 2, when used in conjunction with insulin therapy in patients with type 2 diabetes**. *Diabetes care* (2015) **38** 403-411. DOI: 10.2337/dc14-1237
22. Newman L., Waller J., Palestro C., Schwartz M., Klein M., Hermann G.. **Unsuspected osteomyelitis in diabetic foot ulcers. Diagnosis and monitoring by leukocyte scanning with indium in 111 oxyquinoline**. *JAMA* (1991) **266** 1246-1251. DOI: 10.1001/jama.266.9.1246
23. Pasquel F., Lansang M., Dhatariya K., Umpierrez G.. **Management of diabetes and hyperglycaemia in the hospital**. *lancet. Diabetes and Endocrinol.* (2021) **9** 174-188. DOI: 10.1016/s2213-8587(20)30381-8
24. Perkovic V., Jardine M., Neal B., Bompoint S., Heerspink H., Charytan D.. **Canagliflozin and renal outcomes in type 2 diabetes and nephropathy**. *N. Engl. J. Med.* (2019) **380** 2295-2306. DOI: 10.1056/NEJMoa1811744
25. Prokesch B. C., Tekippe M., Kim J., Raj P., Tekippe E. M., Greenberg D. E.. **Primary osteomyelitis caused by hypervirulent**. *Lancet Infect. Dis.* (2016) **16** e190-e195. DOI: 10.1016/s1473-3099(16)30021-4
26. Ramsey S., Newton K., Blough D., Mcculloch D., Sandhu N., Reiber G.. **Incidence, outcomes, and cost of foot ulcers in patients with diabetes**. *Diabetes care* (1999) **22** 382-387. DOI: 10.2337/diacare.22.3.382
27. Schwegler B., Stumpe K., Weishaupt D., Strobel K., Spinas G., Von Schulthess G.. **Unsuspected osteomyelitis is frequent in persistent diabetic foot ulcer and better diagnosed by MRI than by 18F-FDG PET or 99mTc-MOAB**. *J. Intern. Med.* (2008) **263** 99-106. DOI: 10.1111/j.1365-2796.2007.01877.x
28. Shone A., Burnside J., Chipchase S., Game F., Jeffcoate W.. **Probing the validity of the probe-to-bone test in the diagnosis of osteomyelitis of the foot in diabetes**. *Diabetes care* (2006) **29** 945. DOI: 10.2337/diacare.29.04.06.dc05-2450
29. U.S Food and Drug Administration (2017a). FDA confirms increased risk of leg and foot amputations with the diabetes medicine canagliflozin (Invokana, Invokamet, Invokamet XR). United States: U.S Food and Drug Administration.. *FDA confirms increased risk of leg and foot amputations with the diabetes medicine canagliflozin (Invokana, Invokamet, Invokamet XR)* (2017a)
30. U.S Food and Drug Administration (2017b). FDA removes Boxed Warning about risk of leg and foot amputations for the diabetes medicine canagliflozin (Invokana, Invokamet, Invokamet XR). United States: U.S Food and Drug Administration.. *FDA removes Boxed Warning about risk of leg and foot amputations for the diabetes medicine canagliflozin (Invokana, Invokamet, Invokamet XR)* (2017b)
31. Zaccardi F., Webb D. R., Htike Z. Z., Youssef D., Khunti K., Davies M. J.. **Efficacy and safety of sodium-glucose co-transporter-2 inhibitors in type 2 diabetes mellitus: Systematic review and network meta-analysis**. *Diabetes Obes. Metab.* (2016) **18** 783-794. DOI: 10.1111/dom.12670
32. Zelniker T., Wiviott S., Raz I., Im K., Goodrich E., Bonaca M.. **SGLT2 inhibitors for primary and secondary prevention of cardiovascular and renal outcomes in type 2 diabetes: A systematic review and meta-analysis of cardiovascular outcome trials**. *Lancet (London, Engl.* (2019) **393** 31-39. DOI: 10.1016/s0140-6736(18)32590-x
33. Zhang L., Wang Y., Zhao J., Li Y., Chen H.. **Sex differences in osteomyelitis of the foot in persons with diabetes mellitus: A meta-analysis**. *Wound Manag. Prev.* (2021) **67** 19-25. DOI: 10.25270/wmp.2021.5.1925
34. Zhu J., Yu X., Zheng Y., Li J., Wang Y., Lin Y.. **Association of glucose-lowering medications with cardiovascular outcomes: An umbrella review and evidence map**. *Lancet Diabetes and Endocrinol.* (2020) **8** 192-205. DOI: 10.1016/s2213-8587(19)30422-x
|
---
title: CD4+ T cells expressing CX3CR1, GPR56, with variable CD57 are associated with
cardiometabolic diseases in persons with HIV
authors:
- Celestine N. Wanjalla
- Curtis L. Gabriel
- Hubaida Fuseini
- Samuel S. Bailin
- Mona Mashayekhi
- Joshua Simmons
- Christopher M. Warren
- David R. Glass
- Jared Oakes
- Rama Gangula
- Erin Wilfong
- Stephen Priest
- Tecla Temu
- Evan W. Newell
- Suman Pakala
- Spyros A. Kalams
- Sara Gianella
- David Smith
- David G. Harrison
- Simon A. Mallal
- John R. Koethe
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9971959
doi: 10.3389/fimmu.2023.1099356
license: CC BY 4.0
---
# CD4+ T cells expressing CX3CR1, GPR56, with variable CD57 are associated with cardiometabolic diseases in persons with HIV
## Abstract
Persons with HIV (PWH) on long-term antiretroviral therapy (ART) have a higher incidence and prevalence of cardiometabolic diseases attributed, in part, to persistent inflammation despite viral suppression. In addition to traditional risk factors, immune responses to co-infections such as cytomegalovirus (CMV) may play an unappreciated role in cardiometabolic comorbidities and offer new potential therapeutic targets in a subgroup of individuals. We assessed the relationship of CX3CR1+, GPR56+, and CD57+/- T cells (termed CGC+) with comorbid conditions in a cohort of 134 PWH co-infected with CMV on long-term ART. We found that PWH with cardiometabolic diseases (non-alcoholic fatty liver disease, calcified coronary arteries, or diabetes) had higher circulating CGC+CD4+ T cells compared to metabolically healthy PWH. The traditional risk factor most correlated with CGC+CD4+ T cell frequency was fasting blood glucose, as well as starch/sucrose metabolites. While unstimulated CGC+CD4+ T cells, like other memory T cells, depend on oxidative phosphorylation for energy, they exhibited higher expression of carnitine palmitoyl transferase 1A compared to other CD4+ T cell subsets, suggesting a potentially greater capacity for fatty acid β-oxidation. Lastly, we show that CMV-specific T cells against multiple viral epitopes are predominantly CGC+. Together, this study suggests that among PWH, CGC+ CD4+ T cells are frequently CMV-specific and are associated with diabetes, coronary arterial calcium, and non-alcoholic fatty liver disease. Future studies should assess whether anti-CMV therapies could reduce cardiometabolic disease risk in some individuals.
## Introduction
Persons with HIV (PWH) are at increased risk of cardiovascular and metabolic diseases compared to the general population [1], which has been attributed to persistent systemic inflammation despite the effective suppression of plasma viremia on antiretroviral therapy (ART) (2–4). Among PWH, co-infection with other viruses, such as cytomegalovirus (CMV) and hepatitis B and C, increases the risk of diabetes, cardiovascular disease, and cerebrovascular events, as well as other non-AIDS illnesses (5–8). The adaptive immune system devotes a relatively large proportion of memory CD4+ and CD8+ T cells to the anti-CMV response as compared to other viruses (9–12), and this disproportionate inflation is further exaggerated in PWH compared to age-matched individuals without HIV [10, 13]. As such, HIV presents an important natural model to investigate how sustained exposure to CMV affects various aspects of the immune response and contributes to other aging-related disease processes.
CMV is a herpesvirus that co-evolved with mammals and infects many individuals at a young age, and CMV is highly prevalent in some groups including adult PWH [14, 15]. Growing evidence suggests that anti-CMV T cell responses have a role in metabolic dysregulation in both animal models [16] and humans (17–22). CMV-seropositivity has been shown to predict severe non-AIDS-related illnesses and is an independent risk factor for cardiovascular and cerebrovascular disease in PWH [8, 23, 24]. In PWH, the high prevalence of CMV is such that CMV-seropositivity alone does not stratify individuals at risk of developing these comorbidities. However CMV antibody titers were not associated with cardiovascular mortality in HIV-negative community-dwelling adults, suggesting antibody titers alone may not fully reflect the impact of anti-CMV immune responses on disease risk. [ 25].
Prior studies from our group suggest that an increase in CMV-specific CD4+ T cells in PWH may serve as a novel marker of metabolic and cardiovascular disease risk. We previously showed that adipose tissue CD4+ T cells co-expressing CX3CR1, GPR56, and CD57 (termed ‘CGC+’ cells), a surface marker combination suggestive of antiviral activity, increased with progressive glucose intolerance [26] and with carotid plaque burden in PWH [18]. Furthermore, we showed that CMV-specific CD4+ T cells that recognized an immunodominant peptide epitope (DYSNTHSTRYV from glycoprotein B (gB)) restricted through HLA-DR7 were predominantly CGC+ [17, 18]. Further, we demonstrated that CGC+ CD4+ T cells were cytotoxic and oligoclonal [17]. Despite several studies describing a role for CMV-seropositivity in morbidity and mortality in PWH [8], little is known about the role of CMV-specific CD4+ and CD8+ T cells, target viral epitopes, and the basic mechanisms that mediate these pathologies.
A study of pneumococcal vaccine responses in persons with antineutrophil cytoplasmic antibody-associated vasculitis showed that treatment with valacyclovir reduced subclinical CMV and the proportion of CD4+ CD28- T cells, and improved responses to the vaccine [27]. The expansion of CD4+ CD28- T cells was thought to have reduced the functional capacity of the CD4+ T cell memory compartment, and thereby reduced responses to vaccines. Although valacyclovir is not a recommended first-line therapy against CMV, this study suggests the possible role of anti-CMV therapy in reducing the proportion of CD4+ CD28- T cells, a population with considerable overlap with CGC+ CD4+ T cells [26]. A similar study in PWH showed that treatment with valganciclovir reduced detectable CMV DNA levels and reduced CD8+ T cell activation, defined by CD38 and HLA-DR expression, at 8 weeks and 12 weeks of treatment [28]. Notably, there was no difference in soluble inflammation biomarkers between the placebo group and those treated with valganciclovir, suggesting this effect was primarily on circulating T cells. These findings are important clinically, as they suggest that the anti-CMV T cell response is malleable, and if CD4+ CD28- or CGC+ T cells contribute to cardiometabolic disease pathogenesis the use of anti-viral agents could serve as a novel therapeutic strategy.
The goal of the current study was to [1] characterize the CMV specificity of CGC+ cells using a broader range of tetramer staining, [2] evaluate the relationship between peripheral blood CGC+ T cells with cardiovascular and metabolic diseases among a large cohort of PWH on ART, and [3] determine the relationships between CGC+ T cells and traditional cardiovascular disease risk factors. Using a wide array of class I and II CMV tetramers, we show that CMV-specific T cells are CX3CR1+ and GPR56+ with variable expression of CD57, and are among the cluster defined as CGC+. We show that circulating CGC+ CD4+ T cells and CGC+ CD8+ T cells in PWH are associated with prevalent cardiometabolic conditions (diabetes, subclinical atherosclerosis, and liver disease). Among individual disease risk factors, CGC+ T cells are related most strongly to fasting blood glucose and hemoglobin A1C. CGC+ T cells are also correlated with starch and sucrose metabolites measured in the plasma of PWH. Notably, total memory CD4+ and CD8+ T cells do not have a similar relationship with starch and sucrose metabolites. The relationship between circulating CGC+ T cells and fasting blood glucose does not appear to be driven by a greater dependence on glucose metabolism as a source of energy when compared to other memory T cell subsets. However, higher expression of carnitine palmitoyl transferase 1A (CPT1A) by CGC+ T cells may be due to a greater capacity for fatty acid β-oxidation CPT1A. These findings suggest that CGC+ T cell expansion associated with cardiometabolic disease in PWH may be driven by CMV co-infection. Additional prospective studies defining the antigen specificity of CGC+ T cells and their mechanistic role in cardiometabolic disease pathogenesis are underway.
## Study participants
From August 2017 and November 2019, we recruited 134 adults PWH without diabetes (fasting blood glucose (FBG) <100 mg/dl and/or hemoglobin A1c (HbA1c) <$5.7\%$), with pre-diabetes (FBG 100-125 mg/dl and/or HbA1c 5.7-$6.4\%$) or with diabetes (FBG ≥ 126 mg/dl and/or HbA1c ≥$6.5\%$ or on anti-diabetic medications) to the HIV, Adipose Tissue Immunology and Metabolism (HATIM) study from the Vanderbilt Comprehensive Care Clinic, an academic, urban HIV treatment facility [17]. All participants were on ART combination therapy for ≥18 months, with a minimum of 12 months of sustained plasma viral suppression, a CD4+ T cell count >350 cells/μl, and no known inflammatory or rheumatologic conditions. Exclusion criteria were self-reported heavy alcohol use (>11 drinks/week), known liver cirrhosis, active hepatitis B or C, cocaine or amphetamine use, and use of corticosteroids or growth hormones. Anthropometric measurements including waist circumference, height, weight, and body mass index (BMI) were obtained on the day of recruitment (Table 1). Diabetic PWH were older, with significantly fewer smokers. Participants provided written informed consent, and the study was approved by the Vanderbilt University Institutional Review Board. The study is registered at ClinicalTrials.gov (NCT04451980).
**Table 1**
| Unnamed: 0 | N | Non-Diabetic PWH N=51 | Pre-Diabetic PWH N=44 | Diabetic PWH N=39 | Test Statistic |
| --- | --- | --- | --- | --- | --- |
| Age, yrs | 134 | 45 [36, 52] | 44 [36, 56] | 54 [49, 58] | 0.001 |
| Sex, male | 134 | 0.80 41/ 51 | 0.80 35/ 44 | 0.72 28/ 39 | 0.6 |
| Race, Caucasian | 134 | 0.59 30/ 51 | 0.52 23/ 44 | 0.46 18/ 39 | 0.5 |
| Smoker status, yes | 131 | 0.36 18/ 50 | 0.30 13/ 44 | 0.11 4/ 37 | 0.001 |
| Hepatitis C ab status | 134 | 0.20 10/ 51 | 0.09 4/ 44 | 0.13 5/ 39 | 0.3 |
| HTN, yes | 134 | 0.63 32/ 51 | 0.57 25/ 44 | 0.67 26/ 39 | 0.9 |
| BMI (Kg/m2) | 134 | 30.7 [28.1, 34.1] | 31.8 [29.0, 35.3] | 33.8 [30.3, 39.2] | 0.01 |
| Waist circumference (cm) | 132 | 104 [92, 109] | 106 [95, 113] | 112 [107, 120] | 0.001 |
| Laboratory values | Laboratory values | Laboratory values | Laboratory values | Laboratory values | Laboratory values |
| Hemoglobin A1C, % | 132 | 5.3 [4.9, 5.4] | 5.6 [5.2, 5.9] | 6.8 [6.2, 8.9] | <0.001 |
| FBG, mg/dL | 131 | 90 [83, 94] | 111 [104, 118] | 161 [128, 234] | 0.001 |
| Creatinine, mg/dL | 132 | 1.0 [0.9, 1.1] | 1.0 [0.8,1.1] | 1.0 [0.9, 1.3] | 0.3 |
| LDL, mg/dL | 131 | 96 [84, 120] | 110 [93, 127] | 90 [80, 105] | 0.03 |
| Cholesterol, mg/dL | 133 | 170 [149, 202] | 180 [166, 212] | 175 [150, 196] | 0.3 |
| HDL, mg/dL | 133 | 44 [36, 54] | 41 [34, 50] | 40 [34, 46] | 0.5 |
| Triglycerides, mg/dL | 133 | 104 [77, 170] | 128 [90, 196] | 165 [114, 262] | 0.006 |
| HsCRP, mg/dL | 131 | 2.7 [1.2, 5.1] | 2.7 [1.1, 4.1] | 3.0 [2.1, 7.7] | 0.2 |
| Statin use, yes | 124 | 0.22 10/ 45 | 0.32 14/ 44 | 0.63 22/ 35 | <0.001 |
| Non-contrast CT imaging | Non-contrast CT imaging | Non-contrast CT imaging | Non-contrast CT imaging | Non-contrast CT imaging | Non-contrast CT imaging |
| Pericardial fat, cm3 | 112 | 55 [35,80] | 75 [45,108] | 91 [64,202] | 0.008 |
| Visceral fat, cm3 | 113 | 143 [90, 163] | 169 [120, 215] | 200 [130, 280] | <0.001 |
| Liver mean density, hu | 112 | 63.0 [58.6, 65.6] | 62.3 [55.0, 67.2] | 60.5 [46.2, 63.0] | 0.04 |
| CAC prevalence, yes | 113 | 0.02 1/ 41 | 0.33 12/ 36 | 0.44 16/ 36 | <0.001 |
| HIV-related Laboratory Values | HIV-related Laboratory Values | HIV-related Laboratory Values | HIV-related Laboratory Values | HIV-related Laboratory Values | HIV-related Laboratory Values |
| CD4 at ART start, cells/ml | 130 | 508 [342, 652] | 424 [310, 554] | 462 [249, 620] | 0.6 |
| CD4 T count at enrollment, cells/ml | 134 | 799 [596, 942] | 832 [627, 1016] | 945 [732, 1154] | 0.08 |
| CD4 cell percentage | 134 | 37.0 [31.5, 41.0] | 34.5 [28.8, 38.5] | 40.0 [35.0, 45.5] | 0.004 |
| Duration ART, yrs | 131 | 6.7 [4.3, 11.6] | 7.1 [3.1, 11.2] | 8.9 [5.0, 16.2] | 0.2 |
## Sample collection
Subcutaneous adipose tissue was obtained from participants by liposuction and the stromal vascular fraction (SVF) was processed within 30 minutes to 1 hour of the procedure as previously published in detail [17]. Peripheral blood mononuclear cells (PBMCs) were processed by Ficoll gradient. PBMCs and SVF from all participants were cryopreserved and subsequent assays were performed at a later date in batches. We also re-analyzed data single-cell metabolic profiling of T cells obtained from healthy human donors at the Stanford Blood Center, according to the guidelines of the Stanford Institutional Review Board [29].
## Computed tomography imaging
We performed non-contrast computed tomography (CT) imaging within 1 week of blood collection and anthropometric measurements. This was performed using a Siemens Somatom Force multidetector scanner (Erlangen, Germany). Total coronary arterial calcium (Agatston units, Au) was measured in the left anterior descending (LAD), left main (LM), left circumflex (LCX), and right coronary artery (RCA). For our analysis, coronary arterial calcium (CAC) was treated as a categorical variable (presence or absence of coronary CAC). The mean coronary cross-sectional area (external diameter of the outer walls) was measured at three equidistant points of the LAD. The mean coronary cross-sectional area (corCSA) was derived from the mean of three points and used as a surrogate for arterial remodeling. Non-alcoholic fatty liver disease was defined by liver attenuation, which was averaged from nine total regions using the open-source OsiriX software field [30]. Perivascular adipose tissue (PAT) volume (adipose tissue around the LM coronary, LAD, circumflex, and RCA) and epicardial adipose were measured as previously described [30].
## Flow cytometry
A multiparameter flow cytometry antibody panel was used to stain PBMCs [17, 18, 26]. The panel used to define CGC+ cells included anti-CD3, CD4, CD8, CCR7, CD45RO, GPR56, CX3CR1, CD57, CD14, CD19, and LIVE/DEAD Aqua (Supplemental Table 1). We included Class I and Class II CMV tetramers with this panel to identify virus-specific T cells. CMV Class I (pp65 [HLA-A02 NLVPMVATV (NLV)]) and Class II tetramers (gB [HLA DR1:07 DYSNTHSTRYV (DYS), pp65 [HLA DR1:03 EFFWDANDIYRIF (EFF)], IE2 [HLA DR1:03 TRRGRVKIDEVSRMF (TRR), IE1 [HLA DR1:03 VKQIKVRVDMVRHRI (VKQ)] and pp65 [HLADQB1*06:02 LLQTGIHVRVSQPSL (LLQ)] were obtained from the NIH tetramer facility supported by contract 75N93020D00005 from the National Institute of Allergy and Infectious Diseases. The analysis was performed using the BD FACS Aria II flow cytometer. Bulk and single-cell sorting was performed using a 70μm nozzle into 96 well plates or Eppendorf tubes, respectively, as previously published [17]. An additional panel with fluorescently tagged antibodies was used to further characterize KLRG1, CD27, and CD28 expression on CGC+ T cells (anti-GPR56, CCR7, CD38, KLRG1, CD14, CX3CR1, CD45RO, CXCR3, PD1, CD27, CD57, CD3, Live/Dead stan, CD8, CD4, CD28, and CXCR5) (Supplemental Table 1). These samples were run on a Cytek/Aurora. We used Cytobank to analyze the flow cytometry and mass cytometry data [31]. The gating strategy used to define the immune subsets is shown in the Supplemental Material (Supplemental Figure 1).
## Metabolomics sample extraction
Plasma samples were aliquoted at 25 µl and spiked with 5 µL of metabolomics internal standards solution. Extraction of metabolites was performed by protein precipitation by adding 200 µL of 8:1:1 Acetonitrile: Methanol: Acetone (Fisher Scientific, San Jose, CA) to each sample. Samples were mixed thoroughly, incubated at 4°C for 30 min to allow protein precipitation, and centrifuged at 20,000xg to pellet the proteins. After centrifugation, 190 µl supernatant was transferred into a clean microcentrifuge tube and dried under a gentle stream of nitrogen at 30°C (Organomation Associates, Inc., Berlin, MA). Samples were reconstituted with 25 µL of injection standards solution, mixed, and incubated at 4°C for 10-15 min. Reconstituted samples were centrifuged at 20,000xg and supernatants were transferred into LC-vials for LC-MS analysis.
## Metabolomics LC-MS analysis
LC-MS untargeted metabolomics was performed on a Thermo Q-Exactive Orbitrap mass spectrometer equipped with a Dionex UPLC system (Thermo, San Jose, CA). Separation was achieved on an ACE 18-pfp 100 x 2.1 mm, 2 µm column (Mac-Mod Analytical, Inc., Chadsford, PA) with mobile phase A as $0.1\%$ formic acid in water and mobile phase B as acetonitrile (Fisher Scientific, San Jose, CA). The gradient was run at a flow rate of 350 µL/min and consisted of: 0-3 min, $0\%$ B; 3-13 min, $80\%$ B, 13-16 min, $80\%$ B, 16-16.5 min, $0\%$ B. The total run time was 20.5 min. The column temperature was set at 25°C. The injection volume was 4 µL for negative and 2 µL for positive polarity. All samples were analyzed in positive and negative heated electrospray ionization with a mass resolution of 35,000 at m/z 200 as separate injections. The heated-electrospray conditions are 350°C capillary temperature, 3.5 kV capillary voltage, 50 sheath, and 10 arbitrary units of auxiliary gas. LC-MS injection was done following a sequence of 3 blanks, neat QC, pooled QC, 10 randomized samples, blank, neat QC, pooled QC, 10 randomized samples, and so on.
## Metabolomics data processing
The percent relative standard deviation of internal standard peak areas was calculated to evaluate extraction and injection reproducibility. The raw files were then converted to mzXML using MS Convert (ProteoWizard, Palo Alto, CA). Mzmine 2 was used to identify features, deisotope, align features and perform a gap filling to fill in any features that may have been missed in the first alignment algorithm. The data were searched against an internal retention time metabolite library. All adducts and complexes were identified and removed from the data set.
## Mass cytometry by time of flight
We used cytometry by time of flight (CyTOF) to further define cell surface markers expressed on CGC+ T cells. In brief, cryopreserved PBMCs were thawed and treated with Nuclease S7. After two washes, the cells were stained with LIVE/DEAD Cisplatin stain for 3 minutes, followed by quenching, and then stained with a master mix of CyTOF antibodies against surface markers. Sixteen percent PFA was used to fix cells for 15 minutes at room temperature. After one wash, we resuspended cells in 1mL cold methanol, and caps were sealed with parafilm before incubating overnight at -20°C. On the day the cells were to be analyzed, we washed them with 1X PBS/$1\%$ BSA. They were then stained with the intracellular marker CTLA4 for 20 minutes at room temperature. This was followed by staining with a 25μM DNA intercalator (Ir) in the presence of $1.6\%$ PFA for 20 minutes at room temperature and then transferred to 4C until analyzed. Just before analysis on Helios, we washed cells with PBS followed by a wash with Millipore H2O. We resuspended 500,000 cells/ml (in minimum 500uL) ddH2O for the CyTOF run. We added $\frac{1}{10}$th volume of equilibration beads to the cells and filtered the cells immediately before running. The Cytof panel included antibodies that define T cells (CD3, CD4, CD8) and memory subsets (CD45RA, CD45RO, CCR7).
For the metabolic profiling, cryopreserved PBMCs from healthy donors were thawed in a cell culture medium (CCM; RPMI 1640 containing $10\%$ FBS, and GlutaMAX; Thermo Fisher Scientific) supplemented with 1:10000x Benzonase (Sigma-Aldrich). Cells from different donors were live cell barcoded using CD45 antibodies as previously described [32], then washed and combined for downstream cell staining. Cells were suspended in TruStain FcX Fc blocker (BioLegend) for 10 min at RT and washed in cell staining media (CSM: PBS with $0.5\%$ BSA and $0.02\%$ sodium azide) before staining. Surface staining was performed in CSM for 30 min at RT. Cells were resuspended in monoisotopic cisplatin-195 for 5 min to label non-viable cells (Fluidigm, 0.5 uM final concentration in PBS). Cells were washed in CSM and fixed and permeabilized using the Foxp3/Transcription Factor Staining Buffer Set (eBiosciences). Intracellular staining was performed in a permeabilization buffer for 30 min at RT. Cells were then washed and resuspended in intercalator solution ($1.6\%$ PFA in PBS and 0.5 mM rhodium-intercalator (Fluidigm)) for 1 hr at RT. Cells were washed and resuspended in CSM + $10\%$ DMSO and cryopreserved. Before the acquisition, cells were thawed in CSM and washed twice in Cell Acquisition Solution (CAS; Fluidigm). All samples were filtered through a 35 mm nylon mesh cell strainer, resuspended in CAS supplemented with 1x EQ four-element calibration beads (Fluidigm), and acquired on a Helios mass cytometer (Fluidigm).
## Primary CD4 and CD8 T cell expansion
CGC+ and non-CGC+ CD4+ and CD8+ T cells were sorted from PBMCs stained with the multiparameter flow cytometry panel. Greater than $90\%$ purity was confirmed by spot checks of sorted cells. The CGC+ T cells were expanded using the ImmunoCult™ Human CD3/CD28 T Cell Activator (Stem Cell Technologies, #10991). Cells were expanded per the manufacturer’s protocol with the replacement of media supplemented with human interleukin (IL)-2 (10 to 50ng/ml) every 2-3 days, depending on the density of the cells. Cells were expanded past 14 days and were re-stimulated once more by the addition of CD3/CD28.
## Plasma CMV IgG levels
CMV IgG levels were measured in the plasma by ELISA per the manufacturer’s protocol (Genway, # GWB-BQK12C).
## Single-cell TCR sequencing
Single-cell T cell receptor (TCR) sequencing was performed as published [17]. In brief, we stained PBMCs and index-sorted CGC+CD4+ T cells by flow cytometry into 96- well plates containing 3µL of lysis buffer with a ribonuclease inhibitor [33, 34]. We used uniquely tagged primers (TSOend primer and the constant region primers, TCRA or TCRB) for reverse transcription, which tags the cDNA with well-specific barcodes coupled with a unique molecular identifier (UMI) to allow for multiplexing. Samples from each well were then pooled and amplified using the KAPA HiFi HotStart ReadyMix (Roche, Basel, Switzerland). Nested polymerase chain reactions were performed to target the TCR region specifically. We purified the PCR products using Agencourt AMPure XP (Beckman Coulter, CA, UWA) and indexed libraries were created for sequencing using Truseq adapters. The prepared libraries were quantified using the KAPA Universal qPCR Library Quantification Kit (Kapa Biosystems Inc., MA, USA). The products were sequenced on an Illumina MiSeq using a 2×300bp paired-end chemistry kit (Illumina Inc., CA, USA). Reads were quality-filtered and passed through a demultiplexing tool to assign reads to individual wells and mapped to the TCRB and TCRA loci. We used the MIXCR software package to assign TCR clonotypes. We used the visual genomics analysis studio (VGAS), an in-house program for visualizing and analyzing TCR data (http://www.iiid.com.au/software/vgas).
## RNA transcriptomic analysis
Our group previously sorted CGC+CD4+ and CGC+CD8+ T cells, as well as other memory T cells, for RNA transcriptomic analysis as published [17]. Here, we performed a secondary differential expression analysis to assess differences in 475 genes involved in metabolic pathways between cell types (Supplemental Table 3).
## Cytokine assays
We measured interleukin (IL)-4, IL-10, IL-6, and highly sensitive reactive protein (hs-CRP) in plasma using a multiplex assay (Meso Scale Diagnostics, Rockville, MD) as previously published [35].
## Cellular metabolic assays
We analyzed the metabolic profile of CGC+CD4+ T cells and CGC+CD8+ T cells ex vivo using the SCENITH assay as published [36]. In brief, cryopreserved PBMCs were rapidly thawed and resuspended in RPMI media supplemented with $10\%$ fetal bovine serum. After two washes, the cells were stained with antibodies against CCR7 and CX3CR1 at 37°C for 15 minutes. The cells were added in duplicate per condition to 96-well plates at about 1 million cells per well in 180ul R10 media. They were rested for 15-30 minutes at 37°C. 2-Deoxy-D-glucose (2mM), oligomycin (3mM), and DGO (1mM 2DG and 1.5mM oligomycin) were added to the cells, which were incubated at 37°C for 30 minutes. This was followed by the addition of puromycin (10μM) to each well, incubated at 37°C. We included samples without puromycin as controls. The cells were incubated at 37°C for 30 minutes. Cells were spun down and washed with PBS twice. These were then stained with surface antibodies for 15 minutes at room temperature. The cells were washed twice and fixed using the Foxp3/Transcription factor fixation fix/perm solution (20 minutes) and then washed with the Foxp3/Transcription factor permeabilization buffer. This buffer was used to dilute the anti-puromycin antibody. Cells were incubated with the anti-puromycin antibody for 20 minutes at room temperature. Cells were then washed with PBS and immediately analyzed by flow cytometry.
We sorted CGC+ and non-CGC+ CD4+ and CD8+ T cells and expanded them as above to obtain enough cells for the Seahorse assay. After 12 days of expansion, 100,000 cells per well were plated on Cell-Tak (Corning) coated plates in Seahorse XF Base Medium (Agilent, 102353-100) supplemented with 1 mM L-glutamine and 1 mM pyruvate at pH 7.4. Seeded cells were centrifuged at 200 g without break and incubated for 1 h at 37° C in a non-CO [2] chamber. ECAR measurements were taken using the Agilent Seahorse XF96 analyzer under basal conditions and after consecutive injections with 10 mM Glucose, 1.5 uM Oligomycin, and 50 mM 2-deoxy-glucose (2-DG). Control wells with assay medium lacking cells were used for background measurements.
## Statistical analysis
Continuous variables/clinical demographics are presented as median values [25th and 75th percentiles], and statistical analyses comparing the three metabolic groups were performed using the Kruskal-Wallis test. Differences between categorical variables, represented as proportions, were analyzed using the Chi-squared test. TCR and RNA transcriptomic analyses were performed using the visual genomics analytics studio tool (VGAS) [37]. *Differential* gene expression between CGC+ T cells and non-CGC+ T cells was performed using Kruskal Wallis tests, and adjustment for multiple corrections using the Benjamini Hochberg (BH) method. The top differentially expressed genes, p-value < 0.1, were included in the KEGG pathway and Gene Ontology pathway enrichment analysis using Enrichr and Appster (38–40). The relationship between CGC+ T cells and plasma metabolites was analyzed using Spearman’s rank correlation. Metabolomic pathways represented by metabolites correlated with CGC+ T cells were analyzed using MetaboAnalyst 5.0. Over Representation Analysis (ORA) of the significant plasma metabolites was performed using the hypergeometric test. One-tailed adjusted p values are provided after correcting for multiple testing (FDR). We used the Kruskal-Wallis test to analyze differences in the proportions of immune cell subsets between two groups/treatments, and Wilcoxon tests when there were more than two groups. Relationships between immune subsets and anthropometric or clinical laboratory measurements were determined using Spearman’s rank correlation analysis. We also measured relationships between the immune subsets and other factors after adjustment for potential confounders using partial Spearman’s rank analysis. Statistical analysis was performed using R version 4.1.0 [41] and Prism version 9.
## Data and code availability
The data presented in the study are deposited in the NIH GeneExpression Omnibus repository, accession number GenBank: GSE159759. *Differential* gene expression was performed using custom software, Visual genomics analysis studio (VGAS) [37].
## CMV-specific CD8+ T cells co-express CX3CR1 and GPR56 with variable CD57
We previously defined CMV-specific CD4+ T cells that bind the DYSNTHSTRYV epitope (DYS, HLA-DR7, glycoprotein B (gB)) as CGC+, however the extent to which this subset of cells is CMV-specific is unknown [18]. We characterized CGC+ T cells using additional markers that have been associated with CMV-specific T cells. Two-dimensional plots show CX3CR1, GPR56, and CD57 expression on CD8+ T cells in two HIV-negative donors (CMV-negative and CMV-positive) and four CMV-positive PWH (Figure 1A). We used the Uniform Manifold Approximation and Projection (UMAP) algorithm to visualize clusters of CD8+ T cells related by marker expression. The CGC+ cluster on CD8+ T cells had variable CD57 expression (Figure 1A, UMAPs) and expressed the killer-cell lectin-like receptor G1 (KLRG1, a marker associated with senescence) (Figure 1B). Additional markers included in the panel defined the CGC+ CD8+ T cell cluster as largely made up of T effector memory RA-revertant (TEMRA) cells (CD45RO- CCR7-), CD28-, CD27+/- and CD38+ (Supplemental Figures 2A, B).
**Figure 1:** *CGC+ CD8+ T cells express CX3CR1 and GPR56 with variable expression of CD57. The two-dimensional flow cytometry panel shows the co-expression of CX3CR1 and GPR56 and highlights CD57 expression on these cells compared to non-CGC+ T cells. UMAP shows CGC+ CD8+ cluster and variable expression of CD57 (A). KLRG1 expression on CGC+ CD8+ T cells is demonstrated by two-dimensional plots and UMAP (B). Participants in this analysis included two HIV-negative persons with and without CMV, and four CMV-positive PWH.*
We characterized the CMV-specificity of CD8+ T cells from a subset of HLA-typed individuals (Supplemental Table 2). In participant #1, we evaluated CMV-specific CD8+ T cells in the peripheral blood and adipose tissue using a class I tetramer against the HLA-A*02-01 binding CMV 65 kilodalton phosphoprotein (pp65) epitope495-503 NLVPMVATV (NLV). NLV tetramer+ cells constituted $2.0\%$ of total CD3+ T cells and $3.8\%$ of total CD8+ T cells in the peripheral blood of participant #1 (Figures 2A, B). Two-dimensional flow cytometry plots show that NLV-tetramer+ CD8+ T cells co-express CX3CR1 and GPR56 with variable expression of CD57 (Figure 2C). $65.7\%$ of the NLV-tetramer+ CD8+ T cells are TEMRA and the rest effector memory T cells (TEM) (Figure 2D). NLV tetramer+ cells were present in the cluster of CX3CR1+ and GPR56+ cells with variable expression of CD57 (Figure 2E). We gated on the CGC+ CD8+ cluster and show the expression of the NLV tetramer, CX3CR1, GPR56 and CD57 (Figure 2F). The coloring channel highlights NLV-tetramer+ cells (bright red) and shows that $8.4\%$ of the CGC+ CD8+ T cell cluster from participant #1 express TCRs that bind the NLV tetramer. For this participant #1, we also analyzed NLV tetramer+ CD8+ T cells in the adipose tissue, given the contribution of adipose tissue inflammation to the development of cardiometabolic disease. $1.2\%$ of total CD3+ T cells and $3.4\%$ of total CD8+ T cells expressed the NLV TCR (Figures 2G, H). These NLV-specific CD8+ T cells present in the adipose tissue also co-expressed CX3CR1 and GPR56, with variable CD57+ expression. Like the matched peripheral blood, $54.1\%$ of the NLV-tetramer+ T cells in adipose were TEMRA and the rest were TEM (Figures 2I, J). UMAPs show that NLV-tetramer+ CD8+ T cells present in the adipose tissue from participant #1 cluster with CGC+ CD8+ T cells, with a proportion of ~ $6.0\%$ (Figures 2K, L).
**Figure 2:** *CMV-specific CD8+ T cells are predominantly CGC+. Phenotypic expression of CX3CR1, GPR56, and CD57 by NLV-specific CD8+ T cells in two participants. Peripheral blood NLV-specific CD8+ T cells as a proportion of CD3+ T cells (A) and total CD8+ T cells (B). Two-dimensional plots show the co-expression of CX3CR1, GPR56, and CD57 by NLV-specific CD8 T cells (C). Memory cell phenotypes were classified as TEM (CD45RO+ CCR7-) and TEMRA (CD45RO- CCR7-) (D). UMAP of CD8+ T cells showing the CD8+ T cells with NLV tetramers among the CGC cells (E, F). The proportion of NLV-tetramer+ CD8+ T cells in matched SVF fraction (G, H). Co-expression of CGC markers (I) and memory T cell subsets (J) within UMAPs as the proportion of total CGC+ CD8+ T cells (K, L). A representative sample from the blood of a second participant sample is shown (M-R).*
To assess the heterogeneity among individuals, we analyzed CMV-specific responses in the peripheral blood of additional donors. Participant #2 had NLV+ tetramer cells that constituted $0.6\%$ of total CD3+ T cells (Figure 2M) and $1.4\%$ of total CD8+ T cells (Figure 2N). In this participant, NLV-specific CD8+ T cells were CX3CR1+ and GPR56+, with much less CD57 expression (Figure 2O). There was also a higher proportion of TEM cells among the gated NLV tetramer+ cells (Figure 2P). NLV tetramer-specific cells in participant #2 did not form a tight cluster as seen with participant #1 (Figure 2Q), and $1.9\%$ of CGC+ CD8+ T cells were NLV-tetramer specific (Figure 2R). Two additional HLA-A*02:01 PWH were also evaluated: participant #3 with $2.1\%$ NLV tetramer+ cells as a proportion of total CD3+ T cells (Supplemental Figures 3A-F) and participant #4 with $0.8\%$ NLV tetramer+ cells as a proportion of total CD3+ T cells (Supplemental Figures 3G-L). In summary, despite heterogeneity in immune cell markers, NLV-tetramer+ CD8+ T cells are largely present within the CGC+ cluster.
## CMV-specific CD4+ T cells co-express CX3CR1 and GPR56 with variable CD57
Like CGC+ CD8+ T cells, we also characterized markers expressed on CGC+ CD4+ T cells. The two-dimensional plots show CX3CR1 and GPR56 expression on the y and x-axis and highlight CD57 expression on the z-channel (Figure 3A). The CMV-negative donor had very few CGC+ CD4+ T cells, unlike CGC+ CD8+ T cells. The UMAP shows the separation of the CGC+ CD4+ T cell cluster from the rest of the CD4+ T cells. In addition, CGC+ CD4+ T cells also express KLRG1, which may be less variable than CD57 (Figure 3B). With additional markers, we can define CGC+ CD4+ T cells as TEM (CD45RO+ CCR7-) and TEMRA (CD45RO- CCR7-) cells that are CD28+/-, CD27-, PD1+/- and CD38+/-(Supplemental Figure 5). We used MHC Class II tetramers to identify CD4+ T cells recognizing two immunodominant CMV epitopes: DYS and LLQTGIHVRVSQPSL (LLQ, HLA-DQ06:02, pp65 protein) as previously published [10, 13]. Participants with HLA-DR7 and HLA-DQ6 were selected (Supplemental Table 2). $12.1\%$ of the total CD4+ T cells in participant #2 were DYS tetramer+ (Figure 4A). Two-dimensional flow cytometry plots show that DYS-tetramer+ CD4+ T cells also express CX3CR1 and GPR56 with variable CD57 (Figure 4B). $88.7\%$ of the DYS-tetramer+ CD4+ T cells were TEM, and the rest were TEMRA (Figure 4C). Visualization using the UMAP technique showed that the majority of the DYS tetramer+ cells were within the CGC+ CD4+ cluster (Figures 4D, E). Analysis of the DYS tetramer on the CGC+ CD4+ cluster showed that $42.0\%$ of CGC+ CD4+ T cells in participant #1 had TCRs that recognized the DYS epitope (Figure 4F). In participant #5 (Supplemental Table 2, HLA-DQ6+) LLQ-specific CD4+ T cells comprised $0.28\%$ of CD4+ T cells (Figure 4G). LLQ tetramer+ T cells co-expressed CX3CR1, and GPR56 with variable CD57 expression (Figure 4H). $93.6\%$ tetramer+ cells fell within the CGC+ cluster (Figures 4I-K). $1.3\%$ of CGC+CD4+ T cells in participant #5 were specific for the LLQ epitope (Figure 4L). In summary, the majority of the CD4+ T cells with TCRs that recognize two different immunodominant CMV epitopes are CGC+. CMV-specific CD4+ T cells in PWH are significantly inflated compared to matched HIV-negative controls [13]. These data suggest that in PWH without evidence of acute CMV infection at the time of the study, a large proportion of CGC+ T cells may be CMV-specific.
**Figure 3:** *CGC+ CD4+ T cells express CX3CR1 and GPR56 with variable expression of CD57. The two-dimensional flow cytometry panel shows the co-expression of CX3CR1 and GPR56 and highlights CD57 expression on these cells compared to non-CGC+ T cells. UMAP shows CGC+ CD4+ cluster and variable expression of CD57 (A). KLRG1 expression on CGC+ CD4+ T cells is demonstrated by two-dimensional plots and UMAP (B). Participants in this analysis included two HIV-negative persons with and without CMV, and four CMV-positive PWH.* **Figure 4:** *CMV-specific CD4+ T cells are predominantly CGC+. Phenotypic expression of CX3CR1, GPR56, and CD57 by CMV-specific CD4+ T cells that recognize two different epitopes (DYS and LLQ). Peripheral blood DYS-specific CD4 T cells as a proportion of CD3+ T cells (A). Two-dimensional plots show the co-expression of CX3CR1, GPR56, and CD57 by DYS-specific CD4 T cells (B). Memory cell phenotypes were classified as TEM (CD45RO+ CCR7-) and TEMRA (CD45RO- CCR7-) (C). UMAP of CD4+ T cells showing the CD4+ T cells with DYS tetramers among the CGC cells. Each panel shows the distribution of CX3CR1, GPR56, CD57, CD45RO, CCR7, and HLA-DR expression on the clusters (D-F). A second participant with LLQ-specific CD4+ T cells is shown (G-L).*
## Detection of low-frequency CMV-specific CD4+ T cells among expanded CGC+ T cells
To characterize CMV-specific T cells with TCRs to less immunodominant epitopes with a lower frequency of tetramer+ T cells in PBMCs, we flow-sorted and expanded CGC+ and non-CGC+ T cells as depicted in the schematic (Figure 5A). We made attempts to expand CGC+ CD8+ T cells but could not analyze this population due to a high proportion of cell death. ( Photos of expanded cell subsets on day 10 of culture are shown in Figure 5B). The morphology of the expanded CGC+ CD4+ T cells in some participants was distinct from the non-CGC+CD4+ and CD8+ T cells, with satellite clusters that we speculate may represent clonal expansion. Control MHC class II tetramers (HLA-DR3 and HLA-DQ6) with the CLIP peptides were also used to stain CGC+ CD4+ T cells from expanded cell lines (Figure 5C). We used the CMV tetramers to identify CD4+ T cells with TCRs to less dominant epitopes (TRRGRVKIDEVSRMF (TRR, HLA-DRB1*03:01, IE2 protein) and VKQIKVRVDMVRHRI (VKQ, HLA-DRB1*03:01, IE1 protein)). We measured tetramer+ CD4+ T cells (DYS, TRR, and VKQ) after a 10-day expansion of sorted CGC+ CD4+ T cells and compared them to unsorted PBMCs (Figure 5D). For some of the less dominant epitopes, we had improved detection after expansion in culture (TRR and VKQ). Sorted non-CGC+ CD4+ T cells expanded in culture for 10 days also had some CMV-specific CD4+ T cells by tetramer analysis (Figure 5E). Expanded CGC+ CD4+ cells maintained CX3CR1, GPR56, and CD57 expression on the tetramer+ cells (Supplemental Figures 5A, C, E). The tetramer+ CD4+ T cells that we detected in the expanded T cells from the non-CGC+ CD4+ sort also expressed CX3CR1 and GPR56 compared to the other cells in the same pool. This may suggest that some CGC+ T cells were among the non-CGC+ T cells obtained by sorting before expansion, or that CGC+ CD4+ T cells may be derived from non-CGC+ T cells (Supplemental Figures 5B, D, F). *In* general, CGC+ CD4+ T cells maintained higher levels of CX3CR1, GPR56, and CD57 in culture (Supplemental Figures 5G, H). Notably, there was no significant difference in the mean fluorescence intensity of the CX3CR1 and GPR56 between the tetramer+ cells in the CGC+ CD4+ population versus tetramer+ cells in the non-CGC+ CD4+ expanded T cells, while CD57 expression trended towards being higher in the expanded CGC+ CD4+ T cells (Supplemental Figure 5I). Taken together, CGC+ CD4+ T cells as we have defined them can proliferate in culture after CD3/CD28 stimulation with IL-2 supplementation. This is different from previous studies that failed to show the proliferation of CD57+ CD4+ T cells after stimulation with PHA [42] or HIV antigens [43]. In our studies, the CGC+ CD4+ T cell cluster appears to be driven by CX3CR1 and GPR56, which includes CD4+ T cells with variable CD57+ T cell expression that may undergo several rounds of replication. In summary, CMV-tetramer+ CD4+ T cells in PWH are mainly CX3CR1+ GPR56+ with variable expression of CD57, while similar cells present in the non-CGC+ cells have low expression of CD57.
**Figure 5:** *CMV-specific T cells among expanded CGC+ CD4+ and non-CGC+ CD4+ T cells. Schematic showing workflow for sorting and expansion of CGC+ and non-CGC+ cells (A). Cell expansion cultures from two participants (Supplemental Table 2, participants #4 & #7) on day 10 of expansion. The top figure shows duplicate wells, with the middle panel showing an enlarged image to highlight the satellite cultures in CGC+ CD4+ T cells by direct observation and by light microscopy (B). Tetramer staining controls on expanded cells (DRB1:07:01) and DRB1:03:01) with CLIP peptide (C). Two-dimensional flow cytometry plots showing CMV-specific T cells using tetramers against three different CMV MHC class II epitopes (DR7:DYS) and (DR3:TRR, DR3:VKQ) in non-sorted PBMCs and expanded CGC+ CD4+ T cells (D). Similar analysis on non-CGC+ CD4+ T cells gated from PBMCs and on expanded non-CGC+ CD4+ T cell line (E).*
## CGC+ CD4+ T cells have a large proportion of clonal TCRs and are largely CMV-specific
While we observed that CGC+ CD4+ T cells can recognize several CMV epitopes, it remains unclear the extent to which the CGC+ T cell cluster of cells is CMV-specific as a whole. To understand whether starting with CGC+ CD4+ T cells can help define CMV-specific T cells agnostic to HLA typing, we sorted single CGC+ CD4+ T cells into 96-well plates from two participants (#4 and #6, both HLA-DR7) as shown (Figure 6A). Two-dimensional plots show that $5.5\%$ of CD4+ T cells in participant #4 are DYS tetramer+ (Figures 6B, C) and largely TEM cells (Figure 6D). UMAPs show DYS tetramer+ T cells within the CGC+ T cell cluster (Figures 6E, F). Paired αβ TCR sequences from the sorted CGC+ CD4+ T cells are shown in Circos plots (Figure 6G). Clonal TCRs that did not have paired αβ pairs are not shown on the Circos plots. Out of a total of 41 TCRs with paired αβ pairs, $26.8\%$ had the CDR3 (CASSGGTGGGADTQYF). Other clonal TCRs are shown. Participant #6 was selected because of known DYS-specific CD4+ TCRs identified by bulk sequencing apriori [13]. We also sorted CGC+CD4+ T cells from participant #6 (Figures 6H-M). Out of a total of 70 TCR sequences with matched αβ TCR pairs, 5 of the top 10 clones among the CGC+ CD4+ T cells ($$n = 21$$) had been previously identified as CMV-DYS specific by sequencing TCRs from DYS tetramer+ CD4+ T cells [13]. This suggests CGC+ CD4+ T cells in PWH co-infected with CMV may be largely CMV-specific.
**Figure 6:** *CGC+ CD4+ T cells express clonal TCRs and are largely CMV-specific. Single CGC+ CD4+ T cells were sorted into 96 well plates as shown (A). Two-dimensional plots from participant #4 show DYS tetramer-specific T cells as a proportion of CD4+ T cells (B). CX3CR1, GPR56, and CD57 expression on DYS tetramer+ cells (C) and memory distribution of the tetramer+ cells are shown (D). UMAPs show the distribution of markers (E, F). A total of 40 paired TCRs had identifiable sequences. The Circos plot shows paired αβ TCR CDR3 sequences, and TRβV and paired TCRJ genes are shown (G). A similar analysis was done with participant #6 (H-M). CDR3 sequences in magenta have previously been shown to be DYS-specific, by tetramer staining.*
The frequency of peripheral blood CGC+ CD4+ T cells from participants in the full cohort of PWH was positively correlated with plasma anti-CMV IgG titers (ρ=0.21, $$p \leq 0.03$$), while CGC+ CD8+ T cells were not significant (ρ=0.18, $$p \leq 0.07$$) (Supplemental Figures 6A, B). Finally, CGC+ CD4+ T cells and CGC+ CD8+ T cells were strongly correlated with each other (Supplemental Figure 6C).
## CGC+ CD4+ T cells are higher in PWH with cardiometabolic disease
To investigate the extent to which CGC+ CD4+ and CGC+ CD8+ T cells differ in cardiometabolic disease conditions (diabetes, pre-diabetes, hypertension, coronary arterial calcium, nonalcoholic fatty liver disease, and pericardial fat volume), we measured the frequencies of CGC+ T cells in PBMCs as a proportion of total CD4+ and CD8+ T cells. We found similar proportions of CGC+ CD4+ and CGC+ CD8+ T cells in participants with and without hypertension (Figure 7A), while CGC+ CD4+ T cells were higher in participants with coronary arterial calcium (CAC) on CT imaging ($$p \leq 0.0009$$), (Figure 7B), and non-alcoholic fatty liver disease (NAFLD, defined as absolute liver attenuation less than 58 Hounsfield units [HU] on CT imaging [$$p \leq 0.04$$; Figure 7C]). Both CGC+ CD4+ and CGC+CD8+ T cells were significantly higher among participants with prediabetes and diabetes as compared to non-diabetics (Figure 7D). Notably, the frequency of CGC+ CD8+ T cells was positively correlated with pericardial fat volume as measured by CT imaging ($$p \leq 0.02$$), which was mainly driven by diabetic participants ($$p \leq 0.0008$$) (Figure 7E). A similar relationship was not observed for CGC+ CD4+ T cells and pericardial fat volume ($$p \leq 0.3$$). Collectively, these results indicate that there are more circulating CGC+ CD4+ and CGC+ CD8+ T cells in persons with HIV with subclinical atherosclerosis, NAFLD, pre-diabetes, and diabetes.
**Figure 7:** *HIgher CGC+ CD4+ T cells are associated with cardiometabolic disease. Box plots showing CGC+ CD4+ and CGC+ CD8+ T cells in the presence or absence of hypertension (HTN) (A), coronary arterial calcium (CAC) (B), non-alcoholic fatty liver disease (NAFLD) (C), and diabetes (D). Correlation plots show the relationship between CGC+ T cells and pericardial fat volume (E). Statistical analysis by Mann Whitney, Kruskal Wallis, and Spearman’s rank correlation tests.*
## Circulating CGC+ CD4+ and CGC+ CD8+ T cells are associated with fasting blood glucose and hemoglobin A1C
Prior murine studies showed the adoptive transfer of peripheral senescent CD8+ T cells (CD8+ CD44+ CD153+) from spleens of mice on a high-fat chow diet to mice on a normal chow diet was followed by insulin resistance in the recipient animals, suggesting a role for circulating T cells in the development of metabolic dysregulation [44]. This concept is further supported by epidemiologic analyses in PWH showing an association between a higher circulating CD4+ TEMRA cell frequency and the subsequent development of diabetes mellitus [45]. In light of these findings, we assessed the relationship between peripheral CGC+ CD4+ T cells (as a proportion of total CD4+ T cells) and CGC+ CD8+ T cells (as a proportion of total CD8+ T cells) with several cardiometabolic disease risk factors. CGC+ CD4+ T cells were correlated with waist circumference, and there was a trend towards a correlation with age and BMI (Figures 8A–C). These cells were most strongly correlated with fasting blood glucose in non-diabetic and pre-diabetic individuals ($$p \leq 0.002$$) (Figure 8D). The correlation with fasting blood glucose was stratified by the metabolic group because of the effect of diabetes medications on glucose levels (all diabetic participants were receiving anti-diabetes medications and had a fasting blood glucose range of 73-416 mg/dL). We did not observe a significant relationship between CGC+ CD4+ T cells and fasting blood glucose in diabetic PWH (=-0.13, $$p \leq 0.47$$). T cells can mediate inflammatory and anti-inflammatory effects through plasma cytokines. Therefore, we also assessed whether CGC+ T cells were related to inflammatory markers and anti-inflammatory cytokines important in cardiometabolic disease [35]. The frequency of CGC+ CD4+ T cells correlated with hsCRP ($$p \leq 0.01$$), IL-4 ($$p \leq 0.04$$), IL-10 ($$p \leq 0.04$$) but not IL-6 ($$p \leq 0.5$$) (Figures 8E-H). On the other hand, CGC+ CD8+ T cells were not correlated with age, BMI, or waist circumference, and had a modest correlation with fasting blood glucose in non-diabetic/pre-diabetic PWH ($r = 0.27$, $$p \leq 0.009$$), and a trend toward significance in diabetic PWH ($r = 0.33$, $$p \leq 0.05$$) (Figures 8I-L) CGC+ CD8+ T cells were also correlated with hsCRP (ρ=0.30, $$p \leq 0.005$$) (Figure 8M) but not IL-4, IL-10, or IL-6 (Figures 8N-P). Both CGC+ CD4+ T cells (ρ=0.23, $$p \leq 0.01$$) and CGC+ CD8+ T cells (ρ=0.27, $$p \leq 0.003$$) were correlated with hemoglobin A1C in a partial Spearman’s correlation analysis adjusted for age, sex, body mass index (BMI), hypertension, statin use, and smoking status (Figure 9, top). CGC+ CD4+ T cells had a stronger association with hemoglobin A1C when BMI was removed from the model (ρ=0.27, $$p \leq 0.004$$), while the relationship between CGC+ CD8+ T cells and hemoglobin A1C attenuated (ρ=0.24, $$p \leq 0.01$$) (Figure 9, bottom).
**Figure 8:** *The proportion of circulating CGC+ T cells in peripheral blood is correlated with fasting blood glucose. Spearman’s rank correlation analysis shows relationships between CGC+ CD4+ T cells and age (A), body mass index (BMI) (B), waist circumference (C), and fasting blood glucose stratified by non-diabetic and prediabetic (black) and diabetic (magenta) (D). Correlation plots showing relationships between % CGC+ CD4+ T cells and inflammatory markers including high sensitivity C-reactive protein (HsCRP) (E), IL-4 (F), IL-10 (G), and IL-6 (H). Similar analyses were performed with CGC+ CD8+ T cells (I-P). Statistical analysis by Spearman’s rank test.* **Figure 9:** *CGC+ T cells are associated with higher hemoglobin A1C levels in adjusted analyses. Partial Spearman’s rank correlation analysis adjusted for age, sex, HTN, BMI, statin use, and smoking status (top), and after removal of BMI from the model (bottom).*
## CGC+ CD4+ and CGC+ CD8+ T cells are correlated with circulating concentrations of starch/sucrose metabolites and branch-chain amino acids
Both fasting blood glucose and HbA1c can be influenced by multiple factors, including the time of day, hydration status, medications, red blood cell survival, genetics, and vitamin deficiencies, among others. Plasma metabolites offer an additional profile of metabolic status, and changes in metabolites are frequently present before the development of overt disease [46]. Therefore, we next assessed whether the observed association between CGC+ T cells and blood glucose measurements was also present for other plasma metabolites. We found that frequencies of CGC+ CD4+ T cells and CGC+ CD8+ T cells were positively correlated with plasma levels of branch chain amino acids (isoleucine and norleucine), carbohydrate metabolites (glucose/fructose, glucosamine, galactosamine, and fumarate), acetoacetate, and 2-beta-hydroxybutyric acid among others (Figure 10A). On the other hand, phosphocholine, L-Anserine, 3-Methylhistamine, Sulfino-L-Alanine, and Hydroxy-L-Tryptophan were inversely correlated with the frequencies of CGC+ CD4+ and CGC+ CD8+ T cells. Most of these correlations were not present for total memory CD4+ or CD8+ T cells (Supplemental Figures 7A, B). The frequency of CGC+ CD4+ cells positively correlated with metabolites that enriched for the starch and sucrose metabolism pathways (FDR 0.02) (Figure 10B), and negatively with other metabolites from the taurine and hypotaurine metabolism pathways (FDR 0.09, data not shown). Metabolites that were positively correlated with CGC+ CD8+ cells enriched for the pentose and glucuronate conversions, starch, and sucrose metabolism pathways but were not statistically significant (FDR 0.16) (Figure 10C). Similarly, metabolites that were negatively correlated with CGC+ CD8+ enriched for taurine and hypotaurine metabolism pathways but they were not significant (FDR 0.33, data not shown). Taken together, these results demonstrate that CGC+ T cells are related to starch and sucrose metabolites in plasma, as well as branched-chain amino acids (BCAA) which have been linked with the development of diabetes and cardiovascular disease (46–48).
**Figure 10:** *Starch and sucrose metabolism pathways are enriched among plasma metabolites positively correlated with CGC+ T cells. Forest plots showing Spearman’s rank correlation coefficients between plasma metabolites and the proportion of CGC+ CD4+ T cells over total CD4+ T cells (left panel) and % CGC+ CD8+ T cells over total CD8+ T cells (right panel) (A). The top twenty-five metabolite sets in the enrichment analysis (number of metabolites/expected metabolites per set) were determined based on metabolites that were positively correlated with % CGC+ CD4+ T cells (B) and % CGC+ CD8+ T cells (C). Statistical analysis by Spearman’s rank correlation. Color code: Blue, negative correlation p<0.05; Magenta, positive correlation p<0.05; black, non-significant correlation. Over Representation Analysis (ORA) of plasma metabolites that were associated with CGC+ T cells was performed using MetaboAnalyst 5.0 with the hypergeometric test. One-tailed adjusted p values are provided after correcting for multiple comparisons.*
## CGC+ CD4+ and CGC+ CD8+ T cells are predominantly mitochondrial-dependent
CD4+ TEMRA cells are senescent and bioenergetically flexible compared to CD8+ TEMRA cells, partly due to their ability to effectively engage both glucose metabolism and oxidative phosphorylation [49]. One possible explanation is that CD8+ TEMRA cells may have dysfunctional mitochondria [49]. Given the observed relationship between the frequency of CGC+ T cells with starch and sucrose metabolites, we studied their metabolic profile as a way to understand their functional capacity in PWH. We analyzed the mitochondrial and glucose dependence of T cells using SCENITH, Single Cell ENergetIc metabolism by profiIing Translation in inHibition. Puromycin uptake by CGC+ CD4+ and CGC+ CD8+ T cells after incubation with inhibitors was measured by geometric mean fluorescence (Figures 11A, C). *In* general, naïve cells (CD4+ and CD8+) had lower levels of puromycin uptake compared to T cell memory subsets (Figures 11A, C). We found that unstimulated CD4+ memory (TCM, TEM, TEMRA, and CGC+) T cell subsets were predominantly mitochondrial-dependent (Figure 11B). CD4+ naïve T cells had higher mitochondrial dependence compared to CD4+ TEM ($$p \leq 0.006$$). Although not significant, CGC+ CD4+ T cells had lower mitochondrial dependence and higher glycolytic capacity when compared to CD4+ naïve T cells ($$p \leq 0.05$$) (Figure 11B). CD8+ T cell memory subsets also had higher mitochondrial dependence. However, CD8+ TEM ($$p \leq 0.02$$) and TEMRA ($$p \leq 0.01$$) cells showed significantly higher glycolytic dependence than central memory CD8+ T cells (TCM) (Figure 11D). Unstimulated CGC+ CD8+ T cells also had higher glucose dependence than CD8+ TCM but this was not significant ($$p \leq 0.07$$) (Figure 11D). CGC+ CD4+ T cells that we expanded in culture using CD3/CD28/CD49d did not utilize glycolysis as effectively as non-CGC+ T cells (Supplemental Figures 8A, B). Notably, expanded CGC+ CD8+ T cells utilized glycolysis more than CGC+ CD4+ T cells (Supplemental Figure 8C). Taken together, since T cells are known to be bioenergetically flexible and can undergo metabolic reprogramming to utilize the more abundant nutrition sources in the local environment, it is possible that CGC+ T cells can meet their bioenergetic demands using abundant nutrient sources other than glucose.
**Figure 11:** *Unstimulated CGC+ CD4+ and CGC+ CD8+ T cells are dependent on oxidative phosphorylation. Overlapping histograms showing puromycin geometric mean fluorescence in CD4+ T cells after treatment with 2 deoxy-d-glucose (2DG), oligomycin (o), 2DG, and oligomycin (DGO), and media (Co). Adjacent violin plots show the geometric mean fluorescence (MFI) of puromycin in different memory subsets for the different experimental conditions (Co, DG, O, and DGO groups), and participants by color-coded by diabetes status (A). Truncated violin plots showing the glucose dependence, mitochondrial dependence, and glycolytic capacity in all memory subsets including CGC+ CD4+ T cells (B). Similar analyses were done with CD8+ T cells showing differences in puromycin uptake in all memory subsets (C) and differences in glucose dependence, mitochondrial dependence, and glycolytic capacity (D). Volcano plots show differential gene expression of metabolic genes between CGC+ CD4+ and non-CGC CD4+ T cells (E), and CGC+ CD8+ and non-CGC+ CD8+ T cells (F). Statistical analysis (B, D) was performed using the Mann-Whitney U test and differential gene expression by the Kruskal-Wallis test. GO Biological Processes (GO) enrichment was performed using Enrichr with multiple comparisons correction (Supplemental Tables S5, S7) (38–40).*
Transcriptionally, unstimulated CGC+ CD4+ T cells were enriched for cytotoxic RNA transcripts (GNLY, CD244, GZMH, CTLA4) and were deficient in transcripts that form the aerobic/mitochondrial electron transport chain [SDHC (complex II), UQCR10 (complex III), COX5B (complex IV)] when compared to non-CGC+ CD4+ memory T cells (Figure 11E and Supplemental Tables 4, 5). CGC+ CD8+ T cells, on the other hand, displayed higher CPT1A (fatty acid β oxidation), GRIN1 (glutamate receptor), and lower COX4I1 and COX7C transcripts than non-CGC+ CD8+ T cells (Figure 11F and Supplemental Tables 6, 7). Some studies have suggested that long-chain fatty acid oxidation modulated by CPT1A is important for CD8+ T cell memory development, while knockout studies in mice suggest that CPT1A is dispensable [50].
We performed a direct comparison of CGC+ CD4+ and CGC+ CD8+ T cell transcriptomes to try and explain why CGC+ CD4+ T cells appeared to have a stronger relationship with cardiometabolic disease conditions and risk factors. We found that CGC+ CD8+ T cells were enriched for genes involved in mitochondrial ATP synthesis coupled electron transport [SDHC, UQCR10, COX5B] and fatty-acyl-CoA biosynthetic process [TECR] (Supplemental Figures 8A-C and Supplemental Table 6). Notably, three genes, SDHC, UQCR10, and COX5B, were consistently lower in CGC+ CD4+ T cells as compared to other memory CD4+ T cells and CGC+ CD8+ T cells. While CGC+ CD4+ T cells had higher expression of several genes including SLC16A1, BCL2, BIRC3, ICOS, CTLA4, and IDO1 which enriched several pathways including the pyruvate metabolic process. Taken together, the RNA transcriptomes of both CGC+ CD4+ and CGC+ CD8+ T cells show lower expression of transcripts that encode for mitochondrial complexes than non-CGC+ T cells. However, a direct comparison between CGC+ CD8+ and CGC+ CD4+ T cells suggests differences in bioenergetic sustenance. Notably, CGC+ T cells are largely TEM and TEMRA, compared to other memory cells which would include cells that are TCM, TEM, and TEMRA. Differences that may be driven by TCM in the non-CGC+ memory T cells have not been accounted for in this analysis.
## CGC+ T cells express higher levels of carnitine palmitoyl transferase than other memory T cells
Although CGC+ CD4+ and CGC+ CD8+ T cells were modestly correlated with fasting blood glucose, we did not find significant differences in glucose dependence compared to other memory T cells. Transcriptional analysis suggested that CGC+ CD8+ T cells may rely on long-chain fatty acid oxidation mediated by CPT1A. A previous study showed that PD-1 ligation of T cells induced CPT1A expression leading to an increased rate of fatty acid β oxidation (FAO) while limiting glycolysis [51]. Some CGC+ CD4+ T cells and CGC+ CD8+ T cells express PD-1 (Supplemental Figures 2, 4), and our differential gene expression analysis suggests that CGC+ CD8+ T cells may use FAO as an energy source. We leveraged a separate cohort of HIV-negative donors that had undergone single-cell metabolic profiling of CD4+ and CD8+ T cells by mass cytometry [29]. In this prior cohort, study, we defined CGC+ T cells based on the expression of killer cell lectin-like receptor G1 (KLRG1), and lack of expression of CD27 and CD28 (Figure 12A). Select previously validated metabolic antibodies [29] were used to characterize metabolic proteins/enzymes in CGC+ CD4+ and CGC+ CD8+ T cells. These included metabolic proteins involved in fatty acid metabolism (CPT1A), the tricarboxylic acid cycle and the electron transfer chain (ATP synthase (ATP5A)), mitochondrial expression (voltage-dependent ion channel 1 (VDAC1)), glycolysis and fermentation (hexokinase 2 (HK2)), signaling and transcription (ribosomal protein S6, pS6)) and amino acid metabolism (CD98) (Figure 12B). Of all the metabolic proteins tested, CPT1A expression was higher on CGC+ CD4+ T cells than all other CD4 subsets (Figure 12C), and when compared to all other non-CGC+ CD4+ T cells, both CPT1A and CD98 were higher while pS6 was lower (Figure 12D). CGC+ CD8+ T cells also had higher CPT1A than other CD8+ T cell subsets, while pS6 was significantly lower (Figures 12E, F). Taken together, higher levels of CPT1A in both CGC+ CD4+ and CGC+ CD8+ T cells implicate FAO as a potential source of energy in CGC+ T cells. Lower pS6 compared to other T cells may indicate that CGC+ cells have a lower basal level of translation than other memory subsets.
**Figure 12:** *CGC+ CD4+ T cells have higher expression of carnitine palmitoyl transferase (CPT1A), a rate-limiting enzyme of fatty acid oxidation. UMAPs showing markers used to define the CGC+ T cells (CD28- KLRG1+ CD27-) (A). CPT1A, ATP5A, VDAC1, HK2, pS6, and CD98 expression were measured by mass cytometry (B). Dot plots show the expression of each of the metabolic markers between CD4+ naive, central memory (CM), effector memory (EM), TEMRA (EMRA), and CGC T cells (C), as well as a comparison of CGC+ CD4+ T cells and non-CGC+ T cells [every other CD4+ T cell] (D). A similar analysis is shown with CD8+ T cell subsets (E, F). Statistical analysis (C, E) was performed using pairwise comparisons between the CGC subset and each of the other four populations for each marker, followed by local FDR for multiple hypothesis correction within each marker for the four population comparisons. We only show comparisons with q value<0.05. Statistical analysis of (D, F) was by paired Wilcoxon signed rank test, with no multiple hypothesis testing.*
## Discussion
In PWH, systemic inflammation remains chronically elevated despite consistent suppression of plasma viremia on ART and reconstitution of total CD4+ T cells. CMV co-infection contributes to inflammation by engaging cells from both the innate and adaptive immune arms [52, 53]. Importantly, latent CMV infection permanently alters the immune repertoire in aging individuals regardless of their HIV status, inducing a unique T cell response characterized by an increase in TEM and TEMRA cells [9, 54, 55]. While doing so, CMV alters the responses to other viruses such as the impaired CD8+ T cell response to Epstein *Barr virus* [11]. In PWH, the memory CD4+ T cell pool against CMV is significantly inflated, but the exact mechanism is unknown [13, 56]. It has been proposed that higher CMV viral replication at the tissue level may be an important driver of this T cell expansion; however, the tissue(s) responsible for the inflation of T cell responses has not been identified [10]. To date, we lack a clear mechanism that explains the role of CMV in the pathogenesis of cardiometabolic diseases.
Unlike the innate immune system, cells of the adaptive immune system appear more prone to influence by environmental and physiologic factors [54]. To our knowledge, this is the first study to show a correlation between CGC+ CD4+ T cells and plasma metabolites, including several amino acid and carbohydrate metabolism pathways. Notably, despite the modest relationship between CGC+ CD4+ T cells and fasting blood glucose or starch metabolites, unstimulated CGC+CD4+ T cells exhibited low glucose and high mitochondrial dependence ex-vivo. This was also observed in primary CGC+ CD4+ T cells expanded ex-vivo. Hence, the relationship between CGC+ CD4+ T cells and fasting blood glucose does not appear to stem from higher glucose dependence. While circulating CGC+ CD8+ and CGC+ CD4+ T cells were strongly correlated, they differed in their relationships with CAC, NAFLD, diabetes, and pericardial fat volume. This suggests that although CGC+ CD4+ and CGC+ CD8+ T cells are likely related by their response to CMV antigens, and the circulating proportion of both increases with progressive glucose intolerance, the role or interaction of these cells with the processes contributing to end-organ disease may differ.
CMV-specific T cells can be identified using tetramers, or by the functional expression of inflammatory cytokines after exposure to antigen-presenting cells infected with CMV or pulsed with CMV peptides [13, 57, 58]. Other surface marker combinations including CX3CR1, CD57, KLRG1, and a lack of expression of CD28, have also been used to define CD4+ and CD8+ T cell subsets associated with CMV seropositivity (27, 59–63). However, technical challenges to defining T cell responses to CMV include the size of its genome and HLA restriction of T cell responses. In this study, we showed that CMV-specific T cells in individuals with HIV were predominantly CGC+. Further studies are underway to define the breadth of TCRs that are recognized by the CGC+ subset of CD4+ T cells among PWH and HIV-negative individuals, to further understand the extent to which these cells react to CMV epitopes.
CD4+ CD28- T cells are a subset of cytotoxic cells that are increased in autoimmune diseases and with persistent infections such as HIV [64, 65]. Specifically, high proportions of CD4+ CD28- T cells have been reported in individuals with unstable angina [66], within unstable plaques [67], and in persons with recurrent coronary events [68]. Our group has previously shown that CD4+ CD28- T cells are also increased prior to the development of incident diabetes in PWH [45]. Although CD4+ CD28- T cells are oligoclonal [67], different antigens may stimulate CD4+ CD28- T cells including HIV and CMV. However, studies in which treatment of individuals with anti-CMV therapies has reduced the proportion of CD4+ CD28- T cells in circulation suggest that targeting the CMV viral burden may be a feasible therapeutic approach [27].
Although our study suggests a role for CGC+ CD4+ T cells in the pathogenesis of cardiometabolic disease in PWH, the cross-sectional design precludes an assessment of causality. Following longitudinal cohorts to correlate changes between frequencies of CGC+ CD4+ T cells and cardiometabolic clinical endpoints may define a threshold to classify a subset of persons in which residual inflammation from CMV is a risk factor for the development of comorbidities. Second, our study did not include matched controls without HIV and with metabolic diseases (non-diabetic, pre-diabetic, and diabetic), which would be necessary to understand whether CGC+ CD4+ T cells demonstrate a similar role in HIV-negative individuals. Third, the mechanism by which CGC+ T cells could alter metabolic health is an area of ongoing investigation. Further studies are underway by our group to understand how CGC+ CD4+ T cells impact the pathogenesis of cardiometabolic diseases given their cytotoxic capacity, particularly within adipose tissue and vascular structures [17]. One intriguing possibility is that these cells could be stimulated by non-viral antigens that mimic CMV epitopes and contribute to the development of metabolic disease. The model figure (Figure 13) summarizes the traditional and HIV-specific risk factors that contribute to ectopic fat distribution and the increased prevalence of cardiometabolic disease in PWH. The role of CGC+ cells in cardiometabolic disease risk stratification and its potential role in identifying individuals with a higher risk of developing comorbidities needs to be explored in detail. Eventually, treating CMV or targeting CGC+CD4+ T cells may provide a target that can improve outcomes in PWH and possibly extend to a subset of individuals in the general population.
**Figure 13:** *Conceptual Model. Traditional cardiovascular risk factors (age, sex, low density lipoprotein, body mass index, smoking status, and fasting blood glucose) and HIV-associated factors antiretroviral therapy, viral load, and CD4+ T cell count) may contribute to inflammation that drives cardiometabolic disease. CGC+ CD4+ T cells are largely CMV-specific T cells that are inflated in PWH and may have a diagnostic and mechanistic role in the pathogenesis of cardiometabolic disease. Figure created using Biorender.*
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Vanderbilt University Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
CNW and JK contributed to the conception of the study. CNW, CG, HF, DG, JO, EN, SK, SM, JK contributed to the design of the study. CG, HF, DG, JS, CMW, JO, RG, SPa, SPr contributed to data collection and analysis. SG, DS, DH, MM, SB, TT, SK, SM, JK contributed to the integration of concepts. CNW performed the statistical analysis. CNW and JK wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1099356/full#supplementary-material
## References
1. Bourgi K, Wanjalla C, Koethe JR. **Inflammation and metabolic complications in HIV**. *Curr HIV/AIDS Rep* (2018) **15**. DOI: 10.1007/s11904-018-0411-2
2. Hsue PY, Waters DD. **HIV Infection and coronary heart disease: Mechanisms and management**. *Nat Rev Cardiol* (2019) **16**. DOI: 10.1038/s41569-019-0219-9
3. Armah KA, McGinnis K, Baker J, Gibert C, Butt AA, Bryant KJ. **HIV Status, burden of comorbid disease, and biomarkers of inflammation, altered coagulation, and monocyte activation**. *Clin Infect Dis* (2012) **55**. DOI: 10.1093/cid/cis406
4. Freiberg MS, Bebu I, Tracy R, So-Armah K, Okulicz J, Ganesan A. **D-dimer levels before HIV seroconversion remain elevated even after viral suppression and are associated with an increased risk of non-AIDS events**. *PloS One* (2016) **11**. DOI: 10.1371/journal.pone.0152588
5. Béténé A Dooko C, De Wit S, Neuhaus J, Palfreeman A, Pepe R, Pankow JS. **Interleukin-6, high sensitivity c-reactive protein, and the development of type 2 diabetes among HIV-positive patients taking antiretroviral therapy**. *J Acquir Immune Defic Syndr* (2014) **67**. DOI: 10.1097/QAI.0000000000000354
6. Freiberg MS, Cheng DM, Kraemer KL, Saitz R, Kuller LH, Samet JH. **The association between hepatitis c infection and prevalent cardiovascular disease among HIV-infected individuals**. *AIDS* (2007) **21**. DOI: 10.1097/QAD.0b013e3280118a0d
7. Ishizaka N, Ishizaka Y, Takahashi E, Tooda E, Hashimoto H, Nagai R. **Association between hepatitis c virus seropositivity, carotid-artery plaque, and intima-media thickening**. *Lancet* (2002) **359**. DOI: 10.1016/S0140-6736(02)07339-7
8. Lichtner M, Cicconi P, Vita S, Cozzi-Lepri A, Galli M, Lo Caputo S. **Cytomegalovirus coinfection is associated with an increased risk of severe non-AIDS-defining events in a large cohort of HIV-infected patients**. *J Infect Dis* (2015) **211**. DOI: 10.1093/infdis/jiu417
9. Weltevrede M, Eilers R, de Melker HE, van Baarle D. **Cytomegalovirus persistence and T-cell immunosenescence in people aged fifty and older: A systematic review**. *Exp Gerontol* (2016) **77** 87-95. DOI: 10.1016/j.exger.2016.02.005
10. Pachnio A, Ciaurriz M, Begum J, Lal N, Zuo J, Beggs A. **Cytomegalovirus infection leads to development of high frequencies of cytotoxic virus-specific CD4+ T cells targeted to vascular endothelium**. *PloS Pathog* (2016) **12**. DOI: 10.1371/journal.ppat.1005832
11. Khan N, Hislop A, Gudgeon N, Cobbold M, Khanna R, Nayak L. **Herpesvirus-specific CD8 T cell immunity in old age: Cytomegalovirus impairs the response to a coresident EBV infection**. *J Immunol* (2004) **173**. DOI: 10.4049/jimmunol.173.12.7481
12. Crompton L, Khan N, Khanna R, Nayak L, Moss PA. **CD4+ T cells specific for glycoprotein b from cytomegalovirus exhibit extreme conservation of T-cell receptor usage between different individuals**. *Blood* (2008) **111**. DOI: 10.1182/blood-2007-04-079863
13. Abana CO, Pilkinton MA, Gaudieri S, Chopra A, McDonnell WJ, Wanjalla C. **Cytomegalovirus (CMV) epitope-specific CD4(+) T cells are inflated in HIV(+) CMV(+) subjects**. *J Immunol* (2017) **199**. DOI: 10.4049/jimmunol.1700851
14. Bate SL, Dollard SC, Cannon MJ. **Cytomegalovirus seroprevalence in the united states: the national health and nutrition examination surveys, 1988-2004**. *Clin Infect Dis* (2010) **50**. DOI: 10.1086/652438
15. Parry HM, Zuo J, Frumento G, Mirajkar N, Inman C, Edwards E. **Cytomegalovirus viral load within blood increases markedly in healthy people over the age of 70 years**. *Immun Ageing* (2016) **13**. DOI: 10.1186/s12979-015-0056-6
16. Contreras NA, Sitnik KM, Jeftic I, Coplen CP, Čičin-Šain L, Nikolich-Žugich J. **Life-long control of cytomegalovirus (CMV) by T resident memory cells in the adipose tissue results in inflammation and hyperglycemia**. *PloS Pathog* (2019) **15**. DOI: 10.1371/journal.ppat.1007890
17. Wanjalla CN, McDonnell WJ, Ram R, Chopra A, Gangula R, Leary S. **Single-cell analysis shows that adipose tissue of persons with both HIV and diabetes is enriched for clonal, cytotoxic, and CMV-specific CD4+ T cells**. *Cell Rep Med* (2021) **2**. DOI: 10.1016/j.xcrm.2021.100205
18. Wanjalla CN, Mashayekhi M, Bailin S, Gabriel CL, Meenderink LM, Temu T. **Anticytomegalovirus CD4**. *Arterioscler Thromb Vasc Biol* (2021) **41**. DOI: 10.1161/ATVBAHA.120.315786
19. Yen YF, Jen I, Chen M, Chuang PH, Liu YL, Sharp GB. **Association of cytomegalovirus end-organ disease with stroke in people living with HIV/AIDS: A nationwide population-based cohort study**. *PloS One* (2016) **11**. DOI: 10.1371/journal.pone.0151684
20. Sorlie PD, Adam E, Melnick SL, Folsom A, Skelton T, Chambless LE. **Cytomegalovirus/herpesvirus and carotid atherosclerosis: the ARIC study**. *J Med Virol* (1994) **42**. DOI: 10.1002/jmv.1890420107
21. Nieto FJ, Adam E, Sorlie P, Farzadegan H, Melnick JL, Comstock GW. **Cohort study of cytomegalovirus infection as a risk factor for carotid intimal-medial thickening, a measure of subclinical atherosclerosis**. *Circulation* (1996) **94**. DOI: 10.1161/01.CIR.94.5.922
22. Smieja M, Gnarpe J, Lonn E, Gnarpe H, Olsson G, Yi Q. **Multiple infections and subsequent cardiovascular events in the heart outcomes prevention evaluation (HOPE) study**. *Circulation* (2003) **107**. DOI: 10.1161/01.cir.0000044940.65226.1f
23. Spyridopoulos I, Martin-Ruiz C, Hilkens C, Yadegarfar ME, Isaacs J, Jagger C. **CMV seropositivity and T-cell senescence predict increased cardiovascular mortality in octogenarians: Results from the Newcastle 85+ study**. *Aging Cell* (2016) **15**. DOI: 10.1111/acel.12430
24. Solana R, Tarazona R, Aiello AE, Akbar AN, Appay V, Beswick M. **CMV and immunosenescence: from basics to clinics**. *Immun Ageing* (2012) **9**. DOI: 10.1186/1742-4933-9-23
25. Chen S, Pawelec G, Trompet S, Goldeck D, Mortensen LH, Slagboom PE. **Associations of cytomegalovirus infection with all-cause and cardiovascular mortality in multiple observational cohort studies of older adults**. *J Infect Dis* (2021) **223**. DOI: 10.1093/infdis/jiaa480
26. Wanjalla CN, McDonnell WJ, Barnett L, Simmons JD, Furch BD, Lima MC. **Adipose tissue in persons with HIV is enriched for CD4**. *Front Immunol* (2019) **10**. DOI: 10.3389/fimmu.2019.00408
27. Chanouzas D, Sagmeister M, Faustini S, Nightingale P, Richter A, Ferro CJ. **Subclinical reactivation of cytomegalovirus drives CD4+CD28null T-cell expansion and impaired immune response to pneumococcal vaccination in antineutrophil cytoplasmic antibody-associated vasculitis**. *J Infect Dis* (2019) **219**. DOI: 10.1093/infdis/jiy493
28. Hunt PW, Martin JN, Sinclair E, Epling L, Teague J, Jacobson MA. **Valganciclovir reduces T cell activation in HIV-infected individuals with incomplete CD4+ T cell recovery on antiretroviral therapy**. *J Infect Dis* (2011) **203**. DOI: 10.1093/infdis/jir060
29. Hartmann FJ, Mrdjen D, McCaffrey E, Glass DR, Greenwald NF, Bharadwaj A. **Single-cell metabolic profiling of human cytotoxic T cells**. *Nat Biotechnol* (2021) **39**. DOI: 10.1038/s41587-020-0651-8
30. Gabriel CL, Ye F, Fan R, Nair S, Terry JG, Carr JJ. **Hepatic steatosis and ectopic fat are associated with differences in subcutaneous adipose tissue gene expression in people with HIV**. *Hepatol Commun* (2021) **5**. DOI: 10.1002/hep4.1695
31. Kotecha N, Krutzik PO, Irish JM. **Web-based analysis and publication of flow cytometry experiments**. *Curr Protoc Cytom* (2010) **10** Unit10.17. DOI: 10.1002/0471142956.cy1017s53
32. Hartmann FJ, Simonds EF, Bendall SC. **A universal live cell barcoding-platform for multiplexed human single cell analysis**. *Sci Rep* (2018) **8** 10770. DOI: 10.1038/s41598-018-28791-2
33. Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F, Zaretsky I. **Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types**. *Science* (2014) **343**. DOI: 10.1126/science.1247651
34. Picelli S, Faridani OR, Björklund AK, Winberg G, Sagasser S, Sandberg R. **Full-length RNA-seq from single cells using smart-seq2**. *Nat Protoc* (2014) **9**. DOI: 10.1038/nprot.2014.006
35. Wanjalla CN, Temu TM, Mashayekhi M, Warren CM, Shepherd BE, Gangula R. **Interleukin-17A is associated with flow-mediated dilation and interleukin-4 with carotid plaque in persons with HIV**. *Aids* (2022) **36**. DOI: 10.1097/qad.0000000000003196
36. Argüello RJ, Combes AJ, Char R, Gigan JP, Baaziz AI, Bousiquot E. **SCENITH: A flow cytometry-based method to functionally profile energy metabolism with single-cell resolution**. *Cell Metab* (2020) **32** 1063-75.e7. DOI: 10.1016/j.cmet.2020.11.007
37. Hertzman RJ, Deshpande P, Leary S, Li Y, Ram R, Chopra A. **Visual genomics analysis studio as a tool to analyze multiomic data**. *Front Genet* (2021) **12**. DOI: 10.3389/fgene.2021.642012
38. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV. **Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool**. *BMC Bioinf* (2013) **14**. DOI: 10.1186/1471-2105-14-128
39. Xie Z, Bailey A, Kuleshov MV, Clarke DJB, Evangelista JE, Jenkins SL. **Gene set knowledge discovery with enrichr**. *Curr Protoc* (2021) **1** e90. DOI: 10.1002/cpz1.90
40. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z. **Enrichr: a comprehensive gene set enrichment analysis web server 2016 update**. *Nucleic Acids Res* (2016) **44**. DOI: 10.1093/nar/gkw377
41. **R: A language and environment for statistical computing**. *R Foundation for Statistical Computing* (2021)
42. Palmer BE, Blyveis N, Fontenot AP, Wilson CC. **Functional and phenotypic characterization of CD57+CD4+ T cells and their association with HIV-1-induced T cell dysfunction**. *J Immunol* (2005) **175**. DOI: 10.4049/jimmunol.175.12.8415
43. Brenchley JM, Karandikar NJ, Betts MR, Ambrozak DR, Hill BJ, Crotty LE. **Expression of CD57 defines replicative senescence and antigen-induced apoptotic death of CD8+ T cells**. *Blood* (2003) **101**. DOI: 10.1182/blood-2002-07-2103
44. Yi HS, Kim SY, Kim JT, Lee YS, Moon JS, Kim M. **T-Cell senescence contributes to abnormal glucose homeostasis in humans and mice**. *Cell Death Dis* (2019) **10** 249. DOI: 10.1038/s41419-019-1494-4
45. Bailin SS, Kundu S, Wellons M, Freiberg MS, Doyle MF, Tracy RP. **Circulating CD4+ TEMRA and CD4+ CD28- T cells and incident diabetes among persons with and without HIV**. *Aids* (2022) **36**. DOI: 10.1097/qad.0000000000003137
46. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E. **Metabolite profiles and the risk of developing diabetes**. *Nat Med* (2011) **17**. DOI: 10.1038/nm.2307
47. Felig P, Marliss E, Cahill GF. **Plasma amino acid levels and insulin secretion in obesity**. *N Engl J Med* (1969) **281**. DOI: 10.1056/nejm196910092811503
48. Würtz P, Havulinna AS, Soininen P, Tynkkynen T, Prieto-Merino D, Tillin T. **Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts**. *Circulation* (2015) **131**. DOI: 10.1161/circulationaha.114.013116
49. Callender LA, Carroll EC, Bober EA, Akbar AN, Solito E, Henson SM. **Mitochondrial mass governs the extent of human T cell senescence**. *Aging Cell* (2020) **19**. DOI: 10.1111/acel.13067
50. Raud B, McGuire PJ, Jones RG, Sparwasser T, Berod L. **Fatty acid metabolism in CD8**. *Immunol Rev* (2018) **283**. DOI: 10.1111/imr.12655
51. Patsoukis N, Bardhan K, Chatterjee P, Sari D, Liu B, Bell LN. **PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation**. *Nat Commun* (2015) **6** 6692. DOI: 10.1038/ncomms7692
52. Rossini G, Cerboni C, Santoni A, Landini MP, Landolfo S, Gatti D. **Interplay between human cytomegalovirus and intrinsic/innate host responses: A complex bidirectional relationship**. *Mediators Inflamm* (2012) **2012**. DOI: 10.1155/2012/607276
53. Jackson SE, Mason GM, Wills MR. **Human cytomegalovirus immunity and immune evasion**. *Virus Res* (2011) **157**. DOI: 10.1016/j.virusres.2010.10.031
54. Patin E, Hasan M, Bergstedt J, Rouilly V, Libri V, Urrutia A. **Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors**. *Nat Immunol* (2018) **19**. DOI: 10.1038/s41590-018-0049-7
55. Klenerman P, Oxenius A. **T Cell responses to cytomegalovirus**. *Nat Rev Immunol* (2016) **16**. DOI: 10.1038/nri.2016.38
56. Naeger DM, Martin JN, Sinclair E, Hunt PW, Bangsberg DR, Hecht F. **Cytomegalovirus-specific T cells persist at very high levels during long-term antiretroviral treatment of HIV disease**. *PloS One* (2010) **5** e8886. DOI: 10.1371/journal.pone.0008886
57. Bitmansour AD, Waldrop SL, Pitcher CJ, Khatamzas E, Kern F, Maino VC. **Clonotypic structure of the human CD4+ memory T cell response to cytomegalovirus**. *J Immunol* (2001) **167**. DOI: 10.4049/jimmunol.167.3.1151
58. Lindau P, Mukherjee R, Gutschow MV, Vignali M, Warren EH, Riddell SR. **Cytomegalovirus exposure in the elderly does not reduce CD8 T cell repertoire diversity**. *J Immunol* (2019) **202**. DOI: 10.4049/jimmunol.1800217
59. Chen B, Morris SR, Panigrahi S, Michaelson GM, Wyrick JM, Komissarov AA. **Cytomegalovirus coinfection is associated with increased vascular-homing CD57**. *J Immunol* (2020) **204**. DOI: 10.4049/jimmunol.1900734
60. Sierro S, Rothkopf R, Klenerman P. **Evolution of diverse antiviral CD8+ T cell populations after murine cytomegalovirus infection**. *Eur J Immunol* (2005) **35**. DOI: 10.1002/eji.200425534
61. Stone SF, Price P, Khan N, Moss PA, French MA. **HIV Patients on antiretroviral therapy have high frequencies of CD8 T cells specific for immediate early protein-1 of cytomegalovirus**. *AIDS* (2005) **19**. DOI: 10.1097/01.aids.0000163931.68907.7e
62. Bano A, Pera A, Almoukayed A, Clarke THS, Kirmani S, Davies KA. **CD28null CD4 T-cell expansions in autoimmune disease suggest a link with cytomegalovirus infection**. *F1000Res* (2019) **8** 327. DOI: 10.12688/f1000research.17119.1
63. Bajwa M, Vita S, Vescovini R, Larsen M, Sansoni P, Terrazzini N. **CMV-specific T-cell responses at older ages: Broad responses with a Large central memory component may be key to long-term survival**. *J Infect Dis* (2017) **215**. DOI: 10.1093/infdis/jix080
64. Vallejo AN, Weyand CM, Goronzy JJ. **T-Cell senescence: A culprit of immune abnormalities in chronic inflammation and persistent infection**. *Trends Mol Med* (2004) **10**. DOI: 10.1016/j.molmed.2004.01.002
65. Gerli R, Schillaci G, Giordano A, Bocci EB, Bistoni O, Vaudo G. **CD4+CD28- T lymphocytes contribute to early atherosclerotic damage in rheumatoid arthritis patients**. *Circulation* (2004) **109**. DOI: 10.1161/01.cir.0000131450.66017.b3
66. Liuzzo G, Kopecky SL, Frye RL, O'Fallon WM, Maseri A, Goronzy JJ. **Perturbation of the T-cell repertoire in patients with unstable angina**. *Circulation* (1999) **100**. DOI: 10.1161/01.cir.100.21.2135
67. Liuzzo G, Goronzy JJ, Yang H, Kopecky SL, Holmes DR, Frye RL. **Monoclonal T-cell proliferation and plaque instability in acute coronary syndromes**. *Circulation* (2000) **101**. DOI: 10.1161/01.cir.101.25.2883
68. Liuzzo G, Biasucci LM, Trotta G, Brugaletta S, Pinnelli M, Digianuario G. **Unusual CD4+CD28null T lymphocytes and recurrence of acute coronary events**. *J Am Coll Cardiol* (2007) **50**. DOI: 10.1016/j.jacc.2007.06.040
|
---
title: 'Public awareness, specific knowledge, and worry about mpox (monkeypox): A
preliminary community-based study in Shenzhen, China'
authors:
- Fangmei Ren
- Junchao Liu
- Jianping Miao
- Yucheng Xu
- Ruiyin Zhang
- Jingjie Fan
- Wei Lin
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9971966
doi: 10.3389/fpubh.2023.1077564
license: CC BY 4.0
---
# Public awareness, specific knowledge, and worry about mpox (monkeypox): A preliminary community-based study in Shenzhen, China
## Abstract
### Background
The mpox (monkeypox) outbreak has been declared to be a public health emergency of international concern by the Director-General of World Health Organization in July 2022. However, evidence regarding the awareness, knowledge, and worry about mpox in the general population remains scant.
### Methods
A community-based survey targeting community residents was preliminarily conducted in Shenzhen, China in August 2022 by using a convenience sampling method. Information on mpox-related awareness, knowledge, and worry was collected from each participant. Binary logistic regression analyses with the stepwise procedure were applied to explore the factors associated with awareness, knowledge, and worry about mpox.
### Results
A total of 1028 community residents were included in the analysis (mean age: 34.70 years). Among these participants, $77.9\%$ had ever heard of mpox, and $65.3\%$ were aware of the global outbreak of mpox. However, only about half of them had a high level of knowledge regarding mpox ($56.5\%$) and related symptoms ($49.7\%$). More than one-third of them ($37.1\%$) expressed a high level of worry about mpox. Having high knowledge levels of mpox and related symptoms were positively associated with a high level of worry (OR: 1.79, $95\%$CI: 1.22~2.63 for a single high knowledge level; OR: 1.98, $95\%$CI: 1.47~2.66 for both high knowledge levels).
### Conclusions
This study identified the gaps in public awareness and specific knowledge of mpox in Chinese people, providing scientific evidence for the prevention and control network of mpox at the community level. Targeted health education programs are of urgent need, which should be implemented along with psychological interventions to release public worry if necessary.
## 1. Introduction
The world was greatly impacted by the pandemics caused by infectious diseases in recent years. In 2022, a neglected disease called monkeypox rages and poses a threat to human health, which has been recommended to be replaced with the term of “mpox” by World Health Organization (WHO) [1]. Mpox is known to be a zoonotic and infectious disease caused by the mpox virus. This virus is closely related to the smallpox virus since both of them belong to the genus orthopoxvirus [2]. This disease is usually endemic in West or Central Africa in recent decades [3]. However, many cases have been reported from countries in Europe and North America where the disease was not endemic since early May 2022. The Director-General of WHO declared on July 23 2022 that the multi-country outbreak of mpox was a public health emergency of international concern [4]. As of August 18 2022, a total of 39,110 confirmed cases (including 12 deaths) have been reported to WHO from 94 member states [5]. The current outbreak of mpox has drawn public attention to this neglected disease as the population is generally susceptible to this disease. It has been revealed that mpox spreads from person to person through direct contact with lesions on the skin or indirect contact with contaminated fomites [2]. According to recent reports, most incident cases were men who have sex with men (MSM) that have had recent sexual or close intimate contact, which resulted in an ongoing unprecedented community transmission (6–8). Therefore, raising public awareness about mpox and providing useful recommendations on how to limit further spread among people are of great urgency.
Public awareness and knowledge are crucial for disease prevention and control in the community. Lessons learned from the fight against the Coronavirus disease 2019 (COVID-19) pandemic have suggested a positive role of disease knowledge in facilitating preventive practices [9]. Raising the acceptance of preventive measures, including vaccination, could help to prevent infection and the development of disease [10, 11]. As a neglected disease, mpox should have the priority to be widely aware and understood by the general population, which helps to promote public cooperation and strengthen society-wide efforts in disease prevention and control. In this context, assessment of awareness and knowledge is so essential for the experts to guide the public to capture useful and correct information in an unbiased manner. Only a few previous studies have assessed the knowledge of mpox among physicians, health school, or university students, in which unsatisfactory knowledge levels and gaps were detected (12–15). Moreover, evidence regarding the awareness and knowledge of mpox in the general population remains scant. Investigations based on population-based studies are urgently needed.
It is noteworthy that the current outbreak of mpox may cause a general state of worry. Worrying about the occurrence of a disease pandemic is a key factor of public health significance. On the one hand, worry helps to promote protective health behaviors and contributes to the effective prevention and control of infectious diseases [16, 17]. On the other hand, worry affects people's mental health as researchers have found during the COVID-19 pandemic [18, 19], which may be originated from stringent control measures (e.g., strict home quarantine, prohibition of gathering) and subsequently adverse impacts on the economy and people's daily routines [20]. Previously researchers have detected a low level of worry about mpox in Italian adults [21], however, studies reporting mpox related worry in other countries are still lacking. Given the global outbreak and frequent population flows across countries, it's necessary to explore the public worry in the general population.
Based on a community-based survey in Shenzhen city, this study aimed to investigate the awareness, specific knowledge, and worry about mpox, as well as to find out potential associated factors among Chinese people. These investigations will be helpful to deliver coping strategies to reinforce the prevention and control network of mpox at the community level.
## 2.1. Study design
This is an analytical cross-sectional study followed by the Strengthening the Reporting of Observational Studies in Epidemiology (STORBE) guidelines [22].
## 2.2. Study settings
In August 2022, a cross-sectional survey using a convenience sampling method has been conducted in the Gushu and Haicheng community health service centers of the Baoan district in Shenzhen, China. During the survey period, attenders in these two community health service centers were presented with a survey recruitment notice. The notice marked the study backgrounds, objectives, potential risks and benefits, and question contents formally. A two-dimensional code linked to an online questionnaire was attached at the bottom of the recruitment notice. People who were interested in this survey could access the questionnaire by scanning the two-dimensional code with their smartphones.
## 2.3. Participants
The inclusion criteria of study participants were: visiting above-mentioned survey sites, age from 18 to 60 years, living in Shenzhen city, and voluntary participation. People who could not read or understand the electronic questionnaire were excluded. A confirmation of voluntary participation for informed consent was required before entering the answer interface. The sample quantity was calculated using the formula of the cross-sectional study: n = μα2p(1–p)/δ2. Here, α = 0.05 (two sides), μα = 1.96, δ = 0.03, and the awareness rate of mpox $$p \leq 73.33$$% (according to the pilot study). The required sample size was 836. With the no-response rate controlled within $10\%$, the final sample size was determined to be 920. Between August 5 and August 15, 2022, 1054 community residents clicked the survey link, and 1028 of them met the inclusion criteria and completed the survey.
## 2.4. Instrument
An online self-administered questionnaire was applied to collect data. The questionnaire was prepared in Chinese and designed with a variety of information, including individual characteristics on socio-demography, health conditions, daily health habits, awareness/knowledge/worry about mpox, etc. It has been reviewed and corrected by six professionals in public health (two major in community health, three major in infectious diseases control, and one major in epidemiology and psychological healthcare). A pilot study has been performed among 35 community residents to identify the intelligibility of this questionnaire. All participants in the pilot study confirmed that the content of the questionnaire was clear to read and easy to understand.
## 2.5.1. Demographic characteristics, health conditions, and daily habits
In the self-administrated questionnaire, participants were required to provide information about demographic characteristics, health conditions, and daily habits. Demographic characteristics included age, gender, household registration, marital status, educated level, employment status, monthly income level, and health insurance. Participants were also asked to recall the history of chronic diseases (e.g., hypertension, diabetes, hyperlipidemia) and overweight/obesity diagnosed by a physician. In addition, health habits in daily routines were collected, including smoking, drinking, physical exercise, and family doctor contracted status.
## 2.5.2. Awareness of mpox and the global outbreak
As WHO had not yet changed the name of “monkeypox” to “mpox” during the survey time of this study, the term of “monkeypox” was applied in the survey questions. The awareness of mpox was evaluated by asking “Before the survey time, have you ever heard of monkeypox? ( yes/no).” People who selected “yes” were regarded to be aware of mpox and were further asked to recall where to learn about mpox. Participants were also required to answer “Before the survey time, have you ever heard that WHO has declared the global monkeypox outbreak representing a public health emergency of international concern on July 23 2022? ( yes/no).” Similarly, people who answered “yes” were regarded to be aware of the global outbreak of mpox.
## 2.5.3. Specific knowledge of mpox and related symptoms
People who were aware of mpox were further assessed for their specific knowledge. The common knowledge of mpox was measured by six knowledge items, including “A1: *Monkeypox is* an infectious disease,” “A2: *Monkeypox is* caused by a virus,” “A3: People can get monkeypox by close contact with an infected person or animal,” “A4: People are generally susceptible to monkeypox,” “A5: There are currently no specific treatments for monkeypox,” and “A6: *There is* a vaccine that protects against monkeypox.” Other six question items were applied to measure the awareness of the clinical symptoms in infected individuals, including “B1: Monkeypox can cause fever,” “B2: Monkeypox can cause headache,” “B3: Monkeypox can cause fatigue or exhaustion,” “B4: Monkeypox can cause swollen lymph nodes,” “B5: Monkeypox can cause a body rash,” and “B6: Monkeypox can cause back and muscle aches.” These knowledge items were all correct statements, which were developed and adapted from authoritative health education information [23, 24]. For each item, participants were required to select one of three options (yes/no/don't know). The answer “don't know” was considered an incorrect answer. The number of right answers ≥ 4 out of six items (correct rate ≥ $66.7\%$) was regarded as a high level of specific knowledge, whereas people who were not aware of mpox were regarded with a low knowledge level. The construct validity and reliability of two six-item knowledge scales were tested in the pilot study ($$n = 35$$). Both scales showed satisfactory Kaiser-Meyer-Olkin (KMO) and Bartlett's test of sphericity values (A1–A6: 0.904, χ2 = 370.05, $P \leq 0.001$; B1–B6: 0.905, χ2 = 377.924, $P \leq 0.001$). Two six-item scales were extracted with 93.465 and $93.376\%$ of the total variance in exploratory factor analyses, respectively. The internal consistencies of knowledge items for mpox and related symptoms were also satisfactory in the pilot group (Cronbach's α: 0.984 and 0.986, respectively).
## 2.5.4. Worry about mpox
In this study, worry about mpox was measured by a single question “*At this* moment, how worried are you that monkeypox may cause a global pandemic like COVID-19?”. In line with previous studies on COVID-19 [18, 25, 26], a five-point response was provided to assess the level of worry, which was described in a progressive manner (1 = not at all worried, 2 = not so worried, 3 = somewhat worried, 4 = very worried, and 5 = extremely worried). Here, people who selected very or extremely worried were regarded with a high level of worry about mpox.
## 2.6. Ethics
The study protocol was approved by the Institutional Review Board of Baoan Central Hospital of Shenzhen and in accordance with the ethical standards of the Declaration of Helsinki. Informed consent was obtained from all participants for the study.
## 2.7. Statistical analyses
As all questions were set up to be fully answered before submission, there was no missing data in the final dataset. Descriptive statistical analyses were conducted, showing counts and frequencies for categorical variables. Chi-square tests were used to detect distributed differences of categorical variables across groups. Binary logistic regression analyses were applied to explore the factors associated with awareness, knowledge, and worry about mpox. Variables with a $P \leq 0.20$ in the uni-variable analysis were included in the multivariable analysis. Associated factors were identified with the stepwise procedure. Odds ratios (OR) and $95\%$ confidence intervals (CI) were calculated. A two-tailed test with a $P \leq 0.05$ was considered to be significant. All statistical analyses were performed in SPSS 21.0 (IBM SPSS Statistics, New York, United States).
## 3.1. Characteristics of all participants
In total, 1028 community residents were included in the analysis, with a mean age of 34.70 years (standard deviation: 9.87 years). The characteristics of all participants were displayed in Table 1. Among these participants, more than two-thirds were female gender ($68.3\%$) and well-educated ($69.1\%$ for college or above), $40.3\%$ had local household registration, and nearly four out of five were married ($78.3\%$) and employed ($79.5\%$). Although approximately half of the participants earned a moderate income per month ($46.3\%$ for 5,000 ~ <10,000 RMB), over $90\%$ of them had one or more types of health insurance ($91.6\%$). Less than one-fifth reported smoking ($16.1\%$) and drinking habits ($13.1\%$), respectively, while $30.8\%$ of them had no exercise habit. Nearly one in eight participants had ever been diagnosed with chronic diseases ($12.9\%$) and overweight/obesity ($13.0\%$). Notably, about $69.3\%$ of the participants had contracted with a family doctor.
**Table 1**
| Variable | Number | Frequency (%) |
| --- | --- | --- |
| Age (year) | Age (year) | Age (year) |
| 18~30 | 396 | 38.5 |
| 31~40 | 404 | 39.3 |
| 41~60 | 228 | 22.2 |
| Gender | Gender | Gender |
| Male | 326 | 31.7 |
| Female | 702 | 68.3 |
| Local household registration | Local household registration | Local household registration |
| No | 614 | 59.7 |
| Yes | 414 | 40.3 |
| Marital status | Marital status | Marital status |
| Single/divorced/widow | 223 | 21.7 |
| Married | 805 | 78.3 |
| Education level | Education level | Education level |
| High school or below | 318 | 30.9 |
| College or above | 710 | 69.1 |
| Employment status | Employment status | Employment status |
| No | 211 | 20.5 |
| Yes | 817 | 79.5 |
| Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) |
| < 5,000 | 270 | 26.3 |
| 5,000~ < 10,000 | 476 | 46.3 |
| ≥10,000 | 282 | 27.4 |
| Health insurance | Health insurance | Health insurance |
| No | 86 | 8.4 |
| Yes | 942 | 91.6 |
| Active smoking | Active smoking | Active smoking |
| No | 863 | 83.9 |
| Yes | 165 | 16.1 |
| Drinking | Drinking | Drinking |
| No | 893 | 86.9 |
| Yes | 135 | 13.1 |
| Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise |
| | 317 | 30.8 |
| Less than once a week | 336 | 32.7 |
| More than once a week | 375 | 36.5 |
| Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases |
| No | 895 | 87.1 |
| Yes | 133 | 12.9 |
| Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity |
| No | 894 | 87.0 |
| Yes | 134 | 13.0 |
| Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor |
| No | 316 | 30.7 |
| Yes | 712 | 69.3 |
## 3.2. Awareness, specific knowledge of mpox
In this survey, $77.9\%$ of the participants were aware of mpox, and $65.3\%$ of them were aware of the global outbreak of mpox, without gender differences (Table 2). The most common medium of learning about mpox was the Internet or the social media ($77.5\%$), followed by the TV or the radio ($59.1\%$), people around ($28.8\%$), and the health education activities in the community ($28.1\%$) (Figure 1). The responses to the specific knowledge items of mpox and related symptoms demonstrated significant knowledge gaps (Figures 2, 3). Among people who were aware of mpox, the infectiousness and viral cause of mpox could be recognized by the majority (the correct rates of A1 to A3: 90.4, 86.3, and $82.5\%$, respectively) (Table 2). However, knowledge items regarding population susceptibility, treatments, and vaccination against mpox were not well-known (the correct rates of A4 to A6: 67.9, 26.1, and $42.3\%$, respectively). For mpox related symptoms, the correct rates of knowledge items ranged from 56.3 to $74.5\%$, in which the symptom of lymph nodes got the least awareness (Table 2).
## 3.3. Factors associated with specific knowledge of mpox and related symptoms
Overall, 56.5 and $49.7\%$ of the participants had a high level of knowledge regarding mpox and related symptoms, respectively. Factors associated with specific knowledge of mpox and related symptoms were identified by logistic regression analyses (Table 3). A high knowledge level of mpox was associated with being well-educated (OR: 2.03, $95\%$CI: 1.51~2.73), earning a high income (OR: 1.64, $95\%$CI: 1.12~2.41), having health insurance (OR: 1.65, $95\%$CI: 1.03~2.65), and doing physical exercise more than once a week (OR: 1.62, $95\%$CI: 1.18~2.21). A high knowledge level of mpox related symptoms was associated with having a local household registration (OR: 1.58, $95\%$CI: 1.22~2.05), having health insurance (OR: 1.95, $95\%$CI: 1.20~3.16), doing physical exercise more than once a week (OR: 1.57, $95\%$CI: 1.15~2.13), and contracting with a family doctor (OR: 1.39, $95\%$CI: 1.06~1.84). Moreover, we found that having a diagnosis history of chronic diseases was negatively associated with a high knowledge level of mpox related symptoms (OR: 0.55, $95\%$CI: 0.37~0.80).
**Table 3**
| Variable | Knowledge level of mpox a | Knowledge level of mpox a.1 | Uni-variable OR (95%CI)b | Multivariable OR (95%CI) | Knowledge level of mpox related symptoms a | Knowledge level of mpox related symptoms a.1 | Uni-variable OR (95%CI)b.1 | Multivariable OR (95%CI).1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Low, n (%) | High, n (%) | | | Low, n (%) | High, n (%) | | |
| Age (year) | Age (year) | Age (year) | Age (year) | Age (year) | Age (year) | Age (year) | Age (year) | Age (year) |
| 18~30 | 174 (38.9) | 222 (38.2) | 1.00 (reference) | / | 200 (38.7) | 196 (38.4) | 1.00 (reference) | / |
| 31~40 | 161 (36.0) | 243 (41.8) | 1.18 (0.89, 1.57) | | 195 (37.7) | 209 (40.9) | 1.09 (0.83, 1.44) | |
| 41~60 | 112 (25.1) | 116 (20.0) | 0.81 (0.59, 1.13) | | 122 (23.6) | 106 (20.7) | 0.89 (0.64, 1.23) | |
| Gender | Gender | Gender | Gender | Gender | Gender | Gender | Gender | Gender |
| Male | 138 (30.9) | 188 (32.4) | 1.00 (reference) | / | 164 (31.7) | 162 (31.7) | 1.00 (reference) | / |
| Female | 309 (69.1) | 393 (67.9) | 0.93 (0.72, 1.22) | | 353 (68.3) | 349 (68.3) | 1.00 (0.77, 1.30) | |
| Local household registration | Local household registration | Local household registration | Local household registration | Local household registration | Local household registration | Local household registration | Local household registration | Local household registration |
| No | 299 (66.9) | 315 (54.2) | 1.00 (reference) | / | 344 (66.5) | 270 (52.8) | 1.00 (reference) | 1.00 (reference) |
| Yes | 148 (33.1) | 266 (45.8) | 1.71 (1.32, 2.20) | | 173 (33.5) | 241 (47.2) | 1.78 (1.38, 2.28) | 1.58 (1.22, 2.05) |
| Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status |
| Single/divorced/widow | 91 (20.4) | 132 (22.7) | 1.00 (reference) | / | 106 (20.5) | 117 (22.9) | 1.00 (reference) | / |
| Married | 356 (79.6) | 449 (77.3) | 0.87 (0.64, 1.18) | | 411 (79.5) | 394 (77.1) | 0.87 (0.65, 1.17) | |
| Education level | Education level | Education level | Education level | Education level | Education level | Education level | Education level | Education level |
| High school or below | 185 (41.4) | 133 (22.9) | 1.00 (reference) | 1.00 (reference) | 189 (36.6) | 129 (25.2) | 1.00 (reference) | / |
| College or above | 262 (58.6) | 448 (77.1) | 2.38 (1.82, 3.12) | 2.03 (1.51, 2.73) | 328 (63.4) | 382 (74.8) | 1.71 (1.31, 2.23) | |
| Employment status | Employment status | Employment status | Employment status | Employment status | Employment status | Employment status | Employment status | Employment status |
| No | 109 (24.4) | 102 (17.6) | 1.00 (reference) | / | 125 (24.2) | 86 (16.8) | 1.00 (reference) | / |
| Yes | 338 (75.6) | 479 (82.4) | 1.51 (1.12, 2.05) | | 392 (75.8) | 425 (83.2) | 1.58 (1.16, 2.14) | |
| Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) |
| < 5,000 | 146 (32.7) | 124 (21.3) | 1.00 (reference) | 1.00 (reference) | 158 (30.6) | 112 (21.9) | 1.00 (reference) | / |
| 5,000~ < 10,000 | 211 (47.2) | 265 (45.6) | 1.48 (1.10, 2.00) | 1.14 (0.83, 1.57) | 237 (45.8) | 239 (46.8) | 1.42 (1.05, 1.92) | |
| ≥10,000 | 90 (20.1) | 192 (33.0) | 2.51 (1.78, 3.55) | 1.64 (1.12, 2.41) | 122 (23.6) | 160 (31.3) | 1.85 (1.32, 2.59) | |
| Health insurance | Health insurance | Health insurance | Health insurance | Health insurance | Health insurance | Health insurance | Health insurance | Health insurance |
| No | 52 (11.6) | 34 (5.9) | 1.00 (reference) | 1.00 (reference) | 59 (11.4) | 27 (5.3) | 1.00 (reference) | 1.00 (reference) |
| Yes | 395 (88.4) | 547 (94.1) | 2.12 (1.35, 3.33) | 1.65 (1.03, 2.65) | 458 (88.6) | 484 (94.7) | 2.31 (1.44, 3.71) | 1.95 (1.20, 3.16) |
| Active smoking | Active smoking | Active smoking | Active smoking | Active smoking | Active smoking | Active smoking | Active smoking | Active smoking |
| No | 371 (83.0) | 492 (84.7) | 1.00 (reference) | / | 435 (84.1) | 428 (83.8) | 1.00 (reference) | / |
| Yes | 76 (17.0) | 89 (15.3) | 0.88 (0.63, 1.23) | | 82 (15.9) | 83 (16.2) | 1.03 (0.74, 1.44) | |
| Drinking | Drinking | Drinking | Drinking | Drinking | Drinking | Drinking | Drinking | Drinking |
| No | 386 (86.4) | 507 (87.3) | 1.00 (reference) | / | 444 (85.9) | 449 (87.9) | 1.00 (reference) | / |
| Yes | 61 (13.6) | 74 (12.7) | 0.92 (0.64, 1.33) | | 73 (14.1) | 62 (12.1) | 0.84 (0.58, 1.21) | |
| Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise |
| | 159 (35.6) | 158 (27.2) | 1.00 (reference) | 1.00 (reference) | 178 (34.4) | 139 (27.2) | 1.00 (reference) | 1.00 (reference) |
| Less than once a week | 145 (32.4) | 191 (32.9) | 1.33 (0.97, 1.80) | 1.23 (0.90, 1.69) | 167 (32.3) | 169 (33.1) | 1.30 (0.95, 1.76) | 1.28 (0.94, 1.76) |
| More than once a week | 143 (32.0) | 232 (39.9) | 1.63 (1.21, 2.21) | 1.62 (1.18, 2.21) | 172 (33.3) | 203 (39.7) | 1.51 (1.12, 2.04) | 1.57 (1.15, 2.13) |
| Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases |
| No | 377 (84.3) | 518 (89.2) | 1.00 (reference) | / | 435 (84.1) | 460 (90.0) | 1.00 (reference) | 1.00 (reference) |
| Yes | 70 (15.7) | 63 (10.8) | 0.66 (0.46, 0.94) | | 82 (15.9) | 51 (10.0) | 0.59 (0.41, 0.85) | 0.55 (0.37, 0.80) |
| Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity |
| No | 385 (86.1) | 509 (87.6) | 1.00 (reference) | / | 452 (87.4) | 442 (86.5) | 1.00 (reference) | / |
| Yes | 62 (13.9) | 72 (12.4) | 0.88 (0.61, 1.27) | | 65 (12.6) | 69 (13.5) | 1.09 (0.76, 1.56) | |
| Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor |
| No | 143 (32.0) | 173 (29.8) | 1.00 (reference) | / | 177 (34.2) | 139 (27.2) | 1.00 (reference) | 1.00 (reference) |
| Yes | 304 (68.0) | 408 (70.2) | 1.11 (0.85, 1.45) | | 340 (65.8) | 372 (72.8) | 1.39 (1.07, 1.82) | 1.39 (1.06, 1.84) |
## 3.4. Worry about mpox and associated factors
Of the participants, $37.1\%$ expressed a high level of worry about mpox, where females had a higher proportion than males (40.2 vs. $30.4\%$, $P \leq 0.05$) (Table 2). Factors associated with worry about mpox were also detected (Table 4). Factors that were positively associated with a high level of worry included being female (OR: 1.60, $95\%$CI: 1.20~2.15), having a diagnosis history of overweight/obesity (OR: 1.82, $95\%$CI: 1.23~2.68), and having high knowledge levels of mpox and related symptoms (OR: 1.79, $95\%$CI: 1.22~2.63 for a single high knowledge level; OR: 1.98, $95\%$CI: 1.47~2.66 for both high knowledge levels). Factors that were negatively associated with a high level of worry included having an older age (OR: 0.56, $95\%$CI: 0.38~0.82) and being well-educated (OR: 0.59, $95\%$CI: 0.44~0.80).
**Table 4**
| Variable | Worry about mpox | Worry about mpox.1 | Uni-variable OR (95%CI)b | Multivariable OR (95%CI) |
| --- | --- | --- | --- | --- |
| Variable | Low, n (%) | High, n (%) | | |
| Age (year) | Age (year) | Age (year) | Age (year) | Age (year) |
| 18~30 | 240 (37.1) | 156 (40.9) | 1.00 (reference) | 1.00 (reference) |
| 31~40 | 249 (38.5) | 155 (40.7) | 0.96 (0.72, 1.27) | 0.93 (0.70, 1.25) |
| 41~60 | 158 (24.4) | 70 (18.4) | 0.68 (0.48, 0.94) | 0.56 (0.38, 0.82) |
| Gender | Gender | Gender | Gender | Gender |
| Male | 227 (35.1) | 99 (26.0) | 1.00 (reference) | 1.00 (reference) |
| Female | 420 (64.9) | 282 (74.0) | 1.54 (1.16, 2.04) | 1.60 (1.20, 2.15) |
| Local household registration | Local household registration | Local household registration | Local household registration | Local household registration |
| No | 379 (58.6) | 235 (61.7) | 1.00 (reference) | / |
| Yes | 268 (41.4) | 146 (38.3) | 0.88 (0.68, 1.14) | |
| Marital status | Marital status | Marital status | Marital status | Marital status |
| Single/divorced/widow | 150 (23.2) | 73 (19.2) | 1.00 (reference) | / |
| Married | 497 (76.8) | 308 (80.8) | 1.27 (0.93, 1.74) | |
| Education level | Education level | Education level | Education level | Education level |
| High school or below | 189 (29.2) | 129 (33.9) | 1.00 (reference) | 1.00 (reference) |
| College or above | 458 (70.8) | 252 (66.1) | 0.81 (0.61, 1.06) | 0.59 (0.44, 0.80) |
| Employment status | Employment status | Employment status | Employment status | Employment status |
| No | 129 (19.9) | 82 (21.5) | 1.00 (reference) | / |
| Yes | 518 (80.1) | 299 (78.5) | 0.91 (0.67, 1.24) | |
| Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) | Monthly income level (RMB) |
| < 5,000 | 156 (24.1) | 114 (29.9) | 1.00 (reference) | / |
| 5,000~ < 10,000 | 308 (47.6) | 168 (44.1) | 0.75 (0.55, 1.01) | |
| ≥10,000 | 183 (28.3) | 99 (26.0) | 0.74 (0.53, 1.04) | |
| Health insurance | Health insurance | Health insurance | Health insurance | Health insurance |
| No | 60 (9.3) | 26 (6.8) | 1.00 (reference) | / |
| Yes | 587 (90.7) | 355 (93.2) | 1.40 (0.87, 2.25) | |
| Active smoking | Active smoking | Active smoking | Active smoking | Active smoking |
| No | 535 (82.7) | 328 (86.1) | 1.00 (reference) | / |
| Yes | 112 (17.3) | 53 (13.9) | 0.77 (0.54, 1.10) | |
| Drinking | Drinking | Drinking | Drinking | Drinking |
| No | 556 (85.9) | 337 (88.5) | 1.00 (reference) | / |
| Yes | 91 (14.1) | 44 (11.5) | 0.80 (0.54, 1.17) | |
| Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise | Frequency of physical exercise |
| | 193 (29.8) | 124 (32.5) | 1.00 (reference) | / |
| Less than once a week | 210 (32.5) | 126 (33.1) | 0.93 (0.68, 1.28) | |
| More than once a week | 244 (37.7) | 131 (37.7) | 0.84 (0.61, 1.14) | |
| Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases | Ever been diagnosed with chronic diseases |
| No | 554 (85.6) | 341 (89.5) | 1.00 (reference) | / |
| Yes | 93 (14.4) | 40 (10.5) | 0.70 (0.47, 1.04) | |
| Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity | Ever been diagnosed with overweight/obesity |
| No | 574 (88.7) | 320 (84.0) | 1.00 (reference) | 1.00 (reference) |
| Yes | 73 (11.3) | 61 (16.0) | 1.50 (1.04, 2.16) | 1.82 (1.23, 2.68) |
| Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor | Contracting with a family doctor |
| No | 210 (32.5) | 106 (27.8) | 1.00 (reference) | / |
| Yes | 437 (67.5) | 275 (72.2) | 1.25 (0.94, 1.65) | |
| Knowledge level of mpox and related symptoms a | Knowledge level of mpox and related symptoms a | Knowledge level of mpox and related symptoms a | Knowledge level of mpox and related symptoms a | Knowledge level of mpox and related symptoms a |
| Low/low | 279 (43.1) | 115 (30.2) | 1.00 (reference) | 1.00 (reference) |
| Low/high or High/low | 105 (16.2) | 71 (18.6) | 1.64 (1.13, 2.38) | 1.79 (1.22, 2.63) |
| High/high | 263 (40.6) | 195 (51.2) | 1.80 (1.35, 2.39) | 1.98 (1.47, 2.66) |
## 4. Discussion
The multi-country outbreak of mpox in 2022 has reached the highest level of global public health alert, calling for coordination, cooperation, and global solidarity by the WHO. According to previous findings in other countries, the perception of mpox in the general population was unsatisfactory [15, 21]. Although there were no mpox cases reported in China before the present survey, understanding the public awareness, knowledge, and worry about mpox helps the government and experts to develop appropriate coping strategies. This study detected the gaps in awareness and specific knowledge of mpox among community residents and the positive associations between high knowledge levels and high worry about mpox. These findings shed light on the need for community-based health education and targeted intervention.
This study identified nearly $80\%$ of the survey participants who had ever heard of mpox and about two-thirds knew about the global outbreak. These findings showed an acceptable awareness among the general population in China, as we noticed that <$50\%$ of 651 health school students were aware of the global outbreak in Jordan [12]. Only a few previous studies assessed the knowledge of mpox and most of them were targeting physicians and medical students. Researchers in Indonesia found around $10.0\%$ of 432 general practitioners knew over $80\%$ of the knowledge items of mpox (21 items) [13, 27]. The above studies conducted in Jordan also detected unsatisfactory knowledge levels of mpox (11 items) [12]. A survey among 163 Italian physicians found that only $49.7\%$ had a general knowledge score of mpox exceeding the median (24 items) [14]. A study among 558 university students found about $80\%$ had moderate to good knowledge of mpox (knowing 60–$100\%$ of 21 items) [15]. In our study, around half of the survey participants had high knowledge levels of mpox and related symptoms (correct answers ≥ 4 out of 6 items). Although a direct comparison of knowledge levels across studies cannot be conducted due to the differences in knowledge items and study populations, it's possible to identify the common gaps in mpox related knowledge. The majority of people were lack of knowledge about treatments and vaccines for mpox. In our study, only $26.1\%$ and $42.3\%$ of people who were aware of mpox knew no specific treatments and the existence of a vaccine against mpox, respectively. Among medical students in Jordan, only $26.2\%$ were aware of the presence of mpox vaccination [12]. This phenomenon may partly reflect public misconceptions due to the media news on the development of new drugs and vaccines that are not yet widely used. In line with previous findings, people usually learned about mpox from the Internet or social media [15]. Thus, people may be easily misled by the wrong knowledge or information spread on the Internet. Public health education, especially offline education around people, should be provided along with correct, clear, and understandable messages about mpox.
In the current study, factors associated with high knowledge levels of mpox and related symptoms indicated a relatively high socioeconomic status, such as being well-educated, earning a higher income, and having health insurance. These findings were in line with those reported to be associated with knowledge of COVID-19 [28, 29]. We did not find the effect of age and gender on the knowledge level, which was consistent with the findings in Italian adults [21]. Whereas researchers found a positive association of age and female gender with good knowledge of mpox in a small sample of university students [15]. More population-based investigations are needed to replicate these findings. We also found that people with local household registration tended to have a high level of knowledge, which may result from the better socioeconomic status of local residents than migrant residents. This result was similar to what we found in the investigation of knowledge of HPV and its vaccine [30]. There were opposite effects of physical exercise and a diagnosis history of chronic diseases on specific knowledge levels in our study. The reason may partly lie in the varied attention to novel health information among physically active and inactive persons to some extent. Moreover, contracting with a family doctor seemed to help people maintain a high knowledge level. Given the facilitating effect of family doctor contracting services on health management [31], contracted people are more likely to capture up-to-date health information. Nevertheless, both active and passive access to health knowledge is of great importance for the general population.
There were limited studies that reported worry caused by mpox. A cross-sectional study among 1546 participants in Saudi Arabia detected that more than $60\%$ of them were more worried about COVID-19 than mpox [32]. In our study, a single question item was applied to measure worry about mpox and the average score was 2.96 ± 1.21. This score was relatively higher than that in Italian adults [21]. The proportion of a high level of worry (very or extremely worried) was $37.1\%$ in our study. When compared to studies similarly using a single assessment item of COVID-19 worry, the rate of high worry level tended to be lower than those among Chinese people (Guangdong: $42.05\%$; Henan: $58.61\%$) [18, 25], but higher than that among UK adults ($19.8\%$) [26]. These inconsistencies may be explained by the differences in epidemic status and the effect of prevention and control measures across countries. It's also reasonable that the influence of the mpox outbreak on the public is weaker than COVID-19 as it has not reached pandemic status. Besides, there were other kinds of tools to measure the level of worry. Some studies generally assessed the frequency and severity of worry regarding disease pandemics [16, 33], whereas other studies concentrated on assessing various worry contents, such as worries in social, economic, and life impacts, infection and related outcomes, and so on (34–36). Further studies can draw on the experience of these measurement tools on COVID-19 to explore mpox related worry.
Demographic factors associated with worry about mpox in the current study were in accordance with those found to be associated with COVID-19 worry, including age, gender, and education level [25, 37]. The public worry about mpox may be partly attributed to the age and gender differences in dealing with multiple stresses and maintaining mental health [38, 39]. Moreover, the influence of education level on worry about mpox may lie in that people being well-educated are more easily to obtain resources of psychological healthcare to alleviate worry. Our study also detected that people who had been diagnosed with overweight/obesity had a higher level of worry. Even though there is no evidence regarding the association between being overweight/obesity and mpox, people plagued by overweight/obesity may face more difficulties if the global outbreak becomes serious, such as increased infection risk [40, 41]. It is important to note a positive association between a high level of knowledge and worry in this study. This phenomenon was also reported in other diseases (42–44). The reason for this positive association may be that people with a high knowledge level are more likely to understand the susceptibility and severity associated with mpox and the global outbreak, especially for those affected by the COVID-19 pandemic substantially.
This was a timely study to investigate public awareness, knowledge, and worry about mpox in the Chinese population. The strengths of this study included a proper sample size, a validated questionnaire, and an easily accessible survey platform with smartphones. Notably, this preliminary study may provide important prospects for community health education on mpox. The study findings help to detect vulnerable groups with poor knowledge of mpox and to establish targeted health promotion programs. Moreover, the worrying state along with the increased awareness of mpox also reminds all community health professionals to focus on psychological issues related to health education. Previous study findings suggested that suboptimal level of mpox-related knowledge could be accompanied by a high proportion of vaccine hesitancy among healthcare workers [45]. It's urgently needed to conduct further investigations on the impact of public awareness, knowledge, and worry on attitudes toward mpox vaccination based on the present study sample of the general population.
This study has some limitations. First of all, the convenience sampling process may restrict the generalization of our findings to the overall general population. Moreover, the identified associations cannot help to infer causal relationships because of the cross-sectional design. Second, the assessment of mpox related knowledge was self-draft rather than a standard tool regardless of adaptation from authoritative information. Some newly identified manifestations, for example, oral mucosal enanthema [46], were not included in assessing the knowledge of mpox related symptoms. This attempt may affect the accuracy and completeness of data collection. The subjective nature of questions related to mpox may lead to over- or under-estimation of real impacts. Population-based surveys with a large sample are needed to explore comprehensive mpox knowledge scales in an objective manner. Third, the single-question measurement of worry only reflects one's perceived worry subjectively, which is not a validated diagnostic tool for measuring worry components comprehensively. It has been proposed that a single question may lower the possibility of conflating disease related worry and other existing worries [37]. However, bias raised by self-reported answers could not be avoided due to the online survey design. History of psychological diseases was not collected in this study, which may also affect the worrying state of community residents to some extent. Multi-item measure scales of worry and psychological measurement are recommended to be applied in further studies. Furthermore, given that the recent mpox cases were predominantly reported in MSM, it's possible that sexual orientation may affect the perception and worry about mpox. Specific surveys targeting MSM should be conducted in the future.
## 5. Conclusions
Our study revealed room for improvement in awareness and specific knowledge of mpox in the general population in Shenzhen, China. Most people in China have no history of smallpox vaccination, and are generally susceptible to mpox virus as a result of lacking immunoprotection against infection with orthopox viruses. Health education programs should be established immediately to promote the application of knowledge-attitude-practice (KAP) in fighting against mpox at the community level. This study also highlighted a positive association between a high knowledge level and high worry about mpox. Although worry at a moderate level may help to facilitate preventive practices of mpox, timely psychological interventions are also needed to release public worry if necessary.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Institutional Review Board of Baoan Central Hospital of Shenzhen. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
WL, JF, FR, and YX contributed to the design and implementation of the study. FR, JL, and JM collected the data. FR, RZ, WL, and JF performed the analysis and interpretation of data. FR, JL, WL, and JF drafted and revised the manuscript. All authors discussed the results and commented on the manuscript, reviewed, and approved the final manuscript to be published.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. 1.World Health Organization Who Recommends New Name for Monkeypox Disease. (2022). Available online at: https://www.who.int/news/item/28-11-2022-who-recommends-new-name-for-monkeypox-disease (accessed January 26, 2023).. (2022)
2. Titanji BK, Tegomoh B, Nematollahi S, Konomos M, Kulkarni PA. **Monkeypox: a contemporary review for healthcare professionals**. *Open Forum Infect Dis.* (2022) **9** c310. DOI: 10.1093/ofid/ofac310
3. Bunge EM, Hoet B, Chen L, Lienert F, Weidenthaler H, Baer LR. **The changing epidemiology of human monkeypox-a potential threat? A systematic review**. *PLoS Negl Trop Dis.* (2022) **16** e10141. DOI: 10.1371/journal.pntd.0010141
4. 4.World Health Organization Who Director-General's Statement at the Press Conference Following ihr Emergency Committee Regarding the Multi-country Outbreak of Monkeypox. (2022). Available online at: https://www.who.int/news-room/speeches/item/who-director-general-s-statement-on-the-press-conference-following-ihr-emergency-committee-regarding-the-multi–country-outbreak-of-monkeypox$−$23-july-2022 (accessed August 20, 2022).. *World Health Organization Who Director-General's Statement at the Press Conference Following ihr Emergency Committee Regarding the Multi-country Outbreak of Monkeypox.* (2022)
5. 5.World health organization. 2022 Monkeypox Outbreak: Global Trends. (2022). Available online at: https://worldhealthorg.shinyapps.io/mpx_global/ (accessed August 20, 2022).. *2022 Monkeypox Outbreak: Global Trends.* (2022)
6. Philpott D, Hughes CM, Alroy KA, Kerins JL, Pavlick J, Asbel L. **Epidemiologic and clinical characteristics of monkeypox cases - United States, may 17-july 22, 2022**. *MMWR Morb Mortal Wkly Rep.* (2022) **71** 1018-22. DOI: 10.15585/mmwr.mm7132e3
7. Patel A, Bilinska J, Tam J, Da SFD, Mason CY, Daunt A. **Clinical features and novel presentations of human monkeypox in a central london centre during the 2022 outbreak: descriptive case series**. *BMJ.* (2022) **378** e72410. DOI: 10.1136/bmj-2022-072410
8. Girometti N, Byrne R, Bracchi M, Heskin J, McOwan A, Tittle V. **Demographic and clinical characteristics of confirmed human monkeypox virus cases in individuals attending a sexual health centre in London, UK: an observational analysis**. *Lancet Infect Dis.* (2022). DOI: 10.1016/S1473-3099(22)00411-X
9. Siddiquea BN, Shetty A, Bhattacharya O, Afroz A, Billah B. **Global epidemiology of COVID-19 knowledge, attitude and practice: a systematic review and meta-analysis**. *BMJ Open.* (2021) **11** e51447. DOI: 10.1136/bmjopen-2021-051447
10. Aouissi HA, Kechebar M, Ababsa M, Roufayel R, Neji B, Petrisor AI. **The importance of behavioral and native factors on covid-19 infection and severity: insights from a preliminary cross-sectional study**. *Healthcare (Basel)* (2022) **10** 1341. DOI: 10.3390/healthcare10071341
11. Pan J, A K, Liu Z, Zhang P, Xu Z, Guo X. **Factors that impact acceptance of COVID-19 vaccination in different community-dwelling populations in China**. *Vaccines (Basel)* (2022) **10** 91. DOI: 10.3390/vaccines10010091
12. Sallam M, Al-Mahzoum K, Dardas LA, Al-Tammemi AB, Al-Majali L, Al-Naimat H. **Knowledge of human monkeypox and its relation to conspiracy beliefs among students in Jordanian health schools: filling the knowledge gap on emerging zoonotic viruses**. *Medicina (Kaunas)* (2022) **58** 924. DOI: 10.3390/medicina58070924
13. Harapan H, Setiawan AM, Yufika A, Anwar S, Wahyuni S, Asrizal FW. **Knowledge of human monkeypox viral infection among general practitioners: a cross-sectional study in Indonesia**. *Pathog Glob Health.* (2020) **114** 68-75. DOI: 10.1080/20477724.2020.1743037
14. Riccò M, Ferraro P, Camisa V, Satta E, Zaniboni A, Ranzieri S. **When a neglected tropical disease goes global: knowledge, attitudes and practices of italian physicians towards monkeypox, preliminary results**. *Trop Med Infect Dis* (2022) **7** 135. DOI: 10.3390/tropicalmed7070135
15. Jairoun AA, Al-Hemyari SS, Abdulla NM, El-Dahiyat F, Shahwan M, Hassan N. **Awareness and preparedness of human monkeypox outbreak among university student: time to worry or one to ignore?**. *J Infect Public Health.* (2022) **15** 1065-71. DOI: 10.1016/j.jiph.2022.08.015
16. Li M, Lv G, Hsieh S, Shao R, Yuan J. **Pandemic worry and preventive health behaviors during the COVID-19 outbreak**. *Front Med (Lausanne).* (2021) **8** 700072. DOI: 10.3389/fmed.2021.700072
17. Scherr CL, Jensen JD, Christy K. **Dispositional pandemic worry and the health belief model: promoting vaccination during pandemic events**. *J Public Health (Oxf).* (2017) **39** e242-50. DOI: 10.1093/pubmed/fdw101
18. Yong X, Zhang L. **Covid-19 worry and mental health among the economically active population in Guangdong, China**. *Front Public Health.* (2022) **10** 882177. DOI: 10.3389/fpubh.2022.882177
19. Li Y, Zhao J, Ma Z, McReynolds LS, Lin D, Chen Z. **Mental health among college students during the COVID-19 pandemic in China: a 2-wave longitudinal survey**. *J Affect Disord.* (2021) **281** 597-604. DOI: 10.1016/j.jad.2020.11.109
20. Tee ML, Tee CA, Anlacan JP, Aligam K, Reyes P, Kuruchittham V. **Psychological impact of COVID-19 pandemic in the Philippines**. *J Affect Disord.* (2020) **277** 379-91. DOI: 10.1016/j.jad.2020.08.043
21. Gallè F, Bianco L, Da Molin G, Mancini R, Sciacchitano S, Ferracuti S. **“Monkeypox: what do you know about that?**. *Pathogens* (2022) **11** 1285. DOI: 10.3390/pathogens11111285
22. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. **Strengthening the reporting of observational studies in epidemiology (strobe) statement: guidelines for reporting observational studies**. *BMJ.* (2007) **335** 806-8. DOI: 10.1136/bmj.39335.541782.AD
23. Walter K, Malani PN. **What is monkeypox?**. *JAMA.* (2022) **328** 222. DOI: 10.1001/jama.2022.10259
24. 24.World Health Organization Questions Answers/Monkeypox. (2022). Available online at: https://www.who.int/news-room/questions-and-answers/item/monkeypox (accessed July 20, 2022).. *World Health Organization Questions Answers/Monkeypox.* (2022)
25. Wang Y, Li Q, Tarimo CS, Wu C, Miao Y, Wu J. **Prevalence and risk factors of worry among teachers during the COVID-19 epidemic in Henan, China: a cross-sectional survey**. *BMJ Open.* (2021) **11** e45386. DOI: 10.1136/bmjopen-2020-045386
26. Smith LE, Potts H, Amlo͡t R, Fear NT, Michie S, Rubin GJ. **Worry and behaviour at the start of the COVID-19 outbreak: results from three uk surveys (the COVID-19 rapid survey of adherence to interventions and responses [corsair] study)**. *Prev Med Rep.* (2022) **25** 101686. DOI: 10.1016/j.pmedr.2021.101686
27. Harapan H, Setiawan AM, Yufika A, Anwar S, Wahyuni S, Asrizal FW. **Confidence in managing human monkeypox cases in Asia: a cross-sectional survey among general practitioners in Indonesia**. *Acta Trop.* (2020) **206** 105450. DOI: 10.1016/j.actatropica.2020.105450
28. Zhong BL, Luo W, Li HM, Zhang QQ, Liu XG Li WT. **Knowledge, attitudes, and practices towards covid-19 among Chinese residents during the rapid rise period of the COVID-19 outbreak: a quick online cross-sectional survey**. *Int J Biol Sci.* (2020) **16** 1745-52. DOI: 10.7150/ijbs.45221
29. Alobuia WM, Dalva-Baird NP, Forrester JD, Bendavid E, Bhattacharya J, Kebebew E. **Racial disparities in knowledge, attitudes and practices related to COVID-19 in the USA**. *J Public Health (Oxf).* (2020) **42** 470-8. DOI: 10.1093/pubmed/fdaa069
30. Lin W, Wang Y, Liu Z, Chen B, Yuan S, Wu B. **Inequalities in awareness and attitude towards hpv and its vaccine between local and migrant residents who participated in cervical cancer screening in Shenzhen, China**. *Cancer Res Treat.* (2020) **52** 207-17. DOI: 10.4143/crt.2019.053
31. Huang J, Zhang T, Wang L, Guo D, Liu S, Lu W. **The effect of family doctor-contracted services on noncommunicable disease self-management in Shanghai, China**. *Int J Health Plann Manage.* (2019) **34** 935-46. DOI: 10.1002/hpm.2865
32. Temsah MH, Aljamaan F, Alenezi S, Alhasan K, Saddik B, Al-Barag A. **Monkeypox caused less worry than COVID-19 among the general population during the first month of the who monkeypox alert: experience from Saudi Arabia**. *Travel Med Infect Dis.* (2022) **49** 102426. DOI: 10.1016/j.tmaid.2022.102426
33. Iacob CI, Ionescu D, Avram E, Cojocaru D. **Covid-19 pandemic worry and vaccination intention: the mediating role of the health belief model components**. *Front Psychol.* (2021) **12** 674018. DOI: 10.3389/fpsyg.2021.674018
34. Blix I, Birkeland MS, Thoresen S. **Worry and mental health in the COVID-19 pandemic: vulnerability factors in the general Norwegian population**. *BMC Public Health.* (2021) **21** 928. DOI: 10.1186/s12889-021-10927-1
35. Brown LA, Hamlett GE, Zhu Y, Wiley JF, Moore TM, DiDomenico GE. **Worry about COVID-19 as a predictor of future Insomnia**. *J Sleep Res* (2022) **31** e13564. DOI: 10.1111/jsr.13564
36. Dayton L, Schneider K, Strickland JC, Latkin C. **Determinants of worry using the SARS-CoV-19 worry (cov-wo) scale among United States residents**. *J Community Psychol.* (2021) **49** 1487-504. DOI: 10.1002/jcop.22577
37. Zhou M, Guo W. **Social factors and worry associated with COVID-19: evidence from a large survey in China**. *Soc Sci Med.* (2021) **277** 113934. DOI: 10.1016/j.socscimed.2021.113934
38. Scott SB, Sliwinski MJ, Mogle JA, Almeida DM. **Age, stress, and emotional complexity: results from two studies of daily experiences**. *Psychol Aging.* (2014) **29** 577-87. DOI: 10.1037/a0037282
39. Holingue C, Budavari AC, Rodriguez KM, Zisman CR, Windheim G, Fallin MD. **Sex differences in the gut-brain axis: implications for mental health**. *Curr Psychiatry Rep.* (2020) **22** 83. DOI: 10.1007/s11920-020-01202-y
40. Dobner J, Kaser S. **Body mass index and the risk of infection - from underweight to obesity**. *Clin Microbiol Infect.* (2018) **24** 24-8. DOI: 10.1016/j.cmi.2017.02.013
41. Falagas ME, Kompoti M. **Obesity and infection**. *Lancet Infect Dis.* (2006) **6** 438-46. DOI: 10.1016/S1473-3099(06)70523-0
42. Meltzer GY, Chang VW, Lieff SA, Grivel MM, Yang LH, Des Jarlais DC. **Behavioral correlates of covid-19 worry: stigma, knowledge, and news source**. *Int J Environ Res Public Health* (2021) **18** 11436. DOI: 10.3390/ijerph182111436
43. Joo SH, Jo IS, Kim HJ, Lee CU. **Factors associated with dementia knowledge and dementia worry in the south korean elderly population**. *Psychiatry Investig.* (2021) **18** 1198-204. DOI: 10.30773/pi.2021.0295
44. Tran A, Hwang JH, Choi E, Lee YY, Suh M, Lee CW. **Impact of awareness of breast density on perceived risk, worry, and intentions for future breast cancer screening among Korean women**. *Cancer Res Treat.* (2021) **53** 55-64. DOI: 10.4143/crt.2020.495
45. Riad A, Drobov A, Rozmarinová J, Drapáčová P, Klugarová J, Dušek L. **Monkeypox knowledge and vaccine hesitancy of Czech healthcare workers: a health belief model (hbm)-based study**. *Vaccines (Basel)* (2022) **10** 2022. DOI: 10.3390/vaccines10122022
46. Riad A, Attia S. **Monkeypox-related oral manifestations and implications: should dentists keep an eye out?**. *J Med Virol.* (2023) **95** e28091. DOI: 10.1002/jmv.28091
|
---
title: A randomized pilot trial assessing the reduction of gout episodes in hyperuricemic
patients by oral administration of Ligilactobacillus salivarius CECT 30632, a strain
with the ability to degrade purines
authors:
- Juan M. Rodríguez
- Marco Garranzo
- José Segura
- Belén Orgaz
- Rebeca Arroyo
- Claudio Alba
- David Beltrán
- Leónides Fernández
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC9971985
doi: 10.3389/fmicb.2023.1111652
license: CC BY 4.0
---
# A randomized pilot trial assessing the reduction of gout episodes in hyperuricemic patients by oral administration of Ligilactobacillus salivarius CECT 30632, a strain with the ability to degrade purines
## Abstract
### Introduction
Hyperuricemia and gout are receiving an increasing scientific and medical attention because of their relatively high prevalence and their association with relevant co-morbidities. Recently, it has been suggested that gout patients have an altered gut microbiota. The first objective of this study was to investigate the potential of some *Ligilactobacillus salivarius* strains to metabolize purine-related metabolites. The second objective was to evaluate the effect of administering a selected potential probiotic strain in individuals with a history of hyperuricemia.
### Methods
Inosine, guanosine, hypoxanthine, guanine, xanthine, and uric acid were identified and quantified by high-performance liquid chromatography analysis. The uptake and biotransformation of these compounds by a selection of L. salivarius strains were assessed using bacterial whole cells and cell-free extracts, respectively. The efficacy of L. salivarius CECT 30632 to prevent gout was assessed in a pilot randomized controlled clinical trial involving 30 patients with hyperuricemia and a history of recurrent gout episodes. Half of the patients consumed L. salivarius CECT 30632 (9 log10 CFU/day; probiotic group; $$n = 15$$) for 6 months while the remaining patients consumed allopurinol (100–300 mg/daily; control group; $$n = 15$$) for the same period. The clinical evolution and medical treatment received by the participants were followed, as well as the changes in several blood biochemical parameters.
### Results
L. salivarius CECT 30632 was the most efficient strain for inosine ($100\%$), guanosine ($100\%$) and uric acid ($50\%$) conversion and, therefore, it was selected for the pilot clinical trial. In comparison with the control group, administration of L. salivarius CECT 30632 resulted in a significant reduction in the number of gout episodes and in the use of gout-related drugs as well as an improvement in some blood parameters related to oxidative stress, liver damage or metabolic syndrome.
### Conclusion
Regular administration of L. salivarius CECT 30632 reduced serum urate levels, the number of gout episodes and the pharmacological therapy required to control both hyperuricemia and gout episodes in individuals with a history of hyperuricemia and suffering from repeated episodes of gout.
## Introduction
Purines are part of nucleosides (adenosine and guanosine) from which adenosine monophosphate (AMP) and guanosine monophosphate (GMP) are synthesized and, in turn, used for nucleic acids (DNA/RNA) synthesis. Also, purine derivatives are metabolically relevant molecules involved in cell survival and proliferation, as they act as energy cofactors (ATP, GTP), intracellular signal transduction molecules (cyclic AMP, cyclic GMP), part of coenzymes [nicotinamide adenine dinucleotide (NAD+), nicotinamide adenine dinucleotide phosphate (NADP+), and coenzyme A] and universal methyl donors (S-adenosylmethionine; Rosemeyer, 2004). Most of the purines present in cells come from the recycling of derivatives of cellular metabolism (the savage pathway), but they can also arise from de novo biosynthesis (a process that happens mainly in the liver although small amounts are also produced in the intestine and the vascular endothelium, among other tissues) or from the diet (Pareek et al., 2021). Excess purine nucleosides are removed by breakdown to uric acid involving the sequential action of several enzymes. Adenosine deaminase transforms adenosine into inosine, and the enzyme purine nucleoside phosphorylase converts inosine and guanosine into hypoxanthine and guanine, respectively, which are further transformed to xanthine by xanthine oxidase (acting on hypoxanthine) and guanine deaminase (acting on guanine). In addition, xanthine oxidase, also known as xanthine oxidoreductase, also converts xanthine into uric acid.
Uric acid exists as urate (deprotonated form; pKa = 5.8) at physiological pH (Mandal and Mount, 2015). The normal concentration ranges for urate in human blood are 1.5–6.0 mg/dL in women and 2.5–7.0 mg/dL in men, which are close to saturation levels and are unusually high concentrations compared to other mammalian species due to the lack of uricase, the enzyme responsible of urate degradation (Álvarez-Lario and Macarrón-Vicente, 2010). Urate concentration in the blood depends on the balance between its rate of synthesis in the body, the amount of xanthine of purines from the diet, and on the rate of urate excretion (Lane and Fan, 2015). Approximately, two-thirds of urate elimination takes place in the kidneys, being excreted in the urine, while the remaining one-third occurs in the gastrointestinal tract (Sorensen, 1965; Maesaka and Fishbane, 1998). When there is an imbalance between urate production and excretion, hyperuricemia, defined as an elevated serum urate concentration (>6.0 mg/dL in women and >7.0 mg/dL in men), occurs (Li et al., 2019; George and Minter, 2022). Hyperuricemia increases the risk of precipitation of monosodium urate crystals (solubility limit at 6.8 mg/dL), which can cause gout and urate kidney stones, and also has been associated with the development and severity of many other conditions including chronic renal disease, cardiovascular diseases and metabolic syndrome due to its role in inducing inflammation, endothelial dysfunction, the proliferation of vascular smooth muscle cells and the activation of the renin-angiotensin system (Yanai et al., 2021).
Some risk factors for hyperuricemia and gout development are non-modifiable factors, such as sex (men are at a higher risk than women), age (risk increases with age), race and/or ethnicity, and genetics (due to genetic variants of renal urate transporters), but others are modifiable factors including diet (alcohol consumption, purine-rich foods, fructose-sweetened beverages), medication, and lifestyle (physical activity, body mass index [BMI]) (MacFarlane and Kim, 2014). However, the two main causes for hyperuricemia and gout are, first, a purine-rich diet (seafood, meat, animal offal, alcoholic beverages, and fructose-containing drinks) which induces urate overproduction and, second, a deficient excretion by kidneys and gut.
In the United States, the prevalence rates of hyperuricemia (defined as a serum urate level of >7.0 mg/dL regardless of sex) and gout were 11.9 and $3.9\%$, respectively, and about one-third of gout patients reported the use of urate-lowering therapy (Chen-Xu et al., 2019). Among non-United States populations, the prevalence of hyperuricemia and gout is higher in Asian than in European populations, although wide variability has been reported due to differences in genetic background and non-genetic factors (Butler et al., 2021).
Most treatments for patients affected by hyperuricemia are based on three strategies: (a) uricostatic drugs inhibiting the production of urate (e.g., allopurinol, febuxostat, and topiroxostat) by modulating the activity of a key enzyme (xanthine oxidase) involved in the production of uric acid; (b) uricosuric drugs that promote the excretion of urate in the kidneys by reducing its reabsorption in the renal tubules (e.g., benzbromarone, probenecid, sulfinpyrazone, and lesinurad); or (c) promoting the transformation of urate to more soluble allantoin and hydrogen peroxide (injectable recombinant uricases, such as pegloticase) but, while showing different effectiviness in reducing serum urate levels, most of them are known to have a myriad of side effects (Strilchuk et al., 2019). Unlike other conditions, dietary restriction alone does not always lead to resolution or improvement of hyperuricemia symptoms. Only a few dietary interventions have been described in the literature that resulted in a small decrease in serum urate levels (Vedder et al., 2019).
In the last decade, a wealth of information has arisen on the important role that the human microbiota plays in health and disease (reviewed in Requena and Velasco, 2021; Afzaal et al., 2022). Because alterations in the human microbiota could play a role in the development of various diseases, modifications of the microbiota (probiotics, prebiotics, antibiotics, and fecal microbiota transference) have been proposed as strategies to prevent and treat some illnesses, including hyperuricemia (Rizzatti et al., 2018; Martín and Langella, 2019; Antushevich, 2020; Fan and Pedersen, 2021; Wang et al., 2022). In fact, gout has been linked to gut bacterial dysbiosis: *Bacteroides caccae* and Bacteroides xylanisolvens were enriched in the gut microbiota of patients with clinically diagnosed gout while, simultaneously, *Faecalibacterium prausnitzii* was depleted resulting in reduced butyrate biosynthesis and altered purine degradation in the gut (Guo et al., 2016). More recent studies have confirmed that the profiles of gut microbiota and bacterial metabolites were altered in gout patients, and that they may be partly reversed after urate-lowering treatment with febuxostat (Shao et al., 2017; Chu et al., 2021; Lin et al., 2021). At present, the exact mechanisms relating gut microbiota and purine metabolism are unknown. Modulation of gut microbiota composition using probiotics may be a promising intervention to regulate serum urate levels as it has been shown in animal studies with bacterial strains isolated from fermented foods (Li et al., 2014; Cao et al., 2017; Yamada et al., 2017; Wu et al., 2021). Human vagina and milk have been shown to contain unique microbiotas playing key roles in the initial colonization of the infant gut and, most probably, in the short- and long-term health of the human host (Fernández et al., 2020; France et al., 2022). In the past years, our group has characterized bacterial isolates from both milk and vaginal samples from healthy individuals and some of them were revealed to be good probiotic candidates in clinical trials (Arroyo et al., 2010; Fernández et al., 2016; Cárdenas et al., 2019; Martín et al., 2019; Fernández et al., 2021; Jiménez et al., 2021). Therefore, the objective of this study was, first, to select a potential probiotic strain with the ability to metabolize purine-related metabolites and, second, to evaluate the effect of the administration of this potential probiotic strain on individuals with a history of hyperuricemia in order to evaluate the feasibility of a future multicenter randomized controlled trial.
## Bacterial strains and culture conditions
A collection of 13 *Ligilactobacillus salivarius* strains were initially included in this study. Such strains had been previously isolated from human milk or vaginal exudate samples obtained from healthy individuals. Routinely, bacterial cultures were transferred ($1.5\%$, v/v) from frozen stock cultures to de Man, Rogosa and Sharpe (MRS, Oxoid, Basingstoke, United Kingdom) broth and incubated aerobically at 37°C for 24 h. Viable bacteria were quantified by spreading decimal dilutions onto MRS agar ($1.5\%$, w/v) plates incubated aerobically at 37°C for 24 h. The results were expressed as the number of colony-forming units (CFU).
## Evaluation of the uptake of guanosine, inosine, and uric acid by whole bacterial cells
The initial screening of the bacterial collection was carried out by using the method described by Li et al. [ 2014] with some modifications. In brief, bacterial cells were collected from overnight broth cultures (stationary phase) by centrifugation at 4°C and 19,000 ×g for 10 min and washed once with the same volume of cold saline ($0.85\%$ NaCl, w/v) solution. Then, the cell pellet was suspended in 0.2 mL of phosphate buffer (100 mM K3PO4, pH 7) and an aliquot was taken to determine the number of viable cells. 0.8 mL of 1.3 mM solution of either guanosine, inosine, and/or uric acid, sterilized by filtration (0.22 μm pore size; Nalgene 176–0020 nylon), were mixed with 100 or 25 μL of the suspension of washed cells (about 1010 CFU of viable cells) and the required volume of the phosphate buffer to reach a final volume of 1.4 mL. When indicated, 0.1 mL of either sterile 4 mM glucose or distilled water were added to the mixture to evaluate the uptake of inosine, guanosine, and uric acid in the presence of glucose. The mixtures were incubated in a water bath at 37°C for 60 min. Afterwards, the mixture was centrifuged as above and 540 μL of the cell-free supernatant were mixed with 60 μL of 0.1 M HClO4. Following filtration (0.22 μm pore size; Nalgene 176–0020 nylon) to remove particulate material, the filtrate was kept frozen at −20°C until the determination of the concentration of guanosine, inosine, and uric acid by high-performance liquid chromatography (HPLC) analysis.
## Evaluation of the biotransformation of guanosine, inosine, and uric acid
For this purpose, the same procedure described in the previous section was used but, in this case, cells were substituted by cell-free extracts (CFE). To prepare the CFE, concentrated washed cells suspended in 0.2 mL of cold 0.1 M phosphate buffer (pH 7) were mixed with the same volume of glass beads (0.1 mM bead size; Sigma-Aldrich) and lysed by mechanical disruption in an FP120 FastPrep® Instrument (QbioGene, Irvine, California, United Sates). The mixture was subjected to three processing cycles of 20 s of bead-beating at 5 m s−1 followed by 2 min incubation in an ice bath to avoid protein denaturalization. After bead-beating, the tubes were centrifuged at 4°C and 19,000 ×g for 10 min to sediment cell debris and glass beads. The supernatant (CFE) was transferred to clean tubes and 25 μL were used instead of whole cell suspensions as described above to determine the transformation of guanosine, inosine, and uric acid.
The protein content in the CFE was determined by the Bradford method using a Coomassie Plus (Bradford) Assay Reagent (Thermo Scientific™) and bovine serum albumin (Sigma-Aldrich) as the standard and the microplate procedure in 96-well plates.
## Identification and quantification of inosine, guanosine, hypoxanthine, guanine, xanthine, and uric acid
Inosine, guanosine, hypoxanthine, guanine, xanthine, and uric acid were quantified by HPLC using the method described by Li et al. [ 2014], with slight modifications. Analyses were carried out using a Zorbax SB-C18 (5 μm, 4.6 × 250 mM) column connected to an HPLC device (Agilent 1,260 Infinity Quaternary LC) with a diode array detector (Agilent Technologies, Waldbronn, Germany). Working solutions (1.3 mM) of all compounds were prepared in phosphate buffer (K3PO4, 100 mM, pH 7), cleaned and sterilized by passing the solution through a filter (0.22 μm pore size; Nalgene 176–0020 nylon), and degassed by sonication. The separation of the compounds was achieved by using an isocratic flow (0.5 mL/min) of methanol and $0.1\%$ of acetic acid in Milli-Q water (3:97, v/v). The retention times at 245 nm were 5.00, 4.75, 2.46, 2.36, 2.24, and 2.09 min for guanosine, inosine, xanthine, hypoxanthine, guanine, and uric acid, respectively.
Compound quantification was carried out by developing standard curves built using the corresponding pure compounds (Sigma, Alcobendas, Madrid). The analyses were carried out in triplicate.
## Efficacy of Ligilactobacillus salivarius CECT 30632 to prevent gout: A pilot randomized controlled clinical trial
A total of 30 patients participated in the study and were recruited at Centro de Diagnóstico Médico (Madrid, Spain). All of them shared hyperuricemia (>7 mg/dL), a history of recurrent gout episodes (≥3 episodes/year), characterized by acute arthritis and requiring treatment with colchicine despite taking allopurinol (100–300 mg/day) as a preventive measure. The definition of case (gout) was performed following the criteria of the Spanish Society for Rheumatology. Exclusion criteria included antibiotic or probiotic treatment within the previous 2 months or suffering a gout episode at recruitment. Sample size for this trial was calculated accepting an alpha risk of 0.05 and a beta risk of 0.2 in a one-side test to find a $400\%$ increase in the number of individuals without gout episodes during the 6-month period of the trial and anticipating a drop-out rate of $4\%$. Participants were allocated by simple randomization using a computer-generated list of random numbers prepared by an independent researcher who also prepared the envelopes containing the treatment but with no clinical involvement in the trial. Then, half of the patients ($$n = 15$$; probiotic group) consumed daily, for 6 months, a sachet containing ~9 log10 CFU of L. salivarius CECT 30632 while the other half of the patients ($$n = 15$$; control group) consumed allopurinol (100–300 mg/day) for the same period. Physicians and data analysts were kept blinded to the allocation.
Consumption of drugs used for prevention (allopurinol) or treatment (colchicine, non-steroidal inflammatory drugs [NSAIDs]) of gout was recorded throughout the study. BMI was calculated as weight divided by the square of the height (kg/m2). Blood samples were obtained at the beginning (T1) and at the end (T2; 6 months) of the study. Blood samples were extracted at Unilabs (Madrid, Spain). The first 8 mL-fraction was collected into a Na-heparin tube to analyze oxidative stress (OS)-related parameters in plasma, including markers of oxidative stress (advanced oxidation protein products [AOPPs], sulfhydryl [SH] groups, thiobarbituric acid reactive substances [TBARS], malondialdehyde [MDA], 8-isoprostaglandin F2α) and indicators of vascular function and blood pressure modulation (nitric oxide, nitrite, nitrate); a second 4 mL-fraction was used to obtain serum for standard biochemistry (serum urate, triglycerides, total cholesterol, aspartate transaminase [AST or GOT], alanine transaminase [ALT or GPT]). Hematology and biochemical analyses of blood samples were performed by Unilabs. Metabolites related to OS and nitric oxide metabolism end products (NOx) were measured in duplicate as described previously (Codoñer-Franch et al., 2011, 2012).
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and was approved by the Ethics Committee of the Hospital Clínico San Carlos (Madrid, Spain) (protocol: CEIC $\frac{20}{263}$-E; date of approval: $\frac{01}{04}$/2020, act $\frac{4.1}{20}$).
## Statistical analysis
The normality of data distribution was analyzed using the Shapiro–Wilks test. Quantitative variables are presented as mean and $95\%$ confidence interval (CI) or standard deviation (SD). The Student’s t-test was used to compare the means of continuous variables having a normal distribution. The χ2 test, the Fisher exact test or the Freeman–Halton extension of the Fisher exact probability test for a 2 × 4 contingency table were used to compare percentages. One-way repeated measures ANOVA was used to compare the changes in the mean values of blood parameters during the study. Differences were considered significant when the value of $p \leq 0.05.$ Statistical calculations were performed using Statgraphics Centurion 19 version 19.2.01 (Statgraphics Technologies, Inc., The Plains, VA, United States).
## Initial screening of Ligilactobacillus salivarius strains for nucleoside and uric acid uptake
Most of 13 L. salivarius strains included in this study showed a high ability for guanosine and inosine uptake (Supplementary Table S1). The results of uptake capacity of inosine, guanosine, and uric acid in the presence or not of 4 mM glucose of the most active purine-uptaking strains (5 strains) is shown in Figure 1. The five strains transported inosine, guanosine and uric acid into the cytoplasm, although some variability was observed among them. Most of the strains transported lower amounts of uric acid compared to inosine and guanosine, except MPac90 which showed no preference for any of the three compounds. In the presence of glucose, the transport of the three compounds into the cells increased, being L. salivarius CECT 30632 the only strain that depleted all the inosine, guanosine, and uric acid (Figure 1).
**Figure 1:** *Inosine (blue), guanosine (orange), and uric acid (grey) uptake by whole cells of selected Ligilactobacillus salivarius strains in the presence (+) or not (−) of 4 mM glucose.*
## Biotransformation of inosine and guanosine by whole cells of Ligilactobacillus salivarius
Subsequently, both the transformation of inosine and guanosine inside the cells and the release into the extracellular medium of related metabolites were investigated. The concentrations (as % of transformation) of hypoxanthine, xanthine, and uric acid in the extracellular media when the cells had been incubated in the presence of either inosine or guanosine are shown in Table 1. Guanine was not detected in any sample under these assay conditions.
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Transformation (%) | Transformation (%).1 | Transformation (%).2 |
| --- | --- | --- | --- | --- | --- |
| | | | Product in the extracellular medium | Product in the extracellular medium | Product in the extracellular medium |
| Strain | Substrate | Glucose (4 mM) | Hypoxanthine | Xanthine | Uric acid |
| CECT 30632 | Inosine | − | 97 ± 10 | <1 | 8 ± 3 |
| CECT 30632 | Inosine | + | ND | ND | ND |
| CECT 30632 | Guanosine | − | ND | <1 | <1 |
| CECT 30632 | Guanosine | + | ND | ND | ND |
| MPac32 | Inosine | − | 58 ± 1 | <1 | 19 ± 9 |
| MPac32 | Inosine | + | 33 ± 11 | <1 | <1 |
| MPac32 | Guanosine | − | <1 | <1 | 22 ± 6 |
| MPac32 | Guanosine | + | <1 | <1 | <1 |
| MPac40 | Inosine | − | 89 ± 2 | ND | 89 ± 1 |
| MPac40 | Inosine | + | 6 ± 1 | ND | <1 |
| MPac40 | Guanosine | − | ND | ND | 2 ± 1 |
| MPac40 | Guanosine | + | <1 | 1 | 1 |
| MPac90 | Inosine | − | 90 ± 9 | 2 | <1 |
| MPac90 | Inosine | + | ND | ND | <1 |
| MPac90 | Guanosine | − | ND | 1 | <1 |
| MPac90 | Guanosine | + | ND | ND | ND |
| MPac91 | Inosine | − | 99 ± 9 | <1 | 60 ± 12 |
| MPac91 | Inosine | + | 83 ± 9 | ND | 3 ± 2 |
| MPac91 | Guanosine | − | <1 | 5 ± 3 | 99 ± 5 |
| MPac91 | Guanosine | + | <1 | ND | 13 ± 4 |
Except for the strain MPac32, inosine was efficiently transformed into hypoxanthine (>$89\%$) and released into the extracellular media by the rest of selected strains (Table 1). When glucose was present in the reaction mixture, the release of hypoxanthine decreased in all the strains, although to a different extent; hypoxanthine was not even detected in the extracellular media of L. salivarius CECT 30632 and MPac90. Xanthine was found only in some samples and at a very low concentration (≤ $2\%$ transformation), regardless of the presence of glucose. On the other hand, L. salivarius MPac40 and MPac91 secreted also uric acid in addition to hypoxanthine when inosine was supplied in the reaction mixture. L. salivarius MPac91 was the most efficient at metabolizing guanosine to uric acid and releasing it from cells (Table 1).
## Biotransformation of inosine and guanosine by cell extracts of Ligilactobacillus salivarius
The products obtained after incubation of cell-free intracellular extracts of selected L. salivarius strains with inosine and guanosine are shown in Table 2. L. salivarius CECT 30632 and MPac32 extracts efficiently transformed inosine and guanosine to hypoxanthine and xanthine (>$90\%$), respectively, which were no further converted to uric acid in a large proportion (<$10\%$). The yield of xanthine from guanosine was lower (<$35\%$) and that of uric acid was higher (>$20\%$) in L. salivarius MPac40, MPac90, and MPac91 than in CECT 30632 and MPac32. Most of the inosine present in the reaction mixture containing L. salivarius MPac90 extracts was converted to uric acid (>$90\%$) without accumulation of the intermediates hypoxanthine and xanthine, but a lower efficiency of the guanosine transformation was found, yielding 30–$45\%$ of xanthine and 8–$26\%$ of uric acid. Remarkably, the inclusion of glucose in the reaction mixture did not modify the type and yield of the obtained compounds (Table 2).
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Transformation (%) | Transformation (%).1 | Transformation (%).2 |
| --- | --- | --- | --- | --- | --- |
| Strain | Substrate | Glucose (4 mM) | Hypoxanthine | Xanthine | Uric acid |
| CECT 30632 | Inosine | − | 92 ± 11 | ND | 8 ± 6 |
| CECT 30632 | Inosine | + | 89 ± 9 | ND | 8 ± 2 |
| CECT 30632 | Guanosine | − | ND | 97 ± 7 | 9 |
| CECT 30632 | Guanosine | + | ND | 94 ± 8 | 7 ± 5 |
| MPac32 | Inosine | − | 92 ± 9 | 9 ± 7 | 1 |
| MPac32 | Inosine | + | 98 ± 8 | ND | 13 ± 9 |
| MPac32 | Guanosine | − | ND | 89 ± 10 | 9 ± 5 |
| MPac32 | Guanosine | + | ND | 75 ± 13 | 7 ± 5 |
| MPac40 | Inosine | − | 89 ± 24 | 12 ± 9 | 30 ± 13 |
| MPac40 | Inosine | + | 81 ± 1 | 5 ± 2 | 18 ± 5 |
| MPac40 | Guanosine | − | 3 | 33 ± 18 | 19 ± 9 |
| MPac40 | Guanosine | + | ND | 32 ± 23 | ND |
| MPac90 | Inosine | − | 10 ± 5 | ND | 95 ± 11 |
| MPac90 | Inosine | + | 9 ± 4 | ND | 93 ± 11 |
| MPac90 | Guanosine | − | 3 ± 1 | 30 ± 6 | 8 ± 5 |
| MPac90 | Guanosine | + | 3 ± 1 | 45 ± 12 | 26 ± 5 |
| MPac91 | Inosine | − | 85 ± 18 | ND | 21 ± 9 |
| MPac91 | Inosine | + | 100 ± 20 | 14 ± 9 | 9 |
| MPac91 | Guanosine | − | 1 | 25 ± 8 | 5 ± 3 |
| MPac91 | Guanosine | + | 4 ± 1 | 35 ± 18 | 6 ± 3 |
When considering the protein concentration in the reaction mixtures, to somehow relate the extent of transformation to the bacterial enzyme amount, L. salivarius CECT 30632 was the most efficient for inosine ($100\%$), guanosine ($100\%$) and uric acid ($50\%$) conversion (Table 3). For this reason, this strain was selected for a pilot clinical trial.
**Table 3**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Transformation of | Transformation of.1 | Transformation of.2 |
| --- | --- | --- | --- | --- | --- |
| Strain | Protein in CFE* (μg/mL) | Glucose (mM) | Inosine (%) | Guanosine (%) | Uric acid (%) |
| CECT 30632 | 11.1 ± 1.20 | - | 100 ± 12 | 100 ± 8 | 51 ± 8 |
| CECT 30632 | 11.1 ± 1.20 | 4 | 100 ± 12 | 100 ± 8 | 51 ± 14 |
| MPac32 | 16.3 ± 0.48 | - | 36 ± 26 | 24 ± 23 | 51 ± 14 |
| MPac32 | | 4 | 43 ± 25 | 37 ± 19 | 51 ± 12 |
| MPac40 | 8.1 ± 1.57 | - | 100 ± 12 | 100 ± 8 | 14 ± 3 |
| MPac40 | | 4 | 100 ± 12 | 100 ± 8 | 13 ± 6 |
| MPac90 | 26.4 ± 7.32 | - | 100 ± 12 | 100 ± 8 | 4 ± 2 |
| MPac90 | | 4 | 100 ± 12 | 100 ± 8 | 6 ± 4 |
| MPac91 | 14.2 ± 0.44 | - | 24 ± 18 | 30 ± 21 | 51 ± 15 |
| MPac91 | | 4 | 24 ± 22 | 37 ± 4 | 53 ± 12 |
## Clinical outcomes of the study
This study was conducted between July 2020 and December 2021. A total of 30 volunteers were enrolled and assigned to the probiotic ($$n = 15$$) or the control (allopurinol) ($$n = 15$$) groups. There were no withdrawals during the assay and the compliance rate was high (>$93\%$). Baseline characteristics of the participants were comparable, and no significant differences were identified in terms of age, BMI, and the number of gout episodes in the 9 previous months (Table 4).
**Table 4**
| Unnamed: 0 | Control group (n = 15) | Probiotic group (n = 15) | p-value* |
| --- | --- | --- | --- |
| | Mean [95% CI] or n (%) | Mean [95% CI] or n (%) | p-value* |
| Age (years) | 54.2 [52.2–56.2] | 53.9 [51.9–55.9] | 0.802 |
| BMI (kg/m2) | 32.0 [30.5–33.5] | 31.7 [30.2–33.3] | 0.784 |
| Gout episodes in the previous 9 months | | | |
| 0 | 1 (7) | 1 (7) | 0.772 |
| 1 | 5 (33) | 7 (47) | 0.772 |
| 2 | 8 (53) | 5 (33) | 0.772 |
| 3 | 1 (7) | 2 (13) | 0.772 |
The main outcomes of this pilot study are shown in Tables 5, 6 and Figure 2. Daily administration of the probiotic L. salivarius CECT 30632 to hyperuricemic individuals was well tolerated and resulted in a significant reduction of the number of gout episodes reported during the period that lasted the study in comparison to the participants that received allopurinol in the control group (the Freeman–Halton extension of the Fisher Exact Probability Test for a 2 × 4 contingency table; $$p \leq 0.006$$). In the probiotic group, only 5 out of the 15 volunteers reported a gout episode (only one episode for each of these 5 participants during the trial). In the control group, 13 out of 15 individuals reported gout episodes, and one third of them suffered at least two episodes during the study; the frequency rates reported in the control group during the study were similar to those registered during the 9 months previous to the study (Tables 4, 5).
Colchicine and ibuprofen (or related NSAIDs) doses used during the study by the participants of the control (allopurinol) group (70 and 87 total doses, respectively) were much higher than those required by the volunteers of the probiotic group (30 and 33, respectively) (one-way ANOVA; $$p \leq 0.023$$ for colchicine and $$p \leq 0.022$$ for ibuprofen or related NSAIDs; Figure 2).
There were no differences in the number of doses of either colchicine (t-test: $$p \leq 0.669$$) or NSAIDs (t-test: $$p \leq 0.964$$) taken per individual between the individuals in the control group ($$n = 13$$) or those in the probiotic group ($$n = 5$$) who suffered gout episodes. Among such participants, the mean ($95\%$ CI) of the number of doses per participant was 5.4 (3.6–7.1) doses and 6.7 (4.1–9.3) doses for colchicine or ibuprofen (and related NSAIDs), respectively, in the control group, and 6.0 (3.5–8.5) doses and 6.6 (4.0–9.2) doses for colchicine or ibuprofen (and related NSAIDs), respectively, in the probiotic group. While all the individuals in the control group daily received allopurinol throughout the study, the use of this drug was prescribed for only 5 participants of the probiotic group (Figure 2).
Results of blood analyses performed before the start and at the end of the study to determine the effect of the probiotic intervention in serum urate, markers of oxidative stress (AOPPs, SH groups, TBARS, MDA, 8-isoprostaglandin F2α), indicators of vascular function and blood pressure modulation (NOx, nitrite, nitrate), lipid profile (triglycerides, total cholesterol) and indicators of damage of liver and/or other tissues (AST, ALT) are shown in Table 6. There were no significant changes in the levels of any of the blood parameters tested in the control group individuals. However, relevant changes were observed in the probiotic group in most of the parameters analyzed after the probiotic intervention. Serum urate level was reduced from a mean ($95\%$ CI) value of 9.04 (8.72–9.36) mg/dL to 7.90 (7.58–8.22) mg/dL (one-way repeated measures ANOVA; $p \leq 0.001$). The levels of AOPPs, MDA, and 8-isoprostaglandin F2α were reduced by 16, 6, and $12\%$ (one-way repeated measures ANOVA; $p \leq 0.001$) in the participants of the probiotic group at the end of the study. In contrast, the levels of SH groups and TBARS did not changed. Serum nitrite and nitrate concentrations did not experience variation but that of NOx decreased by a mean value of 0.83 μmol/L (one-way repeated measures ANOVA; $$p \leq 0.027$$). Differences were also found in the mean value of serum triglycerides and total cholesterol that were reduced by 29.52 and 27.88 mg/dL, respectively (one-way repeated measures ANOVA; $$p \leq 0.000$$). Individuals in the probiotic group also experienced an improvement in their serum AST and ALT levels since they were significantly reduced by 1.92 and 2.13 IU/L, respectively (one-way repeated measures ANOVA; $$p \leq 0.037$$ and $$p \leq 0.028$$).
The CONSORT 2010 checklist of information to include when reporting a pilot or feasibility trial1 can be found as Supplementary material (Supplementary Table S2; Eldridge et al., 2016).
## Discussion
Gout is a painful arthritis that may occur in hyperuricemic patients and is caused by the precipitation of monosodium urate crystals in joints and soft tissues, which activates the inflammasome with the concomitant release of IL-1βb (Dalbeth et al., 2021). The intestinal tract is the second organ responsible for the excretion of urate (20–$30\%$) and this process is driven by efflux transporters such as BCRP/ABCG2, which in addition to being present in the kidney is also highly expressed at the apical membrane of the small intestine (Hosomi et al., 2012). It has been hypothesized that the intestinal microbiota might contribute to the reduction of the serum urate levels by different mechanisms: promoting catabolism of purines and urate, secreting microbial metabolites that facilitate urate excretion, and alleviating the intestinal inflammation that is associated with hyperuricemia (Wang et al., 2022). Considering that the gut microbiota profile of gout patients is usually altered, probiotics have been proposed as an interesting alternative to treat hyperuricemia and deposition of urate crystals, without the adverse effects of classical pharmacological therapies (Guo et al., 2016). Different lactobacilli strains have been proven to effectively prevent hyperuricemia and/or reduce serum urate levels in human trials (Li et al., 2014; Yamada et al., 2017; Wang et al., 2019). The results of our pilot study show that serum urate levels and, of utmost relevance, the number of gout episodes and the amount of medication used to prevent or treat acute gout episodes were significantly reduced in individuals with hyperuricemia after the consumption of the probiotic strain L. salivarius CECT 30632, at a daily dose of 109 UFC for 6 months.
Similarly to Li et al. [ 2014], in this study, uric acid and two precursors for uric acid generation (guanosine and inosine) were used as substrates to select potential gout-preventing strains. Our working hypothesis was that increased intestinal clearance of uric acid and/or its precursors by potential probiotic bacteria would eventually lead to lower serum urate levels. Our results showed that almost all of the 13 L. salivarius strains included in the first screening had high inosine and guanosine uptake capacity. Lactic acid bacteria are ubiquitous in rich ecological niches, such as plants, raw and processed foods, wastewater sludge, and the gastrointestinal tract and other mucosal surfaces of a wide variety of host species (George et al., 2018). The adaptation of many lactic acid bacteria to nutrient-rich environments has resulted in an auxotrophy for purines and pyrimidines (Kilstrup et al., 2005). Dedicated transporters have been described for both nucleosides and purine bases, whereas nucleotides (the first degradation product of nucleic acids) must be dephosphorylated by phosphatases before being transported into the bacterial cell. The nucleoside and/or the purine base, once inside the bacterial cell, enter the salvage pathway for nucleotide synthesis or are degraded to purine metabolites (Kilstrup et al., 2005). Nucleoside transport has been proven in many different species of lactic acid bacteria (Li et al., 2014; Hsieh et al., 2021; Lee et al., 2022), but the incorporation of inosine and guanosine by L. salivarius strains of human origin is described for the first time in this study. Additionally, it shows that whole cells of 5 selected L. salivarius strains also uptake uric acid, although to a lesser extent than nucleosides. Under the assay conditions, only one strain, L. salivarius CECT 30632, incorporated all the inosine, guanosine and uric acid present in the extracellular media when abundant glucose was provided. The availability of an easily metabolizable carbon source (glucose) may increase the transport of adenosine, guanosine and uric acid, which will be subsequently used as a nitrogen source for the bacterial cells.
A more detailed characterization of the fate of the nucleosides inosine and guanosine revealed that inosine was metabolized to hypoxanthine which was, then, excreted outside the cell under glucose restriction conditions but not in a glucose-rich medium. Therefore, the absence of glucose will favor the use of the pentose sugar of the nucleosides as a carbon source for L. salivarius CECT 30632, MPac32, and MPac90, as it has been reported in Lactilactobacillus sakei CTC 494 (Rimaux et al., 2011). These strains also transported efficiently guanosine, but xanthine was not detected in the extracellular medium, and glucose availability did not modify this result. Uric acid was not detected in the extracellular media following the uptake of inosine and guanosine by L. salivarius CECT 30632 and MPac90, but different amounts of this metabolite were detected in the case of L. salivarius MPac32, MPac40 and MPac91.
This study also explored the enzymatic potential of the strains to transform inosine, guanosine and uric acid. All the strains, except MPac90, transformed efficiently inosine into hypoxantine. In an animal model, it has been shown that hypoxanthine in the colon modulates energy metabolism (promoting the purine savage pathway) and improves the barrier function in intestinal epithelial cells (Lee et al., 2018). In addition, L. salivarius CECT 30632 and MPac32 also metabolized guanosine to xanthine. This transformation in the gut lumen could be advantageous if purine bases are less efficiently transported across the apical membrane of human intestinal epithelial as it has been reported in animal models (Stow and Bronk, 1993). The transformation of inosine and guanosine into hypoxanthine and xanthine suggests that these strains possess purine nucleosidases (Kilstrup et al., 2005). This transformation would be advantageous because some bacteria favor the uptake of purine bases over purine nucleosides to be incorporated into the savage pathway for the synthesis of nucleotides (Yamada et al., 2017).
The administration of L. salivarius CECT 30632 for 6 months to individuals with hyperuricemia and a history of acute gout episodes included in this study resulted in a significant reduction (~1 mg/dL) of serum uric acid. This reduction was not enough to reset uric acid concentration to normal reference range values, but it was highly effective in terms of reducing the number of gout attacks. This indicates that the mechanism involved in the reduction of gout episodes by L. salivarius CECT 30632 may be related, at least partly, to effects other than the metabolism of nucleosides, purine bases and uric acid. Provision of nutrients to intestinal epithelial cells for energy that promote intestinal uric acid excretion, alleviating inflammation associated with gout and regulation of the expression of uric acid transporters are alternative mechanisms by which intestinal microbiota could reduce hyperuricemia and gout (Yin et al., 2022). Very recently, a study has also described the potential of *Limosilactobacillus fermentum* GR-3 as a therapeutic adjuvant in humans for probiotic treatment of hyperuricemia (Zhao et al., 2022).
The lower requirement of pharmacotherapy is another benefit derived from the administration of the probiotic since adverse events have been linked to most of the current long-term pharmacological strategies to lower serum urate levels, including allopurinol and colchicine (Benn et al., 2018). High doses of allopurinol, a competitive xanthine oxidase inhibitor, can result in severe cutaneous adverse reactions that are associated in some patients with drug-induced liver injury leading to high mortality rates as demonstrated in a 10-year multi-center prospective study (Huang et al., 2021). In patients who experience adverse events related to allopurinol treatment, the alternative is febuxostat (a noncompetitive xanthine oxidase inhibitor), but it has been associated with an increased cardiac risk (White et al., 2018). Colchicine is the most widely used anti-inflammatory drug as a pharmacologic approach to acute gout and is associated with gastrointestinal adverse effects (diarrhea, nausea, vomiting, cramps, and pain) (Qaseem et al., 2017).
In addition, the intake of L. salivarius CECT 30632 resulted in a significant decrease in the blood levels of some markers of oxidative stress (AOPPs, MDA and 8-isoprostaglandin F2α), nitric oxide, triglycerides, total cholesterol, GOT and GPT. Decreases in such parameters are relevant because of the close association between gout and metabolic syndrome (Raya-Cano et al., 2022). Gout is an increasingly relevant condition because of its relatively high prevalence, its impact on well-being and healthcare costs and, most importantly, its association with important co-morbidities that are usually considered as manifestations of the metabolic syndrome including hypertension, hyperlipidemia, cardiovascular disease, liver disease, renal disease, type 2 diabetes, and obesity (Thottam et al., 2017; Kimura et al., 2021). Interestingly, the administration of another L. salivarius strain for lactational mastitis (another inflammatory condition) also led to a significant reduction in the same blood parameters (Espinosa-Martos et al., 2016). Other studies have shown that different L. salivarius strains are able to reduce the blood levels of total cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides in broiler chicken (Shokryazdan et al., 2017) or healthy young humans (Rajkumar et al., 2015), or to attenuate their normal pregnancy-induced rise in women with gestational diabetes, who are at risk of future metabolic syndrome (Lindsay et al., 2015). Chuang et al. [ 2016] evaluated whether heat-killed cells obtained from either a L. salivarius or a *Lactobacillus johnsonii* strain were able to prevent alcoholic liver damage in rats after acute alcohol exposure and found that only those obtained from the L. salivarius strain reduced serum AST and triglyceride levels.
The clinical study presented in this work was an initial pilot trial and, therefore, it has the inherent limitation of the relatively low number of individuals included and, as consequence, the results must be confirmed in future multicenter randomized placebo-controlled trials. Moreover, the treatment was limited to 6 months and the analyses were performed immediately at its end, with no follow-up of participants to check the endurance of the observed positive effects. However, this pilot trial has revealed the high potential of L. salivarius CECT 30632 to reduce the burden associated with hyperuricemia and gout. Measured parameters in this study to assess the potential benefits of the strain for gout prevention were related to clinical or metabolic outcomes (gout episodes, gout-related medication, and standard blood analyses). Such parameters are more appealing or understandable for medical purposes that changes in the fecal microbiome, whose interpretation is frequently very difficult or even impossible for medical practitioners. In future trials, however, it should be evaluated whether changes in the fecal microbiome occur, since the alteration of the intestinal microbiome balance may favor multiple changes in gastrointestinal physiology and inflammatory status. This knowledge may reveal the involvement of specific microorganisms or consortia in the onset and/or progression of hyperuricemia, improve the diagnosis and treatment of this pathology, and help to develop more effective therapeutic strategies by manipulating the gut microbiota, including the selection of probiotics than are even more effective.
In conclusion, in this study, it was shown that the regular administration of L. salivarius CECT 30632 did reduce serum urate levels, the number of gout episodes and, the pharmacological therapy required to control both the hyperuricemia and the gout episodes involving individuals with a history of hyperuricemia and suffering repeated gout episodes.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by The Ethics Committee of the Hospital Clínico San Carlos (Madrid, Spain). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
LF and JR designed and coordinated the study. DB directed the recruitment of participants and the diagnosis of gout episodes. MG, JS, and BO processed the samples and performed the in vitro assays. CA and LF performed the statistical analysis. LF, BO, and JR drafted the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This work was funded by a grant conceded by the Complutense University of Madrid (Spain) to the research group.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.1111652/full#supplementary-material
## References
1. Afzaal M., Saeed F., Shah Y. A., Hussain M., Rabail R., Socol C. T.. **Human gut microbiota in health and disease: unveiling the relationship**. *Front. Microbiol.* (2022) **13** 999001. DOI: 10.3389/fmicb.2022.999001
2. Álvarez-Lario B., Macarrón-Vicente J.. **Uric acid and evolution**. *Rheumatology (Oxford)* (2010) **49** 2010-2015. DOI: 10.1093/rheumatology/keq204
3. Antushevich H.. **Fecal microbiota transplantation in disease therapy**. *Clin. Chim. Acta* (2020) **503** 90-98. DOI: 10.1016/j.cca.2019.12.010
4. Arroyo R., Martín V., Maldonado A., Jiménez E., Fernández L., Rodríguez J. M.. **Treatment of infectious mastitis during lactation: antibiotics versus oral administration of lactobacilli isolated from breast milk**. *Clin. Infect. Dis.* (2010) **50** 1551-1558. DOI: 10.1086/652763
5. Benn C. L., Dua P., Gurrell R., Loudon P., Pike A., Storer R. I.. **Physiology of hyperuricemia and urate-lowering treatments**. *Front. Med (Lausanne).* (2018) **5** 160. DOI: 10.3389/fmed.2018.00160
6. Butler F., Alghubayshi A., Roman Y.. **The epidemiology and genetics of hyperuricemia and gout across major racial groups: a literature review and population genetics secondary database analysis**. *J. Pers. Med.* (2021) **11** 231. DOI: 10.3390/jpm11030231
7. Cao T., Li X., Mao T., Liu H., Zhao Q., Ding X.. **Probiotic therapy alleviates hyperuricemia in C57BL/6 mouse model**. *Biomed. Res.* (2017) **28** 2244-2249
8. Cárdenas N., Martín V., Arroyo R., López M., Carrera M., Badiola C.. **Prevention of recurrent acute otitis media in children through the use of**. *Nutrients* (2019) **11** 376. DOI: 10.3390/nu11020376
9. Chen-Xu M., Yokose C., Rai S. K., Pillinger M. H., Choi H. K.. **Contemporary prevalence of gout and hyperuricemia in the United States and decadal trends: the National Health and nutrition examination survey, 2007-2016**. *Arthritis Rheumatol.* (2019) **71** 991-999. DOI: 10.1002/art.40807
10. Chu Y., Sun S., Huang Y., Gao Q., Xie X., Wang P.. **Metagenomic analysis revealed the potential role of gut microbiome in gout**. *NPJ Biofilms Microbiomes.* (2021) **7** 66. DOI: 10.1038/s41522-021-00235-2
11. Chuang C. H., Tsai C. C., Lin E. S., Huang C. S., Lin Y. Y., Lan C. C.. **Heat-killed**. *Molecules* (2016) **21** 1456. DOI: 10.3390/molecules21111456
12. Codoñer-Franch P., Tavárez-Alonso S., Murria-Estal R., Megías-Vericat J., Tortajada-Girbés M., Alonso-Iglesias E.. **Nitric oxide production is increased in severely obese children and related to markers of oxidative stress and inflammation**. *Atherosclerosis* (2011) **215** 475-480. DOI: 10.1016/j.atherosclerosis.2010.12.035
13. Codoñer-Franch P., Tavárez-Alonso S., Murria-Estal R., Tortajada-Girbés M., Simó-Jordá R., Alonso-Iglesias E.. **Elevated advanced oxidation protein products (AOPPs) indicate metabolic risk in severely obese children**. *Nutr. Metab. Cardiovasc. Dis.* (2012) **22** 237-243. DOI: 10.1016/j.numecd.2010.06.002
14. Dalbeth N., Gosling A. L., Gaffo A., Abhishek A.. **Gout**. *Lancet* (2021) **397** 1843-1855. DOI: 10.1016/S0140-6736(21)00569-9
15. Eldridge S. M., Chan C. L., Campbell M. J., Bond C. M., Hopewell S., Thabane L.. **CONSORT 2010 statement: extension to randomised pilot and feasibility trials**. *BMJ* (2016) **355** i5239. DOI: 10.1136/bmj.i5239
16. Espinosa-Martos I., Jiménez E., de Andrés J., Rodríguez-Alcalá L. M., Tavárez S., Manzano S.. **Milk and blood biomarkers associated to the clinical efficacy of a probiotic for the treatment of infectious mastitis**. *Benef. Microbes.* (2016) **7** 305-318. DOI: 10.3920/BM2015.0134
17. Fan Y., Pedersen O.. **Gut microbiota in human metabolic health and disease**. *Nat. Rev. Microbiol.* (2021) **19** 55-71. DOI: 10.1038/s41579-020-0433-9
18. Fernández L., Cárdenas N., Arroyo R., Manzano S., Jiménez E., Martín V.. **Prevention of infectious mastitis by oral administration of**. *Clin. Infect. Dis.* (2016) **62** 568-573. DOI: 10.1093/cid/civ974
19. Fernández L., Castro I., Arroyo R., Alba C., Beltrán D., Rodríguez J. M.. **Application of**. *Nutrients* (2021) **13** 162. DOI: 10.3390/nu13010162
20. Fernández L., Pannaraj P. S., Rautava S., Rodríguez J. M.. **The microbiota of the human mammary ecosystem**. *Front. Cell. Infect. Microbiol.* (2020) **10** 586667. DOI: 10.3389/fcimb.2020.586667
21. France M., Alizadeh M., Brown S., Ma B., Ravel J.. **Towards a deeper understanding of the vaginal microbiota**. *Nat. Microbiol.* (2022) **7** 367-378. DOI: 10.1038/s41564-022-01083-2
22. George F., Daniel C., Thomas M., Singer E., Guilbaud A., Tessier F. J.. **Occurrence and dynamism of lactic acid bacteria in distinct ecological niches: a multifaceted functional health perspective**. *Front. Microbiol.* (2018) **9** 2899. DOI: 10.3389/fmicb.2018.02899
23. George C., Minter D. A.. **Hyperuricemia**. *StatPearls* (2022)
24. Guo Z., Zhang J., Wang Z., Ang K. Y., Huang S., Hou Q.. **Intestinal microbiota distinguish gout patients from healthy humans**. *Sci. Rep.* (2016) **6** 20602. DOI: 10.1038/srep20602
25. Hosomi A., Nakanishi T., Fujita T., Tamai I.. **Extra-renal elimination of uric acid via intestinal efflux transporter BCRP/ABCG2**. *PLoS One* (2012) **7** e30456. DOI: 10.1371/journal.pone.0030456
26. Hsieh M.-W., Chen H.-Y., Tsai C. -C.. **Screening and evaluation of purine-nucleoside-degrading lactic acid bacteria isolated from winemaking byproducts in vitro and their uric acid-lowering effects in vivo**. *Fermentation* (2021) **7** 74. DOI: 10.3390/fermentation7020074
27. Huang Y. S., Wu C. Y., Chang T. T., Peng C. Y., Lo G. H., Hsu C. W.. **Drug-induced liver injury associated with severe cutaneous adverse drug reactions: a nationwide study in Taiwan**. *Liver Int.* (2021) **41** 2671-2680. DOI: 10.1111/liv.14990
28. Jiménez E., Manzano S., Schlembach D., Arciszewski K., Martin R., Ben Amor K.. *Microorganisms* (2021) **9** 1933. DOI: 10.3390/microorganisms9091933
29. Kilstrup M., Hammer K., Ruhdal Jensen P., Martinussen J.. **Nucleotide metabolism and its control in lactic acid bacteria**. *FEMS Microbiol. Rev.* (2005) **29** 555-590. DOI: 10.1016/j.femsre.2005.04.006
30. Kimura Y., Tsukui D., Kono H.. **Uric acid in inflammation and the pathogenesis of atherosclerosis**. *Int. J. Mol. Sci.* (2021) **22** 12394. DOI: 10.3390/ijms222212394
31. Lane A. N., Fan T. W.. **Regulation of mammalian nucleotide metabolism and biosynthesis**. *Nucleic Acids Res.* (2015) **43** 2466-2485. DOI: 10.1093/nar/gkv047
32. Lee J. S., Wang R. X., Alexeev E. E., Lanis J. M., Battista K. D., Glover L. E.. **Hypoxanthine is a checkpoint stress metabolite in colonic epithelial energy modulation and barrier function**. *J. Biol. Chem.* (2018) **293** 6039-6051. DOI: 10.1074/jbc.RA117.000269
33. Lee Y., Werlinger P., Suh J.-W., Cheng J.. **Potential probiotic**. *Microorganisms* (2022) **10** 851. DOI: 10.3390/microorganisms10050851
34. Li Q., Li X., Wang J., Liu H., Kwong J. S., Chen H.. **Diagnosis and treatment for hyperuricemia and gout: a systematic review of clinical practice guidelines and consensus statements**. *BMJ Open* (2019) **9** e026677. DOI: 10.1136/bmjopen-2018-026677
35. Li M., Yang D., Mei L., Yuan L., Xie A., Yuan J.. **Screening and characterization of purine nucleoside degrading lactic acid bacteria isolated from Chinese sauerkraut and evaluation of the serum uric acid lowering effect in hyperuricemic rats**. *PLoS One* (2014) **9** e105577. DOI: 10.1371/journal.pone.0105577
36. Lin S., Zhang T., Zhu L., Pang K., Lu S., Liao X.. **Characteristic dysbiosis in gout and the impact of a uric acid-lowering treatment, febuxostat on the gut microbiota**. *J. Genet. Genomics* (2021) **48** 781-791. DOI: 10.1016/j.jgg.2021.06.009
37. Lindsay K. L., Brennan L., Kennelly M. A., Maguire O. C., Smith T., Curran S.. **Impact of probiotics in women with gestational diabetes mellitus on metabolic health: a randomized controlled trial**. *Am. J. Obstet. Gynecol.* (2015) **212** 496.e1-496.e11. DOI: 10.1016/j.ajog.2015.02.008
38. MacFarlane L. A., Kim S. C.. **Gout: a review of nonmodifiable and modifiable risk factors**. *Rheum. Dis. Clin. N. Am.* (2014) **40** 581-604. DOI: 10.1016/j.rdc.2014.07.002
39. Maesaka J. K., Fishbane S.. **Regulation of renal urate excretion: a critical review**. *Am. J. Kidney Dis.* (1998) **32** 917-933. DOI: 10.1016/s0272-6386(98)70067-8
40. Mandal A. K., Mount D. B.. **The molecular physiology of uric acid homeostasis**. *Annu. Rev. Physiol.* (2015) **77** 323-345. DOI: 10.1146/annurev-physiol-021113-170343
41. Martín V., Cárdenas N., Ocaña S., Marín M., Arroyo R., Beltrán D.. **Rectal and vaginal eradication of**. *Nutrients* (2019) **11** 810. DOI: 10.3390/nu11040810
42. Martín R., Langella P.. **Emerging health concepts in the probiotics field: streamlining the definitions**. *Front. Microbiol.* (2019) **10** 1047. DOI: 10.3389/fmicb.2019.01047
43. Pareek V., Pedley A. M., Benkovic S. J.. **Human de novo purine biosynthesis**. *Crit. Rev. Biochem. Mol. Biol.* (2021) **56** 1-16. DOI: 10.1080/10409238.2020.1832438
44. Qaseem A., Harris R. P., Forciea M. A., Denberg T. D., Barry M. J.. **Management of acute and recurrent gout: a clinical practice guideline from the American College of Physicians**. *Ann. Intern. Med.* (2017) **166** 58-68. DOI: 10.7326/M16-0570
45. Rajkumar H., Kumar M., Das N., Kumar S. N., Challa H. R., Nagpal R.. **Effect of probiotic**. *J. Cardiovasc. Pharmacol. Ther.* (2015) **20** 289-298. DOI: 10.1177/1074248414555004
46. Raya-Cano E., Vaquero-Abellán M., Molina-Luque R., De Pedro-Jiménez D., Molina-Recio G., Romero-Saldaña M.. **Association between metabolic syndrome and uric acid: a systematic review and meta-analysis**. *Sci. Rep.* (2022) **12** 18412. DOI: 10.1038/s41598-022-22025-2
47. Requena T., Velasco M.. **The human microbiome in sickness and in health**. *Rev. Clin. Esp. (Barc).* (2021) **221** 233-240. DOI: 10.1016/j.rceng.2019.07.018
48. Rimaux T., Vrancken G., Vuylsteke B., De Vuyst L., Leroy F.. **The pentose moiety of adenosine and inosine is an important energy source for the fermented-meat starter culture**. *Appl. Environ. Microbiol.* (2011) **77** 6539-6550. DOI: 10.1128/AEM.00498-11
49. Rizzatti G., Ianiro G., Gasbarrini A.. **Antibiotic and modulation of microbiota: a new paradigm?**. *J. Clin. Gastroenterol.* (2018) **52** S74-S77. DOI: 10.1097/MCG.0000000000001069
50. Rosemeyer H.. **The chemodiversity of purine as a constituent of natural products**. *Chem. Biodivers.* (2004) **1** 361-401. DOI: 10.1002/cbdv.200490033
51. Shao T., Shao L., Li H., Xie Z., He Z., Wen C.. **Combined signature of the fecal microbiome and metabolome in patients with gout**. *Front. Microbiol.* (2017) **8** 268. DOI: 10.3389/fmicb.2017.00268
52. Shokryazdan P., Faseleh Jahromi M., Liang J. B., Ramasamy K., Sieo C. C., Ho Y. W.. **Effects of a**. *PLoS One* (2017) **12** e0175959. DOI: 10.1371/journal.pone.0175959
53. Sorensen L. B.. **Role of the intestinal tract in the elimination of uric acid**. *Arthritis Rheum.* (1965) **8** 694-706. DOI: 10.1002/art.1780080429
54. Stow R. A., Bronk J. R.. **Purine nucleoside transport and metabolism in isolated rat jejunum**. *J. Physiol.* (1993) **468** 311-324. DOI: 10.1113/jphysiol.1993.sp019773
55. Strilchuk L., Fogacci F., Cicero A. F.. **Safety and tolerability of available urate-lowering drugs: a critical review**. *Expert Opin. Drug Saf.* (2019) **18** 261-271. DOI: 10.1080/14740338.2019.1594771
56. Thottam G. E., Krasnokutsky S., Pillinger M. H.. **Gout and metabolic syndrome: a tangled web**. *Curr. Rheumatol. Rep.* (2017) **19** 60. DOI: 10.1007/s11926-017-0688-y
57. Vedder D., Walrabenstein W., Heslinga M., de Vries R., Nurmohamed M., van Schaardenburg D.. **Dietary interventions for gout and effect on cardiovascular risk factors: a systematic review**. *Nutrients* (2019) **11** 2955. DOI: 10.3390/nu11122955
58. Wang J., Chen Y., Zhong H., Chen F., Regenstein J., Hu X.. **The gut microbiota as a target to control hyperuricemia pathogenesis: potential mechanisms and therapeutic strategies**. *Crit. Rev. Food Sci. Nutr.* (2022) **62** 3979-3989. DOI: 10.1080/10408398.2021.1874287
59. Wang H., Mei L., Deng Y., Liu Y., Wei X., Liu M.. *Nutrition* (2019) **62** 63-73. DOI: 10.1016/j.nut.2018.11.018
60. White W. B., Saag K. G., Becker M. A., Borer J. S., Gorelick P. B., Whelton A.. **Cardiovascular safety of febuxostat or allopurinol in patients with gout**. *N. Engl. J. Med.* (2018) **378** 1200-1210. DOI: 10.1056/NEJMoa1710895
61. Wu Y., Ye Z., Feng P., Li R., Chen X., Tian X.. *Gut Microbes* (2021) **13** 1-18. DOI: 10.1080/19490976.2021.1897211
62. Yamada N., Saito-Iwamoto C., Nakamura M., Soeda M., Chiba Y., Kano H.. *Microorganisms* (2017) **5** 10. DOI: 10.3390/microorganisms5010010
63. Yanai H., Adachi H., Hakoshima M., Katsuyama H.. **Molecular biological and clinical understanding of the pathophysiology and treatments of hyperuricemia and its association with metabolic syndrome, cardiovascular diseases and chronic kidney disease**. *Int. J. Mol. Sci.* (2021) **22** 9221. DOI: 10.3390/ijms22179221
64. Yin H., Liu N., Chen J.. **The role of the intestine in the development of hyperuricemia**. *Front. Immunol.* (2022) **13** 845684. DOI: 10.3389/fimmu.2022.845684
65. Zhao S., Feng P., Hu X., Cao W., Liu P., Han H.. **Probiotic**. *iScience* (2022) **25** 105198. DOI: 10.1016/j.isci.2022.105198
|
---
title: Outcomes of beta blocker use in advanced hepatocellular carcinoma treated with
immune checkpoint inhibitors
authors:
- Y. Linda Wu
- Grace van Hyfte
- Umut Özbek
- Marlene Reincke
- Anuhya Gampa
- Yehia I. Mohamed
- Naoshi Nishida
- Brooke Wietharn
- Suneetha Amara
- Pei-Chang Lee
- Bernhard Scheiner
- Lorenz Balcar
- Matthias Pinter
- Arndt Vogel
- Arndt Weinmann
- Anwaar Saeed
- Anjana Pillai
- Lorenza Rimassa
- Abdul Rafeh Naqash
- Mahvish Muzaffar
- Yi-Hsiang Huang
- Ahmed O. Kaseb
- Masatoshi Kudo
- David J. Pinato
- Celina Ang
journal: Frontiers in Oncology
year: 2023
pmcid: PMC9971987
doi: 10.3389/fonc.2023.1128569
license: CC BY 4.0
---
# Outcomes of beta blocker use in advanced hepatocellular carcinoma treated with immune checkpoint inhibitors
## Abstract
### Background
In patients with cirrhosis, portal hypertension increases intestinal permeability, dysbiosis, and bacterial translocation, promoting an inflammatory state that can lead to the progression of liver disease and development of hepatocellular carcinoma (HCC). We aimed to investigate whether beta blockers (BBs), which can mediate portal hypertension, conferred survival benefits in patients treated with immune checkpoint inhibitors (ICIs).
### Methods
We conducted a retrospective, observational study of 578 patients with unresectable HCC treated with ICI from 2017 to 2019 at 13 institutions across three continents. BB use was defined as exposure to BBs at any time during ICI therapy. The primary objective was to assess the association of BB exposure with overall survival (OS). Secondary objectives were to evaluate the association of BB use with progression-free survival (PFS) and objective response rate (ORR) according to RECIST 1.1 criteria.
### Results
In our study cohort, 203 ($35\%$) patients used BBs at any point during ICI therapy. Of these, $51\%$ were taking a nonselective BB. BB use was not significantly correlated with OS (hazard ratio [HR] 1.12, $95\%$ CI 0.9-1.39, $$P \leq 0.298$$), PFS (HR 1.02, $95\%$ CI 0.83-1.26, $$P \leq 0.844$$) or ORR (odds ratio [OR] 0.84, $95\%$ CI 0.54-1.31, $$P \leq 0.451$$) in univariate or multivariate analyses. BB use was also not associated with incidence of adverse events (OR 1.38, $95\%$ CI 0.96-1.97, $$P \leq 0.079$$). Specifically, nonselective BB use was not correlated with OS (HR 0.94, $95\%$ CI 0.66-1.33, $$P \leq 0.721$$), PFS (HR 0.92, 0.66-1.29, $$P \leq 0.629$$), ORR (OR 1.20, $95\%$ CI 0.58-2.49, $$P \leq 0.623$$), or rate of adverse events (OR 0.82, $95\%$ CI 0.46-1.47, $$P \leq 0.510$$).
### Conclusion
In this real-world population of patients with unresectable HCC treated with immunotherapy, BB use was not associated with OS, PFS or ORR.
## Introduction
Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide and often diagnosed in advanced stages when cure is no longer feasible [1]. For patients with advanced HCC, multikinase inhibitors such as sorafenib and lenvatinib had long been the first-line systemic therapy but offered poor outcomes and high toxicity [2]. Recently, the combination of atezolizumab, a programmed death-ligand 1 (PD-L1) inhibitor, and bevacizumab was shown to improve overall survival (OS) compared to sorafenib in patients with unresectable HCC in the IMbrave150 trial [3, 4]. In addition, the phase III HIMALAYA trial recently showed that the combination of durvalumab and tremelimumab had superior efficacy to sorafenib in the first-line treatment of unresectable HCC [5]. Even in patients who had received multikinase inhibitors in the front line, treatment with immunotherapy on progression of disease may induce a response [6]. As a result, immune checkpoint inhibitors (ICIs) have now supplanted multikinase inhibitors as standard of care front line therapy for advanced HCC. However, advanced HCC still carries a poor prognosis, and response to ICIs is limited, underscoring the need to identify markers of ICI response.
Increasingly, there is interest in understanding drug-drug interactions in the context of cancer immunotherapy. In particular, common concomitant medications such as antibiotics, steroids, antacids, metformin, and opioids that may have immunomodulatory effects have been investigated in order to examine their potential role in either enhancing ICI efficacy or contributing to toxicity [7]. The disruption of the gut microbiome, through antibiotic use, for example, has been associated with decreased ICI efficacy and impaired T cell antitumor response [8, 9]. In HCC, a recent study found that patients who responded to ICI had greater gut microbial diversity than non-responders, providing further evidence that the gut microbiome may impact response to ICI [10].
The interaction of the gut microbiome and ICI therapy has important implications for patients with HCC. Liver cirrhosis is well-known to underlie HCC carcinogenesis, and portal hypertension (pHTN) promotes progression of liver disease through immune activation: pHTN causes splanchnic vasodilation and pathological angiogenesis, increasing intestinal permeability and dysbiosis, which leads to bacterial translocation and induces a pro-inflammatory state [11]. Both pHTN and chronic inflammation are risk factors for the development of HCC and tumor progression [12]. Therefore, it is possible that the attenuation of pHTN may decrease aberrant neoangiogenesis and bacterial translocation-mediated inflammation driving HCC tumorigenesis and progression.
Beta blockers (BBs), particularly non-selective BBs, are standard prophylaxis for patients with cirrhosis and pHTN-induced varices [13]. They have been shown to modulate pHTN-associated dysbiosis through a reduction in intestinal bacterial overgrowth, intestinal permeability, and bacterial translocation [14, 15]. Additionally, in preclinical studies, BBs decrease tumor cell proliferation, proinflammatory cytokine load, and catecholamine-driven angiogenesis (16–18). Some clinical studies suggest that BB use is associated with lower incidence of HCC in patients with cirrhosis (19–21). One nationwide population-based study in Taiwan found that propranolol use improved OS in patients with unresectable or metastatic HCC who were treated with sorafenib, locoregional therapy, or radiotherapy [22]. However, there is a paucity of data addressing the effect of beta blockade on outcomes of patients with advanced HCC in the era of immunotherapy. We aimed to evaluate whether BB use conferred survival benefits in patients treated with ICIs using real-world data.
## Study population
The study population consisted of 578 patients with unresectable HCC treated with ICI from 2017 to 2019 at 13 institutions across North America ($$n = 247$$), Europe ($$n = 240$$), and Asia ($$n = 91$$). Patients included in this study had a diagnosis of HCC in accordance with American Association for the Study of Liver Disease [23] and European Association for the Study of the Liver [24] guidelines, received systemic ICI therapy (either monotherapy or in combination), and had measurable disease according to RECIST 1.1 criteria at the start of ICI. All patients were treated according to routine clinical practice, including prescriptions for BB. The decision to start ICI therapy was made at the discretion of the treating physician based on current evidence-based practice guidelines, institutional standards, and often after multidisciplinary tumor board discussions.
## Study design
Patient demographics and clinical data, including Barcelona Clinic Liver Cancer (BCLC) stage, Child-Pugh (CP) class, Eastern Cooperative Oncology Group (ECOG) performance status, alpha fetoprotein (AFP) level, presence of cirrhosis (clinically or radiologically diagnosed), etiology of liver disease, type and duration of ICI therapy, type and indication of BB use, duration of BB use, follow-up and vital status, were collected retrospectively. Baseline data were defined at the time of ICI initiation, and treatment response was evaluated through radiologic staging of the disease using computerized tomography and/or magnetic resonance imaging approximately every 9 weeks during treatment. BB use was defined as exposure at any time during ICI therapy. BBs were classified as nonselective (propranolol, nadolol, carvedilol, labetalol) and cardio-selective (metoprolol, atenolol, bisoprolol, nebivolol), and standard doses were used. Indications for BB use were evaluated and included variceal prophylaxis, cardiovascular disease, and other indications.
The primary outcome was to evaluate the association between BB use and OS, measured from the time of ICI initiation until date of death from any cause or date of last follow-up. Secondary outcomes included assessing the effect of BB use on objective response rate (ORR), defined as the proportion of patients with either radiographic complete response (CR) or partial response (PR), duration of response (DOR), defined as best response of CR, PR, or stable disease (SD), progression-free survival (PFS), measured from the time of ICI initiation until radiographic progression, and development of treatment-related adverse events (AEs) of any grade. All responses were evaluated according to RECIST 1.1 criteria. AEs were defined based on the Common Terminology Criteria for Adverse Events (CTCAE) classification, version 5.0, and identified based on investigator review of clinical notes, radiographic, and laboratory data. Evaluation of BB exposure was based on the presence of an active prescription in the medical record per clinical notes or medication records. Baseline BB use was defined as exposure within 30 days prior to ICI initiation, and concurrent BB use was defined as exposure between the dates of ICI initiation and cessation.
## Statistical analysis
Patient characteristics were summarized descriptively with medians and interquartile ranges for continuous variables and frequencies and proportions for categorical variables. Categorical variables were examined across BB exposure levels utilizing either chi-square tests or Fisher’s exact tests, where appropriate, while the nonparametric Mann-Whitney U test was used for continuous variables. Univariable and multivariable Cox proportional hazard models were fitted for OS and PFS. Univariable and multivariable logistic regression models were generated to evaluate the association of the aforementioned variables with ORR, the presence of any AE, and for AEs graded 2 or higher. Covariates were selected for the multivariable models if they were found to be significant in univariable analysis. Each model is summarized with hazard ratios (HR) or odds ratios (OR) and their coinciding $95\%$ confidence intervals (CIs). Additional subgroup analyses were performed in order to examine the interaction between each covariate and BB exposure. A forest plot was generated which summarizes the subgroup analyses with interaction term and associated p-value. For all survival analyses the proportional hazards assumption was tested and found to be satisfied. Variance inflation factors in multivariable models were below 5 to indicate an absence of multicollinearity. The level of significance was maintained at 0.05. All analyses were carried out in R version 4.1.2 (Vienna, Austria).
## Baseline characteristics
Baseline clinical characteristics are reported in Table 1. The majority of the cohort were male ($$n = 464$$, $80\%$), with a median age of 65 years (IQR: 58-70 years). Most patients ($$n = 406$$, $70\%$) had radiologic or pathologic evidence of cirrhosis at baseline. The causes of underlying liver disease were hepatitis C virus ($$n = 209$$, $36\%$), hepatitis B virus ($$n = 125$$, $22\%$), alcohol-related ($$n = 120$$, $21\%$), and nonalcoholic steatohepatitis (NASH)-associated ($$n = 75$$, $13\%$). Most patients had preserved liver function with CP class A disease ($$n = 413$$, $74\%$) and good performance status with ECOG score either 0 ($$n = 300$$, $52\%$) or 1 ($$n = 259$$, $45\%$).
**Table 1**
| Unnamed: 0 | Unnamed: 1 | No BB Exposure (%) | BB Exposure (%) | P value | All Patients (%) |
| --- | --- | --- | --- | --- | --- |
| N | | 375 | 203 | | 578 |
| Age (years), median | Age (years), median | 64 | 66 | | 65 |
| Male | | 302 (80.5) | 162 (79.8) | 0.9194 | 464 (80.3) |
| Region | USA | 150 (40.0) | 97 (47.8) | <0.0001 | 247 (42.7) |
| Region | Europe | 145 (38.7) | 95 (46.8) | | 240 (41.5) |
| Region | Asia | 80 (21.3) | 11 (5.4) | | 91 (15.7) |
| Cirrhosis | Cirrhosis | 248 (66.1) | 158 (77.8) | 0.0045 | 406 (70.2) |
| Etiology | HBV | 98 (26.1) | 27 (13.3) | 0.0005 | 125 (21.6) |
| Etiology | HCV | 132 (35.2) | 77 (37.9) | 0.5744 | 209 (36.2) |
| Etiology | EtOH | 71 (18.9) | 49 (24.1) | 0.1722 | 120 (20.8) |
| Etiology | NASH | 45 (12.0) | 30 (14.8) | 0.4196 | 75 (13.0) |
| Etiology | Other | 16 (4.3) | 20 (9.9) | 0.0141 | 36 (6.2) |
| BCLC Stage | A | 7 (1.9) | 7 (3.4) | 0.4378 | 14 (2.4) |
| BCLC Stage | B | 52 (13.9) | 25 (12.3) | | 77 (13.3) |
| BCLC Stage | C | 314 (83.7) | 168 (82.8) | | 482 (83.4) |
| Child Pugh Class | A | 286 (78.1) | 127 (64.8) | 0.0016 | 413 (72.3) |
| Child Pugh Class | B | 78 (21.3) | 67 (34.2) | | 145 (25.4) |
| Child Pugh Class | C | 2 (0.5) | 2 (1.0) | | 4 (0.7) |
| ECOG PS | 0 | 210 (56.0) | 90 (44.3) | | 300 (51.9) |
| ECOG PS | 1 | 161 (42.9) | 98 (48.3) | | 259 (44.8) |
| ECOG PS | 2 | 2 (0.5) | 15 (7.4) | | 17 (2.9) |
| ECOG PS | 3 | 2 (0.5) | 0.0 | | 2 (0.3) |
| Portal Vein Thrombosis | Portal Vein Thrombosis | 109 (29.9) | 70 (37.2) | 0.0063 | 179 (32.4) |
| Extrahepatic Metastasis | Extrahepatic Metastasis | 203 (54.1) | 106 (52.2) | 0.7236 | 309 (53.5) |
| Baseline AFP > 400 | Baseline AFP > 400 | 152 (41.5) | 75 (37.3) | 0.3595 | 227 (40.0) |
| Immunotherapy | PD-1 alone | 281 (75.1) | 154 (75.9) | 0.4777 | 435 (75.3) |
| Immunotherapy | PD-1/ CTLA-4 | 28 (7.5) | 9 (4.4) | | 37 (6.4) |
| Immunotherapy | PD-1/TKI | 27 (7.2) | 15 (7.4) | | 42 (7.3) |
| Immunotherapy | Other | 38 (10.2) | 25 (12.3) | | 63 (10.9) |
| First-Line ICI | First-Line ICI | 174 (46.4) | 90 (44.3) | 0.6978 | 264 (45.7) |
| Prior Systemic Therapy | Prior Systemic Therapy | 200 (53.3) | 112 (55.2) | 0.7368 | 312 (54.0) |
| Prior Local Therapy | Prior Local Therapy | 250 (66.7) | 130 (64.0) | 0.5868 | 380 (65.7) |
| Previous Liver Resection | Previous Liver Resection | 133 (35.5) | 53 (26.1) | 0.0274 | 186 (32.2) |
| Antibiotic Exposure | Antibiotic Exposure | 126 (34.8) | 94 (49.2) | 0.0013 | 220 (39.7) |
| Antacid Exposure | Antacid Exposure | 145 (50.5) | 88 (55.3) | 0.3801 | 233 (52.2) |
At the time of initiation of ICI, 482 patients ($83\%$) had BCLC stage C disease. The majority of patients ($$n = 435$$, $75\%$) treated with ICIs received a PD-1 inhibitor alone. ICI was given in the first-line in $46\%$ of patients ($$n = 264$$), and $54\%$ of patients ($$n = 312$$) received at least one prior systemic therapy. Many patients ($$n = 380$$, $66\%$) received prior locoregional therapy, with transarterial chemoembolization being the most common ($$n = 258$$, $45\%$). In addition, 186 patients ($32\%$) had undergone prior surgical resection.
## Treatment outcomes
During a median follow-up of 30.8 months (IQR: 17.2-40.3 months), there were 360 deaths ($62\%$) noted. A total of 541 patients could be evaluated for best radiographic response to ICI therapy per RECIST 1.1 criteria. There were 36 patients with CR ($6.7\%$), 78 with PR ($14.4\%$), and 216 with SD ($39.9\%$), which correspond with an ORR of $21.1\%$ and disease control rate (DCR) of $61.0\%$. At the time of analysis, the median duration of ICI therapy was 4.1 months (IQR: 1.9-9.3 months). Progression of disease was the most common cause of ICI discontinuation ($$n = 303$$, $52\%$).
Treatment-related AEs developed in 336 patients ($58\%$), but only 97 patients ($17\%$) developed grade 3 or higher events. The most common AEs were fatigue ($$n = 127$$, $22\%$), skin toxicity ($$n = 100$$, $17\%$), and liver toxicity ($$n = 96$$, $17\%$), with 142 ($25\%$) experiencing other AEs, such as cytopenias, nausea, fever or infections, neuropathy, and electrolyte imbalances (Table 2). The most common grade 3 or higher AE was hepatotoxicity ($$n = 33$$, $6\%$), followed by fatigue ($$n = 13$$, $2.2\%$) and colitis ($$n = 13$$, $2.2\%$), with 37 patients ($6\%$) experiencing other grade 3 or higher AEs (Table 2). A total of 37 patients ($6.4\%$) also experienced bleeding events, 16 ($43\%$) of which were gastrointestinal or variceal bleeding.
**Table 2**
| Adverse Event | Any Grade, N (%) | Grade 3 or Above, N (%) |
| --- | --- | --- |
| Skin | 100 (17.3%) | 11 (1.9%) |
| Diarrhea/Colitis | 52 (9.0%) | 13 (2.2%) |
| Fatigue | 127 (22.0%) | 13 (2.2%) |
| Hepatitis/Liver Toxicity | 96 (16.6%) | 33 (5.7%) |
| Thyroid Toxicity | 34 (5.9%) | 1 (0.2%) |
| Pituitary Toxicity | 10 (1.7%) | 3 (0.5%) |
| Rheumatologic Toxicity | 11 (1.9%) | 4 (0.7%) |
| Lung Toxicity | 37 (6.4%) | 6 (1.0%) |
| Hypertension | 37 (6.4%) | 4 (0.7%) |
| Proteinuria | 20 (3.5%) | 0 (0%) |
| Other | 142 (24.6%) | 37 (6.4%) |
## Beta blocker exposure
Two hundred and three ($35\%$) patients had BB use at any point during ICI therapy, of which only 4 ($2\%$) patients had used BB up to the time of ICI initiation but not concurrently with ICI. Conversely, 22 ($11\%$) patients started BB after ICI was initiated. However, most patients ($$n = 177$$, $87\%$) had been on BB before the start of ICI and continued on immunotherapy. Furthermore, BBs were long-term medications for patients who had been on BBs prior to ICI, with $96\%$ ($$n = 173$$) being prescribed for more than 4 weeks.
The types of BBs used were evenly divided: $51\%$ ($$n = 103$$) of patients were on a nonselective BB and $49\%$ ($$n = 100$$) were taking a cardio-selective BB. Of those taking a nonselective BB, the indication was predominantly for variceal prophylaxis ($$n = 69$$, $67\%$), followed by cardiovascular indications ($$n = 32$$, $31\%$), with 2 unclear indications. As expected, cardio-selective BBs were prescribed for cardiovascular indications ($$n = 96$$, $96\%$), except for 2 patients who had a cardio-selective BB for variceal prophylaxis and 2 more for other indications.
Overall, the baseline characteristics between patients with or without BB exposure were comparable, but there were some exceptions (Table 1). Patients who had BB exposure were more often from the United States or Europe, had a history of cirrhosis, neoplastic portal vein thrombosis (PVT), and antibiotic exposure. Patients exposed to BBs and antibiotics were most commonly treated with beta-lactams, quinolones, or cephalosporins, typically for a single week-long course for fever of unknown origin and early in the course of ICI therapy (within 30 days). The effect of antibiotic therapy on ICI outcomes in this cohort of HCC patients was previously evaluated [25]. Patients without BB exposure tended to have HBV as an etiology of liver disease, CP class A disease, and a history of prior resection to treat HCC.
## Association of baseline beta blocker use with immunotherapy outcomes
In univariable analysis, BB use was not significantly correlated with OS (HR 1.12, $95\%$ CI 0.9-1.39) (Table 3). Nonselective BB type was also not associated with OS (HR 0.94, $95\%$ CI 0.66-1.33), and these results are illustrated in Figure 1. Variables that were associated with improved OS included CP class A (HR 0.51, $95\%$ CI 0.41-0.64) and performance status ECOG 0 (HR 0.69, $95\%$ CI 0.56-0.84). Factors contributing to worsened OS included presence of neoplastic PVT (HR 1.93, $95\%$ CI 1.55-2.41) and AFP > 400 (HR 1.59, $95\%$ CI 1.29-1.96). Other baseline characteristics tested that did not have associations with OS included age, sex, cirrhosis, viral etiology of liver disease, and presence of extrahepatic metastases. Multivariable analyses identified the same independent predictors of OS, including CP class A disease (HR 0.55, $95\%$ CI 0.44-0.70), presence of neoplastic PVT (HR 1.60, $95\%$ CI 1.27-2.02), and AFP > 400 (HR 1.39, $95\%$ CI 1.11-1.74), but not ECOG 0 (HR 0.81, $95\%$ CI 0.65-1). The effect of BB exposure on OS was not affected by multiple variables evaluated in subgroup analyses (Figure 2), including age, sex, ICI monotherapy vs. combination therapy, line of therapy, cirrhosis, performance status, stage, liver function, presence of neoplastic PVT, extrahepatic metastasis, or AFP level. In particular, given the possible immunomodulatory effects of BBs, concomitant exposure to antibiotics and antacids was examined but not found to influence the effect of BB use on OS.
Next, the PFS was evaluated and results are tabulated in Table 4. Univariable analysis did not find a significant correlation between BB exposure and PFS (HR 1.02, $95\%$ CI 0.83-1.26). Again, nonselective BB use was not determined to be associated with PFS (HR 0.92, $95\%$ CI 0.66-1.29). Variables associated with PFS included CP class A disease (HR 0.71, $95\%$ CI 0.57-0.89), presence of neoplastic PVT (HR 1.41, $95\%$ CI 1.14-1.76), and performance status ECOG 0 (HR 0.77, $95\%$ CI 0.63-0.94). On multivariable analyses, independent predictors of PFS were CP class A disease (HR 0.74, $95\%$ CI 0.58-0.94) and presence of neoplastic PVT (HR 1.37, $95\%$ CI 1.09-1.72). The cirrhosis status and CP class did not significantly affect the PFS of patients with BB exposure in subgroup analyses.
**Table 4**
| Predictor | Univariable HR (95% CI) | P Value | Multivariable HR (95% CI) | P Value.1 |
| --- | --- | --- | --- | --- |
| Age | 0.99 (0.98, 1) | 0.109 | | |
| Sex | 1.04 (0.81, 1.32) | 0.774 | | |
| Cirrhosis | 0.91 (0.73, 1.13) | 0.38 | | |
| Viral etiology | 0.94 (0.77, 1.15) | 0.53 | | |
| Child-Pugh A | 0.71 (0.57, 0.89) | 0.003 | 0.74 (0.58, 0.94) | 0.015 |
| ECOG 0 | 0.77 (0.63, 0.94) | 0.012 | | |
| Portal vein thrombosis | 1.41 (1.14, 1.76) | 0.002 | 1.37 (1.09, 1.72) | 0.008 |
| Extrahepatic metastasis | 1.08 (0.89, 1.32) | 0.441 | | |
| AFP > 400 | 1.21 (0.99, 1.48) | 0.065 | | |
| BB exposure | 1.02 (0.83, 1.26) | 0.844 | | |
| Nonselective BB | 0.92 (0.66, 1.29) | 0.629 | | |
We then evaluated whether BB exposure was associated with ICI response (Table 5). Univariable analyses showed that BB use was also not significantly correlated with ORR (OR 0.84, $95\%$ CI 0.54-1.31). Nonselective BB use did not play a role in objective response (OR 1.20, $95\%$ 0.58-2.49). The other characteristics evaluated, including age, sex, presence of cirrhosis, viral etiology of liver disease, CP class, presence of neoplastic PVT, performance status, presence of extrahepatic metastases, and AFP level were not found to be associated with response to ICI. Subgroup analyses of the effect of BBs on ORR revealed no significant effect of cirrhosis or CP class.
**Table 5**
| Predictor | Univariable OR (95% CI) | P Value |
| --- | --- | --- |
| Age | 1.00 (0.98, 1.02) | 0.967 |
| Sex | 0.72 (0.41, 1.26) | 0.251 |
| Cirrhosis | 0.95 (0.61, 1.48) | 0.816 |
| Viral etiology | 1.03 (0.68, 1.57) | 0.877 |
| Child-Pugh A | 0.85 (0.53, 1.37) | 0.512 |
| ECOG 0 | 1.20 (0.79, 1.82) | 0.386 |
| Portal vein thrombosis | 1.03 (0.65, 1.62) | 0.914 |
| Extrahepatic metastasis | 1.03 (0.68, 1.56) | 0.894 |
| AFP > 400 | 1.19 (0.78, 1.81) | 0.424 |
| BB exposure | 0.84 (0.54, 1.31) | 0.451 |
| Nonselective BB | 1.20 (0.58, 2.49) | 0.623 |
Finally, the effect of BBs on development of AEs was evaluated. BB exposure was not associated with development of any AE (OR 1.38, $95\%$ CI 0.96-1.97). No statistically significant benefit of BB exposure against bleeding events was observed (OR 1.83, $95\%$ CI 0.94-3.58). Only 4 patients in the cohort developed ascites while treated with ICI: 2 patients who had prior BB exposure and 2 who did not. The presence of neoplastic PVT was found to increase the risk of any AE in univariable analysis (OR 1.53, $95\%$ CI 1.05-2.22) and was an independent predictor of AE development in multivariable analysis (OR 1.56, $95\%$ CI 1.06, 2.31) (Table 6). Conversely, patients with a viral etiology of HCC were less likely to develop any AE according to univariable analysis (OR 0.56, $95\%$ CI 0.40-0.79) and multivariable analysis (OR 0.52, $95\%$ CI 0.36-0.75).
**Table 6**
| Predictor | Univariable OR (95% CI) | P Value | Multivariable OR (95% CI) | P Value.1 |
| --- | --- | --- | --- | --- |
| Age | 1.00 (0.98, 1.01) | 0.834 | | |
| Sex | 1.27 (0.84, 1.96) | 0.271 | | |
| Cirrhosis | 0.82 (0.57, 1.19) | 0.296 | | |
| Viral etiology | 0.56 (0.40, 0.79) | 0.001 | 0.52 (0.36, 0.75) | <0.001 |
| Child-Pugh B or C | 0.70 (0.48, 1.03) | 0.07 | | |
| ECOG 1-3 | 1.07 (0.76, 1.49) | 0.701 | | |
| Portal vein thrombosis | 1.53 (1.05, 2.22) | 0.025 | 1.56 (1.06, 2.31) | 0.025 |
| Extrahepatic metastasis | 1.37 (0.98, 1.91) | 0.069 | | |
| AFP > 400 | 1.08 (0.77, 1.53) | 0.654 | | |
| BB exposure | 1.38 (0.96, 1.97) | 0.079 | | |
| Nonselective BB | 0.82 (0.46, 1.47) | 0.51 | | |
## Discussion
In this multicenter, international observational study, we evaluated the effect of BB use in patients with advanced HCC treated with ICI and found no significant association between BB exposure and OS. No significant associations were observed between BB exposure and secondary outcomes, including PFS, ORR, and development of AEs.
The effect of BB exposure on outcomes in HCC had previously been investigated, with some evidence that BB use may improve survival in patients with HCC. One Swedish study of 2104 patients in a national cancer registry between 2006 and 2015 found that BB use, particularly nonselective BB use, was associated with a lower mortality rate, but the mortality benefit appeared to be limited to patients with localized disease [26]. Similarly, a small retrospective study of 36 patients with non-metastatic HCC who had undergone surgical resection or locoregional therapy found that BB use failed to predict HCC recurrence but was associated with improved OS after these curative interventions [27]. As described earlier, a Taiwanese nationwide study of 4680 patients with unresectable and metastatic HCC from 2000 to 2013 found that propranolol reduced the risk of mortality from HCC, but no significant difference in recurrence-free survival (RFS) was observed in the 867 patients with localized disease [22]. However, it is also worthwhile to note that these studies were conducted before ICIs gained widespread use as front line therapy for advanced HCC.
To the best of our knowledge, our study is the first to evaluate the effect of BB use in HCC treated with immunotherapy. Since the IMbrave150 trial changed the treatment paradigm for patients with advanced HCC, it has become more important to develop prognostic markers and to understand the impact of common concomitant medications on response to treatment. The effect of BB use is of particular clinical relevance as many patients with HCC take BBs at baseline as standard of care for variceal prophylaxis and sometimes for cardiovascular indications [28]. Biologically, the role of β-adrenergic signaling has been well described pre-clinically to modulate the tumor microenvironment (TME) and response to immunotherapy [29], and there is significant interest in validating these findings in the clinical setting.
Cancer cells have been shown to express β-adrenergic receptors (βARs) [30], and adrenergic signaling has been linked to tumorigenesis and cancer progression through promotion of processes such as DNA repair, oncogene activation, inflammation and immune response, angiogenesis, survival, and epithelial-mesenchymal transition [29]. Mouse models have been used to mechanistically show the effect of stress on the TME in various solid tumors. For example, Bucsek et al. showed that chronic stress in mice induced by cold exposure increased intratumoral noradrenaline, which subsequently reduced intratumoral CD8+ T cell frequency and functionality [31]. Conversely, the addition of propranolol reduced βAR signaling, which converted tumors to an immunologically active TME with increased CD8+ T cell frequency and effector phenotype, decreased expression of PD-1, and elevated effector CD8+ T cell to CD4+ regulatory T cell ratio, leading to increased efficacy of anti-PD-1 checkpoint blockade [31]. Kokolus et al. also showed that βAR blockade enhanced the antitumor effect of anti-PD-1 checkpoint inhibitor in a murine model of melanoma [32]. Recently, in mouse models of sarcoma and colon cancer, propranolol reduced tumor angiogenesis, increased T cell infiltration, and reduced myeloid-derived suppressor cell infiltration, leading to an up-regulation of PD-L1 on tumor-associated macrophages, ultimately enhancing the efficacy of anti-CTLA4 therapy [33]. These preclinical studies suggest that beta blockade may improve response to ICIs.
In the clinical setting, while no study before ours has evaluated the association between BB exposure and ICIs in HCC, it has been explored in other cancers, and thus far, results have been inconclusive but largely negative. In melanoma, BBs were found to have no independent prognostic effect on RFS in a recent phase III trial of adjuvant pembrolizumab in patients with high-risk stage III resected melanoma [34]. Similarly, in a retrospective study of advanced melanoma, concurrent BB use in patients treated with ICI did not affect ORR, PFS, or OS [35]. In contrast, one retrospective analysis of 195 patients with metastatic melanoma treated with ICI found that nonselective BB exposure improved survival compared to no BB use and β1-selective antagonist use [32]. Another retrospective study of 109 patients with non-small cell lung cancer (NSCLC) treated with ICIs demonstrated that BB use may be associated with improved PFS but not OS [36]. Another study in NSCLC analyzed the effect of multiple concomitant medications with ICIs and found that baseline BB use was not associated with clinical outcomes [37]. A recent meta-analysis of 13 aggregated studies in mostly melanoma, NSCLC, and renal cell carcinoma showed that concurrent BB use with immunotherapy was not significantly associated with improved survival [38]. Our negative findings add to growing evidence that the effect of BBs on clinical outcomes after treatment with ICIs may be limited despite preclinical data, suggesting that multiple interdependent pathways likely modulate the TME in humans and that there is a need to account for differences in experimental and real-world observations.
The unique pathophysiology of liver cirrhosis and microbial dysbiosis also add complexity to understanding of the TME and HCC tumorigenesis, suggesting that beta blockade is unlikely to mediate these interactions in easily predictable ways. BBs have both hemodynamic and non-hemodynamic mechanisms of action. Previously, nonselective BBs have been shown to increase intestinal transit, reducing bacterial overgrowth and translocation, in patients with cirrhosis independently of their hemodynamic functions [14, 39]. While dysbiosis is known to contribute to HCC tumorigenesis, the strategy for targeting the gut microbiota-liver axis is still unclear. It is also unclear how beta blockade changes the microbiome in humans. Additionally, the ways in which ICIs can affect the microbiome are not well-characterized, though the composition of gut microbiota has been shown in preclinical studies to influence immunotherapy efficacy through regulation of immune responses [40]. Our study also assessed whether antacids and antibiotics modified the effect of BB use but found no survival differences. These results are consistent with prior data in HCC demonstrating that antibiotic and antacid exposure during ICI therapy did not affect OS [25, 41] but are in opposition with studies conducted in other solid tumors [42], which may indicate that the HCC microbiome has immunomodulatory effects distinct from that of other cancers. While nonselective BBs may reduce bacterial overgrowth and translocation, further studies are need to better understand whether BB use changes the HCC microbiome and how this may affect outcomes with ICI therapy.
Our study also showed that BB exposure in patients with advanced HCC treated with ICIs did not increase the development of any AEs. On the other hand, and more surprisingly, use of BB also did not reduce the number of bleeding events observed. Given the real-world population evaluated in this cohort, a limitation of our study included the inconsistent reporting of the presence or absence of esophageal varices, as not all patients underwent pretreatment esophagogastroduodenoscopy (EGD). Although the decision to perform an EGD was not standardized, it was made on a case-by-case basis by the treating physician, in line with routine clinical practice. As such, the baseline degree of pHTN could not be fully characterized in the full patient cohort. Regardless, concomitant BB use did not affect the rate of bleeding events or development of ascites, which is in turn consistent with its lack of correlation with clinical outcomes after ICI therapy. In the subgroup analysis of patients with neoplastic PVT, BB use did not confer a statistically significant survival benefit. Given the low rate of bleeding events in the cohort ($6.4\%$), it is possible that the study was underpowered to detect any influence of BB exposure. In the overall analysis, only the presence of neoplastic PVT was identified as an independent predictor of AE development, whereas a viral etiology of HCC was linked with a reduced risk of AE. The presence of neoplastic PVT is a well-known negative prognosticator of HCC and a criterion for classification into advanced stage disease [43], and the increased risk of AE is likely reflective of worse liver function. Further, our findings that viral etiologies of HCC did not increase incidence of AE is supported by prior studies confirming the safety and tolerability of ICIs in patients with HBV and HCV in HCC and other solid tumors [44, 45]. In fact, patients with viral etiologies had a lower incidence of AEs, and while the cause is not entirely clear, it is possible that patients with viral HCC are diagnosed and initiate treatment when liver disease is less advanced as these patients are more likely to receive screening and treatment for liver disease prior to diagnosis of HCC. Studies are underway to better understand the outcomes of patients with viral HCC.
This is the first study investigating the effect of BB exposure in patients with advanced HCC treated with immunotherapy using real-world data. Although other studies of BB use in patients receiving immunotherapy for solid tumors have produced inconsistent results, our findings add to the body of evidence that BB use is not associated with improved survival outcomes. Overall, our international, multicenter cohort offers broad generalizability and is reflective of a diverse, real-world population, including patients with more advanced liver disease (CP classes B and C) who are typically excluded from clinical trial participation. Collection of detailed patient characteristics also allowed us to control for multiple possible confounding factors, such as the patient’s age, performance status, liver function, disease stage, and HCC risk factor.
However, the study also had several limitations, including those inherent to retrospective cohort studies that require validation in prospective studies. Patients taking BBs at baseline likely have increased comorbid conditions, including cirrhosis and cardiovascular disease, that may increase their risk of mortality. We accounted for these possible confounders by controlling for baseline liver function and performance status. However, the OS evaluated in this study was only reflective of all-cause mortality, and the specific cause of death was often unavailable or inconsistently documented in the medical record. While the effect of other potentially confounding concomitant medications such as antibiotics and antacids were assessed, other medications such as aspirin, statins, metformin, and steroids that may affect survival outcomes in HCC were not examined in this study (46–49). In addition, due to the observational design, the definition of BB exposure did not include dose and duration, and BB adherence could not be confirmed based on review of medical records alone. The number of patients in our study may also have been too small to detect an association between BB use and OS, particularly when subdivided by BB type and duration. Finally, our cohort does not have a large proportion of patients treated with the IMbrave150 regimen consisting of atezolizumab and bevacizumab that has now become standard of care for advanced HCC, and prior studies in colon cancer suggest a favorable effect of BB use in bevacizumab-containing therapy [50]. However, studies are currently underway to assess the prognostic impact of concomitant medications on treatment outcomes with this combination.
In conclusion, in our retrospective cohort of patients with unresectable HCC treated with ICI, no statistically significant differences in OS, PFS, or ORR were observed between patients who used BBs and those who did not. Concomitant BB use was safe and did not increase the risk of AEs. Further prospective and larger observational studies, as well as mechanistic studies, are needed to elucidate the effect of beta blockade on HCC and its interaction with the microbiome and immune activation.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by the Institutional Review Board (IRB) at Imperial College London, whose review was accepted by all participating institutions’ IRBs. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
YLW and CA designed the study. YLW conducted the investigation, analysis, interpretation of data, and wrote the original draft. GvH and UÖ contributed to data analysis and methodology. MR, AG, YM, NN, BW, SA, P-CL, BS, LB, MP, AV, AW, AS, AP, LR, AN, MM, YHH, AK, MK, and DP contributed to the investigation and data curation. DP and CA supervised the study. All authors contributed to the article and approved the submitted version.
## Conflict of interest
UÖ is affiliated with Eli Lilly and Company. MR received lecture fees from Falk Foundation e.V. AV received consulting fees from Amgen, AstraZeneca, Baxalta, Bayer, BTG, EISA, Ipsen, Lilly, Novartis, Pierre Fabre, and Roche; travel fees from Bayer, Ipsen, and Roche; and research funding from Novartis. AS received lecture fees from Daiichi Sankyo/AstraZeneca; consulting fees from AstraZeneca, Bristol-Myers Squibb, Daiichi Sankyo/ AstraZeneca, Exelixis, Five Prime Therapeutics, and Pfizer; and institutional funding from Actuate Therapeutics, Astellas Pharma, AstraZeneca/MedImmune, Bristol-Myers Squibb, Clovis Oncology, Daiichi Sankyo/UCB Japan; Exelixis, Fiver Prime Therapeutics, KAHR Medical, Merck Sharp & Dohme, and Seattle Genetics. AK received consulting fees from AstraZeneca, Bayer, Bristol-Myers Squibb, Eisai, Exelixis, Genentech/Roche, and Merck; travel fees from from Bayer/Onyx, Bristol-Myers Squibb, Exelixis, and Merck; and institutional research funding from Adaptimmune, Bayer/Onyx, Bristol-Myers Squibb, Genentech, Hengrui Pharmaceutical, and Merck. AP received consulting fees for Eisai Inc, Exelixis, AstraZeneca, Replimune and Genentech. LR received consulting fees from Amgen, ArQule, AstraZeneca, Basilea, Bayer, Bristol-Myers Squibb, Celgene, Eisai, Exelixis, Genenta Science, Hengrui Therapeutics, Incyte, Ipsen, IQvia, Lilly, Merck Sharp & Dohme, Nerviano Medical Sciences, Roche, Sanofi, Servier, Taiho Oncology, and Zymeworks; lecture fees from AbbVie, Amgen, Bayer, Eisai, Gilead, Incyte, Ipsen, Lilly, Merck Serono, Roche, Sanofi; travel fees from AstraZeneca; and institutional research funding from Agios, ARMO BioSciences, AstraZeneca, BeiGene, Eisai, Exelixis, FibroGen, Incyte, Ipsen, Lilly, Merck Sharp & Dohme, Nerviano Medical Sciences, Roche, and Zymeworks. Y-HH received lecture fees from Bayer, Bristol-Myers Squibb, Eisai, Gilead Sciences, Lilly, MSD, and Roche; and consulting fees from Bayer, Bristol-Myers Squibb, Eisai, Gilead Sciences, Lilly, MSD, and Roche. DP received lecture fees from ViiV Healthcare, Bayer, Falk Pharma and Roche; travel expenses from Bristol-Myers Squibb, Bayer, and MSD Oncology; consulting fees for AstraZeneca, Da Volterra, EISAI, H3 Biomedicine, Ipsen, Mina Therapeutics, and Roche; and institutional research funding from Bristol-Myers Squibb, GlaxoSmithKline, and MSD Oncology.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
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## References
1. Bray F, Ferlay J, Soerjomataram I, Siegel R, Torre L, Jemal A. **Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA Cancer J Clin* (2018) **68** 394-424. DOI: 10.3322/caac.21492
2. Llovet J, Villanueva A, Lachenmayer A, Finn R. **Advances in targeted therapies for hepatocellular carcinoma in the genomic era**. *Nat Rev Clin Oncol* (2015) **12**. DOI: 10.1038/nrclinonc.2015.103
3. Finn R, Qin S, Ikeda M, Galle P, Ducreux M, Kim T. **Atezolizumab plus bevacizumab in unresectable hepatocellular carcinoma**. *N Engl J Med* (2020) **382**. DOI: 10.1056/NEJMoa1915745
4. Cheng AL, Qin S, Ikeda M, Galle PR, Ducreux M, Kim TY. **Updated efficacy and safety data from IMbrave150: Atezolizumab plus bevacizumab vs. sorafenib for unresectable hepatocellular carcinoma**. *J Hepatol* (2022) **76**. DOI: 10.1016/j.jhep.2021.11.030
5. Abou-Alfa GK, Lau G, Kudo M, Chan SL, Kelley RK, Furuse J. **Tremelimumab plus durvalumab in unresectable hepatocellular carcinoma**. *NEJM Evidence* (2022) **1** EVIDoa2100070. DOI: 10.1056/EVIDoa2100070
6. Yau T, Kang Y-K, Kim T-Y, El-Khoueiry AB, Santoro A, Sangro B. **Efficacy and safety of nivolumab plus ipilimumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib: The CheckMate 040 randomized clinical trial**. *JAMA Oncol* (2020) **6**. DOI: 10.1001/jamaoncol.2020.4564
7. Hussain N, Naeem M, Pinato DJ. **Concomitant medications and immune checkpoint inhibitor therapy for cancer: causation or association**. *Hum Vaccin Immunother* (2021) **17** 55-61. DOI: 10.1080/21645515.2020.1769398
8. Pinato DJ, Howlett S, Ottaviani D, Urus H, Patel A, Mineo T. **Association of prior antibiotic treatment with survival and response to immune checkpoint inhibitor therapy in patients with cancer**. *JAMA Oncol* (2019) **5**. DOI: 10.1001/jamaoncol.2019.2785
9. Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT, Daillère R. **Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors**. *Science* (2018) **359**. DOI: 10.1126/science.aan3706
10. Zheng Y, Wang T, Tu X, Huang Y, Zhang H, Tan D. **Gut microbiome affects the response to anti-PD-1 immunotherapy in patients with hepatocellular carcinoma**. *J Immunother Cancer* (2019) **7** 193. DOI: 10.1186/s40425-019-0650-9
11. Simbrunner B, Mandorfer M, Trauner M, Reiberger T. **Gut-liver axis signaling in portal hypertension**. *World J Gastroenterol* (2019) **25**. DOI: 10.3748/wjg.v25.i39.5897
12. Allaire M, Rudler M, Thabut D. **Portal hypertension and hepatocellular carcinoma: Des liaisons dangereuses…**. *Liver Int* (2021) **41**. DOI: 10.1111/liv.14977
13. Garcia-Tsao G, Bosch J. **Management of varices and variceal hemorrhage in cirrhosis**. *N Engl J Med* (2010) **362**. DOI: 10.1056/NEJMra0901512
14. Pérez-Paramo M, Muñoz J, Albillos A, Freile I, Portero F, Santos M. **Effect of propranolol on the factors promoting bacterial translocation in cirrhotic rats with ascites**. *Hepatology* (2000) **31**. DOI: 10.1002/hep.510310109
15. Senzolo M, Fries W, Buda A, Pizzuti D, Nadal E, Sturniolo GC. **Oral propranolol decreases intestinal permeability in patients with cirrhosis: another protective mechanism against bleeding**. *Am J Gastroenterol* (2009) **104**. DOI: 10.1038/ajg.2009.457
16. Wang F, Liu H, Wang F, Xu R, Wang P, Tang F. **Propranolol suppresses the proliferation and induces the apoptosis of liver cancer cells**. *Mol Med Rep* (2018) **17**. DOI: 10.3892/mmr.2018.8476
17. Darnaud M, Faivre J, Moniaux N. **Targeting gut flora to prevent progression of hepatocellular carcinoma**. *J Hepatol* (2013) **58**. DOI: 10.1016/j.jhep.2012.08.019
18. Liao X, Che X, Zhao W, Zhang D, Bi T, Wang G. **The β-adrenoceptor antagonist, propranolol, induces human gastric cancer cell apoptosis and cell cycle arrest**. *Oncol Rep* (2010) **24**. DOI: 10.3892/or_00001032
19. Herrera I, Pascual S, Zapater P, Carnicer F, Bellot P, María Palazón J. **The use of β-blockers is associated with a lower risk of developing hepatocellular carcinoma in patients with cirrhosis**. *Eur J Gastroenterol Hepatol* (2016) **28**. DOI: 10.1097/MEG.0000000000000677
20. Thiele M, Albillos A, Abazi R, Wiest R, Gluud LL, Krag A. **Non-selective beta-blockers may reduce risk of hepatocellular carcinoma: a meta-analysis of randomized trials**. *Liver Int* (2015) **35**. DOI: 10.1111/liv.12782
21. Wijarnpreecha K, Li F, Xiang Y, Xu X, Zhu C, Maroufy V. **Nonselective beta-blockers are associated with a lower risk of hepatocellular carcinoma among cirrhotic patients in the united states**. *Alimentary Pharmacol Ther* (2021) **54**. DOI: 10.1111/apt.16490
22. Chang PY, Chung CH, Chang WC, Lin CS, Lin HH, Dai MS. **The effect of propranolol on the prognosis of hepatocellular carcinoma: A nationwide population-based study**. *PloS One* (2019) **14**. DOI: 10.1371/journal.pone.0216828
23. Heimbach JK, Kulik LM, Finn RS, Sirlin CB, Abecassis MM, Roberts LR. **AASLD guidelines for the treatment of hepatocellular carcinoma**. *Hepatology* (2018) **67**. DOI: 10.1002/hep.29086
24. Galle PR, Forner A, Llovet JM, Mazzaferro V, Piscaglia F, Raoul J-L. **EASL clinical practice guidelines: Management of hepatocellular carcinoma**. *J Hepatol* (2018) **69** 182-236. DOI: 10.1016/j.jhep.2018.03.019
25. Fessas P, Naeem M, Pinter M, Marron TU, Szafron D, Balcar L. **Early antibiotic exposure is not detrimental to therapeutic effect from immunotherapy in hepatocellular carcinoma**. *Liver Cancer* (2021) **10**. DOI: 10.1159/000519108
26. Udumyan R, Montgomery S, Duberg AS, Fang F, Valdimarsdottir U, Ekbom A. **Beta-adrenergic receptor blockers and liver cancer mortality in a national cohort of hepatocellular carcinoma patients**. *Scand J Gastroenterol* (2020) **55** 597-605. DOI: 10.1080/00365521.2020.1762919
27. Yu D, Holmes S, Uhanova J, Lipschitz J, McKay A, Gerald MY. **Predicting hepatocellular carcinoma recurrence and survival**. *Hepatogastroenterology* (2014) **61**
28. Villanueva C, Albillos A, Genescà J, Garcia-Pagan JC, Calleja JL, Aracil C. **β blockers to prevent decompensation of cirrhosis in patients with clinically significant portal hypertension (PREDESCI): A randomised, double-blind, placebo-controlled, multicentre trial**. *Lancet* (2019) **393**. DOI: 10.1016/S0140-6736(18)31875-0
29. Cole SW, Nagaraja AS, Lutgendorf SK, Green PA, Sood AK. **Sympathetic nervous system regulation of the tumour microenvironment**. *Nat Rev Cancer* (2015) **15**. DOI: 10.1038/nrc3978
30. Rains SL, Amaya CN, Bryan BA. **Beta-adrenergic receptors are expressed across diverse cancers**. *Oncoscience* (2017) **4** 95-105. DOI: 10.18632/oncoscience.357
31. Bucsek MJ, Qiao G, MacDonald CR, Giridharan T, Evans L, Niedzwecki B. **β-adrenergic signaling in mice housed at standard temperatures suppresses an effector phenotype in CD8(+) T cells and undermines checkpoint inhibitor therapy**. *Cancer Res* (2017) **77**. DOI: 10.1158/0008-5472.CAN-17-0546
32. Kokolus KM, Zhang Y, Sivik JM, Schmeck C, Zhu J, Repasky EA. **Beta blocker use correlates with better overall survival in metastatic melanoma patients and improves the efficacy of immunotherapies in mice**. *Oncoimmunology* (2018) **7**. DOI: 10.1080/2162402X.2017.1405205
33. Fjæstad KY, Rømer AMA, Goitea V, Johansen AZ, Thorseth ML, Carretta M. **Blockade of beta-adrenergic receptors reduces cancer growth and enhances the response to anti-CTLA4 therapy by modulating the tumor microenvironment**. *Oncogene* (2022) **41**. DOI: 10.1038/s41388-021-02170-0
34. Kennedy OJ, Kicinski M, Valpione S, Gandini S, Suciu S, Blank CU. **Prognostic and predictive value of β-blockers in the EORTC 1325/KEYNOTE-054 phase III trial of pembrolizumab versus placebo in resected high-risk stage III melanoma**. *Eur J Cancer* (2022) **165** 97-112. DOI: 10.1016/j.ejca.2022.01.017
35. Wang DY, McQuade JL, Rai RR, Park JJ, Zhao S, Ye F. **The impact of nonsteroidal anti-inflammatory drugs, beta blockers, and metformin on the efficacy of anti-PD-1 therapy in advanced melanoma**. *Oncologist* (2020) **25**. DOI: 10.1634/theoncologist.2019-0518
36. Oh MS, Guzner A, Wainwright DA, Mohindra NA, Chae YK, Behdad A. **The impact of beta blockers on survival outcomes in patients with non-small-cell lung cancer treated with immune checkpoint inhibitors**. *Clin Lung Cancer* (2021) **22**. DOI: 10.1016/j.cllc.2020.07.016
37. Cortellini A, Di Maio M, Nigro O, Leonetti A, Cortinovis DL, Aerts JG. **Differential influence of antibiotic therapy and other medications on oncological outcomes of patients with non-small cell lung cancer treated with first-line pembrolizumab versus cytotoxic chemotherapy**. *J Immunother Cancer* (2021) **9** e002421. DOI: 10.1136/jitc-2021-002421
38. Zhang Y, Chen H, Chen S, Li Z, Chen J, Li W. **The effect of concomitant use of statins, NSAIDs, low-dose aspirin, metformin and beta-blockers on outcomes in patients receiving immune checkpoint inhibitors: a systematic review and meta-analysis**. *Oncoimmunology* (2021) **10** 1957605. DOI: 10.1080/2162402X.2021.1957605
39. Senzolo M, Cholongitas E, Burra P, Leandro G, Thalheimer U, Patch D. **Beta-blockers protect against spontaneous bacterial peritonitis in cirrhotic patients: a meta-analysis**. *Liver Int* (2009) **29**. DOI: 10.1111/j.1478-3231.2009.02038.x
40. Dai Z, Zhang J, Wu Q, Fang H, Shi C, Li Z. **Intestinal microbiota: a new force in cancer immunotherapy**. *Cell Commun Signal* (2020) **18** 90. DOI: 10.1186/s12964-020-00599-6
41. Jun T, Ozbek U, Dharmapuri S, Hardy-Abeloos C, Zhu H, Lin JY. **Antacid exposure and immunotherapy outcomes among patients with advanced hepatocellular carcinoma**. *Ther Adv Med Oncol* (2021) **13** 17588359211010937. DOI: 10.1177/17588359211010937
42. Spakowicz D, Hoyd R, Muniak M, Husain M, Bassett JS, Wang L. **Inferring the role of the microbiome on survival in patients treated with immune checkpoint inhibitors: causal modeling, timing, and classes of concomitant medications**. *BMC Cancer* (2020) **20** 383. DOI: 10.1186/s12885-020-06882-6
43. Mähringer-Kunz A, Steinle V, Düber C, Weinmann A, Koch S, Schmidtmann I. **Extent of portal vein tumour thrombosis in patients with hepatocellular carcinoma: The more, the worse**. *Liver Int* (2019) **39**. DOI: 10.1111/liv.13988
44. Lee PC, Chao Y, Chen MH, Lan KH, Lee IC, Hou MC. **Risk of HBV reactivation in patients with immune checkpoint inhibitor-treated unresectable hepatocellular carcinoma**. *J Immunother Cancer* (2020) **8** e001072. DOI: 10.1136/jitc-2020-001072
45. Shah NJ, Al-Shbool G, Blackburn M, Cook M, Belouali A, Liu SV. **Safety and efficacy of immune checkpoint inhibitors (ICIs) in cancer patients with HIV, hepatitis b, or hepatitis c viral infection**. *J Immunother Cancer* (2019) **7** 353. DOI: 10.1186/s40425-019-0771-1
46. Ricciotti E, Wangensteen KJ, FitzGerald GA. **Aspirin in hepatocellular carcinoma**. *Cancer Res* (2021) **81**. DOI: 10.1158/0008-5472.CAN-21-0758
47. Wang J, Li X. **Impact of statin use on the risk and prognosis of hepatocellular carcinoma: a meta-analysis**. *Eur J Gastroenterol Hepatol* (2021) **33**. DOI: 10.1097/MEG.0000000000002040
48. Zhou J, Ke Y, Lei X, Wu T, Li Y, Bao T. **Meta-analysis: The efficacy of metformin and other anti-hyperglycemic agents in prolonging the survival of hepatocellular carcinoma patients with type 2 diabetes**. *Ann Hepatol* (2020) **19**. DOI: 10.1016/j.aohep.2019.11.008
49. Cortellini A, Tucci M, Adamo V, Stucci LS, Russo A, Tanda ET. **Integrated analysis of concomitant medications and oncological outcomes from PD-1/PD-L1 checkpoint inhibitors in clinical practice**. *J Immunother Cancer* (2020) **8** e001361. DOI: 10.1136/jitc-2020-001361
50. Fiala O, Ostasov P, Sorejs O, Liska V, Buchler T, Poprach A. **Incidental use of beta-blockers is associated with outcome of metastatic colorectal cancer patients treated with bevacizumab-based therapy: A single-institution retrospective analysis of 514 patients**. *Cancers (Basel)* (2019) **11** 1856. DOI: 10.3390/cancers11121856
|
---
title: A Cross-Sectional Study of Determinants of Type 2 Diabetes Mellitus Among Professional
Drivers in the Perambalur Municipality Area of Tamil Nadu, India
journal: Cureus
year: 2023
pmcid: PMC9972006
doi: 10.7759/cureus.34321
license: CC BY 3.0
---
# A Cross-Sectional Study of Determinants of Type 2 Diabetes Mellitus Among Professional Drivers in the Perambalur Municipality Area of Tamil Nadu, India
## Abstract
Background Professional drivers have a powerful impact on public safety. They are also at a higher risk of obesity, hypertension, and type 2 diabetes mellitus (T2DM) because of their lifestyle. Diabetes and its complications can affect driving and cause increased road traffic accidents. This study aimed to estimate the prevalence of T2DM and determine the risk factors contributing to the development of T2DM among professional drivers in the Perambalur Municipality of Tamil Nadu, India.
Methodology This cross-sectional study was carried out between September 2022 and December 2022 among 118 private bus drivers and full-time, professional, three-wheeler drivers in the Perambalur Municipality. A pre-tested semi-structured proforma was used to collect information on the driver's socio-demographic profile and to inquire about their diabetes history, which was verified with their records. We elicited the risk factors of T2DM among those drivers. We recorded the anthropometric measurements and blood pressure. Data analysis was done using IBM SPSS Statistics for Windows, Version 21.0 (Released 2012; IBM Corp., Armonk, New York, United States).
Results Out of 118 study participants, the majority were in the age group of 51-65 ($37.3\%$). Seventy-seven of the participants have completed their secondary education, and 38 of them belong to the class 2 socioeconomic class. Three-fourths of the sample ($83.1\%$) belonged to nuclear families. Around one-third were current smokers, one-fourth had the habit of chewing tobacco, and more than half of the participants consumed alcohol. Nearly $83.7\%$ had moderate physical activity, followed by $11.9\%$ who had heavy activity, and $5.1\%$ who did not do any physical activity. The prevalence of T2DM among professional drivers was $11.9\%$. The risk factors that contributed to the development of T2DM among professional drivers were age, education, smoking, tobacco chewing, hypertension, elevated BMI, and elevated WC, which are statistically significant (p˂0.05).
Conclusion We found the proportion of obesity, hypertension, and diabetes to be higher among professional drivers than among the general population. This demands an urgent need for preventive and health-promotive interventions to address these chronic diseases.
## Introduction
Drivers are a part of professional groups whose activities have a strong impact on public safety [1]. Since driving is a sedentary occupation with changing day-night shifts affecting their circadian rhythm, professional drivers are at higher risk for developing obesity, hypertension, type 2 diabetes mellitus (T2DM), and cardiovascular diseases in later stages of life [2].
Diabetes mellitus is caused by a complex interaction of genetic and environmental factors [3]. Moreover, the stress of driving and exposure to atmospheric pollutants as risks can influence their performance, and cause sickness and absenteeism, thereby posing a great financial burden to society [4].
Complications of T2DM like diabetic retinopathy and neuropathy can affect driving skills. Diabetic neuropathy can cause muscle weakness, foot ulcers, and even lower extremity amputations [5]. Irregular treatment and skipping medications can result in hyper/hypoglycemia and may lead to increased reaction time, imbalance, and loss of consciousness. Such health issues that affect drivers may result in an increased risk of road accidents [6].
Noncommunicable diseases (NCD) are the major challenges to sustainable development in the 21st century. Goal 3 of Sustainable Developmental Goals (SDG), thus, focuses on reducing premature mortality due to NCDs by at least one-third and providing access to affordable medicines for NCDs [7]. To the extent of our knowledge, very few studies have been done in India to assess other comorbidities like cardiovascular morbidity profile [8].
Many studies done in other parts of the world have reported that professional truck drivers and other road transport professionals have a higher risk of ischemic heart disease and metabolic syndrome [9]. The studies conducted in India showed that the risk factor for the development of T2DM was higher among professional drivers who were chronic smokers, used chewable tobacco products, and addicted to alcohol [10].
In contrast to the above findings, a cross-sectional study conducted in June 2011 among 59 truck drivers in India by Sharma et al. found that the prevalence of risk factors for metabolic disorders was lower among long-haul truck drivers than in the general population [11]. So, we aimed to conduct a study to estimate the prevalence of T2DM and to determine the risk factors contributing to the development of T2DM among professional drivers in Perambalur Municipality of Tamil Nadu, India.
## Materials and methods
Study design and setting This cross-sectional study was conducted among occupational male drivers in the Perambalur Municipality, Tamil Nadu, India, from September 2022 to December 2022. Tamil Nadu (formerly Madras State) is situated in the southeast part of India, with Chennai (formerly Madras city and the southern headquarters of British India) as the state capital. Perambalur is an inland district about 200 miles south that is rich in culture, fortresses, and places of worship.
Ethical clearance and informed consent Before the study began, we got an ethical clearance certificate from the Institutional Ethics Committee (IEC) of Dhanalakshmi Srinivasan Medical College and Hospital, Perambalur, Tamil Nadu, India (Approval number: IECHS/ IRCHS/ N0: 206 B dated August 9, 2022). All participants were provided with information about the study goals before giving consent.
Inclusion and exclusion criteria All full-time professional drivers with a history of driving for the past one year as their primary job were included. Drivers who are unavailable even after two visits, part-time and occasional drivers, and drivers who had been diagnosed with type 1 diabetes were excluded.
Sample Size *Considering previous* data showing that the prevalence of diabetes in India was $7.9\%$ [9], with a $95\%$ confidence level and $5\%$ allowable error, we estimated the sample size to be 112 with a $5\%$ non-response rate. The final estimated sample size was 118. We estimated the sample size using the formula n= Z1-α/22PQ/d2 (Zα=1.96, $$P \leq 7.9$$, $Q = 92.1$, $d = 5$).
Sampling technique We have selected 118 samples selected by convenience sampling in Perambalur Municiplity. Three-wheeler drivers available at the three-wheeler stands during the visit were taken up for the study.
Study tool After getting the IEC approval and informed consent from the participants, a pre-tested, semi-structured questionnaire was used to collect socio-demographic data from the drivers including age, caste, religion, education, and income, and we elicited the risk factors of T2DM like lifestyle, dietary habits, habits of alcohol, tobacco chewing, and smoking with the frequency and duration. We enquired about family history and treatment history to identify the risk factors.
Measurements Height Height was measured with a stadiometer mounted on a weighing scale to the nearest 0.5 cm. Subjects stood upright without shoes with their back and heads against the height rod, heels together, and eyes directed forward [12] Weight
For weight measurement, participants stood barefoot on a standardized weighing scale, and weight was measured in kilograms. We asked subjects to wear light clothing, and we recorded weight to the nearest 1 kg [12].
BMI BMI was calculated using the formula, BMI = weight (kg)/ height (m2). Table 1 describes the Asian criteria-based classification of BMI for adults [13].
**Table 1**
| Body Mass Index (Kg/m2) | Grade |
| --- | --- |
| Below 18.5 | Underweight |
| 18.5-22.9 | Normal weight |
| 23-27.5 | Overweight |
| >27.5 | Obese |
Waist Circumference The waist was measured using a non-stretchable measuring tape. The participants were asked to stand erect in a relaxed position with both feet together; one layer of clothing was accepted. Waist circumference was measured at a level halfway between the costal margin and iliac crest at the level of the umbilicus, at minimal respiration, measured in a horizontal plane to the nearest 1 mm. In males, waist circumference > 90 cm was classified as obese. The average of three readings was considered the final reading [14].
Blood Pressure (BP) BP was recorded using a mercury sphygmomanometer following the auscultatory method. Palpated radial pulse obliteration pressure was used to estimate the systolic BP (SBP). We inflated the cuff 20-30 mm Hg above this level for the auscultatory determinations; cuff deflation rate of 2 mm Hg per second was used. Phase I of the Korotkoff sound is the point at which the first sound heard and was used to define SBP, and phase V of the Korotkoff sound is the point at which the sound disappears, and was used to identify diastolic BP (DBP). While measuring BP, the arm ws positioned at heart level while resting on an armrest, and the patient or provider was not talking. Patients were not have consumed stimulants (including smoking) well before the test [8].
We defined hypertension based on the seventh report of the Joint National Committee of Hypertension (JNC 7), which provides a classification of BP for adults aged 18 years or older. In this, hypertensive is defined as a person having a SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg. A new category designated pre-hypertensive indicates individuals who are at increased risk for progression to hypertension [15]. Table 2 describes the classification of BP for adults [15].
**Table 2**
| JNC 7 Category | Blood Pressure Reading (SBP/DBP) |
| --- | --- |
| Normotensive | < 120 mm Hg / < 80 mm Hg |
| Pre-hypertensive | 120-139 mm Hg / 80-89 mm Hg |
| Hypertension Stage I | 140-159 mm Hg / 90-99 mm Hg |
| Hypertension Stage II | > 160 mm Hg / >100 mm Hg |
Socioeconomic Status *Socioeconomic status* was based on Modified BG Prasad’s Classification, updated in 2020 [16]. Table 3 describes the classification of socioeconomic status.
**Table 3**
| Social Class | Amount in Rupees/month (per capita monthly income limits) |
| --- | --- |
| I | > 7008 |
| II | 3504-7007 |
| III | 2102-3503 |
| IV | 1051-2101 |
| V | < 1050 |
Statistical analysis All collected data were entered into Microsoft Excel (Microsoft Corporation, Redmond, Washington, United States), and IBM SPSS Statistics for Windows, Version 21.0 (Released 2012; IBM Corp., Armonk, New York, United States) was used to analyze the results. Frequency and percentage are used to present qualitative data. We also examined the relationship between the risk factors and T2DM using the Chi-square test or Fisher’s exact test, whichever is applicable. We considered a p-value of less than 0.05 statistically significant.
## Results
A total of 118 participants took part in this study. Out of 118, the majority were in the age group of 51-65 years ($37.3\%$) followed by the age group of 36-50 ($29.7\%$). Most of the participants ($$n = 77$$, $65.3\%$) completed their secondary education and belonged to class 2 socio-economic class ($$n = 38$$, $32.2\%$). Three-fourths of the participants ($83.1\%$) belonged to nuclear families. The complete socio-demographic profile of the participants is given in Table 4.
**Table 4**
| Characteristics | Characteristics.1 | n (%) |
| --- | --- | --- |
| Age | 21-35 | 33 (28.0%) |
| Age | 36-50 | 35 (29.7%) |
| Age | 51-65 | 44 (37.3%) |
| Age | > 65 | 6 (5.1%) |
| Education | Degree | 18 (15.3%) |
| Education | Higher Secondary | 4 (3.4%) |
| Education | Secondary education | 77 (65.3%) |
| Education | Primary education | 13 (11.0%) |
| Education | No education | 6 (5.1%) |
| Type of Family | Joint | 6 (5.1%) |
| Type of Family | Nuclear | 98 (83.1%) |
| Type of Family | Three generations | 14 (11.9%) |
| Socio- economic status | Class 1 | 18 (15.3%) |
| Socio- economic status | Class 2 | 38 (32.2%) |
| Socio- economic status | Class 3 | 33 (28.0%) |
| Socio- economic status | Class 4 | 29 (24.6%) |
The distribution of personal habits among the participants is given in Table 5. Around one-third were current smokers and one-fourth had a habit of chewing tobacco. More than half of the participants consumed alcohol. The majority ($$n = 98$$, $83.1\%$) did moderate physical activity.
**Table 5**
| Characteristic | Characteristic.1 | n (%) |
| --- | --- | --- |
| Smoking* | Current | 44 (37.3%) |
| Smoking* | Ex user | 16 (13.6%) |
| Smoking* | Non-user | 58 (49.2%) |
| Tobacco chewing* | Current | 30 (25.4%) |
| Tobacco chewing* | Ex user | 2 (1.7%) |
| Tobacco chewing* | Non-user | 86 (72.9%) |
| Alcohol consumption** | Current | 68 (57.6%) |
| Alcohol consumption** | Ex user | 20 (16.9%) |
| Alcohol consumption** | Non-user | 30 (25.4%) |
| Diet intake | Mixed diet | 110 (93.2%) |
| Diet intake | Vegetarian | 8 (6.8%) |
| Additional intake of salt while eating | Yes | 34 (28.8%) |
| Additional intake of salt while eating | No | 84 (71.2%) |
| Frequent fried food consumption | Yes | 40 (33.9%) |
| Frequent fried food consumption | No | 78 (66.1%) |
| Predominantly using cooking oil while cooking | Groundnut oil | 44 (37.3%) |
| Predominantly using cooking oil while cooking | Palm oil | 25 (21.2%) |
| Predominantly using cooking oil while cooking | Refined | 4 (3.4%) |
| Predominantly using cooking oil while cooking | Sunflower | 45 (38.1%) |
| Physical activity*** | Heavy | 14 (11.9%) |
| Physical activity*** | Moderate | 98 (83.1%) |
| Physical activity*** | Sedentary | 6 (5.1%) |
The prevalence of diabetes among professional drivers in the study was 14 ($11.9\%$). The prevalence of diabetes is shown in Figure 1.
**Figure 1:** *Prevalence of T2DM among professional driversT2DM: type 2 diabetes mellitus*
The prevalence of risk factors for T2DM is given in Figure 2, where $50.8\%$ of the participants smoked, $27.1\%$ chewed tobacco, $74.6\%$ consumed alcohol, $11.9\%$ had hypertension, $51.7\%$ were obese, overweight, or underweight, and $15.3\%$ had a family history of hypertension.
**Figure 2:** *Prevelance of risk factors for T2DM among professional driversT2DM: type 2 diabetes mellitus*
The association between sociodemographic factors and T2DM among professional drivers is shown in Table 6. Participants aged 21-35 years and >65 years did not develop T2DM unlike the age groups of 36-50 ($5.7\%$) and 51-65 years ($27.3\%$). Likewise, most of the participants who studied up to secondary education ($66.7\%$) developed T2DM followed by those who had primary education ($15.4\%$) or were illiterate ($10.4\%$) while those who studied up to higher secondary or graduated did not have T2DM. Such differences in age group (p-0.001) and education ($p \leq 0.001$) were statistically significant. More participants who belonged to a nuclear family ($14.3\%$) and to class 2 socioeconomic class ($21.1\%$) developed T2DM when compared to their counterparts but this was not statistically significant ($p \leq 0.05$).
**Table 6**
| General characteristics | General characteristics.1 | Type 2 Diabetes Mellitus | Type 2 Diabetes Mellitus.1 | p-value |
| --- | --- | --- | --- | --- |
| General characteristics | General characteristics | Yes | No | p-value |
| Age | 21-35 | 0 | 33 (100%) | 0.001* |
| Age | 36-50 | 2 (5.7%) | 33 (94.3%) | 0.001* |
| Age | 51-65 | 12 (27.3%) | 32 (72.7%) | 0.001* |
| Age | > 65 | 0 | 6 (100%) | 0.001* |
| Education | Degree | 0 | 18 (100%) | <0.001* |
| Education | Higher Secondary | 0 | 4 (100%) | <0.001* |
| Education | Secondary education | 4 (66.7%) | 2 (33.3%) | <0.001* |
| Education | Primary education | 2 (15.4%) | 11 (84.6%) | <0.001* |
| Education | No education | 8 (10.4%) | 69 (89.6%) | <0.001* |
| Type of Family | Joint | 0 | 6 (100%) | 0.198 |
| Type of Family | Nuclear | 14 (14.3%) | 84 (85.7%) | 0.198 |
| Type of Family | Three generations | 0 | 14 (100%) | 0.198 |
| Socio- economic Status | Class 1 | 0 | 18 (100%) | 0.103 |
| Socio- economic Status | Class 2 | 8 (21.1%) | 30 (78.9%) | 0.103 |
| Socio- economic Status | Class 3 | 4 (12.1%) | 29 (87.9%) | 0.103 |
| Socio- economic Status | Class 4 | 2 (6.9%) | 27 (93.1%) | 0.103 |
Table 7 shows the association between risk factors and diabetes among professional drivers. A higher percentage of ex-tobacco chewing ($100\%$), obese ($35.3\%$), hypertensive ($63.6\%$), ex-smoker ($60\%$), and central obesity ($21.1\%$) have developed T2DM when compared to their counterparts. Such differences were statistically significant ($p \leq 0.05$). There was a significant association between T2DM and smoking, tobacco chewing, hypertension, elevated BMI, and elevated waist circumference.
**Table 7**
| Risk Factors | Risk Factors.1 | Type 2 Diabetes Mellitus | Type 2 Diabetes Mellitus.1 | p-value |
| --- | --- | --- | --- | --- |
| Risk Factors | Risk Factors | Yes | No | p-value |
| Smoking | Current | 2 (4.5%) | 42 (95.5%) | 0.002* |
| Smoking | Ex user | 6 (60%) | 10 (40%) | 0.002* |
| Smoking | Non-user | 6 (10.3%) | 52 (89.7%) | 0.002* |
| Tobacco chewing | Current | 2 (6.7%) | 28 (93.3%) | 0.000* |
| Tobacco chewing | Ex user | 2 (100%) | 0 | 0.000* |
| Tobacco chewing | Non-user | 10 (11.6%) | 76 (88.4%) | 0.000* |
| Alcohol consumption | Current | 8 (11.8%) | 60 (88.2%) | 0.360 |
| Alcohol consumption | Ex user | 4 (20%) | 16 (80%) | 0.360 |
| Alcohol consumption | Non-user | 2 (6.7%) | 28 (93.3%) | 0.360 |
| Diet intake# | Mixed diet | 12 (10.9%) | 98 (89.1%) | 0.241 |
| Diet intake# | Vegetarian | 2 (25%) | 6 (75%) | 0.241 |
| Salt intake# | Yes | 2 (5.9%) | 32 (94.1%) | 0.345 |
| Salt intake# | No | 12 (14.3%) | 72 (85.7%) | 0.345 |
| Physical activity | Heavy | 0 | 14 (100%) | 0.198 |
| Physical activity | Moderate | 14 (14.3%) | 84 (85.7%) | 0.198 |
| Physical activity | Sedentary | 0 | 6 (100%) | 0.198 |
| Hypertension# | Yes | 8 (57.1%) | 6 (42.9%) | <0.001* |
| Hypertension# | No | 6 (5.8%) | 98 (94.2%) | <0.001* |
| Hypertension-classification | Hypertensive | 14 (63.6%) | 8 (36.4%) | <0.001* |
| Hypertension-classification | Normotensive | 0 | 51 (100%) | <0.001* |
| Hypertension-classification | Pre-Hypertensive | 0 | 45 (100%) | <0.001* |
| Obesity-BMI index | Underweight | 0 (0%) | 4 (100%) | 0.012* |
| Obesity-BMI index | Normal | 4 (7%) | 53 (95%) | 0.012* |
| Obesity-BMI index | Overweight | 4 (10%) | 36 (90%) | 0.012* |
| Obesity-BMI index | Obese | 6 (35.3%) | 11 (64.7%) | 0.012* |
| Waist circumference# | Central obesity | 12 (21.1%) | 45 (78.9%) | 0.004* |
| Waist circumference# | Normal | 2 (3.3%) | 59(96.7%) | 0.004* |
## Discussion
In our study, the prevalence of diabetes was 14 ($11.9\%$) amongst the professional male drivers in Perambalur Municipality. A study done in South Karnataka showed a similar prevalence of diabetes ($11.1\%$) among drivers [10] along with a study in three regions (East/West/South) of India, which showed a prevalence of 11-$18\%$ [19]. In contrast, in a study conducted by Yosef in Ethiopia among truck drivers [2018], 32 ($8\%$) had diabetes mellitus [2], and a study in Iran also showed a $9.1\%$ prevalence of diabetes [20], which is lower than the current study. Another study by Sangaleti et al. in Brazil states the prevalence of diabetes among truck drivers was $16.4\%$ [21]. The variation observed compared to other studies could be owing to the differences in method, sample size, and operational definitions used. Besides, the socioeconomic, behavioral/lifestyle, and cultural and educational profiles may create a significant variation.
In our study, the risk factors contributing to the development of T2DM among drivers were age, education, smoking, tobacco chewing, hypertension, and obesity. Similarly, a study with Polish drivers by Marcinkiewiz et al. states that increasing age plays an important role in the development of diabetes [1]. Budreviciute et al. 's study showed that harmful habits, such as smoking and drinking alcohol, which were gained by adolescent young people, can significantly contribute to NCD risk [22]. The unhealthy habits may continue during adulthood, which influences the progress of NCDs. So, age plays a major role in the development of diabetes among drivers.
In our study, participants involved in alcohol consumption, smoking, and tobacco chewing were 68 ($57.6\%$), 44 ($37.3\%$), and 30 ($25.4\%$), respectively. Smoking and tobacco chewing were associated with T2DM among drivers ($$p \leq 0.002$$; statistically significant). Similarly, a study conducted by Jaganmohan et al. in Nellore showed an association between smoking and diabetes among drivers ($$p \leq 0.003$$) [23]. A study conducted in Hyderabad showed that $44.07\%$ were chronic smokers, $47.46\%$ used chewable tobacco products, and $57.63\%$ were found to be addicted to alcohol, which were associated with the development of diabetes mellitus [11].
In our study, the prevalence of hypertension among the drivers was 14 ($11.9\%$). In contrast, in studies conducted in Hyderabad [11], South Karnataka [10], Iran [6], and Poland [1], the prevalence of hypertension among long-distance truck drivers was $45.76\%$, $28.9\%$, $42.9\%$, and $36.7\%$, respectively.
Nearly $14.4\%$ of the drivers were obese, while $33.9\%$ were pre-obese, and central obesity was observed among $48.3\%$ of the participants. In comparison with the current study of $14.4\%$ obesity, a survey conducted in South Karnataka [10], Iran [6], and Poland [1] among long-distance truck drivers showed a higher prevalence of obesity of $40\%$, $23\%$, and $17.4\%$, respectively. The longer duration of driving hours creates more hours of sitting while driving resulting in overweight and obesity.
Limitations A causal link between risk variables and T2DM could not be drawn because of the study’s cross-sectional design. We could not infer the risk factors for the development of T2DM among drivers because of the limited sample size and non-probability technique. Based only on their medical history, we assumed that professional drivers had T2DM, which may have resulted in social desirability bias and recollection bias. Additionally, we did not measure fasting blood sugar (FBS), postprandial blood sugar (PPBS), or glycated hemoglobin (HbA1C). Specifically, qualitative data collection and analysis may prove useful in providing insight into how and why various forms of risk factors may influence the development of diabetes among professional drivers differently. Finally, these findings are specific to professional drivers working in Perambalur, India, and should not be generalized.
## Conclusions
The prevalence of T2DM was $11.9\%$. The major risk factors for the development of T2DM among professional drivers were age, education, excessive body weight, high blood pressure, and personal habits like smoking and tobacco chewing. We should conduct similar studies on a larger scale at multiple centers and involve more refined techniques and expertise to yield better results.
The risk factors for T2DM show a need to undertake multidimensional actions that target specific professions and involve various healthcare sectors. To ascertain the prevalence and severity of T2DM, the transportation departments should consider doing pre-placement examinations with the assistance of physicians. These findings further substantiate the need for preventive and health-promotive interventions like the encouragement of regular physical activity and quitting harmful habits like tobacco and alcohol to combat the rising risk factors for T2DM among drivers.
## References
1. Marcinkiewicz A, Szosland D. **Selected risk factors of diabetes mellitus among road transport drivers**. *Int J Occup Med Environ Health* (2010) **23** 175-180. PMID: 20682488
2. Yosef T. **Prevalence and associated factors of chronic non-communicable diseases among cross-country truck drivers in Ethiopia**. *BMC Public Health* (2020) **20** 1564. PMID: 33069207
3. Kumar S, Rao K, Maiya AG, Hande HM, Hazari A. **Need For Early Diabetic Peripheral Neuropathy Screening among Public Transport Professionals - A Case Report**. *Laser Ther* (2016) **25** 141-144. PMID: 27721566
4. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B. **IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045**. *Diabetes Res Clin Pract* (2018) **138** 271-281. PMID: 29496507
5. Callaghan BC, Gao L, Li Y. **Diabetes and obesity are the main metabolic drivers of peripheral neuropathy**. *Ann Clin Transl Neurol* (2018) **5** 397-405. PMID: 29687018
6. Saberi HR, Moravveji AR, Fakharian E, Kashani MM, Dehdashti AR. **Prevalence of metabolic syndrome in bus and truck drivers in Kashan, Iran**. *Diabetol Metab Syndr* (2011) **3** 8. PMID: 21595922
7. Singh Thakur J, Nangia R, Singh S. **Progress and challenges in achieving noncommunicable diseases targets for the sustainable development goals**. *FASEB Bioadv* (2021) **3** 563-568. PMID: 34377953
8. Lakshman A, Manikath N, Rahim A, Anilakumari VP. **Prevalence and Risk Factors of Hypertension among Male Occupational Bus Drivers in North Kerala, South India: A Cross-Sectional Study**. *ISRN Prev Med* (2014) **2014** 318532. PMID: 24971195
9. Mokdad AH, Ford ES, Bowman BA, Dietz WH, Vinicor F, Bales VS, Marks JS. **Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001**. *JAMA* (2003) **289** 76-79. PMID: 12503980
10. Rao CR, Kumar U, Mishra S, Kamath V. **Screening for non-communicable diseases among transport employees of a university: a descriptive analysis**. *Indian J Community Health* (2016) **28** 100-105
11. Sharma PK, Ganguly E. **Morbidity profile of long distance truck drivers in Hyderabad city, India**. *J Dr NTR Univ Health Sci* (2014) **3** 234-237. PMID: 25664312
12. Best C, Shepherd E. **Accurate measurement of weight and height 2: height and BMI calculation**. *Nurs Times* (2020) **116** 42-44
13. **Appropriate body mass index for Asia populations and its implications for policy and intervention strategies**. *Lancet* (2004) **363** 157-163. PMID: 14726171
14. **Boston 677 Huntington Avenue, Ma 02115 + 1495‑1000: Abdominal Obesity Measurement Guidelines for Different Ethnic Groups. Obes. Prev. Source. (2012). Accessed**. (2023)
15. **Program NHBPE: The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.**. *The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure* (2004)
16. Debnath D, Kakkar R. **Modified BG Prasad socio-economic classification, Updated - 2020**. *Indian J Community Health* (2020) **32** 124-125
17. **National Health Interview Survey: Glossary - Adult Tobacco Use**. (2023)
18. Win AM, Yen LW, Tan KH, Lim RB, Chia KS, Mueller-Riemenschneider F. **Patterns of physical activity and sedentary behavior in a representative sample of a multi-ethnic South-East Asian population: a cross-sectional study**. *BMC Public Health* (2015) **15** 318. PMID: 25884916
19. Randhawa S, Dogra V, Bambrah H, Hegde SKB, Sudke A, Phanse V. **Morbidity profile and prevalence of diabetes and high blood pressure among truck drivers in three regions of India [PREPRINT}**. *Research Square* (2019)
20. Izadi N, Malek M, Aminian O, Saraei M. **Medical risk factors of diabetes mellitus among professional drivers**. *J Diabetes Metab Disord* (2013) **12** 23. PMID: 23725484
21. Sangaleti CT, Trincaus MR, Baratieri T. **Prevalence of cardiovascular risk factors among truck drivers in the South of Brazil**. *BMC Public Health* (2014) **14** 1063. PMID: 25304259
22. Budreviciute A, Damiati S, Sabir DK. **Management and Prevention Strategies for Non-communicable Diseases (NCDs) and Their Risk Factors**. *Front Public Health* (2020) **8** 574111. PMID: 33324597
23. Jaganmohan P, Sarma AP. **Studies on changes in hematological and biochemical parameters in smokeless tobacco (Gutka) chewing auto drivers in Nellore district of Andhra Pradesh, India**. *J Appl Nat Sci* (2011) **3** 106-107
|
---
title: 'A phase I trial of metformin in combination with vincristine, irinotecan,
and temozolomide in children with relapsed or refractory solid and central nervous
system tumors: A report from the national pediatric cancer foundation'
authors:
- Jonathan L. Metts
- Matteo Trucco
- Daniel A. Weiser
- Patrick Thompson
- Eric Sandler
- Tiffany Smith
- Jessica Crimella
- Samer Sansil
- Ram Thapa
- Brooke L. Fridley
- Nicholas Llosa
- Thomas Badgett
- Richard Gorlick
- Damon Reed
- Jonathan Gill
journal: Cancer Medicine
year: 2022
pmcid: PMC9972017
doi: 10.1002/cam4.5297
license: CC BY 4.0
---
# A phase I trial of metformin in combination with vincristine, irinotecan, and temozolomide in children with relapsed or refractory solid and central nervous system tumors: A report from the national pediatric cancer foundation
## Abstract
### Background
Patients with relapsed and refractory solid and central nervous system (CNS) tumors have poor outcomes and need novel therapeutic options. Vincristine, irinotecan, and temozolomide (VIT) is a common chemotherapy regimen in relapsed pediatric tumors with an established toxicity profile. Metformin shows preclinical anti‐cancer activity through multiple pathways.
### Methods
The objective of this Phase I trial was to establish the maximum tolerated dose (MTD) and recommended Phase II dose (RP2D) of metformin in combination with VIT in children with relapsed and refractory solid and CNS tumors. A 3 + 3 design was used to test the addition of metformin at five dose levels (666, 999, 1333, 1666, and 2000 mg/m2/day). Therapy toxicity, pharmacokinetics, and radiologic response to treatment were evaluated.
### Results
Twenty‐six patients (median age 13 years, range 2–18 years) were enrolled with 22 evaluable for toxicity. The most common diagnoses were *Ewing sarcoma* ($$n = 8$$), rhabdomyosarcoma ($$n = 3$$) and atypical teratoid/rhabdoid tumor ($$n = 3$$). The MTD was exceeded at Dose Level 5 due to two dose‐limiting toxicities; both were Grade 3 diarrhea requiring prolonged hospitalization and intravenous fluids. The MTD was not determined due to study closure with less than six patients enrolled at Dose Level 4. Frequently observed toxicities were gastrointestinal (most notably diarrhea) and hematologic. Amongst 16 patients evaluable for best overall response, there was one complete response (Ewing sarcoma), three partial responses (Ewing sarcoma, glioblastoma multiforme, and alveolar rhabdomyosarcoma), and five patients with stable disease.
### Conclusions
The MTD of VIT with metformin was not determined due to premature study closure. We recommend an RP2D of Dose Level 4, 1666 mg/m2/day. Radiographic responses were seen in multiple tumor types. Further evaluation for efficacy could be investigated in a Phase II trial.
## BACKGROUND
Solid tumors, including central nervous system (CNS) tumors, represent approximately $60\%$ of all childhood malignancies and account for the majority of cancer deaths in children. 1 Modest improvements in survival have been observed in children with these tumors over the past twenty years. 2 These disappointing results have redoubled efforts to find active agents to combine with traditional chemotherapy, including the repurposing of FDA‐approved medications.
Irinotecan and temozolomide are frequently used in pediatric solid and CNS tumors due to clinical tolerability and preclinical evidence of synergy. 3, 4, 5, 6, 7 Vincristine, irinotecan and temozolomide (VIT) was studied in two Phase I trials in children with refractory or relapsed disease, with radiographic responses and prolonged stable disease seen in sarcomas, neuroblastoma, and CNS tumors. 8, 9 A randomized Phase II trial enrolling mostly children demonstrated an improve overall survival in relapsed rhabdomyosarcoma patients receiving VIT compared to vincristine and irinotecan. 10 Common toxicities of this regimen are hematologic toxicity and irinotecan‐induced diarrhea which can be managed through supportive care including prophylaxis with cephalosporin antibiotics. This well‐tolerated regimen provides a useful backbone to study the addition of novel agents.
Metformin is an FDA‐approved oral biguanide used to treat type II diabetes. An international randomized placebo‐controlled trial showed metformin was safe and effective in children 12 and older, with main side effects of abdominal pain and nausea/vomiting. 11 Metformin has preclinical activity against multiple models of human cancer through multiple mechanisms. Metformin activated the AMP‐activated protein kinase (AMPK) pathway in malignant and nonmalignant tissues, resulting in growth inhibition in several models including breast and p53‐deficient colon cancer. 12, 13, 14 In breast and prostate cancer models, metformin induced inhibition of mammalian target of rapamycin (mTOR) downstream of AMPK and through REDD1 (regulated in development and DNA damage responses 1). 15, 16 Through inhibition of mitochondrial complex I, metformin in combination with glutaminase inhibition inhibited growth of primary tumors and reduced frequency of lung metastases in a murine osteosarcoma model. 17, 18 *Epidemiologic data* suggests metformin provided patients treated for diabetes with protection against development of cancer. 19, 20, 21, 22 The study of metformin for cancer therapy must take into consideration the dosing strategy of metformin in preclinical studies and its volume of distribution. Metformin concentrations above 1 mM are generally used for in vitro cancer experiments. While mouse peak plasma metformin concentrations after IV injections of metformin are much higher than oral administration, the oral route results in more consistent delivery of metformin to tumors. 23 In adult clinical trials, plasma concentrations up to 25 μM at oral doses up to 2500 mg/day have been observed. 23 In regards to potential therapy for solid tumors, metformin has a large volume of distribution of 63–276 liters when given IV, indicating a considerable tissue uptake, which has been confirmed in mouse models. 24 Phase I studies of metformin alone and in combination with other agents in adults with cancer have found a wide range of tolerable metformin doses as high as 2000 mg/day. However in one study in combination with temsirolimus, the starting dose level was not tolerated due to dose limiting toxicities of pneumonitis, fatigue and thrombocytopenia, requiring a dose reduction of temsirolimus and a maximum metformin dose of only 500 mg/day. 25, 26, 27 The Sunshine Project previously reported a Phase I study of metformin in combination with vincristine, dexamethasone, PEG‐asparaginase, and doxorubicin in relapsed and refractory pediatric acute lymphoblastic leukemia (ALL), which demonstrated a metformin maximum tolerated dose (MTD) and recommended Phase II dose (RP2D) of 1000 mg/m2/day. 28 Concurrently to the ALL trial, and based on preclinical evidence of metformin anticancer activity and the lack of established dosing tolerance in adults, we sought to determine the safety and tolerability of metformin when combined with VIT in relapsed and refractory pediatric solid and CNS tumors.
## Objectives
The study's primary objectives were to determine the MTD and RP2D of metformin combined with VIT in pediatric relapsed and refractory solid and CNS tumors, and to describe pharmacokinetics (PKs) of metformin in combination with VIT. The secondary objective was to describe the radiologic responses seen with this regimen.
## Patient eligibility
Patients 1–18 years of age with histologically or radiographically confirmed relapsed or refractory CNS or non‐CNS solid tumors with radiographically measurable disease and no known curative therapy options were eligible. A Karnofsky/Lansky score of 50 or above was required. Prior therapy including vincristine, irinotecan, or temozolomide was permitted, however patients could not have previously received irinotecan and temozolomide in combination. Prior radiation therapy was allowed if completed greater than 14 days prior to the start of protocol therapy for local palliative radiation and greater than six months for total body or craniospinal irradiation. Patients with prior autologous or allogenic stem cell transplant were eligible if more than three months from engraftment, transfusion independent, without graft versus host disease and not taking immunosuppressive medications. Organ function requirements included an absolute neutrophil count ≥1000/mm3, platelet count ≥100,000/mm3 (with no platelet transfusion within seven days of eligibility labs), hemoglobin ≥8.0 gm/dl, calculated or measured creatinine clearance or glomerular filtration rate ≥70ml/min/1.73 m2, total bilirubin ≤1.5 × upper limit of normal for age, alanine aminotransferase ≤5 × upper limit of normal for age, and serum albumin ≥2 gm/dl.
Study exclusions included pregnant or breast‐feeding women, prior allergy or intolerance of vincristine, irinotecan, temozolomide, or metformin, allergy to cephalosporins, or uncontrolled infection. Other exclusions included ongoing treatment with other investigational agents, other concomitant anti‐cancer agents, or hematologic growth factors. CNS tumor subjects on dexamethasone were required to be on a stable or decreasing dose for minimum seven days before enrollment.
## Study design
This study was completed through the Sunshine Project, a multi‐institutional clinical trial consortium sponsored by the National Pediatric Cancer Foundation. Moffitt Cancer Center was the coordinating center, and the study was approved by the Moffitt Cancer Center Institutional Review Board (IRB) and each participating institution's IRB. The trial was registered at www.clinicaltrials.gov (NCT01528046). Written informed consent and assent was obtained according to institutional guidelines.
Dose escalation followed a 3 + 3 design. 29 Common Terminology Criteria for Adverse Events (CTCAE) version 4.0 was used for toxicity grading. All Grade 3 and 4 toxicities and any toxicities possibly, probably, or definitely attributed to metformin were collected. Dose‐limiting toxicity (DLT) evaluation occurred during the first metformin‐containing cycle of treatment, and patients were required to complete a minimum $80\%$ of prescribed metformin doses or to experience a DLT at any time in the cycle to be evaluable for DLT. DLT was defined as any Grade 3 or 4 non‐hematologic toxicity possibly, probably, or definitely attributable to the investigational drug with exclusion of the following Grade 3 events: allergic reactions, nausea, vomiting, dehydration, diarrhea requiring intravenous hydration for 48 h or less, diarrhea not requiring intravenous fluids lasting five days or less, aspartate aminotransferase or alanine aminotransferase elevation returning to Grade ≤1 or baseline prior to the next treatment course, fever, febrile neutropenia, infection, electrolyte abnormalities improving to ≤ Grade 2 within seven days with or without supplements, alopecia, or vincristine‐related neuropathy. The following hematologic toxicities were considered DLTs: Grade 4 neutropenia more than 14 days duration, Grade 3 or 4 thrombocytopenia more than 14 days duration, and failure to recover blood counts to eligibility criteria causing a delay more than 21 days between treatment courses. The MTD of metformin was exceeded if two or more patients in a cohort up to six patients at a given dose level experienced DLT. Once determined, we planned to declare the MTD as the RP2D.
Patients were evaluated with cross‐sectional imaging within 14 days of therapy initiation and before course three, five, and every three cycles thereafter. Responses were evaluated using the Response Criteria in Solid Tumors. 30 To be evaluable for response, a minimum 21 days of metformin therapy with a minimum $80\%$ of metformin doses taken or documented progressive disease after metformin initiation was required. An overall best response assessment required two consecutive determinations of disease status separated by a minimum of three weeks. Responses were characterized as complete response (CR), Partial response (PR), Stable Disease (SD), and Progressive Disease (PD).
## Treatment schema
Initially, protocol therapy included VIT (vincristine 1.5 mg/m2 intravenous Days 1 and 8, irinotecan 50 mg/m2 intravenous Days 1–5, temozolomide 100 mg/m2 oral Days 1–5 in a 21‐day cycle) alone in Cycle 1, with metformin given concurrently beginning in Cycle 2 if the patient had adequate hematologic recovery (absolute neutrophil count ≥1000/mm3 and platelets ≥100,000/mm3) on Day 21 of Cycle 1. If this was not achieved, then a second cycle of VIT was given with a temozolomide dose reduction to 50 mg/m2/dose. If adequate hematologic recovery after Cycle 2 occurred, then the patient began metformin with Cycle 3; if not, the patient was removed from study (Figure 1A). The rationale was to document toxicities from a cycle of non‐metformin‐containing VIT therapy to compare to metformin‐containing cycles for assessment of additive toxicities and possible drug–drug interactions. The trial was amended in 2015 to include metformin at the onset of Cycle 1 and to reduce temozolomide from 100 mg/m2/day to 50 mg/m2/day orally on Days 1–5 (Figure 1B). Adequate hematologic recovery on Day 21 of Cycle 1 was still required post‐amendment. Metformin dosing was assigned at study entry, beginning with Dose Level 1: 666 mg/m2/day divided twice daily on all days of each cycle, equivalent to the typical starting dose of metformin for type II diabetes mellitus. Metformin was purchased commercially through the Moffitt Cancer Center research pharmacy and supplied to sites. Liquid or tablet form of metformin was allowed, and dose rounding was permitted for convenience of administration. Patients were allowed to continue on study for up to 12 cycles. Special dose interruptions were protocol‐mandated to mitigate the risk of metformin‐associated lactic acidosis, including holding metformin for creatinine clearance <60 ml/min/1.73 m2, suspected severe hypovolemia, 24 h prior and 48 h post any procedure or anesthesia/sedation requiring the patient to fast, and 24 h prior and 48 h post any procedure or imaging using intravenous contrast.
**FIGURE 1:** *Trial design: (A) Schema for the original trial VIT cycle without metformin prior to addition of metformin in Cycle 2 (or 3) depending on hematologic tolerance. “Heme Recovery” signifies ANC > 1000 and Platelets >100,000 on Day 21 of a cycle. (B) Post‐amendment schema showing the addition of metformin in Cycle 1. (C) Trial profile accounting for all patients screened for enrollment.*
Temozolomide was required to be given at least one hour prior to the other agents. For Day 1, vincristine was given at least one hour after temozolomide and at least one hour prior to irinotecan. Irinotecan‐induced diarrhea prophylaxis was required with cefpodoxime or cefixime beginning one to two days prior and continuing one to two days post‐irinotecan. Alternative antibiotic prophylaxis was allowed per discretion of the study team. Supportive care for irinotecan early‐ and late‐onset diarrhea was required including use of loperamide, atropine, and octreotide based on symptom timing and intensity.
Tumor resections were allowed on study after the DLT period if deemed clinically indicated by the treating physician. Radiotherapy was not permitted. Subjects were removed from protocol therapy for clinical or radiographic progressive disease, drug‐related adverse events that did not improve or recurred despite dose modifications, refusal of protocol therapy, non‐compliance that in the investigator's opinion precluded ongoing participation, completion of 12 cycles, or if the treating physician determined it was not in the best interest of the patient to continue protocol therapy.
## Pharmacokinetic studies
PK samples for metformin were drawn at Hour 0 (pre‐dose) and Hours 6, 12, and 24 following the first metformin dose on Cycle 1 Day 1. Additional PKs were drawn at Hour 0 before the Cycle 1 Day 8 dose and Hour 6 post‐dose. One ml of whole blood was shipped on cold packs overnight to NMS Labs (Willow Grove, PA) where metformin concentration was determined using a previously published assay. 31 Briefly summarized, pharmacokinetic analysis was conducted using non‐compartmental methods (WinNonlin 8.1; Pharsight). Area under the curve (AUC) for samples obtained up to 12 h were calculated using Linear Trapezoidal Linear Interpolation rule. 32 The observed time to maximum concentration (Tmax), observed maximum plasma concentration (Cmax) and average plasma concentration at steady‐state (Cssavg) were summarized using descriptive statistics.
## Statistical considerations
Patient and clinical characteristics were summarized using descriptive statistics including median and range for continuous measures and proportions and frequencies for categorical measures.
## RESULTS
Twenty‐nine patients consented and were screened (Figure 1C). Three patients failed screening; two due to inadequate renal function and one due to inability to radiographically confirm relapse by the participating institution. Twenty‐six patients were enrolled between October 2012 and June 2019 across seven institutions. The first 11 patients were treated with the initial dosing schema without metformin in Cycle 1 (Figure 1A,C). In this initial cohort, four patients were removed prior to receiving metformin; three due to disease progression, and one due to inability to start metformin by Cycle 3 because of persistent hematologic toxicity. Two additional patients were found to have tumor progression before completing the first metformin‐containing cycle and were not evaluable for DLT. Due to these challenges accruing patients evaluable for DLT and incorporating recommendations of the study's clinical trials oversight committee, the study was amended in 2015 to administer metformin beginning with Cycle 1 and reducing the temozolomide dose of 50 mg/m2/day, and 15 more patients were enrolled (Figure 1B,C).
The median age was 13 years (range 2–18 years), and patients received a median of two prior lines of therapy (range 1–5; Table 1). Two patients received prior irinotecan and six patients received prior temozolomide. Overall 109 treatment cycles (with 94 metformin‐containing cycles) were started, and 100 cycles (with 86 metformin‐containing cycles) were completed, with a median of two cycles completed (range 0–12). With dose‐rounding for metformin, the median prescribed dose of metformin was less than $1\%$ different than the protocol‐specified dose (range $0\%$–$15\%$). No patients underwent tumor resection while on study. The defined dose levels, the number of patients per dose level, and the number evaluable for toxicity and response are in Table 2. Individual patient characteristics in the Table S1.
## Toxicity
Of 26 subjects enrolled, 22 received at least one dose of metformin and were included in toxicity analysis (Table 2). All Grade 3 and 4 toxicities from metformin‐ and non‐metformin‐containing cycles, regardless of attribution, are shown in Table 3. Grade 3 and 4 toxicities possibly, probably or definitely attributed to metformin are shown in the Table S2. Grade 3 and 4 hematologic toxicities in all metformin‐containing cycles regardless of attribution included anemia ($16\%$), thrombocytopenia ($9.6\%$), and neutropenia ($29.8\%$). Grade 3 and 4 thrombocytopenia occurred more frequently in non‐metformin‐containing cycles ($20\%$), which may correlate with the higher dose of temozolomide given prior to the trial amendment, while Grade 3 and 4 neutropenia was more common in metformin‐containing cycles. As expected, a significant portion of non‐hematologic adverse events were gastrointestinal, including abdominal pain, diarrhea, dehydration, nausea, vomiting, and weight loss. Notably, Grade 3 and 4 diarrhea occurred more frequently in metformin‐containing cycles ($6.4\%$ vs. none in non‐metformin‐containing cycles). No patient died on study from treatment related toxicity. Six patients died in the 30‐day follow‐up period after treatment discontinuation, five from progressive disease and one from pulmonary hemorrhage, which was deemed unrelated to study treatment by the investigator. Seven metformin‐containing treatment cycles were delayed due hematologic toxicity.
**TABLE 3**
| Toxicity group | Toxicity type | Metformin (n = 94) | Metformin (n = 94).1 | Metformin (n = 94).2 | No Metformin (n = 15) | No Metformin (n = 15).1 | No Metformin (n = 15).2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Toxicity group | Toxicity type | Grade 3 | Grade 4 | % Cycles | Grade 3 | Grade 4 | % Cycles |
| Blood and lymphatic system disorders | Anemia | 15 | | 16.0 | 1 | | 6.7 |
| Blood and lymphatic system disorders | Febrile neutropenia | 2 | | 2.1 | 1 | | 6.7 |
| Ear and labyrinth disorders | Ear pain | | | | 1 | | 6.7 |
| Ear and labyrinth disorders | Hearing impaired | 1 | | 1.1 | | | |
| Gastrointestinal disorders | Abdominal pain | 2 | | 2.1 | | | |
| Gastrointestinal disorders | Diarrhea | 5 | 1 | 6.4 | | | |
| Gastrointestinal disorders | Nausea | 2 | | 2.1 | 1 | | 6.7 |
| Gastrointestinal disorders | Vomiting | 2 | | 2.1 | | | |
| General disorders and administration site conditions | Fatigue | | | | 1 | | 6.7 |
| General disorders and administration site conditions | Fever | | | | 1 | | 6.7 |
| General disorders and administration site conditions | Gait disturbance | 1 | | 1.1 | | | |
| General disorders and administration site conditions | Pain | 1 | | 1.1 | | | |
| Immune system disorders | Allergic reaction | 1 | | 1.1 | | | |
| Infections and infestations | Enterocolitis infectious | | | | 1 | | 6.7 |
| Investigations | Alanine aminotransferase increased | 7 | | 7.4 | | | |
| Investigations | Aspartate aminotransferase increased | 2 | | 2.1 | | | |
| Investigations | Lymphocyte count decreased | 1 | 1 | 2.1 | | | |
| Investigations | Neutrophil count decreased | 17 | 11 | 29.8 | | 1 | 6.7 |
| Investigations | Platelet count decreased | 4 | 5 | 9.6 | 1 | 2 | 20.0 |
| Investigations | Weight loss | 2 | | 2.1 | | | |
| Investigations | White blood cell decreased | 9 | 1 | 10.6 | | | |
| Metabolism and nutrition disorders | Anorexia | 2 | | 2.1 | | | |
| Metabolism and nutrition disorders | Dehydration | 5 | 1 | 6.4 | 1 | | 6.7 |
| Metabolism and nutrition disorders | Hypokalemia | 3 | | 3.2 | | | |
| Metabolism and nutrition disorders | Hypomagnesemia | 2 | | 2.1 | | | |
| Metabolism and nutrition disorders | Hyponatremia | | | | 1 | | 6.7 |
| Metabolism and nutrition disorders | Hypophosphatemia | 1 | | 1.1 | | | |
| Musculoskeletal and connective tissue disorders | Back pain | 1 | | 1.1 | | | |
| Musculoskeletal and connective tissue disorders | Generalized muscle weakness | 1 | | 1.1 | | | |
| Musculoskeletal and connective tissue disorders | Pain in extremity | 1 | | 1.1 | | | |
| Nervous system disorders | Aphonia | 1 | | 1.1 | | | |
| Nervous system disorders | Depressed level of consciousness | 2 | | 2.1 | | | |
| Nervous system disorders | Dysarthria | 1 | | 1.1 | | | |
| Nervous system disorders | Headache | 2 | | 2.1 | | | |
| Nervous system disorders | Hydrocephalus | 1 | | 1.1 | | | |
| Nervous system disorders | Nervous system disorders ‐ Other, specify | 1 | | 1.1 | | | |
| Nervous system disorders | Peripheral motor neuropathy | 1 | | 1.1 | | | |
| Nervous system disorders | Seizure | | 1 | 1.1 | | | |
| Psychiatric disorders | Confusion | 1 | | 1.1 | | | |
| Psychiatric disorders | Hallucinations | 1 | | 1.1 | | | |
| Renal and urinary disorders | Hematuria | | | | 2 | | 13.3 |
| Respiratory, thoracic and mediastinal disorders | Hypoxia | 1 | | 1.1 | 1 | | 6.7 |
| Vascular disorders | Hypotension | | | | | 1 | 6.7 |
Eighteen patients were evaluable for DLT (Table 2). During the DLT period, all DLT‐evaluable patients received all prescribed doses of VIT, and the median metformin dose compliance was $100\%$ (range $95\%$–$100\%$). No DLTs occurred in Dose Levels 1–4. Two DLTs occurred at Dose Level 5 (metformin 2000 mg/m2/day); both were Grade 3 diarrhea requiring hospitalization and more than 48 h of IV hydration. After completion of Dose Level 5, the study's clinical trials oversight committee recommended closure of the trial. One patient was removed from study after DLT, while the other patient completed Cycle 1 and three subsequent cycles after a metformin dose reduction to 1666 mg/m2/day. No other patients required metformin dose reductions during the study. The MTD was not definitively determined due to lack of six patients treated at Dose Level 4. Due to the tolerability at this dose level, the tolerability of this dose in a Dose Level 5 patient after dose reduction, we propose Dose Level 4 as the RP2D.
## Responses
Nineteen patients achieved the threshold metformin exposure defined in the methods section or progressed after initiation of metformin making them evaluable for response, with 16 patients evaluable for best overall response (Figure 1C). Best overall responses included one patient with CR, three patients with PR, five patients with SD, and seven patients with PD. The CR was a 14‐year‐old with *Ewing sarcoma* who achieved CR after two cycles of chemotherapy, and PRs were seen in patients with glioblastoma multiforme, Ewing sarcoma, and alveolar rhabdomyosarcoma. Four patients received the maximum 12 cycles of therapy. Response timing and length of therapy by patient is shown in Figure 2.The three patients not included in best overall response were a patient with Wilms tumor with PR on first imaging but whose family refused to continue protocol therapy until second imaging, a patient with clear cell sarcoma of the kidney who had SD on first imaging but came off study prior to second imaging per physician discretion to pursue alternative therapy, and a patient with CNS tumor not otherwise specified with SD on first imaging who was removed prior to second imaging per physician discretion for concerns about ongoing metformin compliance.
**FIGURE 2:** *Swimmer's plot of 26 patients treated on study, with timing of overall best response and disease status at end of protocol therapy shown. AA, anaplastic astrocytoma; ARMS, alveolar rhabdomyosarcoma; ATRT, atypical teratoid/rhabdoid tumor of CNS; BSG, brainstem glioma; CCS, clear cell sarcoma of the kidney; CNOS, CNS tumor not otherwise specified; CNSM, central nervous system medulloepithelioma; CR, complete response; ERMS, embryonal rhabdomyosarcoma; ES, Ewing sarcoma; GBM, glioblastoma multiforme; GCT, germ cell tumor; N/A, not applicable as best overall response was unable to be determined; OS, osteosarcoma; PD, progressive disease; PR, partial response; SD, stable disease; WT, Wilms Tumor.*
## Pharmacokinetics
Twenty patients had sufficient PK sample collection for metformin PK analysis on Cycle 1 Day 1. As shown in Table 4, steady‐state concentrations varied minimally between cohorts, independent of dose. The AUC0‐12 of metformin at the 1666 mg/m2 (RP2D cohort) was 1615 h × ng/ml. Average plasma concentration at steady state for evaluable patients at the RP2D was 404 ng/ml. ( Table 4).
**TABLE 4**
| Dose level | N(age range, years) | Tmax (hr) | Cmax (ng/ml) | AUC(0–12 h) (hr × ng/ml) | CSS avg (ng/ml) |
| --- | --- | --- | --- | --- | --- |
| 666 mg/m2/day | 5 (5–18) | 8 ± 4 | 763 ± 268 | 4088 ± 3254 | 413 ± 158 |
| 1000 mg/m2/day | 3 (6–15) | 6 ± 0 | 440 ± 27 | 2530 ± 987 | 297 ± 70 |
| 1333 mg/m2/day | 2 (8–16) | 7 ± 1 | 730 ± 523 | 2920 ± 537 | 516 ± 431 |
| 1666 mg/m2/day | 2 (3–6) | 6 ± 0 | 567 ± 134 | 1615 ± 757 | 404 ± 157 |
| 2000 mg/m2/day | 7(2–16) | 7 ± 2 | 910 ± 345 | 3315 ± 1248 | 557 ± 151 |
## DISCUSSION
This Phase I trial found the MTD was exceeded at Dose Level 5, 2000 mg/m2/day of metformin. Both DLTs at Dose Level 5 were diarrhea requiring prolonged hospitalizations with IV fluids. As expected, the majority of toxicities were gastrointestinal, including diarrhea, and hematologic toxicity. In the absence of a conclusive MTD, Dose Level 4 was declared the RP2D.
Recommended metformin dosing for newly‐diagnosed type 2 diabetes mellitus in children 10 years and older is initially 500–1000 mg daily with escalation as high as 1000 mg twice daily. 33 The MTD/RP2D in our pediatric ALL trial run concurrently with this trial was metformin 1000 mg/m2/day alongside a multiagent reinduction backbone. 28 *It is* possible that the less intensive VIT backbone with reduced temozolomide dosing allowed higher dose escalation of metformin for this trial. The ALL study also included two events of acidosis reported as DLTs. This study outlined several mitigation measures against the development of metformin‐induced lactic acidosis which may have increased tolerability. Finally, several Grade 3 gastrointestinal toxicities that could be confounded by the VIT backbone and managed with supportive care were excluded from the definition of DLT, which may have contributed to the higher MTD in this study.
This study collected all Grade 3 and 4 toxicities and assessed attribution to metformin, but did not assess attribution to VIT. The toxicities attributed to metformin may have overlapping attribution to VIT or may represent interactions between VIT and metformin. The initial design of this trial adding metformin to VIT in the second cycle was designed to assist in determining additive and overlapping toxicities. For hematologic toxicity, rates of Grade 3 and 4 thrombocytopenia appear higher in the VIT‐only cycles ($20\%$ VIT alone vs. $9.6\%$ VIT‐metformin), while Grade 3 and 4 neutropenia was increased in metformin containing cycles ($6.7\%$ for VIT alone vs. $29.8\%$% for VIT‐metformin). Notably, for GI toxicity, rates of Grade 3 and 4 diarrhea appear higher in the VIT‐metformin cycles ($0\%$ for VIT alone vs. $6.4\%$ for VIT‐metformin). This increased frequency along with two DLTs of diarrhea requiring prolonged hospitalization with IV fluids indicate diarrhea was an important additional toxicity from VIT‐metformin. Toxicity comparisons between VIT and VIT‐metformin cycles, especially hematologic, must be interpreted with caution, as the dosing of temozolomide was lowered with an amendment after the 11th patient.
An exact toxicity comparison in the literature using the VIT backbone identical to our trial is unavailable, as our study utilized a decrease dose of temozolomide for most patients. Multiple other prior trials and retrospective experiences of VIT have used varying doses, dosing schedules, and administration routes of these agents (e.g. IV vs. oral irinotecan, five‐ vs ten‐day irinotecan dosing schedule, dosing heterogeneity of temozolomide), making direct comparisons to our trial challenging. 4, 5, 8, 9, 10 A Phase I trial of VIT that included a cohort (termed Schedule B) of patients with a five‐day oral dosing strategy of irinotecan with antibiotic prophylaxis reported less hematologic toxicity than that seen on our trial. 8 Grade 3 and 4 neutropenia occurred in five of 72 Schedule B cycles ($6.9\%$) compared to 28 of 94 metformin‐containing cycles ($29.8\%$) in our trial (Table 3). There was no Grade 3 or 4 anemia reported on Schedule B compared to 15 of 94 cycles ($16\%$) on our trial. While hematologic toxicity is not a frequently‐considered adverse effect with metformin use, there have been reports of metformin lowering neutrophil counts in polycystic ovarian disease and lowering hemoglobin in type 2 diabetes, and its combination with VIT chemotherapy may have exacerbated hematologic toxicity. 34, 35 Comparable to our previous study of metformin in pediatric ALL patients, reported AUC was within $6\%$ at the same dosing cohort (1000 mg/m2/day). 28 The limited sampling scheme affected the PK data from this trial, and 8 out of the 20 PK‐evaluable patients on this study were missing at least one PK time‐point. This limited data may have contributed to the lack of dose‐dependent PK findings on this study. These sampling discrepancies may have occurred because while patients on the ALL study were treated in the hospital, allowing more consistent sample collections, patients on this study were treated outpatient. The sparse data for parameter estimates coupled with varying participants per cohort resulted in non‐correlation between dose and certain PK estimates expected in linear pharmacokinetics, such as AUC and Css. However, previous data supports the linearity between increasing dose levels and these PK estimates for patients who received at least $85\%$ of planned metformin doses. 28 Additionally, metformin has been described to have a very large volume of distribution, which may translate to a tissue sink, and plasma levels may not necessarily reflect cellular exposure. Tumor concentrations of metformin may have been higher with escalated dosing despite the uninformative PK studies.
Our trial has several limitations. First, because of feasibility difficulties during early enrollment, the dose of temozolomide was lowered from 100 mg/m2/day to 50 mg/m2/day, decreasing the treatment intensity of VIT. Notably, there were no DLT in the first 11 patients that received full‐dose temozolomide, however we cannot conclude that full‐dose temozolomide is tolerable at the RP2D of metformin from this study. Secondly, with the MTD not conclusively determined, it is possible that our RP2D causes higher levels of toxicity than expected. Finally, while responses were seen in multiple histologies, and five patients experienced confirmed stable disease lasting at least four cycles of therapy, we are unable to determine the activity that metformin adds to VIT in this study, as responses in these histologies has been reported with VIT alone. The Phase I nature of this trial and heterogeneity of tumors enrolled limits the ability to draw conclusions regarding the activity of this regimen for any specific disease.
In conclusion, the addition of metformin to VIT chemotherapy with modified‐dose temozolomide was found to be tolerable, and our RP2D is metformin 1666 m/m2/day. Notable toxicities that appeared additive with metformin included diarrhea and neutropenia. Due to the uninformative PKs of this study, future studies of metformin in pediatric tumors would benefit from pharmacodynamic testing of the intratumoral accumulation of metformin. Further assessment of anti‐tumor activity will require evaluation of specific disease cohorts in the context of a Phase II trial.
## AUTHOR CONTRIBUTIONS
Jonathan Metts: Data curation (equal); formal analysis (equal); investigation (equal); writing – original draft (lead); writing – review and editing (lead). Matteo Trucco: Investigation (equal); writing – original draft (supporting); writing – review and editing (supporting). Daniel A. A. Weiser: Investigation (equal); writing – review and editing (supporting). Patrick A. Thompson: Investigation (equal); writing – review and editing (supporting). Eric Sandler: Investigation (equal); writing – review and editing (supporting). Tiffany Smith: Data curation (equal); formal analysis (equal); project administration (equal); resources (equal); writing – review and editing (supporting). Jessica Crimella: Data curation (equal); formal analysis (equal); project administration (equal); resources (equal); writing – review and editing (supporting). Samer Sansil: Data curation (equal); formal analysis (equal); writing – review and editing (supporting). Ram Thapa: Data curation (equal); formal analysis (equal); validation (equal); writing – review and editing (supporting). Brooke Fridley: Data curation (equal); formal analysis (equal); methodology (equal); validation (equal); writing – review and editing (supporting). Nicolas J Llosa: Investigation (equal); writing – review and editing (supporting). Thomas Badgett: Investigation (equal); writing – review and editing (supporting). Richard G. Gorlick: Investigation (equal); writing – review and editing (supporting). Damon R. Reed: Funding acquisition (equal); investigation (equal); project administration (equal); supervision (equal); writing – review and editing (supporting). Jonathan B. Gill: Conceptualization (equal); funding acquisition (equal); investigation (equal); methodology (equal); supervision (equal); writing – original draft (supporting); writing – review and editing (supporting).
## FUNDING INFORMATION
This study was funded by the National Pediatric Cancer Foundation and Rally Foundation.
## CONFLICT OF INTEREST
The authors report no conflicts of interest with this work.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
## References
1. Ries L, Smith M, Gurney J. *Cancer Incidence and Survival among Children and Adolescents: United States SEER Program 1975–1995* (1999)
2. Smith MA, Seibel NL, Altekruse SF. **Outcomes for children and adolescents with cancer: challenges for the twenty‐first century**. *J Clin Oncol* (2010) **28** 2625-2634. DOI: 10.1200/JCO.2009.27.0421
3. Wagner LM, Crews KR, Iacono LC. **Phase I trial of temozolomide and protracted irinotecan in pediatric patients with refractory solid tumors**. *Clin Cancer Res* (2004) **10** 840-848. DOI: 10.1158/1078-0432.ccr-03-0175
4. Wagner LM, Villablanca JG, Stewart CF. **Phase I trial of oral irinotecan and temozolomide for children with relapsed high‐risk neuroblastoma: a new approach to neuroblastoma therapy consortium study**. *J Clin Oncol* (2009) **27** 1290-1296. DOI: 10.1200/JCO.2008.18.5918
5. Kushner BH, Kramer K, Modak S, Cheung NK. **Irinotecan plus temozolomide for relapsed or refractory neuroblastoma**. *J Clin Oncol* (2006) **24** 5271-5276. PMID: 17114661
6. Pappo AS, Lyden E, Breitfeld P, Donaldson SS, Wiener E, Parham D, Crews KR, Houghton P, Meyer WH. **Two consecutive phase II window trials of irinotecan alone or in combination with vincristine for the treatment of metastatic rhabdomyosarcoma: the children's oncology group**. *J Clin Oncol* (2007) **25** 362-369. PMID: 17264331
7. Houghton PJ, Stewart CF, Cheshire PJ. **Antitumor activity of temozolomide combined with irinotecan is partly independent of O6‐methylguanine‐DNA methyltransferase and mismatch repair phenotypes in xenograft models**. *Clin Cancer Res* (2000) **6** 4110-4118. PMID: 11051264
8. Wagner LM, Perentesis JP, Reid JM. **Phase I trial of two schedules of vincristine, oral irinotecan, and temozolomide (VOIT) for children with relapsed or refractory solid tumors: a children's oncology group phase I consortium study**. *Pediatr Blood Cancer* (2010) **54** 538-545. DOI: 10.1002/pbc.22407
9. McNall‐Knapp RY, Williams CN, Reeves EN, Heideman RL, Meyer WH. **Extended phase I evaluation of vincristine, irinotecan, temozolomide, and antibiotic in children with refractory solid tumors**. *Pediatr Blood Cancer* (2010) **54** 909-915. DOI: 10.1002/pbc.22460
10. Defachelles AS, Bogart E, Casanova M. **Randomized phase II trial of vincristine‐irinotecan with or without temozolomide, in children and adults with relapsed or refractory rhabdomyosarcoma: a european paediatric soft tissue sarcoma study group and innovative therapies for children with cancer trial**. *J Clin Oncol* (2021) **39** 2979-2990. DOI: 10.1200/JCO.21.00124
11. Jones KL, Arslanian S, Peterokova VA, Park JS, Tomlinson MJ. **Effect of metformin in pediatric patients with type 2 diabetes: a randomized controlled trial**. *Diabetes Care* (2002) **25** 89-94. DOI: 10.2337/diacare.25.1.89
12. Iliopoulos D, Hirsch HA, Struhl K. **Metformin decreases the dose of chemotherapy for prolonging tumor remission in mouse xenografts involving multiple cancer cell types**. *Cancer Res* (2011) **71** 3196-3201. DOI: 10.1158/0008-5472.CAN-10-3471
13. Buzzai M, Jones RG, Amaravadi RK, Lum JJ, DeBerardinis RJ, Zhao F, Viollet B, Thompson CB. **Systemic treatment with the antidiabetic drug metformin selectively impairs p53‐deficient tumor cell growth**. *Cancer Res* (2007) **67** 6745-6752. PMID: 17638885
14. Zakikhani M, Dowling R, Fantus IG, Sonenberg N, Pollak M. **Metformin is an AMP kinase‐dependent growth inhibitor for breast cancer cells**. *Cancer Res* (2006) **66** 10269-10273. PMID: 17062558
15. Dowling RJ, Zakikhani M, Fantus IG, Pollak M, Sonenberg N. **Metformin inhibits mammalian target of rapamycin‐dependent translation initiation in breast cancer cells**. *Cancer Res* (2007) **67** 10804-10812. PMID: 18006825
16. Ben Sahra I, Regazzetti C, Robert G. **Metformin, independent of AMPK, induces mTOR inhibition and cell‐cycle arrest through REDD1**. *Cancer Res* (2011) **71** 4366-4372. DOI: 10.1158/0008-5472.CAN-10-1769
17. Ren L, Ruiz‐Rodado V, Dowdy T. **Glutaminase‐1 (GLS1) inhibition limits metastatic progression in osteosarcoma**. *Cancer & Amp; Metabolism* (2020) **8** 4. DOI: 10.1186/s40170-020-0209-8
18. Wheaton WW, Weinberg SE, Hamanaka RB. **Metformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis**. *Elife* (2014) **3**. DOI: 10.7554/eLife.02242
19. Evans JM, Donnelly LA, Emslie‐Smith AM, Alessi DR, Morris AD. **Metformin and reduced risk of cancer in diabetic patients**. *BMJ* (2005) **330** 1304-1305. PMID: 15849206
20. Li D, Yeung SC, Hassan MM, Konopleva M, Abbruzzese JL. **Antidiabetic therapies affect risk of pancreatic cancer**. *Gastroenterology* (2009) **137** 482-488. DOI: 10.1053/j.gastro.2009.04.013
21. Donadon V, Balbi M, Mas MD, Casarin P, Zanette G. **Metformin and reduced risk of hepatocellular carcinoma in diabetic patients with chronic liver disease**. *Liver Int* (2010) **30** 750-758. DOI: 10.1111/j.1478-3231.2010.02223.x
22. Landman GW, Kleefstra N, van Hateren KJ, Groenier KH, Gans RO, Bilo HJ. **Metformin associated with lower cancer mortality in type 2 diabetes: ZODIAC‐16**. *Diabetes Care* (2010) **33** 322-326. DOI: 10.2337/dc09-1380
23. Dowling RO, Lam S, Bassi C. **Metformin pharmacokinetics in mouse tumors: implications for human therapy**. *Cell Metab* (2016) **23** 567-568. DOI: 10.1016/j.cmet.2016.03.006
24. Graham GG, Punt J, Arora M. **Clinical pharmacokinetics of metformin**. *Clin Pharmacokinet* (2012) **50** 81-98. DOI: 10.2165/11534750-000000000-00000
25. Sehdev A, Karrison T, Zha Y. **A pharmacodynamic study of sirolimus and metformin in patients with advanced solid tumors**. *Cancer Chemother Pharmacol* (2018) **82** 309-317. DOI: 10.1007/s00280-018-3619-3
26. MacKenzie MJ, Ernst S, Johnson C, Winquist E. **A phase I study of temsirolimus and metformin in advanced solid tumours**. *Invest New Drugs* (2012) **30** 647-652. DOI: 10.1007/s10637-010-9570-8
27. Khawaja MR, Nick AM, Madhusudanannair V. **Phase I dose escalation study of temsirolimus in combination with metformin in patients with advanced/refractory cancers**. *Cancer Chemother Pharmacol* (2016) **77** 973-977. DOI: 10.1007/s00280-016-3009-7
28. Trucco M, Barredo JC, Goldberg J. **A phase I window, dose escalating and safety trial of metformin in combination with induction chemotherapy in relapsed refractory acute lymphoblastic leukemia: metformin with induction chemotherapy of vincristine, dexamethasone, PEG‐asparaginase, and doxorubicin**. *Pediatr Blood Cancer* (2018) **65**. DOI: 10.1002/pbc.27224
29. Storer BE. **Design and analysis of phase I clinical trials**. *Biometrics* (1989) **45** 925-937. PMID: 2790129
30. Eisenhauer EA, Therasse P, Bogaerts J. **New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)**. *Eur J Cancer* (2009) **45** 228-247. DOI: 10.1016/j.ejca.2008.10.026
31. Liu A, Coleman SP. **Determination of metformin in human plasma using hydrophilic interaction liquid chromatography‐tandem mass spectrometry**. *J Chromatogr B Analyt Technol Biomed Life Sci* (2009) **877** 3695-3700. DOI: 10.1016/j.jchromb.2009.09.020
32. Chiou WL. **Critical evaluation of the potential error in pharmacokinetic studies of using the linear trapezoidal rule method for the calculation of the area under the plasma level‐‐time curve**. *J Pharmacokinet Biopharm* (1978) **6** 539-546. DOI: 10.1007/BF01062108
33. Arslanian S, Bacha F, Grey M, Marcus MD, White NH, Zeitler P. **Evaluation and management of youth‐onset type 2 diabetes: a position statement by the american diabetes association**. *Diabetes Care* (2018) **41** 2648-2668. DOI: 10.2337/dci18-0052
34. Ibanez L, Jaramillo AM, Ferrer A, de Zegher F. **High neutrophil count in girls and women with hyperinsulinaemic hyperandrogenism: normalization with metformin and flutamide overcomes the aggravation by oral contraception**. *Hum Reprod* (2005) **20** 2457-2462. PMID: 15905296
35. Donnelly LA, Dennis JM, Coleman RL. **Risk of anemia with metformin use in type 2 diabetes: a MASTERMIND study**. *Diabetes Care* (2020) **43** 2493-2499. DOI: 10.2337/dc20-1104
|
---
title: Achieving tolerance modifies cancer susceptibility profiles in liver transplant
recipients
authors:
- Mamatha Bhat
- Elisa Pasini
- Preya Patel
- Jeffrey Yu
- Cristina Baciu
- Sunil M. Kurian
- Josh Levitsky
journal: Cancer Medicine
year: 2022
pmcid: PMC9972022
doi: 10.1002/cam4.5271
license: CC BY 4.0
---
# Achieving tolerance modifies cancer susceptibility profiles in liver transplant recipients
## Abstract
Long‐term survival of transplant recipients is significantly impacted by malignancy. We aimed to determine whether calcineurin inhibitor (CNI)‐treated recipients converted to and weaned off molecular target of rapamycin inhibitor (mTOR‐I) therapy have favorable changes in their molecular profiles in regard to malignancy risk. We performed gene expression profiling from liver biopsy and blood (PBMC) specimens followed by network analysis of key dysregulated genes, associated diseases and disorders, molecular and cellular functions using IPA software. Twenty non‐immune, non‐viremic patients were included, and 8 of them achieved tolerance. Two comparisons were performed: [1] tolerance time point vs tacrolimus monotherapy and [2] tolerance time point vs sirolimus monotherapy. Upon achieving tolerance, IPA predicted significant activation of DNA damage response ($$p \leq 5.40$$e‐04) and inhibition of DNA replication ($$p \leq 7.56$$e‐03). Conversion from sirolimus to tolerance showed decrease in HCC ($$p \leq 1.30$$e‐02), hepatic steatosis ($$p \leq 5.60$$e‐02) and liver fibrosis ($$p \leq 2.91$$e‐02) associated genes. In conclusion, this longitudinal study of patients eventually achieving tolerance reveals an evolving molecular profile associated with decreased cancer risk and improved hepatic steatosis and liver fibrosis. This provides a biological rationale for attempting conversion to mTOR‐I therapy and tolerance following liver transplantation particularly in patients at higher risk of cancer incidence and progression post‐transplant.
Achieving tolerance modifies cancer susceptibility profiles in liver transplant recipients. Liver transplant patients who weaned off sirolimus therapy have favorable changes in their molecular profiles in regard to malignancy risk.
## INTRODUCTION
De novo malignancies arise twice as often in liver transplant (LT) recipients when compared with the general population, significantly impacting long‐term transplant survival. 1, 2, 3 In fact, LT recipients have a 10‐year incidence rate of $11.5\%$ versus a $6.5\%$ cancer incidence in the general population after the age 50. 1 A diagnosis of malignancy is associated with $25\%$ mortality in the first year, and as high as $57.6\%$ at 5 years after a post‐transplant cancer diagnosis. 1 Therefore, a cancer diagnosis is associated with a significantly greater risk of adverse outcome, with a 2.84 times higher risk of cancer‐related death in LT patients than the general population. 4 Though several factors have been shown to contribute to the development of malignancies, immunosuppression is a key risk factor. 5 A higher degree of immunosuppression is associated with an increased risk of malignancy across solid organ transplant recipients. Overall, the decreased cancer‐sensing function of the immune system in immunosuppressed transplant recipients necessitates screening for specific types of cancer more commonly than in the general population. Additionally, the often‐aggressive nature of cancers diagnosed in transplant recipients results in higher mortality. 6 For example, recurrent hepatocellular carcinoma (HCC) is typically more aggressive that pre‐transplant HCC likely due to immunosuppression.
Given this increased susceptibility to cancer, transplant specialists attempt to minimize immunosuppression in LT recipients, as long as there is low concern for developing rejection. The mammalian target of rapamycin inhibitors (mTORi) are immunosuppressive agents with evidence of concomitant antineoplastic effect, thereby potentially serving a dual role in recipients. 7 While controversial and not clinically proven effective, patients at higher risk of de novo or recurrent malignancy are often converted from a CNI to an mTORi for a potential chemopreventive effect. While complete withdrawal of immunosuppression is ideal, it is feasible only in select LT recipients. 8 Nonetheless, the achievement of tolerance in LT recipients is a natural experiment that provides the opportunity to understand the mechanistic basis of cancer susceptibility and metabolic disease in transplant recipients.
Therefore, the aim of our exploratory study was to determine whether patients converted from putatively pro‐neoplastic (CNI) to anti‐neoplastic (mTORi) IS therapy and then weaned off immunosuppression have favorable changes in their molecular profiles in regard to malignancy risk, using a *Network analysis* approach.
## Clinical protocol
This cohort included LT recipients enrolled in previously published clinical trials. 9, 10 Twenty LT recipients, were converted from tacrolimus monotherapy to sirolimus for the indication of tacrolimus toxicity, with clinical characteristics as previously described, 9 Briefly, for conversion to sirolimus, a dose of 2 or 3 mg (< or ≥100 kg body weight)/day was initiated with weekly sirolimus trough level monitoring. When patients reached ≥5 ng/ml, tacrolimus was discontinued followed by initial weekly laboratory tests to monitor sirolimus trough levels (goal 5–8 ng/ml) for one month, then monthly monitoring. Liver/renal function tests, lipid levels, urine protein: creatinine ratios and any signs of sirolimus toxicities were recorded. A subset of these twenty LT recipients 8 were successfully weaned off of sirolimus. 10 For all the patients enrolled, baseline physical examination and laboratory tests were performed and if acceptable, a baseline liver biopsy was performed within one month. Sirolimus was slowly reduced over a period of time of approximately 3–6 months as per our previously described protocol. If liver tests were normal, sirolimus was discontinued completely. Subjects were seen six months later and all baseline assessments were repeated for the final study visit 12 months post‐full withdrawal. Liver tests were performed every 2 weeks at the patient's local laboratory throughout the trial. At any concern for rejection, defined by abnormal liver tests, liver biopsy and blood/tissue biomarkers assays were performed. If rejection was diagnosed, the patient was withdrawn from the study. The study inclusion and exclusion criteria for both studies were as previously described. 9, 10 A written informed consent was obtained from patients for use of liver biopsy tissue and peripheral blood samples. The study protocol was approved by the Northwestern Institutional Review Board and was performed in accordance with the Declaration of Helsinki (https://clinicaltrials.gov/ct2/show/NCT02062944).
## Gene expression microarrays
Liver biopsy tissue and peripheral blood were collected at three time points: [1] prior to conversion from tacrolimus to sirolimus, [2] 6 months following conversion to sirolimus, and [3] 6 months following weaning off of sirolimus. 10 RNA extraction from blood samples was performed using the PaxGene blood RNA kit. RNA was extracted from snap‐frozen liver tissue and purified using RNeasy Mini Kit (Qiagen). RNA quality was verified by Nanodrop spectrophotometer (VWR) and Bioanalyzer (Agilent). Gene expression profiling was performed with Affymetrix HT HG‐U133 Plus PM arrays (Affymetrix) following standard protocols. Two different time points were compared as follows: [1] tolerance time point vs tacrolimus and [2] tolerance time point vs sirolimus. Gene expression analysis was performed as previously described. 10
## Protein–Protein interaction network analysis and pathway enrichment
The list of differentially expressed genes from the previous analysis 4 with FDR correction <0.05 and corresponding log2(fold change) were used as input for pathway and network analyses with Ingenuity Pathway Analysis (QIAGEN Inc., https://www.qiagenbio‐ informatics.com/products/ingenuity‐pathway‐analysis). The core analysis module in IPA was performed to identify differentially regulated diseases and biological functions, and gene networks illustrating the genes and their interactors based on Fisher's exact test, as previously reported. 11 The network is then shown as a graph representing the molecular relationships/interactions as an edge (line) between genes or gene products (nodes). The connectivity of these nodes representing the genes is based on the data collected in the IPA knowledge base. The node color indicates an up‐modulation (red) or down‐modulation (green). Edges are displayed with various colors or labels to better describe the nature of the relationship between the nodes. The Molecular Activity Predictor (MAP) tool was used to predict the crosstalk relationship among our genes and their interactors in the different protein–protein interaction networks. The molecular activity prediction was performed on all the networks considered. Each network considered significant for the study was then overlaid with the diseases and functions database to identify the effect of conversion to sirolimus and achievement of tolerance, and the most common relevant process was identified.
The z‐score was obtained as previously reported. A z‐score ≤−2 (inhibition) or z‐score ≥2 (activation) is considered strongly significant of a predicted change of status. Outside these values, the prediction of activation or inhibition is still present, but less confident. In some cases, the z‐score cannot be calculated, if there is not enough information stored in the IPA knowledge base.
## RESULTS AND DISCUSSION
Twenty non‐immune (without autoimmune liver disease as indication for transplant), non‐viremic patients (without active, ongoing hepatitis B virus (HBV) or hepatitis C virus (HCV) infection) (age 57.2 ± 8; $95\%$ Caucasian; 3.5 ± 2.1 years post‐LT and with no additional immunosuppressive therapy at the moment of enrollment) treated with tacrolimus were successfully converted to sirolimus, 9 and 8 of them achieved tolerance. 9, 10 No differences were identified between the two groups (achieved tolerance vs not achieved tolerance) with respect to median age 63.7 (47.3–76.3) versus 62.7 (44–67.6), time on sirolimus monotherapy 4.2 (0.62–5.5) versus 4.1 (2.5–5.4) years, or time from LT to weaning. 8.1 (4.5–12.0) versus 6.9 (3.0–10.9), respectively, as described before. 10
## Tolerance versus tacrolimus monotherapy
276 genes were found differentially expressed between tolerance versus tacrolimus monotherapy in liver biopsies, with 96 genes being upregulated and 180 downregulated. These were associated with cancer (p‐value range = 1.08e‐02—1.60e‐13), gastrointestinal disease (p‐value range = 1.08e‐02—1.30e‐11) as top diseases, and with cell cycle (p‐value range = 1.08e‐02— 2.57e‐04), DNA replication, recombination and repair (p‐value range = 1.08e‐02—2.57e‐04) as top molecular and cellular functions. Network analysis identified DNA Replication, Recombination, and Repair, Cell Cycle as one of the most significant networks (Figure 1) associated with the DEGs (score = 40), with several pro‐cancer and anti‐cancer genes being involved (Appendix S1).
**FIGURE 1:** *The achievement of tolerance compared with tacrolimus affected genes involved in DNA damage response and DNA replication in the liver tissue. Comparison of tolerance versus tacrolimus timepoint. Tolerance is predicted to improve Repair of DNA (p = 2.78E‐04), DNA damage response (p = 5.40e‐05) and inhibit DNA replication (p = 7.56e‐03) of liver cells based on the overlay with disease and function database. Network 3 in IPA: Cell cycle, DNA replication and Repair*
When overlaying this network with diseases and functions, repair of DNA was predicted to be activated ($$p \leq 2.78$$e‐04) due to the upregulation of transcription regulators RB1 (RB transcriptional Corepressor 1, FC = 2.20) and ELOA (Elongin A, FC = 2.41), and to increased expression of TNRC6A (Trinucleotide Repeat Containing Adaptor 6A, FC = 2.26). The downregulated L3MBTL1 (L3MBTL Histone Methyl‐Lysine Binding Protein 1, FC = 0.43) gene was linked to enriched DNA damage response ($$p \leq 5.40$$e‐04) that was predicted to be highly activated. Upregulated RB1 and SLFN13 (FC = 2.20) were associated with DNA replication, predicted to be enriched ($$p \leq 7.56$$e‐03) and inhibited. Our analysis also revealed neoplasia of cells, as a process, being enriched ($$p \leq 3.15$$e‐04) and highly inhibited (z‐score = −2.1) by DEGs involved (Figure 2). Of the 121 DEGs related to neoplasia (49 upregulated, 72 downregulated), 10 genes were predicted by IPA to significantly reduce this process (Appendix S1).
**FIGURE 2:** *Tolerance is predicted to inhibit Neoplasia of cells (p = 3.15e‐04) in liver tissue. Comparison of tolerance versus tacrolimus timepoint*
Similarly, when using PBMC samples, we found tolerance association with cancer and gastrointestinal diseases being significant, with p‐values ranging from 4.21e‐03 to 1.82e‐20 and from 3.20e‐03 to 4.60e‐6, respectively. As in liver tissue, network analysis identified DNA replication, Recombination and Repair, Cell morphology, Cellular Assembly and Organization as one of the top networks corresponding to the DEGs between tolerance and tacrolimus in blood samples (Appendix S1). By employing IPA overlapping tool with diseases and functions, a significant activation of DNA damage response of cells ($$p \leq 2.92$$e‐06), repair of DNA ($$p \leq 4.20$$e‐04), double‐stranded DNA break repair ($$p \leq 2.93$$e‐03) were revealed (Figure S1).
## Tolerance versus sirolimus monotherapy
When comparing these two groups in liver tissue, 77 DEGs were identified (48 upregulated, 29 downregulated in tolerance group). Tolerance was found to be associated with cancer (p‐value range = 6.56e‐03‐1.51e‐09), organismal injury and abnormalities (p‐value range = 6.56e‐03‐2.14e‐07). Network analysis identified six networks associated with DEGs (Appendix S1). Of these, Network 2 (score = 31) was associated with Cellular Growth and Proliferation, Connective Tissue Development and Function, Tissue Development, with 9 upregulated and 6 downregulated genes (Figure 3). Network overlay with diseases and functions predicted a decrease in HCC (p‐value = 1.30e‐02) and hepatic steatosis (p‐value = 5.60e‐02) acquired by tolerance, with the DEGs involved in cancer and HCC in particular, or in hepatic steatosis being shown in Appendix S1. Based on the links between the molecules in the network and these diseases, inactivation of HCC is due to overexpressed CCN1/CYR61 (FC = 1.55), cellular communication network factor 1, a tumor suppressor in HCC. 12 Upregulation of CCN1/CYR61 was associated with suppression of hepatocarcinogenesis, by limiting the proliferation of oncogenic hepatocytes. 13 Potential decrease in HCC is also linked to prediction in activation of interferon (Ifn), based on the previous studies that found interferon to act as suppressor of carcinogenesis in HCV‐related HCC patients. 14 *In this* network, the decrease in steatosis is linked to downregulated SOCS3 (FC = −1.49), suppressor of cytokine signaling 3, shown to play a role in limiting liver steatosis by inhibition of STAT3 activation, 15 and to downregulated LBP (FC = −1.47), lipopolysaccharide binding protein, with higher levels of LBP being linked to increased liver steatosis in NAFLD patients. 16 *Showing a* similar effect, network 3 (score = 28) associated with Developmental Disorder, Hereditary Disorder, Metabolic disease, with 8 upregulated and 6 downregulated genes, reflected a decrease in HCC (p‐value = 9.83e‐02) and fibrosis of liver (p‐value = 2.91e‐02) in tolerance group (Figure 4), due to predicted inactivation of tumor necrosis factor, TNF, previously shown to affect HCC development and recurrence. 17
**FIGURE 3:** *Achieving tolerance from sirolimus is predicted to decrease HCC (p = 1.30e‐02) and hepatic steatosis (p = 5.60e‐02) in the liver, based on the overlay with the disease and function database. Comparison of tolerance versus sirolimus timepoint. Network 2: Cellular Growth and Proliferation, Connective Tissue Development and Function, Tissue Development* **FIGURE 4:** *Achieving tolerance from sirolimus is predicted to decrease HCC (p = 9.83e‐03) and fibrosis of liver (p = 2.91e‐02), based on the overlay with the disease and function database. Comparison of tolerance versus sirolimus timepoint. Network 3: Developmental Disorder, Hereditary Disorder, Metabolic Disease*
When performing tolerance versus sirolimus comparison analysis in blood samples, the cancer, organismal injury and abnormalities, were also top diseases and disorders found significantly associated with tolerance group (p‐value range = 2.63e—02‐2.39e‐06). In terms of networks determined by the DEGs between the two groups, the one involved similarly as in liver, in Cellular growth and Proliferation, Tissue Morphology, Organismal Functions was overlapped with diseases and functions from IPA database. Neoplasia of hepatocytes was predicted to be significantly inhibited ($$p \leq 5.61$$e‐03) (Figure S2) and linked in the network to transcription regulator HNF4A, hepatocyte nuclear factor 4 alpha, known to suppress the development of HCC. 18 HNF4A was not among the DEGs from our analysis but rather was predicted to be activated by IPA. Liver tumor and fibrosis of liver were also linked to HN4A and inhibited in blood, but not at significant level.
## DISCUSSION
In this study, we use a network analysis approach to examine longitudinal changes in hepatic gene expression during a protocol that resulted in ultimate weaning off of immunosuppression. This longitudinal analysis provides a natural setting to determine molecular profiles that portend cancer risk. Our study reveals molecular evidence of more favorable malignancy risk profiles as LT recipients transition from CNIs to mTOR‐I and tolerance. More specifically, our network analysis reveals favorable changes in hepatic DNA replication and repair genes and generally in HCC upon achieving tolerance. Whether conversion to sirolimus or even full withdrawal has a clinical benefit on de novo malignancy rates or other complications is yet to be determined. While our study provides biological rationale, clinical benefit of mTOR‐I conversion with or without withdrawal needs to be tested in prospective studies.
Our findings on cell cycle and DNA repair gene expression profiles improving with transition to tolerance are concordant with previous literature confirming that genetic variants in the cell cycle confer susceptibility to cancer (lung, ovarian, etc.). 19, 20, 21 The cancer‐promoting effect of CNIs, previously described, and may be proportional to trough levels. 22 Various mechanisms have been proposed, including inhibition of the effector immune responses, 23, 24, 25 upregulation of transforming growth factor‐β (TGF‐B), 26 downregulation of CXCR3‐B, 27, 28 and overexpression of vascular endothelial growth factor (VEGF) that promotes angiogenesis. 29 Contrary to tacrolimus, sirolimus forms a complex FKBP‐12 protein 30 that can bind the kinase mTOR blocking the downstream signaling pathway, crucial for carcinogenic processes such as cell growth and proliferation, cellular metabolism and angiogenesis. 31 Due to its antiproliferative effects, 12 the putative advantages of using sirolimus as an alternative immunosuppressant to decrease the risk of malignancy have been previously described. 32, 33, 34, 35 The inhibition of mTOR may have antiproliferative effects with several mechanisms including a selective decrease in the translation of mRNAs (such as c‐myc, VEGF) essential to tumorigenesis and a decreased phosphorylation of cyclin D1 leading to cycle progression arrest in cancer cells. 36 In fact, conversion of tacrolimus to sirolimus has been shown previously by our group to result in decreased pro‐cancer gene expression, including genes such as eIF2 as well as genes along the mTOR pathway which become downregulated. 37 This exploratory study provides mechanistic and biological data but is limited by the small number of highly select LT recipients studied. This precluded the ability to correlate molecular cancer risk with actual clinical differences in cancer rates between the cohorts, or metabolic risk with cardiovascular events. Longer term follow‐up with larger cohorts is needed to correlate biological and clinical data. Another limitation is that we have inferred changes in cancer‐associated genes in liver in the same individual under different immune conditions to be reflective of systemic effects on cancer susceptibility. Nonetheless, gene expression data from both the liver and PBMCs in the same individual was followed longitudinally, offering a unique opportunity to examine how differences in immunosuppression affect gene expression patterns.
The key finding is that patients have favorable changes in DNA repair, replication‐associated genes upon achieving tolerance, which we have uncovered using a network analysis approach on transcriptomic data from the liver. We thereby use this clinical setting to show how immunosuppression affects cancer risk molecular profiles in a single individual longitudinally. Future validation will include assessment as to whether these gene expression patterns correlate with reduced malignancy risk in patients who are successfully converted off CNI therapy and ultimately achieve tolerance.
## AUTHOR CONTRIBUTIONS
MB, EP, JL study design and writing of manuscript; EP, PP, JY, CB and SK data collection, analysis and compiling; MB, EP, CB and JL input into study design and final manuscript.
## CONFLICT OF INTEREST
Authors declare no conflict of interest.
## ETHICS STATEMENT
A written informed consent was obtained from patients for use of liver biopsy tissue and peripheral blood samples. The study protocol was approved by the Northwestern Institutional Review Board and was performed in accordance with the Declaration of Helsinki (https://clinicaltrials.gov/ct2/show/NCT02062944).
## DATA AVAILABILITY STATEMENT
Raw microarray data will be available upon request.
## References
1. Watt KD, Pedersen RA, Kremers WK, Heimbach JK, Sanchez W, Gores GJ. **Long‐term probability of and mortality from de novo malignancy after liver transplantation**. *Gastroenterology* (2009) **137** 2010-2017. DOI: 10.1053/j.gastro.2009.08.070
2. Sheil AG. **Malignancy following liver transplantation: a report from the Australian Combined Liver Transplant Registry Comparative Study**. *Transplant Proc* (1995) **27** 1247. PMID: 7878867
3. Herrero JI, Lucena JF, Quiroga J. **Liver transplant recipients older than 60 years have lower survival and higher incidence of malignancy. Research Support, Non‐U.S. Gov't**. *Am J Transplant* (2003) **3** 1407-1412. PMID: 14525602
4. Acuna SA, Fernandes KA, Daly C. **Cancer mortality among recipients of solid‐organ transplantation in ontario, Canada**. *JAMA oncology* (2016) **2** 463-469. DOI: 10.1001/jamaoncol.2015.5137
5. Buell JF, Gross TG, Woodle ES. **Malignancy after transplantation. Review**. *Transplantation* (2005) **80** S254-S264. PMID: 16251858
6. Park B, Yoon J, Choi D. **De novo cancer incidence after kidney and liver transplantation: results from a nationwide population based data**. *Sci Rep* (2019) **9**. DOI: 10.1038/s41598-019-53163-9
7. Gaumann A, Schlitt HJ, Geissler EK. **Immunosuppression and tumor development in organ transplant recipients: the emerging dualistic role of rapamycin**. *Transpl Int* (2008) **21** 207-217. DOI: 10.1111/j.1432-2277.2007.00610.x
8. Levitsky J, Feng S. **Tolerance in clinical liver transplantation**. *Hum Immunol* (2018) **79** 283-287. DOI: 10.1016/j.humimm.2017.10.007
9. Levitsky J, Mathew JM, Abecassis M. **Systemic immunoregulatory and proteogenomic effects of tacrolimus to sirolimus conversion in liver transplant recipients**. *Hepatology* (2013) **57** 239-248. DOI: 10.1002/hep.25579
10. Levitsky J, Burrell BE, Kanaparthi S. **Immunosuppression withdrawal in liver transplant recipients on sirolimus**. *Hepatology* (2020) **72** 569-583. DOI: 10.1002/hep.31036
11. Baciu C, Pasini E, Angeli M. **Systematic integrative analysis of gene expression identifies HNF4A as the central gene in pathogenesis of non‐alcoholic steatohepatitis**. *PLoS One* (2017) **12**. DOI: 10.1371/journal.pone.0189223
12. Feng P, Wang B, Ren EC. **Cyr61/CCN1 is a tumor suppressor in human hepatocellular carcinoma and involved in DNA damage response**. *Int J Biochem Cell Biol* (2008) **40** 98-109. DOI: 10.1016/j.biocel.2007.06.020
13. Chen CC, Kim KH, Lau LF. **The matricellular protein CCN1 suppresses hepatocarcinogenesis by inhibiting compensatory proliferation**. *Oncogene* (2016) **35** 1314-1323. DOI: 10.1038/onc.2015.190
14. Iida H, Osaki R, Fujimoto T. **Interval between hepatocellular carcinoma treatment and interferon‐free direct‐acting antiviral agents against hepatitis C is necessary to suppress tumor recurrence**. *Mol Clin Oncol* (2019) **11** 99-105. DOI: 10.3892/mco.2019.1847
15. Bi J, Sun K, Wu H, Chen X, Tang H, Mao J. **PPARgamma alleviated hepatocyte steatosis through reducing SOCS3 by inhibiting JAK2/STAT3 pathway**. *Biochem Biophys Res Commun* (2018) **498** 1037-1044. DOI: 10.1016/j.bbrc.2018.03.110
16. Domper Bardaji F, Gil Rendo A, Illescas Fernandez‐Bermejo S. **An assessment of bacterial overgrowth and translocation in the non‐alcoholic fatty liver of patients with morbid obesity**. *Rev Esp Enferm Dig* (2019) **111** 294-300. DOI: 10.17235/reed.2019.5942/2018
17. Park EJ, Lee JH, Yu GY. **Dietary and genetic obesity promote liver inflammation and tumorigenesis by enhancing IL‐6 and TNF expression**. *Cell* (2010) **140** 197-208. DOI: 10.1016/j.cell.2009.12.052
18. Amicone L, Marchetti A. **Microenvironment and tumor cells: two targets for new molecular therapies of hepatocellular carcinoma**. *Transl Gastroenterol Hepatol* (2018) **3** 24. DOI: 10.21037/tgh.2018.04.05
19. Wang W, Spitz MR, Yang H, Lu C, Stewart DJ, Wu X. **Genetic variants in cell cycle control pathway confer susceptibility to lung cancer**. *Clin Cancer Res* (2007) **13** 5974-5981. DOI: 10.1158/1078-0432.CCR-07-0113
20. Ye Y, Yang H, Grossman HB, Dinney C, Wu X, Gu J. **Genetic variants in cell cycle control pathway confer susceptibility to bladder cancer**. *Cancer* (2008) **112** 2467-2474. DOI: 10.1002/cncr.23472
21. Yurgelun MB, Kulke MH, Fuchs CS. **Cancer susceptibility gene mutations in individuals with colorectal cancer**. *J Clin Oncol* (2017) **35** 1086-1095. DOI: 10.1200/JCO.2016.71.0012
22. Zhu H, Sun Q, Tan C. **Tacrolimus promotes hepatocellular carcinoma and enhances CXCR4/SDF1alpha expression in vivo. Research Support, Non‐U.S. Gov't**. *Mol Med Rep* (2014) **10** 585-592. DOI: 10.3892/mmr.2014.2302
23. Suzuki H, Chikazawa N, Tasaka T. **Intratumoral CD8(+) T/FOXP3 (+) cell ratio is a predictive marker for survival in patients with colorectal cancer**. *Cancer Immunol Immunother* (2010) **59** 653-661. DOI: 10.1007/s00262-009-0781-9
24. Yokoyama I, Hayashi S, Kobayashi T. **Immunosuppressive drugs and their effect on experimental tumor growth**. *Transpl Int* (1995) **8** 251-255. PMID: 7546145
25. Yokoyama I, Hayashi S, Sato E. **Enhancement of tumor proliferation by cyclosporine A in early phase of experimental hepatic metastasis**. *Jpn J Cancer Res* (1994) **85** 704-709. PMID: 8071112
26. Datta D, Contreras AG, Basu A. **Calcineurin inhibitors activate the proto‐oncogene Ras and promote protumorigenic signals in renal cancer cells**. *Cancer Res* (2009) **69** 8902-8909. DOI: 10.1158/0008-5472.CAN-09-1404
27. Hojo M, Morimoto T, Maluccio M. **Cyclosporine induces cancer progression by a cell‐autonomous mechanism**. *Nature* (1999) **397** 530-534. DOI: 10.1038/17401
28. Datta D, Contreras AG, Grimm M, Waaga‐Gasser AM, Briscoe DM, Pal S. **Calcineurin inhibitors modulate CXCR3 splice variant expression and mediate renal cancer progression**. *J Am Soc Nephrol* (2008) **19** 2437-2446. DOI: 10.1681/ASN.2008040394
29. Basu A, Contreras AG, Datta D. **Overexpression of vascular endothelial growth factor and the development of post‐transplantation cancer**. *Cancer Res* (2008) **68** 5689-5698. DOI: 10.1158/0008-5472.CAN-07-6603
30. Schreiber SL. **Chemistry and biology of the immunophilins and their immunosuppressive ligands**. *Science* (1991) **251** 283-287. DOI: 10.1126/science.1702904
31. Laplante M, Sabatini DM. **mTOR signaling in growth control and disease**. *Cell* (2012) **149** 274-293. DOI: 10.1016/j.cell.2012.03.017
32. Zhou J, Fan J, Wang Z. **Conversion to sirolimus immunosuppression in liver transplantation recipients with hepatocellular carcinoma: report of an initial experience**. *World J Gastroenterol* (2006) **12** 3114-3118. DOI: 10.3748/wjg.v12.i19.3114
33. Ling S, Feng T, Zhan Q. **Sirolimus‐based immunosuppression improves outcomes in liver transplantation recipients with hepatocellular carcinoma beyond the Hangzhou criteria**. *Ann Transl Med* (2020) **8** 80. DOI: 10.21037/atm.2020.01.10
34. Chinnakotla S, Davis GL, Vasani S. **Impact of sirolimus on the recurrence of hepatocellular carcinoma after liver transplantation**. *Liver Transpl* (2009) **15** 1834-1842. DOI: 10.1002/lt.21953
35. Ye Q, Ling S, Jiang G. **Sirolimus‐based immunosuppression improves the prognosis of liver Transplantation Recipients with low TSC1/2 expression in hepatocellular carcinoma beyond the Milan Criteria**. *Eur J Surg Oncol* (2021) **47** 2533-2542. DOI: 10.1016/j.ejso.2021.04.001
36. Guba M, Graeb C, Jauch KW, Geissler EK. **Pro‐ and anti‐cancer effects of immunosuppressive agents used in organ transplantation**. *Transplantation* (2004) **77** 1777-1782. DOI: 10.1097/01.tp.0000120181.89206.54
37. Mathew JM, Kurian S, Cravedi P. **Immune and gene expression profiling during tacrolimus to everolimus conversion early after liver transplantation**. *Hum Immunol* (2021) **82** 81-88. DOI: 10.1016/j.humimm.2020.10.012
|
---
title: Growth in eligibility criteria content and failure to accrue among National
Cancer Institute (NCI)‐affiliated clinical trials
authors:
- John S. Peterson
- Deborah Plana
- Danielle S. Bitterman
- Skyler Bryce Johnson
- Hugo J. W. L. Aerts
- Benjamin Harris Kann
journal: Cancer Medicine
year: 2022
pmcid: PMC9972031
doi: 10.1002/cam4.5276
license: CC BY 4.0
---
# Growth in eligibility criteria content and failure to accrue among National Cancer Institute (NCI)‐affiliated clinical trials
## Abstract
### Background
Cancer trial accrual is a national priority, yet up to $20\%$ of trials fail to accrue. Trial eligibility criteria growth may be associated with accrual failure. We sought to quantify eligibility criteria growth within National Cancer Institute (NCI)‐affiliated trials and determine impact on accrual.
### Methods
Utilizing the Aggregated Analysis of ClinicalTrials.gov, we analyzed phase II/III interventional NCI‐affiliated trials initiated between 2008 and 2018. Eligibility criteria growth was assessed via number of unique content words within combined inclusion and exclusion criteria. Association between unique word count and accrual failure was evaluated with multivariable logistic regression, adjusting for known predictors of failure. Medical terms associated with accrual failure were identified via natural language processing and categorized.
### Results
Of 1197 trials, 231 ($19.3\%$) failed due to low accrual. Accrual failure rate increased with eligibility criteria growth, from $11.8\%$ in the lowest decile (12–112 words) to $29.4\%$ in the highest decile (445–750 words). Median eligibility criteria increased over time, from 214 (IQR [23, 282]) unique content words in 2008 to 417 (IQR [289, 514]) in 2018 (r 2 = 0.73, $P \leq 0.001$). Eligibility criteria growth was independently associated with accrual failure (OR: 1.09 per decile, $95\%$ CI [1.03–1.15], $$p \leq 0.004$$). Eighteen exclusion criteria categories were significantly associated with accrual failure, including renal, pulmonary, and diabetic, among others (Bonferroni‐corrected $p \leq 0.001$).
### Conclusions
Eligibility criteria content growth is increasing dramatically among NCI‐affiliated trials and is strongly associated with accrual failure. These findings support national initiatives to simplify eligibility criteria and suggest that further efforts are warranted to improve cancer trial accrual.
## INTRODUCTION
Cancer clinical trial enrollment is a national priority. 1 Significant resources are spent on encouraging trial accrual, recognizing that clinical trial enrollment may lead to improved survival over standard of care. 1 Despite these efforts, it is estimated that only $3\%$ of adults with cancer in the United States participate in clinical trials and $24\%$ of trials fail to reach more than half of their recruitment goals. 2, 3, 4 Reasons for failure to accrue are complex, and might include changes in eligibility criteria characteristics. 2, 5 Restrictive eligibility criteria have been implicated in low accrual, and in widening demographic disparities between the patients enrolled in clinical trials and the larger population, which the results of the studies are meant to serve. 6, 7 Overall, it is estimated that one in five cancer patients is ineligible to participate in clinical trials based on eligibility criteria. 2 A prior study investigating a cooperative group's lung cancer trial protocols showed that eligibility criteria were increasing. 8, 9 Broader trends in eligibility criteria among cancer trials remain unexplored, and the relationship between trial accrual failure and eligibility criteria growth remains undefined. Recent developments in natural language processing (NLP), a branch of artificial intelligence that extracts quantitative data from free text, have proven effective in analyzing large volume medical text in situations that would otherwise require prohibitive and error‐prone manual curation processes. 10 Leveraging publicly available clinical trial data and NLP tools, we sought to investigate the impact of eligibility criteria content changes on cancer trial accrual and identify criteria characteristics that are associated with accrual failure.
## Data source and selection
A retrospective study of clinical trial data gathered from the Aggregated Analysis of ClinicalTrials.gov (AACT) and the Cancer Trials Support Unit (CTSU) websites was conducted. The AACT is a daily‐updated, public online relational database containing all protocols and results data on every clinical trial listed on Clinicaltrials. Gov. 11 The Clinical Trial Support Unit (CTSU) hosts up‐to‐date information on cancer clinical trials supported by the National Cancer Institute (NCI). 12 Given the public nature of all data used, no institutional review board approval was required. The AACT and CTSU data were extracted in their entirety on February 2, 2021 for analysis, yielding 365,685 unique studies. AACT data were curated and organized by individual study identifier, and 20 descriptive variables were extracted, including the entire text of each trial's eligibility criteria. The 1197 trials included for analysis were initiated between January 1, 2008 and December 31, 2018, were interventional Phase II or III, affiliated with the National Cancer Institute (NCI), and were first classified as either “Completed” or “Failed” as per the study definition. “ Failed trials” were defined as those whose current status was “Terminated,” “Suspended,” “Withdrawn,” or those who failed to achieve $50\%$ of target enrollment after 2 years (Appendix 1). Accrual failure, the primary endpoint of the study, was defined as failed trials whose reason for failure was specified as an accrual problem and/or trials that failed to achieve $50\%$ of target enrollment after 2 years. This enrollment cut‐off was based on previous literature demonstrating that most clinical trials fail if they do not reach half of the target enrollment after 2 years. 3, 4 Trials were excluded if they were not yet completed and also did not meet our definition of failure.
## Analysis of trial accrual failures
The cause of trial failure was determined using the “why_stopped” data field, which provides a brief explanation of why a study was halted or terminated (Supplementary Tables S1). Previous research has identified specific trial characteristics to be associated with NCI‐affiliated trial failure. 4 We adapted the search terms used in this work to identify cancer types and grouped trials that studied common solid and liquid tumors (Supplementary Table S2); common solid tumors were defined as breast, colorectal, lung, and prostate and common liquid cancers were leukemia, lymphoma, and myeloma. 4 To identify all clinical trials that used an NCI‐approved targeted therapy, we collected the names of all NCI‐approved cancer drugs from www.cancer.gov/about‐cancer/treatment/drugs (accessed January 13, 2021) and then cross‐checked them within each trial's intervention Medical Subject Headings (MeSH). We identified trials that required tissue samples by searching for the phrases “biopsy,” “sample,” “specimen,” “paraffin‐embedded,” “paraffin embedded,” and “tumor tissue” in the eligibility criteria. Trials done in a metastatic setting were found by searching for “metasta” and “stage iv” in the trial title or trial description.
## Analysis of eligibility criteria content
Eligibility criteria content was quantified using number of unique content words, utilizing the natural language processing (NLP) package, Natural Language Tool Kit (NLTK) with Python version 3.9.2 (Python Software Foundation, Python Language Reference, available at http://www.python.org). 13 Specifically, we tokenized individual, unique words in the eligibility criteria content of each trial and removed “stop words,” or common words with little interpretive value (e.g., “and,” “the,” “at”). 14 The number of unique content words in each trial's eligibility criteria was then counted.
The median number of unique content words in eligibility criteria was plotted for clinical trials initiated in each year. The fraction of clinical trials that failed due to low accrual was similarly plotted by initiation year. Accrual failure rate was analyzed by decile of eligibility criteria unique word count. The association between eligibility criteria content and accrual failure rate was analyzed using univariable and multivariable logistic regression. Multivariable regression included the following variables that had been previously associated with accrual failure among NCI‐affiliated clinical trials between 2000 and 2013: tumor site, biopsy requirement, use of approved targeted therapy, radiation therapy, clinical trial phase, and metastatic setting. 4 Odds ratios with $95\%$ confidence intervals were calculated, and a p value of <0.05 was considered statistically significant. An analogous analysis was also performed using the number of individual eligibility criteria as a surrogate for complexity, instead of unique word count. Additionally, we conducted regression analyses investigating the association between accrual failure and unique content words within inclusion criteria and exclusion criteria separately. Statistical analyses were performed using R software, version 3.6.0 (R Project, Vienna, Austria).
A sensitivity analysis was performed using a more permissive definition of “completion”, including all trials whose status was “Completed,” or “Recruiting,” or “Active, not recruiting” with greater than $50\%$ accrual, so as to test whether the trends identified would remain significant (Appendix 2).
## Medical term association with accrual failure and feature importance
The specialized biomedical NLP library, ScispaCy was used to identify biomedical terms within the exclusion criteria in each clinical trial that could be associated with accrual failure (Appendix 1). Upon qualitative review of trial exclusion and inclusion criteria individually, it was observed that there was substantial variation in the way in which the inclusion criteria were written—sometimes inclusively, sometimes exclusively, and sometimes neutral. This variation was far less prevalent among exclusion criteria, which were primarily list of barriers to enrollment. For this reason, we chose to conduct this analysis only on exclusion criteria to capture terms that were restrictive to enrollment. ScispaCy is an open‐source software package built on the Python‐based spaCy model and retrained on large volumes of biomedical text from the United Medical Language System (UMLS), RxNorm, GeneOntology, Medical Subject Headings (MeSH), and the Human Phenotype Ontology. 15 Several specialized pipelines are available through ScispaCy; we opted to utilize the model “en_core_sci_md,” which offers name entity recognition for approximately 360,000 terms and 50,000 word vectors. The en_core_sci_md pipeline identified 12,870 unique n‐grams of five words or less. To increase processing speed, all n‐grams that only appeared in the dataset once were excluded leaving 5909 n‐grams. Clinical terms that appeared significantly more frequently in accrual failure trials were identified via two‐sided chi‐squared tests of independence with post hoc Bonferroni correction for multiple hypothesis testing at a significance level of $$p \leq 6.7$$ × 10−6. The resulting biomedical terms were then reviewed by authors (JP, BK) for medical relevancy and qualitatively grouped into thematic clinical categories (Table 2).
Given the hypothesized complex and non‐linear relationships between exclusion clinical categories and accrual failure, we chose to model accrual failure and analyze category predictive importance with machine learning, using a gradient‐boosted trees classifier via the Python XGboost package. 16 As model input, we utilized the clinical categories referenced above as categorical variables and also included a continuous variable representing the sum of the total number of medical terms in a given trial. The data were randomly divided into a training set ($80\%$) and test set ($20\%$), stratified by accrual failure. Model hyperparameters were optimized using a grid search with 10‐fold cross validation (Appendix 1). The model with the highest performing area under the receiver operating characteristic curve (AUC) was chosen for testing. We calculated each feature's importance via the Gini (impurity‐based) importance. 17
## Trial failure rate and reasons
A total of 1197 trials met our inclusion criteria for analysis (Figure 1). Of these, 405 ($33.8\%$) trials failed. The following categories were identified as the most common reasons for failure: low accrual ($$n = 231$$; $57.0\%$), an administrative decision ($6.7\%$), poor interim results ($5.4\%$), funding issues ($4.4\%$), logistical challenges ($4.0\%$), toxicity and safety concerns ($3.2\%$), and competing clinical trials ($1.0\%$) (Supplementary Table S1). The proportion of trials that failed due to poor accrual increased over time, while the proportion of trial failures due to non‐accrual reasons stayed fairly constant (Figure 2A). There were 42 ($10.4\%$) trials for which no reason was given for closure and 28 ($6.9\%$) closed for other reasons.
**FIGURE 1:** *Data Selection and organization flowchart* **FIGURE 2:** *Trial failure rates and increasing eligibility criteria content among NCI‐affiliated cancer trials between January 1, 2008 and December 31, 2018 (n = 1197 trials). (A) Trial failure rates and reasons for failure among trials by initiation year. (B) Increase in median number of unique content words by trial initiation year*
## Trends in eligibility criteria content
The median number of unique content words in trial eligibility criteria increased by $95\%$ over a 10‐year period, from 214 (IQR: 23, 289) in 2008 to 416 (IQR: 289, 514) in 2018 ($$p \leq 0.0008$$, r 2 = 0.732; Figure 2B). Trial failure rate was associated with increased unique word count decile from $11.8\%$ in the first decile to $29.4\%$ in the tenth decile (r 2 = 0.529, Figure 3).
**FIGURE 3:** *Rates of low‐accrual failure for each decile of unique word count of eligibility criteria of NCI‐affiliated cancer trials (n = 1197 trials)*
## Predictors of trial accrual failure
On multivariable analysis, unique word count decile was associated with accrual failure (OR 1.09, $95\%$ CI: [1.03–1.15]; $$p \leq 0.004$$), as was phase (Phase III vs. Phase II, OR: 1.74, $95\%$ CI: [1.13–2.64]; $$p \leq 0.01$$) (Table 1). Total number of eligibility criteria was also independently associated with accrual failure, though the association was not as strong as that of unique word count (Supplementary Tables S4 and S5).
**TABLE 1**
| Variable | OR (95% CI) | p value |
| --- | --- | --- |
| Number of unique content words (per decile) | 1.09 (1.03–1.15) | 0.004 |
| Common tumors | 1.13 (0.84–1.52) | 0.41 |
| Target therapy as intervention | 1.32 (0.91–1.95) | 0.15 |
| Metastatic setting | 1.26 (0.91–1.74) | 0.16 |
| Tissue sample required | 1.00 (0.73–1.36) | 0.99 |
| Radiation therapy as intervention | 0.85 (0.24–2.35) | 0.78 |
| Phase III (vs. Phase II) | 1.74 (1.13–2.64) | 0.01 |
## Clinical categories within exclusion criteria and association with accrual failure
After removal of trials that did not have distinct exclusion criteria section in the AACT, 887 clinical trials remained for analysis. After extraction of biomedical entities and statistical analysis, we found 276 terms out of 12,870 terms that appeared more frequently in trials with accrual failures (Bonferroni‐corrected threshold of $p \leq 6.72$ × 10−6, Appendix 1). Of these, 128 terms were grouped into the following 18 categories: “age,” “allergy and immunology,” “bleeding and coagulopathy,” “cardiovascular,” “chemotherapy,” “gastrointestinal,” “reproductive,” “hematology/oncology,” “hepatic,” “imaging,” “infection,” “diabetes,” “neuropsychiatric,” “orthopedic,” “pulmonary,” “radiation therapy,” “renal,” and “surgery” (Table 2). The remaining 148 terms were excluded from categorization as they were ambiguous, nonspecific, or were effectively stop words (e.g., “course,” “criteria,” “associated with”) (Appendix 2). Following optimal fit with gradient‐boosted trees classifier, feature importance analysis demonstrated that quantity of medical terms present was the most important feature. Other important exclusion criteria categories were renal, pulmonary, immunologic, diabetic, and age restrictions (Supplementary Figure S2). Sensitivity analyses of exclusion and inclusion criteria quantified separately demonstrated a strong association between accrual failure and increasing unique content words of exclusion criteria, while association with inclusion criteria was not statistically significant (Appendix 3). Sensitivity analysis including trials with unknown phase demonstrated that a strong association between eligibility criteria unique content words and accrual failure was maintained (Appendix 4).
**TABLE 2**
| Category | Medical terms | Successful no. (%) | Failed no. (%) | p value |
| --- | --- | --- | --- | --- |
| Reproductive | Breast, breastfeeding, cervical, childbearing, childbearing potential, contraception, female, females, lactating, mother, potential, pregnancy test, pregnant, pregnant women, women | 617 (86.1%) | 159 (93.5%) | 9.69 E‐61 |
| Infectious | Abscess, active, active infection, antibiotics, antiretroviral, antiretroviral therapy, hiv, human, human immunodeficiency, human immunodeficiency virus, immunodeficiency, immunodeficiency virus, infection, infections, viral, virus | 585 (81.6%) | 150 (88.2%) | 6.17 E‐58 |
| Hematology/oncology | Cancer, carcinoma, in situ, invasive, leukemia, malignancy, metastases, metastatic, prostate cancer, skin cancer, squamous, squamous cell, tumor | 541 (75.5%) | 140 (82.4%) | 2.75 E‐53 |
| Allergy and immunology | Allergic, allergic reactions, allergy, corticosteroids, hypersensitivity, immunotherapy, inflammatory, steroids | 504 (70.3%) | 127 (74.7%) | 6.50 E‐51 |
| Cardiovascular | Angina, angina pectoris, arrhythmia, blood pressure, cardiac, cardiac arrhythmia, cardiac disease, congestive, congestive heart failure, coronary, ejection, ejection fraction, failure, fraction, heart, heart failure, infarction, myocardial, myocardial infarction, nyha, pectoris, systolic blood pressure, unstable, unstable angina, unstable angina pectoris, venous, ventricular, ventricular arrhythmias, york, york heart | 507 (70.7%) | 134 (78.8%) | 3.98 E‐49 |
| Neuropsychiatric | Brain, central, central nervous system, cns, neurologic, neuropathy, nervous system, psychiatric, psychiatric illness | 479 (66.8%) | 123 (72.4%) | 1.06 E‐47 |
| Age | Age | 488 (68.1%) | 131 (77.1%) | 1.08 E‐46 |
| Surgery | Allogeneic, major surgery, resection, surgery, surgical, transplant | 366 (51.0%) | 94 (55.3%) | 7.43 E‐37 |
| Chemotherapy | Adjuvant, chemotherapy, mitomycin, nitrosureas | 343 (47.8%) | 84 (49.4%) | 4.87 E‐36 |
| Radiation therapy | Radiation, radiation therapy, radiotherapy | 344 (48.0%) | 95 (55.9%) | 1.43 E‐32 |
| Hepatic | Bilirubin, hepatic, hepatitis, liver | 312 (43.5%) | 84 (49.4%) | 2.16 E‐30 |
| Gastrointestinal | Bowel, gastrointestinal, obstruction, oral, perforation, swallow, ulcer | 306 (42.7%) | 86 (50.6%) | 1.10 E‐28 |
| Bleeding and coagulopathy | Anticoagulation, bleeding diathesis, wound | 129 (18.0%) | 36 (21.2%) | 4.49 E‐13 |
| Renal | Creatinine, renal | 133 (19.4%) | 47 (27.6%) | 1.52 E‐11 |
| Pulmonary | Pulmonary | 179 (18.5%) | 44 (25.9%) | 2.24 E‐11 |
| Orthopedic | Bone, bone fracture, fracture | 119 (16.6%) | 37 (21.8%) | 5.19 E‐11 |
| Imaging | Imaging, mri | 89 (12.4%) | 22 (12.9%) | 2.03 E‐10 |
| Diabetes | Diabetes | 107 (14.9%) | 35 (20.6%) | 1.52 E‐09 |
| Median no. of terms | All terms (n = 128) | 93 | 99 | 2.39 E‐03 |
## DISCUSSION
In this large study of NCI‐affiliated cancer trials, we found that the incidence of accrual failures is rising and strongly associated with increasing eligibility criteria content. This association of content growth with accrual failure persisted after adjustment for multiple known contributors to accrual failure. We additionally demonstrated how natural language processing and machine learning can be leveraged to determine specific exclusionary biomedical terms that are most associated with accrual failure. We found that, more than the quality of the criteria, it was the quantity of eligibility criteria content that most strongly associated with accrual failure. We interrogated several surrogates for criteria complexity and robustly demonstrated their association with accrual failure: unique content word decile, number of criteria, and the number of medical term categories represented in the exclusion criteria. Our study advances the understanding of eligibility criteria as barriers to trial accrual and supports ongoing efforts to simplify eligibility criteria.
Our findings of increased accrual failures are consistent with prior studies. In 2014, Stenslund et al. reported that poor enrollment was the most common cause for failure among the clinical cancer trials initiated by the NCI Cooperative Group between 2005 and 2011, responsible for $36.3\%$ of these 935 failures. 18 The causes of increased accrual failures are likely multifactorial and may include the increasingly complex and precision‐based nature of oncology care, the outpacing of trials compared to number of patients, the funding‐related issues for non‐industry sponsored trial, and eligibility criteria characteristics. 19, 20, 21 Recently, there has been an increasing number of precision oncology trials, which inherently come with more stringent eligibility requirements. Our study suggests that eligibility content quantity, in and of itself, as a surrogate for eligibility exclusivity, is associated with poor accrual. Given that each criterion introduces a potential restriction on enrollment population that trial staff members must assess for each patient prior to registration, this association with poor accrual is plausible. Intriguingly, the data suggest no clear threshold for risk of accrual failure with unique content words, but a relatively linear correlation. This suggests that most, if not all, cancer trials may benefit from streamlining of eligibility criteria to improve trial accrual, particularly in when it comes to baseline patient‐centric (as opposed to tumor‐centric) factors, such as comorbidities and demographics.
Organizing content into medical entity categories allowed us to probe the impact of specific exclusion criteria themes on accrual. NLP allowed for the identification of lexical associations with low‐accrual failure—associations that corroborate contemporary research and expert opinion. Exclusions pertaining to age, HIV status, and renal function, for example, are all identified as traditional areas of exclusion in the 2017 ASCO‐Friends recommendations for enrollment expansion, and each individually appeared more frequently among the exclusion criteria of low‐accruing trials in our study. 22, 23, 24 Likewise, reproductive factors, like pregnancy and breastfeeding, were more represented in the exclusion criteria of low‐accruing trials. Demographic disparities, including by race and geographic region, between the participants in clinical cancer trials and the rest of the cancer population are well‐documented. 25, 26 In 2004, Murthy et al. found lower enrollment rates among elderly patients and females compared to their younger and male counterparts. 27 More recently, Ludmir et al. found cancer trial participants to be 6.49 years younger on average than the general cancer population and the disparity to be growing by about 10 weeks each year. 21 We additionally identified accrual failure associations with several restriction categories that had not been documented previously, such as diabetes and neuropsychiatric conditions. These themes and the others identified in our analysis should be explored in greater depth in future work.
Our findings not only largely support ongoing efforts by national organizations to reduce barriers to cancer trial enrollment, but also highlight that more may be needed to reverse the alarming trend of increasing accrual failure. Beyond the general simplification of criteria, our results encourage trialists to consider certain clinical and demographic characteristics of the population, such as age, sex, and comorbidity restrictions, when designing eligibility criteria. Our results demonstrated that the presence of renal, pulmonary, immunologic, and diabetic restrictions was particularly associated with accrual failure and highlight that there should be attempts to include patients with these comorbidities if sufficiently safe to do so. When lab cut‐offs are placed in the exclusion criteria, levels that balance inclusivity and safety are recommended.
This study is not without limitations. While we found a robust association between eligibility criteria growth and accrual failure, we were not practically able to control for all potential confounders. Given the retrospective nature of the study, we cannot prove a causal relationship between eligibility criteria and accrual failure, and as discussed above, there are many contributing factors to accrual failure. Despite this, there is a plausible basis for the link between eligibility criteria complexity and accrual, and we have mitigated the potential for confounding by limiting our sample to Phases II and III, interventional NCI‐affiliated trials and conducting a multivariable analysis that controlled for numerous established risk factors of low accrual. While reducing confounding, we recognize that this limited sample makes the findings less generalizable to other type of cancer trials, including single‐institution and industry‐sponsored trial, as well as those enrolling pediatric populations or rare cancers, and this should be explored in future studies. Additionally, it is not possible to track accrual in real time via the AACT data. Thus, while our definition of accrual failure (<$50\%$ 2‐year post‐initiation) is a definition based on best available evidence, it may have overestimated accrual failure for trials with initially slow, but eventually successful enrollment, particularly for trials initiated in the later years of the study period. Notwithstanding, low enrollment was the most common reason for trial failure every year between 2008 and 2018, and while other causes of failure remained relatively stable, low‐accrual failure showed a positive trend between 2008 and 2016. This remained the case during the sensitivity analysis, using a more liberal definition of “completion” (Appendix 2). Regarding the analysis of medical term categories, the NLP tools we used are limited in their ability to capture contextual relationships between words and sentences that may modify the effects of specific terms and categories on accrual. More advanced applications of NLP, such as use of deep learning‐based language models pretrained on large textual datasets may provide a means to incorporate more complex text relationships. 28 Despite the limitations, we believe these findings highlight the strong association of eligibility criteria on patient accrual and provide valuable information for patients, investigators, government agencies, and advocates involved in the design and execution of clinical trials.
## CONCLUSIONS
Eligibility criteria content among NCI‐affiliated clinical cancer trials is increasing in quantity and complexity, and this growth is strongly associated with trial accrual failure. Certain clinical exclusions, such as organ dysfunction, age, and neuropsychiatric conditions, were identified as particularly associated with accrual failure. These findings support national recommendations to simplify eligibility criteria. They suggest that further efforts may be needed to actualize these recommendations and improve cancer trial accrual.
## AUTHOR CONTRIBUTIONS
Writing original draft—JSP. Writing, editing, and revision—JSP, BHK, DP, DSB, HJWLA, SBJ. Methodology—BHK, JSP. Data collection—JSP, BHK. Data analysis—JSP, BHK.
## FUNDING INFORMATION
Not applicable.
## ETHICS STATEMENT
This study did not involve interaction with human subjects and was therefore deemed IRB exempt.
## DATA AVAILABILITY STATEMENT
All data are publicly accessible via the AACT and CTSU. Derived datasets used in the study will be shared on reasonable request to the corresponding author.
## References
1. Chow CJ, Habermann EB, Abraham A. **Does enrollment in cancer trials improve survival?**. *J Am Coll Surg* (2013.0) **216** 774-780. DOI: 10.1016/j.jamcollsurg.2012.12.036
2. Unger JM, Vaidya R, Hershman DL, Minasian LM, Fleury ME. **Systematic review and meta‐analysis of the magnitude of structural, clinical, and physician and patient barriers to cancer clinical trial participation**. *JNCI J Natl Cancer Inst* (2019.0) **111** 245-255. DOI: 10.1093/jnci/djy221
3. Schroen AT. **Clinical trial development as a predictor of accrual performance—letter**. *Clin Cancer Res* (2011.0) **17** 4614. DOI: 10.1158/1078-0432.CCR-10-3418
4. Bennette CS, Ramsey SD, McDermott CL, Carlson JJ, Basu A, Veenstra DL. **Predicting low accrual in the National Cancer Institute's cooperative group clinical trials**. *JNCI J Natl Cancer Inst* (2016.0) **108**. DOI: 10.1093/jnci/djv324
5. Treweek S, Lockhart P, Pitkethly M. **Methods to improve recruitment to randomised controlled trials: Cochrane systematic review and meta‐analysis**. *BMJ Open* (2013.0) **3**. DOI: 10.1136/bmjopen-2012-002360
6. Abbasi J. **Older patients (still) left out of cancer clinical trials**. *JAMA* (2019.0) **322** 1751-1753. DOI: 10.1001/jama.2019.17016
7. Singh H, Kanapuru B, Smith C. **FDA analysis of enrollment of older adults in clinical trials for cancer drug registration: a 10‐year experience by the U.S. Food and Drug Administration**. *J Clin Oncol* (2017.0) **35**. DOI: 10.1200/JCO.2017.35.15_suppl.10009
8. Garcia S, Bisen A, Yan J. **Thoracic oncology clinical trial eligibility criteria and requirements continue to increase in number and complexity**. *J Thorac Oncol* (2017.0) **12** 1489-1495. DOI: 10.1016/j.jtho.2017.07.020
9. Bradley J, Kelly K, Stinchcombe TE. **The ever‐increasing number of trial eligibility criteria: time to bend the curve**. *J Thorac Oncol* (2017.0) **12** 1459-1460. DOI: 10.1016/j.jtho.2017.08.003
10. Spasic I, Nenadic G. **Clinical text data in machine learning: systematic review**. *JMIR Med Inform* (2020.0) **8**. DOI: 10.2196/17984
11. **Database | Clinical Trials Transformation Initiative**
12. **Accessed July 17, 2021**
13. Bird S, Klein E, Loper E. *Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit* (2009.0)
14. Manning C, Raghavan P, Schutze H. *Introduction to Information Retrieval* (2009.0)
15. Neumann M, King D, Beltagy I, Ammar W. **ScispaCy: fast and robust models for biomedical natural language processing**. *Proc 18th BioNLP Workshop Shar Task* (2019.0) 319-327. DOI: 10.18653/v1/W19-5034
16. Chen T, Guestrin C. **XGBoost: a scalable tree boosting system**. *Proc 22nd ACM SIGKDD Int Conf Knowl Discov Data Min* (2016.0) 785-794. DOI: 10.1145/2939672.2939785
17. Menze BH, Kelm BM, Masuch R. **A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data**. *BMC Bioinformatics* (2009.0) **10** 213. DOI: 10.1186/1471-2105-10-213
18. Stensland KD, McBride RB, Latif A. **Adult cancer clinical trials that fail to complete: an epidemic?**. *JNCI J Natl Cancer Inst* (2014.0) **106** 1-6. DOI: 10.1093/jnci/dju229
19. Kolata G. *A cancer conundrum: too many drug trials, too few patients* (2017.0)
20. Kann BH, Johnson SB, Aerts HJWL, Mak RH, Nguyen PL. **Changes in length and complexity of clinical practice guidelines in oncology, 1996‐2019**. *JAMA Netw Open* (2020.0) **3**. DOI: 10.1001/jamanetworkopen.2020.0841
21. Ludmir EB, Mainwaring W, Lin TA. **Factors associated with age disparities among cancer clinical trial participants**. *JAMA Oncol* (2019.0) **5** 1769-1773. DOI: 10.1001/jamaoncol.2019.2055
22. Lichtman SM, Harvey RD, Damiette Smit MA. **Modernizing clinical trial eligibility criteria: recommendations of the American Society of Clinical Oncology‐friends of cancer research organ dysfunction, prior or concurrent malignancy, and comorbidities working group**. *J Clin Oncol off J Am Soc Clin Oncol* (2017.0) **35** 3753-3759. DOI: 10.1200/JCO.2017.74.4102
23. Lin NU, Prowell T, Tan AR. **Modernizing clinical trial eligibility criteria: recommendations of the American Society of Clinical Oncology‐friends of cancer research brain metastases working group**. *J Clin Oncol off J Am Soc Clin Oncol* (2017.0) **35** 3760-3773. DOI: 10.1200/JCO.2017.74.0761
24. Uldrick TS, Ison G, Rudek MA. **Modernizing clinical trial eligibility criteria: recommendations of the American Society of Clinical Oncology‐friends of cancer research HIV working group**. *J Clin Oncol off J Am Soc Clin Oncol* (2017.0) **35** 3774-3780. DOI: 10.1200/JCO.2017.73.7338
25. Grant SR, Lin TA, Miller AB. **Racial and ethnic disparities among participants in US‐based phase 3 randomized cancer clinical trials**. *JNCI Cancer Spectr* (2020.0) **4**. DOI: 10.1093/jncics/pkaa060
26. Brierley CK, Zabor EC, Komrokji RS. **Low participation rates and disparities in participation in interventional clinical trials for myelodysplastic syndromes**. *Cancer* (2020.0) **126** 4735-4743. PMID: 32767690
27. Murthy VH, Krumholz HM, Gross CP. **Participation in cancer clinical trials: race‐, sex‐, and age‐based disparities**. *JAMA* (2004.0) **291** 2720-2726. DOI: 10.1001/jama.291.22.2720
28. Morin O, Vallières M, Braunstein S. **An artificial intelligence framework integrating longitudinal electronic health records with real‐world data enables continuous pan‐cancer prognostication**. *Nat Cancer* (2021.0) **2** 709-722. PMID: 35121948
|
---
title: BMAL1 promotes colorectal cancer cell migration and invasion through ERK‐ and
JNK‐dependent c‐Myc expression
authors:
- Lina Shan
- Wenqian Zheng
- Bingjun Bai
- Jinghui Hu
- Yiming Lv
- Kangke Chen
- Xiaowei Wang
- Yangtao Pan
- Xuefeng Huang
- Hongbo Zhu
- Sheng Dai
journal: Cancer Medicine
year: 2022
pmcid: PMC9972036
doi: 10.1002/cam4.5129
license: CC BY 4.0
---
# BMAL1 promotes colorectal cancer cell migration and invasion through ERK‐ and JNK‐dependent c‐Myc expression
## Abstract
Our study demonstrate that BMAL1 plays a critical role in cell migration and invasion of CRC cells via MAPK‐c‐Myc pathway. Our results may deepen our understanding in the relationship of BMAL1 and tumorigenic phenotypes and be developed into a promised therapeutic target for the BMAL1‐overexpression CRC.
### Background
Cancer metastasis is still a life threat to patients with colorectal cancer (CRC). Brain and muscle ARNT‐like protein 1 (BMAL1) is an important biological proteins that can regulate the behavior of cancer cells and their response to chemotherapy. However, the role of BMAL1 in the tumorigenic phenotype of CRC remains unclear. Here, we aim to investigate the functional role and mechanisms of BMAL1 in CRC.
### Methods
The mRNA expression of BMAL1 was studied using the Cancer Genome Atlas (TCGA) databases. The protein level in clinical tissues was confirmed by immunohistochemistry (IHC). The effects of BMAL1 on the epithelial‐to‐mesenchymal transition (EMT) and proliferation of CRC cell lines (including BMAL1 overexpressed or silencing cells) were studied by Transwell, wound healing, CCK‐8 and colony formation experiments. A series of experiments were conducted to demonstrate the mechanisms of BMAL1 regulating EMT and cancer proliferation in vitro and in vivo.
### Results
We found that BMAL1 expression was closely related to the poor prognosis of CRC. BMAL1 overexpression promoted cell proliferation and migration. Mechanistically, we found that BMAL1 may activate the epithelial‐to‐mesenchymal transition (EMT) pathway and induce the β‐catenin release further promotes the expression of oncogene c‐Myc and the migration of colorectal cells by activating MAPK pathway. However, BMAL1 silencing achieved the opposite effect. In addition, blocking MAPK‐signaling pathway with specific inhibitors of ERK$\frac{1}{2}$ and JNK can also downregulate the expressions of c‐Myc in vitro. Taken together, these results suggested that the BMAL1/ c‐Myc‐signaling pathway may regulate the metastasis of CRC through the JNK/ERK$\frac{1}{2}$ MAPK‐dependent pathway.
### Conclusions
Our study showed that BMAL1 promotes CRC metastasis through MAPK‐c‐Myc pathway. These results deepen our understanding of the relationship between BMAL1 and tumorigenic phenotypes, which may become a promising therapeutic target for BMAL1 overexpressing CRC.
## BACKGROUND
CRC is one of the most aggressive cancers in European and American countries. The incidence and mortality rate of CRC also have an increasing trend in China. 1, 2 CRC is a common and complex disease caused by genetic and environmental factors and their interactions. As the early symptoms of CRC are not obvious, tumor metastasis is common at diagnosis and during follow‐up. It is well known that distant invasion and metastasis are adverse prognostic factors leading to CRC‐related mortality. In recent years, there has been no obvious breakthroughs in surgical technology or molecular targeted therapy. Therefore, searching for effective methods and key pathogenic mechanisms are crucial for improving colorectal cancer management. 3, 4, 5 BMAL1, one of the important biological clock proteins in mammals, which is located on human chromosome 11. 6, 7 Recently, many studies have shown that the biological clock system plays an important role in cancer cell proliferation, apoptosis, growth, metabolism, and tumor treatment. 8 Circadian rhythm may regulate the expression of various genes in multiple cell types, and its disruption may increase the risk of cancer. As an important core clock protein, BMAL1 is closely related to tumorigenesis. Karantanos T and colleagues proposed that the expression of BMAL1, a key clock gene, is more common in colorectal carcinomas, but less common in colorectal adenomas. 9 Additionally, circadian rhythm genes might substantially influence the efficacy and toxicity of some antitumor drugs. 10, 11, 12 According to a recent study, BMAL1 may play a role in acquisition chemotherapy and targeted treatment resistance in patients with CRC. 13 Based on this evidence, we hypothesized that BMAL1 may contribute to tumorigenesis and metastasis of CRC. The study of molecular events driving metastasis is crucial to reveal the mechanisms involving tumorigenesis and metastasis in CRC.
One of the key events of CRC invasion and metastasis is epithelial‐to‐mesenchymal transition (EMT). 14 In the process of cell migration, polarized epithelial cells lose their cell polarity and intercellular connection, and obtain the characteristics of mesenchymal cell. The cancer cells become more invasive so as to result in metastasis. This event is defined as the epithelial‐to‐mesenchymal transition. Recent study have found that BMAL1 was a CRC‐related gene and was associated with poor prognosis. Gain‐ and loss‐of function analyses revealed that BMAL1 play a role in inducing proliferation and metastasis of colorectal cancer cells both in vitro and in vivo. Further functional experiments indicated that BMAL1 induces CRC progression and maintains the EMT phenotype via the MAPK (ERK$\frac{1}{2}$ and JNK)/c‐Myc pathway in CRC. These findings provide a new feasible target for the colorectal cancer treatment.
## Analyses of TCGA and public gene expression datasets
The datasets generated and analyzed during the current study are available in the TCGA database (https://www.proteinatlas.org) (ENSG00000133794). We obtained the RNA sequencing data and paired clinical information of CRC samples from the TCGA database. Then, we used the cut‐off value recommended by the website for separating the high and low levels of BMAL1 expression in the survival analysis and performed a survival analysis to determine the correlation between BMAL1 mRNA expression and the prognosis of the 434 cases with colon adenocarcinoma.
## Patients and samples
The tissue samples including tumor and corresponding normal tissues were collected from 84 patients disgnosed with CRC by Sir Run Run Shaw Hospital of Zhejiang University School of Medicine. No other malignancies were observed in these patients. The written informed consent of patient and the approval from the Institutional Research Ethics Committee were obtained.
## Cell culture and transfections
RKO, HCT116, and SW480 colorectal cancer cell lines were purchased from the American Type Culture Collection (ATCC) and cultured in RPMI1640 or DMEM containing $10\%$ FBS. Ubi‐MCS‐3FLAG‐SV40‐EGFP‐IRES‐puromycin‐BMAL1 expression lentiviruses, hU6‐MCS‐Ubiquitin‐EGFP‐IRES‐puromycin BMAL1 inhibitor lentiviruses and their corresponding negative controls were purchased from Shanghai Genechem Co. These lentiviruses were transfected to establish stable cell lines. After 10 days of culture with puromycin, the stably expressed cell lines were selected. After selection, stably expressed cell lines were harvested and confirmed using qRT–PCR and Western blotting assays.
## Immunohistochemistry
BMAL1 Immunohistochemistry (IHC) assay was based on the previous studies. 15 In short, the BMAL1 expression of clinical tissues was assessed by IHC analysis using anti‐BMAL1 antibody (1:200; Proteintech). The primary antibody was cultured with tissue sections overnight at 4°C. The final evaluation criteria depend on the percentage of positive cells and the degree of staining. The staining intensity score was defined and classified as negative, weak, moderate, or high‐strong, which were counted as 0, 1, 2, and 3, respectively. The cell proportion score was defined as follows: <$10\%$ = 0, >$10\%$ to $25\%$ = 1, >$25\%$ to $50\%$ = 2, >$50\%$ to $75\%$ = 3, and >$75\%$ = 4. Finally, these two scores were then calculated by multiplying. High BMAL1 expression: score > 4; low BMAL1 expression: the final score was ≤4. The operation and the scoring of the c‐Myc staining were the same as BMAL1. 16 If the final score was ≥4, the sample was considered c‐Myc‐positive.
## Western blotting
Total‐cell protein was extracted using RIPA buffer with protease inhibitors and phosphatase inhibitor (Life Technologies). The BCA Protein Assay Kit (Beyotime Bio‐ technology) was used to quantify the protein concentration. An equal amount of protein was loaded onto $8\%$–$12\%$ SDS‐PAGE and then transferred to PVDF membrance (Hercules bio rad). After incubation with $5\%$ skimmed milk at room temperature for 1 h, the membrance was incubated with the following antibodies: anti‐BMAL1, anti‐E‐cadherin, anti‐N‐cadherin, anti‐vimentin, anti‐β‐catenin, anti‐phospho‐MEK, anti‐phospho‐ERK$\frac{1}{2}$ (Cell Signaling Technology), anti‐p38, anti‐JNK, anti‐c‐Myc, anti‐RAF, and anti‐Ki67 (Abcam). Anti‐GAPDH mouse monoclonal antibody (Cell Signaling Technology) was used as load control. The ECL system was used for blots (Amersham Biosciences).
## Colony formation assay of cell proliferation
A total of 500 transfected cells were incubated in 6 cm dishes for 10 days. Then, cell clones were fixed with $10\%$ formaldehyde for 5 minutes, stained with $1\%$ crystal violet and counted with an optical microscope. Each assay was performed in triplicate.
## Wound healing assay and invasion assay
For the wound healing test, different groups of 1 × 106 RKO, HCT116 and SW480 cells, and cells were cultured to near $90\%$ confluence in the plates. Then scratched with a sterile 1 ml pipette tip. After the damaged cell layer was washed, it was cultured in complete medium without FBS. The wounds were photographed with a light microscope at 0 and 24 h.
Transwell cell migration plates (Corning Incorporated) were used for invasion and migration assays. Transwell migration experiments were carried out using Corning 8‐μm chambers according to the manufacturer's instructions (BD Biosciences). For the invasion assay, cancer cells (5 × 104 cells/well) from different groups (RKO/Control; RKO/BMAL1;RKO/shControl; RKO/shBMAL1; SW480/Control; SW480/BMAL1; SW480/shControl; SW480/shBMAL1) with 200 μl serum‐free medium were placed into the upper chamber, with 600 μl medium supplemented with $10\%$ FBS in the bottom chamber. After 36 h, the migrated cells in the underside of the membrance were immersed and washed with PBS, fixed with $4\%$ paraformaldehyde, stained using $0.1\%$ crystal violet, washed three times with water, and counted under light microscopy.
## Xenograft tumor model
Four‐week‐old Male BALB/c nude mice were placed in an animal facility for 12‐h light on–off cycle at consant room temperature, and then the animals were randomly divided into four groups (6 mice per group). BALB/c nude mice were subcutaneously injected with 5 × 106 viable cells suspended in 200 μl PBS in the right flank. The size of subcutaneous tumor was measured with Vernier calipers on Days 10, 15, 20, 25, 30, and 35. The tumors were dissected and embedded in paraffin after 35 days. The final volume of tumor tissues was calculated using the following equation: tumor length (mm) = (tumor length + tumor width)/2. The tumors were also IHC stained with antibodies against BMAL1, E‐cadhrein, and c‐Myc. Animal studies were approved by the Animal Care Institutional and Use Committee of Sir Run Run Shaw Hospital of Zhejiang University.
## Statistical analysis
All data were analyzed using SPSS version 16.0 statistical software packages (IBM). The data are expressed as means ± standard deviations (SD) to indicate the variability of the data. Chi‐square test or Fisher's exact test were used to compare the correlation between BMAL1 and clinicopathological feactures. Statistically significant differences between groups were compared using the Student's t test. The patients' survival outcomes were evaluated according to the Kaplan–Meier Plotter and compared using log‐rank test. A two‐tailed p‐value <0.05 was statistically significant.
## High BMAL1 expression predicted poor survival of patients with CRC
We analyzed the BMAL1 mRNA information in samples from The Cancer Genome Atlas (TCGA) and studied the clinical relevance of BMAL1. Survival analysis indicated that BMAL1 expression was an independent prognostic factor in patients with CRC ($p \leq 0.05$, Figure 1A). At the same time, in the Sir Run Run Shaw Hospital (SRRSH) cohort, we first evaluated BMAL1 expression level in 84 primary colorectal adenocarcinoma specimens and examined the correlation between BMAL1 expression and clinicopathological parameters. The typical IHC staining of BMAL1 protein in CRC tissues were shown in Figure 1B. Tissues used were obtained from 84 patients with CRC treated between January 2012 and November 2015 in SRRSH. The baseline characteristics of the patients are summarized in Table 1. In addition, $58.3\%$ of patients exhibited BMAL1 positive in primary CRC tumor, which was common among younger patients ($p \leq 0.05$). Importantly, the expression of BMAL1 played an important role in determining the prognosis of patients with CRC ($p \leq 0.05$, Figure 1A). However, univariate Cox regression model revealed that BMAL1 expression was not related to T stage, lymphatic invasion, tumor size or tumor location. Taken together, these results confirmed that BMAL1 was associated with CRC outcomes.
**FIGURE 1:** *High BMAL1 expression was significantly associated with poor prognosis in CRC. (A) Patients in The Cancer Genome Atlas (TCGA) and Sir Run Run Shaw cohorts ($$n = 84$$) were stratified according to the BMAL1 gene expression signature. A total of $58.3\%$ ($\frac{49}{84}$) CRC tissues were BMAL1 overexpression. Kaplan–Meyer overall survival curves of CRC with different BMAL1 genotypes ($p \leq 0.05$). (B) Representative photographs showing immunohistochemical expression of BMAL1 in the adjacent normal tissue (0 = negtive), colorectal cancer and the lymph node. 40× & 200× magnification; 1 = mild, 2 = moderate, 3 = strong.* TABLE_PLACEHOLDER:TABLE 1
## BMAL1‐KD inhibited Ki67 activation and the proliferation of colorectal cancer cells
Three primary CRC cell lines (HCT116, RKO, and SW480) were transfected with recombinant lentivirus in order to increase or silence BMAL1 expression in vitro and study the involvement of BMAL1 in proliferation during colorectal tumorigenesis. The overexpression or knockdown efficacy was quite sufficient, which has been verified by Western blotting (Figure 2A). Using this model, we effectively investigated the role of BMAL1 in cancer cell lines.
**FIGURE 2:** *BMAL1 affects colorectal cancer cells' growth. (A) Representative western blot analysis of BMAL1 and Ki‐67 protein expression in transfected cell lines. (B) SW480 cells transfected with BMAL1 and shBMAL1 were subjected to the CCK‐8 assay after transfection. (C, D) Stably transfected RKO and SW480 cells were seeded onto 6‐well plates. The number of colonies was counted on the 10th day after seeding. (E) Western blots were quantified by ImageJ densitometric analysis and normalized to controls. Data are expressed as mean ± SD (n = 3). *p < 0.05, **p < 0.01 and ***p < 0.001.*
Here, we investigated the biological effect of BMAL1. BMAL1 expression was knocked down in RKO and SW480 CRC cell lines (RKO/sh‐BMAL1 and SW480/sh‐BMAL1) and the BMAL1‐overexpressing cell lines (RKO/BMAL1, SW480/BMAL1, and HCT116/BMAL1) were also constructed, thereby modulating different cellular activities, such as apoptosis, proliferation, cell cycle, and metastasis. The colony formation assay and MTT assay revealed that BMAL1 downregulation significantly decreased cell proliferation compared to that of control cells ($p \leq 0.05$, Figure 2B–D). Ki67 has been widely used as a proliferation marker to assess cell proliferation in cancer research. Western blotting analysis of Ki67 expression in colorectal carcinoma cells revealed that the level of the proliferation‐related protein Ki67 was significantly downregulated in the BMAL1 knockdown cell lines compared with the control cell lines ($p \leq 0.05$, Figure 2A,E). Conversely, BMAL1 overexpression significantly upregulated the Ki‐67 expression in these cell lines. Taken together, these results suggested that BMAL1 increased the proliferation of colorectal cancer cells.
## BMAL1 promotes CRC cell metastasis via the EMT pathway
We next tested whether BMAL1 affects the migration and invasion of CRC cells. Wound healing assays and Transwell assays were performed to assess the effect of BMAL1 on CRC cell motility. RKO/BMAL1 and SW480/BMAL1 cells migrated significantly quicker than the corresponding control cells (Figure 3A,B). Transwell assays showed that BMAL1 markedly promoted the migration and invasive abilities of CRC cells. The opposite results were observed in the BMAL1‐silenced cell lines (Figure 3C,D). Collectively, BMAL1 promoted CRC migration and invasion.
**FIGURE 3:** *Migration activity was regulated by BMAL1. (A, C) Wound‐healing assay showed that migration activity was enhanced by BMAL1. BMAL1 silencing suppressed the migration ability of SW480 cells. (B, D) Transwell assays showed that BMAL1 enhanced the migration and invasion potential of CRC. BMAL1 silencing decreased the migration and invasion of RKO and SW480 cells. (E, G) Western blot assay showed that BMAL1 significantly increased the expression of β‐catenin, N‐cadhrein, vimentin but inhibited E‐cadhrein expression. (F, H) Western blots were quantified by ImageJ densitometric analysis and normalized to controls. Data are expressed as mean ± SD (n = 3). *p < 0.05, **p < 0.01 and ***p < 0.001.*
During the multi‐step process of malignant tumors, which are initially benign, epithelial cells acquire some obvious mesenchymal features, which makes them have the ability to locally spread to adjacent tissues and to distant organs. Much of this phenotypic progression of increased invasiveness depends largely on the activation of EMT. Activation of the epithelial–mesenchymal transition increases the abilities of colorectal cancer cells to migrate, invade and extravasate.
We then determined the expression of EMT markers vimentin, N‐cadherin, and E‐cadherin to confirm whether BMAL1 affected cell motility via the EMT pathway. We observed increases in vimentin and N‐cadherin expression in RKO/BMAL1 and SW480/BMAL1 cells, whereas E‐cadherin level was remarkably decreased (Figure 3E,F). E‐cadherin act as the “caretaker” of the epithelial phenotype and its loss is considered a critical feature of the EMT experience. Additionally, the nuclear accumulation of β‐catenin is an important hallmark of the EMT at the invasive of CRC. Our study indicated that β‐catenin was activated in BMAL1‐positive CRC cell lines. In contrast, BMAL1 silencing reduced the expression of the vimentin, β‐catenin, and N‐cadherin proteins (Figure 3G,H). Taken together, BMAL1 regulated the activation of EMT process and the migration and invasion behavious of colorectal cancer.
## BMAL1 upregulates c‐Myc expression
We speculated that BMAL1 induces cell migration in vitro by moderating the expression of target genes. First, distant metastasis of CRC is driven by the EMT of cancer cells. c‐*Myc is* one of the most important regulators of the EMT that contributes to metastatic processes in CRC and other carcinomas. Additionally, β‐catenin was recently considered an important transcriptional activator of c‐Myc. 17, 18, 19, 20 Upregulation of c‐*Myc is* a hallmark of colorectal cancer. 21, 22, 23 More importantly, previous studies have implied that c‐*Myc is* also a target of aberrant clock gene expression. 24 We hypothesized that c‐Myc may be essential for BMAL1 to promote cell metastasis and activate the EMT pathway. Therefore, c‐Myc expression was investigated in RKO/BMAL1 and SW480/BMAL1 cell lines using Western blotting. c‐Myc expression was significantly upregulated in BMAL1‐positive cell lines. In contrast, c‐Myc expression was remarkably decreased in BMAL1‐silenced cell lines (Figure 4A,B).
**FIGURE 4:** *BMAL1 regulates c‐Myc expression. (A) BMAL1 activated c‐Myc protein expression in RKO/BMAL1 and SW480/BMAL1 cells, which detected by western blot. (B) Western blots were quantified by ImageJ densitometric analysis and normalized to controls. Data are expressed as mean ± SD (n = 3). (C) Immunohistochemical detection of c‐Myc in representative tumor samples, the adjacent normal tissue and lymph node from CRC of the indicated genotypes. 40× & 200× magnification. 1 = mild, 2 = moderate, 3 = strong. *p < 0.05, **p < 0.01 and ***p < 0.001.*
For further confirmation, we next explored whether the same results would be obtained in the clinical samples. We investigated BMAL1 and c‐MYC protein levels via IHC in clinical samples. Among the specimens from patients with colorectal cancer, $72.1\%$ had c‐Myc overexpression (Figure 4C). The prevalence of c‐Myc overexpression was significantly higher in the BMAL1‐positive group than in the BMAL1‐negative group ($p \leq 0.05$). Interestingly, all patients in the BMAL1 overexpression group were c‐Myc‐positive (Table 2). Based on these results, we conclude that a high level of BMAL1 was associated with c‐Myc overexpression. All of the aforementioned findings support the role of c‐Myc as a potential target of BMAL1 that contributes to cell invasion and metastasis.
**TABLE 2**
| Unnamed: 0 | c‐Myc | c‐Myc.1 | Total | p‐value |
| --- | --- | --- | --- | --- |
| | Negative | Positive | Total | p‐value |
| BMAL1 | | | | |
| Negative | 19 | 11 | 30 | |
| Positive | 0 | 38 | 38 | <0.01 |
| Total | 19 | 49 | 68 | |
## BMAL1 activates c‐Myc through the MAPK signaling pathway
From the above results, BMAL1 activates the target gene c‐Myc and the EMT pathway. Next, we would like to address how BMAL1 induces the EMT in CRC cells. To date, little is known about the effect of BMAL1 on intracellular signal transduction in CRC cells. As shown here, BMAL1 promoted the expression of c‐Myc. Therefore, we explored the potential relationship between these two genes and investigated several important signaling pathways. The MAPK pathway is not only a key regulator of the cell cycle distribution, survival, and drug sensitivity. It is also related to cell growth and migration. c‐*Myc is* one of the most important targets of the MAPK signaling pathway. The levels of MAPK pathway related molecules and their phosphorylated forms were detected to determine whether BMAL1 regulates the migration of CRC cells through this pathway. The results of Western blot analyses showed that the levels of RAF, p‐MEK, P‐ERK$\frac{1}{2}$, and P‐JNK (phosphorylated forms of MEK$\frac{1}{2}$, ERK$\frac{1}{2}$ and JNK) were significantly higher in RKO/BMAL1 and SW480/BMAL1 cells, but no changes were detected in P38 levels. In addition, significant changes in the levels of MAPK proteins were attenuated by BMAL1 silencing (Figure 5A,B). MAPK pathway antagonists were used to elucidate whether MAPKs, ERK, and JNK are linked to the BMAL1 stimulated c‐Myc expression. In the presence of ERK$\frac{1}{2}$ (APExBIO PD98059, 10 μM) and JNK inhibitors (SP600125, 10 μM), the overexpression of c‐Myc induced by BMAL1 was attenuated in RKO and SW480 cells, and cell migration was also dramatically decreased (Figure 6A,B), indicating that the ERK/JNK pathway mediated the upregulation of c‐Myc expression and increased cell migration by BMAL1. Hence, we speculated that BMAL1 promoted cell invasion via the ERK/JNK‐cMyc axis.
**FIGURE 5:** *MAPK‐signaling pathway is involved in BMAL1‐induced migration. (A) BMAL1 activated MAPK‐signaling pathway in RKO/BMAL1 and SW480/BMAL1 cells. The related proteins in MAPK‐signaling pathway were detected by western blot. BMAL1 actived the RAF, P‐MEK, P‐ERK, JNK expression, while the P38 was not affected. (B) Data are expressed as mean ± SD (n = 3). *p < 0.05, **p < 0.01 and ***p < 0.001.* **FIGURE 6:** *BMAL1 modulates the c‐Myc expression through ERK‐ and JNK‐dependent signaling pathway. (A) Inhibiting the MAPK pathway suppressed c‐Myc but increased E‐cadhrein expression in vitro using ERK1/2 (APExBIO PD98059, 10 μM) and JNK inhibitor (SP600125, 10 μM). (B) MAPK pathway inhibition markedly decreased the protein expression of c‐Myc. *p < 0.05, **p < 0.01 and ***p < 0.001.*
## BMAL1 silencing inhibited the tumorigenicity of colorectal cancer cells in nude mice
Based on the results described above, silencing BMAL1 in colorectal cancer cell lines significantly inhibited cell proliferation and migration in vitro. A xenograft model was constructed to facilitate an investigation of these findings in vivo. RKO/Ctrl+ cells, RKO/BMAL1 cells, RKO/Ctrl‐ cells, and RKO/shBMAL1 cells were injected into the flanks of female nude mice. The tumors produced by the injection of RKO/BMAL1 cells were significantly larger and heavier than those in the control group after 5 weeks of management. RKO/BMAL1 cells exhibited markedly increased tumor growth in the subcutaneous xenograft model ($p \leq 0.05$, Figure. 7A,B).
**FIGURE 7:** *BMAL1 promotes tumorigenesis and tumor metastasis in mice. (A) BMAL1 knockdown significantly inhibited CRC proliferation in vivo. Cancer cells were injected into the nude mice. The tumors were resected from mice after 35 days. (B) At the indicated times, subcutaneous tumor size (mm) was measured with calipers (mean ± SD, n = 4). (C) Immunohistochemistry staining of BMAL1 and E‐cadhrein in xenograft tissues. 40× & 200× magnification. 1 = mild, 2 = moderate, 3 = strong. *p < 0.05, **p < 0.01 and ***p < 0.001.*
Tissue sections (4‐μm‐thick slice) were cut from paraffin blocks generated after the tumors were measurable. In addition, the IHC staining and quantitative data also confirmed the results, and the percentage of the E‐cadherin staining was significantly increased in the RKO/shBMAL1 injection group (Figure 7C). Collectively, these results indicate that BMAL1 increased tumor cell growth and metastasis in nude mice.
## DISCUSSION
CRC remains one of the most aggressive cancers due to its rapid recurrence and early metastasis. BMAL1 upregulation has been reported in various carcinomas, including colorectal cancer. 25, 26, 27 Some studies reported that BMAL1 upregulation was associated with aggressive clinical phenotype and resistance to chemotherapy or targeted therapy. 28 In contrast, others believed that BMAL1 was a protective factor for CRC, 29 which might increase the sensitivity to oxaliplatin therapy. 30 Our current study supports the role of BMAL1 in tumorgenicity and metastasis of CRC. To date, a few data on the mechanism of BMAL1 as a biomarker of CRC.
The EMT pathway is widely accepted to be a crucial biological process driving the invasion and metastasis of cancer cells. In this report, we provided evidence that increased BMAL1 expression in tumors promoted EMT‐like changes in colorectal cancer cell lines and lead to a dismal prognosis in patients with CRC. Previous studies have confirmed that circadian rhythm‐related genes have a significant influence on tumorigenesis, progression, and drug resistance. In our study, we measured BMAL1 expression levels in tissues of CRC patients. Clinical evidence suggests that BMAL1 overexpression is usually associated with poor outcome, and BMAL1 overexpression is more common in younger patients. Consistently, we investigated the levels of several typical EMT and proliferation markers, such as N‐cadherin, Ki67, β‐catenin, and vimentin. The expression of the proliferation‐related factor Ki67 significantly increased. Conversely, the levels of classical epithelial marker E‐cadherin remarkably decreased. Collectively, these evidences may prove that BMAL1 promotes the proliferate and migrate ability of colorectal cancer cell lines.
Our results reveal that BMAL1 activates the EMT pathway, which is the key and early stage in metastasis and invasion of CRC. As the ablation of E‐cadherin is a key feature of the EMT, cancer cells lose the connections between cells due to the decline of E‐cadherin. After EMT experience, cells gain greater motility to spread into surrounding or even distant tissues. Moreover, β‐catenin has been widely recognized as a major EMT marker that supported tumor attachment to the extracellular matrix. 31, 32 EMT progress is not simply a binary process, but a highly complex program. Under normal conditions, β‐catenin is involved in the connecting between E‐cadherin and actin cytoskeleton. During EMT, β‐catenin separates from the E‐cadherin/β‐catenin complexes and accumulates in the cytoplasm. Specifically, disruption of the E‐cadherin/β‐catenin complex induces β‐catenin release and further avtivates Wnt/β‐catenin pathway. In addition to β‐catenin, c‐*Myc is* also the mediator of EMT and enhances the migration and invasion of CRC cells. Recent studies have shown that c‐Myc mutation or overproduction is related to circadian clock disorder. 33, 34 The circadian clock gene Per2 has been suggested to lead to c‐Myc overexpression and an increased tumor incidence. 35 Okazaki F showed that c‐Myc expression was controlled by the circadian clock in colon cancer cells. 36 *In this* study, we studied the relationship between the BMAL1 and c‐Myc. We found that the level of c‐Myc changed with the expression of BMAL1. This association was confirmed in clinical patients treated at our hospital.
Additionally, the proto oncogene c‐*Myc is* one of the transcriptional targets of β‐catenin,which acts as a key downstream effector of WNT/β‐catenin signaling in several carcinogenesis processes. 37 Our recent study evaluated the c‐Myc and β‐catenin expression in cancer cell lines, confirming the activation of β‐catenin/c‐Myc signaling in BMAL1‐positive colorectal cancer cells. In the BMAL1‐negative colorectal cancer cell lines, the expression of these two oncogenes was significantly decreased. Altogether, our data suggested that c‐Myc may be a target gene of BMAL1 in colon tumorigenesis. Next, we investigated the relationship between BMAL1 and c‐Myc in the CRC cells. c‐Myc was known as an important target gene of the MAPK pathway. 38 MAPK signaling pathway consists of p38 MAPK, extracellular signal‐regulated kinase (ERK) and c‐Jun N‐terminal kinase (JNK). The relationship between MAPK pathway and cell proliferation and invasion in colorectal cancer has been fully studied. 39 Therefore, we analyzed the expression of core proteins in these pathways to determine whether the function of BMAL1 in CRC depends on MAPK signaling pathways. The results showed that the levels of phosphorylated ERK$\frac{1}{2}$, JNK, and MEK$\frac{1}{2}$ in BMAL1 high expression group were significantly higher than BMAL1 silencing group. The invasiveness of the CRC cells was decreased after the administration of the corresponding inhibitors of MAPK signal pathway. Unexpectedly, p38 inhibition did not affect the BMALl‐induced migratory. Therefore, only the ERK/JNK MAPK pathway was activated by high BMAL1 expression in the present study. Next, we further examined the relationship between BMAL1‐induced MAPK pathway activation and c‐Myc expression. Stably transfected cells were treated with PD98059 or SP600125, then the MAPK and c‐Myc levels were analyzed by Western blotting. c‐Myc expression was significantly decreased. Moreover, the invasion activity was also suppressed. Therefore, the activation of ERK$\frac{1}{2}$ and JNK in MAPK pathway might play a critical role in BMAL1‐induced migration activation. c‐*Myc is* a key functional target gene modulated by BMAL1.
This study documented the malignant biological functions of BMAL1, such as increasing colorectal cancer cell metastasis and proliferation. Meanwhile, we report for the first time that BMAL1 increased the expression of c‐*Myc via* the MAPK pathway in CRC. Inhibition of the ERK/JNK MAPK pathway suppressed the c‐Myc and EMT axes. We hypothesize that in BMAL1‐overexpressing cell lines, β‐catenin accumulates in the nucleus, and the MAPK pathway (ERK$\frac{1}{2}$ and JNK) is activated, resulting in increased c‐Myc expression and EMT pathway activation. Finally, decreased E‐cadherin expression leads to increased migration and ultimately promotes the metastasis of colorectal cancer (Figure 8).
**FIGURE 8:** *BMAL1 promotes colorectal cancer cells proliferation and migration via BMAL1/MAPK/c‐Myc pathway. This figure was created with BioRender.com.*
In summary, our report specifically elucidated the interaction between BMAL1 and c‐Myc and illustrated that BMAL1 upregulates c‐Myc in CRC via activation of the MAPK‐signaling pathway.
## CONCLUSION
Our report provides a new insight into the role of BMAL1 in colorectal cancer growth and metastasis. The BMAL1/MAPK/c‐Myc signaling loop in colorectal cancer may represent a novel mechanism to regulate colorectal cancer growth. Thus, targeting BMAL1 should be seen as a potential therapeutic opportunity for drug development to improve outcomes of patients with metastatic CRC.
## AUTHORS' CONTRIBUTIONS
LNS conceived and designed the study. LNS, WQZ, BJB and KKC performed the most part of experiments and wrote the manuscript. JHH and XWW provides help for the specific ideas of the article. The data were analyzed by YML. HBZ, XFH and SD reviewed the manuscript. All authors read and approved the manuscript.
## FUNDING INFORMATION
This work was supported by grants from the National Natural Science Foundation of China (grants no. 81703076 and 82072628) and the key research and development program of Zhejiang (grants no. 2022C03032).
## CONFLICT OF INTEREST
The authors declare that they have no competing interests.
## ETHICS APPROVAL AND CONSENT TO PARTICIPATE
This study was approved by the Ethics Committee of the Sir Run Run Shaw Hospital of Zhejiang University. Animal welfare and experimental procedures were carried out in accordance with the Guide for the Care and Use of Laboratory Animals and approved by the animal ethics committee of Zhejiang University (SRRSH201606035).
## CONSENT FOR PUBLICATION
All contributing authors agree to the publication of this article.
## DATA AVAILABILITY STATEMENT
The datasets generated and analyzed during the current study are available in the TCGA database (https://www.proteinatlas.org) (ENSG00000133794). All the data and material in this paper are available when requested.
## References
1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. **Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA Cancer J Clin* (2018) **68** 394-424. PMID: 30207593
2. Chen W, Zheng R, Baade PD. **Cancer statistics in China, 2015**. *CA Cancer J Clin* (2016) **66** 115-132. PMID: 26808342
3. Chiorean EG, Nandakumar G, Fadelu T. **Treatment of patients with late‐stage colorectal cancer: ASCO resource‐stratified guideline**. *JCO Glob Oncol* (2020) **6** 414-438. PMID: 32150483
4. Siegel RL, Miller KD, Goding Sauer A. **Colorectal cancer statistics, 2020**. *CA Cancer J Clin* (2020) **70** 145-164. PMID: 32133645
5. Dekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB. **Colorectal cancer**. *Lancet* (2019) **394** 1467-1480. PMID: 31631858
6. Lowrey PL, Takahashi JS. **Genetics of circadian rhythms in mammalian model organisms**. *Adv Genet* (2011) **74** 175-230. PMID: 21924978
7. Bunger MK, Wilsbacher LD, Moran SM. **Mop3 is an essential component of the master circadian pacemaker in mammals**. *Cell* (2000) **103** 1009-1017. PMID: 11163178
8. Karantanos T, Theodoropoulos G, Pektasides D, Gazouli M. **Clock genes: their role in colorectal cancer**. *World J Gastroenterol* (2014) **20** 1986-1992. PMID: 24587674
9. Karantanos T, Theodoropoulos G, Gazouli M. **Expression of clock genes in patients with colorectal cancer**. *Int J Biol Markers* (2013) **28** 280-285. PMID: 23712462
10. Li S, Zhai J, Chu W, Geng X, Chen ZJ, Du Y. **Altered circadian clock as a novel therapeutic target for constant darkness‐induced insulin resistance and hyperandrogenism of polycystic ovary syndrome**. *Transl Res* (2020) **219** 13-29. PMID: 32119846
11. Rakshit K, Matveyenko AV. **Induction of core circadian clock transcription factor Bmal1 enhances β‐cell function and protects against obesity‐induced glucose intolerance**. *Diabetes* (2021) **70** 143-154. PMID: 33087455
12. Yu F, Zhang T, Zhou C. **The circadian clock gene Bmal1 controls intestinal exporter MRP2 and drug disposition**. *Theranostics* (2019) **9** 2754-2767. PMID: 31244920
13. Burgermeister E, Battaglin F, Eladly F. **Aryl hydrocarbon receptor nuclear translocator‐like (ARNTL/BMAL1) is associated with bevacizumab resistance in colorectal cancer via regulation of vascular endothelial growth factor a**. *EBioMedicine* (2019) **45** 139-154. PMID: 31300350
14. Zhao J, Ou B, Han D. **Tumor‐derived CXCL5 promotes human colorectal cancer metastasis through activation of the ERK/Elk‐1/snail and AKT/GSK3β/β‐catenin pathways**. *Mol Cancer* (2017) **16** 70. PMID: 28356111
15. Zeng Z, Luo HY, Yang J. **Overexpression of the circadian clock GeneBmal1 increases sensitivity to oxaliplatin in colorectal cancer**. *Clin Cancer Res* (2014) **20** 1042-1052. PMID: 24277452
16. Tang Q, Chen J, di Z. **TM4SF1 promotes EMT and cancer stemness via the Wnt/β‐catenin/SOX2 pathway in colorectal cancer**. *J Exp Clin Cancer Res* (2020) **39** 232. PMID: 33153498
17. He TC, Sparks AB, Rago C. **Identification of c‐MYC as a target of the APC pathway**. *Science* (1998) **281** 1509-1512. PMID: 9727977
18. van de Wetering M, Sancho E, Verweij C. **The beta‐catenin/TCF‐4 complex imposes a crypt progenitor phenotype on colorectal cancer cells**. *Cell* (2002) **111** 241-250. PMID: 12408868
19. Cadigan KM, Waterman ML. **TCF/LEFs and Wnt signaling in the nucleus**. *Cold Spring Harb Perspect Biol* (2012) **4** a007906. PMID: 23024173
20. Higgs MR, Lerat H, Pawlotsky JM. **Hepatitis C virus‐induced activation of β‐catenin promotes c‐Myc expression and a cascade of pro‐carcinogenetic events**. *Oncogene* (2013) **32** 4683-4693. PMID: 23108410
21. Yue C, Chen J, Li Z, Li L, Chen J, Guo Y. **microRNA‐96 promotes occurrence and progression of colorectal cancer via regulation of the AMPKα2‐FTO‐m6A/MYC axis**. *J Exp Clin Cancer Res* (2020) **39** 240. PMID: 33183350
22. Fang Y, Shen ZY, Zhan YZ. **CD36 inhibits β‐catenin/c‐myc‐mediated glycolysis through ubiquitination of GPC4 to repress colorectal tumorigenesis**. *Nat Commun* (2019) **10** 3981. PMID: 31484922
23. Al‐Kuraya K, Novotny H, Bavi P. **HER2, TOP2A, CCND1, EGFR and C‐MYC oncogene amplification in colorectal cancer**. *J Clin Pathol* (2007) **60** 768-772. PMID: 16882699
24. Al‐Nuaimi Y, Hardman JA, Bíró T. **A meeting of two chronobiological systems: circadian proteins Period1 and BMAL1 modulate the human hair cycle clock**. *J Invest Dermatol* (2014) **134** 610-619. PMID: 24005054
25. Wang J, Li S, Li X. **Circadian protein BMAL1 promotes breast cancer cell invasion and metastasis by up‐regulating matrix metalloproteinase9 expression**. *Cancer Cell Int* (2019) **19** 182. PMID: 31346317
26. Oshima T, Takenoshita S, Akaike M. **Expression of circadian genes correlates with liver metastasis and outcomes in colorectal cancer**. *Oncol Rep* (2011) **25** 1439-1446. PMID: 21380491
27. Dong P, Wang Y, Liu Y. **BMAL1 induces colorectal cancer metastasis by stimulating exosome secretion**. *Mol Biol Rep* (2021) **49** 373-384. PMID: 34727291
28. Zhang Y, Devocelle A, Desterke C. **BMAL1 knockdown leans epithelial‐mesenchymal balance toward epithelial properties and decreases the chemoresistance of colon carcinoma cells**. *Int J Mol Sci* (2021) **22**
29. Papagiannakopoulos T, Bauer MR, Davidson SM. **Circadian rhythm disruption promotes lung tumorigenesis**. *Cell Metab* (2016) **24** 324-331. PMID: 27476975
30. Zeng ZL, Luo HY, Yang J. **Overexpression of the circadian clock gene Bmal1 increases sensitivity to oxaliplatin in colorectal cancer**. *Clin Cancer Res* (2014) **20** 1042-1052. PMID: 24277452
31. Angers S, Moon RT. **Proximal events in Wnt signal transduction**. *Nat Rev Mol Cell Biol* (2009) **10** 468-477. PMID: 19536106
32. Nowicki A, Sporny S, Duda‐Szymańska J. **β‐Catenin as a prognostic factor for prostate cancer (PCa)**. *Cent European J Urol* (2012) **65** 119-123
33. Liu Z, Selby CP, Yang Y. **Circadian regulation of c‐MYC in mice**. *Proc Natl Acad Sci U S A* (2020) **117** 21609-21617. PMID: 32817420
34. Altman BJ, Hsieh AL, Sengupta A. **MYC disrupts the circadian clock and metabolism in cancer cells**. *Cell Metab* (2015) **22** 1009-1019. PMID: 26387865
35. Fu L, Pelicano H, Liu J, Huang P, Lee CC. **The circadian gene Period2 plays an important role in tumor suppression and DNA damage response in vivo**. *Cell* (2002) **111** 41-50. PMID: 12372299
36. Okazaki F, Matsunaga N, Okazaki H. **Circadian rhythm of transferrin receptor 1 gene expression controlled by c‐Myc in colon cancer‐bearing mice**. *Cancer Res* (2010) **70** 6238-6246. PMID: 20631077
37. Vallée A, Guillevin R, Vallée JN. **Vasculogenesis and angiogenesis initiation under normoxic conditions through Wnt/β‐catenin pathway in gliomas**. *Rev Neurosci* (2018) **29** 71-91. PMID: 28822229
38. Xu W, Gu J, Ren Q. **NFATC1 promotes cell growth and tumorigenesis in ovarian cancer up‐regulating c‐Myc through ERK1/2/p38 MAPK signal pathway**. *Tumour Biol* (2016) **37** 4493-4500. PMID: 26501422
39. Wang B, Qi X, Liu J. **MYH9 promotes growth and metastasis via activation of MAPK/AKT signaling in colorectal cancer**. *J Cancer* (2019) **10** 874-884. PMID: 30854093
|
---
title: Identification of miR‐30c‐5p as a tumor suppressor by targeting the m6A reader
HNRNPA2B1 in ovarian cancer
authors:
- Qiulei Wu
- Guoqing Li
- Lanqing Gong
- Jing Cai
- Le Chen
- Xiaohan Xu
- Xiaoli Liu
- Jing Zhao
- Ya Zeng
- Rui Gao
- Lili Yu
- Zehua Wang
journal: Cancer Medicine
year: 2022
pmcid: PMC9972042
doi: 10.1002/cam4.5246
license: CC BY 4.0
---
# Identification of miR‐30c‐5p as a tumor suppressor by targeting the m6A reader HNRNPA2B1 in ovarian cancer
## Abstract
miR‐30c‐5p inhibits OvCa progression by inhibiting m6A reader HNRNPA2B. Meanwhile, HNRNPA2B1 might regulate CDK19 mRNA stability to alter m6A level.
### Background
microRNAs (miRNAs) and N6‐methyladenosine (m6A) play important roles in ovarian cancer (OvCa). However, the mechanisms by which miRNAs regulate m6A in OvCa have not been elucidated so far.
### Methods
To screen m6A‐related miRNAs, Pearson's correlation analysis of miRNAs and m6A regulators was implemented using The Cancer Genome Atlas database (TCGA). To determine the level of m6A, RNA m6A quantitative assays were used. Then, colony formation assays, EdU assays, wound healing assays, and Transwell assays were performed. The dual‐luciferase reporter assay was used to confirm the miRNA target genes. Protein–protein interaction (PPI) analysis of the target genes was performed, and hub genes were discovered using the cytoHubba/Cytoscape software. The underlying molecular mechanisms were explored by bioinformatics and RNA stability assays.
### Results
A total of 126 miRNAs were identified as m6A‐related miRNAs by Pearson's correlation analysis. Among them, the high level of miR‐30c‐5p was associated with good prognosis in OvCa patients. In vitro, the miR‐30c‐5p agomir lowered the m6A level and inhibited OvCa cell proliferation, migration, and invasion. The hub target genes of miR‐30c‐5p were identified as (i) XPO1, (ii) AGO1, (iii) HNRNPA2B1, of which m6A reader HNRNPA2B1 was highly expressed in OvCa tissues and related with poor prognosis. In vitro, knockdown of HNRNPA2B1 significantly reduced m6A level and hampered the proliferation and migration of OvCa cells. The inhibition of m6A reader HNRNPA2B1 attenuated the suppression of proliferation and migration and the low m6A level induced by the miR‐30c‐5p downregulation. Mechanistically, m6A reader HNRNPA2B1 might regulate CDK19 mRNA stability to alter m6A level.
### Conclusions
miR‐30c‐5p inhibits OvCa progression and reduces the m6A level by inhibiting m6A reader HNRNPA2B1, thus providing new insights into the m6A regulatory mechanism in OvCa.
## BACKGROUND
Ovarian cancer (OvCa) has been the most lethal cancer in females, 1 and about $80\%$ patients are identified with metastatic cancer at an advanced stage. 2 Although surgery and platinum‐based chemotherapy respond well to initial treatment, the recurrence rate remains high for advanced patients. Thus, more research into the underlying molecular mechanism of tumor aggressiveness is critically needed to improve treatment strategies.
Epigenetics refers to gene expression and cell phenotypic changes that do not alter the DNA sequence. 3 Recent studies indicate that epigenetic modification is potential to improve tumor treatment therapies. 4, 5 Over the past few decades, studies on epigenetic modifications have focused on genomic modifications, including DNA methylation, and histone acetylation. Researchers have discovered in recent years that widespread RNA modifications in organisms serve important roles in tumorigenesis and development. 6 N6‐methyladenosine (m6A) is the most frequent RNA modification, occurring in almost all phases of RNA life. 7, 8 The m6A modification supports embryonic development and sustains hematopoietic stem cell function and other normal cellular functions by delicate balance. 9, 10 The disturbance of this balance leads to a series of pathophysiological alterations, resulting in various human diseases, such as aging, 6 metabolic disorders, 11 cardiomyopathy, 12 and cancers. 13 The m6A methyltransferase complex (METTL3, METTL14, and WTAP) assembles the m6A modification, which is then erased by demethylase (FTO and ALKBH5) and identified by a collection of RNA‐binding proteins (HNRNPs, YTHDF$\frac{1}{2}$/3, IGF2BPs, and RBMX), also called as “writers”, “erasers”, and “readers”, respectively. 14 These m6A regulators affect the RNA stability, which leads to OvCa malignancy and poor prognosis. 15 Recently, drugs targeting m6A modification have been developed to treat cancer. 16 The identification of critical molecules that alter m6A modification is significant for improving the treatment of OvCa patients.
microRNA (miRNAs), a set of evolutionarily conserved noncoding RNAs (ncRNAs), regulate gene expression at the post‐transcription level. 17, 18 Recent research has shown that m6A modifications are linked to ncRNAs, especially miRNAs. 19 On the one hand, m6A regulators execute m6A‐dependent modification to promote the maturity of miRNAs involved in carcinogenesis. 20 On the other hand, miRNAs target m6A regulators to alter the m6A modification profiles to influence cancer development. 21, 22, 23 However, the mechanisms by which miRNAs regulate m6A to manifest malignant behaviors of OvCa are less known.
In this finding, we identified that miR‐30c‐5p reduced the m6A level and limited cell growth and motility abilities by targeting the m6A reader HNRNPA2B1 in OvCa. Our findings reveal a new molecular mechanism for altering m6A level and identify miR‐30c‐5p as a potential therapeutic target for OvCa.
## Clinical samples
Sixty‐four tumor tissues were obtained from OvCa patients after surgical resection. Twenty‐two normal fallopian tubal and ovarian tissues were collected from patients who received a total hysterectomy and bilateral salpingo‐oophorectomy with benign gynecological diseases for use as controls in this study. All clinical samples were prepared into paraffin‐embedded sections. Written consent was obtained before surgery. The study protocol was approved by the Ethics Committee. Table 1 summarizes the clinical information of the patients.
**TABLE 1**
| Variables | N | Score of m6A (Mean ± SD) | p value |
| --- | --- | --- | --- |
| Age (years) | | | |
| <50 | 25.0 | 3.815 ± 2.296 | 0.268 |
| ≥50 | 39.0 | 4.496 ± 2.497 | |
| FIGO stage | | | |
| Stages I–II | 18.0 | 3.389 ± 1.771 | 0.369 |
| Stages III–IV | 46.0 | 4.235 ± 2.531 | |
| Histological Type | | | |
| HGSOC | 42.0 | 3.473 ± 1.744 | 0.209 |
| Non‐HGSOC | 22.0 | 4.271 ± 2.602 | |
| Omentum metastasis | | | |
| No | 29.0 | 3.234 ± 2.054 | 0.017 |
| Yes | 35.0 | 4.629 ± 2.436 | |
## Immunohistochemistry (IHC) staining
After dewaxing and rehydrating paraffin sections, antigen retrieval was carried out in sodium‐citrate buffer at 95°C. The endogenous peroxidase was then inactivated with $3\%$ H2O2, and nonspecific binding sites were blocked with $10\%$ goat serum. The sections were incubated with primary antibodies against m6A (1:200, Abcam, ab151230) at 4°C overnight and cultured with the biotin‐conjugated IgG the next day. The sections were then stained with hematoxylin and 3,3'diaminobenzidine (DAB). By multiplying the intensity score by the percentage staining area, the total score was calculated (total from 0 to 9).
## Source of datasets
The Cancer Genome Atlas (TCGA; http://tcga‐data.nci.nih.gov/tcga) database was used to identify m6A‐related miRNAs in OvCa. The expression datasets of the normal tissues were obtained from the Genotype‐Tissue Expression (GTEx; https://gtexportal.org/home/) database. Other OvCa datasets, including GSE101976, GSE119055, GSE83693, GSE27651, GSE66957, and GSE27651, were downloaded from the Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo/) database.
## Cell culture and transfection
Human OvCa cell lines (SKOV3, ES2, A2780, CAOV3, OVCAR3, and OVCAR4) were obtained from the China Center for Type Culture Collection (Wuhan University). OVCAR3 cells were cultivated in RPMI‐1640 medium containing $20\%$ fetal bovine serum (FBS, Gibco), whereas SKOV3, ES2, A2780, CAOV3, and OVCAR4 cells were cultured in DMEM/F12 medium supplemented with $10\%$ FBS in a humidified incubator (37°C, $5\%$ CO2). The HNRNPA2B1 small interfering RNAs (siRNAs) and miR‐30c‐5p agomirs/antagomirs were obtained from RiboBio. Lipofectamine 3000 (Invitrogen) was used for transfection assays, which were performed according to the manufacturer's instructions. The corresponding sequences are listed in Table S1.
## Quantitative real‐time PCR (qRT‐PCR)
Total RNA from OvCa cells was isolated using TRIzol reagent (Takara). The HiScript III qRT SuperMix Kit (Vazyme) and Prime‐Script RT Master Mix Kit (Takara) were used to reverse transcription, and the relative level of each RNA was determined using SYBR Green (Vazyme). Table S1 listed the corresponding primer sequences. Each experiment has three replicates.
## RNA m6A quantitative assay
The EpiQuik m6A RNA Methylation Quantification Kit (Epigentek) was used to measure the m6A level of total RNAs. The m6A level was quantified by reading the absorbance at 450 nm using a Spectramax plate reader (SpectraMax i3). Each experiment has three replicates.
## Transwell assay
Forty‐eight hours after transfection, 4 × 104 cells in serum‐free medium were seeded into the upper chamber without (Transwell migration assay) or with (Transwell invasion assay) Matrigel (BD Biosciences). After 24 h of incubation, non‐migrated or invaded cells were scraped off using a cotton swab, and cells on the bottom of the chamber were fixed with methanol for 10 min and stained using $0.1\%$ crystal violet. Then, five fields were selected and photographed randomly. Each experiment was repeated three times.
## Wound healing assay
OvCa cells formed a confluent monolayer in six‐well plates, wounded with a 200‐μl pipette tip. After replacing the culture media with serum‐free medium, the wound was closed after 24 h. Each experiment has three replicates. The wound healing areas were observed by and measured by ImageJ software (version 1.51).
## 5‐Ethynyl‐2′‐Deoxyuridine (EdU) cell proliferation assay
According to the manual (RiboBio) to perform the assays, all images were taken with an Olympus fluorescence microscope of five random fields. All experiments have three replicates at least.
## Colony formation assay
Five hundred cells per well were plated in 6‐well plates and cultured until colonies were visible. After being fixed with $4\%$ formaldehyde, the colonies were treated with $0.1\%$ crystal violet. All experiments have three replicates at least.
## miRNA target prediction and protein–protein interaction (PPI) network
Online prediction tools included miRWalk databases (http://mirwalk.umm.uni‐heidelberg.de/), miRDB databases (http://mirdb.org/), miRTarBase databases (http://mirtarbase.mbc.nctu.edu.tw/index.html), and TargetScan databases (http://www.targetscan.org/vert_80/). The online websites Kaplan–Meier plotter (KM plotter) and UALCAN were used to determine the prognostic significance and the expression of genes. 24 In addition, the interaction among the genes was analyzed by the STRING database (https://string‐db.org/). The *Hub* genes were analyzed by the Molecular Complex Detection (MCODE) plugin in Cytoscape (version 3.8.2). ( degree cut‐off = 2, node score cut‐off = 0.2, k‐core = 2, and max. Depth = 100).
## Functional enrichment analysis
The clusterProfiler package was used to reveal the functions and pathways of miR‐30c‐5p target genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. In addition, miRNACancerMAP was utilized to investigate the miR‐30c‐5p cancer pathways. 25
## HNRNPA2B1 immunostaining
For immunocytofluorescence (ICF) assays, the cells were fixed with $4\%$ formaldehyde. After incubating with $0.1\%$ Triton X‐100 and $3\%$ bovine serum albumin (BSA), the cells were incubated with anti‐HNRNPA2B1 antibody (1:400, Proteintech, 14,813‐1‐AP) overnight at 4°C. Then, Cy3–conjugated IgG and FITC phalloidin (Yeasen) were used to stain the cells, followed by DAPI staining. For immunocytochemistry (ICC) assays, the cells were treated with biotin‐conjugated IgG at 37°C and stained with DAB, hematoxylin, and eosin. A fluorescent microscope was used to image the cell morphology (Olympus). All experiments have three replicates at least.
## Western blotting
RIPA buffer was used to extract total cellular protein. Protein extracts were separated in $10\%$ SDS‐PAGE and transferred to PVDF membranes. Membranes were blocked with $5\%$ defatted milk. After incubation with the primary antibodies at 4°C overnight, the membrane was treated with HRP‐conjugated secondary antibody (1:5000, CST). Protein bands were detected using an advanced chemiluminescence kit (Pierce, ThermoScientific) in Molecular Imager ChemiDoc XRS+ and Image Lab software. The primary antibodies used were anti‐HNRNPA2B1 (1:8000, Proteintech, 14,813‐1‐AP) and anti‐β‐actin (1:5000, Proteintech, 66,009‐1‐Ig). All experiments were repeated thrice at least.
## Luciferase reporter gene assay
The wild‐type or mutated sequence of the HNRNPA2B1 mRNA 3'UTR was cloned into pmirGLO vector. Cells were co‐transfected with the specific luciferase reporter plasmids. The assays were detected by the Dual‐Luciferase Reporter Assay (E1910, Promega). Each experiment was repeated at least three times.
## Prediction of RNA‐protein interaction and m6A site
The potential interaction of HNRNPA2B1 and miR‐30c‐5p was assessed by use of the RNA–protein interaction prediction tool (RPISeq), which is based on random forest (RF) or support vector machine (SVM). Predictions with probabilities >0.5 were considered positive. The online URL: http://pridb.gdcb.iastate.edu/RPISeq/references.php. The potential m6A sites were predicted using an online tool, SRAMP (http://www.cuilab.cn/sramp/).
## RNA stability analysis
The cells were treated with mRNA transcription inhibitor actinomycin D (5 μg/ml) (MCE, HY‐17559) for 0, 2, 4, and 6 h. Cells were collected, and the total RNA was extracted by TRIzol Reagent (Invitrogen) and analyzed by qPCR. Each experiment was repeated at least three times.
## Statistical analysis
Statistical analyses were carried out by using R Statistical Software (version 3.6.3) and GraphPad Prism (version 8.0.1). All data are expressed as mean ± standard deviation (mean ± SD). Pearson's correlation analysis was used to measure correlation. Student's t test or one‐way ANOVA was used to evaluate the differences between two or multiple groups, respectively. The Mann–Whitney test and Kruskal‐Wallis test were used for nonnormally distributed data. Statistical significance was set at $p \leq 0.05.$
## Dysregulation of m6A level and m6A regulators in OvCa
Analysis of the IHC revealed that the m6A level was markedly upregulated in OvCa tissues than in normal controls (Figure 1A,B and Figure S1). Moreover, the overall survival (OS) of patients with high m6A level exhibited was shorter than of those in the low group (Figure 1C). These results suggest that m6A modification involves in the progression of OvCa.
**FIGURE 1:** *Disregulation of m6A level and m6A regulators in OvCa. (A) Representative IHC images of m6A in tissues. Scale bar 50 μm. (B) m6A level in normal tissues and OvCa tissues. (C) Kaplan–Meier analysis of m6A level in OvCa patients. (D) Heatmap for the mRNA levels of 22 m6A regulators. *p < 0.05; **p < 0.01; ***p < 0.001; ns. not significant*
Considering that m6A modification is mediated by m6A regulators, we identified the levels of the 22 m6A regulators between OvCa tissues and normal controls using datasets from the TCGA and GTEx databases. The findings revealed that the 22 m6A regulators were expressed variably, indicating that the m6A alternation might participate in OvCa tumorigenesis and development (Figure 1D and Figure S2A).
## Identification of m6A‐related miRNAs in OvCa
To identify m6A‐related miRNAs in OvCa, the TCGA database was used to identify the matrix expression of 22 m6A regulators and 2155 miRNAs. Then, we defined m6A‐related miRNAs as miRNAs that had a strong relationship with one or more of the 22 m6A regulators. Pearson's analysis was performed to determine the correlation (|Pearson R| > 0.3 and $p \leq 0.05$), and the m6A regulators‐miRNAs co‐expression network was visualized by a Sankey diagram (Figure 2A). A total of 126 miRNAs were identified as m6A‐related miRNAs, and univariate Cox regression analysis was implemented to distinguish prognostic‐related miRNAs. As shown in Figure 2B, miR‐30c‐5p, miR‐1248, miR‐199a‐5p, and miR‐6504‐5p were significantly related to the OS of OvCa patients. The correlation between 22 key m6A regulators and four OS‐associated miRNAs in TCGA is shown in Figure 2C.
**FIGURE 2:** *Identification of m6A‐related miRNAs in OvCa. (A) Sankey relational diagram for 22 m6A regulators and miRNAs. (B) Forest plot for the OS‐associated miRNAs. (C) Heatmap for the correlations between 22 m6A regulators and the four prognostic m6A‐related miRNAs. (D) Level of miR‐30c‐5p in OvCa in different FIGO stages in the TCGA database. (E) Kaplan–Meier OS curve for OvCa patients in the TCGA database. Kaplan–Meier analysis of OvCa patients in GSE101976 of OS (F) and PFS (G) Level of miR‐30c‐5p for OvCa patients in GSE119055 (H) and GSE83693 (I). *p < 0.05; **p < 0.01; ***p < 0.001; ns. not significant*
To single out the key m6A‐related miRNAs in OvCa, survival analyses from the TCGA database were performed on the four miRNAs selected above. miR‐30c‐5p ($p \leq 0.001$) and miR‐1248 ($$p \leq 0.004$$) were associated with OS, but miR‐199a‐5p ($$p \leq 0.169$$) and miR‐6504‐5p ($$p \leq 0.088$$) were not (Figure 2D and Figure S2B). The GSE101976 dataset showed that the OvCa patients with high level of miR‐30c‐5p had longer OS and progression‐free survival (PFS), which is consistent with the TCGA database. Considering that the International Federation of Gynecology and Obstetrics (FIGO) staging of OvCa is closely related to prognosis, the level of miRNAs in different stages was analyzed, revealing that miR‐30c‐5p expression was decreased in patients with advanced‐stage disease (FIGO stages III–IV) compared with early‐stage disease (FIGO stages I–II); however, this was not the case for miR‐1248 (Figure 2E and Figure S2C). The GSE119055 and GSE83693 datasets validated our results, revealing that the expression of miR‐30c‐5p is decreased in OvCa tissues compared with normal controls, especially in recurrent OvCa lesions (Figure 2H,I). These findings suggest that miR‐30c‐5p, an m6A‐related miRNA, inhibits the progression of OvCa.
## miR‐30c‐5p decreases m6A level and inhibits OvCa cells proliferation, migration and invasion in vitro
To investigate how miR‐30c‐5p regulates the biological function in vitro, we transfected miR‐30c‐5p agomir (an agonist) and miR‐30c‐5p antagomir (an inhibitor) into OvCa cells and performed the qRT‐PCR assays to validate the transfection efficiency (Figure 3A). The RNA m6A quantitative experiment showed that the m6A level in the agomiR‐30c‐5p group was lower than that of the negative control (NC) group and vice versa (Figure 3B). In SKOV3 and ES2 cells, overexpression of miR‐30c‐5p reduced motility and invasion (Figure 5C‐F). Meanwhile, the miR‐30c‐5p agomir markedly reduced the number of clones and EdU‐positive proliferating cells (Figure 5G‐I). Accordingly, the migrative, invasive, and proliferative abilities of OvCa cells increased after downregulation of miR‐30c‐5p. These results suggested that miR‐30c‐5p could decrease m6A level and inhibit the progression of OvCa in vitro.
**FIGURE 3:** *miR‐30c‐5p decreases m6A level and inhibits OvCa cells progression in vitro. (A) RT‐qPCR for the interference efficiencies of agomiR‐30c‐5p and antagomiR‐30c‐5p. (B) m6A level of ES2 and SKOV3 cells transfected with agomiR‐30c‐5p and antagomiR‐30c‐5p. Transwell assays (C, D), wound healing assays (E, F), EdU assays (G, H), and colony formation assays (I) of ES2 and SKOV3 cells transfected with agomiR‐30c‐5p and antagomiR‐30c‐5p. Wound healing scale bar 200 μm. EdU assays and Transwell assays scale bar 20 μm. Each experiment has three replicates at least. *p < 0.05; **p < 0.01; ***p < 0.001; ns. not significant*
## miR‐30c‐5p targets m6A reader
HNRNPA2B1
Four online miRNA target analysis websites (TargetScan, miTarBase, miRDB, and miWALK) were used to predict the potential target genes, and the 22 overlapping genes were recognized (Figure 4A). PPI networks of the 22 overlapping genes were then visualized in CytoScape, and the top three hub genes (XPO1, AGO1, and HNRNPA2B1) were clustered using MCODE (Figure 4B,C). As presented in Figure 4D,E, GO and KEGG enrichment analyses of the 22 overlapping genes were mainly enriched in the transcription and posttranscriptional modification and the JAK–STAT/HIF‐1 signaling pathway. Moreover, the miRNACancerMAP online website showed that miR‐30c‐5p is closely related to the tumor‐associated signaling pathways, including the AKT pathway and ErbB pathway (Figure S3A). These target genes appeared to play important roles in OvCa carcinogenesis by regulating cancer cell proliferation and motility. *Hub* genes are highly connected genes in gene expression networks and are inclined to play important roles in biological mechanisms. Further studies were performed to investigate the prognostic significance of the three hub genes. Kaplan–Meier survival curves and log‐rank test analyses indicated that patients with high HNRNPA2B1 protein expression were related to decreased PFS and OS (Figure 4F). Meanwhile, HNRNPA2B1 expressed more highly in OvCa lesions than in normal tissues (Figure 4G), which was consistent with the results from GSE23554, GSE27651, and GSE66957 (Figure 4H,I), while XPO1 and AGO1 had the opposite expression and prognosis pattern (Figure S3B‐E). *In* general, m6A reader HNRNPA2B1 plays oncogenic roles in OvCa.
**FIGURE 4:** *Hub target genes of miR‐30c‐5p. (A) Venn diagram of miR‐30c‐5p target genes. PPI networks (B) and three hub genes (C) of 22 overlapping genes. KEGG (D) and GO enrichment analysis (E) of the 22 overlapping genes. (F) Kaplan–Meier OS and PFS curve of HNRNPA2B1 in OvCa patients. (G) Protein expressions of the HNRNPA2B1. (H) Kaplan–Meier analysis of OvCa patients in GSE30161 of OS and PFS. (I) Different mRNA levels of the HNRNPA2B1 between normal and tumor tissue in GSE27651 and GSE66957. *p < 0.05; **p < 0.01; ***p < 0.001; ns. not significant*
Considering that the protein function is determined by its location, ICF and ICC assays were performed and revealed the nuclear location of HNRNPA2B1 in different OvCa cell lines (Figure 5A and Figure S4A). And, qRT‐PCR assays and Western blotting assays were performed and revealed that miR‐30c‐5p agomir reduced HNRNPA2B1 expression and vice versa (Figure 5B,C). After validating transfection efficiency (Figure S4B), similar results were validated in other OvCa cells, including A2780, CAOV3, OVCAR3, and OVCAR4 cells (Figure S4C). Then, dual‐luciferase reporter assays showed that miR‐30c‐5p significantly reduced the luciferase activity of the wild‐type group of HNRNPA2B1, but no significant reduction was observed in the HNRNPA2B1 mutation group (Figure 5D,E). In conclusion, miR‐30c‐5p targets m6A reader HNRNPA2B1.
**FIGURE 5:** *miR‐30c‐5p targets m6A reader HNRNPA2B1. (A) Representative ICC and ICF images of HNRNPA2B1 in SKOV3 and ES2 cells. Scale bar 20 μm. (B) qRT‐PCR for the mRNA level of HNRNPA2B1. (C) Representative images of Western blotting for the HNRNPA2B1 protein. (D) Schematic diagram for the sequences of wild‐type and mutation of HNRNPA2B1. (E) Dual‐luciferase reporter assay in SKOV3 and ES2 cells. Each experiment has three replicates at least. *p < 0.05; **p < 0.01; ***p < 0.001; ns. not significant*
## miR‐30c‐5p inhibits m6A level and OvCa cells proliferation, migration and invasion by targeting HNRNPA2B1 in vitro
To further study the biological function of HNRNPA2B1 in OvCa in vitro, small interfering RNAs (siRNAs) were used to decrease m6A reader HNRNPA2B1 expression. The silencing efficiency was validated by qRT‐PCR and Western blotting assays (Figure S4D,E), and si#2 and si#3 were chosen to conduct the following experiments because of their relatively high silencing efficiency. Transwell migration assays and wound‐healing assays showed that the number of migrated ES2 and SKOV3 cells significantly decreased after HNRNPA2B1. Moreover, Transwell invasion assays demonstrated that HNRNPA2B1 knockdown significantly reduced the invasion abilities (Figure 6A‐D). Thus, HNRNPA2B1 knockdown inhibited the migration and invasion capacity of OvCa cells. EdU assays and cloning formation assays showed that the proliferation ability of OvCa cells, including ES2 and SKOV3 cells, decreased after knocking down HNRNPA2B1 (Figure 6E‐G). The RNA m6A quantitative experiments were performed and showed that downregulation of HNRNPA2B1 reduced the m6A level (Figure 6H).
**FIGURE 6:** *miR‐30c‐5p inhibits m6A level and OvCa cells progression by targeting HNRNPA2B1 in vitro. Transwell assays (A, B), wound healing assays (C, D), EdU assays (E, F), and colony formation assays (G) of ES2 and SKOV3 cells after downregulation of HNRNPA2B1. (H) m6A level of ES2 and SKOV3 cells after downregulation of HNRNPA2B1. Wound healing scale bar 200 μm. EdU assays and Transwell assays scale bar 20 μm. Each experiment has three replicates at least. *p < 0.05; **p < 0.01; ***p < 0.001; ns. not significant*
To further confirm that the function of miR‐30c‐5p in OvCa progression is dependent on HNRNPA2B1, EdU assays were performed and demonstrated that the miR‐30c‐5p antagomir increased the proliferation of OvCa cells (Figure 7A,C), which was attenuated by the downregulation of HNRNPA2B1. Transwell migration and invasion assays and m6A quantitative assay showed that HNRNPA2B1 downexpression rescued the cell mobility (Figure 7B,D) and the m6A level (Figure 7E) of miR‐30c‐5p knockdown cells. The miR‐30c‐5p antagomir increased the proliferation, migration, and invasion of OvCa cells (Figure 7A‐D) and the m6A level (Figure 7E), which was attenuated by the downregulation of HNRNPA2B1. Collectively, we demonstrate that miR‐30c‐5p restrains OvCa progression by inhibiting the oncogenic activity of HNRNPA2B1 and the m6A level, suggesting that miR‐30c‐5p acts as the upstream regulator for the m6A modification system by targeting m6A reader HNRNPA2B1.
**FIGURE 7:** *Downregulation of HNRNPA2B1 offsets the oncogenic activities of OvCa cells transfected with antagomiR‐30c‐5p. Edu assays (A, C) and Transwell assays (B, D) showed that inhibition of miR‐30c‐5p reversed the migration, invasion, and proliferation ability repressed by downregulation of HNRNPA2B1. (E) m6A level of ES2 and SKOV3 cells transfected with si‐HNRNPA2B1 and antagomiR‐30c‐5p. Each experiment has three replicates at least. *p < 0.05; **p < 0.01; ***p < 0.001; ns. not significant*
## HNRNPA2B1 regulates the stability of
CDK19 mRNA
Considering that HNRNPA2B1 has an important effect on the stabilization of mRNA, Pearson's correlation analysis was used to determine the correlation between the HNRNPA2B1 and mRNA in the TCGA database (Pearson R > 0.3 and $p \leq 0.05$). Then, univariate Cox regression analysis was performed and showed that eight genes had negative effects on survival among them (Figure 8A). Co‐expression analysis of the eight genes in OvCa patients in the TCGA dataset was carried out using Pearson's correlation coefficients (Figure 8B). As shown in Figure 8C,D, qRT‐PCR results revealed that HNRNPA2B1 knockdown could significantly downregulate CDK19 expression in ES2 and SKOV3 cells, indicating that CDK19 might act as a downstream target of HNRNPA2B1. RNA stability assays further identified that the half‐life of CDK19 mRNA was reduced in ES2 and SKOV3 cells after knockdown of HNRNPA2B1 (Figure 8E,F). As shown in Figure 8G, we demonstrated that the possible interaction between HNRNPA2B1 and CDK19 mRNA was very high, which was assessed by RPISeq websites (RF classifier = 0.8, SVM classifier = 0.98). Meanwhile, several potential m6A sites were found on CDK19 mRNA using the online tool SRAMP (Figure 8H). These data suggest that HNRNPA2B1 might bind to the m6A site of CDK19 mRNA to promote the stability of CDK19 mRNA, which alters the m6A level of total RNA.
**FIGURE 8:** *HNRNPA2B1 regulates the stability of CDK19 mRNA. (A) Forest plot for the OS‐associated genes. (B) Pearson's correlation analysis was used to determine correlations between the eight genes. Relative expression of eight genes in ES2 (C) and SKOV3 (D) cells after downregulation of HNRNPA2B1 compared with control cells by using RT‐qPCR. Knockdown of the HNRNPA2B1 resulted in the degradation of CDK19 mRNA in ES2 (E) and SKOV3 (F) cells. (G) Prediction of the interaction probabilities of HNRNPA2B1 with CDK19 mRNA by RPISeq. (H) Potential m6A modification sites along mRNA of CDK19 predicted by SRAMP. Each experiment has three replicates at least. *p < 0.05; **p < 0.01; ***p < 0.001; ns. not significant*
## DISCUSSION
m6A modification, the most prevalent type of epigenetic regulation in cells, is an emerging field in the study of tumorigenicity of OvCa. However, the function of miRNAs involved in m6A regulation is still poorly understood. This study investigated how miR‐30c‐5p regulated HNRNPA2B1 to modulate OvCa progression in an m6A‐dependent manner. Our findings will help develop novel therapies targeting m6A modification in OvCa.
A series of miRNAs have been verified to regulate m6A modification. 21, 26 *In this* study, we identified that four m6A‐related miRNAs (miR‐30c‐5p, miR‐1248, miR‐199a‐5p, and miR‐6504‐5p) were significantly related to the OS of OvCa patients. miR‐30c‐5p has been reported as a tumor suppressor in various cancers. 27, 28, 29 Several studies also have shown that miR‐1248 and miR‐6504‐5p are related to various cancers, such as lung cancer, 30 prostate cancer, 31 and breast cancer. 32 Gan, et al. demonstrated that miR‐199a‐5p targes Beclin1 and RUNX1 to regulate proliferation and invasion in OvCa. 33 Among the four m6A‐related miRNAs, miR‐30c‐5p was chosen to further study because of its being significantly related to survival. Consistently, we observed that miR‐30c‐5p inhibited OvCa cell proliferation, migration, and invasion. Furthermore, we validated that miR‐30c‐5p reduced the m6A level in OvCa cells for the first time. These results prompt us to investigate how miR‐30c‐5p acts as a tumor suppressor to regulate the m6A level in OvCa.
miRNAs influence the translation or stability of mRNA by binding to the 3'UTR of mRNA. miR‐30c‐5p has been reported to target different genes to promote cancer cell proliferation and invasion. 27, 28 Other studies have shown that miRNAs target m6A regulators to alter the m6A level. 21, 22, 23 For example, miR‐501‐3p diminishes m6A level by targeting WTAP in kidney cancer. 23 Sun, et al. also clarified the critical role of the miR‐103‐3p/METTL14/m6A signaling axis in osteoblast activity. 26 *In this* study, bioinformatics approaches were utilized to predict and identify the target genes of miR‐30c‐5p, and we found that m6A reader HNRNPA2B1 was a novel target of miR‐30c‐5p in OvCa, as indicated by bioinformatics approaches and the luciferase assay. Further experiments proved that miR‐30c‐5p could target HNRNPA2B1 to reduce m6A level.
m6A reader HNRNPA2B1 is mainly localized in the nucleus and selectively binds to m6A‐containing transcripts to regulate RNA production and metabolism, including maintaining RNA stability, RNA splicing, and RNA processing. 34, 35 The role of HNRNPA2B1 as an oncogene in OvCa development has been described in existing studies. 36, 37, 38 For instance, HNNRPA2B1 facilitates the proliferation abilities of OvCa cells by regulating Lin28B expression. 39 Wang, et al. showed that HNRNPA2B1 enhances drug sensitivity in OvCa cells. 40 Here, we found that HNRNPA2B1 promoted the proliferation, migration, and invasion of OvCa cells, verifying its potential role as an oncogene in the OvCa progression. *In* general, the m6A level is usually regulated by the m6A methyltransferase complex and demethylase, while m6A RNA‐binding proteins (“readers”) could also alter the m6A level indirectly by regulating the mRNA stability. 41, 42, 43 Lu, et al. proved that m6A reader IMP2 regulates m6A level by stabilizing the ZFAS1/OLA1 axis in colorectal cancer. 44 Previous studies also revealed that HNRNPA2B1 stabilizes ILF3 and TCF7L2 mRNA via an m6A‐dependent manner. 38, 45 However, whether the m6A reader HNRNPA2B1 regulates the mRNA stability in OvCa has not been reported so far. Herein, we demonstrated that the mRNA stability of CDK19 mRNA was significantly reduced after hnRNPA2B1 knockdown. CDK19, as a critical regulatory enzyme, has been reported to promote tumor cell migration and proliferation in prostate cancer and ovarian cancer. 46, 47 Meanwhile, we found that the possibility of HNRNPA2B1 binding to CDK19 mRNA was very high, and CDK19 mRNA had several potential m6A modification sites by bioinformatics analysis, suggesting that HNRNPA2B1 might regulate CDK19 mRNA stability to alter m6A level.
miRNAs are being studied in preclinical and clinical studies in recent years, while targeted therapies for m6A regulators are still under investigation. 16, 48, 49 Our results identify potential regulators of m6A modification and show that miR‐30c‐5p may serve as the key target to regulate m6A modification. Meanwhile, further animal models and experiments need to be investigated. Collectively, our study may help elucidate the processes and mechanisms of m6A modification in OvCa and provide novel insights into the further study of m6A‐related miRNAs.
## CONCLUSION
This study concludes that miR‐30c‐5p reduces the m6A level and inhibits OvCa progression by targeting m6A reader HNRNPA2B1. Our findings not only identify a new potential molecular mechanism for altering m6A modification but also facilitate the development of novel therapies for OvCa.
## AUTHOR CONTRIBUTIONS
The study was designed by LY and ZW. QW and GL performed the experiment. LG, JC, and LC collected the clinical data. XX, XL, and JZ. YZ and RG performed bioinformatic analyses and statistical analysis.
## FUNDING INFORMATION
The National Natural Science Foundation of China (No. 81974413 and No. 81902665) provided funding for this research.
## CONFLICT OF INTEREST
No conflicts.
## ETHICS APPROVAL STATEMENT
The study protocol was approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (No: IORG0003571).
## DATA AVAILABILITY STATEMENT
All data created and/or analyzed during this work included in this article.
## References
1. Siegel RL, Miller KD, Fuchs HE, Jemal A. **Cancer Statistics, 2021**. *CA Cancer J Clin* (2021) **71** 7-33. PMID: 33433946
2. Torre LA, Trabert B, DeSantis CE. **Ovarian cancer statistics, 2018**. *CA Cancer J Clin* (2018) **68** 284-296. PMID: 29809280
3. Martinez‐Useros J, Martin‐Galan M, Florez‐Cespedes M, Garcia‐Foncillas J. **Epigenetics of Most Aggressive Solid Tumors: Pathways Targets and Treatments**. *Cancers (Basel)* (2021) **13** 3209. PMID: 34198989
4. Chatterjee A, Rodger EJ, Eccles MR. **Epigenetic drivers of tumourigenesis and cancer metastasis**. *Semin Cancer Biol* (2018) **51** 149-159. PMID: 28807546
5. Davies A, Zoubeidi A, Selth LA. **The epigenetic and transcriptional landscape of neuroendocrine prostate cancer**. *Endocr Relat Cancer* (2020) **27** R35-R50. PMID: 31804971
6. Zhao LY, Song J, Liu Y, Song CX, Yi C. **Mapping the epigenetic modifications of DNA and RNA**. *Protein Cell* (2020) **11** 792-808. PMID: 32440736
7. Deng X, Su R, Weng H, Huang H, Li Z, Chen J. **RNA N(6)‐methyladenosine modification in cancers: current status and perspectives**. *Cell Res* (2018) **28** 507-517. PMID: 29686311
8. Roundtree IA, Evans ME, Pan T, He C. **Dynamic RNA modifications in gene expression regulation**. *Cell* (2017) **169** 1187-1200. PMID: 28622506
9. Liu J, Yue Y, Han D. **A METTL3‐METTL14 complex mediates mammalian nuclear RNA N6‐adenosine methylation**. *Nat Chem Biol* (2014) **10** 93-95. PMID: 24316715
10. Mapperley C, van de Lagemaat LN, Lawson H. **The mRNA m6A reader YTHDF2 suppresses proinflammatory pathways and sustains hematopoietic stem cell function**. *J Exp Med* (2021) **218**. PMID: 33156926
11. Wang J, Wang K, Liu W, Cai Y, Jin H. **m6A mRNA methylation regulates the development of gestational diabetes mellitus in Han Chinese women**. *Genomics* (2021) **113** 1048-1056. PMID: 33667648
12. Meng L, Lin H, Huang X, Weng J, Peng F, Wu S. **METTL14 suppresses pyroptosis and diabetic cardiomyopathy by downregulating TINCR lncRNA**. *Cell Death Dis* (2022) **13** 38. PMID: 35013106
13. Sun T, Wu R, Ming L. **The role of m6A RNA methylation in cancer**. *Biomed Pharmacother* (2019) **112**. PMID: 30784918
14. Wang T, Kong S, Tao M, Ju S. **The potential role of RNA N6‐methyladenosine in Cancer progression**. *Mol Cancer* (2020) **19** 88. PMID: 32398132
15. Zhang C, Liu J, Guo H. **m6A RNA methylation regulators were associated with the malignancy and prognosis of ovarian cancer**. *Bioengineered* (2021) **12** 3159-3176. PMID: 34187307
16. Huang Y, Su R, Sheng Y. **Small‐molecule targeting of oncogenic FTO demethylase in acute myeloid leukemia**. *Cancer Cell* (2019) **35** 677-691 e610. PMID: 30991027
17. Bartel DP. **Metazoan MicroRNAs**. *Cell* (2018) **173** 20-51. PMID: 29570994
18. Han X, Guo J, Fan Z. **Interactions between m6A modification and miRNAs in malignant tumors**. *Cell Death Dis* (2021) **12** 598. PMID: 34108450
19. Huang H, Weng H, Chen J. **m(6)A Modification in Coding and Non‐coding RNAs: Roles and Therapeutic Implications in Cancer**. *Cancer Cell* (2020) **37** 270-288. PMID: 32183948
20. Chen X, Xu M, Xu X. **METTL14 suppresses CRC progression via regulating N6‐methyladenosine‐dependent primary miR‐375 processing**. *Mol Ther* (2020) **28** 599-612. PMID: 31839484
21. Chen T, Hao YJ, Zhang Y. **m(6)A RNA methylation is regulated by microRNAs and promotes reprogramming to pluripotency**. *Cell Stem Cell* (2015) **16** 289-301. PMID: 25683224
22. Yi YC, Chen XY, Zhang J, Zhu JS. **Novel insights into the interplay between m(6)A modification and noncoding RNAs in cancer**. *Mol Cancer* (2020) **19** 121. PMID: 32767982
23. He L, Chen S, Ying Y. **MicroRNA‐501‐3p inhibits the proliferation of kidney cancer cells by targeting WTAP**. *Cancer Med* (2021) **10** 7222-7232. PMID: 34595849
24. Chandrashekar DS, Bashel B, Balasubramanya SAH. **UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses**. *Neoplasia* (2017) **19** 649-658. PMID: 28732212
25. Tong Y, Ru B, Zhang J. **miRNACancerMAP: an integrative web server inferring miRNA regulation network for cancer**. *Bioinformatics* (2018) **34** 3211-3213. PMID: 29897412
26. Sun Z, Wang H, Wang Y. **MiR‐103‐3p targets the m(6) A methyltransferase METTL14 to inhibit osteoblastic bone formation**. *Aging Cell* (2021) **20**. PMID: 33440070
27. Guo Y, Guo Y, Chen C. **Circ3823 contributes to growth, metastasis and angiogenesis of colorectal cancer: involvement of miR‐30c‐5p/TCF7 axis**. *Mol Cancer* (2021) **20** 93. PMID: 34172072
28. Yuan LQ, Zhang T, Xu L, Han H, Liu SH. **miR‐30c‐5p inhibits glioma proliferation and invasion via targeting Bcl2**. *Transl Cancer Res* (2021) **10** 337-348. PMID: 35116264
29. Cao JM, Li GZ, Han M, Xu HL, Huang KM. **MiR‐30c‐5p suppresses migration, invasion and epithelial to mesenchymal transition of gastric cancer via targeting MTA1**. *Biomed Pharmacother* (2017) **93** 554-560. PMID: 28686969
30. Yang T, Li M, Li H, Shi P, Liu J, Chen M. **Downregulation of circEPSTI1 represses the proliferation and invasion of non‐small cell lung cancer by inhibiting TRIM24 via miR‐1248 upregulation**. *Biochem Biophys Res Commun* (2020) **530** 348-354. PMID: 32828310
31. Yu Y, Song Y, Cheng L. **CircCEMIP promotes anoikis‐resistance by enhancing protective autophagy in prostate cancer cells**. *J Exp Clin Cancer Res* (2022) **41** 188. PMID: 35655258
32. Luo X, Wang H. **LINC00514 upregulates CCDC71L to promote cell proliferation, migration and invasion in triple‐negative breast cancer by sponging miR‐6504‐5p and miR‐3139**. *Cancer Cell Int* (2021) **21** 180. PMID: 33757509
33. Gan X, Zhu H, Jiang X. **CircMUC16 promotes autophagy of epithelial ovarian cancer via interaction with ATG13 and miR‐199a**. *Mol Cancer* (2020) **19** 45. PMID: 32111227
34. Wu B, Su S, Patil DP. **Molecular basis for the specific and multivariant recognitions of RNA substrates by human hnRNP A2/B1**. *Nat Commun* (2018) **9** 420. PMID: 29379020
35. Alarcon CR, Goodarzi H, Lee H, Liu X, Tavazoie S, Tavazoie SF. **HNRNPA2B1 Is a Mediator of m(6)A‐Dependent Nuclear RNA Processing Events**. *Cell* (2015) **162** 1299-1308. PMID: 26321680
36. Petri BJ, Piell KM, South Whitt GC. **HNRNPA2B1 regulates tamoxifen‐ and fulvestrant‐sensitivity and hallmarks of endocrine resistance in breast cancer cells**. *Cancer Lett* (2021) **518** 152-168. PMID: 34273466
37. Guo H, Wang B, Xu K. **m(6)A reader HNRNPA2B1 promotes esophageal cancer progression via up‐regulation of ACLY and ACC1**. *Front Oncol* (2020) **10**. PMID: 33134163
38. Jiang F, Tang X, Tang C. **HNRNPA2B1 promotes multiple myeloma progression by increasing AKT3 expression via m6A‐dependent stabilization of ILF3 mRNA**. *J Hematol Oncol* (2021) **14** 54. PMID: 33794982
39. Yang Y, Wei Q, Tang Y. **Loss of hnRNPA2B1 inhibits malignant capability and promotes apoptosis via down‐regulating Lin28B expression in ovarian cancer**. *Cancer Lett* (2020) **475** 43-52. PMID: 32006618
40. Wang JM, Liu BQ, Zhang Q. **ISG15 suppresses translation of ABCC2 via ISGylation of hnRNPA2B1 and enhances drug sensitivity in cisplatin resistant ovarian cancer cells**. *Biochim Biophys Acta Mol Cell Res* (2020) **1867**. PMID: 31926942
41. Li J, Wu L, Pei M, Zhang Y. **YTHDF2, a protein repressed by miR‐145, regulates proliferation, apoptosis, and migration in ovarian cancer cells**. *J Ovarian Res* (2020) **13** 111. PMID: 32948220
42. Li J, Meng S, Xu M. **Downregulation of N(6)‐methyladenosine binding YTHDF2 protein mediated by miR‐493‐3p suppresses prostate cancer by elevating N(6)‐methyladenosine levels**. *Oncotarget* (2018) **9** 3752-3764. PMID: 29423080
43. Yang Z, Li J, Feng G. **MicroRNA‐145 modulates N6‐methyladenosine levels by targeting the 3′‐untranslated mRNA region of the N6‐methyladenosine binding YTH domain family 2 protein**. *J Biol Chem* (2017) **292** 3614-3623. PMID: 28104805
44. Lu S, Han L, Hu X. **N6‐methyladenosine reader IMP2 stabilizes the ZFAS1/OLA1 axis and activates the Warburg effect: implication in colorectal cancer**. *J Hematol Oncol* (2021) **14** 188. PMID: 34743750
45. Liu H, Li D, Sun L. **Interaction of lncRNA MIR100HG with hnRNPA2B1 facilitates m(6)A‐dependent stabilization of TCF7L2 mRNA and colorectal cancer progression**. *Mol Cancer* (2022) **21** 74. PMID: 35279145
46. Bragelmann J, Klumper N, Offermann A. **Pan‐cancer analysis of the mediator complex transcriptome identifies CDK19 and CDK8 as therapeutic targets in advanced prostate cancer**. *Clin Cancer Res* (2017) **23** 1829-1840. PMID: 27678455
47. Fan L, Lei H, Lin Y. **Hotair promotes the migration and proliferation in ovarian cancer by miR‐222‐3p/CDK19 axis**. *Cell Mol Life Sci* (2022) **79** 254. PMID: 35451651
48. Janssen HL, Reesink HW, Lawitz EJ. **Treatment of HCV infection by targeting microRNA**. *N Engl J Med* (2013) **368** 1685-1694. PMID: 23534542
49. Bouchie A. **First microRNA mimic enters clinic**. *Nat Biotechnol* (2013) **31** 577. PMID: 23839128
|
---
title: 'Association between dietary intake of n‐3 polyunsaturated fatty acids and
risk of colorectal cancer in the Japanese population: The Japan Collaborative Cohort
Study'
authors:
- Ayako Kato
- Chika Okada
- Ehab S. Eshak
- Hiroyasu Iso
- Akiko Tamakoshi
journal: Cancer Medicine
year: 2022
pmcid: PMC9972092
doi: 10.1002/cam4.5098
license: CC BY 4.0
---
# Association between dietary intake of n‐3 polyunsaturated fatty acids and risk of colorectal cancer in the Japanese population: The Japan Collaborative Cohort Study
## Abstract
### Background
Epidemiological studies of the dietary intake of specific n‐3 polyunsaturated fatty acids (PUFA) and anatomical subsite‐specific colorectal cancer (CRC) are limited. We examined the prospective associations of total n‐3 PUFA, marine‐derived n‐3 PUFA [combined eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA)], and alpha‐linolenic acid (ALA) as plant‐derived n‐3 PUFA with the risk of CRC by subsite in the Japan Collaborative Cohort Study.
### Methods
The participants completed a self‐administered food frequency questionnaire and had no prior history of CRC. Cox proportional hazards model was used to determine the associations between n‐3 PUFAs intake and CRC risk overall and by anatomical subsite.
### Results
During the median 13.8‐year follow‐up period, 699 of the 42,536 participants aged 40–79 years developed incident CRC. An inverse association was found between dietary ALA intake and the risk of distal colon cancer; the multivariable hazard ratios and $95\%$ confidence intervals for the highest quartiles (Q4) were 0.41 (0.21–0.81; p trend = 0.01) compared with the lowest quartiles (Q1). Marine n‐3 PUFA intake was not associated with CRC risk in the overall or anatomical subsite‐specific analyses.
### Conclusion
Our findings suggest that higher ALA intake may be beneficial for lowering the risk of distal colon cancer.
## INTRODUCTION
Colorectal cancer (CRC) is the third most common cancer worldwide, followed by breast and lung cancer. 1 The incidence of CRC has been decreasing in some countries, 2, 3, 4 but not in Japan. 5, 6 In 2017, 153,193 patients were diagnosed with CRC, which became the second most common cause of cancer‐related deaths in 2019 ($13.7\%$) in Japan. 5 *Diet is* among the established lifestyle factors affecting the risk of CRC, which also include smoking, alcohol consumption, obesity, and physical inactivity. 7, 8 N‐3 polyunsaturated fatty acids (PUFAs) are fatty acids containing more than one double bond in their chemical structure, with the first double bond occurring on the third carbon from the methyl carbon end. 9 According to the length of the carbon chain, n‐3 PUFAs with 18‐carbon atoms include alpha‐linolenic acid (ALA), which is derived mainly from plant oils, and n‐3 PUFAs with 20 or more carbon atoms include eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA), which are derived from marine oils. 9 The anti‐inflammatory and anticarcinogenic effects of n‐3 PUFAs support the hypothesis that n‐3 PUFAs could protect against cancer risk, including CRC. 10 Recently, a large European cohort study on marine‐derived n‐3 PUFAs intake and CRC risk was conducted. 11 Subsequently, a meta‐analysis reported that high blood n‐3 PUFA levels decreased CRC risk, and dietary n‐3 PUFA intake tended to be inversely associated with CRC risk. 12 In that meta‐analysis, dietary ALA intake was not associated with CRC risk. The evidence on the effect of plant‐derived n‐3 PUFA (ALA) and anatomical subsite‐specific CRC is limited. 13, 14 *In this* study, we aimed to determine the association between dietary n‐3 PUFAs intake and CRC risk in a large‐scale cohort study of cancer risk in Asia. We hypothesized different associations by anatomical subsite because the colon cancer risk varies by anatomical subsite due to different embryological and physiological features, 15 and by specific n‐3 PUFAs due to different sources and chemical structures. 9
## Study population
The Japan Collaborative Cohort Study for Evaluation of Cancer Risk (JACC) study was established in the late 1980s. Details of the JACC study are described in a previous publication. 16 In summary, a sample of 110,585 residents from 45 administrative districts in Japan (46,395 men and 64,190 women) aged 40–79 years at baseline (between 1988 and 1990) participated in municipal health screening examinations. Participants completed self‐administered questionnaires containing questions related to their lifestyle and medical history. Individual informed consent was obtained in 36 areas, and group consent was obtained from the area leader (which was common in Japan before the Japanese government first established the ethical guidelines in 2002 17) in the remaining nine areas prior to study participation. Depending on the guideline for Informed Consent in Epidemiologic Research, 18 proposed by the research group funded by the Ministry of Health and Welfare, the appropriateness of the entire study was ethically reviewed and approved by the Ethical Board of the Nagoya University School of Medicine in 2000. Subsequently, the study was approved by the Ethical Board of Osaka University Graduate School of Medicine. *In* general, the JACC study was conducted in accordance with the Declaration of Helsinki.
Our analysis included only 65,042 participants because the data on cancer incidence were collected in 24 of the 45 areas from population‐based cancer registers or by reviewing the records of major local hospitals. Furthermore, 972 participants with a previous history of cancer at baseline and 21,534 participants without dietary data were excluded. The remaining 42,536 participants were included in the final analysis. The included participants were younger; had a lower prevalence of men, smoking, and a history of diabetes mellitus; and had a higher prevalence of drinking, walking, sedentary work, higher education, and family history of CRC than the participants excluded from the analysis (Supporting Information Table S1). Therefore, the CRC risk profile was unlikely to differ materially between the included and excluded participants.
## Assessment of dietary intake
Food frequency questionnaire (FFQ) was used at baseline to obtain information on the intake of various food items. The FFQ included 33 food items with five multiple‐choice responses: almost never, 1–2 times/month, 1–2 times/week, 3–4 times/week, and almost daily. The responses were rated as follows: 0 ($\frac{0}{30}$ days), 0.05 ($\frac{1.5}{30}$ days), 0.214 ($\frac{1.5}{7}$ days), 0.5 ($\frac{3.5}{7}$ days), and 1.0 ($\frac{30}{30}$ days), respectively. The dietary intake of n‐3 PUFAs (total n‐3 PUFA, EPA, DPA, DHA, and ALA) was computed using the fifth edition of the Japan Food Tables to measure the amount of n‐3 PUFAs in each food. These contents were multiplied by the participants' scores for each food item, and then, the contents were summed. The portion size of the FFQ food items was estimated based on the intake reported by a subsample of the JACC study in a validation study. 19 The n‐3 PUFA intakes from the FFQ [mean (SD) = 5.0 (0.9) mg/day] was validated against that estimated from 12‐day dietary records [mean (SD) = 10.0 (1.7) mg/day] in the validation study with a Spearman's rank correlation coefficient of 0.21 ($$p \leq 0.058$$). 19 Data on supplementation (e.g., fish oil supplement) was not included in the survey; however, supplement use was uncommon among the Japanese population at baseline. The energy‐adjusted nutrient intake was computed using the residual method. 20
## Ascertainment of colorectal cancer
CRC cases were defined using the codes C18 to C20, as stated in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD‐10). Colon cancer was defined using CRC code C18. Colon cancer was further classified by subsite: C18.0 to C18.5 for proximal colon cancer and C18.6 and 18.7 for distal colon cancer. The codes C18.8 and C18.9 were not used for further classification by subsite because they were overlapping sites and were unspecified by subsite. C19 and C20 codes were used to define rectal cancer.
## Statistical analysis
The person‐years of follow‐up were computed from the baseline (1988–1990) to the first censoring, which included death, moving out of the study area, diagnosis of any cancer, or termination of the follow‐up. Participants who moved out of the study area were considered censored cases because the incidence rates after such moves could not be followed in our system. The follow‐up was terminated in 2009 in most areas. However, this treatment was discontinued in some areas before 2009. The lowest consumption category in quartiles (Q1) of each n‐3 PUFA energy‐adjusted intake for all participants was regarded as the reference. Multivariable‐adjusted hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for CRC, colon cancer, proximal colon cancer, distal colon cancer, and rectal cancer were calculated using Cox proportional hazards models. The confounding variables included age, sex, body mass index (BMI; <18.5 kg/m2, 18.5 to 24.9 kg/m2, or ≥25.0 kg/ m2), smoking status (never smoker, former smoker, current smoker of 1–19 cigarettes/day, or current smoker of ≥20 cigarettes/day), alcohol intake (never drinker, former drinker, current drinker of <2 Japanese drinks [46 g ethanol]/day, or of ≥2 Japanese drinks/day), walking duration (walking <30 min/day or ≥30 min/day), sports duration (sports <1 h/week or ≥1 h/week), education (attended school at ages ≤18 years or >18 years), sedentary work (yes or no), family CRC history in parents or siblings (yes or no), diabetes history (yes or no), and quartiles of energy, calcium, iron, saturated fatty acids, and fiber intake. Missing values for all covariates were grouped into an additional category and included in the model. In the sensitivity analyses, the HRs for any observed significant associations were calculated after excluding participants who had been diagnosed with CRC within the first 3, 5, or 10 years of follow‐up. SAS 9.4 (SAS Institute Inc., Cary, NC, USA) was used to perform all analyses. Statistical significance was set at p‐value <0.05.
## RESULTS
Table 1 presents the baseline characteristics of the participants according to quartiles of total n‐3 PUFA, marine n‐3 PUFA, and ALA intake. The mean intake was 927 mg/day in the lowest quartile (Q1) and 2143 mg/day in the highest quartile (Q4) for total n‐3 PUFA; 280 mg/day and 1031 mg/day Q4 for marine n‐3 PUFA; and 480 mg/day and 1113 mg/day for ALA, respectively. Total n‐3 PUFA intake was positively associated with age, walking, and the selected nutrient intakes and inversely associated with being men, smoking, drinking, and sedentary work. Similar trends were observed for marine n‐3 PUFA and ALA intakes.
**TABLE 1**
| Unnamed: 0 | Unnamed: 1 | Quartile of total n‐3 PUFA intake b | Quartile of total n‐3 PUFA intake b.1 | Quartile of total n‐3 PUFA intake b.2 | Quartile of total n‐3 PUFA intake b.3 | Quartile of total n‐3 PUFA intake b.4 | Quartile of total n‐3 PUFA intake b.5 | Quartile of total n‐3 PUFA intake b.6 | Quartile of marine n‐3 PUFA intake b , c | Quartile of marine n‐3 PUFA intake b , c.1 | Quartile of marine n‐3 PUFA intake b , c.2 | Quartile of marine n‐3 PUFA intake b , c.3 | Quartile of marine n‐3 PUFA intake b , c.4 | Quartile of marine n‐3 PUFA intake b , c.5 | Quartile of marine n‐3 PUFA intake b , c.6 | Quartile of marine n‐3 PUFA intake b , c.7 | Quartile of ALA intake b | Quartile of ALA intake b.1 | Quartile of ALA intake b.2 | Quartile of ALA intake b.3 | Quartile of ALA intake b.4 | Quartile of ALA intake b.5 | Quartile of ALA intake b.6 | Quartile of ALA intake b.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | Q1 | Q2 | Q2 | Q3 | Q3 | Q4 | Q4 | Q1 | Q1 | Q2 | Q2 | Q3 | Q3 | Q4 | Q4 | Q1 | Q1 | Q2 | Q2 | Q3 | Q3 | Q4 | Q4 |
| Dietary intake | (mg/day) | 927±326 | 1202±312 | 1202±312 | 1536±314 | 1536±314 | 2143±439 | 2143±439 | 280±119 | 280±119 | 428±124 | 428±124 | 638±153 | 638±153 | 1031±214 | 1031±214 | 480±149 | 480±149 | 647±175 | 647±175 | 814±164 | 814±164 | 1113±243 | 1113±243 |
| No. of participants | | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 |
| Age | (year) | 55.2±9.9 | 56.2±10.2 | 56.2±10.2 | 56.7±10.1 | 56.7±10.1 | 57.7±9.7 | 57.7±9.7 | 55.9±10.2 | 55.9±10.2 | 56.3±10.2 | 56.3±10.2 | 56.3±10.0 | 56.3±10.0 | 57.3±9.5 | 57.3±9.5 | 55.1±9.6 | 55.1±9.6 | 56.0±10.0 | 56.0±10.0 | 56.7±10.0 | 56.7±10.0 | 58.2±10.1 | 58.2±10.1 |
| Body mass index | (kg/m2) | 22.8±3.1 | 22.8±2.9 | 22.8±2.9 | 22.7±3.0 | 22.7±3.0 | 22.9±3.6 | 22.9±3.6 | 22.8±3.1 | 22.8±3.1 | 22.8±3.0 | 22.8±3.0 | 22.8±3.3 | 22.8±3.3 | 22.9±3.2 | 22.9±3.2 | 22.9±3.1 | 22.9±3.1 | 22.7±3.2 | 22.7±3.2 | 22.7±3.0 | 22.7±3.0 | 22.9±3.4 | 22.9±3.4 |
| Men | (%) | 54.8 | 36.9 | 36.9 | 33.6 | 33.6 | 30.2 | 30.2 | 51.7 | 51.7 | 37.3 | 37.3 | 34.2 | 34.2 | 32.3 | 32.3 | 53.2 | 53.2 | 38.1 | 38.1 | 33.6 | 33.6 | 30.1 | 30.1 |
| Current smoker | (%) | 34.7 | 22.5 | 22.5 | 19.9 | 19.9 | 17.6 | 17.6 | 31.7 | 31.7 | 22.6 | 22.6 | 20.8 | 20.8 | 19.7 | 19.7 | 34.7 | 34.7 | 23.3 | 23.3 | 20.0 | 20.0 | 16.8 | 16.8 |
| Regular drinker (≥46 g ethanol/day) | (%) | 55.3 | 40.2 | 40.2 | 37.1 | 37.1 | 33.0 | 33.0 | 50.6 | 50.6 | 39.7 | 39.7 | 37.7 | 37.7 | 37.8 | 37.8 | 56.5 | 56.5 | 42.4 | 42.4 | 36.8 | 36.8 | 30.0 | 30.0 |
| Daily walking time (≥30 min/day) | (%) | 68.4 | 70.1 | 70.1 | 71.7 | 71.7 | 72.4 | 72.4 | 70.3 | 70.3 | 70.2 | 70.2 | 70.6 | 70.6 | 71.3 | 71.3 | 67.4 | 67.4 | 69.1 | 69.1 | 72.4 | 72.4 | 73.9 | 73.9 |
| Sports (≥1 h/week) | (%) | 26.1 | 26.5 | 26.5 | 28.3 | 28.3 | 27.7 | 27.7 | 26.5 | 26.5 | 27.6 | 27.6 | 27.1 | 27.1 | 27.4 | 27.4 | 25.2 | 25.2 | 27.0 | 27.0 | 28.3 | 28.3 | 28.2 | 28.2 |
| Sedentary work | (%) | 16.9 | 16.7 | 16.7 | 15.9 | 15.9 | 13.9 | 13.9 | 16.1 | 16.1 | 17.1 | 17.1 | 16.1 | 16.1 | 14.4 | 14.4 | 16.7 | 16.7 | 17.4 | 17.4 | 15.9 | 15.9 | 13.3 | 13.3 |
| Education (ages >18 years) | (%) | 15.5 | 15.1 | 15.1 | 14.3 | 14.3 | 14.4 | 14.4 | 15.1 | 15.1 | 14.5 | 14.5 | 14.2 | 14.2 | 15.5 | 15.5 | 14.9 | 14.9 | 16.3 | 16.3 | 14.6 | 14.6 | 13.3 | 13.3 |
| History of diabetes mellitus | (%) | 5.4 | 4.7 | 4.7 | 5.0 | 5.0 | 5.0 | 5.0 | 5.1 | 5.1 | 4.8 | 4.8 | 4.7 | 4.7 | 5.4 | 5.4 | 5.8 | 5.8 | 5.0 | 5.0 | 4.9 | 4.9 | 4.4 | 4.4 |
| Family history of colorectal cancer | (%) | 1.5 | 1.6 | 1.6 | 1.4 | 1.4 | 1.3 | 1.3 | 1.4 | 1.4 | 1.5 | 1.5 | 1.3 | 1.3 | 1.5 | 1.5 | 1.5 | 1.5 | 1.6 | 1.6 | 1.4 | 1.4 | 1.3 | 1.3 |
| Energy | (kcal/day) | 1588±472 | 1439±419 | 1439±419 | 1482±410 | 1482±410 | 1588±405 | 1588±405 | 1598±452 | 1598±452 | 1432±412 | 1432±412 | 1487±439 | 1487±439 | 1580±402 | 1580±402 | 1580±474 | 1580±474 | 1457±427 | 1457±427 | 1476±395 | 1476±395 | 1585±414 | 1585±414 |
| Calcium intake | (mg/day) | 381±141 | 431±138 | 431±138 | 480±137 | 480±137 | 552±145 | 552±145 | 421±151 | 421±151 | 435±146 | 435±146 | 466±147 | 466±147 | 521±152 | 521±152 | 367±136 | 367±136 | 432±133 | 432±133 | 481±131 | 481±131 | 564±143 | 564±143 |
| Iron intake | (mg/day) | 576±194 | 655±190 | 655±190 | 744±191 | 744±191 | 875±212 | 875±212 | 649±217 | 649±217 | 662±209 | 662±209 | 723±216 | 723±216 | 815±224 | 815±224 | 554±183 | 554±183 | 647±182 | 647±182 | 746±177 | 746±177 | 901±201 | 901±201 |
| Saturated fatty acids intake | (mg/day) | 870±349 | 937±342 | 937±342 | 1019±342 | 1019±342 | 1168±375 | 1168±375 | 915±367 | 915±367 | 946±352 | 946±352 | 1011±350 | 1011±350 | 1121±375 | 1121±375 | 864±342 | 864±342 | 951±336 | 951±336 | 1008±338 | 1008±338 | 1170±390 | 1170±390 |
| Total dietary fiber intake | (g/day) | 10.6±3.3 | 11.3±3.3 | 11.3±3.3 | 12.4±3.3 | 12.4±3.3 | 14.1±3.5 | 14.1±3.5 | 11.8±3.6 | 11.8±3.6 | 11.4±3.5 | 11.4±3.5 | 12.1±3.5 | 12.1±3.5 | 13.2±3.6 | 13.2±3.6 | 10.0±3.1 | 10.0±3.1 | 11.1±3.0 | 11.1±3.0 | 12.5±2.9 | 12.5±2.9 | 14.9±3.3 | 14.9±3.3 |
In the 585,644 person‐years of follow‐up (median years = 13.8) of the 42,536 participants, 699 patients developed CRC, including 462 with colon cancer (204 proximal colon, 187 distal colon cancer, and 71 overlapping or unspecified subsite cases) and 237 with rectal cancer.
Table 2 shows the HRs for the quartiles of the total n‐3 PUFA intake. No association was found between total n‐3 PUFA and CRC, colon cancer (both entire colon and colon by subsite), or rectal cancer.
**TABLE 2**
| Unnamed: 0 | Quartile of total n‐3 PUFA intake a | Quartile of total n‐3 PUFA intake a.1 | Quartile of total n‐3 PUFA intake a.2 | Quartile of total n‐3 PUFA intake a.3 | Quartile of total n‐3 PUFA intake a.4 | Quartile of total n‐3 PUFA intake a.5 | Quartile of total n‐3 PUFA intake a.6 | Quartile of total n‐3 PUFA intake a.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Q1 | Q2 | Q2 | Q3 | Q3 | Q4 | Q4 | p for trend |
| No. of participants | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | |
| Person‐years | 130614 | 141366 | 141366 | 151573 | 151573 | 162192 | 162192 | |
| Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer |
| No. of cases | 157 | 174 | 174 | 184 | 184 | 184 | 184 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.08 | (0.87–1.34) | 1.07 | (0.86–1.32) | 0.99 | (0.80–1.23) | 0.85 |
| Multivariable HR b | 1.00 | 1.20 | (0.95–1.52) | 1.22 | (0.95–1.57) | 1.11 | (0.84–1.46) | 0.55 |
| Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer |
| No. of cases | 104 | 111 | 111 | 127 | 127 | 120 | 120 | |
| Age‐ and sex‐adjusted HR | 1.00 | 0.99 | (0.76–1.30) | 1.05 | (0.80–1.36) | 0.91 | (0.69–1.19) | 0.54 |
| Multivariable HR b | 1.00 | 1.16 | (0.87–1.55) | 1.29 | (0.95–1.75) | 1.11 | (0.79–1.56) | 0.51 |
| Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer |
| No. of cases | 38 | 48 | 48 | 56 | 56 | 62 | 62 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.09 | (0.71–1.67) | 1.14 | (0.75–1.74) | 1.13 | (0.75–1.71) | 0.56 |
| Multivariable HR b | 1.00 | 1.25 | (0.79–1.98) | 1.42 | (0.88–2.30) | 1.40 | (0.83–2.36) | 0.22 |
| Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer |
| No. of cases | 56 | 42 | 42 | 52 | 52 | 37 | 37 | |
| Age‐ and sex‐adjusted HR | 1.00 | 0.77 | (0.52–1.16) | 0.90 | (0.61–1.33) | 0.61 | (0.40–0.94) | 0.06 |
| Multivariable HR b | 1.00 | 0.84 | (0.55–1.30) | 1.03 | (0.66–1.60) | 0.68 | (0.40–1.16) | 0.30 |
| Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer |
| No. of cases | 53 | 63 | 63 | 57 | 57 | 64 | 64 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.27 | (0.88–1.83) | 1.10 | (0.75–1.61) | 1.18 | (0.81–1.71) | 0.59 |
| Multivariable HR b | 1.00 | 1.29 | (0.87–1.92) | 1.09 | (0.71–1.69) | 1.13 | (0.70–1.80) | 0.87 |
Marine n‐3 PUFA intake (combined with EPA, DPA, and DHA) was not associated with CRC at any anatomical subsite (Table 3).
**TABLE 3**
| Unnamed: 0 | Quartile of marine n‐3 PUFA intake a | Quartile of marine n‐3 PUFA intake a.1 | Quartile of marine n‐3 PUFA intake a.2 | Quartile of marine n‐3 PUFA intake a.3 | Quartile of marine n‐3 PUFA intake a.4 | Quartile of marine n‐3 PUFA intake a.5 | Quartile of marine n‐3 PUFA intake a.6 | Quartile of marine n‐3 PUFA intake a.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Q1 | Q2 | Q2 | Q3 | Q3 | Q4 | Q4 | p for trend |
| No. of participants | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | |
| Person‐years | 139482 | 143172 | 143172 | 147961 | 147961 | 155129 | 155129 | |
| Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer |
| No. of cases | 171 | 173 | 173 | 162 | 162 | 193 | 193 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.05 | (0.85–1.30) | 0.97 | (0.78–1.20) | 1.09 | (0.88–1.34) | 0.62 |
| Multivariable HR b | 1.00 | 1.06 | (0.86–1.32) | 0.97 | (0.77–1.21) | 1.07 | (0.86–1.34) | 0.75 |
| Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer |
| No. of cases | 112 | 114 | 114 | 105 | 105 | 131 | 131 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.03 | (0.79–1.33) | 0.92 | (0.70–1.20) | 1.08 | (0.83–1.39) | 0.75 |
| Multivariable HR b | 1.00 | 1.03 | (0.79–1.35) | 0.94 | (0.71–1.24) | 1.09 | (0.83–1.43) | 0.69 |
| Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer |
| No. of cases | 41 | 49 | 49 | 55 | 55 | 59 | 59 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.14 | (0.75–1.73) | 1.24 | (0.82–1.86) | 1.22 | (0.82–1.83) | 0.31 |
| Multivariable HR b | 1.00 | 1.17 | (0.77–1.78) | 1.28 | (0.84–1.96) | 1.25 | (0.81–1.92) | 0.30 |
| Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer |
| No. of cases | 54 | 50 | 50 | 33 | 33 | 50 | 50 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.01 | (0.68–1.48) | 0.65 | (0.42–1.00) | 0.95 | (0.65–1.41) | 0.41 |
| Multivariable HR b | 1.00 | 0.98 | (0.66–1.46) | 0.64 | (0.41–1.00) | 0.94 | (0.62–1.43) | 0.41 |
| Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer |
| No. of cases | 59 | 59 | 59 | 57 | 57 | 62 | 62 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.11 | (0.77–1.59) | 1.06 | (0.74–1.53) | 1.10 | (0.77–1.58) | 0.66 |
| Multivariable HR b | 1.00 | 1.13 | (0.78–1.63) | 1.02 | (0.70–1.49) | 1.04 | (0.70–1.53) | 0.99 |
Table 4 shows the HRs for the ALA intake quartiles. No associations were observed between ALA and overall CRC risk; however, ALA intake was inversely associated with distal colon cancer risk in a dose‐dependent manner [HR ($95\%$CI) for Q2:0.93 (0.60–1.43), Q3:0.76 (0.44–1.29), and Q4:0.41 (0.21–0.81), p for trend = 0.01].
**TABLE 4**
| Unnamed: 0 | Quartile of ALA intake a | Quartile of ALA intake a.1 | Quartile of ALA intake a.2 | Quartile of ALA intake a.3 | Quartile of ALA intake a.4 | Quartile of ALA intake a.5 | Quartile of ALA intake a.6 | Quartile of ALA intake a.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Q1 | Q2 | Q2 | Q3 | Q3 | Q4 | Q4 | p for trend |
| No. of participants | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | 10634 | |
| Person‐years | 127096 | 139111 | 139111 | 154211 | 154211 | 165326 | 165326 | |
| Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer | Colorectal cancer |
| No. of cases | 167 | 174 | 174 | 172 | 172 | 186 | 186 | |
| Age‐ and sex‐adjusted HR | 1.00 | 0.98 | (0.80–1.22) | 0.87 | (0.70–1.08) | 0.85 | (0.69–1.06) | 0.09 |
| Multivariable HR b | 1.00 | 1.11 | (0.87–1.42) | 1.06 | (0.79–1.42) | 0.97 | (0.69–1.36) | 0.75 |
| Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer | Colon cancer |
| No. of cases | 113 | 116 | 116 | 117 | 117 | 116 | 116 | |
| Age‐ and sex‐adjusted HR | 1.00 | 0.93 | (0.72–1.21) | 0.82 | (0.63–1.07) | 0.72 | (0.55–0.94) | 0.01 |
| Multivariable HR b | 1.00 | 1.10 | (0.81–1.48) | 1.07 | (0.75–1.52) | 0.85 | (0.56–1.29) | 0.43 |
| Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer | Proximal colon cancer |
| No. of cases | 39 | 49 | 49 | 52 | 52 | 64 | 64 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.07 | (0.70–1.63) | 0.96 | (0.63–1.47) | 1.02 | (0.68–1.54) | 0.94 |
| Multivariable HR b | 1.00 | 1.25 | (0.77–2.01) | 1.32 | (0.75–2.31) | 1.43 | (0.76–2.69) | 0.31 |
| Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer |
| No. of cases | 58 | 52 | 52 | 45 | 45 | 32 | 32 | |
| Age‐ and sex‐adjusted HR | 1.00 | 0.88 | (0.60–1.28) | 0.70 | (0.47–1.04) | 0.46 | (0.30–0.72) | <0.001 |
| Multivariable HR b | 1.00 | 0.93 | (0.60–1.43) | 0.76 | (0.44–1.29) | 0.41 | (0.21–0.81) | 0.01 |
| Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer | Rectal cancer |
| No. of cases | 54 | 58 | 58 | 55 | 55 | 70 | 70 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.10 | (0.76–1.60) | 0.98 | (0.67–1.43) | 1.17 | (0.81–1.69) | 0.52 |
| Multivariable HR b | 1.00 | 1.13 | (0.74–1.74) | 1.05 | (0.63–1.73) | 1.24 | (0.70–2.19) | 0.55 |
The results remained unchanged after excluding distal colon cancer cases diagnosed within 3, 5, or 10 years from the baseline; the HRs ($95\%$ CIs) after excluding cases that developed during the first 5 years from the baseline were as follows: Q2, 0.86 (0.48–1.53); Q3, 0.54 (0.26–1.10); and Q4, 0.23 (0.09–0.56) (p for trend = 0.001). The respective HRs ($95\%$ CIs) after excluding incident cases within the first 10 years were as follows: Q2, 1.02 (0.46–2.27); Q3, 0.67 (0.24–1.86); and Q4, 0.44 (0.12–1.54) (p for trend = 0.18) (Table 5).
**TABLE 5**
| Unnamed: 0 | Quartile of ALA intake a | Quartile of ALA intake a.1 | Quartile of ALA intake a.2 | Quartile of ALA intake a.3 | Quartile of ALA intake a.4 | Quartile of ALA intake a.5 | Quartile of ALA intake a.6 | Quartile of ALA intake a.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Q1 | Q2 | Q2 | Q3 | Q3 | Q4 | Q4 | p for trend |
| Exclude 3 years | Exclude 3 years | Exclude 3 years | Exclude 3 years | Exclude 3 years | Exclude 3 years | Exclude 3 years | Exclude 3 years | Exclude 3 years |
| Dietary intake (mg/day) b | 481 ± 180 | 648 ± 175 | 648 ± 175 | 815 ± 164 | 815 ± 164 | 1115 ± 244 | 1115 ± 244 | |
| No. of participants | 10329 | 10329 | 10329 | 10330 | 10330 | 10329 | 10329 | |
| Person‐years | 126787 | 139022 | 139022 | 153807 | 153807 | 164258 | 164258 | |
| Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer |
| No. of cases | 47 | 42 | 42 | 35 | 35 | 27 | 27 | |
| Age‐ and sex‐adjusted HR | 1.00 | 0.87 | (0.57–1.32) | 0.66 | (0.42–1.03) | 0.48 | (0.29–0.77) | 0.001 |
| Multivariable HR c | 1.00 | 0.87 | (0.54–1.41) | 0.65 | (0.36–1.19) | 0.38 | (0.18–0.79) | 0.01 |
| Exclude 5 years | Exclude 5 years | Exclude 5 years | Exclude 5 years | Exclude 5 years | Exclude 5 years | Exclude 5 years | Exclude 5 years | Exclude 5 years |
| Dietary intake (mg/day) b | 497 ± 183 | 670 ± 176 | 670 ± 176 | 832 ± 165 | 832 ± 165 | 1129 ± 243 | 1129 ± 243 | |
| No. of participants | 9290 | 9290 | 9290 | 9291 | 9291 | 9290 | 9290 | |
| Person‐years | 126871 | 137730 | 137730 | 147115 | 147115 | 152873 | 152873 | |
| Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer |
| No. of cases | 31 | 33 | 33 | 27 | 27 | 17 | 17 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.02 | (0.62–1.68) | 0.79 | (0.46–1.34) | 0.48 | (0.26–0.89) | 0.01 |
| Multivariable HR c | 1.00 | 0.86 | (0.48–1.53) | 0.54 | (0.26–1.10) | 0.23 | (0.09–0.56) | 0.001 |
| Exclude 10 years | Exclude 10 years | Exclude 10 years | Exclude 10 years | Exclude 10 years | Exclude 10 years | Exclude 10 years | Exclude 10 years | Exclude 10 years |
| Dietary intake (mg/day) b | 512 ± 185 | 698 ± 175 | 698 ± 175 | 853 ± 165 | 853 ± 165 | 1148 ± 243 | 1148 ± 243 | |
| No. of participants | 7823 | 7824 | 7824 | 7824 | 7824 | 7823 | 7823 | |
| Person‐years | 126286 | 130669 | 130669 | 133727 | 133727 | 135411 | 135411 | |
| Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer | Distal colon cancer |
| No. of cases | 15 | 17 | 17 | 12 | 12 | 9 | 9 | |
| Age‐ and sex‐adjusted HR | 1.00 | 1.12 | (0.56–2.27) | 0.77 | (0.35–1.69) | 0.60 | (0.26–1.40) | 0.16 |
| Multivariable HR c | 1.00 | 1.02 | (0.46–2.27) | 0.67 | (0.24–1.86) | 0.44 | (0.12–1.54) | 0.18 |
No significant sex interaction was observed in analyses for Tables 2, 3, 4, 5.
## DISCUSSION
In this large prospective study of a Japanese population, dietary intake of ALA was inversely associated with distal colon cancer risk in a dose‐dependent manner. After excluding the diagnosed distal colon cancer cases that occurred within 3, 5, and 10 years of baseline, the observed association remained unchanged. Dietary intakes of marine n‐3 PUFA and total n‐3 PUFA were, however, not associated with the risk of CRC at any subsites.
As only a small proportion of ALA can be transformed into EPA and DHA in the human body, 21, 22 ALA per se may reduce the risk of distal colon cancer. The beneficial effects of ALA include reduction of aberrant crypt foci in the colon, 23, 24, 25 induction of apoptosis, 26, 27 and inhibition of proliferation, adhesion, and invasion of cancer cells. 28 In rats, flaxseed oil, rich in ALA, inhibited inflammation and led to a reduction in tumorous lesions in the intestine and pancreas. 29 Previous studies evaluating the association between ALA intake and CRC risk have yielded inconsistent findings. A previous Japanese prospective study showed no associations between dietary ALA intake and colon cancer risk; the multivariable relative risks ($95\%$ CIs) for the highest (median: 2760 mg/day in men and 2640 mg/day in women) quintiles of intake were 0.84 (0.56–1.28) (p for trend = 0.31) in men and 1.01 (0.65–1.57) (p for trend = 0.69) in women compared with that for the lowest (median: 1210 mg/day in men and 1350 mg/day in women) quintiles of intake. 13 In that study, the association between ALA intake and the risk of distal colon cancer was analyzed only for invasive cancers, defined as tumors over the mucosal layer. A previous prospective study conducted in the United States (US) in 123,529 health professionals aged 40–75 years and nurses aged 30–55 years with 24 to 26 years of follow‐up also showed no association between dietary ALA intake and the risk of CRC, proximal colon, and distal colon cancer. The multivariable HRs ($95\%$ CIs) of CRC for the highest (≥1300 mg/day in men and ≥1200 mg/day in women) quartiles were 0.89 (0.70–1.13) (p for trend = 0.45) in men and 1.05 (0.86–1.29) (p for trend = 0.56) in women compared with those for the lowest (<900 mg/day both in men and in women) quartiles. The respective multivariable HRs ($95\%$ CIs) of proximal colon cancer were 0.81 (0.70–1.13) (p for trend = 0.45) in men and 1.04 (0.78–1.40) (p for trend = 0.89) in women. The respective multivariable HRs ($95\%$ CIs) of distal colon cancer were 1.15 (0.75–1.75) (p for trend = 0.39) in men and 1.18 (0.81–1.71) (p for trend = 0.25) in women. 14 In contrast, some prospective studies investigating the association between dietary ALA intake and CRC risk reported a positive trend. 30, 31, 32, 33 A 22‐year prospective study of 4967 Caucasians aged ≥55 years in Rotterdam, Netherlands, reported that dietary n‐3 PUFA from non‐fish sources was associated with an increased risk of CRC, with the HR ($95\%$ CI) for the highest (median:1400 mg/day) tertile of 1.76 (1.25–2.47) (p for trend = 0.001) compared with that for the lowest (median:700 mg/day) tertile. 30 Another 6‐year follow‐up study on American individuals aged 50–74 years ($97\%$ of participants were white 34) revealed a non‐significant positive association between dietary ALA intake and CRC risk among women. The multivariable HR ($95\%$ CI) for the highest (≥1190 mg/day) quartile of ALA was 1.38 (1.02–1.85) (p for trend = 0.13) compared with that for the lowest (<780 mg/day) quartile of ALA intake, and a non‐significant inverse association was observed between dietary ALA intake and the risk of CRC among men. 31 The multivariable HR ($95\%$ CI) for the highest (≥1260 mg/day) quartile of ALA intake was 0.87 (0.66–1.14) (p for trend = 0.09) compared with that for the lowest (<820 mg/day) quartile of ALA intake among men. 31 According to a 20‐year prospective study of 48,223 Swedish women aged 29–49 years, the multivariable HRs ($95\%$ CIs) of CRC and colon cancer for the highest (≥1160 mg/day) quartile of ALA intake were 1.17 (0.86–1.59) (p for trend = 0.112) and 0.95 (0.65–1.41) (p for trend = 0.833), respectively, compared with that for the lowest (<840 mg/day) quartile of ALA. 32 An 11‐year prospective study of 59,986 Chinese men aged 40–74 years showed that the multivariable HRs ($95\%$ CIs) of CRC and colon cancer for the highest quartiles of ALA were 1.15 (0.92–1.43) (p for trend = 0.18) and 0.98 (0.74–1.31) (p for trend = 0.82), respectively, compared with that for the lowest quartile of ALA intake (amount of intake was not reported). 33 In our study, the mean ALA intakes in the highest and lowest quartiles were 1175 and 525 mg/day among men and 1086 and 429 mg/day among women, respectively. The distribution of ALA intake in our study did not differ materially from those reported in previous studies.
The disparity between our study and previous studies that reported a positive trend may be due to the different dietary sources and different age distributions, shorter follow‐up period, and lack of information on the anatomical subsite of CRC in previous studies. For example, the primary sources of non‐marine n‐3 PUFAs associated with a higher risk of CRC 30 in the Rotterdam study were butter and margarine. 35 However, ALA is also present in flaxseed, canola, and soybean oils, 22 among which soybeans are common in Japanese foods. 36 Our results indicate an inverse association between dietary ALA intake and CRC risk only in patients with distal colon cancer. In rats, the incidence of aberrant crypt foci in the distal colon was higher than that in the proximal colon, 25 and the number of aberrant crypt foci was reduced in the distal colon by flaxseed intake (rich in ALA) compared to that in the proximal colon. 23, 24 In addition, the mucosa of the distal colon has a higher apoptotic index than that of the proximal colonic. 15 In our study, no association was found between marine n‐3 PUFA intake and CRC risk, including in distal colon cancer. However, a US study conducted among health professionals and nurses suggested a positive association between marine n‐3 PUFA intake and the risk of distal colon cancer, with the multivariable HRs ($95\%$ CIs) in the highest quartiles of intake being 1.43 (0.97–2.11) among men and 1.36 (1.03–1.80) among women compared with those in the lowest quartiles of intake. 14 The discrepancy between the findings from the US and our studies may be due to a large difference in the distribution of marine n‐3 PUFA intake; the highest quartiles among the US men (562 mg/day) and women (412 mg/day) 14 corresponded to that between the second and third quartiles among Japanese men (473 and 710 mg/day) and that in the second quartile among Japanese women (402 mg/day) in our study. The high mean levels of marine n‐3 PUFA intake were observed across middle‐aged and older Japanese; 595 mg/day for the ages of 40–64 and 588 mg/day for the ages of 65–79 among men, and 603 mg/day and 568 mg/day, respectively, among women.
Moreover, no association was observed between marine n‐3 PUFA intake and risk of proximal colon cancer. Meanwhile, a previous Japanese study revealed a protective effect of marine n‐3 PUFA intake against invasive proximal colon cancer, with multivariable relative risks being 0.35 (0.14–0.88) among men and 0.59 (0.24–1.45) among women in the highest quintile compared with that in the lowest quintile. 13 Furthermore, a recent report of the US health professionals and nurses' study revealed that a high marine n‐3 PUFA intake was associated with a lower risk of FOXP3 + T‐cell‐high CRC; the multivariable HRs ($95\%$ CIs) in the highest quartile were 0.57 (0.40–0.81, p for trend<0.001) for FOXP3 + T‐cell‐high CRC and 1.14 (0.81–1.60, p for trend = 0.77) for FOXP3 + T‐cell‐low CRC compared with those in the lowest quartile. 37 The potency of marine n‐3 PUFA in proximal colon cancer may differ depending on the invasion levels and immune infiltrate of cancer; however, future research is needed to confirm this finding.
The strengths of our study were as follows: the prospective nature of the study, which avoided the occurrence of exposure recall bias; its large sample size with high follow‐up rates; and the informative analyses under sufficient quality of the cancer registry, which reduced the misclassification of outcomes. In addition, our study results were not affected by reverse causation because of the similar trend before and after excluding the early diagnosis of dietary ALA intake.
Our study had several limitations. First, although we adjusted for multiple potential confounders, some confounding and other unmeasured factors, such as the use of nonsteroidal anti‐inflammatory drugs, are suggested to reduce the CRC risk due to its anti‐inflammatory effect 38; however, they remain unaccounted for. Second, dietary assessment using the FFQ is subject to recall bias, differences in the nutrient content of foods, and inability to capture the absorption and metabolism of PUFAs by the questionnaire. Furthermore, the data on n‐3 PUFAs intake obtained using the FFQ were available only at baseline; hence, it did not reflect the possible changes in the participants' dietary habits during the follow‐up period; however, such changes would have occurred randomly. Third, because we did not have information on the histological depth of CRC, the HRs depending on the invasion level could not be estimated.
In conclusion, our findings provide evidence that a high ALA intake is associated with a lower risk of distal colon cancer in a dose‐dependent manner. Further studies focusing on the health benefits of ALA intake on CRC risk are warranted.
## AUTHOR CONTRIBUTIONS
Ayako Kato, Chika Okada, and Hiroyasu Iso designed the study and methods of the analyses. Ayako Kato and Chika Okada analyzed the data. Ayako Kato drafted the manuscript. Ehab S Eshak, Hiroyasu Iso, and Akiko Tamakoshi provided a critical review of the content. Akiko Tamakoshi and Hiroyasu Iso coordinated the research. All authors approved the final manuscript.
## CONFLICT OF INTEREST
The authors declare that they have no competing interests.
## ETHICS STATEMENT
This study was approved by the Ethical Board of Nagoya University School of Medicine, Aichi, Japan, and Osaka University Graduate School of Medicine, Osaka, Japan.
## DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be made available by the authors, upon justified requests to the steering committee of the JACC study.
## References
1. Sung H, Ferlay J, Siegel RL. **Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA A Cancer J Clin* (2021.0) **71** 209-249
2. Henley SJ, Ward EM, Scott S. **Annual report to the nation on the status of cancer, part I: national cancer statistics**. *Cancer* (2020.0) **126** 2225-2249. PMID: 32162336
3. Arnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F. **Global patterns and trends in colorectal cancer incidence and mortality**. *Gut* (2017.0) **66** 683-691. PMID: 26818619
4. Hong S, Won YJ, Lee JJ. **Community of population‐based regional cancer registries. Cancer statistics in Korea: incidence, mortality, survival, and prevalence in 2018**. *Cancer Res Treat* (2021.0) **53** 301-315. PMID: 33735559
5. **Cancer statistics in Japan 2021**. (2021.0)
6. Katanoda K, Hori M, Saito E. **Updated trends in cancer in Japan: incidence in 1985–2015 and mortality in 1958–2018 – a sign of decrease in cancer incidence**. *J Epidemiol* (2021.0) **31** 426-450. PMID: 33551387
7. 7
World Cancer Research Fund
. International/American Institute for Cancer Research. Diet, Nutrition, Physical Activity and Colorectal Cancer
2017; 8–10. Available at: https://www.wcrf.org/wp‐content/uploads/2021/02/Colorectal‐cancer‐report.pdf (Accessed: January 20, 2022).. *International/American Institute for Cancer Research* (2017.0) 8-10
8. Sasazuki S, Inoue M, Shimazu T. **Development and evaluation of cancer prevention strategies in Japan. Evidence‐based cancer prevention recommendations for Japanese**. *Jpn J Clin Oncol* (2018.0) **48** 576-586. PMID: 29659926
9. Calder PC. **Very long‐chain n‐3 fatty acids and human health: fact, fiction and the future**. *Proc Nutr Soc* (2018.0) **77** 52-72. PMID: 29039280
10. Cockbain AJ, Toogood GJ, Hull MA. **Omega‐3 polyunsaturated fatty acids for the treatment and prevention of colorectal cancer**. *Gut* (2012.0) **61** 135-149. PMID: 21490374
11. Aglago EK, Huybrechts I, Murphy N. **Consumption of fish and long‐chain n‐3 polyunsaturated fatty acids is associated with reduced risk of colorectal cancer in a large European cohort**. *Clin Gastroenterol Hepatol* (2020.0) **18** 654-666.e6. PMID: 31252190
12. Kim Y, Kim J. **Intake or blood levels of n‐3 polyunsaturated fatty acids and risk of colorectal cancer: a systematic review and meta‐analysis of prospective studies**. *Cancer Epidemiol Biomarkers Prev* (2020.0) **29** 288-299. PMID: 31767566
13. Sasazuki S, Inoue M, Iwasaki M. **Japan public health center‐based prospective study group. Intake of n‐3 and n‐6 polyunsaturated fatty acids and development of colorectal cancer by subsite: Japan public health center‐based prospective study**. *Int J Cancer* (2011.0) **129** 1718-1729. PMID: 21120874
14. Song M, Chan AT, Fuchs CS. **Dietary intake of fish, ω‐3 and ω‐6 fatty acids and risk of colorectal cancer: a prospective study in U.S. men and women**. *Int J Cancer* (2014.0) **135** 2413-2423. PMID: 24706410
15. Iacopetta B. **Are there two sides to colorectal cancer?**. *Int J Cancer* (2002.0) **101** 403-408. PMID: 12216066
16. Tamakoshi A, Ozasa K, Fujino Y. **Cohort profile of the Japan collaborative cohort study at final follow‐up**. *J Epidemiol* (2013.0) **23** 227-232. PMID: 23583921
17. **The Ministry of Health, Labour and Welfare, Ministry of Education, Culture, Sports, Science and Technology (2002): Ethical Guidelines for Epidemiologic Research**. 2002
18. **Working Group on Development of Ethics Guidelines for Informed Consent in Epidemiologic Research. Ethical guidelines on informed consent in epidemiologic research Version1.0. April 10, 2000**
19. Date C, Fukui M, Yamamoto A. **JACC study group. Reproducibility and validity of a self‐administered food frequency questionnaire used in the JACC study**. *J Epidemiol* (2005.0) **15** S9-S23. PMID: 15881192
20. Willett WC, Howe GR, Kushi LH. **Adjustment for total energy intake in epidemiologic studies**. *Am J Clin Nutr* (1997.0) **65** 1220S-1228S. PMID: 9094926
21. Williams CM, Burdge G. **Long‐chain n‐3 PUFA: plant v. marine sources**. *Proc Nutr Soc* (2006.0) **65** 42-50. PMID: 16441943
22. Barceló‐Coblijn G, Murphy EJ. **Alpha‐linolenic acid and its conversion to longer chain n‐3 fatty acids: benefits for human health and a role in maintaining tissue n‐3 fatty acid levels**. *Prog Lipid Res* (2009.0) **48** 355-374. PMID: 19619583
23. Serraino M, Thompson LU. **Flaxseed supplementation and early markers of colon carcinogenesis**. *Cancer Lett* (1992.0) **63** 159-165. PMID: 1314132
24. Jenab M, Thompson LU. **The influence of flaxseed and lignans on colon carcinogenesis and beta‐glucuronidase activity**. *Carcinogenesis* (1996.0) **17** 1343-1348. PMID: 8681453
25. Williams D, Verghese M, Walker LT, Boateng J, Shackelford L, Chawan CB. **Flax seed oil and flax seed meal reduce the formation of aberrant crypt foci (ACF) in azoxymethane‐induced colon cancer in fisher 344 male rats**. *Food Chem Toxicol* (2007.0) **45** 153-159. PMID: 17045379
26. Nagel JM, Brinkoetter M, Magkos F. **Dietary walnuts inhibit colorectal cancer growth in mice by suppressing angiogenesis**. *Nutrition* (2012.0) **28** 67-75. PMID: 21795022
27. González‐Fernández MJ, Ortea I, Guil‐Guerrero JL. **α‐Linolenic and γ‐linolenic acids exercise differential antitumor effects on HT‐29 human colorectal cancer cells**. *Toxicol Res (Camb)* (2020.0) **9** 474-483. PMID: 32905142
28. Chamberland JP, Moon HS. **Down‐regulation of malignant potential by alpha linolenic acid in human and mouse colon cancer cells**. *Fam Cancer* (2015.0) **14** 25-30. PMID: 25336096
29. Sasaki A, Nagatake T, Egami R. **Obesity suppresses cell‐competition‐mediated apical elimination of RasV12‐transformed cells from epithelial tissues**. *Cell Rep* (2018.0) **23** 974-982. PMID: 29694905
30. Kraja B, Muka T, Ruiter R. **Dietary fiber intake modifies the positive association between n‐3 PUFA intake and colorectal cancer risk in a Caucasian population**. *J Nutr* (2015.0) **145** 1709-1716. PMID: 26041678
31. Daniel CR, McCullough ML, Patel RC. **Dietary intake of omega‐6 and omega‐3 fatty acids and risk of colorectal cancer in a prospective cohort of U.S. men and women**. *Cancer Epidemiol Biomarkers Prev* (2009.0) **18** 516-525. PMID: 19190143
32. Shin A, Cho S, Sandin S, Lof M, Oh MY, Weiderpass E. **Omega‐3 and ‐6 fatty acid intake and colorectal cancer risk in Swedish women's lifestyle and health cohort**. *Cancer Res Treat* (2020.0) **52** 848-854. PMID: 32138465
33. Nguyen S, Li H, Yu D. **Dietary fatty acids and colorectal cancer risk in men: a report from the Shanghai Men's health study and a meta‐analysis**. *Int J Cancer* (2021.0) **148** 77-89. PMID: 32638381
34. Calle EE, Rodriguez C, Jacobs EJ. **The American Cancer Society cancer prevention study II nutrition cohort: rationale, study design, and baseline characteristics**. *Cancer* (2002.0) **94** 2490-2501. PMID: 12015775
35. Muka T, Kiefte‐de Jong JC, Hofman A, Dehghan A, Rivadeneira F, Franco OH. **Polyunsaturated fatty acids and serum C‐reactive protein: the Rotterdam study**. *Am J Epidemiol* (2015.0) **181** 846-856. PMID: 25899092
36. Nagata C, Mizoue T, Tanaka K. **Soy intake and breast cancer risk: an evaluation based on a systematic review of epidemiologic evidence among the Japanese population**. *Jpn J Clin Oncol* (2014.0) **44** 282-295. PMID: 24453272
37. Song M, Nishihara R, Cao Y. **Marine ω‐3 polyunsaturated fatty acid intake and risk of colorectal cancer characterized by tumor‐infiltrating T cells**. *JAMA Oncol* (2016.0) **2** 1197-1206. PMID: 27148825
38. Rothwell PM, Wilson M, Elwin CE. **Long‐term effect of aspirin on colorectal cancer incidence and mortality: 20‐year follow‐up of five randomised trials**. *Lancet* (2010.0) **376** 1741-1750. PMID: 20970847
|
---
title: Vaspin accelerates the proliferation, invasion and metastasis of Triple‐Negative
breast cancer through MiR‐33a‐5p/ABHD2
authors:
- Xin‐Hui Cao
- Xiu Chen
- Kai Yang
- Ya‐Lin Wang
- Ming‐Xing Liang
- Yin‐Jiao Fei
- Jin‐Hai Tang
journal: Cancer Medicine
year: 2022
pmcid: PMC9972114
doi: 10.1002/cam4.5241
license: CC BY 4.0
---
# Vaspin accelerates the proliferation, invasion and metastasis of Triple‐Negative breast cancer through MiR‐33a‐5p/ABHD2
## Abstract
We found that compared with normal‐weight breast cancer patients, serum vaspin levels in overweight patients were significantly increased, and vaspin could promote the progression of triple‐negative breast cancer by regulating the miR‐33a‐5p/ABHD2 pathway
### Objective
To explore the influence and the underlying mechanism of vaspin (visceral adipose tissue‐derived serpin) on the development of triple‐negative breast malignancy.
### Methods
First, we analyzed medical records and screened out 22 breast cancer patients with different BMI according to inclusion and exclusion criterion, and measured serum vaspin of those patients. Then we studied the effects of vaspin on TNBC cell lines by using EdU assay, colony formation, transwell and wound‐healing assay. Later, we used bioinformatics analysis to identify downstream effectors and verify with qRT‐PCR, luciferase assay, western blot, etc.
### Results
We found the vaspin level was positively correlated with BMI in breast malignant patients and vaspin could significantly enhance the proliferation, infiltration and transferring of triple‐negative breast cancer cells by restraining the expression of miR‐33a‐5p. By using bioinformatic analysis and luciferase assay, we identified miR‐33a‐5p directly regulating ABHD2.
### Conclusion
Vaspin, as a cancer‐promoting cytokine, may inhibit miR‐33a‐5p thus increasing the level of ABHD2 to promote the development of the triple‐negative breast cancer.
## INTRODUCTION
Breast cancer is one of the upmost causes of female cancer deaths worldwide. 1, 2 According to latest study, the incidence of breast cancer accounted for $19.2\%$ of female cancers in China in 2018, and the mortality rate of breast cancer patients was about $9.1\%$. 3 Clinical case analysis showed that obesity could be a risk factor for awful prognosis among breast cancer patients. 4, 5 Among all subtypes of breast cancer, triple‐negative breast cancer (TNBC) is the most aggressive subtype with poor prognosis. Analysis of different subtypes of obese breast cancer patients showed that obese patients with TNBC would develop into a worse prognosis. 6 Adipokines are components of the cancer cells associated microenvironment and play important roles in the occurrence and development of cancer cells. 7 In 2005, researchers discovered a novel type of adipose factors called vaspin, which was derived from visceral fat and belonged to the serine protein inhibitor family. 8 *Human serum* vaspin protein consists of 415 amino groups and has $40.5\%$ identity with α1‐antitrypsin. 8 A variety of tissues in the human body produced vaspin, like adipose tissue, liver, pancreas, skin, skeletal muscle and adipose tissue, and among them, the production of vaspin in the liver is the highest. 9, 10 The study has found that vaspin concentrations in serum are positively correlated with Body Mass Index (BMI), waist circumference, and body fat percentage. 11 Meanwhile, other study also showed that vaspin levels could be significantly increased in obese subjects. 12 Previous study showed the elevated serum vaspin level in patients with endometrial cancer and colorectal cancer was associated with the malignant degree. 13, 14 Regarding breast cancer, so far there are few studies about the influences of vaspin upon breast cancer.
MicroRNAs (miRNAs) belong to the class of non‐coding RNA molecules that possess approximately endogenous 22 bases. Literatures had already reported the diverse features of dysregulated miRNAs in progression of varieties of cancers including breast, stomach, lung and prostate cancer. 15 It has been determined that miRNAs related to cell behavior, such as proliferation, inflammation, stress response, migration, invasion, differentiation, and apoptosis. 15, 16 Interestingly, microRNA‐33a‐5p (miR‐33a‐5p) was confirmed to be down‐regulated in melanoma as well as hepatocellular carcinoma, representing its suppression on cancer progression. 17, 18 However, the role of miR‐33a‐5p in breast tumor progression holds obscure.
We recently found vaspin could inhibit the expression of miR‐33a‐5p in TNBC cell lines and identified a latent target of miR‐33a‐5p called a/b‐hydrolase domain containing 2 (ABHD2). Previous studies showed that in ovarian cancer ABHD2 lead to a series of adverse reactions like anoikis resistance, chemoresistance and so on. For prostate cancer, ABHD2 promoted cell proliferation and migration. ABHD2 was related to modulating Akt/p70S6K and JNK, thus promoting the malignant progression of cells. 19 Thus, we hypothesized that vaspin could promote the progress of the TNBC by inhibiting miR‐33a‐5p thus inducing ABHD2 expression.
In the present study, we showed that vaspin was associated with poor prognosis of TNBC patients. Additionally, we found vaspin promoted the proliferation, invasion, and metastasis of triple‐negative breast cancer by suppressing the expression of miR‐33a‐5p. Furthermore, we proved that ABHD2 was a direct target of miR‐33a‐5p in TNBC and miR‐33a‐5p inhibited cell proliferation, migration and invasion in TNBC cells by regulating ABHD2 expression. Therefore, our study reveals vaspin as a novel cancer‐promoting cytokine through miR‐33a‐5p.
## Patients and samples
After screening, 22 Asian patients with invasive breast cancer diagnosed in the Department of Breast Surgery of Jiangsu Provincial People's Hospital were included in the study (Figure 1). The selective criteria were as follows, inclusion criteria: [1] age: from 18 to 75 year‐old; [2] gender: female; [3] never received surgery, chemotherapy, radiotherapy or other anti‐tumor treatment previously; [4] the breast cancer diagnosis was confirmed by needle biopsy or surgical pathology; [5] all participants voluntarily signed an informed consent before sample collections; [6] the size of the mass was greater than or equal to 150 mg. Exclusion criteria: [1] Complicated with basic diseases such as hypertension and diabetes; [2] Complicated with infection or other inflammatory reactions; [3] Combined with other primary tumors, such as gastric cancer, colorectal cancer, ovarian cancer; [4] Those who have used other drugs in clinical trials within 4 weeks before the first medication; [5] Subjects have congenital or acquired immune deficiencies (such as autoimmune hepatitis, interstitial pneumonia, uveitis, enteritis).
**FIGURE 1:** *Flow chart of inclusion and exclusion of serum vaspin levels in breast cancer patients.*
Then we used test tubes free of pyrogen and endotoxin to collect blood from patients as required. After centrifugation at 3000 rpm for 10 min, the serum was quickly and carefully stored at −80°C. Next, we measured the vaspin in the serum according to the instructions of the human vaspin ELISA test kit (Xinquan, China). Based on the BMI, patients were divided into normal (BMI < 24) and overweight (BMI:24–28) groups, with 11 patients in each group.
## Cells and cell culture
Human breast cancer cell lines MDA‐MB‐231 were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China) and Human breast cancer cell lines SUM1315MO2 were sponsored by Stephen Ethier (University of Michigan, Ann Arbor, MI, USA). The culture condition were Dulbecco's modified Eagle's medium (DMEM) with high glucose (HyClone), $10\%$ fetal bovine serum (FBS), and $1\%$–$1.5\%$ penicillin with streptomycin (Cellbio). And the atmosphere of culture were humidity with $5\%$ CO2 at 37°C.
## Real‐time quantitative PCR (RT‐qPCR)
To evaluate the optimal concentration and culture time of vaspin affecting the level of miR‐33a‐5p, as well as the transfection efficiency of miRNA mimics, we extracted the total RNAs with TRIzol reagent on the basis of manufacturer's instructions and converted them into complementary DNAs through HisScript II Q RT SuperMix for qPCR (Vazyme). The procedure of PCR was preformed following the instructions of ChamQ SYBR qPCR Master Mix (High ROX Premixed) (Vazyme) on the StepOne Plus Real‐Time PCR System (Thermo Fisher Scientific, USA). The relative expression of miR‐33a‐5p was calculated by the 2−ΔΔCt method, and the internal reference was U6. Human miR‐33a‐5p reverse transcription primer:5′‐GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGCAAT‐3′ Forward Primer:5′‐CG CGGTGCATTGTAGTTGC‐3′ Reverse Primer:5′‐AGTGC AGGGTCCGAGGTATT‐3′.
In the same way, we measure the downstream genes of miR‐33a‐5p, and the internal reference was GAPDH. The primer sequences are as follows: ABHD2: Forward Primer:5′‐CATGCTGGAGACTCCCGAAC‐3′ Reverse Primers:5′‐CAAACACCGGACGATCACGTA‐3′; ARID5B:Forward Primer:5′‐TCTTAAAGGCAGACCACGCAA‐3′ Reverse Primers:5′‐TGCCATCGGAATTGTTGTTGG‐3′; ABCA1:Forward Primer:5′‐ACCCACCCTATGAACAACA TGA‐3′ Reverse Primers:5′‐GAGTCGGGTAACGGAAAC AGG‐3′; IRS2:Forward Primer:5′‐CGGTGAGTTCTACGG GTACAT‐3′ Reverse Primers:5′‐TCAGGGTGTATTCATCC AGCG‐3′. All the above primers are synthesized by Sangon Biotech Co. Ltd.
## Transfection of miR‐33a‐5p
Human miR‐33a‐5p mimics were purchased from RiboBio Company (Guangzhou, China). According to the protocol, miR‐33a‐5p mimics were transfected into MDA‐MB‐231 and SUM1315MO2 cells (5 × 105 cells/well) through an usage of Lipofectamine™ 3000 transfection reagent (Invitrogen). After 8 h, cells were continued to culture for 48 h with the replaced medium containing $10\%$ FBS.
## Western blot assay
Cells were rinsed with cold PBS and with RIPA buffer (Beyotime) on ice for half an hour. The lysate was centrifuged at 12,000 g, 4°C for 20 min. BCA protein determination kit (Beyotime) was employed to measure the protein concentration. Then the protein was separated by SDS‐PAGE and transferred to a polyvinylidene fluoride (PVDF) membrane, which was blocked with $5\%$ skimmed milk powder in TBST (Tris $0.05\%$ Tween‐20), incubated with primary antibody against ABHD2 (1:1000 dilution. Proteintech) and GAPDH (1:10000 dilution. Proteintech) at 4°C overnight, washed with TBST, and incubated with HRP‐conjugated secondary antibody (1:1000 dilution. Proteintech) for 1 h at room temperature. Then bands were visualized with an enhanced chemiluminescence (Beyotime) system.
## Cell migration and invasion assay
By the transwell experiment, the functions of vaspin and miR‐33a‐5p upon the migration and invasion of MDA‐MB‐231 and SUM1315MO2 cells were determined. For transwell migration assay, MDA‐MB‐231 and SUM1315MO2 cells were firstly treated with vaspin or transfected with miR‐33a‐5p mimics. After 48 h, cells were digested with trypsin, resuspended in a serum‐free medium and placed in the upper layer of the chamber (1x104 cells/well). Completed medium containing $20\%$ serum were added to the lower chamber. The chamber was fixed with $4\%$ paraformaldehyde (Servicebio)and dyed with $1\%$ crystal violet (Sangon biotech) 24 h later. The cells inside the upper layer of the chamber were gently cleaned by a cotton swab. Then the cells migrated at the bottom of the chamber were imaged. For the invasion experiment, first of all, the chamber was covered with diluted Matrigel. The subsequent procedures were in accordance with the migration experiment. Because SUM1315MO2 has a weaker invasion ability, the incubation time is 48 h, while MDA‐MB‐231 is still 24 h.
## Wound healing
First, the treated MDA‐MB‐231 or SUM1315MO2 cells in 6‐well plates were cultured to a confluency of $90\%$–$95\%$. Then we used a sterile 200 μL pipette to scrape a linear wound across the cell layer. Floated cells and debris were washed by PBS and adherent cells were cultured in serum‐free medium. Finally, the width of the wounds was pictured via the microscope camera (Canon, Japan) instantly. Because of the differences in cell growth rate and migration ability between, the width of the wounds of MDA‐MB‐231 cells were taken after 24 h, and the SUM1315MO2 cells were taken after 48 h. The ratio of (1‐existent area/initial area) × $100\%$ represents the migration ability of cells.
## Cell proliferation assay
The colony formation experiment and the EdU assay (RiboBio) were performed to evaluate the proliferation ability of cells. MDA‐MB‐231 and SUM1315MO2 cells were treated with vaspin or transfected with miR‐33a‐5p mimics. After 48 h, cells were digested with trypsin and inoculated into a 6‐well plate (800 cells/well) for the colony formation experiment or into 96‐well plates (104 cells/well) for the EdU assay. For the colony formation experiment, the cells were fixed and stained with $4\%$ paraformaldehyde and $1\%$ crystal violet, respectively, after 10 days of culture. Then we observed and counted the generated cell colonies. For the Edu cell proliferation assay, after 24 h of growth, the cells were operated in terms of the instructions of the Edu cell proliferation kit. The picture was obtained by using an inverted fluorescence microscope and the data were analyzed.
## ABHD2 3'UTR construct and luciferase reporter gene detection
Design PCR amplification primers for ABHD2‐WT or ABHD2‐MUT, PCR amplify the 3'UTR sequence of the gene, purify and recover the target fragment, connect it to a dual fluorescent reporter vector, transform DH5α competent E. coli, spread the plate, pick a single clone for colony PCR identification, and finally DNA sequencing verified the constructed plasmid vector (RiboBio). Using Lipofectamine 3000 (Invitrogen), MDA‐MB‐231 or SUM1315MO2 cells were co‐transfected with ABHD2‐WT or ABHD2‐MUT and miR‐33a‐5p or miR‐NC. After 48 h, the luciferase activity was evaluated by the dual‐luciferase reporter system (Vazyme) in accordance with the manufacturer's protocol.
## Bioinformatics and statistical analysis
*Target* genes of miR‐33a‐5p were predicted by miRDB (http://mirdb.org/), TargetScan (http://www.targetscan.org/), and miRTarBase (http://mirtarbase.cuhk.edu.cn/php/index.php). GEPIA database (http://gepia.cancer‐pku.cn/) was used to analyze difference in the gene expression of breast cancer. Kaplan–Meier Plotter website (http://www.kmplot.com/breast) was utilized to analyze the survival correlation between ABHD2 expression and breast cancer. The results were displayed as the mean ± SD. All experiments were independently repeated three times. GraphPad Prism version 8.0.2 software was used for data analysis. Differences between two groups were calculated by Student's t‐test. Differences among three or more groups were performed with the one‐way analysis of variance. $p \leq 0.05$ was regarded as statistically significant.
## Serum vaspin level was positively correlated with BMI among breast cancer patients
Table 1 showed the clinicopathology information of 22 patients included in the following analysis. We first analyzed vaspin level in patients'serum and found that the average vaspin content in the serum of the overweight group was higher than that of the normal one with statistical significance (Figure 2A). We further analyzed whether there was a linear relationship between vaspin and BMI. As shown in Figure 2B, the result came that a certainly positive linear relationship between serum vaspin content and BMI was emerged.
## Effect of vaspin on the expression of miR‐33a‐5p in breast cancer cells
Previous studies showed vaspin inhibits miR‐33a‐5p in THP‐1 macrophages‐derived foam cells. To reveal the influence of vaspin on miR‐33a‐5p expression in breast cancer cells, we treated MDA‐MB‐231 cell line with 0, 0.1, 1, 10, 100 ng/mL concentration gradient for 24 h, and then the expression of miR‐33a‐5p was measured by qPCR. As demonstrated in Figure 3A, vaspin decreased the expression of miR‐33a‐5p in MDA‐MB‐231 cell, with largest inhibitory effect at 10 ng/mL. Next, we treated the MDA‐MB‐231 cell line with 10 ng/mL vaspin for 0, 8, 24, and 48 h. As shown in Figure 3B, 48 h treatment of vaspin had largest inhibitory effect on miR‐33a‐5p expression. To further confirm the inhibitiory influence of vaspin on miR‐33a‐5p, we treated SUM1315MO2 with 10 ng/mL for 48 h and used qPCR to evaluate miR‐33a‐5p level. As exhibited in Figure 3C, vaspin treatment significantly decreased the miR‐33a‐5p expression in SUM1315MO2 cells.
**FIGURE 3:** *Vaspin inhibited the expression of miR‐33a‐5p in breast cancer cells. (A, B) MDA‐MB‐231 cells were cultured by medium added multipy concentrations of vaspin (0, 0.1, 1.0, 10, 100 ng/mL) for 24 h or 10 ng/mL vaspin for different time periods (0, 8, 24, 48 h) culture, measure the expression of miR‐33a‐5p; (C) The different expression of miR‐33a‐5p SUM1315MO2 cells interfered with 0 or 10 ng/mL vaspin for 48 h. All data were the mean ± SD of three independent experiments, and each performed in triplicate. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001 versus 0 ng/mL or 0 h.*
## Vaspin promotes proliferation, invasion, and metastasis of TNBC cells
We next studied the functional role of vaspin on TNBC cell. We treated the MDA‐MB‐231 and SUM1315MO2 cells with 10 ng/mL vaspin for 2 days. The differences in migration and invasion ability between the treated group and the untreated group were observed through transwell and wound healing experiments. The results were shown in Figure 4A–F, vaspin could significantly promote the cell migration and invasion. Figure 4G–J verified the effect of vaspin on the cell proliferation via the Edu and colony formation assays. Results showed that vaspin might enhance the evolution of TNBC cells.
**FIGURE 4:** *Vaspin promoted migration, invasion and metastasis of triple‐negative breast cancer cells. (A, B) The 24 h Transwell migration test results and analysis of MDA‐MB‐231 and SUM1315MO2 cells interference group and control group (100 × magnification); (C)The 24 h Matrigel invasion test results and analysis of MDA‐MB‐231 cells interference group and control group (100 × magnification); (D) The 48 h Transwell invasion test results and analysis of SUM1315MO2 cells a interference group and control group (100 × magnification); (E)The 24 h wound‐healing experiment results and analysis of MDA‐MB‐231 cells interference group and control group (40 × magnification); (F)The 48 h wound‐healing experiment results and analysis of SUM1315MO2 cells interference group and control group (40 × magnification); (G, H) The 14d Colony formation experiment results and analysis of MDA‐MB‐231 and SUM1315MO2 cells interference group and control group; (I, J) The 24 h EdU cell proliferation assay results and analysis of MDA‐MB‐231 and SUM1315MO2 cells interference group and control group (100 × magnification); All data were calculated by the mean ± SD of three independent experiments. *p < 0.05, **p < 0.01 versus the control.*
## miR‐33a‐5p inhibited the proliferation, invasion, and metastasis of TNBC cell lines
Previous studies have shown that vaspin could reduce the expression of miR‐33a‐5p. Here we validated the influence of miR‐33a‐5p TNBC cell lines. Both MDA‐MB‐231 and SUM1315MO2 cell lines were transfected with miR‐33a‐5p mimics and scrabble mimics. And 48 h after transfection, the miR‐33a‐5p level were significantly increased in both cell lines (Figure 5A,B). Migration assay and wound healing assay showed overexpression of miR‐33a‐5p could make the TNBC cells migration ability decline (Figure 5C,D, G,H). Invasion assay showed overexpression of miR‐33a‐5p weakened invasion ability in TNBC cell line (Figure 5E,F). Both colony formation assay and EdU assay showed that TNBC cells proliferation ability could be promoted by overexpression of miR‐33a‐5p (Figure 5I–L). These results confirmed the proliferation, invasion, and migration abilities of TNBC cell could be inhibited by miR‐33a‐5p.
**FIGURE 5:** *miR‐33a‐5p inhibited migration, invasion and metastasis of triple‐negative breast cancer cells. (A, B) RT‐qPCR results demonstrated that miR‐33a‐5p expression were obviously elevated in MDA‐MB‐231 and SUM1315MO2 cells transfected with miR‐33a‐5p mimic in comparison with the control groups.; (C, D) The 24 h Transwell migration test results and analysis of MDA‐MB‐231 and SUM1315MO2 miR‐33a‐5p‐mimic group and NC group (100 × magnification); (E) The 24 h Transwell invasion test results and analysis of MDA‐MB‐231 experimental group and NC group (100 × magnification); (F) The 48 h Transwell invasion test results and analysis of SUM1315MO2 experimental and NC group (100 × magnification); (G)The 24 h wound‐healing experiment results and analysis of MDA‐MB‐231 cells experimental and NC group (40 × magnification); (H)The 48 h wound‐healing experiment results and analysis of SUM1315MO2 cells experimental and NC group (40 × magnification); (I, J) The 14d Colony formation experiment results and analysis of MDA‐MB‐231 and SUM1315MO2 cells experimental and NC group; (K, L) The 24 h EdU cell proliferation assay results and analysis of MDA‐MB‐231 and SUM1315MO2 cells experimental and NC group (100 × magnification); All data were showed as the mean ± SD and triplicates were performed. *p < 0.05, **p < 0.01 versus NC group.*
## miR‐33a‐5p reverses the effect of vaspin on TNBC
To further seek whether miR‐33a‐5p can inhibit the promotion of vaspin on the invasion, invasion, metastasis and proliferation of MAD‐MB‐231 and SUM1315MO2 cells, miR‐33a‐5p mimic, vaspin, vapsin+miR‐33a‐5p mimic and blank groups were subjected to phenotypic experiments. Results of the transwell experiment discovered that the cell number in the vapsin+miR‐33a‐5p group was remarkably less than that in the vaspin group, indicating that miR‐33a‐5p might abate the ability of vaspin to promote cell migration and infiltration (Figure 6A–D). Wound‐healing experiments presented that in comparison to the vaspin group, the wound healing speed of vapsin+miR‐33a‐5p was significantly slower, declaring that miR‐33a‐5p would eliminate the metastasis‐promoting energy of vaspin (Figure 6E,F). Similarly, in the colony generation experiment (Figure 6G,H) and EdU (Figure 6I,J) experiment, we found that the number of colonies and the proportion of proliferating cells produced by the miR‐33a‐5p group and the vapsin+miR‐33a‐5p group were similar. While, compared to the vaspin group, cells in the vapsin+miR‐33a‐5p group were significantly reduced. Therefore, miR‐33a‐5p might reverse the proliferative action of vaspin in the triple‐negative breast cancer cells.
**FIGURE 6:** *miR‐33a‐5p reversed the effect of vaspin on triple‐negative breast cancer (A–J) MDA‐MB‐231 cells and SUM1315MO2 cells were given transfection miR‐33a‐5p, vaspin interference, transfection miR‐33a‐5p and vaspin interference, or no operation. Consistent with the previous conditions, the cells in the above treatment groups were subjected to transwell migration experiment, transwell invasion experiment, wound‐healing experiment, cell colony formation experiment and EdU cell proliferation assay. At the same time, an analysis of differences between treatment groups was performed. All experiments were performed in triplicates. *p < 0.05, **p < 0.01.*
## miR‐33a‐5p directly targets ABHD2
We used miRDB (http://mirdb.org), miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/index.html) and TargetScan (http://www.targetscan.org)databases to analyze the downstream genes of miR‐33a‐5p, and intersected the results of the three data. As it showed in Figure 7A, 19 genes showed in all predicted results from three database. As it showed in Figure 7B, ARID5B, ABCA1, IRS2, and ABHD2 were differentially expressed in breast cancer tissues based on GEPIA (http://gepia.cancer‐pku.cn) database among those 19 genes. Then we transfected the miR‐33a‐5p/mimics and NC groups and used qPCR to evaluate mRNA level of those 4 genes. The result showed miR‐33a‐5p could inhibit ABHD2 in both MDA‐MB‐231 and SUM1315MO2 cells with significant differences, while ARID5B was obviously decreased in MDA‐MB‐231 but without significant differences in SUM1315MO2, also other 2 genes were not significantly affected in either cell lines (Figure 7C). Previous studies showed ABHD2 promoted cell proliferation and migration. Here we found ABHD2 was overexpressed in breast cancers. In order to verify that miR‐33a‐5p binds to ABHD2 wild‐type 3'‐UTR or mutant 3'‐UTR, luciferase assay was applied in MDA‐MB‐231 cells and SUM1315MO2 cells (Figure 7D). The luciferase assay showed that the overexpression of miR‐33a‐5p inhibited the luciferase activity of the ABHD2‐MT‐3'‐UTR reporter, but not the ABHD2‐MUT‐3'‐UTR reporter (Figure 7E,F).
**FIGURE 7:** *(A) Used Venn diagram to analyze miR‐33a‐5p downstream target genes in miRDB, miRTarBase and TargetScan databases. (B) GEPIA database shows the differential expression ARID5B, ABCA1, ZC3H12C, IRS2, SEMA7A, HMGA2, and ABHD2 in breast cancer. (C) RT‐qPCR results showed differential expression of ABHD2, ABCA1, ARID5B and IRS2 in miR‐33a‐5p/mimic or NC‐transfected MDA‐MB‐231 and SUM1315M02 cells. (D) The TargetScan software predicts that there is a potential binding site for miR‐33a‐5p on the 3'UTR of the ABHD2 gene. (E, F) MDA‐MB‐231 cells and SUM1315MO2 cells were co‐transfected with a wild‐type or mutant 3′‐UTR sequence of ABHD2 and the miR33a‐5p mimic or NC, and the luciferase activity was measured at 48 h and analyzed. (G, H) Western blot experiment to determine the expression and analysis results of the ABHD2 gene in MDA‐MB‐231 cells and SUM1315MO2 cells miR‐33a‐5p/mimic, vaspin, vaspin+miR‐33a‐5p/mimic and the control group. (I) The Kaplan–Meier Plotter database was used to estimate PFS with low (n = 309) or high (n = 309) levels of ABHD2 in patients with triple‐negative breast cancer. (J) The Kaplan–Meier Plotter database was applied to estimate the OS of TNBC patients with low (n = 180) or high (n = 61) levels of ABHD2. Experimental data were performed in triplicates. *p<0.05, **p<0.01*
Next, we divide the MDA‐MB‐231 and SUM1315MO2 cells into vaspin+miR‐33a‐5p/mimic, vaspin, miR‐33a‐5p/mimic and blank treatments, and then collected the protein to observe the downstream gene ABHD2 gene protein expression level. The results are shown in Figure 7G,H. These results indicated vaspin could induce ABHD2 expression by inhibiting miR‐33a‐5p amplification, and upregulation of miR‐33a‐5p could decrease ABHD2 expression with or without vaspin induction.
Finally, We used the database K‐M Plotter (https://kmplot.com/analysis) to figure up the impact of ABHD2 on the disease‐free survival (DFS) rate and overall survival (OS) rate of breast cancer patients. High expression of ABHD2 is detrimental to DFS and OS of breast cancer patients (Figure 7I–G).
## DISCUSSION
According to recent study, Asians have a higher body fat percentage than Caucasians with the same BMI. 20 Survey studies have found that the proportion of obese and overweight people in China gradually increases over the age, peaks at middle‐aged and elder groups. 21 Obesity and overweight not only increases the risk of chronic diseases such as high blood pressure, diabetes, and hyperlipidemia but also affects the incidence and prognosis of cancer. 22 TNBC is a subtype of breast cancer with awful prognosis. 23 Clinical studies have proved that TNBC patients with obese and overweight have worse prognosis. 24 In our study, we put forward the idea that the high expression of vaspin in obese patients affected the miR‐33a‐5p/ABHD2 thus promoting the progression of TNBC.
Previous studies showed that people with high BMI had higher vaspin levels in serum. 12 *In this* study, we found that breast cancer patients who were overweight had higher serum vaspin level than patients with normal weight. In addition, we showed that increased vaspin might promote the progression of triple‐negative breast cancer. As serum vaspin level can be managed through intervention in weight control (weight loss surgery or diet control), 25, 26 the weight control may be able to reduce the risk of TNBC progression.
It has been previously delivered that miR‐33a‐5p was a tumor suppressor in several types of cancer. Research by Yili Wang and his colleagues discovered that upregulation of miR‐33a‐5p in vitro could target methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) to hold up the growth and migration of rectal cancer cells. 27 Zhaohui Gong and his coworkers delivered that the tumor suppressors like miR‐33a‐5p and miR‐128‐3p, in the whole blood of lung cancer patients were highly stable and were supposed to be biomarkers for early lung cancer diagnosis. 28 At present, there are few studies on miR‐33a‐5p in breast cancer. For example, the study of Ji Wu et al. uncovered that the overexpression of miR‐33a‐5p could significantly improve the sensitivity of TNBC to doxorubicin. 29 Our study first reported that vaspin could significantly down‐regulate miR‐33a‐5p in TNBC cells, overexpression of miR‐33a‐5p could rescue the cell phenotype change induced by vaspin, indicating vaspin could be used as one of potential upstream targets of miR‐33a‐5p.
ABHD2 is an abhydrolase domain containing 2 which called α/β‐hydrolase containing protein 2. Previous studies have found that ABHD2 played a key role in the infiltration of macrophages into atherosclerosis. 30 Recent studies have shown that ABHD2 could promote cancer progression. For example, ABHD2 is a direct target of miR‐140‐3p in skin melanoma cells. It could reverse the anti‐tumor effect of miR‐140‐3p, and the influence of miR‐140‐3p on Akt/p70S6K and JNK pathways were reversed by ABHD2 overexpressing. 19 Currently, there was no report on the role of ABHD2 in breast cancer. Through online database analysis and dual‐luciferase experiments, we supposed that ABHD2 may be a direct target of miR‐33a‐5p in breast cancer cells. Besides, we also proved that the mutual effect between vaspin and miR‐33a‐5p could change the quantity of ABHD2. In this study, we did not focus on downstream signaling of ABHD2. Further studies will be required to reveal the detailed roles and respective mechanisms of ABHD2 in breast cancer.
In conclusion, we found that compared with normal weight breast cancer patients, serum vaspin level in overweight patients significantly increased, and vaspin could promote the progression of TNBC by regulating the miR‐33a‐5p/ABHD2 pathway. Through this study, we could gain an in‐depth understanding of the role of vaspin/miR‐33a‐5p/ABHD2 in obese and overweight TNBC, thus better understanding the mechanism of breast cancer deterioration in obese and overweight people. This study will provide new therapeutic insights for inhibiting the progression of TNBC. In the future study, we will reveal the detailed molecular mechanism of ABHD2 in breast cancer.
## AUTHOR CONTRIBUTIONS
Xinhui Cao and Xiu Chen completed most of the cell experiments and drafted the manuscript. Kai Yang performed clinical case sample collection, measurement, and data analysis. Ya‐Lin Wang and Ming‐Xing Liang complete the data analysis of cell experiments. Yin‐Jiao Fei conducted database data analysis. Jinhai Tang He provided experimental protocol, experimental guidance, and data review.
## FUNDING INFORMATION
Our research was supported by the National Natural Science Foundation of China (No. 81902987 and 81872365), National Key Research and Development Program of China (No. 2016YFC0905900) and Jiangsu Provincial Key Research Development Program (No. BE2019731).
## CONFLICT OF INTEREST
In the future study, we will reveal the detailed molecular mechanism of ABHD2 in breast cancer. All authors declare no conflict of interest.
## ETHICS APPROVAL AND CONSENT TO PARTICIPATE
The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of The First Affiliated Hospital of Nanjing Medical University. Written informed consent was obtained from all patients before enrollment.
## DATA AVAILABILITY STATEMENT
The data in this study are open access via https://osf.io/jsnw9/?view_only=12c829a3ac834012b5a82d5a8f0bff05.
## References
1. Siegel RL, Miller KD, Jemal A. **Cancer statistics, 2019**. *CA Cancer J Clin* (2019) **69** 7-34. PMID: 30620402
2. Jung K, Won Y, Kong H, Lee E. **Prediction of cancer incidence and mortality in Korea, 2019**. *Cancer Res Treat* (2019) **51** 431-437. PMID: 30913864
3. Feng RM, Zong YN, Cao SM, Xu RH. **Current cancer situation in China: good or bad news from the 2018 global cancer statistics?**. *Cancer Commun (London, England)* (2019) **39** 22
4. Andò S, Gelsomino L, Panza S. **Obesity, leptin and breast cancer: epidemiological evidence and proposed mechanisms**. *Cancers* (2019) **11** 62. PMID: 30634494
5. Dibaba DT, Ogunsina K, Braithwaite D, Akinyemiju T. **Metabolic syndrome and risk of breast cancer mortality by menopause, obesity, and subtype**. *Breast Cancer Res Treat* (2019) **174** 209-218. PMID: 30465158
6. Widschwendter P, Friedl TW, Schwentner L. **The influence of obesity on survival in early, high‐risk breast cancer: results from the randomized SUCCESS a trial**. *Breast Cancer Res* (2015) **17** 129. PMID: 26385214
7. Umar MI, Hassan W, Murtaza G. **The adipokine component in the molecular regulation of cancer cell survival, proliferation and metastasis**. *Pathol Oncol Res* (2021) **27** 1609828. PMID: 34588926
8. Hida K, Wada J, Eguchi J. **Visceral adipose tissue‐derived serine protease inhibitor: a unique insulin‐sensitizing adipocytokine in obesity**. *Proc Natl Acad Sci USA* (2005) **102** 10610-10615. PMID: 16030142
9. Goktas Z, Owens S, Boylan M, Syn D, Shen CL, Reed DB, San Francisco S, Wang S. **Associations between tissue visfatin/nicotinamide, phosphoribosyltransferase (Nampt), retinol binding protein‐4, and vaspin concentrations and insulin resistance in morbidly obese subjects**. *Mediators Inflamm* (2013) **2013** 1-9. PMID: 24367155
10. Körner A, Neef M, Friebe D. **Vaspin is related to gender, puberty and deteriorating insulin sensitivity in children**. *Int J Obes (2005)* (2011) **35** 578-586
11. Tan BK, Heutling D, Chen J. **Metformin decreases the adipokine vaspin in overweight women with polycystic ovary syndrome concomitant with improvement in insulin sensitivity and a decrease in insulin resistance**. *Diabetes* (2008) **57** 1501-1507. PMID: 18375437
12. Feng R, Li Y, Wang C. **Higher vaspin levels in subjects with obesity and type 2 diabetes mellitus: a meta‐analysis**. *Diabetes Res Clin Pract* (2014) **106** 88-94. PMID: 25151227
13. Cymbaluk‐Płoska A, Chudecka‐Głaz A, Jagodzińska A. **Evaluation of biologically active substances promoting the development of or protecting against endometrial cancer**. *Onco Targets Ther* (2018) **11** 1363-1372. PMID: 29559794
14. Kan JY, Lee YC, Lin YD, Ho WY, Moi SH. **Effect of baseline characteristics and tumor burden on Vaspin expression and progressive disease in operable colorectal cancer**. *Diagnostics (Basel, Switzerland)* (2020) **10** 801. PMID: 33050209
15. Farazi TA, Hoell JI, Morozov P, Tuschl T. **MicroRNAs in human cancer**. *Adv Exp Med Biol* (2013) **774** 1-20. PMID: 23377965
16. Di Leva G, Garofalo M, Croce CM. **MicroRNAs in cancer**. *Annu Rev Pathol* (2014) **9** 287-314. PMID: 24079833
17. Zhang ZR, Yang N. **MiR‐33a‐5p inhibits the growth and metastasis of melanoma cells by targeting SNAI2**. *Neoplasma* (2020) **67** 813-824. PMID: 32305057
18. Lin C, Xiang Y, Sheng J, Liu S, Cui M, Zhang X. **Long non‐coding RNA CRNDE promotes malignant progression of hepatocellular carcinoma through the miR‐33a‐5p/CDK6 axis**. *J Physiol Biochem* (2020) **76** 469-481. PMID: 32621257
19. He Y, Yang Y, Liao Y. **miR‐140‐3p inhibits cutaneous melanoma progression by disrupting AKT/p70S6K and JNK pathways through ABHD2**. *Mol Ther Oncolytics* (2020) **17** 83-93. PMID: 32322665
20. Chooi YC, Ding C, Magkos F. **The epidemiology of obesity**. *Metab Clin Exp* (2019) **92** 6-10. PMID: 30253139
21. Lu J, Bi Y, Ning G. **Curbing the obesity epidemic in China**. *Lancet Diab Endocrinol* (2016) **4** 470-471
22. Smith KB, Smith MS. **Obesity statistics**. *Prim Care* (2016) **43** 121-135. PMID: 26896205
23. Vagia E, Mahalingam D, Cristofanilli M. **The landscape of targeted therapies in TNBC**. *Cancer* (2020) **12** 916
24. Choi Y, Park SK, Ahn KJ. **Being overweight or obese increases the risk of progression in triple‐negative breast cancer after surgical resection**. *J Korean Med Sci* (2016) **31** 886-891. PMID: 27247497
25. Golpaie A, Tajik N, Masoudkabir F. **Short‐term effect of weight loss through restrictive bariatric surgery on serum levels of vaspin in morbidly obese subjects**. *Eur Cytokine Netw* (2011) **22** 181-186. PMID: 22266100
26. Blüher M, Rudich A, Klöting N. **Two patterns of adipokine and other biomarker dynamics in a long‐term weight loss intervention**. *Diabetes Care* (2012) **35** 342-349. PMID: 22190676
27. Yan Y, Zhang D, Lei T. **MicroRNA‐33a‐5p suppresses colorectal cancer cell growth by inhibiting MTHFD2**. *Clin Exp Pharmacol Physiol* (2019) **46** 928-936. PMID: 31209892
28. Pan J, Zhou C, Zhao X. **A two‐miRNA signature (miR‐33a‐5p and miR‐128‐3p) in whole blood as potential biomarker for early diagnosis of lung cancer**. *Sci Rep* (2018) **8** 16699. PMID: 30420640
29. Guan X, Gu S, Yuan M, Zheng X, Wu J. **MicroRNA‐33a‐5p overexpression sensitizes triple‐negative breast cancer to doxorubicin by inhibiting eIF5A2 and epithelial‐mesenchymal transition**. *Oncol Lett* (2019) **18** 5986-5994. PMID: 31788073
30. Miyata K, Nakayama M, Mizuta S. **Elevated mature macrophage expression of human ABHD2 gene in vulnerable plaque**. *Biochem Biophys Res Commun* (2008) **365** 207-213. PMID: 17980156
|
---
title: Construction of a cuproptosis‐related lncRNA signature for predicting prognosis
and immune landscape in osteosarcoma patients
authors:
- Shumin Ni
- Jinjiong Hong
- Weilong Li
- Meng Ye
- Jinyun Li
journal: Cancer Medicine
year: 2022
pmcid: PMC9972154
doi: 10.1002/cam4.5214
license: CC BY 4.0
---
# Construction of a cuproptosis‐related lncRNA signature for predicting prognosis and immune landscape in osteosarcoma patients
## Abstract
Using the TARGET‐OS database, we developed a risk score prognosis model using the cuproptosis‐related lncRNAs. The novel cuproptosis‐related lncRNA signature have high reliability and accuracy for predicting outcome and immunotherapy response in osteosarcoma patients.
### Background
Long noncoding RNAs (lncRNAs) influence the onset of osteosarcoma. Cuproptosis is a novel cell death mechanism. We attempted to identify a cuproptosis‐related lncRNA signature to predict the prognosis and immune landscape in osteosarcoma patients.
### Methods
Transcriptional and clinical data of 85 osteosarcoma patients were derived from the TARGET database and randomly categorized into the training and validation cohorts. We implemented the univariate and multivariate Cox regression, along with LASSO regression analyses for developing a cuproptosis‐related lncRNA risk model. Kaplan–Meier curves, C‐index, ROC curves, univariate and multivariate Cox regression, and nomogram were used to assess the capacity of this risk model to predict the osteosarcoma prognosis. Gene ontology, KEGG, and Gene Set Enrichment (GSEA) analyses were conducted for determining the potential functional differences existing between the high‐risk and low‐risk patients. We further conducted the ESTIMATE, single‐smaple GSEA, and CIBERSORT analyses for identifying the different immune microenvironments and immune cells infiltrating both the risk groups.
### Results
We screened out four cuproptosis‐related lncRNAs (AL033384.2, AL031775.1, AC110995.1, and LINC00565) to construct the risk model in the training cohort. This risk model displayed a good performance to predict the overall survival of osteosarcoma patients, which was confirmed by using the validation and the entire cohort. Further analyses showed that the low‐risk patients have more immune activation and immune cells infiltrating as well as a good response to immunotherapy.
### Conclusions
We developed a novel cuproptosis‐related lncRNA signature with high reliability and accuracy for predicting outcome and immunotherapy response in osteosarcoma patients, which provides new insights into the personalized treatment of osteosarcoma.
## INTRODUCTION
Osteosarcoma is a very prevalent form of malignant bone cancer that is primarily detected in teenagers and young adults. 1, 2 The primary affecting site of this tumor is generally noted in the metaphysis of the long bones, like the proximal tibia, distal femur, and proximal humeral. 3 Osteosarcoma shows poor prognosis owing to its higher rate of recurrence and distant metastasis, particularly lung metastasis. 4, 5 Over the past 50 years, although many advanced treatment strategies have been used for osteosarcoma, such as chemotherapy, limb‐sparing surgery, amputation, stereotactic radiotherapy, and immunotherapy, there has not been a significant improvement in the survival rate of the osteosarcoma patients, and the 5‐year survival value is approximately 60–$70\%$. 6, 7 Owing to the lack of accurate and reliable biomarkers, approximately $20\%$ of all osteosarcoma patients show the presence of metastases upon diagnosis, which can challenge the management of osteosarcoma patients. 8 Additionally, due to genetic heterogeneity, those osteosarcoma patients with the same treatments might have different prognoses. 9, 10 Therefore, the molecular mechanisms underlying osteosarcoma metastasis and progression should be further explored. Besides, more reliable and efficient prognostic biomarkers are urgently identified to design novel techniques for treating and determining the prognosis of osteosarcoma patients.
Long noncoding RNAs (LncRNAs) are a subtype of noncoding RNAs and have >200 nucleotides. 11 In the past few years, many researchers have presented evidence proving that the lncRNAs show a wide range of biological functions in regulating diverse physiological and pathological progression, 12 including chromatin remodeling, 13 transcriptional regulation, and posttranslational modification. 14 Numerous studies have observed that the mutations and dysregulations in the lncRNAs cause many human diseases, such as cerebrovascular diseases, 15 ischemic injuries, 16 endocrinologies, 17 immune system diseases, 18 and especially cancer. 19, 20, 21 Due to the characteristics of tissue specificity, high stability, abundance, and species conservation, lncRNAs have been identified as a possible diagnostic and prognostic biomarker in the clinical management of cancer. 22 Additionally, lncRNAs are also associated with tumor metabolism, tumor microenvironment, and drug resistance, 23 consequently recognized as potential cancer drug targets. 24, 25 Programmed cell death is an essential physiological and pathological process to remove superfluous or damaged cells to ensure healthy development and tissue homeostasis, 26 including apoptosis, autophagy, pyroptosis, ferroptosis, and necroptosis. 27, 28, 29 Recently, a novel cell death pathway was reported by Tsvetkov et al. 30 *Cuproptosis is* defined as the copper‐dependent death of the cells, where the copper binds directly to the lipoylated components of the tricarboxylic acid (TCA) cycle. It is well known that copper is an important cofactor that is essential for the activity of many cellular enzymes in all living organisms. 31, 32 Excess copper causes mitochondrial protein aggregation and triggers cuproptosis. 33 Several researchers have shown that the level of copper in cancer patients was higher than in healthy controls. 34 Additionally, programmed cell death was seen to play a dual role in tumor regulation, as it stimulates tumor progression and also prevents tumor progression. Previous research showed that copper can induce tumor cell death as it helps in accumulating the reactive oxygen species (ROS), inhibits proteasomes, and causes antiangiogenesis. 35 Therefore, these new findings invigorated studies exploring the use of copper to treat cancer. However, the mechanism of cuproptosis in osteosarcoma progression is still unclear, and the research regarding the role played by cuproptosis‐related lncRNAs in osteosarcoma is still not completely clear.
In this study, we screened the cuproptosis‐related lncRNAs in osteosarcoma patients from the TARGET database to develop a prognostic model and validate the capacity of this model for anticipating the prognosis of osteosarcoma patients. We also carried out the functional enrichment analysis using the data of the high‐risk and low‐risk patients for investigating the underlying mechanism of cuproptosis‐related lncRNAs in osteosarcoma. Finally, we examined the relationship between the novel risk model and immune characteristics present in osteosarcoma patients. These results could offer a lot of useful data that could improve the prognosis of osteosarcoma patients.
## Data source
We downloaded the transcriptome sequencing data and the clinical information related to the 88 osteosarcoma patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET, https://ocg.cancer.gov/programs/target) database. After filtering out three samples without survival information, we randomly categorized the 85 patients into the training ($$n = 43$$) and validation ($$n = 42$$) cohorts, in a 1:1 ratio for further analysis. Table 1 presents the clinical data of all patients. Table 2 presents the clinical features of patients in the training and validation cohorts.
## Identification of the cuproptosis‐related lncRNAs
We acquired 19 cuproptosis‐related genes from the previously reported literature. 33, 35, 36 We extracted the list of cuproptosis‐related genes and identified the cuproptosis‐related lncRNAs using the |correlation coefficient| > 0.4 and a p‐value <0.001, with the help of the “limma” package. Then, we conducted the univariate Cox regression analysis for screening and detecting the prognostic cuproptosis‐related lncRNAs after setting a threshold value of p‐value <0.05.
## Development and validation of the cuproptosis‐related lncRNA prognostic signature
Using the training cohort, we carried out the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis for optimizing the cuproptosis‐related prognostic lncRNAs and preventing data overfitting. We also conducted the multivariate *Cox analysis* for simplifying the number and estimating the coefficient of cuproptosis‐related lncRNAs in the risk model. We determined the risk score of every patient included in the training and validation cohorts, using the formula below: Risk score=∑$i = 1$ncoefi×cuproptosis−related lncRNA expression We categorized osteosarcoma patients into high‐ and low‐risk groups, based on the median risk score value of the training set. Then, we used the principal component analysis (PCA) for determining whether this signature could distinguish the osteosarcoma patients between the two groups. We used the Kaplan–Meier curve, univariate Cox regression, and multivariate Cox regression analyses for assessing the prognostic value of cuproptosis‐related lncRNA signature. We also used the receiver operator characteristic (ROC) curves along with the C‐index for assessing the model accuracy. To determine whether our cuproptosis‐related lncRNA had a superior predictive ability for osteosarcoma patients, we compared it with three published lncRNA prognostic signatures related to pyroptosis, 37 iron metabolism, 38 and N6‐methyladenosine. 39
## Construction of nomogram
We used the risk scores and the clinical factors to develop a nomogram that could accurately anticipate the 1‐, 3‐, and 5‐year overall survival (OS) rate of osteosarcoma patients. We used the time‐dependent calibration and ROC curves for evaluating the ability of the novel nomogram for prognosis prediction.
## Functional enrichment analysis
We used the “limma” package for screening the differentially expressed genes (DEGs) between the high‐risk and low‐risk groups with |log2FC| >1 and FDR q < 0.05. We further carried out the GO (i.e., Molecular Function, Biological Processes, and Cellular Component) and KEGG enrichment analyses on these DEGs using the “clusterProfiler” package. Subsequently, we performed the Gene Set Enrichment Analysis (GSEA) between high‐risk and low‐risk groups. 40 A total of 209 DEGs were identified between high‐ and low‐risk groups (Figure 8A). We carried out a GO analysis that indicated that the DEGs were enriched in the immune activation and immune response pathways. Figure 8B,C present the top 10 GO terms showing the highest significant enrichment. The KEGG analysis indicated that the DEGs were related to the inflammation‐ and immune‐related pathways, the top 20 KEGG terms were presented in Figure 8D,E. We also conducted the GSEA analysis for determining the potential biological processes and the pathways in the high‐ and low‐risk patients. The results indicated that the immune cell‐related pathways and immune signaling pathways were enriched in patients in the low‐risk group (Figure 8F), such as the chemokine signaling, B‐cell receptor signaling, and the NK‐cell‐mediated cytotoxicity pathways.
**FIGURE 8:** *The functional enrichment analyses between the high‐risk and low‐risk patients. (A) Volcano plots for the differentially expressed genes (DEGs) were derived from the high‐ and low‐risk groups. The red dot indicated the upregulated genes, whereas the green dot denoted the downregulated genes. (B) Gene Ontology (GO) analysis for the DEGs between the high‐ and low‐risk groups. (C) Circle plots for the GO analysis. (D) KEGG analysis of the DEGs between the high‐ and low‐risk patients. (E) Circle plot of the KEGG analysis. (F) GSEA in the high‐ and low‐risk groups.*
## Correlation between the cuproptosis‐related lncRNAs prognostic signature and immune characteristics
ESTIMATE package 41 was used to calculate the ImmuneScore, StromalScore, and ESTIMATEScore of osteosarcoma patients. The ESTIMATEScore was calculated as the sum of the ImmuneScore and the StromalScore and indicated the general ratio of these factors in the tumor microenvironment (TME). We conducted the single‐sample GSEA (ssGSEA) 42 to determine the activity of the immune‐linked functions between the high‐ and low‐risk groups, applying the GSVA package. We used the CIBERSORT technique 43 for analyzing the infiltration levels of the 22 immune cells in the patients categorized into both risk groups. We analyzed the correlation between the proportion of infiltrating immune cells and the risk scores. Last, we compared and assessed the expression of the immune checkpoint‐related genes (ICGs) among patients in both risk groups, with the help of the “reshape2,” “limma,” “ggplot2,” and “ggpubr” packages, for determining the response of the osteosarcoma patients to immunotherapy.
## Statistical analysis
We used the R software (v4.1.0) to statistically analyze all data and visualize the results. We conducted the Chi‐square and the Wilcoxon signed‐rank tests to compare the difference observed across subgroups. We used the log‐rank test for comparing the Kaplan–Meier survival curves. We also conducted the univariate and multivariate Cox regression analyses for estimating the hazard ratio (HR) and the $95\%$ confidence interval (CI) values for the risk scores and other important clinical parameters. We used the ROC curves and the values of the area under the curve (AUC) for determining the predictive accuracy of the risk model. A p value of <0.05 was considered statistically significant.
## Identifying the cuproptosis‐related prognostic lncRNAs in the osteosarcoma patients
The flow chart of this study is shown in Figure 1. First, we recorded the expression levels of 19 cuproptosis‐related genes derived from the TARGET‐OS cohort. Thereafter, we carried out the Pearson correlation analysis to identify 696 cuproptosis‐related lncRNAs (Table S1), wherein their expression levels could be related to a single or multiple cuproptosis‐related genes (Figure 2A). Last, we screened 33 of these lncRNAs using the univariate Cox regression analysis, which served as the cuproptosis‐related prognostic lncRNAs in osteosarcoma patients for further analysis (Figure 2B).
**FIGURE 1:** *The flow chart of this study.* **FIGURE 2:** *Developing a four cuproptosis‐related long noncoding RNA (lncRNAs) signature for osteosarcoma patients. (A) Sankey diagram highlighting the relationship between the cuproptosis genes and the cuproptosis‐related lncRNAs. (B) Forest plot highlighting the univariate Cox regression analysis results. (C) A general cross‐validation curve of the paired likelihood deviance. (D) Elucidation for the LASSO coefficient profiles of the prognostic lncRNAs.*
## Developing a cuproptosis‐related lncRNA prognostic model
To develop a cuproptosis‐related lncRNA prognostic model, we categorized 85 osteosarcoma patients into the training and validation cohorts (1:1 ratio), using the random sampling technique. Then, in the training cohort, we used the LASSO regression analysis and introduced a new lambda value in the data set to decrease the total number of variables (Figure 1C,D). We further used the multivariate Cox regression analysis for screening four lncRNAs out of the remaining nine lncRNAs and calculating the coefficients. Figure 3A presents the correlation between the four LncRNAs incorporated in the signature and cuproptosis‐related genes. The Kaplan–*Meier analysis* results (Figure 3B) indicated that the osteosarcoma patients who had a higher AL033384.2 expression level showed a worse prognosis. On the other hand, patients with a higher AL031775.1, AC110995.1, and LINC00565 expression level showed a favorable prognosis. We calculated the risk score as follows: Expression value of AL033384.2 × 1.743 + expression value of AL031775.1 × −1.495 + expression value of AC110995.1 × −1.785 + expression value of LINC00565 × −2.283.
**FIGURE 3:** *The prognosis of cuproptosis‐related lncRNA signature using the training cohort. (A) Relationship between the cuproptosis genes and four cuproptosis‐related lncRNAs in the training cohort. (B) Kaplan–Meier curves for the four lncRNAs in the signature. (C) Distribution of the overall survival (OS) state, OS time, and risk scores of the data included in the training cohort. (D) Kaplan–Meier curve for the OS of the high‐ and low‐risk patients included in the training cohort. (E) The area under the curves (AUCs) of the time‐dependent receiver operator characteristic (ROC) curves validated the prognostic accuracy of the risk scores in the training cohort. (F) AUCs of the ROC curves in comparison to the prognostic accuracy of risk scores and other clinical parameters in the training cohort. (G) The C‐index was used for determining the prognostic accuracy of risk scores and a few additional clinical factors in the training cohort.*
## Validating the established risk model
After calculating the risk score values, we categorized the patients from the training and validation cohorts into the high‐ and low‐risk groups depending on the median value of the training cohort. Figure 3C presents the results of the risk score distribution, OS state of osteosarcoma patients, and expression levels of four LncRNAs in the training cohort. The Kaplan–Meier curve revealed that the high‐risk osteosarcoma patients showed a poor prognosis in comparison with the low‐risk patients in the training cohort (Figure 3D). The AUCs of ROC curves for the 1‐, 3‐, and 5‐year OS (Figure 3E) were 0.900, 0.907, and 0.938, respectively. However, the other clinical factors showed lower values (Figure 3F; age: 0.595; gender: 0.484; and metastasis: 0.710). Furthermore, as depicted in Figure 3G, this risk score showed the highest C‐index compared with other clinical factors. Figure 4A presents the distribution of the risk scores and the OS state of osteosarcoma patients included in the validation cohort. Figure 4B presents the expression levels of four LncRNAs in osteosarcoma patients categorized into the high‐ and low‐risk groups. Similar to the results noted for the training cohort, the Kaplan–Meier curve (Figure 4C) for the validation cohort revealed that the high‐risk patients displayed a poor OS than the low‐risk patients. The AUC values for ROC curves developed for the 1‐, 3‐, and 5‐year OS for patients in the validation cohort were seen to be 0.804, 0.769, and 0.612, respectively (Figure 4D). We then merged both the training and the validation cohorts for increasing the sample size. We carried out the PCA analysis of the entire cohort and noted that the signature incorporating four cuproptosis‐related lncRNAs (Figure 5A) could differentiate the risk status of the osteosarcoma patients more effectively in the whole genome expression (Figure 5B), cuproptosis‐related genes (Figure 5C), and the cuproptosis‐related lncRNA (Figure 5D). Figure 6A presents the risk score distribution and the OS state of osteosarcoma patients included in the entire cohort. Figure 6B describes the expression level of the four cuproptosis‐related LncRNAs in the high‐ and low‐risk patients. The Kaplan–Meier curve (Figure 6C) developed for the entire cohort demonstrated that the high‐risk patients showed a short OS compared to the low‐risk patients. For the entire cohort, the AUCs of ROC curves for the 1‐, 3‐, and 5‐year OS were 0.831, 0.842, and 0.768, respectively (Figure 6D), which were higher than that previous LncRNA signature for osteosarcoma patients (Table S1). Univariate (Figure 6E) and multivariate cox regression analyses (Figure 6F) indicated that the risk scores (HR = 8.075, $p \leq 0.001$) and metastasis status (HR = 5.446, $p \leq 0.001$) can be regarded as independent prognostic parameters for osteosarcoma patients. The OS analysis of the subgroups depending on factors like age (Figure 6G), gender (Figure 6H), and metastatic state (Figure 6I) revealed that the high‐risk patients displayed a poor prognosis compared to the low‐risk patients in every subgroup.
**FIGURE 4:** *Prognosis of the cuproptosis‐related lncRNA signature in the validation cohort. (A) Distribution of the overall survival (OS) time, OS state, and risk scores for patients categorized into the validation cohort. (B) Heatmap of the four cuproptosis‐related lncRNA expression profiles in the validation cohort. (C) Kaplan–Meier curve for the OS of the high‐ and low‐risk patients included in a validation cohort. (D) AUCs of time‐dependent receiver operating characteristic curves validated the prognostic accuracy of the risk scores in the validation cohort.* **FIGURE 5:** *Principal component analysis. Four cuproptosis‐related lncRNA signatures (A) could better distinguish the risk status of patients than the whole‐genome expression (B), cuproptosis‐related genes (C), and cuproptosis‐related lncRNA (D).* **FIGURE 6:** *Prognosis of the cuproptosis‐related lncRNA signature in osteosarcoma patients. (A) Distribution of the overall survival (OS) time, OS state, and risk scores in all patients. (B) Heatmap of four cuproptosis‐related lncRNA expression profiles in all samples. (C) Kaplan–Meier curves for the OS of the high‐ and low‐risk osteosarcoma patients. (D) The area under the curves of the time‐dependent ROC curves that validated the prognostic accuracy of risk scores in all samples. (E) Univariate Cox regression analysis of cuproptosis‐related lncRNA signature in all samples. (F) Multivariate cox regression analysis of cuproptosis‐related lncRNA signature in all samples. (G) Subgroup analysis of the Kaplan–Meier curve related to the patients' age. (H) Subgroup analysis of the Kaplan–Meier curves related to the patients' gender. (I) Subgroup analysis of the Kaplan–Meier curves related to the patients' metastasis status.*
## Development of a novel nomogram based on the risk scores and clinical data
We developed a nomogram that used the risk score values of the patients as well as their available clinical data for accurately predicting the OS status of osteosarcoma patients (Figure 7A). The AUCs of ROC curves in the nomogram (Figure 7B) for the 1‐, 3‐, and 5‐year OS were 0.959, 0.908, and 0.857, respectively, demonstrating that this novel nomogram could accurately anticipate the prognosis of osteosarcoma patients. The calibration diagram (Figure 7C) showed that the predicted curve was very close to an ideal curve (a line drawn at an angle of 45° with slope = 1 and passing through the origin of coordinate axes), which further indicated that the predicted and observed results were in good agreement.
**FIGURE 7:** *Construction of a nomogram after combining the risk scores and clinical properties to predict the survival of osteosarcoma patients. (A) A nomogram combined risk score and clinical information. (B) Time‐dependent receiver operating characteristic curves were used for validating the prognostic accuracy of the developed nomogram. (C) Calibration curves were developed for this nomogram, and they showed that the predicted and the actual 1‐, 3‐, and 5‐year overall survival values were in good agreement.*
## Tumor microenvironment and immune cells infiltrating
We assessed the number of immune and stromal components, independently, using the ESTIMATE method, and noted that the ImmuneScore, StromalScore, and ESTIMATEScore values were significantly higher in low‐risk patients than in high‐risk patients (Figure 9A). The above results implied that the low‐risk patients had more immune and stromal components. We carried out the ssGSEA analysis and noted that the 12 immune‐related functions (except Type‐II‐IFN‐Response) were significantly downregulated in the high‐risk patients (Figure 9B,C). We also employed the CIBERSORT technique for assessing the variations in the infiltration levels of the 22 immune cells in osteosarcoma patients. Violin plots (Figure 10A) revealed that high‐risk patients had a lower proportion of two immune effective cells (CD8+ T cells and M1 Macrophages) and a higher proportion of naive CD4+ T cells compared with the low‐risk patients. Furthermore, the Pearson correlation revealed that the risk scores of the patients were significantly and negatively related to the CD8+ T cell (Figure 10B, R = −0.34) and M1 macrophages (Figure 10C, R = −0.25), and significantly and positively related to the naive CD4+ T cells (Figure 10D, $R = 0.33$).
**FIGURE 9:** *Correlation between the cuproptosis‐related lncRNA signature and the tumor microenvironment and immune functions. (A) Correlation analysis of ImmuneScore, StromalScore, and ESTIMATEScore with cuproptosis‐related lncRNA signature. The heatmap (B) and box plots (C) for the immune‐related functions noted in high‐ and low‐risk patients.* **FIGURE 10:** *Relationship between the cuproptosis‐related lncRNA signature and immune cell infiltration. (A) Violin plots presented the different proportions of immune cell infiltration in the high‐risk and low‐risk patients. (B) CD8+ T cells were negatively associated with the risk scores of the patients. (C) M1 macrophages were negatively related to the risk scores of the osteosarcoma patients. (D) Naive CD4+ T cells were positively associated with the risk scores of the patients.*
## Relationship between the risk scores and the ICGs
The expression of the ICGs has been regarded as the biomarker for anticipating the effectiveness of immunotherapy in osteosarcoma patients. In this report, we noted that the low‐risk patients showed a higher expression level of the many ICGs (such as IDO1, LAG3, GZMB, CD8A, PRF1, TNF, HAVCR2, CTLA4, CD274, and PDCD1), indicating that these patients could respond better to immunotherapy (Figure 11).
**FIGURE 11:** *Different expressions of the immune check point‐related genes between the high‐risk and low‐risk patients. *p < 0.05, **p < 0.01, and ***p < 0.001.*
## DISCUSSION
The recent cancer statistics, published in 2022, by the American Cancer Society stated that despite advances in treatment techniques, osteosarcoma patients showed a 5‐year OS of only $68\%$. 2 This highlights a crucial need to determine a specific biomarker for assessing the risk and monitoring the recurrence of osteosarcoma patients for precise treatment, especially for metastatic and recurrent patients. Recently, a novel cell death manner, cuproptosis, has been reported. 30 Similar to the other cell death modalities, cuproptosis is also responsible for initiating the death of tumor cells 44, 45 and also displays a high potential for application in tumor treatment. 35 A lot of recent evidence has been presented that indicates that osteosarcoma onset and advancement are facilitated by the abnormal expression of the lncRNAs. 46, 47 Additionally, abnormally expressed lncRNAs have been used as the predictive and prognostic biomarkers for osteosarcoma patients. 48, 49 Furthermore, despite thorough investigation, we have not been able to conduct any previous studies that explored the correlation between the cuproptosis‐related lncRNAs and osteosarcoma.
In this study, we employed the TARGET database for screening the cuproptosis‐related prognostic lncRNAs using Pearson correlation analysis and univariate Cox regression analysis. In the training cohort, we developed a novel risk‐score model that was based on the four cuproptosis‐related lncRNAs (AL033384.2, AL031775.1, AC110995.1, and LINC00565), which were associated with tumorigenesis and prognosis. Amer et al. reported that LINC00565 was linked to the poor OS and progression‐free survival in glioblastoma multiforme. 50 LINC00565 also targets the miR‐665/AKT3 axis in gastric cancer, enhances proliferation, and prevents apoptosis. 51 Additionally, AL031775.1 was involved in several prognostic predicting models for patients with bladder cancer. 52, 53, 54 In the current study, we showed that AL033384.2 was associated with a worse prognosis, and AL031775.1, AC110995.1, and LINC00565 were associated with a favorable prognosis. Subsequently, we classified patients into the high‐ and low‐risk groups depending on their median risk scores. According to the Kaplan–Meier survival curves, the high‐risk patients showed poor OS compared to the low‐risk patients. The AUC values of 1‐, 3‐, and 5‐year in ROC curves were >0.9, revealing that this model showed a strong prediction ability. Meanwhile, the AUCs were higher than that previous LncRNA signature, 37, 38, 39 indicating a superior predictive ability for osteosarcoma patients. Furthermore, this model showed higher AUC values and C‐index, compared to factors like gender, age, and metastasis, implying that risk scores could accurately anticipate the prognosis of osteosarcoma patients compared to the traditional clinical risk markers. Importantly, these findings were confirmed in the validation and the entire cohorts. Consistently, the univariate and multivariate cox regression analyses confirmed that the risk scores could be considered a reliable and independent prognostic biomarker for osteosarcoma patients.
It is challenging to put a single indicator into clinical practice, even though the risk score shows the potential to be regarded as a promising predictor of clinical outcomes in osteosarcoma patients. 55 Predictions made using the nomogram are more accurate and useful since they can take into account the relative importance of each variable. 56, 57 Therefore, we integrated the risk score and other clinicopathological parameters to develop a nomogram. The results indicated that the AUC values of ROC curves were significantly higher compared to the single‐factor values (age, risk score, gender, and metastasis). The calibration diagram indicated that the predicted curve was closer to the ideal curve, proving that the developed nomogram could improve the predictive ability and accuracy to monitor osteosarcoma patients.
We also investigated the differential functions of the genes between the high‐ and low‐risk groups. The functional analyses of GO and KEGG implied that the DEGs in both groups were primarily enriched in the immune response and inflammatory response processes. Additionally, the GSEA and ssGSEA revealed that the low‐risk patients showed more prevalent immunological signaling and immune cell‐related pathways compared to the high‐risk patients. The aforementioned results suggested that low‐risk osteosarcoma patients may have a better immune activation capacity to trigger the anticancer responses. The hypothesis that low‐risk patients have a better prognosis is supported by the fact that an active immune response provides an environment for suppressing the development of cancer cells.
The tumor microenvironment (TME) includes the immune cells, stromal cells, cancer cells, and the extracellular matrix, and this complicated milieu played a vital role in promoting immune evasion, immune tolerance, tumorigenesis, and tumor progression. 58, 59, 60 The balance of the TME is a critical factor that influences cancer patients' survival and response to immunotherapy. 61 LncRNAs are involved in regulating the TME. 23 Here, the ESTIMATE analysis revealed that the low‐risk patients showed higher ImmuneScore and StromalScore values, representing more immune or stromal cellular components in the TME of low‐risk patients. Consistent with this result, the immune cell infiltration analysis confirmed that the osteosarcoma patients categorized into the low‐risk group showed a high proportion of CD8+ T cells and M1 macrophages. The cytotoxic CD8+ cells exert a suppressive function and act as significant effector cells, 62 which can interact directly with the tumor cells. Several studies indicated that the CD8+ T cells are positively related to the favorable clinical prognosis in numerous cancers. 40, 63, 64, 65 Similarly, macrophages are known to be major components of the TME, including M1 macrophages and M2 macrophages, which usually maintain a balanced state. 66 If M1 macrophages predominate, the balance may shift to an antitumor microenvironment. 67, 68, 69 M1 macrophages can attack tumor cells and prevent tumor growth, 70 and contribute to the favorable clinical outcome. 71 Therefore, these two effective immune cell enrichments might partially explain the risk score of the model that could predict osteosarcoma patients' survival.
Recently, immune checkpoint‐modulating agents (represented by anti‐CTLA4 and anti‐PD antibodies), CAR‐T cells, and neoantigen vaccines have been seen to be successful in enhancing the antitumor effect in numerous types of cancer, bringing a paradigm shift to cancer treatment. 72, 73, 74, 75 However, only a small number of osteosarcoma patients can benefit from immunotherapy, owing to the intratumor heterogeneity. 76 Therefore, a biomarker is necessary for clinical management and prediction of immunotherapy efficacy in osteosarcoma. Previous studies showed that LncRNAs could target ICGs in cancer, which determines the immune activation level. 77 *In this* study, we noted a higher expression level of the ICGs (CTLA4, CD274, PDCD1C, HAVCR2, and LAG3) in the osteosarcoma patients categorized into the low‐risk group, which suggested that these patients could gain more benefits from the immune checkpoint blockade therapy.
Nevertheless, this study also has a few shortcomings. First, the results presented in this study were solely confined to the data derived from the TARGET‐OS set, with a smaller sample size. Second, though we confirmed the effectiveness of this signature in the internal validation cohort, it is still necessary to verify the results using an external validation cohort with additional experiments. Third, the underlying mechanisms that differ in the high‐ and low‐risk patients were assessed using only the bioinformatics‐based prediction. We need to carry out additional in vitro and in vivo experiments for supporting these findings.
In conclusion, using the TARGET‐OS database, we initially categorized the osteosarcoma patients into the training and validation cohorts and then developed a risk score prognosis model using the cuproptosis‐related lncRNAs in the training cohort. We validated that this cuproptosis‐related lncRNA signature showed satisfactory performance and could accurately predict the OS for osteosarcoma patients, which was confirmed by the validation cohort and the entire cohort. The nomogram combining the risk score with the clinical characteristics could improve the prediction efficiency of the model. Furthermore, the low‐risk patients showed more immune active and more immune cells infiltrating, as well as had a better response to immunotherapy.
## AUTHOR CONTRIBUTIONS
Shumin Ni: Data curation (lead); writing – original draft (lead). Jinjiong Hong: Data curation (supporting); software (lead). Weilong Li: *Formal analysis* (equal); software (equal). Meng Ye: Conceptualization (equal); funding acquisition (equal); investigation (equal); visualization (equal); writing – review and editing (equal). Jinyun Li: Conceptualization (equal); formal analysis (equal); investigation (equal); supervision (equal); writing – review and editing (equal).
## FUNDING INFORMATION
The study was funded by the Zhejiang Key Laboratory of Pathophysiology [202204].
## CONFLICT OF INTEREST
The authors declare that there are no financial or other conflicts of interest associated with this study.
## ETHICAL APPROVAL STATEMENT
All data of this study were public and required no ethical approval.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are publicly available from TARGET data sets (https://ocg.cancer.gov/programs/target).
## References
1. Rojas GA, Hubbard AK, Diessner BJ, Ribeiro KB, Spector LG. **International trends in incidence of osteosarcoma (1988‐2012)**. *Int J Cancer* (2021) **149** 1044-1053. PMID: 33963769
2. Siegel RL, Miller KD, Fuchs HE, Jemal A. **Cancer statistics, 2022**. *CA Cancer J Clin* (2022) **72** 7-33. PMID: 35020204
3. Biazzo A, De Paolis M. **Multidisciplinary approach to osteosarcoma**. *Acta Orthop Belg* (2016) **82** 690-698. PMID: 29182106
4. Kager L, Zoubek A, Potschger U. **Primary metastatic osteosarcoma: presentation and outcome of patients treated on neoadjuvant cooperative osteosarcoma study group protocols**. *J Clin Oncol* (2003) **21** 2011-2018. PMID: 12743156
5. Simpson S, Dunning MD, de Brot S, Grau‐Roma L, Mongan NP, Rutland CS. **Comparative review of human and canine osteosarcoma: morphology, epidemiology, prognosis, treatment and genetics**. *Acta Vet Scand* (2017) **59** 71. PMID: 29065898
6. Fan Z, Huang G, Zhao J. **Establishment and characterization of a highly metastatic human osteosarcoma cell line from osteosarcoma lung metastases**. *J Bone Oncol* (2021) **29**. PMID: 34221892
7. Mirabello L, Troisi RJ, Savage SA. **Osteosarcoma incidence and survival rates from 1973 to 2004: data from the surveillance, epidemiology, and end results program**. *Cancer* (2009) **115** 1531-1543. PMID: 19197972
8. Lin YH, Jewell BE, Gingold J. **Osteosarcoma: molecular pathogenesis and iPSC modeling**. *Trends Mol Med* (2017) **23** 737-755. PMID: 28735817
9. Guo J, Tang H, Huang P. **Single‐cell profiling of tumor microenvironment heterogeneity in osteosarcoma identifies a highly invasive subcluster for predicting prognosis**. *Front Oncol* (2022) **12**. PMID: 35463309
10. Rosemann M, Gonzalez‐Vasconcellos I, Domke T. **A Rb1 promoter variant with reduced activity contributes to osteosarcoma susceptibility in irradiated mice**. *Mol Cancer* (2014) **13** 182. PMID: 25092376
11. Chen LL. **Linking long noncoding RNA localization and function**. *Trends Biochem Sci* (2016) **41** 761-772. PMID: 27499234
12. Cong R, Kong F, Ma J, Li Q, Yang H, Ma X. **The PVT1/miR‐612/CENP‐H/CDK1 axis promotes malignant progression of advanced endometrial cancer**. *Am J Cancer Res* (2021) **11** 1480-1502. PMID: 33948369
13. Han P, Chang CP. **Long non‐coding RNA and chromatin remodeling**. *RNA Biol* (2015) **12** 1094-1098. PMID: 26177256
14. Karakas D, Ozpolat B. **The role of LncRNAs in translation**. *Noncoding RNA* (2021) **7** 16. PMID: 33672592
15. Weng R, Jiang Z, Gu Y. **Noncoding RNA as diagnostic and prognostic biomarkers in cerebrovascular disease**. *Oxid Med Cell Longev* (2022) **2022** 8149701-8149712. PMID: 35498129
16. Cao Y, Liu J, Lu Q. **An update on the functional roles of long noncoding RNAs in ischemic injury (review)**. *Int J Mol Med* (2022) **50** 1-15
17. Zhao Y, Yan G, Mi J. **The impact of lncRNA on diabetic kidney disease: systematic review and in silico analyses**. *Comput Intell Neurosci* (2022) **2022** 8400106-8400117. PMID: 35528328
18. Liu Y, Cheng L, Zhan H. **The roles of noncoding RNAs in systemic sclerosis**. *Front Immunol* (2022) **13**. PMID: 35464474
19. Chao HM, Wang TW, Chern E, Hsu SH. **Regulatory RNAs, microRNA, long‐non coding RNA and circular RNA roles in colorectal cancer stem cells**. *World J Gastrointest Oncol* (2022) **14** 748-764. PMID: 35582099
20. Chen L, Xie Y, Yu M, Gou Q. **Long noncoding RNAs in lung cancer: from disease markers to treatment roles**. *Cancer Manag Res* (2022) **14** 1771-1782. PMID: 35634537
21. Huang Y, Jiang L, Liu Y, Liu L, Wang J, Shi L. **Long non‐coding RNAs in virus‐related cancers**. *Rev Med Virol* (2022) e2364. DOI: 10.1002/rmv.2364
22. Trevisani F, Floris M, Vago R, Minnei R, Cinque A. **Long non‐coding RNAs as novel biomarkers in the clinical management of papillary renal cell carcinoma patients: a promise or a pledge?**. *Cell* (2022) **11** 1658
23. Yang P, Ding J, Bian Y, Ma Z, Wang K, Li J. **Long non‐coding RNAs and cancer mechanisms: immune cells and inflammatory cytokines in the tumor microenvironment**. *Med Oncol* (2022) **39** 108. PMID: 35578054
24. Chai B, Ma Z, Wang X, Xu L, Li Y. **Functions of non‐coding RNAs in regulating cancer drug targets**. *Acta Biochim Biophys Sin (Shanghai)* (2022) **54** 279-291. PMID: 35538038
25. Zhong C, Xie Z, Shen J, Jia Y, Duan S. **LINC00665: an emerging biomarker for cancer diagnostics and therapeutics**. *Cell* (2022) **11** 1540
26. Fuchs Y, Steller H. **Programmed cell death in animal development and disease**. *Cell* (2011) **147** 742-758. PMID: 22078876
27. He Q, Qu M, Xu C. **The emerging roles of nitric oxide in ferroptosis and pyroptosis of tumor cells**. *Life Sci* (2022) **290**. PMID: 34952041
28. Li M, Wang ZW, Fang LJ, Cheng SQ, Wang X, Liu NF. **Programmed cell death in atherosclerosis and vascular calcification**. *Cell Death Dis* (2022) **13** 467. PMID: 35585052
29. Lin L, Zhang MX, Zhang L, Zhang D, Li C, Li YL. **Autophagy, Pyroptosis, and ferroptosis: new regulatory mechanisms for atherosclerosis**. *Front Cell Dev Biol* (2021) **9**. PMID: 35096837
30. Tsvetkov P, Coy S, Petrova B. **Copper induces cell death by targeting lipoylated TCA cycle proteins**. *Science* (2022) **375** 1254-1261. PMID: 35298263
31. Festa RA, Thiele DJ. **Copper: an essential metal in biology**. *Curr Biol* (2011) **21** R877-R883. PMID: 22075424
32. Tapiero H, Townsend DM, Tew KD. **Trace elements in human physiology and pathology**. *Copper Biomed Pharmacother* (2003) **57** 386-398. PMID: 14652164
33. Kahlson MA, Dixon SJ. **Copper‐induced cell death**. *Science* (2022) **375** 1231-1232. PMID: 35298241
34. Lelievre P, Sancey L, Coll JL, Deniaud A, Busser B. **The multifaceted roles of copper in cancer: a trace metal element with dysregulated metabolism, but also a target or a bullet for therapy**. *Cancers (Basel)* (2020) **12** 3594. PMID: 33271772
35. Jiang Y, Huo Z, Qi X, Zuo T, Wu Z. **Copper‐induced tumor cell death mechanisms and antitumor theragnostic applications of copper complexes**. *Nanomedicine (Lond)* (2022) **17** 303-324. PMID: 35060391
36. Cobine PA, Brady DC. **Cuproptosis: cellular and molecular mechanisms underlying copper‐induced cell death**. *Mol Cell* (2022) **82** 1786-1787. PMID: 35594843
37. Bu X, Liu J, Ding R, Li Z. **Prognostic value of a Pyroptosis‐related long noncoding RNA signature associated with osteosarcoma microenvironment**. *J Oncol* (2021) **2021** 2182761-2182713. PMID: 34804157
38. Hong‐Bin S, Wan‐Jun Y, Chen‐Hui D, Xiao‐Jie Y, Shen‐Song L, Peng Z. **Identification of an iron metabolism‐related lncRNA signature for predicting osteosarcoma survival and immune landscape**. *Front Genet* (2022) **13**. PMID: 35360864
39. Yang K, Wang F, Li K, Peng G, Yang H, Xu H, Xiang Y, Sun H. **N6‐methyladenosine modification‐related long non‐coding RNAs are potential biomarkers for predicting the prognosis of patients with osteosarcoma**. *Technol Cancer Res Treat* (2022) **21**
40. Zhou C, Shen Y, Jin Y. **A novel Pyroptosis‐related long non‐coding RNA signature for predicting the prognosis and immune landscape of head and neck squamous cell carcinoma**. *Cancer Med* (2022). DOI: 10.1002/cam4.4819
41. Yoshihara K, Shahmoradgoli M, Martinez E. **Inferring tumour purity and stromal and immune cell admixture from expression data**. *Nat Commun* (2013) **4** 2612. PMID: 24113773
42. Subramanian A, Tamayo P, Mootha VK. **Gene set enrichment analysis: a knowledge‐based approach for interpreting genome‐wide expression profiles**. *Proc Natl Acad Sci USA* (2005) **102** 15545-15550. PMID: 16199517
43. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. **Profiling tumor infiltrating immune cells with CIBERSORT**. *Methods Mol Biol* (2018) **1711** 243-259. PMID: 29344893
44. da Silva DA, De Luca A, Squitti R. **Copper in tumors and the use of copper‐based compounds in cancer treatment**. *J Inorg Biochem* (2022) **226**. PMID: 34740035
45. De Luca A, Barile A, Arciello M, Rossi L. **Copper homeostasis as target of both consolidated and innovative strategies of anti‐tumor therapy**. *J Trace Elem Med Biol* (2019) **55** 204-213. PMID: 31345360
46. Xu S, Gong Y, Yin Y, Xing H, Zhang N. **The multiple function of long noncoding RNAs in osteosarcoma progression, drug resistance and prognosis**. *Biomed Pharmacother* (2020) **127**. PMID: 32334375
47. Xue Y, Liu H, Nie G, Ren X. **lncRNA DARS‐AS1 promoted osteosarcoma progression through regulating miR‐532‐3p/CCR7**. *Dis Markers* (2022) **2022** 4660217-4660219. PMID: 35422889
48. Lee AM, Ferdjallah A, Moore E. **Long non‐coding RNA ANRIL as a potential biomarker of chemosensitivity and clinical outcomes in osteosarcoma**. *Int J Mol Sci* (2021) **22** 11168. PMID: 34681828
49. Liu M, Yang P, Mao G. **Long non‐coding RNA MALAT1 as a valuable biomarker for prognosis in osteosarcoma: a systematic review and meta‐analysis**. *Int J Surg* (2019) **72** 206-213. PMID: 31734255
50. Amer RG, Ezz El Arab LR, Abd El Ghany D, Saad AS, Bahie‐Eldin N, Swellam M. **Prognostic utility of lncRNAs (LINC00565 and LINC00641) as molecular markers in glioblastoma multiforme (GBM)**. *J Neurooncol* (2022) **158** 435-444. PMID: 35668225
51. Hu J, Ni G, Mao L. **LINC00565 promotes proliferation and inhibits apoptosis of gastric cancer by targeting miR‐665/AKT3 axis**. *Onco Targets Ther* (2019) **12** 7865-7875. PMID: 31576144
52. Chen M, Nie Z, Li Y. **A new ferroptosis‐related lncRNA signature predicts the prognosis of bladder cancer patients**. *Front Cell Dev Biol* (2021) **9**. PMID: 34869304
53. Cui Y, Zhou Z, Chai Y, Che X, Zhang Y. **Identification of a nomogram from ferroptosis‐related long noncoding RNAs signature to analyze overall survival in patients with bladder cancer**. *J Oncol* (2021) **2021** 8533464-8533418. PMID: 34484338
54. Tong H, Li T, Gao S, Yin H, Cao H, He W. **An epithelial‐mesenchymal transition‐related long noncoding RNA signature correlates with the prognosis and progression in patients with bladder cancer**. *Biosci Rep* (2021) **41**. PMID: 33289830
55. Vincent JL, Moreno R. **Clinical review: scoring systems in the critically ill**. *Crit Care* (2010) **14** 207. PMID: 20392287
56. Li H, Zhou F, Cao Z. **Development and validation of a nomogram based on nutritional indicators and tumor markers for prognosis prediction of pancreatic ductal adenocarcinoma**. *Front Oncol* (2021) **11**. PMID: 34136406
57. Zhu X, Liu W, Cao Y. **Development and validation of multicenter predictive nomograms for locally advanced pancreatic cancer after chemoradiotherapy**. *Front Oncol* (2021) **11**. PMID: 34169000
58. Elmusrati A, Wang J, Wang CY. **Tumor microenvironment and immune evasion in head and neck squamous cell carcinoma**. *Int J Oral Sci* (2021) **13** 24. PMID: 34341329
59. Guo JY, Chiu CH, Wang MJ, Li FA, Chen JY. **Proteoglycan serglycin promotes non‐small cell lung cancer cell migration through the interaction of its glycosaminoglycans with CD44**. *J Biomed Sci* (2020) **27** 2. PMID: 31898491
60. Pyzer AR, Cole L, Rosenblatt J, Avigan DE. **Myeloid‐derived suppressor cells as effectors of immune suppression in cancer**. *Int J Cancer* (2016) **139** 1915-1926. PMID: 27299510
61. Song G, Shi Y, Zhang M. **Global immune characterization of HBV/HCV‐related hepatocellular carcinoma identifies macrophage and T‐cell subsets associated with disease progression**. *Cell Discov* (2020) **6** 90. PMID: 33298893
62. Chang WT, Lai TH, Chyan YJ. **Specific medicinal plant polysaccharides effectively enhance the potency of a DC‐based vaccine against mouse mammary tumor metastasis**. *PLoS One* (2015) **10**. PMID: 25825910
63. Carstens JL, Correa de Sampaio P, Yang D. **Spatial computation of intratumoral T cells correlates with survival of patients with pancreatic cancer**. *Nat Commun* (2017) **8**. PMID: 28447602
64. Nadella S, Burks J, Al‐Sabban A. **Dietary fat stimulates pancreatic cancer growth and promotes fibrosis of the tumor microenvironment through the cholecystokinin receptor**. *Am J Physiol Gastrointest Liver Physiol* (2018) **315** G699-G712. PMID: 29927319
65. Sharma P, Shen Y, Wen S. **CD8 tumor‐infiltrating lymphocytes are predictive of survival in muscle‐invasive urothelial carcinoma**. *Proc Natl Acad Sci USA* (2007) **104** 3967-3972. PMID: 17360461
66. Lewis CE, Pollard JW. **Distinct role of macrophages in different tumor microenvironments**. *Cancer Res* (2006) **66** 605-612. PMID: 16423985
67. Riihijarvi S, Fiskvik I, Taskinen M. **Prognostic influence of macrophages in patients with diffuse large B‐cell lymphoma: a correlative study from a Nordic phase II trial**. *Haematologica* (2015) **100** 238-245. PMID: 25381134
68. Wanderley CW, Colon DF, Luiz JPM. **Paclitaxel reduces tumor growth by reprogramming tumor‐associated macrophages to an M1 profile in a TLR4‐dependent manner**. *Cancer Res* (2018) **78** 5891-5900. PMID: 30104241
69. Xiang W, Shi R, Kang X. **Monoacylglycerol lipase regulates cannabinoid receptor 2‐dependent macrophage activation and cancer progression**. *Nat Commun* (2018) **9** 2574. PMID: 29968710
70. Bai Y, Yin K, Su T, Ji F, Zhang S. **CTHRC1 in ovarian cancer promotes M2‐like polarization of tumor‐associated macrophages via regulation of the STAT6 signaling pathway**. *Onco Targets Ther* (2020) **13** 5743-5753. PMID: 32606786
71. Beane JE, Mazzilli SA, Campbell JD. **Molecular subtyping reveals immune alterations associated with progression of bronchial premalignant lesions**. *Nat Commun* (2019) **10** 1856. PMID: 31015447
72. Gajra A, Zalenski A, Sannareddy A, Jeune‐Smith Y, Kapinos K, Kansagra A. **Barriers to chimeric antigen receptor T‐cell (CAR‐T) therapies in clinical practice**. *Pharmaceut Med* (2022) **36** 163-171. PMID: 35672571
73. Popovic A, Jaffee EM, Zaidi N. **Emerging strategies for combination checkpoint modulators in cancer immunotherapy**. *J Clin Invest* (2018) **128** 3209-3218. PMID: 30067248
74. Shang S, Zhao Y, Qian K. **The role of neoantigens in tumor immunotherapy**. *Biomed Pharmacother* (2022) **151**. PMID: 35623169
75. Sun L, Chen L, Li H. **Checkpoint‐modulating immunotherapies in tumor treatment: targets, drugs, and mechanisms**. *Int Immunopharmacol* (2019) **67** 160-175. PMID: 30553199
76. Lu Y, Zhang J, Chen Y. **Novel immunotherapies for osteosarcoma**. *Front Oncologia* (2022) **12**
77. Jiang Y, Zhao L, Wu Y. **The role of NcRNAs to regulate immune checkpoints in cancer**. *Front Immunol* (2022) **13**. PMID: 35464451
|
---
title: Optimized decision support for selection of transoral robotic surgery or (chemo)radiation
therapy based on posttreatment swallowing toxicity
authors:
- Mehdi Hemmati
- Carly Barbon
- Abdallah S. R. Mohamed
- Lisanne V. van Dijk
- Amy C. Moreno
- Neil D. Gross
- Ryan P. Goepfert
- Stephen Y. Lai
- Katherine A. Hutcheson
- Andrew J. Schaefer
- Clifton D. Fuller
journal: Cancer Medicine
year: 2022
pmcid: PMC9972156
doi: 10.1002/cam4.5253
license: CC BY 4.0
---
# Optimized decision support for selection of transoral robotic surgery or (chemo)radiation therapy based on posttreatment swallowing toxicity
## Abstract
The primary aim of transoral robotic surgery (TORS) in overall toxicity level reduction in eligible oropharyngeal squamous cell cancer (OPSCC) patients may be jeopardized in the presence of postoperative extranodal extension (ENE) and/or positive margin (PM) that necessitate adjuvant therapy. Our decision analysis model shows that Moderate‐to‐high likelihoods associated with postoperative PM or ENE that trigger adjuvant therapy leave definitive non‐surgical therapies as the optimal treatment for TORS‐eligible OPSCC patients leading to less overall toxicity burden.
### Background
A primary goal in transoral robotic surgery (TORS) for oropharyngeal squamous cell cancer (OPSCC) survivors is to optimize swallowing function. However, the uncertainty in the outcomes of TORS including postoperative residual positive margin (PM) and extranodal extension (ENE), may necessitate adjuvant therapy, which may cause significant swallowing toxicity to survivors.
### Methods
A secondary analysis was performed on a prospective registry data with low‐ to intermediate‐risk human papillomavirus–related OPSCC possibly resectable by TORS. Decision trees were developed to model the uncertainties in TORS compared with definitive radiation therapy (RT) and chemoradiation therapy (CRT). Swallowing toxicities were measured by Dynamic Imaging Grade of Swallowing Toxicity (DIGEST), MD Anderson Dysphagia Inventory (MDADI), and the MD Anderson Symptom Inventory–Head and Neck (MDASI‐HN) instruments. The likelihoods of PM/ENE were varied to determine the thresholds within which each therapy remains optimal.
### Results
Compared with RT, TORS resulted in inferior swallowing function for moderate likelihoods of PM/ENE (>$60\%$ in short term for all instruments, >$75\%$ in long term for DIGEST and MDASI) leaving RT as the optimal treatment. Compared with CRT, TORS remained the optimal therapy based on MDADI and MDASI but showed inferior swallowing outcomes based on DIGEST for moderate‐to‐high likelihoods of PM/ENE (>$75\%$ for short‐term and >$40\%$ for long‐term outcomes).
### Conclusion
In the absence of reliable estimation of postoperative PM/ENE concurrent with significant postoperative PM, the overall toxicity level in OPSCC patients undergoing TORS with adjuvant therapy may become more severe compared with patients receiving nonsurgical treatments thus advocating definitive (C)RT protocols.
## INTRODUCTION
Recent studies indicate that the incidence of human papillomavirus–associated (HPV+) head‐and‐neck (HNC) cancer has been on a sharp rise, and the incidence of this malignancy is projected to nearly double by the year 2030. 1, 2 *With a* yearly incidence of 600,000 cases worldwide, there are 62,000 HNC cases annually in the United States with an estimated 13,000 deaths, 3, 4 driven by the endemic rise of oropharyngeal squamous cell cancer (OPSCC). 5 Historical OPSCC surgical treatment for OPSCC involved surgical procedures including transmandibular and transcervical pharyngotomy, which are associated with significant functional morbidity, notably dysphagia. 6, 7, 8 To reduce postoperative morbidity, high‐dose radiation therapy (RT) in combination with chemotherapy became the standard organ‐preserving approach, offering comparable locoregional control and survival. However, nonsurgical chemoradiation therapy (CRT) treatments also put the patient at risk for multiple posttreatment toxicities, including radiation‐associated dysphagia.
Transoral robotic surgery (TORS) is a surgical approach that was approved by FDA in 2009 and involves minimal disturbance to critical nerves and swallow musculature of the laryngopharynx, thus promising superior acute postoperative swallowing outcomes compared with traditional open surgical approaches 9 and therapeutic nonsurgical organ‐preserving regimens in OPSCC 10 as reported in prospective cohort studies. 11, 12, 13, 14 Proponents of a primary TORS approach further cite potential for de‐escalation protocols or avoidance of adjuvant therapies, altogether as a major functional advantage of TORS. 10 Despite TORS promise, it is reported that only $9\%$–$27\%$ of patients treated with frontline TORS avoid postoperative adjuvant RT, and $34\%$–$45\%$ avoid adjuvant CRT. 10 As probabilistic outcomes of TORS, postoperative positive margins (PMs) or pathological extranodal extensions (ENEs) indicate increased risk of recurrence, and necessitate adjuvant (C)RT (Figure 1 [Top]) with resultant short‐ and long‐term radiation associated toxicities. 14, 15 *In a* recent study on a cohort of low‐ and intermediate‐risk OPSCC patients receiving definitive (C)RT and TORS (possibly followed by adjuvant therapy), swallowing outcomes (at 3–6 months posttreatment) were reported to be similar regardless of the primary treatment modality. 14 This suggests that patients undergoing TORS, when followed by adjuvant therapy, may not incur less severe dysphagia compared with receiving definitive CRT as the primary treatment modality.
**FIGURE 1:** *(Top) The underlying process for determining the eligibility of the patient for TORS and its probabilistic outcomes; (Bottom) General decision Tree for TORS decision‐making; scenario 1: definitive RT versus TORS; scenario 2: CRT versus TORS; pM+pM−: probability of having (not having) positive margins after surgery; pN+pN−: probability of having (not having) extranodal extension after surgery; pTM+pTM−: probability of having tumor resection margin of more (less) than 2 mm after surgery; CRT, chemoradiation therapy; RT, radiation therapy; TORS, transoral robotic surgery*
At present, the relative selection criteria for surgery are primarily qualitative and based on subjective assessment by the physician. Studies suggest that, at least in current practices, physicians are quite poor at predicting the necessity of adjuvant therapy based on presurgical or imaging risk features. This leaves the provider and patient with an upfront pretherapy choice: choose definitive (C)RT with known quantized patient‐specific toxicity risk probability OR choose an a priori quantifiable toxicity risk of surgery and an undefined probability of the risk toxicity of adjuvant therapy. The premise of this study is to define the proportional likelihood of surgical risk features (and resultant indication for adjuvant therapy‐associated toxicity) and determine mathematically optimal decision between primary therapies: (chemo)RT or TORS. Put simply, we address the question of how numerically confident the surgeon and radiation oncologist must be in the risk of pretreatment pathologic margin positivity or ENE be to rationally select TORS for the purposes of minimizing toxicity assuming equivalent locoregional control.
The primary focus of the present study was to develop a decision support tool that aids in selecting the best primary treatment protocol by incorporating the likelihood of postoperative PM and/or ENE to quantify both overall therapy‐related burden level and swallowing function impairment based on short‐ and long‐term toxicities, using an existing prospective data set. The aims of this study were as follows: (i) to quantify the swallowing‐related toxicity levels of definitive therapies and TORS based on subjective and objective instruments using short‐term and long‐term assessments of toxicities and (ii) to determine the required confidence level of likelihood of ENE/PM to determine the optimal primary therapy and its' associated risk level. To achieve these aims, we incorporate the quantified expected swallowing‐related toxicity burden of each primary treatment based on probabilistic postoperative PM and/or ENE events. This is the first application of decision analysis, a widely established tool for decision‐making in uncertain environments. We use this tool to quantify the risk of postoperative swallowing‐related toxicities and the impact on quality of life, measured using highly reliable functional endpoints frequently used in OPSCC.
## Study design
This secondary analysis was conducted using prospective registry data from the MD Anderson Oropharynx Cancer Registry (PA14‐0947) Patient‐Reported Outcomes and Function (PROF) Core. The PROF registry enrolls all consenting OPSCC/HNC patients at the University of Texas MD Anderson Cancer Center (MDACC). The sample for this secondary analysis included patients enrolled on PA14‐0914 from March 2015 to February 2018 with the following eligibility criteria: (i) cancer of the oropharynx and (ii) TORS, RT, or CRT as primary treatment approaches at MDACC. All primary treatment was determined by Multidisciplinary Tumor Board. Data analysis occurred under approval of the Institutional Review Board (protocol PA11‐0809). 14
## Demographics
Demographics and treatment characteristics of the cohort are listed in Table 1. 14
**TABLE 1**
| Characteristic | All patients | Primary TORS | Primary (C)RT |
| --- | --- | --- | --- |
| Enrollment | (n = 257) | No adjuvant therapy, n = 38 | RT Alone, n = 30 CRT, n = 152 |
| Enrollment | (n = 257) | With adjuvant RT (TORS+RT), n = 22 | RT Alone, n = 30 CRT, n = 152 |
| Enrollment | (n = 257) | With adjuvant CRT (TORS + CRT), n = 15 | RT Alone, n = 30 CRT, n = 152 |
| Total | 257 | 75 | 182 |
| Age at primary treatment start, mean (SD), y | 59.54 (9.07) | 58.70 (9.60) | 59.89 (8.84) |
| Sex | Sex | Sex | Sex |
| Female | 35 (13.6) | 10 (13.3) | 25 (13.7) |
| Male | 222 (86.4) | 65 (86.7) | 157 (86.3) |
| Primary tumor site | Primary tumor site | Primary tumor site | Primary tumor site |
| Tonsil | 135 (52.5) | 38 (50.7) | 97 (53.3) |
| BOT | 116 (45.1) | 34 (45.3) | 82 (45.0) |
| GPS | 6 (2.3) | 3 (4.0) | 3 (1.6) |
## Toxicity instruments
Prospective collection of clinician‐ and patient‐graded outcome measures occurred at routine timepoints. The MD Anderson Dysphagia Inventory (MDADI) is a patient‐administered 20‐item questionnaire that evaluates the impact of dysphagia on quality of life. The MDADI includes one question regarding global function and 19 items that focus on the physical, emotional and functional aspects of swallowing, which are pooled and averaged to obtain a composite score (varying from 20 (poor swallowing‐related quality of life) to 100 (optimal swallowing‐related quality of life)). 16 The MD Anderson Symptom Inventory–Head and Neck Module (MDASI‐HN) is a validated multi‐symptom inventory of patient‐reported swallowing and chewing difficulties based on scores varying from 0 (symptom not present) to 10 (highest imaginable severity of the symptom) and represents a generalizable pan‐symptom toxicity metric. 17 Lastly, the Dynamic Imaging Grade of Swallowing Toxicity (DIGEST) is a validated and reliable objective tool that measures the presence and severity of pharyngeal dysphagia. The DIGEST conforms to CTCAE criteria for toxicity reporting with a 4‐point grading scale, 0 (no pharyngeal dysphagia), 1 (mild), 2 (moderate), 3 (severe), to 4 (life‐threatening dysphagia). 14, 18 The study was conducted using multiple instruments to avoid risk/decision calibration predicated only based on a subset of the patient toxicity profile, thus accounting for inter‐therapy differential toxicity.
## Measures
A total of six measures were developed for this study. Each instrument was assessed for all treatment cohorts (TORS, RT alone, CRT alone, TORS with adjuvant RT (TORS+RT), TORS with adjuvant CRT (TORS+CRT)) pretherapy (baseline), 3–6 months and 18–24 months after primary treatment (Table 2). For each cohort, MDADI‐based and MDASI‐based absolute short‐term deterioration in swallowing function (ΔSMDADI and ΔSMDASI) were defined as the reduction in MDADI baseline score and the increment in MDASI baseline score, respectively, within 3–6 months. MDADI‐based and MDASI‐based absolute long‐term deterioration in swallowing function (ΔLMDADI and ΔLMDASI) were defined analogously with respect to 18–24 months scores. For each treatment cohort, DIGEST‐based short‐ and long‐term deteriorations in swallowing functions (DDIGEST and RDIGEST) were calculated as the fraction of baseline population whose DIGEST baseline grades evolved into any worse grade within 3–6 and 18–24 months after receiving therapy, respectively (Appendix A [A1‐A3]).
**TABLE 2**
| Instrument | CRT | RT | TORS+CRT | TORS+RT | TORS |
| --- | --- | --- | --- | --- | --- |
| MDADI | MDADI | MDADI | MDADI | MDADI | MDADI |
| Baseline (mean) | 93.26 | 87.17 | 94.04 | 90.18 | 88.69 |
| 3–6 months | 81.14 | 82.02 | 82.89 | 83.80 | 82.55 |
| 18–24 months | 86.56 | 81.98 | 85.16 | 88.28 | 86.05 |
| Absolute short‐term deterioration ΔSMDADI (bootstrapped) | 12.1 a , b | 5.34 | 11.27 a , b | 6.43 | 6.3 |
| Absolute long‐term deterioration ΔLMDADI (bootstrapped) | 6.71 | 5.25 | 8.81 | 2.05 | 2.57 |
| MDASI | MDASI | MDASI | MDASI | MDASI | MDASI |
| Baseline (mean) | 0.47 | 0.99 | 0.59 | 0.32 | 0.81 |
| 3–6 months | 1.43 | 1.37 | 1.09 | 0.91 | 1.20 |
| 18–24 months | 0.99 | 1.35 | 0.62 | 0.54 | 0.93 |
| Absolute short‐term deterioration ΔSMDADI (bootstrapped) | 0.95 | 0.38 | 0.51 | 0.59 | 0.39 |
| Absolute long‐term deterioration ΔLMDADI (bootstrapped) | 0.51 | 0.36 | 0.05 | 0.22 | 0.12 |
| DIGEST | DIGEST | DIGEST | DIGEST | DIGEST | DIGEST |
| 3–6 months incidence with worsen grade | 60 (120) | 10 (23) | 4 (10) | 11 (16) | 5 (24) |
| 18–24 months incidence with worsen grade | 23 (66) | 3 (11) | 3 (6) | 7 (7) | 1 (14) |
| Absolute short‐term deterioration DDIGEST (bootstrapped) | 0.5 | 0.44 | 0.4 | 0.69 | 0.21 |
| Absolute long‐term deterioration RDIGEST (bootstrapped) | 0.35 | 0.27 | 0.5 | 1.00 | 0.07 |
## Statistical analysis
This study was based on a published analysis conducted by Hutcheson et al. 14 using the same patient cohort. Bootstrapping‐based resampling 19, 20 was applied to mitigate the effects of unequal sample sizes across treatment cohorts ($$n = 10$$,000) as well as reducing the variability among of the constructed measures. For MDADI and MDASI scores, bootstrapping was employed based on the empirical distribution computed from the frequency of observed scores. The values of ΔSMDADI, ΔLMDADI, ΔSMDASI, and ΔLMDASI were calculated using bootstrapped data sets for each treatment cohort. For DIGEST grades, bootstrapping was employed based on the assumption that the evolution of baseline grades into 3–6 months and 18–24 months grades follows multinomial distribution based on the reported incidence (Appendix C). Next, the values of RDIGEST and DDIGEST were calculated using the bootstrapped data set (Table 2).
## Decision tree analysis
The aim of this study is to seek the required confidence level with respect to the likelihoods of postoperative ENE/PM for TORS to become the optimal treatment, that is, to outperform definitive (C)RT's expected swallowing‐related toxicity burden. An expected‐value decision tree was constructed following the clinical flow depicted in Figure 1 (Top) allowing the measure‐based comparison of definitive (C)RT with deterministic outcomes and TORS with probabilistic outcomes (Figure 1 [Bottom]). Decision trees are extremely efficient for implementing medical guidelines for scenarios with probabilistic outcomes to determine the optimal decision based on expected values of decisions. 21 The decision tree model was constructed under two distinct scenarios: (i) TORS versus definitive RT and (ii) TORS versus definitive CRT, based on the assumption that the patients studied under each scenario are eligible for both surgical and definitive therapy with comparable locoregional control and survival. For each scenario, the decision model was analyzed for each measure: using the collected measure values (Table 2), the expected toxicity burden of TORS was calculated as a function of postoperative ENE and PM likelihoods (pN+ and pM+, respectively, ranging from 0 to 1) based on the assumption that when an adjuvant therapy is required, it is equally likely that the patient will undergo adjuvant RT or adjuvant CRT. ( *Sensitivity analysis* was performed to study the effects of this assumption as reported in Appendix B.) In each scenario, the optimal choice between TORS and the definitive therapy was made based on the observation that for both short‐ and long‐term swallowing‐related toxicity levels, the treatment protocol having lower expected toxicity burden is more favorable. For each scenario and for each short‐or long‐term measure, the cut‐off value for TORS (cS,cL) was computed as the highest expected toxicity burden of TORS under which TORS remains the optimal treatment.
The results of decision tree analysis were demonstrated as 2D heatmaps, for each measure, revealing the combination of likelihoods of postoperative ENE and PM for which TORS swallowing toxicity burden is lower than the definitive therapy. The heatmaps were also employed to derive individual postoperative ENE and PM likelihoods for which definitive therapy becomes the optimal treatment having lower swallowing toxicity level. Finally, to account for the inherent difficulty in pretherapy estimation of postoperative ENE/PM likelihoods, measure‐based risk associated with TORS (r) were developed as the fraction of possible combinations of postoperative ENE and PM likelihoods for which definitive therapy offers lower toxicity burden compared with TORS.
## TORS versus definitive RT (Scenario 1)
Figure 2 shows how expected deterioration in swallowing function, computed through six measures (see “Methods” section), can be used to determine the likelihood regions in which either TORS or definitive RT remains the optimal treatment selection. For each of the three short‐term measures, the blue region in Figure 2A indicates the ranges of likelihoods associated with postoperative events (i.e., PM and ENE) for which TORS outperforms definitive RT in terms of swallowing outcomes. The red region, on the other hand, indicate combination of postoperative likelihoods for which TORS results in higher swallowing‐related injuries compared with definitive RT, thus leaving the latter treatment as the optimal choice. Figure 2B provides similar results based on the three long‐term measures.
**FIGURE 2:** *Expected deterioration in swallowing function for the first scenario based on (A) short‐term measures and (B) long‐term measures. cS, cut‐off value for TORS; DDIGEST, DIGEST‐based absolute short‐term deterioration in swallowing function; RDIGEST, DIGEST‐based absolute long‐term deterioration in swallowing function; ΔLMDADI, MDADI‐based absolute long‐term deterioration; ΔLMDASI, MDASI‐based absolute long‐term deterioration; ΔSMDADI, MDADI‐based absolute short‐term deterioration; ΔSMDASI, MDASI‐based absolute short‐term deterioration; r, risk associated with TORS; RT, radiation therapy; TORS, transoral robotic surgery*
## Short‐term (3–6 month) outcomes
The decision tree analysis using both MDADI and MDASI instruments implied that definitive RT remained the optimal treatment for any postoperative ENE and PM likelihoods (Figure 2A). This is justified based on the observation that the short‐term MDADI‐ and MDASI‐based swallowing‐related toxicities of TORS (ΔSMDADI and ΔSMDASI, respectively) were always more severe compared with definitive RT (Table 2). According to the DIGEST‐measure (presence and severity of dysphagia), if the likelihood associated with either ENE or PM is, at least, $75\%$, definitive RT remained the optimal treatment. For the cases in which the likelihood of neither ENE or PM is more than $40\%$, TORS was the optimal treatment. Furthermore, in the absence of pretherapy likelihood estimation of ENE or PM, TORS risk level was at least $65\%$ (according to DIGEST), and definitive therapy outperformed TORS based on MDADI‐ and MDASI‐based measures.
When comparing TORS with definitive CRT using short‐term measures, TORS remained the optimal treatment based on both MDADI and MDASI instruments for any likelihoods associated with postoperative ENE and/or PM (Figure 3A). This observation was evident from the related measure values reported in Table 2. Based on the DIGEST instrument, the swallowing toxicity burden of TORS remained higher compared with definitive CRT if the likelihood for any of the postoperative events is more than $80\%$, thus making definitive CRT the optimal treatment. TORS remained the optimal treatment when both postoperative events have a likelihood of, at most, $55\%$. Furthermore, in the absence of pretherapy estimation of ENE or PM likelihoods, TORS risk level is at most $45\%$ according to DIGEST, while it carries no risk according to the other instruments.
## Long‐term (18–24 month) outcomes
For long‐term measures, TORS was the optimal treatment based on MDASI instrument for any confidence level regarding the likelihood of postoperative ENE and PM (Figure 2B). However, based on the MDADI instrument, definitive RT became the optimal treatment when either postoperative ENE or PM are extremely likely (>$90\%$). TORS was the optimal treatment if the likelihood of both postoperative events remained less than $70\%$. According to the DIGEST instrument, however, definitive RT remained the optimal treatment even if either of the events was likely, at least, $25\%$. In this case, TORS becomes the optimal treatment only if both events are extremely unlikely (<$10\%$). Finally, in the absence of pretherapy information about ENE or PM likelihood, TORS risk level is at $21\%$ according to MDADI, and $97\%$ according to DIGEST with TORS carrying no risk based on MDASI instrument.
Table 3 summarizes the confidence level of postoperative events likelihoods required to ensure TORS (definitive RT) is the optimal treatment under the first scenario.
**TABLE 3**
| Scenario I | Scenario I.1 | Scenario I.2 |
| --- | --- | --- |
| Instrument/measure | Confidence level of postoperative events for which TORS is optimal | Confidence level of postoperative events for which definitive RT is optimal |
| MDADI | MDADI | MDADI |
| Short term (3–6 months) | — | Any likelihood associated with ENE and/or PM |
| Long term (18–24 months) | When both ENE and PM have likelihood <70% | If either of ENE or PM has a likelihood >90% |
| MDASI | MDASI | MDASI |
| Short term (3–6 months) | — | Any likelihood associated with ENE and/or PM |
| Long term (18–24 months) | Any likelihood associated with ENE and/or PM | — |
| DIGEST | DIGEST | DIGEST |
| Short term (3–6 months) | When both ENE and PM have likelihood <40% | If either of ENE or PM has a likelihood >75% |
| Long term (18–24 months) | When both ENE and PM have likelihood <10% | If either of ENE or PM has a likelihood >25% |
For this scenario, the results of decision tree analysis for long‐term measures were almost similar to those for the first scenario (Figure 3B). TORS remained the optimal treatment based on both MDADI and MDASI instruments being insensitive to the likelihood of postoperative ENE or PM. However, according to the DIGEST instrument, even moderate likelihood (>$40\%$) for either of postoperative events implies the superiority of definitive CRT over TORS. The latter becomes the optimal treatment when both postoperative events have a small likelihood (<$20\%$). Furthermore, when no pretherapy information about the likelihoods is available, TORS carried a risk of level of $91\%$ based on DIGEST, while MDASI and MDASI indicate that there is no risk for TORS (Figure 3B).
A summary of the confidence level of postoperative events likelihoods required for the optimality of TORS (definitive CRT) is given in Table 3.
## TORS versus definitive CRT (Scenario 2)
Figure 3 demonstrates the comparative analysis for TORS versus definitive CRT using each of the six measures introduced in the “Methods” section. Analogous to Figure 2, for each measure, blue regions in Figure 3 demonstrate the combinations of postoperative events' likelihoods for which TORS is expected to have superior swallowing outcomes compared with definitive CRT. Red regions in Figure 3 indicate the ranges of likelihoods for which definitive CRT is expected to outperform TORS in terms of swallowing injuries. Figure 3A,B provide the results for short‐term and long‐term measures, respectively.
**FIGURE 3:** *Expected deterioration in swallowing function for the second scenario based on (A) short‐term measures and (B) long‐term measures. cS, cut‐off value for TORS; DDIGEST, DIGEST‐based absolute short‐term deterioration in swallowing function; RDIGEST, DIGEST‐based absolute long‐term deterioration in swallowing function; ΔLMDADI, MDADI‐based absolute long‐term deterioration; ΔLMDASI, MDASI‐based absolute long‐term deterioration; ΔSMDADI, MDADI‐based absolute short‐term deterioration; ΔSMDASI, MDASI‐based absolute short‐term deterioration; CRT, chemoradiation therapy; r, risk associated with TORS; TORS, transoral robotic surgery*
## DISCUSSION
Since the approval of TORS by FDA as a minimally invasive surgical treatment protocol for HNC patients, there have been several studies reporting on the success of TORS as an option for treatment of early‐stage oropharyngeal carcinomas due to its favorable oncologic outcomes and its potential to mitigate the toxicities incurred by patients in other surgical techniques or primary CRT. 22, 23, 24, 25 Consequently, TORS has been increasingly used for low‐ to intermediate‐risk OPSCC patients having small‐volume primary tumor and near‐normal baseline function. 14 However, studies suggest a considerable percentage of patients have undergone postoperative adjuvant CRT, 26, 27, 28 despite being theoretically believed to be candidates for surgical therapy alone. The current data suggest that surgeons and radiation oncologists are decidedly poor at predicting whether a patient will require adjuvant treatment from pretherapy exam, and thus many patients offered surgical resection are in fact being offered not TORS alone, but rather some unquantified probability of double‐ or triple‐modality therapy (and the concomitant additional toxicities therefrom). This is primarily due to absence of extreme presurgical certitude regarding of post‐TORS histopathological features, which makes it a challenging decision‐making problem to choose between initial TORS or definitive nonsurgical treatment protocols.
Decision analysis provides an integrated framework to study decision‐making scenarios that involve uncertain outcomes. The decision analysis model developed in this study incorporates the imputed pretherapy physic‐assessed statistical likelihood of the two major postoperative indicator events that trigger adjuvant therapy: pretherapy physician‐estimated probability of margin positivity and ENE. Through quantifying posttherapy swallowing‐related toxicities using well‐established patient‐reported and objective instruments, the model in this study captures the probabilistic outcomes of TORS which in comparison with the definitive (C)RT's outcome can aid the clinical team in choosing the optimal treatment protocol. While objective instruments are developed based on standard test results, hence providing more concrete results in comparing the change in the patient's quality of life, they might be less indicative of the patient's lived experience. The developed decision support tool in this study is developed under the premise that the physician will include both objective and patient‐reported measures when deciding about the optimal therapy. The results of this model can also be utilized to compute the risk level associated with TORS in developing higher swallowing‐related toxicity burden compared with definitive (C)RT in the absence of pretherapy estimation of the likelihood of postoperative events that can trigger the need for adjuvant therapy.
Three observations are notable from the current analysis: (i) there are distinctly different optimal choices based on the probability of postoperative events that differ whether radiotherapy‐alone or chemoradiotherapy is the comparator for surgical treatment; (ii) there are divergent optimal choice of therapy regarding subjective multisymptom (MDASI), subjective swallowing (MDADI) or objective swallowing (DIGEST) is the toxicity metric of interest; and (iii) the choice of therapy based on early (3–6 month) swallowing outcomes may not reflect the optimal therapy selection for later time‐points (18–24 months).
The decision analysis model in this work has its own limitations. It currently relies on a single institutional database with a cohort of 257 patients. Bootstrapping was used to mitigate the effects of small‐size population allowing the model to make assumptions as “real‐life” as possible. Furthermore, expected‐value decision analysis has its own disadvantages, namely the sensitivity to the probability values as well as measures. Sensitivity analysis was performed to determine the variation of TORS risk level as a function of the likelihood of postoperative events as well as associated quantified short‐ and long‐term toxicity of all treatment protocols. We would like to emphasize that while our model has been constructed based on single‐institutional data, it can be easily instantiated using validated possibly multi‐institutional data or even from randomized trials.
Ultimately, the aim of this effort is to quantize decision‐making for HNC/OPSCC patients eligible for alternative treatment protocols. The vast majority of cases selected for definitive or surgical therapy (potentially followed by adjuvant radiotherapy) are typically made using heuristic physician‐decision processes, which appear to be speciously high estimates of the potential for single‐modality surgery. However, advanced approaches such as improved standardized radiologic assessment, 29 AI‐assisted imaging analysis, or risk‐models 30 could improve outcomes by bringing quantitative decision support to surgeons and radiation oncologists. Furthermore, these data serve to define preoperative assessment tools for decision support for future explorations.
## CONCLUSION
Our models demonstrated optimal decision thresholds for selection of surgical possibly with adjuvant therapy or organ preservation with (chemo)radiotherapy based on clinically‐representative subjective and objective toxicity outcomes. The resultant thresholds for physician certainty for prediction of clinical risk features necessitating adjuvant therapy should be considered with these decision tools as a component of multidisciplinary patient‐centric therapy selection for early‐stage oropharyngeal cancer patients.
## AUTHOR CONTRIBUTION
Mehdi Hemmati, Clifton D. Fuller, and Andrew J. Schaefer contributed to the study concept and design. Mehdi Hemmati implemented the decision support tool and performed the analysis. Mehdi Hemmati and Carly Barbon contributed to the data analysis. Abdallah S.R. Mohamed and Lisanne V. van Dijk contributed to data collection and interpretation. Amy C. Moreno, Neil D. Gross, Ryan P. Goepfert, Stephen Y. Lai, and Katherine A. Hutcheson contributed to the analysis and interpretation of the results. Mehdi Hemmati drafted the manuscript. All authors contributed to the interpretation of data and provided feedback on the manuscript.
## CONFLICT OF INTEREST
Dr. Fuller received/receives funding and salary support from directly related to this project from: NIH National Institute of Dental and Craniofacial Research (NIDCR) Academic Industrial Partnership Grant (R01DE028290); NIDCR Establishing Outcome Measures for Clinical Studies of Oral and Craniofacial Diseases and Conditions award (R01DE025248); NIH/NSF NCI Smart Connected Health Program (R01CA257814). Dr. Fuller received/receives funding and salary support from directly unrelated to this project from: NCI Parent Research Project Grant (R01CA258827); NCI Ruth L. Kirschstein NRSA Institutional Research Training Grant (T32CA261856); NIH NIDCR Exploratory/Developmental Research Grant Program (R21DE031082); National Institutes of Health (NIH) National Cancer Institute (NCI) Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Program (R01CA214825); NSF/NIH Joint Initiative on Quantitative Approaches to Biomedical Big Data program (R01CA225190); NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Programs for Residents and Clinical Fellows Grant (R25EB025787); NCI Early Phase Clinical Trials in Imaging and Image‐Guided Interventions Program (1R01CA218148); NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672); Small Business Innovation Research Grant *Program a* sub‐award from Oncospace, Inc. (R43CA254559); The Human BioMolecular Atlas Program (HuBMAP) Integration, Visualization & Engagement (HIVE) Initiative (OT2OD026675) sub‐award; Patient‐Centered Outcomes Research Institute (PCS‐1609‐36,195) sub‐award from Princess Margaret Hospital; National Science Foundation (NSF) Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) grant (NSF 1933369). Dr. Fuller receives grant and infrastructure support from MD Anderson Cancer *Center via* the Charles and Daneen Stiefel Center for Head and Neck Cancer Oropharyngeal Cancer Research Program; the Program in Image‐guided Cancer Therapy; and the NIH/NCI Cancer Center Support Grant (CCSG) Radiation Oncology and Cancer Imaging Program (P30CA016672). Dr. Fuller has received direct industry grant/in‐kind support, honoraria, and travel funding from Elekta AB. TCS was supported by The University of Texas Health Science Center at Houston Center for Clinical and Translational Sciences TL1 Program (TL1 TR003169).
## ETHICS STATEMENT
The ethics approval was obtained from the University of Texas MD Anderson Cancer Center Institutional Review Board with protocol number: PA11‐0809.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are openly available in figshare at the following URL: https://doi.org/10.6084/m9.figshare.19636755.v1
## References
1. Baskin RM, Boyce BJ, Amdur R. **Transoral robotic surgery for oropharyngeal cancer: patient selection and special considerations**. *Cancer Manag Res* (2018.0) **10** 839-846. PMID: 29719420
2. Chaturvedi AK, Engels EA, Pfeiffer RM. **Human papillomavirus and rising oropharyngeal cancer incidence in the United States**. *J Clin Oncol* (2011.0) **29** 4294-4301. PMID: 21969503
3. 3
National Cancer Institute – Head and Neck Cancers
. Accessed September 10, 2021. https://www.cancer.gov/types/head‐and‐neck/head‐neck‐fact‐sheet
4. 4
Centers for Disease Control and Prevention – Head and Neck Cancers
. Accessed September 29, 2021. https://www.cdc.gov/cancer/headneck/
5. Jamal Z, Anjum F. *Oropharyngeal Squamous Cell Carcinoma* (2020.0)
6. Lazarus CL, Ganz C, Ru M, Miles BA, Kotz T, Chai RL. **Prospective instrumental evaluation of swallowing, tongue function, and QOL measures following transoral robotic surgery alone without adjuvant therapy**. *Head Neck* (2019.0) **41** 322-328. PMID: 30552845
7. Rieger JM, Zalmanowitz JG, Li SYY. **Functional outcomes after surgical reconstruction of the base of tongue using the radial forearm free flap in patients with oropharyngeal carcinoma**. *Head Neck* (2007.0) **29** 1024-1032. PMID: 17405174
8. O'Connell DA, Rieger J, Harris JR. **Swallowing function in patients with base of tongue cancers treated with primary surgery and reconstructed with a modified radial forearm free flap**. *Arch Otolaryngol Head Neck Surg* (2008.0) **134** 857-864. PMID: 18711061
9. Chia SH, Gross ND, Richmon JD. **Surgeon experience and complications with transoral robotic surgery (TORS)**. *Otolaryngol Head Neck Surg* (2013.0) **149** 885-892. PMID: 24013139
10. Hutcheson KA, Holsinger FC, Kupferman ME, Lewin JS. **Functional outcomes after TORS for oropharyngeal cancer: a systematic review**. *Eur Arch Oto‐Rhino‐Laryngol* (2015.0) **272** 463-471
11. More YI, Tsue TT, Girod DA. **Functional swallowing outcomes following transoral robotic surgery vs primary chemoradiotherapy in patients with advanced‐stage oropharynx and supraglottis cancers**. *JAMA Otolaryngol Head & Neck Surg* (2013.0) **139** 43-48. PMID: 23247974
12. Amit M, Hutcheson K, Zaveri J. **Patient‐reported outcomes of symptom burden in patients receiving surgical or nonsurgical treatment for low‐intermediate risk oropharyngeal squamous cell carcinoma: a comparative analysis of a prospective registry**. *Oral Oncol* (2019.0) **91** 13-20. PMID: 30926057
13. Heah H, Goepfert RP, Hutcheson KA. **Decreased gastrostomy tube incidence and weight loss after transoral robotic surgery for low‐ to intermediate‐risk oropharyngeal squamous cell carcinoma**. *Head Neck* (2018.0) **40** 2507-2513. PMID: 30102824
14. Hutcheson KA, Warneke CL, Yao CMKL. **Dysphagia after primary transoral robotic surgery with neck dissection vs nonsurgical therapy in patients with low‐ to intermediate‐risk oropharyngeal cancer**. *JAMA Otolaryngol Head Neck Surg* (2019.0) **145** 1053-1063. PMID: 31556933
15. Hutcheson KA, Lewin JS, Barringer DA. **Late dysphagia after radiotherapy‐based treatment of head and neck cancer**. *Cancer* (2012.0) **118** 5793-5799. PMID: 23640737
16. Chen AY, Frankowshi R, Bishop‐Leone J. **The development and validation of a dysphagia‐specific quality‐of‐life questionnaire for patients with head and neck cancer: the M. D. Anderson dysphagia inventory**. *Arch Otolaryngol Head Neck Surg.* (2001.0) **127** 870-876. PMID: 11448365
17. Rosenthal DI, Mendoza TR, Chambers MS. **Measuring head and neck cancer symptom burden: the development and validation of the M. D. Anderson symptom inventory, head and neck module**. *Head Neck* (2007.0) **29** 923-931. PMID: 17358040
18. Goepfert RP, Lewin JS, Barrow MP. **Grading dysphagia as a toxicity of head and neck cancer: differences in severity classification based on MBS DIGEST and clinical CTCAE grades**. *Dysphagia* (2018.0) **33** 185-191. PMID: 28836005
19. James G, Witten D, Hastie T, Tibshirani R. *An introduction to statistical learning* (2013.0)
20. Efron B, Tibshirani RJ. *An introduction to the bootstrap* (1994.0)
21. Sox HS, Blatt MA, Higgins MC, Marton KI. *Medical decision making* (2007.0)
22. Ling DC, Chapman BV, Kim J. **Oncologic outcomes and patient‐reported quality of life in patients with oropharyngeal squamous cell carcinoma treated with definitive transoral robotic surgery versus definitive chemoradiation**. *Oral Oncol* (2016.0) **61** 41-46. PMID: 27688103
23. Lira RB, Kowalski LP. **Robotic head and neck surgery: beyond TORS**. *Curr Oncol Rep* (2020.0) **22** 1-7. PMID: 31960161
24. Achim V, Bolognone RK, Palmer AD. **Long‐term functional and quality‐of‐life outcomes after transoral robotic surgery in patients with oropharyngeal cancer**. *JAMA Otolaryngol & Neck Surg* (2018.0) **144** 18-27
25. De Almeida JR, Li R, Magnuson JS. **Oncologic outcomes after transoral robotic surgery: a multi‐institutional study**. *JAMA Otolaryngol & Neck Surg.* (2015.0) **141** 1043-1051
26. Sinclair CF, McColloch NL, Carroll WR, Rosenthal EL, Desmond RA, Magnuson JS. **Patient‐perceived and objective functional outcomes following transoral robotic surgery for early oropharyngeal carcinoma**. *Arch Otolaryngol & Neck Surg* (2011.0) **137** 1112-1116
27. Hurtuk A, Agrawal A, Old M, Teknos TN, Ozer E. **Outcomes of transoral robotic surgery: a preliminary clinical experience**. *Otolaryngol Neck Surg* (2011.0) **145** 248-253
28. Weinstein GS, O'Malley BW, Cohen MA, Quon H. **Transoral robotic surgery for advanced oropharyngeal carcinoma**. *Arch Otolaryngol Head Neck Surg* (2010.0) **136** 1079-1085. PMID: 21079160
29. Faraji F, Aygun N, Coquia SF. **Computed tomography performance in predicting extranodal extension in HPV‐positive oropharynx cancer**. *Laryngoscope* (2020.0) **130** 1479-1486. PMID: 31411751
30. Hararah MK, Stokes WA, Jones BL. **Nomogram for preoperative prediction of nodal extracapsular extension or positive surgical margins in oropharyngeal squamous cell carcinoma**. *Oral Oncol* (2018.0) **83** 73-80. PMID: 30098782
|
---
title: Metabolic pathways enriched according to ERG status are associated with biochemical
recurrence in Hispanic/Latino patients with prostate cancer
authors:
- Natalia L. Acosta‐Vega
- Rodolfo Varela
- Jorge Andrés Mesa
- Jone Garai
- Melody C. Baddoo
- Alberto Gómez‐Gutiérrez
- Silvia J. Serrano‐Gómez
- Marcela Nuñez Lemus
- Martha Lucía Serrano
- Jovanny Zabaleta
- Alba L. Combita
- María Carolina Sanabria‐Salas
journal: Cancer Medicine
year: 2022
pmcid: PMC9972164
doi: 10.1002/cam4.5301
license: CC BY 4.0
---
# Metabolic pathways enriched according to ERG status are associated with biochemical recurrence in Hispanic/Latino patients with prostate cancer
## Abstract
Differentially expressed genes (DEGs) between ERG high and ERG low tumors revealed clustering of most of the non‐BCR cases into de ERG high group and most of the BCR cases in ERG low group. Subsequent analyses confirmed an association between ERG status with BCR, showing a worst BCR‐free survival for ERG low patients compared to the ERG high group. Enrichment pathway analysis of the 150 DEGs identified an important participation of metabolic‐related pathways in the BCR progression.
### Background
The role of ERG‐status molecular subtyping in prognosis of prostate cancer (PCa) is still under debate. In this study, we identified differentially expressed genes (DEGs) according to ERG‐status to explore their enriched pathways and implications in prognosis in Hispanic/Latino PCa patients.
### Methods
RNA from 78 Hispanic PCa tissues from radical prostatectomies (RP) were used for RNA‐sequencing. ERG high /ERG low tumor groups were determined based on the 1.5‐fold change median expression in non‐tumor samples. DEGs with a False Discovery Rate (FDR) < 0.01 and a fold change >2 were identified between ERG high and ERG low tumors and submitted to enrichment analysis in MetaCore. Survival and association analyses were performed to evaluate biochemical recurrence (BCR)‐free survival.
### Results
The identification of 150 DEGs between ERG high and ERG low tumors revealed clustering of most of the non‐BCR cases ($60\%$) into de ERG high group and most of the BCR cases ($60.8\%$) in ERG low group. Kaplan–Meier survival curves showed a worst BCR‐free survival for ERG low patients, and a significant reduced risk of BCR was observed for ERG high cases (OR = 0.29 ($95\%$CI, 0.10–0.8)). Enrichment pathway analysis identified metabolic‐related pathways, such as the renin‐angiotensin system and angiotensin maturation system, the linoleic acid metabolism, and polyamines metabolism in these ERG groups.
### Conclusions
ERG low tumor cases were associated with poor BCR‐free survival in our Hispanic/Latino patients, with metabolism‐related pathways altered in the BCR progression.
### Impact
Our findings suggest the need to dissect the role of diet, metabolism, and lifestyle as risk factors for more aggressive PCa subtypes.
## INTRODUCTION
Prostate cancer (PCa) is the second most common cancer and the fifth leading cause of death from cancer in men worldwide. 1 In Colombia, PCa is the most common cancer in men with estimated age‐standardized incidence rates of 49.8 cases per 100,000 inhabitants and second highest mortality rates with 12–12.6 per 100,000 inhabitants. 1, 2 The understanding of PCa molecular alterations has increased with the definition of molecular subtypes and the identification of prognostic gene‐expression signatures. 3 The establishment of subtypes began with the identification of the fusion of genes ERG and TMPRSS2 as a common somatic alteration in PCa. TMPRSS2:ERG (T2E) gene fusion results in overexpression of ERG, a known oncogene and member of the ETS transcription factor family. 4, 5 Around $50\%$ of Caucasians PCa patients harbor T2E‐positive tumors, but lower frequencies have been reported in men of African or Asian ethnicities. 6 Later, it was established that negative ERG tumor status was associated with poorer BCR‐free survival in Caucasian, but no relation was found in African–American patients. 7 Moreover, there are currently several publications that have shown an association between high expression of ERG with good prognosis, 7, 8, 9 whereas others report an inverse association. 10, 11 A meta‐analysis including 48 studies showed no evidence of an association with recurrence‐free or disease‐specific survival, 12 though authors conclude ERG status might allow patient stratification for different management strategies. 13, 14 Therefore, there is still conflicting evidence as to whether the T2E fusion and/or the level of ERG expression have prognostic implications. 15 Also, the dissimilarities in the frequency of the fusion and prognosis may be given by differences in the genetic structure of the ethnic groups.
Given that the T2E gene fusion is an early event in PCa, fusion‐positive tumors are believed to represent a distinct molecular subtype of PCa involving activation of specific oncogenic pathways, 16, 17 as well as different metabolic profiles compared with the T2E fusion‐negative tumors. 18, 19 Therefore, in this study, we aimed to explore molecular differences associated to progression in ERG high and ERG low PCa tumors through a differential expression analysis and enrichment pathway analysis in Hispanic/Latino patients with localized/regionally advanced PCa.
## Patients and sample collection
Localized/regionally advanced PCa patients diagnosed at Instituto Nacional de Cancerología (INC) in Colombia between 2007 and 2011 were included. Samples were obtained from FFPE (Formalin Fixed Paraffin Embedded) tissues from radical prostatectomies (RP). This protocol was approved by the Research Ethics Board at the INC and was designated as an exempt study for informed consent.
One hundred and one ($$n = 101$$) suitable cases were identified through histologic review by an expert pathologist. All tumor samples with Gleason pattern over 3 + 3 and high‐density areas of tumor cells ≥$65\%$, as well as non‐tumor regions, were selected. Section cores were extracted for each type of tissue. From each RP, only the focus with the highest Gleason pattern from each patient was used in this study. Clinical information was obtained from INC databases. BCR was defined as the elevation of serum PSA levels over 0.2 ng/mL on two successive measurements, as previously established 20; for this work, BCR was defined within the 5 years of follow‐up after RP surgery.
## RNA extraction
Total RNA was extracted using AllPrep DNA/RNA FFPE kit® (Qiagen, Hilden, Germany) following the manufacturer's recommendations. RNA quantity and quality were determined with Nanodrop 2000 Spectrophotometer® (ThermoFisher Scientific, Wilmington, USA) and the Agilent RNA 6000 Nano kit® (Agilent Technologies, Santa Clara, CA), respectively. All samples were suitable for library preparation.
## RNA‐Seq library preparation and sequencing
For library preparation, 1 μg of total RNA and the TruSeq Stranded Total RNA Library Prep kit® with Ribo‐Zero Human/Mouse/Rat (Illumina, Inc., San Diego, CA, USA) were used. Fragmentation step was omitted in most of the samples due to sample quality, while in five samples, it was done according to recommendations from Illumina®. Validation of libraries was performed in the Agilent 2100 Bioanalyzer® system and then normalization to 10 nM was done with the Qubit dsDNA HS Assay kit® (ThermoFisher Scientific, Wilmington, USA), before cluster generation. Sequencing was performed in 12‐pooled samples at 1 × 75 bp with single‐end strategy in a NextSeq 500® (Illumina Inc., San Diego, CA, USA), with no sequencing results in six tumor samples.
## RNA‐seq data analysis
Reads were checked for quality control (QC) using FASTQC and then aligned to human ribosomal RNA using STAR® v.2.5.2. 21 Fifteen tumor samples had rRNA content higher than $70\%$ and were excluded. Unmapped reads were used to map to human reference Homo_sapiens. GRCh38.78 (Ensembl) using RSEM® v1.2.31 22 to generate the read counts and expression calculations. Filtering according to ENSEMBL protein coding genes was done. Filtering of outlier samples through principal component analysis (PCA) excluded 2 tumor samples remaining 78 tumor samples. Genes with median counts of zero in all samples were also filtered out and PCA was used to check batch effects to correct in further analyses.
## Determining
ERG
high
and
ERG
low
To determine tumor cases with ERG high and ERG low, we calculated a 1.5‐fold change over the ERG median expression value in non‐tumor tissues as the cut‐off point. Tumor cases above the defined value were classified as ERG high, otherwise tumors were categorized as ERG low.
## Ancestry estimation
DNA from adjacent non‐tumoral FFPE tissue from 101 cases was extracted using AllPrep DNA/RNA FFPE kit® (Qiagen, Hilden, Germany) following the manufacturer's recommendations. Samples were sent to the University of Minnesota Genomics Center for genotyping of 106 autosomal Ancestry‐Informative Markers (AIMs), 23 in a Sequenom iPLEX® Genotyping Platform. Single nucleotide polymorphisms (SNPs) with call rate lower than $90\%$ were removed, leaving 101 for ancestry estimation; similarly, 25 samples with a call rate lower than $85\%$ were excluded, remaining 76 cases. The concordance score for genotyping was $97.4\%$ between 22 duplicated samples. Additionally, all AIMs were in Hardy–Weinberg equilibrium. These analyses were done in PLINK® v1.90b4.1 64‐bit. Finally, proportions of European, African, and Indigenous *American* genetic ancestry were estimated for each case with the ADMIXTURE® software V1.3.0 under an admixture model. To perform a supervised analysis, three parental reference populations were included: European (42 individuals from Coriell's North American Caucasian panel), African (37 non‐admixed Africans living in United Kingdom and South Carolina—USA), and Indigenous Central American populations (15 Mayan and 15 Nahuas). 23
## Differential gene expression analysis
Filtered raw counts from 78 samples were used as input data for analysis in DESeq2® package v1.20.0. 24 Pre‐filtering to include transcripts with at least 1 read count in more than $80\%$ of the samples to count data was applied. Comparisons between ERG high and ERG low tumors were made to identify the differentially expressed genes (DEGs). *Estimated* genetic ancestry was also included as a variable to determine the effect of ancestry in the differential gene expression analysis, we only included Indigenous and European ancestries proportion since we found a low representation of African ancestry in our cases (median percentage of $5\%$). All the comparisons included batch correction following recommendations documented for the package. Genes with False Discovery Rate (FDR) less than 0.01 and fold change over 2 were selected as DEGs. Unsupervised clustering was done with normalized expression data of DEGs by using Pheatmap® package v1.0.10.
## ERG
expression dataset from GEO repositories
The GEO dataset GSE70770 25 was used to confirm the association of ERG expression with BCR. ERG was categorized as high and low based on the median normalized counts of expression to determine the implication of ERG with BCR‐free survival through Kaplan–Meier survival curve and log‐rank test.
## Statistical analysis
For clinical‐pathological characteristics, continuous variables were analyzed applying analysis of variance test (ANOVA) and Kruskal–Wallis for multiple comparisons and Student's T test and Wilcoxon rank‐sum test for comparisons between two groups. Categorical variables were analyzed with X 2 test and Fisher's exact test. The assumption of normally distributed data was tested by Shapiro–Wilk test. Principal component analysis with RNA‐seq data was done by using singular value decomposition (SVD) on the Log2 transformed counts. Kaplan–Meier survival curves and log‐rank test were done with R Survival® and Survminer® packages for associations between ERG status with BCR‐free survival. p‐value <0.05 was considered statistically significant. Univariate logistic regressions with estimated ORs and $95\%$ confidence intervals (CI) were assessed for associations between ERG and clinical‐pathological variables with BCR. Variables with statistically significant p‐values <0.1 were included in a multivariate model for logistic regression. All the assumptions were verified, and to assess model fit we used goodness of fit measurements. Statistical analyses were done in Rstudio® v1.1.463.
## Patient clinicopathological characteristics
The 78 sequenced tumor cases are described in Table 1. BCR information was available for 73 cases from which 34 ($46.6\%$) presented BCR within a median time of 16.59 months (range 2.1–55.07 months) in the 5‐years of follow‐up after the RP surgery. ERG expression was low in $47.4\%$ of cases and high in $52.6\%$ (Table 1).
**TABLE 1**
| Characteristics | N = 78 | ERG low n = 37 | ERG high n = 41 | p |
| --- | --- | --- | --- | --- |
| Age ‐ years (median, range) | 65 (32–73) | 66 (32–73) | 64 (42–73) | 0.616 |
| Age ‐ years (%) | Age ‐ years (%) | Age ‐ years (%) | Age ‐ years (%) | Age ‐ years (%) |
| <50 | 7 (9.0) | 2 (5.4) | 5 (12.2) | 0.681 |
| 50–60 | 12 (15.4) | 7 (18.9) | 5 (12.2) | |
| 60–70 | 46 (59.0) | 22 (59.5) | 24 (58.5) | |
| >70 | 13 (16.7) | 6 (16.2) | 7 (17.1) | |
| BMI (median [range]) | 26.57 (17.28–36) | 26.75 (17.28–35.19) | 25.47 (17.71–36) | 0.216 |
| Ancestry (median, range) | Ancestry (median, range) | Ancestry (median, range) | Ancestry (median, range) | Ancestry (median, range) |
| European ancestry | 0.57 (0.19–0.81) | 0.54 (0.19–0.81) | 0.58 (0.23–0.78) | 0.756 |
| Indigenous ancestry | 0.38 (0.01–0.66) | 0.39 (0.05–0.66) | 0.37 (0.01–0.65) | 0.713 |
| African ancestry | 0.05 (0.00–0.58) | 0.06 (0.00–0.56) | 0.04 (0.00–0.58) | 0.76 |
| Pre‐operative characteristic | Pre‐operative characteristic | Pre‐operative characteristic | Pre‐operative characteristic | Pre‐operative characteristic |
| Preoperative PSA (median, range) | 9.41 (2.94–45.21) | 9.40 (2.94–45.21) | 9.41 (3.7–44) | 0.579 |
| Clinical stage (%) | Clinical stage (%) | Clinical stage (%) | Clinical stage (%) | Clinical stage (%) |
| I | 24 (30.8) | 10 (27.0) | 14 (34.1) | 0.545 |
| II | 53 (67.9) | 26 (70.3) | 27 (65.9) | |
| IV | 1 (1.3) | 1 (2.7) | 0 (0.0) | |
| Gleason Grade Group at biopsy (%) | Gleason Grade Group at biopsy (%) | Gleason Grade Group at biopsy (%) | Gleason Grade Group at biopsy (%) | Gleason Grade Group at biopsy (%) |
| GG1 | 41 (56.9) | 13 (40.6) | 28 (70.0) | 0.063 |
| GG2 | 15 (20.8) | 8 (25.0) | 7 (17.5) | |
| GG3 | 11 (15.3) | 7 (21.9) | 4 (10.0) | |
| GG4 and GG5 | 5 (6.9) | 4 (12.5) | 1 (2.5) | |
| D'Amico risk groups (%) | D'Amico risk groups (%) | D'Amico risk groups (%) | D'Amico risk groups (%) | D'Amico risk groups (%) |
| LR | 21 (26.9) | 9 (24.3) | 12 (29.3) | 0.806 |
| IR | 36 (46.2) | 17 (45.9) | 19 (46.3) | |
| HR | 21 (26.9) | 11 (29.7) | 10 (24.4) | |
| Post‐operative characteristic | Post‐operative characteristic | Post‐operative characteristic | Post‐operative characteristic | Post‐operative characteristic |
| % tumor in RP (median, range) | 18.50 (1–90) | 21 (1–90) | 16 (1–75) | 0.357 |
| Gleason Grade Group at PR (%) | Gleason Grade Group at PR (%) | Gleason Grade Group at PR (%) | Gleason Grade Group at PR (%) | Gleason Grade Group at PR (%) |
| GG1 | 22 (28.2) | 5 (13.5) | 17 (41.4) | 0.036 |
| GG2 | 24 (30.8) | 12 (32.4) | 12 (29.3) | |
| GG3 | 18 (23.1) | 11 (29.8) | 7 (17.1) | |
| GG4 and GG5 | 14 (17.9) | 9 (24.3) | 5 (12.2) | |
| Pathological stage (%) | Pathological stage (%) | Pathological stage (%) | Pathological stage (%) | Pathological stage (%) |
| T1/T2 | 41 (52.6) | 18 (48.6) | 23 (56.1) | 0.65 |
| T3 | 37 (47.4) | 19 (51.4) | 18 (43.9) | |
| Lymphovascular Invasion in RP (%) | Lymphovascular Invasion in RP (%) | Lymphovascular Invasion in RP (%) | Lymphovascular Invasion in RP (%) | Lymphovascular Invasion in RP (%) |
| No | 55 (85.9) | 25 (80.6) | 30 (90.9) | 0.296 |
| Yes | 9 (14.1) | 6 (19.4) | 3 (9.1) | |
| Perineural invasion in RP (%) | Perineural invasion in RP (%) | Perineural invasion in RP (%) | Perineural invasion in RP (%) | Perineural invasion in RP (%) |
| No | 12 (16.4) | 7 (19.4) | 5 (13.5) | 0.543 |
| Yes | 61 (83.6) | 29 (80.6) | 32 (86.5) | |
| Extracapsular extension in RP (%) | Extracapsular extension in RP (%) | Extracapsular extension in RP (%) | Extracapsular extension in RP (%) | Extracapsular extension in RP (%) |
| No | 38 (50.0) | 18 (50.0) | 20 (50.0) | 1 |
| Yes | 38 (50.0) | 18 (50.0) | 20 (50.0) | |
| Lymph node compromise (%) | Lymph node compromise (%) | Lymph node compromise (%) | Lymph node compromise (%) | Lymph node compromise (%) |
| No | 67 (85.9) | 31 (83.8) | 36 (87.8) | 0.748 |
| Yes | 11 (14.1) | 6 (16.2) | 5 (12.2) | |
| Follow‐up characteristic | Follow‐up characteristic | Follow‐up characteristic | Follow‐up characteristic | Follow‐up characteristic |
| Additional treatment (%) a | Additional treatment (%) a | Additional treatment (%) a | Additional treatment (%) a | Additional treatment (%) a |
| No | 55 (70.5) | 22 (59.5) | 33 (80.5) | 0.05 |
| ADT | 23 (29.5) | 15 (40.5) | 8 (19.5) | |
| BCR (%) | BCR (%) | BCR (%) | BCR (%) | BCR (%) |
| No | 39 (53.4) | 12 (36.4) | 27 (67.5) | 0.01 |
| Yes | 34 (46.6) | 21 (63.6) | 13 (32.5) | |
| PSA at BCR (median [range]) | 0.27 (0.20–3.54) | 0.32 (0.20–3.54) | 0.26 (0.21–0.41) | 0.232 |
| Time to BCR – months (median [range]) | 16.59 (2.10–55.07) | 22.2 (4–55.1) | 8.1 (2.1–42.1) | 0.074 |
| Time of follow‐up ‐ months (median [range]) | 67.60 (3.37–112.63) | 66.9 (4.9–111.4) | 69.4 (3.4–112.6) | 0.806 |
Clinicopathological characteristics compared between ERG groups showed that ERG low group have higher frequency of higher Gleason Grades at RP ($54.1\%$ accounting for GG3‐GG4/GG5) compared with ERG high ($29.3\%$ for GG3‐GG4/GG5) (Table 1) while ERG high group was enriched in lower Gleason Grades ($70.7\%$ for GG1‐GG2; $$p \leq 0.036$$) (Table 1). Of notice, BCR was also statistically significant different between the two groups, with $63.6\%$ of BCR cases in ERG low and only $32.5\%$ of BCR cases in ERG high group (Table 1), which suggest an association of ERG‐status with prognosis for these localized and regionally advanced PCa patients.
## Differentially expressed genes between ERG groups
DESeq2 results between the two ERG tumor groups showed 532 DEGs, including 284 overexpressed and 248 with low expression in the group of ERG high compared to ERG low tumors using an FDR <0.01 (Figure S1). Setting a fold change over 2, 150 DEGs remained which, based on their expression, were able to separate most ERG high from ERG low tumors into two clusters through a hierarchical clustering analysis (Figure 1). Interestingly, as it is shown in the heatmap, most of the PCa cases with BCR were grouped within the ERG low cluster ($\frac{14}{23}$, $60.8\%$), while most of the non‐BCR cases were grouped within the ERG high tumors ($\frac{30}{50}$, $60\%$). These results are in line with the suggested associations that we found in the clinicopathological analysis in which most of the BCR cases were in the ERG low group and most non‐BCR in the ERG high group (Table 1).
**FIGURE 1:** *Heatmap for the 150 DEGs. Unsupervised hierarchical clustering analysis for 150 DEGs in 78 tumor samples from PCa patients. DEGs were obtained from comparison between ERG
high
and ERG
low
groups (FDR <0.01, fold change >2). Normalized counts of expression were scaled, and expression values for each gene were color labeled (blue to red). Patients are represented in columns and genes in rows. The separation in clusters shows the 15 samples forming a sub‐cluster within the ERGhigh tumors.*
In addition, it is noteworthy that a small group of 15 samples form a sub‐cluster within the ERG high tumors (Figure 1). These 15 samples, located in the sub‐cluster to the left under the ERGhigh cluster in the dendrogram, differ in the gene expression pattern (see genes in rows) compared with the whole cluster for ERG high, especially in the first panel of genes. Of notice, these cases are enriched in ERG low cases (9 out of 15) (Figure 1). Clinicopathological characteristics between this group compared with the ERG high and ERG low clusters showed significant statistical differences within Gleason GG at RP, with $33.3\%$ in each of the Gleason groups GG3 and GG4/GG5, for a total of $66.6\%$ of cases associated with the higher Gleason groups (GG3 and GG4/GG5) (Table S1). This contrasts with findings in ERG low and ERG high groups presenting $46.1\%$ and $27\%$ of cases, respectively, associated with Gleason groups GG3 and GG4/GG5. However, we did not find association with BCR (Table S1).
## Effect of genetic ancestry in the identification of DEGs
We wanted to determine whether genetic ancestry modulates the expression of genes associated to the ERG expression. We included the Indigenous and *European* genetic ancestries in the analysis of differential expression in DESeq2 comparing the ERG groups. No DEGs were found other than the obtained without including this variable, suggesting that the European and Indigenous ancestries, as analyzed here, do not modify differentially expressed genes found between ERG groups in our patients. We included only Indigenous and European ancestries since they sum for the major genetic component in the population included in this study.
## BCR‐free survival analysis according to
ERG
groups
To evaluate the impact of the ERG‐status on prognosis, we assessed the association between both groups with BCR‐free survival by Kaplan–Meier analysis. It showed that ERG low group was correlated with worse BCR‐free survival within 5 years of RP (log‐rank test $$p \leq 0.029$$) (Figure 2A) and univariate logistic regressions confirmed the association between risk of BCR with ERG high group as a protector factor (OR = 0.28; $95\%$CI, 0.10–0.71; $$p \leq 0.009$$) (Table 2).
**FIGURE 2:** *Survival curves for ERG tumor groups. (A) Kaplan–Meier curve for BCR‐free survival in years for 73 PCa patients with ERGlow (blue) and ERGhigh
(red) expression. (B) Kaplan–Meier curve for BCR‐free survival in the GSE70770 dataset by ERGlow (blue) and ERGhigh
(red) expression. The comparison method for the survival curves was Log‐rank test.* TABLE_PLACEHOLDER:TABLE 2 We also determined the associations of clinical‐pathological variables with the risk of BCR. In the univariate logistic regression, only Gleason GG2 at RP was associated (OR = 3.73; $95\%$CI 1.12–13.69; $$p \leq 0.037$$) (Table 2). For the multivariate logistic regression model, we included Gleason GGs at RP and ERG groups, since they were significant in the univariate analyses. Only ERG groups maintained significant, with an OR of 0.29 ($95\%$CI, 0.10–0.8; $$p \leq 0.020$$) for ERG high group (Table 2).
Next, we used the GSE70770 dataset ($$n = 203$$) to validate in an independent set whether ERG expression is associated with BCR (BCR, $$n = 59$$). Cases were divided according to the normalized ERG expression into ERG high and ERG low. These groups analyzed by Kaplan–Meier showed statistically significant differences in BCR‐free survival confirming ERG low as the group with shorter BCR‐free survival compared with ERG high group ($$p \leq 0.005$$) (Figure 2B). Cox proportional hazard model regression also showed higher BCR risk for ERG low group (Hazard ratio = 2.2; $95\%$CI, 1.3–3.9; $$p \leq 0.004$$).
## Signaling pathway analysis
Given that DEGs were able to separate most of the samples between ERG high and ERG low tumor groups, we explored how these DEGs participate in signaling pathways and processes that could contribute to the prognosis of the disease. Among the most significant pathways maps identified, as is shown in Figure 3A, we found those related with Angiotensin, such as are Protein folding and maturation_Angiotensin system maturation and Renin‐Angiotensin‐Aldosterone System; pathways maps related with metabolism, such as linoleic acid metabolism and polyamine metabolism. Other pathways identified were Beta‐catenin‐dependent transcription regulation in colorectal cancer; Development_ROBO2, ROBO3, and ROBO4 signaling pathways, Notch signaling in oligodendrocyte precursor cell differentiation in multiple sclerosis and Signal transduction_mTORC1 upstream signaling. The DEGs that participate in each of these pathways and direction of expression in ERG high and ERG low tumor groups are listed in Table 3.
**FIGURE 3:** **Enrichment analysis* for 150 DEGs obtained for ERGhigh vs. ERGlow. (A) Pathway maps. (B) Networks. (C) Processes. (D) Diseases. DEGs were selected with and FDR <0.01 and fold change >2 and submitted to MetaCore for analysis.* TABLE_PLACEHOLDER:TABLE 3 The most significant GO processes included various processes related to regulation of ion transport, multicellular and anatomical development, digestive system process, and regulation of trans‐synaptic signaling (Figure 3C).
## DISCUSSION
Reports widely describe the relevance of molecular subtyping of PCa and the T2E translocation as a different subtype for localized PCa tumors, although it has different frequencies across populations/ethnicities. 7, 26 However, the contradictory evidence for these subtypes with prognosis remains. In this study, we compared gene expression between ERG high and ERG low tumors and found 150 DEGs with an FDR <0.01 and FC >2 that differentiated both groups and interestingly DEGs clustered most of the non‐BCR cases ($60\%$) into the ERG high group and most of the BCR cases ($60.8\%$) in the ERG low group, through unsupervised clustering hierarchical analysis. In accordance, the clinicopathological analysis revealed that more than $60\%$ of the BCR cases occurred in the ERG low group, and the survival analysis showed a correlation between the ERG low group with a lower BCR‐free survival, while ERG high tumors exhibited a better prognosis.
This correlation has been previously reported, showing a BCR‐free survival significantly longer in patients with ERG overexpression, 8 and ERG negative status associated with poorer BCR‐free survival in Caucasians, although no association was found for African Americans. 7 Also, low expression levels of ERG have been proposed as an independent predictor for BCR in low‐risk patients. 9 Moreover, a very recent report also found that negative ERG expression measured by immunohistochemistry was associated with biochemical progression after RP. 27 Our findings may indicate that measurement of ERG expression could also be used as a predictor of disease progression in patients treated with RP, including admixed populations such as are Hispanic/Latino population.
However, some studies have also reported opposite results, with ERG expression associated with unfavorable outcomes 10, 11 or no correlation with the progression of the disease. 28, 29, 30 These dissimilarities across studies may be due to the sampling of the tissues studied, in which only sections of tissue cores, 28 biopsies, 29 or frozen tumors 31 were used, and therefore multifocality and heterogeneity might be underrepresented.
Another important finding in our clinicopathological analyses was the significant differences in Gleason Grade at RP between ERG high and ERG low groups ($$p \leq 0.036$$), with ERG low group having higher frequency of higher Gleason Grades and ERG high group enriched in lower Gleason Grades. Although Gleason Grades G1 ($$n = 22$$) and G2 ($$n = 24$$) were more frequent in our population with a total of $59\%$ of the patients, the distribution of cases in the ERG groups was an interesting finding in our study. This result is consistent with previous evidence showing a correlation between the overexpression of ERG or presence of the T2:E translocation with a favorable pathology of lower Gleason scores (≤7), lower primary Gleason pattern (≤ grade 3) and lower Clinical T‐stage (T1 + T2). 32, 33 Hence, our findings between Gleason Grades and ERG groups suggest that ERG low cases may be associated with more advance stages of the disease while ERG high cases with lower stages.
We also found through univariate analysis that Gleason GG2 at RP was associated with risk of BCR (OR = 3.73; $95\%$CI 1.12–13.69; $$p \leq 0.037$$), and for GG$\frac{4}{5}$, we observed the same risk direction, although it was not statistically significant (OR = 4; $95\%$CI 0.86–21.05; $$p \leq 0.084$$). This result is consistent with previous evidence showing the prognostic value of Gleason for BCR. 34, 35, 36 Nevertheless, in our multivariate analysis including the ERG status, the association of Gleason Grade with BCR was lost, which may reflect the influence of other factors not considered in this paper involved in the development of BCR. However, we cannot omit the limitation in our sample size, discussed ahead in the limitations section.
Since it has been previously shown that a higher incidence, mortality, and aggressive presentation of PCa is associated with African ancestry compared with other ethnic groups, 37, 38, 39 we wanted to determine whether genetic ancestry in Colombian patients plays a role in the aggressiveness of PCa. Only Indigenous and European ancestries were tested since they sum for the major genetic component in the population included in this study, but a modification of DEGs was not seen. African ancestry was not included given that the median percentage in our cases accounted for a $5\%$, representing an extremely low component in most of them. Our results suggest that Indigenous and *European* genetic ancestry have no influence in differential expression profiles between ERG high and ERG low cases, while for African ancestry, the data were insufficient to draw conclusions. A more representative population of this ancestry is needed.
To further understand the molecular drivers related to the progression of PCa tumors, we submitted the 150 DEGs to enrichment analysis in MetaCore. It identified signaling pathways related to the angiotensin system, the Protein folding and maturation_Angiotensin system maturation and the renin‐angiotensin system (RAS). RAS is well known for its role in maintaining cardiovascular homeostasis, electrolyte balance, renal physiology, blood pressure, and cell survival. 40 However, the role of the dysregulated pathway in tumors and the effects on cancer of inhibitors of RAS (RASi) is still unclear. 41, 42 In normal prostate tissue, RAS signaling contributes to spermiogenesis, sperm motility, and survival. 42 However, different studies imply a dysregulated expression of RAS signaling associated with increased risk of PCa and progression, such as the case of Angiotensin II affecting cell morphology, proliferation, and survival of normal prostate cells through increasing metalloproteinases and regulation of BAX and BCL2. 43 BCL2 contributes to the release and infiltration of CCL2 protein, which accelerates cancer progression and correlates with high PSA. 44 Angiotensin II also triggers the IGFR1/AKT pathway in androgen‐dependent PCa cells transforming them into androgen‐resistant. 45 Another member of the RAS pathway, the angiotensin II receptor type 1 (AGTR1) was also associated with metastatic PCa cells, 46 while the angiotensin II receptor type 2 inhibits tumor growth, induces apoptosis, and reduces Ki‐67 and AR expression. 47 Nevertheless, expression of RAS components has been identified in prostate tissues, and especially highly expressed in resistant PCa cases compared with untreated and normal prostate tissue. 48 RASi are widely used for the treatment of cardiovascular and renal diseases in the form of angiotensin receptor blockers (ARB) or angiotensin‐converting enzyme inhibitors (ACEIs). 50 Studies evaluating the impact of RASi on PCa show consistent and favorable results. Among hypertensive patients, long‐term use of ARBs or ACEI reduced the risk of PCa, 51 ARB‐treated veterans showed a small but significant reduction in the incidence of PCa, 52 and the intake of ACEIs/ARBs associated with a significantly reduced risk of BCR after radiotherapy with adjuvant/neoadjuvant hormone treatment. 53 Given the growing evidence that RASi have a role in reducing the risk and progression of PCa, and that we identified angiotensin related signaling as enriched pathways in ERG high tumors, which were associated with a reduced risk of BCR compared with the ERG low tumor group, together these results warrant further research exploring RASi in patients with T2E arrangements or differential expression of ERG and different subtypes. Nonetheless, the RAS system appears to be implicated in PCa tumors for which RASi could improve patient management and outcomes.
Another enriched pathway identified through MetaCore and related to metabolism was the linoleic acid metabolism signaling pathway, with members of this pathway downregulated (CYP2J2, ALOX15B, FAD52, [Table 3]) in the ERG high group and overexpressed in ERG low tumors. The involvement of linoleic acid in cancer and in general in cardiovascular health is still unclear. Considered as an essential omega‐6 fatty acid, the consumption of linoleic acid was markedly increased in the past century given by the dietary recommendations in the U.S and Western countries. 53 Then, a relationship between dietary linoleic acid consumption and the development of some cancers was suggested, however, the findings are contradictory. 54, 55, 56 For PCa, some of the reports show no association of individual n‐6 fatty acids with this type of cancer, 57, 58 but a trend for n‐3 fatty acids as a protective factor was reported for Latinos and Whites (compared with African Americans and Japanese Americans). 58 A higher ratio of n‐6/n‐3 fatty acids intake, however, was associated with an increased risk of high‐grade PCa. 59 Another study reported that intakes of saturated fats were related to the risk of advanced or fatal PCa, but no association between total n‐6 or ratio n‐6/n‐3 fatty acids and risk of PCa was found. 60 Recently, Figiel et al. 61 indicated that low levels of linoleic acid and high levels of saturates characterized the profile associated with PCa aggressiveness in African‐Caribbeans, which was analyzed in the periprostatic adipose tissue of patients. In contrast, animal and in vitro experiments have had more consistent findings, suggesting that n‐6 fatty acids stimulate PCa growth, whereas n‐3 inhibits it. 62, 63 Meller et al. 64 discovered that tumors with the T2E translocation have a different metabolome profile compared with the T2E negative tumors, particularly enriched in fatty acids and suggesting that the metabolism of fatty acids in PCa tumors could be modulated depending on the presence of the translocation; it was found however that linoleic acid was increased in T2E positive tumors. Considering these findings, studies exploring the role of linoleic acid metabolism in a T2E translocation context should be done.
Finally, the polyamine metabolism pathway is frequently dysregulated in cancer given the need for polyamines for transformation and tumor progression. 65 The polyamines include putrescine, spermidine, and spermine, which are polycations involved in cell growth, survival, protein and nucleic acid synthesis, stabilization of chromatin structure, differentiation, apoptosis, nucleic acid depurination, and major components of prostate fluid. 66 Different known oncogenic pathways lead to the dysregulation of polyamine metabolism, including MYC signaling, RAS/RAF/MEK/ERK signaling pathway, AKT signaling 66 PTEN/PI3K/mTORC1, 67 and the activation of the non‐canonical WNT signaling pathway which appears to be associated with decreased citrate and spermine levels in the most aggressive phenotypes of PCa. 68 In line with this evidence, Meller et al. 64 reported that putrescine and spermine were decreased in prostate cancer compared to normal tissues, while spermidine was increased, and under the context of T2E translocation, a negative correlation of spermine and putrescine with ERG rearrangement was described. 18, 64 In our study, we observed downregulation of some members of the polyamine metabolism pathway in ERG high tumors, but we cannot infer which polyamines are affected. Thus, this data indicates that alteration of polyamine metabolism is involved in prostate tumors, however, their participation in tumor progression under the context of ERG rearrangements should be addressed. Finally, polyamines and polyamine metabolites measured in either urine or serum have shown potential as biomarkers for prostate cancer, 69 which also could assist in tumor subtyping and personalized medicine.
Since this is a Hospital‐based retrospective study, there are some limitations that are important to discuss and that could be explained in part by our institutional context, that is, a high number of the patients treated at the INC experience many barriers to health access during their treatment and follow‐up, including transportation and a deeply fragmented and segmented health system that affects patients every time their health insurer decides to change the cancer institute for their management, causing delays in their clinical interventions. The retrospective design and short follow‐up restricted the analysis for other survival events. The small sample size and the inclusion of only PCa patients with localized/regionally advanced PCa that underwent RP surgery, may explain the distribution in the frequencies of low and high Gleason Grades and the strength of associations. Given the nature of a retrospective study with follow‐up information, FFPE tissues were used. To overcome quality issues, we used optimal and suited laboratory protocols for nucleic acids extraction, combined with feasible protocols for subsequent NGS‐based analysis of these molecules. 71 A a bias in the *African* genetic ancestry representation for analysis might not allow conclusive results given that patients from the INC were mainly from the Andean region and few from the Coastal region, which have higher African ancestry in our country. Finally, incident metastatic PCa cases were ruled out since they were inoperable, therefore, aggressive cases were underrepresented in our study.
Overall, our study confirmed a differential BCR‐free survival for ERG tumor groups, defined as ERG high and ERG low, in Hispanic/Latino patients with localized/regionally advanced PCa, with ERG low tumor cases having the worst survival. Analysis of enriched pathways in these groups found metabolism‐related pathways, such as the renin‐angiotensin system, the linoleic acid metabolism, and polyamines metabolism. Since the pathways we found are altered in ERG high and ERG low tumors, and ERG low tumors were correlated with a shorter BCR‐free survival, the results may suggest an involvement of these pathways in BCR. Although these findings should be confirmed in larger and more diverse populations in Hispanics, it warrants further research concerning diet, metabolism and lifestyle factors into prevention and management of this type of cancer, since these are considered as modifiable risk factors, as well as to better understand the interaction between the ERG status with Gleason Grades and the prognosis of PCa.
## AUTHOR CONTRIBUTIONS
Natalia L. Acosta‐Vega: Conceptualization (equal); data curation (lead); formal analysis (lead); investigation (lead); methodology (equal); writing – original draft (lead). Rodolfo Varela: Conceptualization (equal); data curation (equal); supervision (supporting); writing – review and editing (equal). Jorge Andrés Mesa: Conceptualization (equal); data curation (equal); supervision (supporting); writing – review and editing (equal). Jone Garai: Data curation (equal); methodology (equal); writing – review and editing (equal). Melody C Baddoo: Data curation (equal); formal analysis (equal); methodology (equal); software (equal); writing – review and editing (equal). Alberto Gómez‐Gutiérrez: Conceptualization (equal); data curation (equal); formal analysis (equal); methodology (supporting); resources (supporting); supervision (lead); writing – review and editing (equal). Silvia J. Serrano‐Gómez: *Formal analysis* (equal); methodology (equal); writing – review and editing (equal). Marcela Nuñez Lemus: Methodology (supporting); writing – review and editing (supporting). Martha Lucía Serrano: *Formal analysis* (supporting); writing – review and editing (supporting). Jovanny Zabaleta: Conceptualization (equal); data curation (equal); formal analysis (equal); funding acquisition (equal); methodology (equal); resources (equal); supervision (supporting); writing – review and editing (equal). Alba Lucia Combita: Conceptualization (equal); data curation (equal); formal analysis (equal); funding acquisition (equal); methodology (equal); project administration (equal); resources (equal); supervision (lead); writing – review and editing (equal). María Carolina Sanabria‐Salas: Conceptualization (equal); data curation (equal); formal analysis (equal); funding acquisition (equal); methodology (equal); project administration (equal); resources (equal); supervision (lead); writing – review and editing (equal).
## FUNDING INFORMATION
Instituto Nacional de Cancerología (MCSS project funding C41030310118 and ALC project funding C19010300456). Translational Genomics Core Laboratory at LSUHSC‐New Orleans ‐ USA (JZ grants: P30GM114732, P20GM121288–01, P20CA202922).
## CONFLICT OF INTEREST
The authors declare no potential conflicts of interest.
## ETHICS STATEMENT
This study was approved by the Research Ethics Board of the Colombian National Cancer Institute and was designated as an exempt study for informed consent.
## DATA AVAILABILITY STATEMENT
The data generated in this manuscript is deposited in NCBI’s Gene Expression Omnibus (GEO) through the accession number GSE216490. Expression profile data to confirm the association between ERG expression with BCR was obtained from the Gene Expression Omnibus (GEO) at GSE70770.
## References
1. Sung H, Ferlay J, Siegel RL. **Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries**. *CA Cancer J Clin* (2021.0) **71** 209-249. PMID: 33538338
2. Pardo C, Cendales R. **Incidencia, mortalidad y prevalencia de cáncer en Colombia 2007–20112015**. 148
3. Tomlins SA, Alshalalfa M, Davicioni E. **Characterization of 1577 primary prostate cancers reveals novel biological and clinicopathologic insights into molecular subtypes**. *Eur Urol* (2015.0) **68** 555-567. PMID: 25964175
4. Tomlins SA, Rhodes DR, Perner S. **Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer**. *Science* (2005.0) **310** 644-648. PMID: 16254181
5. Rubin MA. **ETS rearrangements in prostate cancer**. *Asian J Androl* (2012.0) **14** 393-399. PMID: 22504874
6. Magi‐Galluzzi C, Tsusuki T, Elson P. **TMPRSS2‐ERG gene fusion prevalence and class are significantly different in prostate cancer of Caucasian**. *African‐American and Japanese Patients Prostate* (2011.0) **71** 489-497. PMID: 20878952
7. Cullen J, Young D, Chen Y. **Predicting prostate cancer progression as a function of ETS‐related gene status, race, and obesity in a longitudinal patient cohort**. *Eur Urol Focus* (2018.0) **4** 818-824. PMID: 28753864
8. Kim SH, Joung JY, Lee GK. **Overexpression of ERG and wild‐type PTEN are associated with favorable clinical prognosis and low biochemical recurrence in prostate cancer**. *PLoS One* (2015.0) **10**. PMID: 25897494
9. Yan W, Jamal M, Tan SH. **Molecular profiling of radical prostatectomy tissue from patients with no sign of progression identifies**. *Oncotarget* (2019.0) **10** 6466-6483. PMID: 31741711
10. Demichelis F, Fall K, Perner S. **TMPRSS2:ERG gene fusion associated with lethal prostate cancer in a watchful waiting cohort**. *Oncogene* (2007.0) **26** 4596-4599. PMID: 17237811
11. Nam RK, Sugar L, Wang Z. **Expression of TMPRSS2:ERG gene fusion in prostate cancer cells is an important prognostic factor for cancer progression**. *Cancer Biol Ther* (2007.0) **6** 40-45. PMID: 17172822
12. Pettersson A, Graff RE, Bauer SR. **The TMPRSS2:ERG rearrangement, ERG expression, and prostate cancer outcomes: a cohort study and meta‐analysis**. *Cancer Epidemiol Biomarkers Prev* (2012.0) **21** 1497-1509. PMID: 22736790
13. Attard G, Parker C, Eeles RA. **Prostate cancer**. *Lancet* (2016.0) **387** 70-82. PMID: 26074382
14. Rubin MA, Maher CA, Chinnaiyan AM. **Common gene rearrangements in prostate cancer**. *J Clin Oncol* (2011.0) **29** 3659-3668. PMID: 21859993
15. Boström PJ, Bjartell AS, Catto JW. **Genomic predictors of outcome in prostate cancer**. *Eur Urol* (2015.0) **68** 1033-1044. PMID: 25913390
16. Paulo P, Ribeiro FR, Santos J. **Molecular subtyping of primary prostate cancer reveals specific and shared target genes of different ETS rearrangements**. *Neoplasia* (2012.0) **14** 600-611. PMID: 22904677
17. Gasi Tandefelt D, Boormans J, Hermans K, Trapman J. **ETS fusion genes in prostate cancer**. *Endocr Relat Cancer* (2014.0) **21** R143-R152. PMID: 24659477
18. Hansen AF, Sandsmark E, Rye MB. **Presence of TMPRSS2‐ERG is associated with alterations of the metabolic profile in human prostate cancer**. *Oncotarget* (2016.0) **7** 42071-42085. PMID: 27276682
19. Dudka I, Thysell E, Lundquist K. **Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2‐ERG fusion status**. *BMC Cancer* (2020.0) **20** 437. PMID: 32423389
20. Cookson MS, Aus G, Burnett AL. **Variation in the definition of biochemical recurrence in patients treated for localized prostate cancer: the American urological association prostate guidelines for localized prostate cancer update panel report and recommendations for a standard in the reporting of surgical outcomes**. *J Urol* (2007.0) **177** 540-545. PMID: 17222629
21. Dobin A, Davis CA, Schlesinger F. **STAR: ultrafast universal RNA‐seq aligner**. *Bioinformatics* (2013.0) **29** 15-21. PMID: 23104886
22. Li B, Dewey CN. **RSEM: accurate transcript quantification from RNA‐seq data with or without a reference genome**. *BMC Bioinformatics* (2011.0) **12** 323. PMID: 21816040
23. Fejerman L, John EM, Huntsman S. **Genetic ancestry and risk of breast cancer among U.S**. *Latinas Cancer Res* (2008.0) **68** 9723-9728. PMID: 19047150
24. Love MI, Huber W, Anders S. **Moderated estimation of fold change and dispersion for RNA‐seq data with DESeq2**. *Genome Biol* (2014.0) **15** 550. PMID: 25516281
25. Ross‐Adams H, Lamb AD, Dunning MJ. **Integration of copy number and transcriptomics provides risk stratification in prostate cancer: a discovery and validation cohort study**. *EBioMedicine* (2015.0) **2** 1133-1144. PMID: 26501111
26. Network CGAR. **The molecular taxonomy of primary prostate cancer**. *Cell* (2015.0) **163** 1011-1025. PMID: 26544944
27. Brady L, Carlsson J, Baird AM. **Correlation of integrated ERG/PTEN assessment with biochemical recurrence in prostate cancer**. *Cancer Treat Res Commun* (2021.0) **29**. PMID: 34507017
28. Hoogland AM, Jenster G, van Weerden WM. **ERG immunohistochemistry is not predictive for PSA recurrence, local recurrence or overall survival after radical prostatectomy for prostate cancer**. *Mod Pathol* (2012.0) **25** 471-479. PMID: 22080055
29. Raymundo EM, Diwa MH, Lapitan MC. **Increased association of the ERG oncoprotein expression in advanced stages of prostate cancer in Filipinos**. *Prostate* (2014.0) **74** 1079-1085. PMID: 24909781
30. Minner S, Enodien M, Sirma H. **ERG status is unrelated to PSA recurrence in radically operated prostate cancer in the absence of antihormonal therapy**. *Clin Cancer Res* (2011.0) **17** 5878-5888. PMID: 21791629
31. Schaefer G, Mosquera JM, Ramoner R. **Distinct ERG rearrangement prevalence in prostate cancer: higher frequency in young age and in low PSA prostate cancer**. *Prostate Cancer Prostatic Dis* (2013.0) **16** 132-138. PMID: 23381693
32. Fine SW, Gopalan A, Leversha MA. **TMPRSS2‐ERG gene fusion is associated with low Gleason scores and not with high‐grade morphological features**. *Mod Pathol* (2010.0) **23** 1325-1333. PMID: 20562851
33. Baohong J, Sedarsky J, Srivastava S, Sesterhenn I, Dobi A, Quanlin L. **ERG tumor type is less frequent in high grade and high stage prostate cancers of Chinese men**. *J Cancer* (2019.0) **10** 1991-1996. PMID: 31205559
34. Swanson GP, Trevathan S, Hammonds KAP, Speights VO, Hermans MR. **Gleason score evolution and the effect on prostate cancer outcomes**. *Am J Clin Pathol* (2021.0) **155** 711-717. PMID: 33079976
35. Epstein JI, Zelefsky MJ, Sjoberg DD. **A contemporary prostate cancer grading system: a validated alternative to the Gleason score**. *Eur Urol* (2016.0) **69** 428-435. PMID: 26166626
36. Kawase M, Ebara S, Tatenuma T. **The impact of Gleason grade 3 as a predictive factor for biochemical recurrence after robot‐assisted radical prostatectomy: a retrospective multicenter cohort study in Japan (the MSUG94 group)**. *Medicina (Kaunas)* (2022.0) **58** 990. PMID: 35893105
37. Faisal FA, Sundi D, Cooper JL. **Racial disparities in oncologic outcomes after radical prostatectomy: long‐term follow‐up**. *Urology* (2014.0) **84** 1434-1441. PMID: 25432835
38. Henderson BE, Lee NH, Seewaldt V, Shen H. **The influence of race and ethnicity on the biology of cancer**. *Nat Rev Cancer* (2012.0) **12** 648-653. PMID: 22854838
39. Sundi D, Faisal FA, Trock BJ. **Reclassification rates are higher among African American men than Caucasians on active surveillance**. *Urology* (2015.0) **85** 155-160. PMID: 25440814
40. Kawai T, Forrester SJ, O'Brien S, Baggett A, Rizzo V, Eguchi S. **AT1 receptor signaling pathways in the cardiovascular system**. *Pharmacol Res* (2017.0) **125** 4-13. PMID: 28527699
41. George AJ, Thomas WG, Hannan RD. **The renin‐angiotensin system and cancer: old dog, new tricks**. *Nat Rev Cancer* (2010.0) **10** 745-759. PMID: 20966920
42. Cui Y, Wen W, Zheng T. **Use of antihypertensive medications and survival rates for breast, colorectal, lung, or stomach cancer**. *Am J Epidemiol* (2019.0) **188** 1512-1528. PMID: 31062847
43. Almutlaq M, Alamro AA, Alamri HS, Alghamdi AA, Barhoumi T. **The effect of local renin angiotensin system in the common types of cancer**. *Front Endocrinol (Lausanne)* (2021.0) **12**. PMID: 34539580
44. Domińska K, Ochędalski T, Kowalska K, Matysiak‐Burzyńska ZE, Płuciennik E, Piastowska‐Ciesielska AW. **A common effect of angiotensin II and relaxin 2 on the PNT1A normal prostate epithelial cell line**. *J Physiol Biochem* (2016.0) **72** 381-392. PMID: 27119161
45. Shirotake S, Miyajima A, Kosaka T. **Regulation of monocyte chemoattractant protein‐1 through angiotensin II type 1 receptor in prostate cancer**. *Am J Pathol* (2012.0) **180** 1008-1016. PMID: 22226738
46. Domińska K, Ochędalski T, Kowalska K, Matysiak‐Burzyńska ZE, Płuciennik E, Piastowska‐Ciesielska AW. **Interaction between angiotensin II and relaxin 2 in the progress of growth and spread of prostate cancer cells**. *Int J Oncol* (2016.0) **48** 2619-2628. PMID: 27035428
47. Kowalska K, Nowakowska M, Domińska K, Piastowska‐Ciesielska AW. **Coexpression of CAV‐1, AT1‐R and FOXM1 in prostate and breast cancer and normal cell lines and their influence on metastatic properties**. *Acta Biochim pol* (2016.0) **63** 493-499. PMID: 27213551
48. Ito Y, Naiki‐Ito A, Kato H. **Chemopreventive effects of angiotensin II receptor type 2 agonist on prostate carcinogenesis by the down‐regulation of the androgen receptor**. *Oncotarget* (2018.0) **9** 13859-13869. PMID: 29568400
49. Uemura H, Hasumi H, Ishiguro H, Teranishi J, Miyoshi Y, Kubota Y. **Renin‐angiotensin system is an important factor in hormone refractory prostate cancer**. *Prostate* (2006.0) **66** 822-830. PMID: 16482568
50. Ponikowski P, Voors AA, Anker SD. **2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)developed with the special contribution of the heart failure association (HFA) of the ESC**. *Eur Heart J* (2016.0) **37** 2129-2200. PMID: 27206819
51. Pai PY, Hsieh VC, Wang CB. **Long term antihypertensive drug use and prostate cancer risk: a 9‐year population‐based cohort analysis**. *Int J Cardiol* (2015.0) **193** 1-7. PMID: 26002406
52. Rao GA, Mann JR, Bottai M. **Angiotensin receptor blockers and risk of prostate cancer among United States veterans**. *J Clin Pharmacol* (2013.0) **53** 773-778. PMID: 23686462
53. Alashkham A, Paterson C, Windsor P, Struthers A, Rauchhaus P, Nabi G. **The incidence and risk of biochemical recurrence following radical radiotherapy for prostate cancer in men on angiotensin‐converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs)**. *Clin Genitourin Cancer* (2016.0) **14** 398-405. PMID: 27053500
54. Blasbalg TL, Hibbeln JR, Ramsden CE, Majchrzak SF, Rawlings RR. **Changes in consumption of omega‐3 and omega‐6 fatty acids in the United States during the 20th century**. *Am J Clin Nutr* (2011.0) **93** 950-962. PMID: 21367944
55. Sauer LA, Blask DE, Dauchy RT. **Dietary factors and growth and metabolism in experimental tumors**. *J Nutr Biochem* (2007.0) **18** 637-649. PMID: 17418560
56. Zock PL, Katan MB. **Linoleic acid intake and cancer risk: a review and meta‐analysis**. *Am J Clin Nutr* (1998.0) **68** 142-153. PMID: 9665108
57. Azrad M, Turgeon C, Demark‐Wahnefried W. **Current evidence linking polyunsaturated fatty acids with cancer risk and progression**. *Front Oncol* (2013.0) **3** 224. PMID: 24027672
58. Park SY, Murphy SP, Wilkens LR, Henderson BE, Kolonel LN. **Fat and meat intake and prostate cancer risk: the multiethnic cohort study**. *Int J Cancer* (2007.0) **121** 1339-1345. PMID: 17487838
59. Schuurman AG, van den Brandt PA, Dorant E, Brants HA, Goldbohm RA. **Association of energy and fat intake with prostate carcinoma risk: results from The Netherlands cohort study**. *Cancer* (1999.0) **86** 1019-1027. PMID: 10491529
60. Williams CD, Whitley BM, Hoyo C. **A high ratio of dietary n‐6/n‐3 polyunsaturated fatty acids is associated with increased risk of prostate cancer**. *Nutr Res* (2011.0) **31** 1-8. PMID: 21310299
61. Pelser C, Mondul AM, Hollenbeck AR, Park Y. **Dietary fat, fatty acids, and risk of prostate cancer in the NIH‐AARP diet and health study**. *Cancer Epidemiol Biomarkers Prev* (2013.0) **22** 697-707. PMID: 23549401
62. Figiel S, Pinault M, Domingo I. **Fatty acid profile in peri‐prostatic adipose tissue and prostate cancer aggressiveness in African‐Caribbean and Caucasian patients**. *Eur J Cancer* (2018.0) **91** 107-115. PMID: 29413967
63. Meng H, Shen Y, Shen J, Zhou F, Shen S, Das UN. **Effect of n‐3 and n‐6 unsaturated fatty acids on prostate cancer (PC‐3) and prostate epithelial (RWPE‐1) cells in vitro**. *Lipids Health Dis* (2013.0) **12** 160. PMID: 24168453
64. Brown M, Roulson JA, Hart CA, Tawadros T, Clarke NW. **Arachidonic acid induction of rho‐mediated transendothelial migration in prostate cancer**. *Br J Cancer* (2014.0) **110** 2099-2108. PMID: 24595005
65. Meller S, Meyer HA, Bethan B. **Integration of tissue metabolomics, transcriptomics and immunohistochemistry reveals ERG‐ and Gleason score‐specific metabolomic alterations in prostate cancer**. *Oncotarget* (2016.0) **7** 1421-1438. PMID: 26623558
66. Murray‐Stewart TR, Woster PM, Casero RA. **Targeting polyamine metabolism for cancer therapy and prevention**. *Biochem J* (2016.0) **473** 2937-2953. PMID: 27679855
67. Casero RA, Murray Stewart T, Pegg AE. **Polyamine metabolism and cancer: treatments, challenges and opportunities**. *Nat Rev Cancer* (2018.0) **18** 681-695. PMID: 30181570
68. Zabala‐Letona A, Arruabarrena‐Aristorena A, Martín‐Martín N. **mTORC1‐dependent AMD1 regulation sustains polyamine metabolism in prostate cancer**. *Nature* (2017.0) **547** 109-113. PMID: 28658205
69. Sandsmark E, Hansen AF, Selnæs KM. **A novel non‐canonical Wnt signature for prostate cancer aggressiveness**. *Oncotarget* (2017.0) **8** 9572-9586. PMID: 28030815
70. Giskeødegård GF, Bertilsson H, Selnæs KM. **Spermine and citrate as metabolic biomarkers for assessing prostate cancer aggressiveness**. *PLoS One* (2013.0) **8**. PMID: 23626811
71. Lee SR, Choi YD, Cho NH. **Association between pathologic factors and ERG expression in prostate cancer: finding pivotal networking**. *J Cancer Res Clin Oncol* (2018.0) **144** 1665-1683. PMID: 29948147
|
---
title: 'Describing settings of care in the last 100 days of life for cancer decedents:
a population‐based descriptive study'
authors:
- Abe Hafid
- Michelle Howard
- Colleen Webber
- Ana Gayowsky
- Mary Scott
- Aaron Jones
- Amy T. Hsu
- Peter Tanuseputro
- James Downar
- Katrin Conen
- Doug Manuel
- Sarina R. Isenberg
journal: Cancer Medicine
year: 2022
pmcid: PMC9972173
doi: 10.1002/cam4.5291
license: CC BY 4.0
---
# Describing settings of care in the last 100 days of life for cancer decedents: a population‐based descriptive study
## Abstract
A population‐based descriptive study using linked health administrative data in Ontario, Canada, described where patients dying of cancer spent their last 100 days of life. From 2013 to 2017, patients dying with cancer spent most of their time in either institutions (25.9 days) or at home without any care (48.3 days).
### Background
Few studies have described the settings cancer decedents spend their end‐of‐life stage, with none considering homecare specifically. We describe the different settings of care experienced in the last 100 days of life by individuals with cancer and how settings of care change as they approached death.
### Methods
A retrospective cohort study from January 2013 to December 2017, of decedents whose primary cause of death was cancer, using linked population‐level health administrative datasets in Ontario, Canada.
### Results
Decedents 125,755 were included in our cohort. The average age at death was 73, $46\%$ were female, and $14\%$ resided in rural regions. And $24\%$ died of lung cancer, $7\%$ breast, $7\%$ colorectal, $7\%$ pancreatic, $5\%$ prostate, and $50\%$ other cancers. In the last 100 days of life, decedents spent 25.9 days in institutions, 25.8 days receiving care in the community, and 48.3 days at home without any care. Individuals who died of lung and pancreatic cancers spent the most days at home without any care (52.1 and 52.6 days), while individuals who died of prostate and breast cancer spent the least days at home without any care (41.6 and 45.1 days). Regardless of cancer type, decedents spent fewer days at home and more days in institutions as they approached death, despite established patient preferences for an end‐of‐life experience at home.
### Conclusions
In the last 100 days of life, cancer decedents spent most of their time in either institutions or at home without any care. Improving homecare services during the end‐of‐life may provide people dying of cancer with a preferred dying experience.
## BACKGROUND
Patients with cancer have high care needs toward the end of life. Patients dying with cancer tend to have multiple comorbidities, 1 and are more likely to experience severe pain at the end‐of‐life stage compared to other end‐of‐life trajectories. 2 They also experience aggressive interventions during the end‐of‐life phase, 3, 4 with almost a quarter of decedents who died of cancer in Ontario, Canada experiencing at least one aggressive intervention (i.e., receiving chemotherapy in the last 14 days of life, having more than one emergency room visit or hospitalization in the last 30 days of life). 3 A retrospective cohort study of cancer decedents from 7 countries reported that between $44\%$ to $60\%$ of decedents experienced at least one hospitalization during the last 30 days of life and $5\%$ to $13\%$ of decedents received chemotherapy during this period as well. 5 Research has been conducted on preferred settings of death for individuals with cancer. A qualitative study of patients dying with cancer reported that patients desire a comfortable end‐of‐life experience with limited suffering, maintaining their independence and autonomy, and, ultimately, dying in their own homes. 6 A survey of bereaved caregivers in Ontario also reported that patients with terminal illnesses (e.g., cancer) overwhelmingly preferred to die at home as well. 7 Similarly, a systematic review of 210 studies from over 33 countries identified that most individuals prefer to die at home instead of an institution and that most individuals' preferences remained static as they approached death. 8 Despite this, $53\%$ of cancer decedents die in a hospital setting in Ontario, 1 thereby contradicting the established benefits of homecare during this phase 9 and patient preferences in spending their final days at home.
Palliative care in Ontario—as provided by physicians and nurse practitioners‐‐ is delivered in the hospital (both in a consultative model and a dedicated palliative care unit), in complex continuing care/sub‐acute care (in the form of a palliative care unit), at outpatient clinics, in nursing homes, and in the home. Almost half of decedents receive at least 1 palliative care service in the last year of life, with the majority of it being delivered in acute care and outpatient settings. 10 Hospice in Ontario refers only to dedicated facilities and there are only approximately 271 hospice beds in the entire province. 11 Unfortunately, health services information for hospice facilities is not captured through health administrative datasets in Ontario, Canada. Nevertheless, a 2014 cross‐country comparison study estimated that $16\%$ to $30\%$ of decedents received hospice palliative care services in Canada, compared to $23\%$ in England, $12\%$ in Germany, and $41\%$ in the United States. 12 Few studies have explored which settings decedents spend their end‐of‐life stage in. 13, 14, 15, 16 Of these studies, only two limited their study cohorts to cancer decedents, of which one was specific to hematologic cancers. 13, 15 Cheung et al. and Andersen et al. both reported that cancer decedents spend the majority of their final days at home. However, they only studied the number of days spent at home in the last 6 months of life; neglecting to differentiate between days at home with home care and days at home without any care, nor reporting on days spent in other settings.
To our knowledge, no studies have described the different settings of care experienced by cancer decedents during the last 100 days of life using population‐level health administrative data. Therefore, the objective of this study was to describe the different settings of care experienced in the last 100 days of life by decedents whose primary cause of death was cancer and how settings of care change as the decedent approached death. The last 100 days of life is a timeframe of interest as healthcare utilization and cost increase significantly during this timeframe for decedents with a history of cancer. 17 In addition, a systematic review of retrospective studies using linked‐health administrative datasets demonstrated that the use of various healthcare resources increased toward the end‐of‐life phase for patients dying with cancer. 5, 18, 19
## Study design and data sources
We conducted a retrospective cohort study using linked population‐level health administrative datasets in Ontario, Canada, held at ICES (formerly known as the Institute for Clinical Evaluative Sciences). ICES is an independent, non‐profit research institute whose legal status under Ontario's health information privacy law allows it to collect and analyze health care and demographic data, without consent, for health system evaluation and improvement. Data holdings at ICES are derived from across all healthcare sectors in Ontario, with access to individual‐level data for services across the continuum of care. Datasets are linked using unique encoded identifiers and are analyzed internally at ICES (please see Supplementary File S1 for a description of datasets accessed). The use of data in this project was authorized under section 45 of Ontario's Personal Health Information Protection Act, which does not require review by a Research Ethics Board.
## Population
The cohort initially consisted of all decedents aged 19 years or older at death who died between January 1, 2013, and December 31, 2017. Decedents were excluded if they were older than 105 years at death (in case of administrative error) if they were ineligible to the Ontario Health Insurance Plan (OHIP) during their last year of life,* if they had no healthcare encounters in the five years before death, and if they had an address outside Ontario at death. Additional data quality exclusions include decedents whose primary cause of death was not cancer or female decedents whose cause of death was prostate cancer.
Decedents were classified into six categories of major cancer sites for their cause of death: lung, breast, colorectal, pancreatic, prostate, and other. Cause of death was first captured through the Ontario Registrar General – Deaths database. Cancer site was identified via death data from the Ontario Cancer Registry. Please see Supplementary File S2 for the ICD10 diagnosis codes used to categorize cancer sites.
## Cohort characteristics
Decedent characteristics were captured at 100 days before death using information from the administrative databases. Age and sex were obtained from the Registered Persons Database (RPDB). Rurality status and neighborhood income level were collected by linking the decedents' postal code in RPDB to the 2011 *Canadian census* data if their death date occurred between 2012 and 2015, and 2016 *Canadian census* data if their death date occurred after 2016. Comorbidity status for conditions other than cancer was obtained using previously developed algorithms that use diagnosis codes and medication data to identify prevalent chronic conditions 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 and was captured using a 265‐day period, from the start of the last year of life to the first day of the last 100 days of life.
## Defining healthcare settings
Past studies have defined the end‐of‐life stage as the last 30 days of life 2, 3, 4 or the last 180 days of life 1, 13, 14, 15, 16; in this study, we define this stage as the last 100 days of life as healthcare utilization increases with an increasing focus on palliative care delivery. Therefore, this study described the settings in which decedents received care in their last 100 days of life, including days spent both in institutions and in the community. Days in institutions were categorized as emergency room, inpatient hospital, palliative care units, complex continuing care or rehabilitation, and long‐term care.
Days in the emergency room were derived from any encounter documented in the National Ambulatory Care Reporting System (NACRS) database, which included scheduled visits to and transfers from the ER, visits that resulted in an inpatient admission, and registrations that did not result in a physician encounter.
Days in inpatient hospital settings were derived from any encounter in the Discharge Abstract Database (DAD) and the Ontario Mental Health Reporting System metadata.
Palliative care units are exclusively located in acute care hospitals and complex continuing care facilities and differ from hospice facilities, which through dedicated facilities, are considered to be in the community. We used an approach to approximate whether an individual was in a palliative care unit. Days in palliative care units in complex continuing care facilities were captured using OHIP physician billings and the Continuing Care (CCRS). Specifically, an individual was considered to be admitted to a palliative care unit if at least $50\%$ of the OHIP billing claims between a given admission and discharge date in CCRS were palliative in nature and if the individual had at least 1 encounter with a palliative care specialist. Palliative care specialists were identified using a validated algorithm that identified physicians as palliative care specialists if they billed $10\%$ or more palliative fee codes during a 2‐year time frame. 30 Days in palliative care units in acute hospitals were captured using DAD. An individual was considered to be admitted to a palliative care unit if the main patient service was palliative care and the most responsible diagnosis on the hospitalization record was palliative care (ICD‐10 diagnosis code Z51.5 for palliative care). This approach has been used in previous Ontario‐based studies. 31, 32, 33 Days in complex continuing care or rehabilitation were derived from admission and discharge dates in CCRS and the National Rehabilitation Reporting *System metadata* respectively, while subtracting time spent in other settings if hospitalizations and ER visits fell within those dates. Days in long‐term care were also derived from admission and discharge dates from CCRS‐LTC, while subtracting time spent in other settings if hospitalizations and ER visits fell within those dates.
Days in the community were categorized as home with outpatient care, home with publicly funded home care (including physician home visits), and home without healthcare. Days at home with outpatient care were derived from OHIP billing fee codes whose location of service was office or phone. Days at home with home care were derived from OHIP billing fee codes whose location of service was home or from service dates from the Home Care Dataset. Consequently, days at home without any care were derived from the remaining days not spent in any of the above settings.
Please see Supplementary File S3 for a hierarchy order and administrative codes used to define settings of care.
## Statistical analyses
Descriptive results are presented as counts and proportions for categorical variables, and as mean and standard deviations (SD) for continuous variables. Absolute and relative differences are presented for changes in days spent in different healthcare settings across the last 100 days of life. Results are reported by cancer type and socio‐demographic factors. All analyses were conducted using SAS Enterprise Guide v.7.15.
## RESULTS
From January 1, 2013, to December 31, 2017, there were 475,108 decedents aged 19 years and older after 8655 excluding decedents ineligible for OHIP during the last year of life, decedents aged <19 years or > 105 years at death, decedents who did not have healthcare contact within the last 5 years of life, and decedents with non‐Ontario residence status. After excluding non‐cancer decedents, females with prostate cancer as their cause of death and missing cause of death information or record in the Ontario Cancer Registry, 125,755 decedents remained in the study cohort (Figure 1). The number of excluded females being recorded as having prostate cancer was suppressed in Figure 1 to prevent reidentification due to small cell issues. The mean age of decedents was 73.0 (SD 12.9), $46.1\%$ were female, $14.1\%$ resided in rural regions, and $86.2\%$ had at least one other prevalent condition in addition to cancer before the last 100 days of life. Of the total cohort, $23.6\%$ died of lung cancer, $7.2\%$ breast cancer, $7.2\%$ colorectal cancer, $6.6\%$ pancreatic cancer, $5.2\%$ prostate cancer, and $50.3\%$ other cancer types, consisting of various cancers with small proportions preventing functional grouping (Table 1). Decedents experienced 1.7 emergency department visits and 1.2 inpatient hospital admissions in the last 100 days of life respectively.
**FIGURE 1:** *Cohort creation flow diagram* TABLE_PLACEHOLDER:TABLE 1 In the last 100 days of life, decedents spent a mean of 1.7 days in emergency rooms, 12.6 days in inpatient hospitals, 4.4 days in palliative care units, 2.5 days in complex continuing care or rehabilitation, 4.7 days in long‐term care, 4.6 days at home with outpatient care, 21.2 days at home with home care, and 48.3 days at home without any care (Figure 2). Of note, the median days were less than the means for all settings, except for days at home without healthcare (Supplementary File S4), suggesting a right skew to the data.
**FIGURE 2:** *Mean number of days spent in healthcare settings in the last 100 days of life amongst cancer decedents (n = 125,755) in Ontario from 2013 to 2017. CCC, complex continuing care.*
Results were stratified by cancer type, age category, and neighborhood income quintile (Figure 3). Lung cancer and pancreatic cancer decedents spent an average of 52.1 and 52.6 days at home without care, while prostate cancer and breast cancer decedents spent an average of 41.6 and 45.1 days at home without care. Decedents spent between an average of 3.8 (pancreatic) and 5.2 (breast) days in a palliative care unit, between 9.9 (breast) and 14.2 (other) days in an inpatient hospital, and between 3.2 (pancreatic) and 8.0 (prostate) days in long‐term care. As age increased, decedents spent fewer days at home without care (19–44 = 47.3 days; 95 + = 37.7 days) and in hospital inpatient settings (19–44 = 17.6 days; 95 + = 7.2 days). Days spent in long‐term care increased as decedents' age increased, with decedents aged 95+ spending 24.9 days in long‐term care. Differences in places of care according to neighborhood income quintile were marginal; however, there was a general pattern that as income increased, days in institutions decreased and days at home increased. Please see Supplementary File S5 for results stratified by both cancer type and age.
**FIGURE 3:** *Mean number of days spent in healthcare settings in the last 100 days of life per cancer type, age category, and neighborhood income quintile, amongst cancer decedents (n = 125,755) in Ontario from 2013 to 2017. Note: Data labels report mean days spent in each respective settings in the last 100 days of life.*
Places of care were analyzed weekly across the last 14 weeks of life (i.e., approximately the last 100 days) to measure changes in setting as decedents approached death (Figure 4). Absolute and relative differences were produced for each setting to measure change between 14 weeks away from death, until the last week before death. Overall, the average number of weekly days spent at home decreased while days in institutions increased as decedents approached death. Days at home without care decreased by 3.5 days, representing a $76\%$ reduction, while days in hospital inpatient settings increased by 1.5 days, representing a $371\%$ increase, and days at home with home care increased by 0.9 days, representing a $1242\%$ increase. Please refer to Supplementary File S6 for results stratified by cancer type.
**FIGURE 4:** *Mean Days spent in healthcare settings in the last 14 weeks of life (approximately last 100 days of life), amongst cancer decedents (n = 125,755) in Ontario from 2013 to 2017. Note: Data labels report absolute and relative difference in days from the last 14 week of life to the last week of life.*
Transitions between home, hospital, long‐term care, and palliative care units, were captured from the last 4 months of life (Table 2). Between 4 months to 3 months before death, most decedents remained in their original location, while some moved from home to hospital ($$n = 6317$$; $5\%$) and hospital to home ($$n = 3040$$; $2\%$). Most decedents remained in their original setting as they approached death, while the percentage of decedents moving from home to hospital continued to increase in the last month ($$n = 22$$,612; $18\%$).
**TABLE 2**
| Source | Unnamed: 1 | Target | Months 4 to 3, N (%) | Months 3 to 2, N (%) | Months 2 to 1, N (%) | Total number of transitions |
| --- | --- | --- | --- | --- | --- | --- |
| Source | | Target | N = 125,755 | N = 125,755 | N = 125,755 | N = 377,265 |
| Home | ➔ | Home | 103,298 (82.1%) | 92,504 (73.6%) | 67,146 (53.4%) | 262,948 (69.7%) |
| Home | ➔ | Hospital | 6317 (5.0%) | 11,655 (9.3%) | 22,612 (18.0%) | 40,584 (10.8%) |
| Home | ➔ | Long‐term care | 188 (0.2%) | 251 (0.2%) | 222 (0.2%) | 661 (0.2%) |
| Home | ➔ | Palliative care unit | 753 (0.6%) | 2095 (1.7%) | 6410 (5.1%) | 9258 (2.5%) |
| Hospital | ➔ | Home | 3040 (2.4%) | 3638 (2.9%) | 4300 (3.4%) | 10,978 (2.9%) |
| Hospital | ➔ | Hospital | 4483 (3.6%) | 6251 (5.0%) | 11,645 (9.3%) | 22,379 (5.9%) |
| Hospital | ➔ | Long‐term care | 193 (0.6%) | 255 (0.2%) | 272 (0.2%) | 720 (0.2%) |
| Hospital | ➔ | Palliative care unit | 364 (0.3%) | 754 (0.6%) | 1865 (1.5%) | 2983 (0.8%) |
| Long‐term care | ➔ | Home | 27 (0.0%) | 28 (0.0%) | 36 (0.0%) | 91 (0.0%) |
| Long‐term care | ➔ | Hospital | 67 (0.1%) | 119 (0.1%) | 343 (0.3%) | 529 (0.1%) |
| Long‐term care | ➔ | Long‐term care | 5549 (4.4%) | 5772 (4.6%) | 5824 (4.6%) | 17,145 (4.5%) |
| Long‐term care | ➔ | Palliative care unit | 7 (0.0%) | 18 (0.0%) | 89 (0.1%) | 114 (0.0%) |
| Palliative care unit | ➔ | Home | 140 (0.1%) | 220 (0.2%) | 344 (0.3%) | 704 (0.2%) |
| Palliative care unit | ➔ | Hospital | 31 (0.0%) | 57 (0.1%) | 157 (0.1%) | 245 (0.1%) |
| Palliative care unit | ➔ | Long‐term care | 7 (0.0%) | 14 (0.0%) | 21 (0.0%) | 42 (0.0%) |
| Palliative care unit | ➔ | Palliative care unit | 1291 (1.0%) | 2124 (1.7%) | 4469 (3.6%) | 7884 (2.1%) |
## DISCUSSION
In this retrospective study describing the different settings of care experienced in the last 100 days of life in Ontario, Canada, cancer decedents spent almost half of their final days at home without receiving any care, 25 days in institutions, and 21 days at home with home care services, including physician home services. Further, decedents increasingly spent more days in institutions as they approached death, resulting in fewer days spent at home consequently.
Cancer decedents of less aggressive cancers, like breast, colorectal, and prostate, spent more days in institutions compared to decedents with more aggressive cancers like pancreatic and lung cancers. These findings may reflect the differences in treatment on survival rates between less and more aggressive cancers. For instance, people with breast, colorectal, and prostate cancers, have significantly higher five‐ and 10‐year survival rates than people with lung or pancreatic cancers, 34 demonstrating the differences in treatment effectiveness between cancers. In turn, people dying from less aggressive cancers may spend more time in institutions as they may receive more treatment. This may be further compounded by aggressive cancers, such as lung and pancreatic cancers, rapidly progressing which may make it difficult for physicians to coordinate homecare or palliative care. Nevertheless, further statistical analyses would be required to verify that differences between cancer types are not attributed to random variation. Regardless of cancer type, decedents spent more days in institutions as they approached death, complementing existing literature. 14 Further, our findings demonstrate that almost one in five decedents transitioned from home to hospital in the last month of life, which reinforces previous findings as well. 35, 36 Despite transitioning to hospital settings, decedents spent little time in palliative care units that provide end‐of‐life care to referred patients. This may be attributed to there only being 31 palliative care units in Ontario and few in rural areas. 37 *As a* result, rural decedents may experience difficulties in accessing palliative care units, as described in the literature. 38 Further, limited time spent in palliative care units may be due to the units having limited financial support and inadequate human resources, 37 which may prevent higher uptake.
Our findings highlight the role of homecare services for people dying from cancer in Ontario, Canada. While there was an increase in days at home with home care toward death, most cancer decedents spent their final days either in an institution or at home without any care. Palliative home care resources are stretched, and many people lack the social supports to achieve a home death, even when resources are available. Increasing access to homecare services at the end‐of‐life can have positive outcomes, such as decreased acute care usage 39, 40 and providing patients with a dignified end‐of‐life experience by maintaining patient autonomy and integrity, 41 while also aligning with patient preferences and values regarding their preferred location of death. 4, 6 Therefore, our findings, coupled with the literature, emphasize the importance of improving end‐of‐life care delivery to keep patients in the community and out of institutions during this important life stage.
## Strengths and limitations
Since December 2017, the federal and provincial governments have developed frameworks for improving palliative care provision. 42, 43 Along with these frameworks, the Ontario Palliative Care Teams were developed in 2017 to improve access to palliative home care, whose impacts may not be captured in our study as we used health administrative data dating from January 1, 2013, until December 31, 2017. Also, cause of death information was only available until 2017, as a result, we limited our decedent cohort to 2017 to enable us to categorize decedents into specific cancer sub‐groups. Therefore, our findings may not reflect the current standard of end‐of‐life care which may have resulted in cancer decedents receiving more homecare in their last 100 days of life. In addition, our study did not explore how our outcomes of interest varied by important decedent characteristics such as race, ethnicity, religious background, or gender. This information is unavailable at the individual‐level using health administrative datasets in Ontario. Moreover, this study was conducted using health administrative data from one Canadian province, which may not reflect the end‐of‐life experience of cancer decedents from other jurisdictions; however, we believe there are similarities to other regions with similar health systems. Further, this study uses a definition for identifying care in a palliative care unit that is not validated. It is often assumed that patients prefer to be at home; however, for various reasons, some patients may prefer to receive care at and die in an institution, and often only people with financial resources and strong social support networks have the privilege of dying at home. In addition, we do not have information on whether decedents had additional support in the home—either provided by private services or family or friends, which would impact whether decedents were able to stay at home. Regardless, this study adds granular information over time about the last 100 days of life for cancer decedents and their transitions across care settings.
## CONCLUSION
In the last 100 days of life, people dying of cancer in Ontario, Canada, spend 25 days in institutions and nearly 50 days at home without any care, and only 21 days at home with home care services, including physician home visits. As individuals approach death, they increasingly spend more time in institutions instead of in the community, contradicting established patient preferences. It is unlikely or desirable to avoid all institutional admissions during the end‐of‐life. At a population level, increasing days spent in acute care indicate that we can expect a certain number of transitions to acute care, despite the receipt of homecare. This realization may prompt decision‐makers to shift resources to where needed most instead of a blanket approach in increasing services based on a diagnosis; for example, the often‐recommended bolstering of homecare or physician home visits to address the deterioration during the end‐of‐life stage.
## AUTHOR CONTRIBUTIONS
Abe Hafid: Conceptualization (equal); investigation (equal); methodology (equal); project administration (lead); visualization (lead); writing – original draft (lead); writing – review and editing (equal). Michelle Howard: Conceptualization (equal); funding acquisition (lead); investigation (equal); methodology (equal); writing – review and editing (equal). Colleen Webber: Conceptualization (equal); investigation (equal); methodology (equal); writing – review and editing (equal). Ana Gayowsky: *Formal analysis* (equal); methodology (equal); writing – review and editing (equal). Mary Scott: Methodology (equal); writing – review and editing (equal). Aaron Jones: Methodology (equal); writing – review and editing (equal). Amy T Hsu: Funding acquisition (equal); methodology (equal); writing – review and editing (equal). Peter Tanuseputro: Funding acquisition (equal); methodology (equal); writing – review and editing (equal). James Downar: Funding acquisition (equal); methodology (equal); writing – review and editing (equal). Katrin Conen: Methodology (equal); writing – review and editing (equal). Doug Manuel: Funding acquisition (equal); methodology (equal); writing – review and editing (equal). Sarina R Isenberg: Conceptualization (equal); funding acquisition (equal); investigation (equal); methodology (equal); writing – review and editing (equal).
## FUNDING INFORMATION
This study was funded by a grant from the Canadian Institutes of Health Research project #159771. This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and Long‐Term Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
## ETHICS
The use of data in this project was authorized under section 45 of Ontario's Personal Health Information Protection Act, which does not require review by a Research Ethics Board.
## CONFLICT OF INTEREST
All authors declare that they have no competing interests.
## DATA AVAILABILITY STATEMENT
The data set from this study is held securely in coded form at ICES. While data sharing agreements prohibit ICES from making the data set publicly available, access may be granted to those who meet prespecified criteria for confidential access, available at www. ices.on.ca/DAS. The full data set creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.
## References
1. Barbera L, Seow H, Sutradhar R. **Quality of end‐of‐life cancer care in Canada: a retrospective four‐province study using administrative health care data**. *Curr Oncol* (2015.0) **22** 341-355. PMID: 26628867
2. Hagarty AM, Bush SH, Talarico R, Lapenskie J, Tanuseputro P. **Severe pain at the end of life: a population‐level observational study**. *BMC Palliat Care* (2020.0) **19** 60. PMID: 32354364
3. Ho TH, Barbera L, Saskin R, Lu H, Neville BA, Earle CC. **Trends in the aggressiveness of end‐of‐life cancer care in the universal health care system of Ontario, Canada**. *J Clin Oncol* (2011.0) **29** 1587-1591. PMID: 21402603
4. Barbera L, Paszat L, Chartier C. **Indicators of poor quality end‐of‐life cancer care in Ontario**. *J Palliat Care* (2006.0) **22** 12-17. PMID: 16689410
5. Bekelman JE, Halpern SD, Blankart CR. **Comparison of site of death, health care utilization, and hospital expenditures for patients dying with cancer in 7 developed countries**. *JAMA* (2016.0) **315** 272-283. PMID: 26784775
6. Kastbom L, Milberg A, Karlsson M. **A good death from the perspective of palliative cancer patients**. *Support Care Cancer* (2017.0) **25** 933-939. PMID: 27837324
7. Brazil K, Howell D, Bedard M, Krueger P, Heidebrecht C. **Preferences for place of care and place of death among informal caregivers of the terminally ill**. *Palliat Med* (2005.0) **19** 492-499. PMID: 16218162
8. Gomes B, Calanzani N, Gysels M, Hall S, Higginson IJ. **Heterogeneity and changes in preferences for dying at home: a systematic review**. *BMC Palliat Care* (2013.0) **12**. DOI: 10.1186/1472-684X-12-7
9. Brumley R, Enguidanos S, Jamison P. **Increased satisfaction with care and lower costs: results of a randomized trial of in‐home palliative care**. *J Am Geriatr Soc* (2007.0) **55** 993-1000. PMID: 17608870
10. Tanuseputro P, Budhwani S, Bai YQ, Wodchis WP. **Palliative care delivery across health sectors: a population‐level observational study**. *Palliat Med* (2017.0) **31** 247-257. PMID: 27317412
11. **Annual Report 2014, Chapter 3, Section 3.08. 2014**
12. Klinger CA, Howell D, Zakus D, Deber RB. **Barriers and facilitators to care for the terminally ill: a cross‐country case comparison study of Canada, England, Germany, and the United States**. *Palliat Med* (2014.0) **28** 111-120. PMID: 23801462
13. Andersen SK, Croxford R, Earle CC, Singh S, Cheung MC. **Days at home in the last 6 months of life: a patient‐determined quality indicator for cancer care**. *J Oncol Pract* (2019.0) **15** e308-e315. PMID: 30849006
14. Dalmau‐Bueno A, García‐Altés A, Amblàs J, Contel JC, Santaeugènia S. **Determinants of the number of days people in the general population spent at home during end‐of‐life: results from a population based cohort analysis**. *PLoS One* (2021.0) **16**. PMID: 34264956
15. Cheung MC, Croxford R, Earle CC, Singh S. **Days spent at home in the last 6 months of life: a quality indicator of end of life care in patients with hematologic malignancies**. *Leuk Lymphoma* (2020.0) **61** 146-155. PMID: 31437075
16. Gill TM, Gahbauer EA, Leo‐Summers L, Murphy TE, Han L. **Days spent at home in the last six months of life among community‐living older persons**. *Am J Med* (2019.0) **132** 234-239. PMID: 30447203
17. Langton JM, Reeve R, Srasuebkul P. **Health service use and costs in the last 6 months of life in elderly decedents with a history of cancer: a comprehensive analysis from a health payer perspective**. *Br J Cancer* (2016.0) **114** 1293-1302. PMID: 27115468
18. Langton JM, Blanch B, Drew AK, Haas M, Ingham JM, Pearson SA. **Retrospective studies of end‐of‐life resource utilization and costs in cancer care using health administrative data: a systematic review**. *Palliat Med* (2014.0) **28** 1167-1196. PMID: 24866758
19. Fassbender K, Fainsinger RL, Carson M, Finegan BA. **Cost trajectories at the end of life: the Canadian experience**. *J Pain Symptom Manage* (2009.0) **38** 75-80. PMID: 19615630
20. Muggah E, Graves E, Bennett C, Manuel DG. **The impact of multiple chronic diseases on ambulatory care use; a population based study in Ontario, Canada**. *BMC Health Serv Res* (2012.0) **12** 452. PMID: 23217125
21. Lane NE, Maxwell CJ, Gruneir A, Bronskill SE, Wodchis WP. **Absence of a socioeconomic gradient in older adults' survival with multiple chronic conditions**. *EBioMedicine* (2015.0) **2** 2094-2100. PMID: 26844290
22. Jaakkimainen RL, Bronskill SE, Tierney MC. **Identification of physician‐diagnosed Alzheimer's disease and related dementias in population‐based administrative data: a validation study using family physicians' electronic medical records**. *J Alzheimers Dis* (2016.0) **54** 337-349. PMID: 27567819
23. Mondor L, Maxwell CJ, Bronskill SE, Gruneir A, Wodchis WP. **The relative impact of chronic conditions and multimorbidity on health‐related quality of life in Ontario long‐stay home care clients**. *Qual Life Res* (2016.0) **25** 2619-2632. PMID: 27052421
24. Mondor L, Maxwell CJ, Hogan DB. **Multimorbidity and healthcare utilization among home care clients with dementia in Ontario, Canada: a retrospective analysis of a population‐based cohort**. *PLoS Med* (2017.0) **14**. PMID: 28267802
25. Mondor L, Cohen D, Khan AI, Wodchis WP. **Income inequalities in multimorbidity prevalence in Ontario, Canada: a decomposition analysis of linked survey and health administrative data**. *Int J Equity Health* (2018.0) **17** 90. PMID: 29941034
26. Gruneir A, Bronskill SE, Maxwell CJ. **The association between multimorbidity and hospitalization is modified by individual demographics and physician continuity of care: a retrospective cohort study**. *BMC Health Serv Res* (2016.0) **16** 154. PMID: 27122051
27. Pefoyo AJ, Bronskill SE, Gruneir A. **The increasing burden and complexity of multimorbidity**. *BMC Public Health* (2015.0) **15** 415. PMID: 25903064
28. Petrosyan Y, Bai YQ, Kone Pefoyo AJ. **The relationship between diabetes care quality and diabetes‐related hospitalizations and the modifying role of comorbidity**. *Can J Diabetes* (2017.0) **41** 17-25. PMID: 27789111
29. Thavorn K, Maxwell CJ, Gruneir A. **Effect of socio‐demographic factors on the association between multimorbidity and healthcare costs: a population‐based, retrospective cohort study**. *BMJ Open* (2017.0) **7**
30. Barbera L, Hwee J, Klinger C, Jembere N, Seow H, Pereira J. **Identification of the physician workforce providing palliative care in Ontario using administrative claims data**. *CMAJ Open* (2015.0) **3** E292-E298
31. Isenberg SR, Meaney C, May P. **The association between varying levels of palliative care involvement on costs during terminal hospitalizations in Canada from 2012 to 2015**. *BMC Health Serv Res* (2021.0) **21** 331. PMID: 33849539
32. Webber C, Hsu AT, Tanuseputro P, Fitzgibbon E, Li C. **Acute care utilization and place of death among patients discharged from an inpatient palliative care unit**. *J Palliat Med* (2020.0) **23** 54-59. PMID: 31305204
33. Quinn KL, Hsu AT, Meaney C. **Association between high cost user status and end‐of‐life care in hospitalized patients: a national cohort study of patients who die in hospital**. *Palliat Med* (2021.0) **35** 1671-1681. PMID: 33781119
34. **Canadian Cancer Statistics 2021**
35. Abraham S, Menec V. **Transitions between care settings at the end of life among older homecare recipients: a population‐based study**. *Gerontol Geriatr Med* (2016.0) **2** 2333721416684400. PMID: 28680944
36. Van Den Block L, Pivodic L, Pardon K. **Transitions between health care settings in the final three months of life in four EU countries**. *Eur J Public Health* (2015.0) **25** 569-575. PMID: 25829502
37. Towns K, Dougherty E, Kevork N. **Availability of services in Ontario hospices and hospitals providing inpatient palliative care**. *J Palliat Med* (2012.0) **15** 527-534. PMID: 22512831
38. Evans R, Stone D, Elwyn G. **Organizing palliative care for rural populations: a systematic review of the evidence**. *Fam Pract* (2003.0) **20** 304-310. PMID: 12738700
39. Seow H, Barbera L, Howell D, Dy SM. **Using more end‐of‐life homecare services is associated with using fewer acute care services: a population‐based cohort study**. *Med Care* (2010.0) **48** 118-124. PMID: 20057327
40. Seow H, Barbera L, Pataky R. **Does increasing home care nursing reduce emergency department visits at the end of life? A population‐based cohort study of cancer decedents**. *J Pain Symptom Manage* (2016.0) **51** 204-212. PMID: 26514717
41. Karlsson C, Berggren I. **Dignified end‐of‐life care in the patients' own homes**. *Nurs Ethics* (2011.0) **18** 374-385. PMID: 21558113
42. **Framework on Palliative Care in Canada**. (2018.0)
43. **Ontario Provincial Framework for Palliative Care**. (2021.0)
|
---
title: IGF2BP2 promotes pancreatic carcinoma progression by enhancing the stability
of B3GNT6 mRNA via m6A methylation
authors:
- Pei Cao
- Yufan Wu
- Ding Sun
- Weigang Zhang
- Junyi Qiu
- Zuxiong Tang
- Xiaofeng Xue
- Lei Qin
journal: Cancer Medicine
year: 2022
pmcid: PMC9972174
doi: 10.1002/cam4.5096
license: CC BY 4.0
---
# IGF2BP2 promotes pancreatic carcinoma progression by enhancing the stability of B3GNT6 mRNA via m6A methylation
## Abstract
1. Bioinformatics reveals that m6A regulators are generally elevated in PC. 2. IGF2BP2 enhances the stability of B3GNT6 mRNA via m6A methylation. 3. IGF2BP2 plays a carcinogenic role, promoting tumor formation in vivo.
### Background
Pancreatic carcinoma (PC) is a highly lethal cancer with an increasing mortality rate, its five‐year survival rate is only approximately $4\%$. N6‐methyladenosine (m6A) modification is the most common posttranscriptional modification of RNA, it could affect tumor formation by regulating m6A modifications in the mRNA of key oncogenes or tumor suppressor genes. However, its role in PC remains unclear.
### Methods
We combined bioinformatic analysis with in vitro and in vivo experiments to investigate the expression profile of methylation modulators and identify key m6A regulators in the progression of PC. Further study focused on exploring the target genes binding to the regulators through RIP and immunofluorescence staining experiment.
### Results
TCGA and Gene Expression Omnibus (GEO) analyses revealed an overall increasing trend in the expression of m6A regulators in PC, and consensus clustering analysis of m6A modification showed that the expression of regulators was negatively correlated with the survival rate. LASSO‐Cox regression analysis revealed that IGF2BP2, METTL3, ALKBH5 and KIAA1429 were associated with hazard ratios (HR), but only IGF2BP2 was sufficiently appropriate for the m6A survival prognosis model. The IHC and WB results verified high protein expression of IGF2BP2 in PC, and IGF2BP2 knockdown inhibited the proliferation and migration of PC cells. We predicted and verified B3GNT6 was observably regulated by IGF2BP2 via RIP assays. In addition, IF staining confirmed the co‐expression of IGF2BP2 and B3GNT6. The tumor‐promoting effect of IGF2BP2 and its co‐expression with B3GNT6 were verified in an animal model.
### Conclusions
Elevated m6A levels promote PC progression. IGF2BP2 is a credible marker and modulates B3GNT6 mRNA stability, indicating that IGF2BP2 is a potential prognostic marker and therapeutic target in PC progression.
## BACKGROUND
Pancreatic carcinoma has long been one of the most devastating and highly malignant tumors, the five‐year survival rate is only $4\%$. 1 In both the United States and China, the morbidity and mortality of PC are less than satisfactory. 2 Currently, the most common and effective treatment is surgery combined with radiotherapy and chemotherapy, but this regimen exhibits little therapeutic effect. 3 Considering that PC is characterized by a strong interstitial hyperplastic reaction around cancer cells, 4 its drug resistance and early invasive metastasis control the high mortality of PC patients. 5 During the progression of PC, different signaling pathways mediate cancer typing, growth, proliferation and invasion, which makes the tumor microenvironment more complex. 6 *It is* well known that RNA is subject to a variety of internal modifications, among which N6‐methyladenosine (m6A) methylation is the most abundant and conserved epigenetic chemical modification. 7 Scientists have found that m6A methylation is a dynamic reversible process mediated by m6A methylation‐related proteins. 8 m6A methyltransferase “Writers” (METTL$\frac{3}{14}$, WTAP) facilitate the installation of methyl groups on RNA, and its demethylation is mediated by “Erasers” (FTO and ALKBH5), then m6A could be recognized by “Readers” (YTHDF$\frac{1}{2}$/3, IGF2BP$\frac{1}{2}$/3 and YTHDC$\frac{1}{2}$) to perform related functions, 9 including the regulation of mRNA stability, 10 transcription and translation efficiency, 11 RNA processing events 12 and miRNA maturation. It has been found that m6A mRNA methylation plays an essential role in the occurrence and development of tumors, 13, 14 including hepatoblastoma, 15 bladder cancer 16 and gastric cancer. 17 However, research on m6A in PC is still limited. It has been found that WTAP is highly expressed in PC and associated with clinicopathological features, but related mechanisms have not been reported. 18 IGF2BP2, a member of the IGF2 mRNA‐binding protein family, binds to the 5' UTR of the insulin‐like growth factor 2 (IGF2) mRNA and regulating its translation. It is overexpressed and promotes tumor progression in a variety of cancers. Xu revealed the oncogenic role of IGF2BP2 in PC and could promote cancer proliferation by activating the PI3K/Akt signaling pathway. 19 Research has found it could regulate DANCR in PC through m6A modification to promote stemness‐like properties and pathogenesis. 20 Since little is known about IGF2BP2 and m6A methylation in PC. It is urgent to identify the regulation of m6A in PC. Here we classified PC patients according to the overall expression pattern of m6A RNA methylation regulators in the Cancer Genome Atlas (TCGA) database and constructed a risk score based on the least absolute shrinkage and selection operator. We identified a crucial target IGF2BP2, which mediates the malignant progression of PC, promotes B3GNT6 mRNA stability, contributes to further progression of PC.
## Downloading of the PC dataset in the cancer genome atlas (TCGA) and gene expression omnibus (GEO)
We used the R package TCGAbiolinks to download the PC transcriptome data from TCGA database and combined them with healthy human tissue transcriptome data in GTEx to explore differential gene expression in PC. Also, RNA sequencing data was downloaded in GEO to measure the expression of regulators in PC. Patients with rational survival data and clinicopathological characteristics were included in the analysis. For group analysis, the median value was used as the cut‐off site.
## Clinical bioinformatic analysis
We employed the R software package ConsensusClusterPlus to divide 176 PC patients into two subtypes (PA1 and PA2) according to the expression of the regulators and used principal component analysis (PCA) to evaluate the difference in the gene expression distribution between the two clusters. Additionally, we used the R software package limma to conduct differential gene expression analysis on the subgroups, where fold change (FC) > 2 and $p \leq 0.05$ were used as the cut‐off values for DEGs. KEGG and GO enrichment analyses were performed with the above DEGs. The risk signature was calculated using the LASSO‐Cox regression algorithm according to the minimum criteria. The risk score was calculated using the formula, where *Coefi is* the coefficient: Risk score=∑$i = 1$nCoefi*xi
## Prediction of IGF2BP2 target genes
To predict and verify the possible downstream targets regulated by IGF2BP2, we analyzed and compared the differential genes between the two subgroups (PA1 and PA2), and the differential genes in PC. In addition, the CLIP dataset in m6A2Target database was used to filter the downstream mRNAs of IGF2BP2, the possible targets of IGF2BP2 were then obtained by intersection, and the follow‐up experiments were carried out to verify it.
## Clinical PC specimens
PC together with counterpart adjacent normal tissue samples were obtained from department of General Surgery, the First Affiliated Hospital of Soochow University. The research was ratified by the Ethical Committee of the first affiliated hospital of Soochow University and written informed consent were successively obtained from all participants before the study.
## Cell culture
Human CFPAC1 and PANC1 PC cells were purchased from GenePharma Technology Co., Ltd. (Suzhou, China) and used in our research. These cells were cultured in Dulbecco's modified Eagle's medium supplemented with $10\%$ foetal bovine serum (FBS) in an incubator with a $5\%$ CO2 atmosphere at 37°C. All cells were free of bacterial and mycoplasma contamination.
## Transfection
The target mRNA interference fragments and the NC construct si‐NC were synthesized (Synbio Technologies, China) and co‐transfected with Lipofectamine 2000 reagent (Invitrogen, USA) to knock down gene expression in PC cells; their sequences are shown in Table S1.
## Quantification of overall m6A RNA methylation
We used an EpiQuik m6A RNA Methylation Quantification Kit (colorimetric) (Epigentek, USA) to quantify the overall m6A methylation level in PC cells. First, we extracted total RNA and approximately 200 ng RNA was separated as an initial input. Then, the sample RNA, standard positive/negative control and binding solution were bound to the wells respectively for 1 h to assay and capture RNA according to the manufacturer's instruction. After washing, the detection antibody and enhancer solution were added, and color developer solution and stop solution was added prior to measurement of the absorbance. The values were calculated using linear regression equations.
## RNA extraction and quantitative real‐time polymerase chain reaction (qRT‐PCR)
Total RNA was extracted with TRIzol from PC cells and tissues. All primers used for qRT‐PCR were designed and blasted in the National Center for Biotechnology Information database. RNA was reverse transcribed to cDNA and PCR was then performed using SYBR Green qPCR Master Mix (GenePharma, China) in triplicate. Human β‐actin was used as the internal control for mRNA expression. The primer sequences are shown in Table S1.
## CCK‐8 proliferation assay
CCK‐8 kit (Dojindo Laboratories, Japan) was used to detect the proliferation ability of PC cells. Transfected cells were seeded in 96‐well plates at a concentration of 5 × 103 cells per well. After cultured for 0, 24, 48, 72 and 96 h under the same conditions, PC cells were incubated with diluted CCK‐8 reagent following the instructions. Then, the absorbance was measured in a microplate reader at a wavelength of 450 nm.
## EdU assay
An EdU kit (RiboBio, China) was used to detect the cell proliferation capability. In brief, after stable transfection for 48 h, cells were incubated with 5 μM EdU reagent diluted with DMEM for 3 h. Then, cells were permeabilized with $0.5\%$ Triton X‐100 for 20 minutes prior to fixation with $4\%$ paraformaldehyde for 30 minutes. Apollo and DAPI dyes were used to stain DNA and then nuclei. Images of EdU‐ and DAPI‐positive cells were acquired under a fluorescence microscope.
## Colony formation assay
To verify colony formation ability, transfected cells were seeded into 6‐well plates at a density of 5000 cells per well and distributed evenly by pipetting. After 14 days, the cells were washed with PBS twice, fixed with $4\%$ paraformaldehyde for 30 min and stained with $1\%$ crystal violet for 30 min. Eventually, the cells were imaged and the colonies were counted.
## Transwell invasion assay
To evaluate the cell migration ability, cells were cultured in serum‐free medium in the upper chamber of a Transwell plate (Corning, USA), and 700 μl of DMEM containing $10\%$ FBS was added to the lower chamber. After 48 h, the cells were fixed and stained with crystal violet for 20 min, and the cells migrating through the upper chamber membrane were counted. Images were acquired by microscopy.
## Luciferase reporter assay
The B3GNT6 fragment containing the possible m6A modification site and the corresponding mutated fragment were synthesized and cloned into the pmirGLO vector (Promega, USA) named B3GNT6‐WT and B3GNT6‐Mut. The synthesized vectors were separately co‐transfected with IGF2BP2‐si or si‐NC into 293 T cells. After co‐incubation for 24 h, cells were collected, and luciferase activities were measured with a Dual Luciferase Reporter Assay kit (GenePharma, China).
## m6A IP and IGF2BP2 IP assays
Immunoprecipitation assays of m6A and IG2BP2 were performed with an RIP kit (BersinBio Biotech, China). In brief, approximately 1 × 107 treated cells were collected and lysed for 30 min. DNA impurities were removed with DNase, the supernatant was collected after centrifugation, and the supernatant was then incubated with anti‐m6A and anti‐IGF2BP2 primary antibodies overnight at 4°C in a vertical orientation. Then, magnetic beads were added to incubate for 1 h. Finally, RNA was extracted, and target gene expression was detected by qRT‐PCR.
## 3‐DAA demethylation treatment
The overall methylation inhibitor 3‐DAA (Cayman, USA) was used to inhibit RNA methylation levels. After inoculation, cells were treated with 10 ug/ml 3‐DAA for 48 hours, then cells were collected for protein analysis.
## IHC analysis
Paraffin sections of 5 paired clinical pancreatic specimens were prepared and baked in an oven at 65°C for 30 min. After routine dewaxing, hydration and antigen repair, sections were sealed with $5\%$ goat serum for 30 min, placed in a wet box and incubated overnight with the IGF2BP2 primary antibody (ID:OTI3C8, OriGene, American) at 4°C. The next day, sections were incubated with the secondary antibody for 30 min, incubated with DAB for color development for 3 min and stained with hematoxylin for 3 min. Sections were sealed with neutral gum, and images were acquired with a microscope.
## IF detection assay
After dewaxing, hydration and antigen repair, paraffin sections were blocked with immunostaining blocking solution for 1 h, incubated with IGF2BP2 and B3GNT6 (ID:21291‐1‐AP, Proteintech, American) primary antibody at 4°C overnight and secondary antibody for 1 h. After counterstaining with DAPI, sections were sealed with an anti‐fluorescence quenching agent and imaged under a fluorescence microscope. Treated PC cells were prepared as cell slides and subsequent experiments were performed as described above.
## Western blot analysis
We used a RIPA mixture containing $1\%$ PMSF lysate to lyse and obtain total protein from transfected cells. A BCA protein detection kit (Thermo Scientific Pierce, USA) was used to determine the protein concentration. Protein separation under 80 V, 30 min and 120 V, 1 h electrophoresis conditions, followed by transferring to polyvinylidene difluoride membranes at 200 mA for 1.5 h. Then, membranes were blocked with $5\%$ skim milk and incubated with IGF2BP2 primary antibody (OriGene, American) and B3GNT6 (Proteintech, American) primary antibody overnight at 4°C. After washing, immunoreactions were visualized with an electroluminescence detection system.
## Animal experiment
To evaluate the effect of IGF2BP2 on the tumor formation ability in vivo, 2 × 106 cells were injected into the axilla of nude mice for subcutaneous tumor formation. After 9 days, modified IGF2BP2‐si or si‐NC was injected into the tumors every 3 days for 3 weeks. Tumor size was measured every 3 days. Finally, the subcutaneous tumors were excised and weighed to compare the tumor sizes, and protein expression was detected by immunohistochemistry and immunofluorescence. The care of laboratory animals was in accordance with the guidelines and ethical requirements of the Laboratory Animal Centre of Soochow University.
## Statistical analysis
We used GraphPad 8.0 for charting. For statistical analysis, two‐tailed Student's t‐test between two groups were performed by SPSS Statistic 26 for windows. The Kaplan–Meier method was used for survival analysis. Data was reported as mean ± SD from three duplicate experiments. Results was considered statistically significant when $p \leq 0.05$ (*$p \leq 0.05$).
## Overall expression pattern of m6A regulators
Considering that methylation regulators perform different biological roles, we first analyzed the expression of 15 regulators in PC and found that compared with normal pancreatic expression profiles in the GTEx database, the regulators were generally upregulated in TCGA, it was also observed in the GEO database (GSE15471) (Figure 1A,B). Spearman correlation analysis showed a close relationship between the factors (Figure 1C). Analysis of the STRING database also revealed a positive correlation between multiple regulators (Figure 1D). Our CNV analysis showed tumor size with regulators CNV was bigger than that without CNV (Figure 1E), suggesting that CNV is an important element leading to the upregulation of regulators and the progression of PC.
**FIGURE 1:** *Expression of m6A methylation regulators and identification of consensus clusters by m6A regulators in PC. (A)–(B) Expression levels of 15 m6A RNA methylation regulators in PC in TCGA (A) and GEO (B) (red is up‐regulated and blue is down‐regulated). (C) Spearman correlation analysis of 15 m6A regulators in PC. (D) PPI network shows the interaction among 15 m6A regulators. (E) Relationship between overall CNV and tumor size. (F) Consensus clustering cumulative distribution function (CDF) for k = 2 to 10. (G) Consensus clustering matrix for k = 2. (H) Kaplan–Meier analysis for two subgroups.*
## Identification of two subgroups of PC by consensus clustering
To visualize the specific functions of m6A methylation regulators, we removed normal pancreatic samples and PC samples with no suitable clinical data, and used the ConsensusClusterPlus R package to perform consensus clustering. According to the expression similarity of the regulators, the cumulative distribution function (CDF) of our data seemed to be ideal when $K = 2$, and the PA1 (96 samples) and PA2 (80 samples) subgroups were then defined (Figure 1F,G). We compared the clinicopathological characteristics of PA1 and PA2 and found them to be consistent with the above expectations. K‐M survival analysis showed significant differences between the two subgroups (Figure 1H).
The edgeR software package was used to analyze the difference between the expression profiles of PA1 and PA2, and the DEGs were selected based on the criteria |logFC| ≥ 1 and p ≤ 0.05. Finally, 2681 genes were identified, of which 724 genes were upregulated in PA1, and 1957 genes were upregulated in PA2. KEGG and GO enrichment analyses showed that the DEGs were mainly enriched in homeostasis or regulation of membrane potential (Figure 2A,B), such as Cytokine−cytokine receptor interaction and pancreatic secretion, indicating that mRNA methylation affects multiple biological processes.
**FIGURE 2:** *Enrichment analysis between subgroups and risk signature with four m6A RNA methylation regulators. (A–B) KEGG and GEO analysis of the subgroups. (C) The process of building the signature containing 15 m6A RNA methylation regulators. (D–E) The coefficients calculated by multivariate Cox regression using LASSO are shown. (F) Overall survival analysis between low‐ and high‐risk groups stratified by the risk score.(G) Overall survival analysis of the four m6A regulators. (H) The heatmap shows the expression of four m6A methylation regulators and related clinicopathological features in low‐ and high‐risk PC patients. (I–J) Univariate and multivariate Cox regression analyses of the association between clinicopathological factors and overall survival in TCGA.*
## Analysis of associations between m6A methylation regulators and clinicopathological characteristics
To further clarify the role of the regulators in survival, we conducted univariate Cox regression analysis. Among the 15 regulators, 5 were related to prognosis: IGF2BP2 (HR = 2.19), IGF2BP3 (HR = 1.53), METTL3 (HR = 0.53), ALKBH5 (HR = 0.6) and KIAA1429 (HR = 1.55) (Figure 2C). Survival models based on high reliability and LASSO regression are widely used to screen prognostic genes from high‐dimensional data; thus, we established risk characteristics and performed LASSO‐Cox regression analysis to calculate the risk score, and found that KIAA1429, METTL3, IGF2BP2 and ALKBH5 were the main contributors (Figure 2D,E), where the coefficient of KIAA1429, METTL3, IGF2BP2 and ALKBH5 were 0.00027, −0.00056, 0.00015 and −0.00019, respectively. According to the median risk score, low‐risk patients had higher survival status (Figure 2F). The respective survival rate analysis showed higher IGF2BP2 contributed to lower survival rate (Figure 2G). Heatmap showed that ALKBH5 and METTL3 were upregulated in the low‐risk group, while IGF2BP2 and KIAA1429 were upregulated in the high‐risk group (Figure 2H). We evaluated the correlations between the risk subgroups and the clinicopathological features. Univariate and multivariate Cox regression analyses showed that the risk score ($p \leq 0.05$) and lymph node metastasis ($p \leq 0.05$) were highly significant (Figure 2I,J). In summary, we believe that the accuracy of m6A methylation regulators for predicting the prognosis of PC was further corroborated. Considering that IGF2BP2 is highly expressed in PC, with a high HR, and is strongly independently associated with prognosis, we selected it as the main regulator for subsequent studies.
## IGF2BP2 is a credible molecular prognostic marker in PC
TCGA and IHC analysis revealed high expression of IGF2BP2 in PC (Figure 3A,B). What's more, quantitative analysis of RNA methylation indicates an overall upregulation of m6A modification in PC tissues (Figure 3C). Combining positive experiments and bioinformatics results, we then sought to investigate some specific reasons for high IGF2BP2 and m6A expression. TCGA data analysis showed that increasing CNV contributed to a higher mRNA level (Figure 3D), while the DNA methylation level was negatively correlated with the mRNA expression (Figure 3E), indicating that DNA methylation and CNV were indispensable cooperative factors for high IGF2BP2 expression. These results indicated that IGF2BP2 was a marker of unfavorable prognosis in PC patients.
**FIGURE 3:** *Expression of IGF2BP2 in pancreatic carcinoma. (A) Expression of IGF2BP2 mRNA in TCGA and GTEx database. (B) IHC (IGF2BP2)‐stained paraffin‐embedded sections verified the expression of IGF2BP2 protein in PC and normal tissue. (C) Quantification of overall m6A RNA methylation in PC tissues. (D) Relationship between different CNV types and IGF2BP2 expression level. (E) Relationship between IGF2BP2 DNA methylation and mRNA expression. (F) IGF2BP2 mRNA expression in si‐NC and IGF2BP2‐si groups. (G–H) IGF2BP2 protein expression in si‐NC and IGF2BP2‐si groups. (*p < 0.05).*
## IGF2BP2 promotes PC cell proliferation and migration
For further investigation of the biological function of IGF2BP2 in PC, we knocked down its expression in CFPAC1 and PANC1 cells (Figure 3F,H) and quantitative analysis of m6A revealed no changes (Figure S1). CCK‐8 and EdU assays indicated that silencing IGF2BP2 obviously suppressed the proliferation of PC cells (Figure 4A–D). Also IGF2BP2 KO decreased cell migration ability with Transwell assay (Figure 4E). The colony formation assay showed that inhibition of IGF2BP2 decreased cell viability (Figure 4F). These results illustrate that IGF2BP2 exerts oncogenic effects in PC.
**FIGURE 4:** *IGF2BP2 KO suppresses pancreatic carcinoma proliferation and migration ability. (A)–(D) CCK‐8 and EdU assay were used to detect cell proliferation ability. (E) Transwell experiment revealed PC cell migration ability with or without IGF2BP2 KO. (F) PC cell colony formation ability detection after IGF2BP2 KO. (*p < 0.05).*
## IGF2BP2 regulates B3GNT6 expression
As an RNA binding protein, IGF2BP2 has been proven to bind to a variety of RNAs to regulate their expression. To identify potential target genes of IGF2BP2, we analyzed IGF2BP2 CLIP data and identified 6249 targets with relatively high associativity. Subsequently, PC patients were divided into high IGF2BP2 expression and low IGF2BP2 expression group, and differential gene expression analysis was conducted between the groups. In TCGA, we identified 8335 DEGs between PC and normal tissue, among which nine were common highly reliable genes (Figure 5A). qRT‐PCR confirmed that after knockdown of IGF2BP2, some genes were downregulated to varying degrees, of which, B3GNT6, DHRS9 and ALPP had the largest decreases (Figure 5B). We performed RIP with IGF2BP2 and m6A antibody (Figure S2) and results showed that B3GNT6 was more highly enriched than DHRS9 and ALPP (Figure 5C). Also, TCGA data analysis indicated that B3GNT6, ALPP and DHRS9 was upregulated in PC (Figure 5D; Figure S3). B3GNT6 is a beta‐1,3‐N‐acetylglucosaminyl transferase that adds an N‐acetylglucosamine moiety to N‐acetylgalactosamine‐modified serine or threonine residues to affect biosynthesis and metabolism. Studies have found that B3GNT6 is closely related to a variety of disease processes, such as immunity deficiency, 21 colorectal cancer and m6A modification, 22 Together, these results indicate that B3GNT6 may be the downstream target of IGF2BP2. Survival analysis with TCGA data showed high B3GNT6 was related to lower survival status, and Spearman correlation analysis also showed the co‐expression of IGF2BP2 and B3GNT6 (Figure 5E). Given that B3GNT6 is clinically relevant, closely related to m6A and understudied in PC, we mainly focused our research on it.
**FIGURE 5:** *IGF2BP2 regulates B3GNT6 mRNA expression to promote pancreatic carcinoma. (A) Prediction of IGF2BP2 target genes, Venn diagram shows substantial and significant overlap among TCGA‐GTEx, IGF2BP2‐CLIP and DEGs between PA1 and PA2 subgroups. (B) Expression of predicted genes after IGF2BP2 KO. C m6A IP and IGF2BP2 IP verified the binding efficacy of target genes. (D) TCGA‐GTEx reveals the high expression of B3GNT6 in PC. (E) Spearman correlation analysis reveals positive co‐expression between IGF2BP2 and B3GNT6. (F–H) CCK‐8 and EdU assay revealed the PC cell proliferation ability after B3GNT6 KO. (I) Transwell experiment revealed PC cell migration ability after B3GNT6 KO. (J) B3GNT6 KO inhibited colony formation ability in PC cells. (*p < 0.05).*
## Inhibition of B3GNT6 decreased the proliferative ability of PC cells
We first knocked down B3GNT6 in cells, CCK‐8 and EdU incorporation assays indicated that silencing B3GNT6 overtly inhibited the proliferation of PC cells (Figure 5F–H). In addition, Transwell and colony formation assay showed that B3GNT6‐si inhibited cell migration and viability (Figure 5I,J). These results indicate that B3GNT6 can exert oncogenic effects in PC.
## IGF2BP2 recognizes m6A methylation on B3GNT6 and increases its stability
To further identify the underlying mechanism of IGF2BP2‐mediated B3GNT6 expression, we sought to determine whether and how IGF2BP2 interacts with B3GNT6 to affect its expression. We knockdown IGF2BP2 and RIP showed lower B3GNT6 was enriched (Figure 6A). Dual luciferase reporter assay was conducted and the results revealed that B3GNT6 was possibly recognized and regulated by m6A modification (Figure 6B,C).
**FIGURE 6:** *IGF2BP2 regulates B3GNT6 mRNA stability via an m6A‐dependent manner and promotes m6A content in PC. (A) IGF2BP2 IP verified B3GNT6 expression after IGF2BP2 KO. (B‐C) Dual luciferase reporter assays verified the methylation of B3GNT6 by IGF2BP2. (D) B3GNT6 mRNA stability in cells treated with IGF2BP2‐si. (E) B3GNT6 protein expression in cells after treated with IGF2BP2‐si and 3‐DAA. (F–G) Immunofluorescence staining confirmed the co‐expression of IGF2BP2 and B3GNT6 in PC tissues (F) and cells (G). (H–J) Detection of the tumorigenic ability of PC cells, including tumor size and tumor weight. (K) Immunofluorescence staining confirmed the co‐expression of IGF2BP2 and B3GNT6 in mice tumor tissue. (L) IHC(B3GNT6)‐stained paraffin‐embedded sections verified the low expression of B3GNT6 protein after IGF2BP2 KO. (*p < 0.05).*
Considering that IGF2BPs could regulate RNA stability, we sought to evaluate B3GNT6 mRNA stability. PANC1 cells were transfected with IGF2BP2‐si or the si‐NC, then treated with actinomycin D (Fdbio science, China) at 10 ng/ml at different time point (0, 4, 8 and 12 h). As shown, B3GNT6 mRNA decay rate in the IGF2BP2‐siRNA group was faster than that in the si‐NC group (Figure 6D), IGF2BP2‐KO and 3‐DAA could decreased B3GNT6 protein expression (Figure 6E), suggesting IGF2BP2 can increase B3GNT6 mRNA stability and contribute to the high B3GNT6 protein level in an m6A manner. IF staining revealed that IGF2BP2 and B3GNT6 had similar expression trends in PC tissue (Figure 6F), and knockdown of IGF2BP2 led to an obvious decrease in the B3GNT6 protein level (Figure 6G). Together, these results suggest that B3GNT6 could undergo m6A methylation, and IGF2BP2 functions as a reader of methylated B3GNT6 to increase its stability.
## IGF2BP2 promotes tumorigenesis in vivo
Subcutaneous tumorigenesis experiments in mice showed that knockdown of IGF2BP2 contributed to a decreased tumor size (Figure 6H,I) and tumor weight (Figure 6J), showing its significant ability in tumor formation. We evaluated the B3GNT6 expression level by IHC and IF, finding that knockdown of IGF2BP2 contributed to a lower B3GNT6 expression level (Figure 6K,L). Together, these data indicate that IGF2BP2 regulates B3GNT6 in an m6A manner and serves as an oncogene in PC (Figure 7).
**FIGURE 7:** *Mechanism diagram of IGF2BP2 promoting B3GNT6 expression via m6A methylation.*
## DISCUSSION
We conducted a comprehensive expression profiling analysis of 15 methylation regulators and found overall high expression in PC samples. We divided PC samples into two subgroups and performed differential gene expression analysis. Then, we used LASSO‐Cox regression analysis to construct a methylation model and obtained risk score, finding that the survival rate in the high‐risk group was significantly lower than that in the low‐risk group. Finally, we identified the potential m6A regulator IGF2BP2 and downstream target gene B3GNT6. Subsequent in vivo and in vitro experiments were performed to verify the gene methylation modification. IGF2BP2 was proved to modulate cellular biological function through post‐transcriptional regulation and participate in the development and progression of cancers in an m6A manner, such as head and neck squamous carcinoma, 23 thyroid cancer, 24 colorectal cancer 25 and macrophage phenotypic activation. 26 The mortality of PC varies greatly in different regions. In addition, due to late detection and the scarcity of effective treatments, the survival rate is generally low. 27 Currently, effective treatment for PC is still based on traditional surgical treatment, but still with a poor survival rate. 28, 29 RNA methylation is the most common epigenetic modification, and m6A is the most universal methylation modification. 9, 30 By modifying the methylation of specific sites in mRNA, one m6A methylation regulator may have different functions in different cancers. 31 m6A modifies and regulates different aspects of mRNA, including its structure, stability, splicing, nuclear export, translation, decay, etc., and is also involved in cell fate determination, cell cycle regulation, and cell differentiation. Among the regulators, m6A writers mainly increase the level of methylation on RNA, which is the key step in m6A modification. 32 *As a* reversible modification, m6A eraser has the opposite function of a writer to reduce the level of m6A modification, but may eventually contribute to similar functional results, because there are also m6A readers, a group of proteins that can recognize m6A, 33, 34 thus, when writers or erasers cooperate with different readers, they can contribute to various biological functions, which also leads to the complexity of m6A modification. 35 To date, m6A methylation has been found to function as a carcinogenic or tumor‐suppressive mechanism in glioblastoma, 36 hepatocellular carcinoma, 37, 38 and breast cancer, 39 but the function of m6A methylation in PC is still unknown. Previous studies have shown that YTHDF2 inhibits adhesion, invasion, migration and EMT through YAP signaling. 40 WTAP stabilizes Fak mRNA in PC to promote metastasis and gemcitabine resistance, 41 PIK3CB m6A methylation promotes the progression of PTEN‐deficient PC by regulating the AKT signaling pathway, 42 but the pattern of methylation and the progression of malignancy in PC remain to be explored. Our results show that KIAA1429, METTL3, IGF2BP2 and ALKBH5 contribute to the classification of PC and is closely related to the clinicopathological characteristics of PC. METTL3 was originally identified as a methyltransferase responsible for m6A modification, 43 and growing evidence shows it functions in mRNA shearing, 44 3'‐UTR modification, 45 translational regulation 34 and decay. 31 In contrast to YTHDF2's ability to promote mRNA attenuation, 36 IGF2BP2 can promote the stability and maintenance of its target mRNA in an m6A‐dependent manner, 46, 47 which is usually achieved after binding to an mRNA stabilizer.
B3GNT6 was identified as IGF2BP2 target in an m6A manner, and our research verified its oncogenic role in PC, considering that B3GNT6 is an essential enzyme for the synthesis of core 3 O‐glycan 48 and to reduce malignant biological characteristics in colon, but was upregulated and a favorable prognostic factor in PC, 49 we believed m6A played an essential role in glucose metabolism.
In theory, the reader, eraser and writer should have different expression patterns due to their different functions. However, our research found that the expression patterns of most regulators, whether promoting or inhibiting methylation, tended to be the same. We speculated that on one hand, there may be a proportional relationship. For example, the ratio of methylation/demethylation regulators exceeding a certain range will promote cancer or suppress cancer. On the other hand, different regulators have different affinities for the same gene and contributions to abnormal downstream activities caused by m6A methylation. 47, 50 Third, there may be other molecules affecting m6A methylation modification, indeed, these still need to be further verified.
## CONCLUSIONS
In summary, we have confirmed the elevated m6A level and revealed the significant effect of RNA methylation in PC. Besides we authenticate a novel gene IGF2BP2 is a credible marker and upregulated in PC, PCR and WB indicate that it promotes the B3GNT6 mRNA stability to contribute to the deterioration of PC, indicating that IGF2BP2 is a potential prognostic marker and therapeutic target in PC progression. Hence, our study provides some evidence for research of RNA methylation in PC.
## ADVANTAGES AND LIMITATIONS
In this study, we first verified that m6A modification features play an important role in the development of PC through bioinformatics analysis. Combined with experiments, it was verified that IGF2BP2 can regulate B3GNT6 stability through m6A and may regulate the metabolic pathway of PC. However the deficiency is that we only carried out cell function experiments on B3GNT6 and no follow‐up pathway analysis. Clinically, only IHC analysis of specimens was performed. Due to insufficient number of specimens and insufficient follow‐up time, clinical research has not been carried out in depth. we will further expand the sample size for verification.
## AUTHORS' CONTRIBUTIONS
Xiaofeng Xue and Lei Qin contributed to the conception and design of the article. Pei Cao, Ding Sun and Weigang Zhang contributed to the acquisition of the data. Pei Cao and Yufan Wu contributed to the implementation of the experiment. Zuxiong Tang, Junyi Qiu and Ding Sun contributed to the acquisition of specimens. Pei Cao, Yufan Wu and Xiaofeng Xue contributed to the bioinformatics analysis. Xiaofeng Xue, Pei Cao and Junyi Qiu contributed to the analysis and interpretation of the data. Xiaofeng Xue, Lei Qin and Pei Cao contributed to the writing, review, and revision of the paper.
## FUNDING INFORMATION
This research was supported by the Youth Science Foundation of National Natural Science Foundation of China (No. 81802365).
## CONFLICT OF INTEREST
The authors declare no competing financial interests.
## ETHICS APPROVAL AND CONSENT TO PARTICIPATE
All animal experiments were performed according to the guide for the Laboratory Animal Centre of Soochow University. The care of laboratory animals was in accordance with the guidelines and ethical requirements of the Laboratory Animal Centre of Soochow University.
## CONSENT FOR PUBLICATION
All authors have read and approved the final manuscript.
## PUBLISHER'S NOTE
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## DATA AVAILABILITY STATEMENT
The experimental data that support this study are available upon request. Bioinformatics data was downloaded from TCGA and GEO database with R studio, the experimental data are all independently completed by the research group.
## References
1. Vincent A, Herman J, Schulick R, Hruban RH, Goggins M. **Pancreatic cancer**. *Lancet* (2011) **378** 607-620. DOI: 10.1016/S0140-6736(10)62307-0
2. Chen W, Zheng R, Baade PD. **Cancer statistics in China, 2015**. *CA Cancer J Clin* (2016) **66** 115-132. DOI: 10.3322/caac.21338
3. Zhu H, Li T, Du Y, Li M. **Pancreatic cancer: challenges and opportunities**. *BMC Med* (2018) **16** 214. DOI: 10.1186/s12916-018-1215-3
4. Ligorio M, Sil S, Malagon‐Lopez J. **Stromal microenvironment shapes the Intratumoral architecture of pancreatic cancer**. *Cell* (2019) **178** 160-175.e27. DOI: 10.1016/j.cell.2019.05.012
5. Maitra A, Hruban RH. **Pancreatic cancer**. *Annu Rev Pathol* (2008) **3** 157-188. PMID: 18039136
6. Feig C, Gopinathan A, Neesse A, Chan DS, Cook N, Tuveson DA. **The pancreas cancer microenvironment**. *Clin Cancer Res* (2012) **18** 4266-4276. DOI: 10.1158/1078-0432.CCR-11-3114
7. Liu J, Harada BT, He C. **Regulation of gene expression by N‐methyladenosine in cancer**. *Trends Cell Biol* (2019) **29** 487-499. DOI: 10.1016/j.tcb.2019.02.008
8. Jia G, Fu Y, Zhao X. **N6‐methyladenosine in nuclear RNA is a major substrate of the obesity‐associated FTO**. *Nat Chem Biol* (2011) **7** 885-887. DOI: 10.1038/nchembio.687
9. Zhao BS, Roundtree IA, He C. **Post‐transcriptional gene regulation by mRNA modifications**. *Nat Rev Mol Cell Biol* (2017) **18** 31-42. DOI: 10.1038/nrm.2016.132
10. Zhao X, Yang Y, Sun BF. **FTO‐dependent demethylation of N6‐methyladenosine regulates mRNA splicing and is required for adipogenesis**. *Cell Res* (2014) **24** 1403-1419. DOI: 10.1038/cr.2014.151
11. Su R, Dong L, Li C. **R‐2HG exhibits anti‐tumor activity by targeting FTO/mA/MYC/CEBPA signaling**. *Cell* (2018) **172** 90-105.e23. DOI: 10.1016/j.cell.2017.11.031
12. Bartosovic M, Molares HC, Gregorova P, Hrossova D, Kudla G, Vanacova S. **N6‐methyladenosine demethylase FTO targets pre‐mRNAs and regulates alternative splicing and 3'‐end processing**. *Nucleic Acids Res* (2017) **45** 11356-11370. DOI: 10.1093/nar/gkx778
13. Cui Q, Shi H, Ye P. **mA RNA methylation regulates the self‐renewal and tumorigenesis of glioblastoma stem cells**. *Cell Rep* (2017) **18** 2622-2634. DOI: 10.1016/j.celrep.2017.02.059
14. Barbieri I, Tzelepis K, Pandolfini L. **Promoter‐bound METTL3 maintains myeloid leukaemia by mA‐dependent translation control**. *Nature* (2017) **552** 126-131. DOI: 10.1038/nature24678
15. Liu L, Wang J, Sun G. **M(6)a mRNA methylation regulates CTNNB1 to promote the proliferation of hepatoblastoma**. *Mol Cancer* (2019) **18** 188. DOI: 10.1186/s12943-019-1119-7
16. Han J, Wang JZ, Yang X. **METTL3 promote tumor proliferation of bladder cancer by accelerating pri‐miR221/222 maturation in m6A‐dependent manner**. *Mol Cancer* (2019) **18** 110. DOI: 10.1186/s12943-019-1036-9
17. Zhang C, Zhang M, Ge S. **Reduced m6A modification predicts malignant phenotypes and augmented Wnt/PI3K‐Akt signaling in gastric cancer**. *Cancer Med* (2019) **8** 4766-4781. DOI: 10.1002/cam4.2360
18. Li B‐Q, Huang S, Shao QQ. **WT1‐associated protein is a novel prognostic factor in pancreatic ductal adenocarcinoma**. *Oncol Lett* (2017) **13** 2531-2538. DOI: 10.3892/ol.2017.5784
19. Xu X, Yu Y, Zong K, Lv P, Gu Y. **Up‐regulation of IGF2BP2 by multiple mechanisms in pancreatic cancer promotes cancer proliferation by activating the PI3K/Akt signaling pathway**. *J Exp Clin Cancer Res* (2019) **38** 497. DOI: 10.1186/s13046-019-1470-y
20. Hu X, Peng WX, Zhou H. **IGF2BP2 regulates DANCR by serving as an N6‐methyladenosine reader**. *Cell Death Differ* (2020) **27** 1782-1794. DOI: 10.1038/s41418-019-0461-z
21. Lim CK, Bronson PG, Varade J, Behrens TW, Hammarström L. **STXBP6 and B3GNT6 genes are associated with selective IgA deficiency**. *Front Genet* (2021) **12**. DOI: 10.3389/fgene.2021.736235
22. Li N, Guo Q, Zhang Q, Chen BJ, Li XA, Zhou Y. **Comprehensive analysis of differentially expressed profiles of mRNA N6‐Methyladenosine in colorectal cancer**. *Frontiers in Cell and Developmental Biology* (2021) **9**. DOI: 10.3389/fcell.2021.760912
23. Yu D, Pan M, Li Y. **RNA N6‐methyladenosine reader IGF2BP2 promotes lymphatic metastasis and epithelial‐mesenchymal transition of head and neck squamous carcinoma cells via stabilizing slug mRNA in an m6A‐dependent manner**. *J Exp Clin Cancer Res* (2022) **41** 6. DOI: 10.1186/s13046-021-02212-1
24. Dong L, Geng Z, Liu Z, Tao M, Pan M, Lu X. **IGF2BP2 knockdown suppresses thyroid cancer progression by reducing the expression of long non‐coding RNA HAGLR**. *Pathol Res Pract* (2021) **225**. DOI: 10.1016/j.prp.2021.153550
25. Cui J, Tian J, Wang W. **IGF2BP2 promotes the progression of colorectal cancer through a YAP‐dependent mechanism**. *Cancer Sci* (2021) **112** 4087-4099. DOI: 10.1111/cas.15083
26. Wang X, Ji Y, Feng P. **The m6A reader IGF2BP2 regulates macrophage phenotypic activation and inflammatory diseases by stabilizing TSC1 and PPARγ**. *Adv Sci (Weinheim, Baden‐Wurttemberg, Germany)* (2021) **8**. DOI: 10.1002/advs.202100209
27. Karim‐Kos HE, de Vries E, Soerjomataram I, Lemmens V, Siesling S, Coebergh JWW. **Recent trends of cancer in Europe: a combined approach of incidence, survival and mortality for 17 cancer sites since the 1990s**. *Eur J Cancer* (2008) **44** 1345-1389. DOI: 10.1016/j.ejca.2007.12.015
28. Oberstein PE, Olive KP. **Pancreatic cancer: why is it so hard to treat?**. *Therap Adv Gastroenterol* (2013) **6** 321-337. DOI: 10.1177/1756283X13478680
29. Kleeff J, Korc M, Apte M. **Pancreatic cancer**. *Nat Rev Dis Primers* (2016) **2** 16022. DOI: 10.1038/nrdp.2016.22
30. Wang T, Kong S, Tao M, Ju S. **The potential role of RNA N6‐methyladenosine in cancer progression**. *Mol Cancer* (2020) **19** 88. DOI: 10.1186/s12943-020-01204-7
31. Meyer KD, Saletore Y, Zumbo P, Elemento O, Mason CE, Jaffrey SR. **Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons**. *Cell* (2012) **149** 1635-1646. DOI: 10.1016/j.cell.2012.05.003
32. Liu J, Yue Y, Han D. **A METTL3‐METTL14 complex mediates mammalian nuclear RNA N6‐adenosine methylation**. *Nat Chem Biol* (2014) **10** 93-95. DOI: 10.1038/nchembio.1432
33. Wang X, Lu Z, Gomez A. **N6‐methyladenosine‐dependent regulation of messenger RNA stability**. *Nature* (2014) **505** 117-120. DOI: 10.1038/nature12730
34. Wang X, Zhao BS, Roundtree IA. **N(6)‐methyladenosine modulates messenger RNA translation efficiency**. *Cell* (2015) **161** 1388-1399. DOI: 10.1016/j.cell.2015.05.014
35. He L, Li H, Wu A, Peng Y, Shu G, Yin G. **Functions of N6‐methyladenosine and its role in cancer**. *Mol Cancer* (2019) **18** 176. DOI: 10.1186/s12943-019-1109-9
36. Visvanathan A, Patil V, Arora A. **Essential role of METTL3‐mediated mA modification in glioma stem‐like cells maintenance and radioresistance**. *Oncogene* (2018) **37** 522-533. DOI: 10.1038/onc.2017.351
37. Chen M, Wei L, Law CT. **RNA N6‐methyladenosine methyltransferase‐like 3 promotes liver cancer progression through YTHDF2‐dependent posttranscriptional silencing of SOCS2**. *Hepatology* (2018) **67** 2254-2270. DOI: 10.1002/hep.29683
38. Chen M, Wong C‐M. **The emerging roles of N6‐methyladenosine (m6A) deregulation in liver carcinogenesis**. *Mol Cancer* (2020) **19** 44. DOI: 10.1186/s12943-020-01172-y
39. Cai X, Wang X, Cao C. **HBXIP‐elevated methyltransferase METTL3 promotes the progression of breast cancer via inhibiting tumor suppressor let‐7g**. *Cancer Lett* (2018) **415** 11-19. DOI: 10.1016/j.canlet.2017.11.018
40. Chen J, Sun Y, Xu X. **YTH domain family 2 orchestrates epithelial‐mesenchymal transition/proliferation dichotomy in pancreatic cancer cells**. *Cell Cycle* (2017) **16** 2259-2271. DOI: 10.1080/15384101.2017.1380125
41. Li B‐Q, Liang ZY, Seery S. **WT1 associated protein promotes metastasis and chemo‐resistance to gemcitabine by stabilizing Fak mRNA in pancreatic cancer**. *Cancer Lett* (2019) **451** 48-57. DOI: 10.1016/j.canlet.2019.02.043
42. Tian J, Zhu Y, Rao M. **N‐methyladenosine mRNA methylation of regulates AKT signalling to promote PTEN‐deficient pancreatic cancer progression**. *Gut* (2020) **69** 2180-2192. DOI: 10.1136/gutjnl-2019-320179
43. Bokar JA, Shambaugh ME, Polayes D, Matera AG, Rottman FM. **Purification and cDNA cloning of the AdoMet‐binding subunit of the human mRNA (N6‐adenosine)‐methyltransferase**. *RNA* (1997) **3** 1233-1247. PMID: 9409616
44. Liu N, Dai Q, Zheng G, He C, Parisien M, Pan T. **N(6)‐methyladenosine‐dependent RNA structural switches regulate RNA‐protein interactions**. *Nature* (2015) **518** 560-564. DOI: 10.1038/nature14234
45. Ke S, Alemu EA, Mertens C. **A majority of m6A residues are in the last exons, allowing the potential for 3' UTR regulation**. *Genes Dev* (2015) **29** 2037-2053. DOI: 10.1101/gad.269415.115
46. Huang H, Weng H, Sun W. **Recognition of RNA N‐methyladenosine by IGF2BP proteins enhances mRNA stability and translation**. *Nat Cell Biol* (2018) **20** 285-295. DOI: 10.1038/s41556-018-0045-z
47. Li J, Chen F, Peng Y. **N6‐Methyladenosine regulates the expression and secretion of TGFβ1 to affect the epithelial‐mesenchymal transition of cancer cells**. *Cells* (2020) **9** 296. DOI: 10.3390/cells9020296
48. Iwai T, Kudo T, Kawamoto R. **Core 3 synthase is down‐regulated in colon carcinoma and profoundly suppresses the metastatic potential of carcinoma cells**. *Proc Natl Acad Sci USA* (2005) **102** 4572-4577. DOI: 10.1073/pnas.0407983102
49. Doi N, Ino Y, Angata K, Shimada K, Narimatsu H, Hiraoka N. **Clinicopathological significance of core 3 O‐glycan synthetic enzyme, β1,3‐N‐acetylglucosaminyltransferase 6 in pancreatic ductal adenocarcinoma**. *PloS One* (2020) **15**. DOI: 10.1371/journal.pone.0242851
50. Lin X, Chai G, Wu Y. **RNA mA methylation regulates the epithelial mesenchymal transition of cancer cells and translation of snail**. *Nat Commun* (2019) **10** 2065. DOI: 10.1038/s41467-019-09865-9
|
---
title: 'The Metaverse, the Built Environment, and Public Health: Opportunities and
Uncertainties'
journal: Journal of Medical Internet Research
year: 2023
pmcid: PMC9972199
doi: 10.2196/43549
license: CC BY 4.0
---
# The Metaverse, the Built Environment, and Public Health: Opportunities and Uncertainties
## Abstract
There has been a growing interest in the “metaverse,” and discourse about how this platform may contribute to different fields of science is already beginning to emerge. In this paper, we discuss key opportunities and uncertainties about how a metaverse might contribute to advancing knowledge in the interdisciplinary field of the built environment and public health aimed at reducing noncommunicable diseases.
## Introduction
Since 2021, there has been a growing interest in the “metaverse,” and discourse about how this platform will contribute to different fields of science is already beginning to emerge [1-5]. However, scientific discussions regarding the potential research applications of the metaverse are still in their infancy. A PubMed search of published literature, including the keyword “metaverse,” undertaken in July 2022, revealed only 58 articles, of which 49 were published in 2022. Of these, several viewpoint articles discussed how a metaverse could contribute to different aspects of health, such as medicine [5], cardiovascular health [2,4], dentistry [5], ophthalmology [6], and intelligence health care [7]. Nevertheless, none of these previous viewpoint articles focused on the “built environment” and health. The built environment can be defined as “the human-made [physical] space in which people live, work, and recreate on a day-to-day basis” [8], such as houses, shops, roads, parks, and public spaces. The built environment can potentially impact health over the long-term via its influences on health behaviors [9,10]. As a first study, to our knowledge, the purpose of this paper is to discuss key opportunities and uncertainties about how a metaverse might contribute to advancing knowledge in the interdisciplinary field of the built environment and public health aimed at reducing noncommunicable diseases. The rationale for selecting noncommunicable diseases as a topic of focus is that these diseases (eg, diabetes, heart diseases, strokes, chronic respiratory disease, cancers, and mental illness) are potentially avoidable but remain the primary causes of deaths worldwide, accounting for $74\%$ of all deaths in 2019 [11]. As life expectancy increases, the burden of noncommunicable diseases will continue to increase, especially in aging societies, such as Japan. Modifiable risk factors that can lead to noncommunicable diseases (eg, physical inactivity, unhealthy diet, and smoking) are prevalent in many populations. The built environment plays a pivotal role in shaping these modifiable risk factors. Population-level interventions affecting many people over a relatively long time that reduce these risk factors are necessary to manage the increase in noncommunicable diseases [12]. This paper will be of interest to experts in public health, urban design, epidemiology, medicine, sport, and environmental sciences, especially those considering using the metaverse for research and intervention purposes.
## Existing Knowledge of the Relationships Between the Built Environment and Noncommunicable Diseases
Modifying the built environment is an essential population-level strategy for changing noncommunicable disease risk factors [10]. Several systematic reviews have provided evidence on the relationships between built environment attributes and noncommunicable disease risk factors [13,14]. Several pathways through which the built environment may influence noncommunicable diseases have also been discussed in previous studies [15,16]. For example, Koohsari et al [15] proposed a conceptual framework by which built environment characteristics impact cardiovascular diseases via behavioral (eg, physical activity, sedentary behavior, diet, sleep), ecological (eg, air/noise pollution and heat), and physiological risk factors.
## What Is a Metaverse?
In 1992, the term “metaverse” first appeared in Neil Stevenson’s science fiction novel, Snow Crash, where people relate with each other through avatars in a virtual world. The term has recently regained considerable attention after Facebook announced the change of their name to “Meta” in October 2021 as a metaverse company. Since then, academics from several scientific disciplines have developed definitions of a metaverse and proposed research opportunities that it may offer.
Since many disciplines are involved in developing a metaverse, reaching a universally accepted definition of this term may be challenging. Therefore, there is yet to be a consensus on the definition of a metaverse. In the hospitality and tourism field, Gursoy et al [3] defined a metaverse as “a parallel reality where humans can work, play, and communicate.” *In a* more comprehensive definition, a metaverse is “a three-dimensional virtual world where avatars engage in political, economic, social, and cultural activities” [17]. In the field of medicine, Yang et al [1] defined a metaverse as “the internet accessed via virtual reality and augmented reality glasses.” In relation to cardiovascular health, Mesko [4] referred to a metaverse as “a virtual reality space in which users can interact with other users in a computer-generated social environment.” Several technologies such as virtual reality, augmented reality, and artificial intelligence are necessary elements in building a metaverse. However, a broader definition is needed as the technologies that facilitate how people interact in a metaverse will change over time.
## Opportunities and Uncertainties
Knowledge of how the built environment may influence noncommunicable diseases has improved over the past decade. However, there are a number of overarching limitations to this research, which may be enhanced by conducting research in a metaverse.
## Conducting Randomized Experimental Studies
Because of ethical and budgetary constraints, there is a lack of randomized experimental study designs examining the causal effects of the built environment on noncommunicable disease and health more broadly [18,19]. Two theoretical concepts of immersion and presence in a metaverse can potentially facilitate conducting these experiments. In a metaverse, the concept of immersion is defined as “users’ engagement with a virtual reality system that results with being in a flow state” [20]. A more subjective concept is that of presence, which refers to a feeling and sense of “being there” in a mediated and virtual world [21,22]. Within a metaverse, study participants could be randomized to experience different built environment exposures such as high and low density, high and low walkability, or different levels of nature or urban environments. A better understanding of how experiences of these virtual built environmental exposures affect people’s biomarkers, physiological and psychological responses, and health behavior decision-making in the short-term and noncommunicable diseases over the long-term could be tested in a metaverse [23]. It should be noted that if participants are required to experience certain environmental conditions virtually, such as extremely population-dense, loud, and overwhelming built environments in a metaverse, these experiences could potentially change attitudes or perceptions toward their environments, in reality, leading to changes in behavior [24]. Additionally, a metaverse can provide an opportunity to test how the built environment and social interactions contribute to people’s health. For instance, whether social interactions in a virtual park, reproduced from the real-world model, may contribute to health.
## Offsetting or Alleviating the Effects of a Poor-Quality Built Environment
The experiments may reveal that interventions exposing individuals to supportive health environments in a metaverse positively impact psychological responses and behaviors in the physical world. For example, exposure to nature and green space in a metaverse may encourage individuals to seek out and use such spaces in the physical world. There may be political, financial, and logistical challenges to modifying built environment attributes such as street layouts, land uses, sidewalks, and green spaces. Changes to the built environment also often take a long time to implement. In some cases, modifying the built environment (eg, creating a new park) may be impossible, especially in densely built-up and populated areas lacking free spaces. In these situations, a metaverse may alleviate health problems by enabling people to experience a modified improved version of their physical environments virtually and feel a presence in those places. These virtual places may also provide opportunities for social interactions among avatars. For instance, workers in workplaces lacking green spaces can immerse themselves in greenery using a metaverse, which may offer mental health benefits. Alternatively, suppose a worker works from a virtual office within a metaverse. In that case, they could situate their virtual office anywhere—a café, warm cabin, mountain views, or the moon, which may alleviate stress. Based on the Yerkes-Dodson law, an individual’s performance improves up to a certain point with increasing mental arousal [25]. Such virtual offices can provide opportunities for workers to reach their optimal levels of positive arousal, which can promote their work performance. Nevertheless, at this stage, there is a lack of empirical studies examining the effects of exposing people to a health-supportive built environment in a metaverse on their health outcomes in the real world. Therefore, these opportunities remain to be confirmed or refuted by future studies.
## Participation in Healthy Built Environment Design Interventions
Involving various stakeholders in the planning and design of health-support built environment interventions is necessary. Aligned with the immersion and presence concepts, a metaverse could allow stakeholders to experience, build, and collaboratively modify the proposed changes to the built environment before these interventions are implemented in the physical world. The application of virtual reality and its related technologies in participatory urban planning has already been discussed [26]. Additionally, with sufficient advances in health impact assessment, a metaverse could produce estimates of the health impacts of potential changes in real time. This allows urban designers and public health practitioners to optimize designs based on stakeholder feedback, social and cultural norms, health, socioeconomic, and inclusivity considerations (Figure 1).
**Figure 1:** *The built environment and human health: the opportunities offered by a metaverse (Part of the figure was created with BioRender.com).*
## Uncertainties in the Impacts of a Metaverse on Health Behavior in the Physical World
At the time of writing this paper, there may also be some uncertainties about using a metaverse for research in the built environment and public health interdisciplinary field. Notable, how a metaverse impacts human behavior in the physical world will be a critical ongoing question. First, it is likely that not all the existing issues in the relationships between built environment and health can be discussed and tested in a metaverse in its current form. For instance, replicating the traditional research on the role of walkability on walking behavior may be challenging to undertake in a metaverse. It is then necessary to explore and identify the senses in the real world, which can be experienced in a metaverse and how different people can immerse themselves to those virtual environments. Second, a metaverse is a virtual manifestation and way to interact with people in a virtual world. It is uncertain but likely that current social structures may be recreated in a metaverse, particularly owing to the digital divide, defined as disparities between people due to their digital access, skills, and knowledge [27]. Thus, certain populations, including low-income or less-educated individuals and minority groups, may be excluded from a metaverse (or studies/research involving a metaverse) due to their lack of access, computer literacy, or feelings of safety to participate in research in a metaverse. Therefore, new ethical concerns about avoiding the digital divide may need to be addressed in relation to the research involving a metaverse [28]. Third, if individuals undertake much of their daily activities (and time) in a metaverse, they may isolate themselves from the physical world in favor of a metaverse, causing mental health consequences such as social isolation, depression, and antisocial behaviors. Notably, the social isolation triggered by online environments is an important consideration, especially in children and adolescents [29-31]. For example, an increase in online gaming was found to be associated with more minor adolescents’ social circles [31]. Fourth, this adoption of a “metaverse lifestyle” could also have potential unintentional physical health consequences by increasing physical inactivity. For instance, spending several hours per day in a metaverse could accumulate too much sitting time, which has been found to have several adverse physical health effects [32,33]. To avoid too much sitting, interacting in the metaverse should require physical movement in the real world. Active interaction in the virtual world can also promote exercise and physical activity. For instance, a review of randomized controlled trials found that engaging in active video games was beneficial for physical activity in adolescents [34]. Fifth, some of the most common acute health concerns experienced by patients using virtual environments are headaches, eye strain, vertigo, and nausea [35]. These health symptoms may negatively affect people’s physical function and further discourage them from engaging in healthy behaviors such as exercise and walking. Sixth, there may also be a reluctance to engage in urban design or community health decision-making because the physical world may seem less important to immersed people as the popularity of the metaverse increases. Finally, there are some concerns about the vital roles of technologies in creating a metaverse as a virtual space where we work, play, and communicate. Specifically, artificial intelligence (mimicking human intelligence in computers) technologies will be used for many functions in a metaverse. Artificial intelligence has been shown to recreate social patterns in multiple areas [36,37]. A metaverse relying heavily on artificial intelligence may recreate existing social patterns, including inequitable, ageist, racist, and heteronormative virtual environments. Considering these critical questions, recommendations or guidelines for healthy and safe use of a metaverse may need to be developed and promoted.
## Conclusion
A metaverse is evolving rapidly and has the potential to influence all aspects of human life. It is then best, sooner rather than later, to face the prospects and challenges a metaverse can offer to different scientific fields. Multidisciplinary research examining the impacts of the built environment on noncommunicable diseases in a metaverse will require our discipline to advance rapidly in many scientific areas. Thus, the current generation of the built environment and public health researchers will need to be versed in computer-assisted design and programming for artificial intelligence. They will need to work collaboratively with other disciplines (eg, information technology, software engineering, computer science) to use a metaverse to advance evidence-based knowledge on the built environment and health.
## References
1. Yang D, Zhou J, Chen R, Song Y, Song Z, Zhang X, Wang Q, Wang K, Zhou C, Sun J, Zhang L, Bai L, Wang Y, Wang X, Lu Y, Xin H, Powell CA, Thüemmler C, Chavannes NH, Chen W, Wu L, Bai C. **Expert consensus on the metaverse in medicine**. *Clinical eHealth* (2022) **5** 1-9. DOI: 10.1016/j.ceh.2022.02.001
2. Skalidis I, Muller O, Fournier S. **The metaverse in cardiovascular medicine: applications, challenges, and the role of non-fungible tokens**. *Can J Cardiol* (2022) **38** 1467-1468. DOI: 10.1016/j.cjca.2022.04.006
3. Gursoy D, Malodia S, Dhir A. **The metaverse in the hospitality and tourism industry: an overview of current trends and future research directions**. *J Hospitality Marketing Manage* (2022) **31** 527-534. DOI: 10.1080/19368623.2022.2072504
4. Mesko B. **The promise of the metaverse in cardiovascular health**. *Eur Heart J* (2022) **43** 2647-2649. DOI: 10.1093/eurheartj/ehac231
5. Locurcio LL. **Dental education in the metaverse**. *Br Dent J* (2022) **232** 191. DOI: 10.1038/s41415-022-3990-7
6. Tan T, Li Y, Lim J, Gunasekeran D, Teo Z, Ng W, Ting DS. **Metaverse and virtual health care in ophthalmology: opportunities and challenges**. *Asia Pac J Ophthalmol (Phila)* (2022) **11** 237-246. DOI: 10.1097/APO.0000000000000537
7. Wang G, Badal A, Jia X, Maltz JS, Mueller K, Myers KJ, Niu C, Vannier M, Yan P, Yu Z, Zeng R. **Development of metaverse for intelligent healthcare**. *Nat Machine Intelligence* (2022) **4** 922-929. DOI: 10.1038/s42256-022-00549-6
8. Saelens B, Handy S. **Built environment correlates of walking: a review**. *Med Sci Sports Exerc* (2008) **40** S550-66. DOI: 10.1249/MSS.0b013e31817c67a4
9. Marteau TM, Hollands GJ, Fletcher PC. **Changing human behavior to prevent disease: the importance of targeting automatic processes**. *Science* (2012) **337** 1492-5. DOI: 10.1126/science.1226918
10. Gostin LO. **Non-communicable diseases: healthy living needs global governance**. *Nature* (2014) **511** 147-9. DOI: 10.1038/511147a
11. **The top 10 causes of death**. *World Health Organization* (2020)
12. Chokshi DA, Farley TA. **Health. Changing behaviors to prevent noncommunicable diseases**. *Science* (2014) **345** 1243-4. DOI: 10.1126/science.1259809
13. Fazeli Dehkordi ZS, Khatami SM, Ranjbar E. **The associations between urban form and major non-communicable diseases: a systematic review**. *J Urban Health* (2022) **99** 941-958. DOI: 10.1007/s11524-022-00652-4
14. Malambo P, Kengne AP, De Villiers A, Lambert EV, Puoane T. **Built environment, selected risk factors and major cardiovascular disease outcomes: a systematic review**. *PLoS One* (2016) **11** e0166846. DOI: 10.1371/journal.pone.0166846
15. Koohsari MJ, McCormack GR, Nakaya T, Oka K. **Neighbourhood built environment and cardiovascular disease: knowledge and future directions**. *Nat Rev Cardiol* (2020) **17** 261-263. DOI: 10.1038/s41569-020-0343-6
16. Frank L, Engelke P, Schmid T. *Health and Community Design: The Impact of the Built Environment on Physical Activity* (2003)
17. Park S, Kim Y. **A metaverse: taxonomy, components, applications, and open challenges**. *IEEE Access* (2022) **10** 4209-4251. DOI: 10.1109/access.2021.3140175
18. Martin A, Ogilvie D, Suhrcke M. **Evaluating causal relationships between urban built environment characteristics and obesity: a methodological review of observational studies**. *Int J Behav Nutr Phys Act* (2014) **11** 142. DOI: 10.1186/s12966-014-0142-8
19. Laddu D, Paluch AE, LaMonte MJ. **The role of the built environment in promoting movement and physical activity across the lifespan: implications for public health**. *Prog Cardiovasc Dis* (2021) **64** 33-40. DOI: 10.1016/j.pcad.2020.12.009
20. Berkman M, Akan E, Lee N. **Presence and immersion in virtual reality**. *Encyclopedia of Computer Graphics and Games* (2019) 1-10
21. Green MC, McAllister CA. **Presence**. *The International Encyclopedia of Media Psychology* (2020) 1-5
22. Clemente M, Rodriguez A, Rey B, Alcaniz M, Wiederhold B, Riva G. **Measuring presence during the navigation in a virtual environment using EEG**. *Annual Review of Cybertherapy and Telemedicine 2013: Positive Technology and Health Engagement for Healthy Living and Active Ageing* (2013) 136
23. Mazumder R, Spiers H, Ellard C. **Exposure to high-rise buildings negatively influences affect: evidence from real world and 360-degree video**. *Cities Health* (2020) **6** 1081-1093. DOI: 10.1080/23748834.2020.1839302
24. Humpel N, Marshall AL, Leslie E, Bauman A, Owen N. **Changes in neighborhood walking are related to changes in perceptions of environmental attributes**. *Ann Behav Med* (2004) **27** 60-67. DOI: 10.1207/s15324796abm2701_8
25. Yerkes RM, Dodson JD. **The relation of strength of stimulus to rapidity of habit-formation**. *J Comp Neurol Psychol* (1908) **18** 459-482. DOI: 10.1002/cne.920180503
26. Hudson‐Smith A, Batty M. **Ubiquitous geographic information in the emergent Metaverse**. *Trans GIS* (2022) **26** 1147-1157. DOI: 10.1111/tgis.12932
27. Valdez VB, Javier SP, Filho WL, Azul AM, Brandli L, Salvia AL, Özuyar PG, Wall T. **Digital divide: from a peripheral to a core issue for all SDGs**. *Reduced Inequalities* (2020) 1-14
28. Abràmoff MD, Roehrenbeck C, Trujillo S, Goldstein J, Graves AS, Repka MX, Silva Iii EZ. **A reimbursement framework for artificial intelligence in healthcare**. *NPJ Digit Med* (2022) **5** 72. DOI: 10.1038/s41746-022-00621-w
29. Irie T, Shinkawa H, Tanaka M, Yokomitsu K. **Online-gaming and mental health: loot boxes and in-game purchases are related to problematic online gaming and depression in adolescents**. *Curr Psychol* (2022) 1-12. DOI: 10.1007/s12144-022-03157-0
30. Smohai M, Urbán R, Griffiths MD, Király O, Mirnics Z, Vargha A, Demetrovics Z. **Online and offline video game use in adolescents: measurement invariance and problem severity**. *Am J Drug Alcohol Abuse* (2017) **43** 111-116. DOI: 10.1080/00952990.2016.1240798
31. Kowert R, Domahidi E, Festl R, Quandt T. **Social gaming, lonely life? The impact of digital game play on adolescents’ social circles**. *Comput Hum Behav* (2014) **36** 385-390. DOI: 10.1016/j.chb.2014.04.003
32. Bailey DP, Hewson DJ, Champion RB, Sayegh SM. **Sitting time and risk of cardiovascular disease and diabetes: a systematic review and meta-analysis**. *Am J Prev Med* (2019) **57** 408-416. DOI: 10.1016/j.amepre.2019.04.015
33. Li S, Lear SA, Rangarajan S, Hu B, Yin L, Bangdiwala SI, Alhabib KF, Rosengren A, Gupta R, Mony PK, Wielgosz A, Rahman O, Mazapuspavina MY, Avezum A, Oguz A, Yeates K, Lanas F, Dans A, Abat MEM, Yusufali A, Diaz R, Lopez-Jaramillo P, Leach L, Lakshmi PVM, Basiak-Rasala A, Iqbal R, Kelishadi R, Chifamba J, Khatib R, Li W, Yusuf S. **Association of sitting time with mortality and cardiovascular events in high-income, middle-income, and low-income countries**. *JAMA Cardiol* (2022) **7** 796-807. DOI: 10.1001/jamacardio.2022.1581
34. Williams WM, Ayres CG. **Can active video games improve physical activity in adolescents? A review of RCT**. *Int J Environ Res Public Health* (2020) **17** 669. DOI: 10.3390/ijerph17020669
35. Park S, Lee G. **Full-immersion virtual reality: adverse effects related to static balance**. *Neurosci Lett* (2020) **733** 134974. DOI: 10.1016/j.neulet.2020.134974
36. Buolamwini J, Gebru T. **Gender shades: intersectional accuracy disparities in commercial gender classification**. *Proceedings of Machine Learning Research* (2018) 1-15
37. Moore CM. **The challenges of health inequities and AI**. *Intelligence-Based Med* (2022) **6** 100067. DOI: 10.1016/j.ibmed.2022.100067
|
---
title: 'A Serious Game About Hematology for Health Care Workers (SUPER HEMO): Development
and Validation Study'
journal: JMIR Serious Games
year: 2023
pmcid: PMC9972200
doi: 10.2196/40350
license: CC BY 4.0
---
# A Serious Game About Hematology for Health Care Workers (SUPER HEMO): Development and Validation Study
## Abstract
### Background
Complete blood count (CBC) and hemostatic screening tests are among the most commonly prescribed blood tests worldwide. All health care workers (nurse practitioners, pharmacists, dentists, midwives, and physicians) are expected to correctly interpret the results in their daily practice. Currently, the undergraduate hematology curriculum consists predominantly of lecture-based teaching. Because hematology combines basic science (blood cells and hemostasis physiology) and clinical skills, students report that they do not easily master hematology with only lecture-based teaching. Having interviewed students at the University of Lorraine, we considered it necessary to develop new teaching approaches and methods.
### Objective
We aimed to develop and validate a serious game about CBC analysis for health care students. Our primary objective was to help students perceive hematology as being a playful and easy topic and for them to feel truly involved in taking care of their patients by analyzing blood tests. We considered that this game-based approach would be attractive to students as an addition to the classic lecture-based approach and improve their knowledge and skills in hematology.
### Methods
We developed an adventure game called SUPER HEMO, a video game in which the player assumes the role of a protagonist in an interactive story driven by exploration and problem-solving tests. Following validation with beta testing by a panel of volunteer students, we used a novel, integrated teaching approach. We added 1.5 hours of gaming to the standard curriculum for a small group of volunteer students. Physician and pharmacy students in their third year at a single French university were invited to attend this extracurricular course. Pregame and postgame tests and satisfaction surveys were immediately recorded. Final hematology exam results were analyzed.
### Results
A total of 86 of 324 physician students ($26.5\%$) and 67 of 115 pharmacy students ($58\%$) opted to participate. Median scores on the pre- and posttests were 6 out of 10 versus 7 out of 10, respectively, for the physician students, ($P \leq .001$) and 7.5 out of 10 versus 8 out of 10, respectively, for the pharmacy students ($P \leq .001$). At the final hematology evaluation, physician students who played SUPER HEMO had a slightly better median score than those who did not: 13 out of 20 versus 12 out of 20, respectively ($$P \leq .002$$). Pharmacy students who played SUPER HEMO had a median score of 21.75 out of 30; this was not significantly different from pharmacy students who did not play SUPER HEMO ($\frac{20}{30}$; $$P \leq .12$$). Among the participants who answered the survey ($$n = 143$$), more than $86\%$ ($\frac{123}{143}$) believed they had strengthened their knowledge and nearly $80\%$ ($\frac{114}{143}$) of them had fun.
### Conclusions
Feedback from this game session provided evidence to support the integration of interactive teaching methods in undergraduate hematology teaching. The development of SUPER HEMO is intended to be completed so that it can become a support tool for continuing education.
## Introduction
Numerous serious games (SGs) have been developed to improve nursing [1] and medical [2] knowledge and skills. The main subjects are surgery [3-5], emergency medicine [6,7], pharmacy [8] for health care students, and other subjects, such as preventive medicine for adolescents and young people [9-12]. Systematic reviews of SGs conclude that they seem to be at least as effective as other digital education modalities [13], but pedagogical effectiveness, participant behavior, and patient health outcomes have barely been evaluated [14-16].
In the context of the global COVID-19 pandemic, the demand for online learning increased worldwide. However, it has been reported that reduced peer and teacher interaction can cause motivation issues [17,18]. Recently, online learning has been described negatively, especially by health care students in clinical practice [19]. Many health care students see working in the health care setting as a vocation rather than a job, with patient-centered and compassionate care being the basis of this view [20]. Therefore, video games that enable specifically situated, experiential learning by introducing different unwell characters represent an attractive learning tool for today’s students, who are very receptive to computer-based learning.
SGs for hematology education are rare and deal only with transfusion [21,22]. One important part of hematology education is complete blood count (CBC). CBC is a quantitative and qualitative evaluation of blood cells and stands as one of the most common laboratory tests in medicine, being indicated for a vast number of conditions. CBC interpretation is taught only with lecture-based courses in hematology. An internal survey among physician and pharmacy students at the University of Lorraine revealed that more than $50\%$ of students considered CBC interpretation hard to master. Because correctly applying CBC knowledge to form patient diagnoses and make clinical decisions is a requirement for all health professionals, we decided to implement an SG for hematology education.
We developed an SG (Figure 1) named SUPER HEMO with a structured, 3-phase development framework that included preparation and design, development, and formative evaluation, as described previously [23].
**Figure 1:** *SUPER HEMO development.*
## Framework
As a reference for the creation and development of the game, we took inspiration from role-playing and adventure games (eg, point-and-click games and visual novels), as these are commonly played at home by students. The game’s contents, including a database with questions, answers, and feedback, were based on knowledge from the Collège national des enseignants en hématologie (the French national college of hematology teachers).
At first, we created an educational committee composed of 3 hematology experts (1 pharmacist and 2 medical doctors), a pharmacist with gamification expertise (with no expertise in hematology), and an instructional designer. Meetings took place every month for the first 6 months of the project. The goals of this committee were to define pedagogical objectives, game design, and game modes, and to write the clinical cases’ dialogue. During the writing of the clinical cases, physician students volunteered to test some of the cases before their integration into the game. The committee also looked for funding. The raised funds were mainly used to hire a graphics designer and a game developer.
Then, a working committee was created with 2 medical doctors, a pharmacist with hematology expertise, a pharmacist with gamification expertise, an instructional designer, a graphics designer, and a game developer. Meetings occurred every 2 months to discuss practical issues arising from game conception. Some members worked together outside of the meetings in groups, such as the graphics designer with the hematology expert or the game developer with the instructional designer and the hematology expert.
The project was then presented to the pedagogical council of each faculty.
## Story
SUPER HEMO is set in a dreamlike “Red Cell World” (Multimedia Appendix 1), with red-blood-cell trees and depictions of the organs involved in erythropoiesis, including a “lung mountain,” “medullar cave,” “kidney rock,” “spleen fortress,” and “thyroid isthmus.” Players can choose a female or a male avatar on the home screen. They then complete the introduction, in which “Lady Stem Cell” explains the world and the game’s settings and instructions and gives the player the “CBC asset,” which is the power to check the blood parameters of the unwell characters encountered and interpret the corresponding results. Players assume the role of a hematology superhero named SUPER HEMO. SUPER HEMO can meet 5 unwell characters in 5 different steps. The player must answer their questions and find the best way to diagnose and cure them. “ Magicians” (radiologists, pathologists, hematology-biologists, pharmacists, and geneticists) can be summoned to help the player find the right answer (Multimedia Appendix 2) in exchange for gold coins.
## Mechanics
To complete the world’s challenges (Figure 2), SUPER HEMO must explore 5 clinical cases.
**Figure 2:** *"Red World" mechanics.*
Each stop (represented by a light blue button) corresponds to the clinical case of an unwell character. Players choose to explore the clinical cases in the order that they decide. When SUPER HEMO successfully ends a case, players are rewarded with 1, 2, or 3 stars depending on the number of errors they made. Moreover, a hidden hematopoietic cell can be caught to unlock a minigame to earn gold coins. A wrong answer to a skill question makes SUPER HEMO lose the patient’s trust and lose gold coins, and a wrong answer to a treatment question makes the hero lose the patient’s trust. Lady Stem Cell gives immediate feedback to every question, thus maintaining the user’s motivation and commitment to the game [24]. If all gold coins and the patient’s trust are lost, Mister Insurance takes SUPER HEMO back to the beginning of the case, where the player can immediately play again if they have enough gold coins; otherwise, they can still earn gold coins in minigames before trying the case again. The hidden collected cells constitute an atlas of hematopoietic cells that contains a precise description and a real picture (taken by microscopy) of each blood cell. Minigames are always related to hematology in a fun way.
When SUPER HEMO reaches $80\%$ success in the world’s 5 clinical cases (and achieves a total of 12 stars of a possible 15) the player wins a new asset (a myelogram) and is thereby allowed to move on to a higher level, which corresponds to another hematopoietic world.
Throughout the game, to maintain the dreamlike atmosphere and immersion, different theme songs accompany the player (with the possibility to mute them). The player also has the option to reset the game on the home screen (not pictured).
## Technology
The game was programmed in January 2019 using C# in the Unity game engine, for computers only (ie, there was no mobile app). The invention was protected with an Inter Deposit Digital Number on September 9, 2020, for the University of Lorraine. To date, the game is available in French only and is free of charge at the University.
## Video Game Beta Tests
Three beta-test sessions were organized with small groups of undergraduate and graduate physician and pharmacy volunteer students. The goal was to obtain a primary evaluation of the game design and concept and to debug the game. The volunteers played the game for 1 hour and were then asked to fill in a questionnaire (Multimedia Appendix 3) about several aspects of the game, including the graphical interface, gameplay, use of multimedia, and educational content, and provide ideas for improvement and general comments. We collected the questionnaires and analyzed the answers to modify or improve the game and the teaching methods, if required.
## Ethics Approval
The study was conducted in accordance with the Helsinki Declaration and Resolution. The study was approved by the Pedagogic Committee of the Faculté de Pharmacie, Université de Lorraine (June 17, 2021) and Faculté de Médecine, Université de Lorraine (July 7, 2021).
## Recruitment and Game Evaluation
Students in their third year of medicine and pharmacy courses were identified as the most appropriate study participants, since hematology is a regular and mandatory course unit during this academic year. These student cohorts were expected to benefit the most from additional exposure to clinical learning while using SUPER HEMO, as their upcoming final exams were planned to take place shortly after game exposure. The game evaluation was performed after 2 weeks of classical hematology teaching. The gaming course consisted of 1.5 hours of gaming (in the Red World) that covered the following standard red blood cell disorders: anemia and polycythemia. The game evaluation was split into 2 phases examining [1] the immediate knowledge acquisition of the players and [2] whether the players successfully completed their final hematology examination.
First, to evaluate the effectiveness of this teaching method, voluntary participants from each course were asked to complete a knowledge test consisting of 10 multiple-choice questions (Multimedia Appendix 4); one point was obtained for each correct answer. The participants completed the 10-question test before (pretest) and after playing the game for 1 hour (posttest). An online questionnaire was designed to assess playability and the students’ understanding of SUPER HEMO. Students were also asked to rate their level of confidence after the gaming session on a questionnaire. Qualitative data considering the students’ general feedback was also collected. All answers were anonymized.
Second, successful completion of the final hematology examination was extracted for students who had participated in SUPER HEMO and those who had not by the pedagogic committee, and mean results were compared.
## Statistics
Statistical analyses were carried out using Prism (version 5.0; GraphPad). Comparisons of participation rates for the physician and pharmacy students, as well as the female to male ratio of the groups, were made with the Fisher exact test. Pre- and posttest scores and the final evaluation were expressed as median values, with the range and 25th to 75th percentiles. Pre- and posttest results were compared using a paired Wilcoxon signed-rank test in the 3 groups (ie, the overall population, the physician students, and the pharmacy students). Final evaluation results were compared using the Mann-Whitney test. As participation was on a voluntary basis, a post hoc power analysis of the final evaluation’s scores was also performed.
## Beta Tests
The results of the 3 beta tests (Figure 3) indicated a significant interest in this new SG.
The interface received a score of 3.7 of 5. As for the multimedia aspect, the graphics and music received scores of 4.1 and 3.7 of 5, respectively. All students stated that they enjoyed the game, $86\%$ ($\frac{19}{22}$) found the game fun to use, and $68\%$ ($\frac{15}{22}$) even lost track of time while playing. Regarding the educational content, $90\%$ of the students ($\frac{20}{22}$) found that SUPER HEMO represented an efficient method to learn hematology.
The beta tests allowed us to detect a few points to improve SUPER HEMO and address issues encountered by the students. Some students had difficulties answering certain types of questions (for example, ones that used drag-and-drop), or they could not find the minigames. This led us to integrate tutorials at the beginning of each game, such as on how to answer questions encountered for the first time. We also modified some questions and minigame rules to better drive the players. Moreover, some players forgot the CBC results while talking to the unwell characters and frequently wanted to check them; therefore, we added a button to allow the players to check the CBC results as often as they wanted to. Finally, as the game tasks could be interrupted, we added a “log info” button to remind the students of dialogue text.
The free comments included mostly thanks for the initiative and anticipation for the sequels.
**Figure 3:** *Beta-test results. SH: SUPER HEMO.*
## Integrated Teaching Approach in Addition to the Standard Hematology Undergraduate Curriculum
A total of 153 volunteer students were recruited, including 86 of 324 physician students ($26.5\%$) and 67 of 115 pharmacy students ($58\%$), who agreed to participate in this complementary session at the end of the standard lecture-based hematology course. Of note, the proportion of female students was higher in the participant group (Table 1).
Overall, the volunteer students were evaluated on a 10-point scale (Table 2 and Figure 4). They had a higher posttest score (median 7.5, range 2.5-10) than pretest score (median 6.5, range 1.5-9; $$P \leq .001$$), indicating that the game slightly improved their immediate knowledge acquisition. The median pre-and posttest scores were 6 (range 1.5-8) and 7 (range 2.5-10), respectively, for the physician students ($P \leq .001$) and 7.5 (range 3-9) and 8 (range 3.5-10), respectively, for the pharmacy students ($P \leq .001$).
At the final examination (Figure 4), we observed that the physician students who played SUPER HEMO obtained a slightly higher score (median $\frac{13}{20}$, range 6-17) than those who did not play SUPER HEMO (median $\frac{12}{20}$, range 5-17; $$P \leq .002$$), with a satisfactory study power ($83\%$). The pharmacy students who played SUPER HEMO had a score (median $\frac{21.75}{30}$, range 12.25-27.25) that was not statistically significantly different from those who did not play SUPER HEMO (median $\frac{19.8}{30}$, range 9.5-26.75; $$P \leq .12$$). Unfortunately, the study power was low ($64\%$) for this group.
## Gameplay Satisfaction
Among 153 volunteer students, 143 answered the questionnaire ($93.4\%$). Game experience received a score of 4.7 out of 5, and $79\%$ of the students ($$n = 113$$) found the game fun to use. Regarding multimedia, graphics received a score of 4.7 out of 5, and music received 3.9 out of 5. The minigames were scored as attractive ($\frac{4.1}{5}$; Figure 5).
Concerning the educational content, most of the 153 students indicated that they had augmented their knowledge ($$n = 130$$, $91\%$), had made progress in hematology ($$n = 137$$, $96\%$), and better understood their courses ($$n = 124$$, $87\%$) after playing SUPER HEMO. In addition, 71 students ($50\%$) indicated that they specifically aimed to obtain the top 3 stars at the end of each case in the game, and 70 students ($49\%$) dedicated specific effort to collect all the hidden cells. While the effect of rewards on memory appears well documented, it has recently been reported that incentives can also have counterproductive effects on memory [25]. Our videogame reward system was, however, developed to create a realistic game environment with well-known reinforcement and reward schedules. Of the 153 participants, 106 ($74\%$) indicated that the flow of the game suited their knowledge, zero indicated they were bored by the game, 5 ($3\%$) became lost in the game, and only 1 gave up.
**Figure 5:** *Gameplay satisfaction.*
## Principal Findings
SUPER HEMO was developed to increase students’ motivation for learning hematology. In fact, as previously described, when students feel involved, they are more likely to achieve educational goals [26]. But with only 4 hematology teachers at the university, bedside (for clinical symptoms) and laboratory (for cell recognition) teaching in undergraduate medical education could not be fully accomplished for that year’s 324 physician and 115 pharmacy students. Consequently, SUPER HEMO was developed to confront students with different clinical situations, improve their cell observation skills, and supplement the classical teaching model. Because players may have different skills, we developed the pedagogic content concomitantly with gamification [27]. Special characters, such as Lady Stem Cell, were created to regularly debrief the players on wrong or right answers to questions [28]. SUPER HEMO level 1 (the Red World) was developed and beta tested over a 2-year period. Subsequently, SUPER HEMO’s approach was integrated to the standard hematology undergraduate curriculum of third-year physician and pharmacy students and the SG was evaluated.
In health care education, studies comparing SGs to other teaching methods with prospective evaluations are scarce [29,30]. We assumed that this prospective evaluation might be useful to justify SUPER HEMO’s integration with the standard hematology curriculum. The satisfaction questionnaire clearly showed that most students enjoyed playing SUPER HEMO ($\frac{113}{142}$, $80\%$) and felt that they learned from it ($\frac{123}{142}$, $86\%$). These results demonstrate the students’ strong commitment to the game. This result was reinforced by the free comments (eg, “I find the interface truly attractive; it really makes me want to do well” and “A good experience, dialogs or context are quite funny but still remain consistent!”). Several students inquired about playing the rest of the game (eg, “We’re looking forward to the white and yellow worlds’ release” and “I hope I can play again soon!”). Students highlighted the link between the game and seriousness (eg, “It allowed me to study in a fun way” and “Fun way to learn hematology or to practice without the feeling that I have studied”).
Knowledge improvement with this complementary method to the standard course was more difficult to evaluate. If a positive effect of SUPER HEMO was clearly observed for short-term knowledge (ie, the results of the pre- and posttests), the measurement of learning outcome (ie, the results of the final examination) should also be discussed. First, the first SUPER HEMO session ran through the COVID-19 pandemic, when restrictions were in place on attending courses. Therefore, we could not evaluate the effectiveness of this SG with a randomized controlled trial. After discussion with the pedagogic committee during the pandemic, courses were transmitted virtually to students who wanted to stay at home; less than $10\%$ of students attended on site. In order not to penalize medically or psychologically fragile students, we decided to invite students to participate as they chose to do so. We can hypothesize that volunteer students who came to the SUPER HEMO session were the most motivated in the class, thus biasing the results of the final exams. Second, we observed that pharmacy students were more likely to attend the SUPER HEMO session than the physician students. Of note, the pharmacy students were probably more motivated, as their final exam took place a few days after the gaming session, whereas the physician students had their final exam 1 month after the gaming session. For the pharmacy students, although the participation rate was satisfactory, the study was underpowered, suggesting that follow-up studies (within the next few years) with more students might confirm that playing SUPER HEMO allowed students to obtain a better score in the final hematology evaluation (as was observed for the physician students). Third, our conclusions on interest in SUPER HEMO should be moderated by consideration of the SUPER HEMO session design. The session had a time limit of 90 minutes and was based only on the available “Red World,” which dealt with anemia and polycythemia, while the final evaluation contained questions on red blood cells, white blood cells, and platelets. We therefore propose that in the future [1] three worlds corresponding to the three lineages of hematopoietic cells in the CBC be made available to encompass the whole hematology program, [2] game-based learning should have no time restrictions, and [3] new evaluations with more participants should be organized with other universities.
Currently, we consider that our research contributes to the literature by providing a new game for teaching hematology and an investigation of the effectiveness of the SG context for hematology learning. This first experiment with SUPER HEMO commits us to develop this SG for hematology in future academic years. Future work will focus on developing levels 2, 3, and 4, the “White World,” “Yellow World,” and “Complex World,” respectively, with all parameters varying in the latter. We propose an evolving game that covers the entire program of hematology and is accessible on the digital platform of the University of Lorraine. SGs have been poorly developed for hematology education. Tan et al [21] were the first to report an SG, developed for nurses in Singapore, that dealt with the safe administration of blood transfusions. This SG was prospectively tested with 103 second-year undergraduate nursing students randomized into control or experimental groups. Posttest knowledge and mean scores for confidence improved significantly in the experimental group ($P \leq .001$) after the SG intervention compared to mean pretest scores and mean posttest scores for the control group ($P \leq .001$). However, no significant differences ($$P \leq .11$$) were found between the experimental and control groups for mean posttest performance scores. Four years later, the participants evaluated the SG positively as an innovative and stimulating learning tool; however, more rigorous efforts to improve the interface and alleviate technical issues were suggested as ways to make the SG more accessible and intuitive for all levels of nursing staff [22]. To our knowledge, SUPER HEMO is the first SG developed for CBC interpretation to be used by teachers of hematology for students in medicine, pharmacy, dentistry, and midwifery. During the COVID-19 pandemic, education was suddenly disrupted, and universities were mandated to switch to online teaching. The game-based computer app that we designed represents a potentially effective teaching tool for health care students.
## Conclusion
This study has provided evidence that SUPER HEMO met our primary objective: to develop an SG for hematology that was playable and acceptable overall. The usability of SUPER HEMO was demonstrated beyond the initial beta-testing pilot study; we obtained preliminary evidence that SUPER HEMO might be a useful educational tool. We will use it as a supplement to lecture-based courses and trace the time and frequency of logins to SUPER HEMO. Last, we propose to correlate this continuous training to student results for the third-year final exams, final graduation exams (at the end of the fifth year), and for the student’s specialty choice.
## References
1. Johnsen HM, Fossum M, Vivekananda-Schmidt P, Fruhling A, Slettebø Åshild. **Teaching clinical reasoning and decision-making skills to nursing students: Design, development, and usability evaluation of a serious game**. *Int J Med Inform* (2016) **94** 39-48. DOI: 10.1016/j.ijmedinf.2016.06.014
2. Adjedj J, Ducrocq G, Bouleti C, Reinhart L, Fabbro E, Elbez Y, Fischer Q, Tesniere A, Feldman L, Varenne O. **Medical student evaluation with a serious game compared to multiple choice questions assessment**. *JMIR Serious Games* (2017) **5** e11. DOI: 10.2196/games.7033
3. Chon S, Hilgers S, Timmermann F, Dratsch T, Plum PS, Berlth F, Datta R, Alakus H, Schlößer Hans Anton, Schramm C, Pinto Dos Santos Daniel, Bruns C, Kleinert R. **Web-based immersive patient simulator as a curricular tool for objective structured clinical examination preparation in surgery: development and evaluation**. *JMIR Serious Games* (2018) **6** e10693. DOI: 10.2196/10693
4. Chon S, Timmermann F, Dratsch T, Schuelper N, Plum P, Berlth F, Datta RR, Schramm C, Haneder S, Späth Martin Richard, Dübbers Martin, Kleinert J, Raupach T, Bruns C, Kleinert R. **Serious games in surgical medical education: a virtual emergency department as a tool for teaching clinical reasoning to medical students**. *JMIR Serious Games* (2019) **7** e13028. DOI: 10.2196/13028
5. Kleinert R, Heiermann N, Wahba R, Chang D, Hölscher Arnulf H, Stippel DL. **Design, realization, and first validation of an immersive web-based virtual patient simulator for training clinical decisions in surgery**. *J Surg Educ* (2015) **72** 1131-8. DOI: 10.1016/j.jsurg.2015.05.009
6. Breckwoldt J, Ludwig JR, Plener J, Schröder Torsten, Gruber H, Peters H. **Differences in procedural knowledge after a "spaced" and a "massed" version of an intensive course in emergency medicine, investigating a very short spacing interval**. *BMC Med Educ* (2016) **16** 249. DOI: 10.1186/s12909-016-0770-6
7. de Sena DP, Fabrício Daniela D, da Silva VD, Bodanese LC, Franco AR. **Comparative evaluation of video-based on-line course versus serious game for training medical students in cardiopulmonary resuscitation: A randomised trial**. *PLoS One* (2019) **14** e0214722. DOI: 10.1371/journal.pone.0214722
8. Fens T, Hope DL, Crawshaw S, Tommelein E, Dantuma-Wering C, Verdel BM, Trečiokienė I, Solanki V, van Puijenbroek EP, Taxis K. **The international pharmacy game: a comparison of implementation in seven universities world-wide**. *Pharmacy (Basel)* (2021) **9** 125. DOI: 10.3390/pharmacy9030125
9. Cates JR, Fuemmeler BF, Stockton LL, Diehl SJ, Crandell JL, Coyne-Beasley T. **Evaluation of a serious video game to facilitate conversations about human papillomavirus vaccination for preteens: pilot randomized controlled trial**. *JMIR Serious Games* (2020) **8** e16883. DOI: 10.2196/16883
10. Lima IDDA, Ponce de Leon CGRM, Ribeiro LM, Silva ICRD, Vilela DM, Fonseca LMM, Góes Fernanda Dos Santos Nogueira de, Funghetto SS. **A serious game (Immunitates) about immunization: development and validation study**. *JMIR Serious Games* (2022) **10** e30738. DOI: 10.2196/30738
11. Darville G, Burns J, Chavanduka T, Anderson-Lewis C. **Utilizing theories and evaluation in digital gaming interventions to increase human papillomavirus vaccination among young males: qualitative study**. *JMIR Serious Games* (2021) **9** e21303. DOI: 10.2196/21303
12. Mack I, Reiband N, Etges C, Eichhorn S, Schaeffeler N, Zurstiege G, Gawrilow C, Weimer K, Peeraully R, Teufel M, Blumenstock G, Giel KE, Junne F, Zipfel S. **The Kids Obesity Prevention Program: cluster randomized controlled trial to evaluate a serious game for the prevention and**. *J Med Internet Res* (2020) **22** e15725. DOI: 10.2196/15725
13. Gentry Sarah Victoria, Gauthier Andrea, L'Estrade Ehrstrom Beatrice, Wortley David, Lilienthal Anneliese, Tudor Car Lorainne, Dauwels-Okutsu Shoko, Nikolaou Charoula K, Zary Nabil, Campbell James, Car Josip. **Serious gaming and gamification education in health professions: systematic review**. *J Med Internet Res* (2019) **21** e12994. DOI: 10.2196/12994
14. Graafland M, Schraagen J M, Schijven M P. **Systematic review of serious games for medical education and surgical skills training**. *Br J Surg* (2012) **99** 1322-30. DOI: 10.1002/bjs.8819
15. Abdulmajed Hind, Park Yoon Soo, Tekian Ara. **Assessment of educational games for health professions: a systematic review of trends and outcomes**. *Med Teach* (2015) **37 Suppl 1** S27-32. DOI: 10.3109/0142159X.2015.1006609
16. Gorbanev Iouri, Agudelo-Londoño Sandra, González Rafael A, Cortes Ariel, Pomares Alexandra, Delgadillo Vivian, Yepes Francisco J, Muñoz Óscar. **A systematic review of serious games in medical education: quality of evidence and pedagogical strategy**. *Med Educ Online* (2018) **23** 1438718. DOI: 10.1080/10872981.2018.1438718
17. Sinclair Peter, Kable Ashley, Levett-Jones Tracy. **The effectiveness of internet-based e-learning on clinician behavior and patient outcomes: a systematic review protocol**. *JBI Database System Rev Implement Rep* (2015) **13** 52-64. DOI: 10.11124/jbisrir-2015-1919
18. Keis Oliver, Grab Claudia, Schneider Achim, Öchsner Wolfgang. **Online or face-to-face instruction? A qualitative study on the electrocardiogram course at the University of Ulm to examine why students choose a particular format**. *BMC Med Educ* (2017) **17** 194. DOI: 10.1186/s12909-017-1053-6
19. Hoss Thomas, Ancina Amancay, Kaspar Kai. **Forced remote learning during the COVID-19 pandemic in Germany: a mixed-methods study on students' positive and negative expectations**. *Front Psychol* (2021) **12** 642616. DOI: 10.3389/fpsyg.2021.642616
20. Kotera Yasuhiro, Gorchakova Valentina, Maybury Sarah, Edwards Ann-Marie, Kotera Hiromasa. **Comparison of academic motivation between business and healthcare students in online learning: a concurrent nested mixed-method study**. *Healthcare (Basel)* (2022) **10** 1580. DOI: 10.3390/healthcare10081580
21. Tan Apphia Jia Qi, Lee Cindy Ching Siang, Lin Patrick Yongxing, Cooper Simon, Lau Lydia Siew Tiang, Chua Wei Ling, Liaw Sok Ying. **Designing and evaluating the effectiveness of a serious game for safe administration of blood transfusion: A randomized controlled trial**. *Nurse Educ Today* (2017) **55** 38-44. DOI: 10.1016/j.nedt.2017.04.027
22. Aloweni Fazila, See Min Ting Alicia, Ng Xin Ping, Ang Shin Yuh. **Employing serious game for assessing knowledge of blood transfusion procedure among nurses: A qualitative evaluation and feedback improvement study**. *Nurse Educ Today* (2021) **101** 104873. DOI: 10.1016/j.nedt.2021.104873
23. Olszewski Aleksandra E, Wolbrink Traci A. **Serious gaming in medical education: a proposed structured framework for game development**. *Simul Healthc* (2017) **12** 240-253. DOI: 10.1097/SIH.0000000000000212
24. Nurmi Johanna, Knittle Keegan, Ginchev Todor, Khattak Fida, Helf Christopher, Zwickl Patrick, Castellano-Tejedor Carmina, Lusilla-Palacios Pilar, Costa-Requena Jose, Ravaja Niklas, Haukkala Ari. **Engaging users in the behavior change process with digitalized motivational interviewing and gamification: development and feasibility testing of the Precious app**. *JMIR Mhealth Uhealth* (2020) **8** e12884. DOI: 10.2196/12884
25. Hamel R, De La Fontaine É, Lepage J F, Bernier P M. **Punishments and rewards both modestly impair visuomotor memory retention**. *Neurobiol Learn Mem* (2021) **185** 107532. DOI: 10.1016/j.nlm.2021.107532
26. Lemoine Elizabeth R, Nassim Janelle S, Rana Jasmine, Burgin Susan. **Teaching & Learning Tips 4: Motivation and emotion in learning**. *Int J Dermatol* (2018) **57** 233-236. DOI: 10.1111/ijd.13715
27. Desurvire Heather, El-Nasr Magy Seif. **Methods for game user research: studying player behavior to enhance game design**. *IEEE Comput Graph Appl* (2013) **33** 82-7. DOI: 10.1109/MCG.2013.61
28. Kätsyri Jari, Hari Riitta, Ravaja Niklas, Nummenmaa Lauri. **Just watching the game ain't enough: striatal fMRI reward responses to successes and failures in a video game during active and vicarious playing**. *Front Hum Neurosci* (2013) **7** 278. DOI: 10.3389/fnhum.2013.00278
29. Drummond David, Delval Paul, Abdenouri Sonia, Truchot Jennifer, Ceccaldi Pierre-François, Plaisance Patrick, Hadchouel Alice, Tesnière Antoine. **Serious game versus online course for pretraining medical students before a simulation-based mastery learning course on cardiopulmonary resuscitation: A randomised controlled study**. *Eur J Anaesthesiol* (2017) **34** 836-844. DOI: 10.1097/EJA.0000000000000675
30. Middeke Angélina, Anders Sven, Schuelper Madita, Raupach Tobias, Schuelper Nikolai. **Training of clinical reasoning with a Serious Game versus small-group problem-based learning: A prospective study**. *PLoS One* (2018) **13** e0203851. DOI: 10.1371/journal.pone.0203851
|
---
title: 'A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older
Adults After Stroke or Transient Ischemic Attack: Application Design Study'
journal: JMIR Cardio
year: 2023
pmcid: PMC9972205
doi: 10.2196/41691
license: CC BY 4.0
---
# A Smartwatch System for Continuous Monitoring of Atrial Fibrillation in Older Adults After Stroke or Transient Ischemic Attack: Application Design Study
## Abstract
### Background
The prevalence of atrial fibrillation (AF) increases with age and can lead to stroke. Therefore, older adults may benefit the most from AF screening. However, older adult populations tend to lag more than younger groups in the adoption of, and comfort with, the use of mobile health (mHealth) apps. Furthermore, although mobile apps that can detect AF are available to the public, most are designed for intermittent AF detection and for younger users. No app designed for long-term AF monitoring has released detailed system design specifications that can handle large data collections, especially in this age group.
### Objective
This study aimed to design an innovative smartwatch-based AF monitoring mHealth solution in collaboration with older adult participants and clinicians.
### Methods
The Pulsewatch system is designed to link smartwatches and smartphone apps, a website for data verification, and user data organization on a cloud server. The smartwatch in the Pulsewatch system is designed to continuously monitor the pulse rate with embedded AF detection algorithms, and the smartphone in the Pulsewatch system is designed to serve as the data-transferring hub to the cloud storage server.
### Results
We implemented the Pulsewatch system based on the functionality that patients and caregivers recommended. The user interfaces of the smartwatch and smartphone apps were specifically designed for older adults at risk for AF. We improved our Pulsewatch system based on feedback from focus groups consisting of patients with stroke and clinicians. The Pulsewatch system was used by the intervention group for up to 6 weeks in the 2 phases of our randomized clinical trial. At the conclusion of phase 1, 90 trial participants who had used the Pulsewatch app and smartwatch for 14 days completed a System Usability Scale to assess the usability of the Pulsewatch system; of 88 participants, 56 ($64\%$) endorsed that the smartwatch app is “easy to use.” For phases 1 and 2 of the study, we collected 9224.4 hours of smartwatch recordings from the participants. The longest recording streak in phase 2 was 21 days of consecutive recordings out of the 30 days of data collection.
### Conclusions
This is one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring. In this paper, we report on the system’s usability and opportunities to increase the acceptability of mHealth solutions among older patients with cognitive impairment.
### Trial Registration
ClinicalTrials.gov NCT03761394; https://www.clinicaltrials.gov/ct2/show/NCT03761394
### International Registered Report Identifier (IRRID)
RR2-10.1016/j.cvdhj.2021.07.002
## Background
Atrial fibrillation (AF) is the most frequent cardiac arrhythmia [1], and the prevalence of AF in the United *States is* increasing, from an estimated 5.2 million in 2010 to a projected 12.1 million in 2030 [2]. AF, whether paroxysmal, persistent, or permanent, and whether symptomatic or silent, significantly increases the risk of thromboembolic ischemic stroke [3]. Owing to the difficulty in diagnosis, paroxysmal AF (pAF) is the most common AF pattern found in all patients presenting with acute ischemic stroke [4]. Diagnosing pAF remains critically important and often requires monitoring for >24 hours [5]. The popularity of noninvasive wearable devices and user-friendly, informative mobile apps may play an important role in long-term heart rhythm monitoring for the population at high risk of pAF [6]. In addition, the COVID-19 pandemic has fundamentally altered the landscape of clinical care in the United States, with many older adults and their clinicians shifting to internet-based visits and increasing the acceptability of wearable devices as medical monitors. Because the prevalence of AF increases with age, reaching $9\%$ in people ≥80 years [7], the ideal population to screen for AF is of older adults. However, familiarity with wearable devices remains low among older adults. Physical, as well as cognitive, impairments can interfere with the ability of older adults to use mobile health (mHealth) apps and commercial wearables [8]. We aimed to design a system for AF monitoring that would be highly usable by older adults by incorporating their feedback into the design of a smartphone app.
Traditional electrocardiogram (ECG) monitoring uses hydrogel-based adhesive electrodes, which leads to poor patient acceptance and usability in long-term monitoring apps. Most current mobile noninvasive technologies with automated pulse or ECG acquisition have highly accurate AF detection, calculated from embedded advanced signal processing algorithms [6]. However, initial manifestations of this technically required an individual to perform self-checks by placing fingers on a smartphone camera lens or pairing them with an ECG unit for 30 seconds to 2 minutes of recording. This spot-check approach does not provide the continuous, passive monitoring needed to detect brief episodes of AF in high-risk populations, such as those with cryptogenic stroke.
## Prior Work
A better method to monitor pAF passively and near-continuously is to use a photoplethysmography (PPG) sensor on the back of a smartwatch to record pulse information. Recently, Apple Heart study [9,10], Huawei Heart study [11,12], and Fitbit Heart study [13] evaluated systems that use this approach to monitor pAF. However, all 3 studies targeted users who already owned each brand’s smartphones and smartwatches. Therefore, the Apple Heart study, Huawei Heart study, and Fitbit Heart study were skewed toward younger participants. In fact, each study included only $5.9\%$, $1.8\%$, and $12.5\%$ of the total cohort of participants aged ≥65 years, respectively. No details were provided on whether the apps were specifically designed by or for older adults, and no details on the design of the rhythm collection system have been described [10-13]. In addition, Including the details of the design is important, as long-term monitoring that generates enormous data could overload wearable devices that have limited storage.
## Goal of This Study
As with any novel technological development, end user guidance in design and development is paramount. This is especially true for pAF monitoring because the target population of older stroke patients is unique, slower to adopt new technology, and understudied. Accordingly, in our clinical trial [14], we collaborated with survivors of stroke and their clinicians to develop Pulsewatch, an innovative smartwatch-based AF detection mHealth system. This comprised a Samsung smartwatch for long-term (up to 6 weeks), near-continuous (24 h/d) pulse monitoring and on-demand ECG into which we embedded our novel algorithms for noise elimination, contact sensing, and automated AF detection and which communicated with a smartphone app with user interface (UI). In this study, we provide the details of our system design and its acceptability among older survivors of stroke or transient ischemic attack (TIA).
## Methods
In this section, we discuss the design of the functionalities of the Pulsewatch system. Details of the final implementation of the Pulsewatch system are provided in the Results section.
## Overview of the Functionality of the Pulsewatch System
The Pulsewatch system consisted of a pair of smartphone and smartwatch apps and was intended to be used for at least 6 weeks in an at-home ambulatory setting by older adults who survived a stroke or TIA [14]. The aim of the Pulsewatch system was to provide a passive AF monitoring solution with minimal user attention required during recording with real-time display of the monitoring results. Importantly, owing to the cognitive impairment of our target population, pulse monitoring had to be passive, wherein the participants were only asked to hold still when a rhythm abnormality was detected. Although the system required no action on the part of the user, users can access historical data through the Pulsewatch app. Users can input symptoms or notes for their clinicians, as the system was designed to facilitate the sharing of information about the AF status between the user and their clinicians. The participants’ clinicians can also remotely check the Pulsewatch system’s wearing time information, user symptoms, and AF detection results.
The Pulsewatch system was designed to be used by 3 groups of users, as described in Textbox 1.
During the design process of the Pulsewatch app, inputs from both patients and their clinicians were taken to facilitate communication. Our patients’ focus group consisted of 5 screened patients with a history of stroke or TIA at the University of Massachusetts Chan Medical School’s (UMass Chan) Stroke Prevention Clinic, whereas our clinicians’ focus group participants consisted of 5 clinicians (neurologists and cardiologists) from the UMass Chan Neurology and Cardiology groups. Developers (algorithm developer and software engineer) also joined the hack-a-thon and agile programming team meeting at UMass Chan to finalize the Pulsewatch system to be deployed in the clinical trial.
The use case diagram [15] depicted in Figure 1 shows the main functionalities provided by our Pulsewatch system. The Pulsewatch experiment began with a smartwatch app that could automatically and independently detect AF. After a participant received the dyad composed of the smartwatch and smartphone, turned on the devices, and donned the smartwatch, the data collection and signal processing procedure automatically started without any configuration needed from the participant. We believe that this is the most feasible way to start monitoring AF in older adult participants.
**Figure 1:** *Use case diagram of the data collection system. AF: atrial fibrillation; UConn: University of Connecticut; UMass Chan: University of Massachusetts Chan Medical School.*
## Ethics Approval
Formal ethical approval for this study was obtained from the Institutional Review Board of the UMass Chan’s Institutional Review Board (Approval Number H00016067). Written informed consent was obtained from all patients involved in the hack-a-thon and clinical trials. Verbal consent was obtained from clinicians enrolled in the hack-a-thon development phase. Because of the unprecedented challenges posed by COVID-19 regarding in-person human participant research, we adopted an alternate protocol to allow for all study encounters to occur over the phone [14]. The consent form was adopted for telephone and approved by the Institutional Review, and eligible participants were contacted via their contact information in the electronic health record and consented [14]. This alternate remote protocol was initiated in July 2020, and all participants who had been approached over the phone were also offered the option to participate in person as per the original study protocol [14].
All participants’ identifiable information was deidentified by the UMass team. The authors designed the study and gathered and analyzed the data according to the Helsinki Declaration guidelines for human research.
The 5 patient participants who completed the hack-a-thon were each awarded a US $50 visa gift card. During the clinical trial, enrolled participants received a US $100 visa gift card after completing the baseline questionnaire for phase 1 of the study and another US $100 gift card for completing the follow-up questionnaire. Participants who were randomly selected for a usability interview received an additional US $60 visa gift card. After completing phase 2 and the 44- day follow-up questionnaire, the participants were compensated with a US $50 visa gift card.
## Functionality of Independent AF Detection on Smartwatch
Figure 2 illustrates the core functionality and highlights of the Pulsewatch system, that is, the signal processing system on the smartwatch.
We designed a novel sensor modulation program to turn the sensor on every 10 minutes to preserve the smartwatch battery while maximizing the monitoring span. The duration of the sensor-on stage was 5 minutes but could be extended based on the instantaneous AF detection results [14].
**Figure 2:** *Use case diagram of pulse monitoring (smartwatch signal processing) system. ACC: accelerometry; AF: atrial fibrillation; PPG: photoplethysmography.*
## Functionality of Automated File Transferring Between Smartwatch and Smartphone
Once data were collected and processed on the smartwatch, the next critical step was when and how to upload them to the smartphone so that the storage on the watch could be freed. As shown in Figure 3, the file transferring procedure initiated by the watch occurred immediately when the sensor was turned off, thereby ensuring that no new files were created. The file sender program inside the smartwatch app first confirmed that a Bluetooth connection was established between the smartwatch and the paired smartphone. When the criteria were met, a file transfer process was initiated. Three types of data were transferred from the watch to the phone: PPG raw data, accelerometry (ACC) raw data, and results of signal processing and AF detection methods. Details of the output data are provided in the Results section. Once the smartphone successfully received the watch data, the watch app deleted the transferred data to free the storage space for future data collection. The smartphone app file receiver application programming interface (API) continuously ran in the background of the smartphone to ensure that any spontaneous file uploading requests were received from the watch.
**Figure 3:** *Use case diagram for phone-watch file transfer system. ACC: accelerometry; AF: atrial fibrillation; PPG: photoplethysmography.*
## Functionality of Automated File Backup Between the Smartphone and the Cloud Server
Even if the smartphone received data transferred from the smartwatch, the physical dyad could be damaged or lost at any moment. To address this, we designed a Pulsewatch system to ensure that the data were backed up to the cloud server. As shown in Figure 4, when the smartphone was turned on, the file sender in the Pulsewatch phone app automatically ran in the background constantly to ensure that files were uploaded to the cloud at any given moment. Before uploading unsent files to the server, the file sender must establish internet connection, either through Wi-Fi or cellular data. Once a file was uploaded through the internet connection successfully, the phone app did not delete it but instead moved it to another folder to have a second local backup. This was because during transfer, the data could be corrupted, and therefore, it was critical to keep the original files in the phone.
**Figure 4:** *Use case diagram of the phone-server file transfer system. UConn: University of Connecticut.*
## Functionality of Symptom Logging in the Smartphone
An important functionality required by cardiologists, as shown in Figure 5, was participants’ symptom tracking within the Pulsewatch smartphone app, which mirrors the same functionality as clinical heart rhythm monitors. Regardless of the AF status results in the phone app, participants were able to select predefined symptoms or log free text in the smartphone app at any time to inform their clinician. To separate different participants’ inputs on the cloud, the participants had to first log-in to the phone app with their user ID (UID) and password. This log-in process required internet connection to contact the cloud server for log-in credentials. After log-in, the participants used the start time point from when the watch sensor was turned on every 10 minutes to input their symptoms and notes. Once the participant clicked the save button, all edits were automatically uploaded to the server through an internet connection. The UMass research staff could read the participant’s symptom input immediately on the webpage.
**Figure 5:** *Use case diagram of the symptom logging system. AF: atrial fibrillation; HR: heart rate; UConn: University of Connecticut; UID: user ID; UMass Chan: University of Massachusetts Chan Medical School.*
## Results
In this section, we explain the implementation of all the functionalities proposed in the Methods section for our Pulsewatch system. We then describe the output of our Pulsewatch clinical trial, including the total number of recordings collected from the Pulsewatch system and the usability ratings of our system among our participants.
## Implemented Structure of the Pulsewatch System
On the basis of the functionality mentioned in the Overview of the Functionality of the Pulsewatch System in the Methods section, we designed the structure of our Pulsewatch system, as shown in Figure 6.
At near real-time speed (<1 s), the smartwatch processed the recorded data and displayed an AF status (normal or abnormal) on the watch UI, together with the time of day. There was no notification of AF status other than the watch face color and text, reflecting the preferences of older adult patients. After collecting and processing the data, they were transferred to the paired smartphone via self-initiated communication within the dyad. The data were backed up both in the phone’s local storage and on the cloud server through the phone app.
**Figure 6:** *Structure of the Pulsewatch system. Admin: administrator; UConn: University of Connecticut; UID: user ID; UMass Chan: University of Massachusetts Chan Medical School.*
## Implemented UI of the Smartwatch App
On the basis of the functionality requested in Figure 2, we implemented the logic as shown in Figure 7. During the sensor-on stage, PPG and ACC data were recorded, and AF detection was performed [16,17] based on the PPG heart rate value [18] from clean PPG segments [16]. The results of AF detection were displayed accordingly on the watch face. The enrolled participants were able to observe the AF status from the smartwatch face passively when they checked the time displayed on the watch. If the system detected AF, the status lingered on the watch face until the participant clicked the watch face to acknowledge it. This AF detection cycle was repeated all day while the watch was powered on.
For the watch app UI shown in Figure 7, the AF status was displayed with the watch face color and text at the top, based on suggestions from the patient focus group members in the hack-a-thon [14]. To avoid inducing worry among participants, the watch face color for abnormality was deliberately chosen as blue (in lieu of a red color). Color blindness was considered and factored into the choice of warning color, following a suggestion by clinician participants in the hack-a-thon [14]. In addition to the AF status (normal, possible AF active, possible AF previously detected), the watch face also displays the time and 2 distinct heart rate features. Details of the smartwatch UI are provided in Multimedia Appendix 1. As the sensor was turned off half of the time, we used the darkest color for the background to minimize the brightness of the watch face and disturbance to participants’ sleep. The frequency of normal watch face could be high as well, if the participant did not have long episodes of AF or had lots of motion artifact; thus, we used a darker green color in (4-W-1) of Figure 7, compared with the dazzling green color on the watch face shown in the normal screen of Figure 3 in [14]. We did not design any night mode for the watch face in case participants preferred to be woken up at night for any AF alert. To indicate the Bluetooth connection and the remaining battery percentage, we added 2 icons at the bottom of the watch face to help participants debug the file transferring issue if the study staff had not seen their data for several days in phase I.
**Figure 7:** *The state machine of the Pulsewatch smartwatch app. AF: atrial fibrillation; bpm: beats per minute; HR: heart rate.*
## Implementation of Smartwatch App
Before we introduced the final implementation of the watch app, we needed to provide background knowledge on the paradigms of mobile watch apps. On the basis of the required functionalities, we designed the watch app architecture shown in Figure 8 [19]. The Pulsewatch watch application uses both types of apps that the Samsung Tizen (Samsung) Wearable OS supports: a web app, which is for the watch face application, and a native application, which is for the Pulsewatch service application—a job manager for calling the sensors to collect data or calling the algorithm to process the data. The watch face application, written in web language (ie, HTML, Cascading Style Sheets, and JavaScript), is a special type of application that runs automatically and consistently on the home screen of a watch with the Samsung Tizen Wearable OS when the watch is turned on. The watch faces shown in Figure 7 are the only apps that the participants could see. All automated procedures are described below for both native and service apps.
According to the designed functionality of the watch app (signal processing functionality in Figure 2, UI changes in Figure 7, and file transferring functionality in Figure 3), we summarize the workflow of our watch app as follows. As mentioned previously, every time the smartwatch was powered on, the watch face application ran first, followed by the native application. The watch face color or pattern changes according to the input sent from our service application through the message port. Our service application then initialized all APIs equipped and started the AF detection procedure. A service application turns on the watch sensors through the sensor scheduler and then sends the initiated sensor status to the watch face application through the same message port. Then, the AF detection API collects and processes all sensor data and transmits the AF detection results to the watch face application through the same message port. Finally, when the 5-minute sensor-on stage is completed, the sensor scheduler turns off the sensor and updates the sensor status on the watch face application through the same message port.
Every smartwatch model was powerful enough to process the pulse signal and detect AF in near real time. The initial model of the smartwatch was Gear S3 Classic [2016]. After nearly a year of heavy use in the clinical trial, the battery health of the Gear S3s degraded significantly and could not support more than 3 hours of use, and this older model was discontinued by the manufacturer. We replaced it with a Galaxy Watch 3 (41 mm; 2020) watch, which had better sensors for data collection and larger RAM and internal storage compared with Gear S3. Both Gear S3 and Galaxy Watch 3 run an open source Samsung Tizen Wearable operating system (OS) from the Linux Foundation [20], allowing researchers to have access to the original sensor data using a free open-source API without any commercial license fees to Samsung [21].
**Figure 8:** *The architecture of the Pulsewatch smartwatch app. ACC: accelerometry; AF: atrial fibrillation; API: application programming interface; PPG: photoplethysmography; UI: user interface.*
## Implementation of Smartphone App
When deploying a mobile phone app, which is slightly different from the deployment of the mobile watch app, we decided to use a hybrid application [22], where we display content using the web application and then fit it inside the native application structure. The reasons for this are presented in Table 1.
We structured the relationships between pages and other content components [23] of the Pulsewatch phone app in a top-down informational architecture, as shown in Figure 9. After log-in, the user entered the home page, which consisted of 4 large buttons for 4 main categories: “My Preferences,” “Results,” “My History,” and “Get Help.” These 4 main pages can also be navigated quickly using the side menu (screenshots 10-17 in Figure 10). Details of the content displayed on each page are provided in the Multimedia Appendix 1.
The “Login,” “Results,” and “My History” pages are actually 3 web-based apps that run on the cloud server. We chose this application type mainly because the large amount and importance of the content displayed on these 3 pages led to many UI revisions. Thus, compared with using a native app paradigm, using web apps significantly reduced the developer’s burden and end user’s pain in reinstalling the apps. However, if the cloud server was shut down for maintenance, no users could use the phone app. Therefore, it was necessary to schedule a time slot for server maintenance (eg, around 3 AM to 5 AM), because not many users were logged in and used the app during these hours.
The design of the “Login” page can shorten the dyad cleaning time (eg, removal of patient information and data) between any 2 participants in the experiment for UMass study staff. When the app was first used by a participant, the phone app required a 1-time log-in for the participants to input their UID and passwords. This process could also be performed by the study staff to reduce app use difficulties before handing out the dyad. All the data recorded from the smartwatch will be tagged with this UID. Once the experiment was finished, the UMass study coordinators were able to manually log out the current participant in the side menu from any page to prepare the dyad for the next participant. Once the current UID was logged out, the next user could not see any history of information from previous users, and the newly recorded data can be isolated from the previous data with a different UID.
The “My Preferences” and “Get Help” pages are native app pages because they need access to system-level features and have to work offline. The “My Preferences” page was mainly designed to let a user set up twice daily self-check alarms. This allows users to set the time and notification types for self-check alarms. The number of acceptable prompts that Pulsewatch can deliver per day was determined by the patient focus group participants in the hack-a-thon [14]. In addition to alarm time, users could also set a do-not-disturb time to avoid unwanted interruptions from the Pulsewatch app. Notification options such as flashlights, sound, and vibration could also be turned on or off. Self-check alarms appeared in the notification center to allow the user to tap it and open our Pulsewatch phone app quickly. This alarm could be deleted using either a swipe left or right action.
For the phone UI shown in Figure 10, a larger font size and capitalized letters were appreciated by end users [14], many of whom had visual impairments. In the phone app, a pie chart on the results page was added, as one of the participating clinicians suggested [14] that it would be important for clinicians to have a report that showed the AF burden recorded by the system.
As some active participants might wonder if the watch files were successfully uploaded to the phone, we placed a steady notification banner, using the native app structure, in the notification center to show the last time that a file was transferred from the watch to the phone. This notification banner was not visible unless the participants swiped down the notification center.
## Implemented Data Tracking Website on the Cloud Server
Most electronic health records are now web and client-server–based and use relational databases [24]. Although not indispensable, it is state-of-the-art to provide a web service for clinicians and research staff to track the status of data uploading on the cloud. The details of our data tracking website are provided in Figure 11 and Multimedia Appendix 1.
**Figure 11:** *The information architecture of the data tracking website and the final implementation of the data tracking website. AF: atrial fibrillation; UID: user ID.*
## Large Data Collected From the Pulsewatch System
In total, our Pulsewatch system collected 33,207,780 seconds (approximately 9224.38 h) and 182 GB of physiological recordings from the 90 participants who participated in phase 1 in the intervention group of 14 days and the 60 participants who participated in phase 2 (30 days).
For the functionality of the Pulsewatch system, as described in Figure 7, we recorded the raw PPG and ACC data for post hoc analysis because of the scarcity of smartwatch data sets for those with AF. The details of our output files from the Pulsewatch system are provided in the Multimedia Appendix 1.
As a staggering number of files were generated during the 44 days of continuous monitoring of the clinical trial, we had to consider the size of each file, as the cumulative size could be massive and pose challenges for the smartwatch, smartphone, and even the cloud server storage. The final file sizes were recorded during our clinical trial, and were found to be as large as 10 GB per participant if the Pulsewatch app was used daily. This amounts to a significantly large data storage requirement, considering that as many as 60 subjects could wear our Pulsewatch system for the entire study, lasting 44 days.
Because the quantity of data was quite substantial, we had to seek a large, long-term storage space on the cloud storage with a lower cost because the original storage space on the cloud server shown in Figure 6 is faster in read and write speed but costs much more. Details of the storage are described in Multimedia Appendix 1.
## Usability of the Pulsewatch System
Before phase 1 started in this clinical trial, we held a hack-a-thon meeting [14] to optimize the interactivity and usability of Pulsewatch, guided by information gleaned from 2 focus groups. From the hack-a-thon, the patients unanimously agreed that the UI layout shown above for both the phone and watch was clear and preferable to other options that were discussed.
After phase 1, researchers at UMass Chan studied the human factor aspects of our Pulsewatch system by surveying 90 patients who used it [25]. The participants used the System Usability Scale to quantify usability [25]. As documented in [25], patients ($$n = 90$$) who received the Pulsewatch system had an average age of 65 years, $41\%$ ($\frac{37}{90}$) were female individuals, and $87\%$ ($\frac{78}{90}$) were of a White racial background. The baseline characteristics of the participants can be found in Table 1 of the study by Ding et al [25]. A total of $39\%$ (32 /83) of patients found the system to be highly usable (System Usability Scale>68; [26]). For the watch app, $64\%$ ($\frac{56}{88}$) of patients agreed or strongly agreed that it was easy to use. When considering only the phone app, $52\%$ ($\frac{46}{88}$) of patients agreed or strongly agreed that it was easy to use. In addition, $42\%$ ($\frac{37}{88}$) of patients felt that using Pulsewatch made them feel more connected to their clinicians. About one-eighth of the patients thought it was stressful to use the devices ($\frac{11}{88}$, $13\%$ patients), but more than half enjoyed their experience using the system ($\frac{45}{88}$, $51\%$ patients) [25].
## Formation of Smartwatch Wearing Habit
On the first day of phase 2, 44 out of 57 ($77\%$) participants in the intervention group wore the smartwatch of the Pulsewatch system [27]; however, it quickly dropped to only 14 out of 57 participants ($25\%$) on the last day of phase 2. This fading enthusiasm for wearing a smartwatch for AF monitoring suggests that engineers and clinicians must consider the burden of the Pulsewatch system before designing its functionalities and user groups. In their book [28], Eyal described the “Hook” model to build user’s habit-forming products. In Figure 12, we illustrate how to use the Hook model to increase Pulsewatch system use among the targeted older adult population.
**Figure 12:** *The Hook model forms the habit of using the Pulsewatch system. AF: atrial fibrillation.*
The Hook model consists of 4 steps to form the habit of using a new product. The first step is trigger, which means the actuator of a user’s behavior. An external trigger could be the health caregiver approaching the participant, and an internal trigger could be the participant’s concern for their health after stroke or TIA. We believe that all the enrolled participants experienced strong external and internal triggers in this study.
After the trigger comes the action step, which is defined as the user’s behavior in anticipation of a reward. The simple action in this study was to wear the Pulsewatch watch and sporadically check the AF results on the Pulsewatch phone app. This step could be challenging because the watch battery lasted only for 8 hours of recording. Consequently, participants may have lost patience with the frequent need to charge the watch. This situation will improve if developers have root access to the OS of smartwatches to shut down irrelevant smartwatch services for better battery management, and the battery capacity will increase as technology advances.
The third step is the variable reward, which is the key part that we believe should be improved upon. There are 3 variable types of rewards, and we believe that we can let end users select one or more types of rewards when they start to use the phone app. For rewards of the tribe, that is, when the users prefer to be supported by a community, they can see other participants’ wearing times and encourage other older adults to participate and adhere to the guidelines. If the caregivers or the users’ beloved families and friends are interested in helping the users, they can also join and provide in-app encouragement to the user. The momentum of wearing a watch can be continuously driven by social connections with others.
For those users who prefer rewards of the hunt, meaning they want information-intensive material, our phone app should provide a summary of AF burden and heart rate trends on the home screen that is easy for participants to find. Providing straightforward information reduces fatigue and encourages better adherence to data collection.
For users who prefer rewards of the self, that is, the need for intrinsic rewards of mastery, competence, and completion, one of the best ways to reward is to show the cumulative wearing time every time the user places the smartwatch on its charger. This is also helpful for participants with impaired cognitive function, as we could tailor some customized audio announcements to passively inform them of their accomplishments and health status. To increase the entertainment value of self-reward, we could provide occasional pop-up rewarding messages to remark on certain milestones of wearing time.
In the last step, which is the investment step, we can introduce a weekly report to the users summarizing their daily events, including exercise, sleep, heart health, and information on the time and effort the users have invested to leverage a new round of triggers.
## Principal Findings
As one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring, we successfully designed and implemented an innovative smartwatch-based AF monitoring mHealth solution for the older adult population with feedback from patients with AF and their clinicians. The usability of the system indicated an increase in the acceptability of mHealth solutions among older patients with cognitive impairment.
## Comparison With Prior Work
Although few manuscripts describe designs for health monitoring apps, Chae et al [29] described an approach to the design of a web-based upper limb home-based rehabilitation system using a smartwatch and smartphone for chronic stroke survivors. From the brief description of the system design by Chae et al [29], the recording of smartwatch data was initiated by touching the start button on the smartphone app, which required considerable attention from older adult patients. This was found to be difficult for the older adult participants to adhere to. Moreover, the frequency of rehabilitation exercises was as low as a few times a day; thus, the recording length was incomparable with the near-continuous recording of PPG data for AF monitoring in this study. Data processing was performed using the phone’s microprocessor, and not the watch, which would be an issue for real-time AF detection if *Bluetooth is* disconnected. Lutze et al [30-32] designed a stand-alone smartwatch app using the Samsung Simband smartwatch to handle health hazards for older people. However, other than simple tasks such as at-home checking through Wi-Fi connection, health reminders, and emergency calling, it neither performed other complicated tasks nor collected a large amount of data. Others have attempted to design combined smartphone and smartwatch apps as a diary self-management tool for diabetics [33]. The design of the app involved diabetic patients, but because the daily diary recording was discrete, the amount of data collected was minimal when compared with our AF monitoring; hence, cloud-based storage requirements were not needed. None of the above studies could provide a solution for freeing storage after recording a large amount of data, which is crucial for long-term monitoring, as storage space is limited in smartwatches (<8 GB).
## Limitations
The Pulsewatch system was designed to collect data for long-term monitoring, and the smartwatch file transfer process proceeded smoothly when the Bluetooth connection between the smartwatch and the smartphone was stable. However, several issues were encountered. For example, our longest recording streak among all participants was only 21 days during the combined 44 days of the phase 1 and phase 2 experiments. This was caused by improper functioning of the smartphone app during the 30-day phase 2. For example, the smartphone app could be terminated by the smartphone OS because of high battery consumption, or the participants may have their own smartphone device; therefore, they tend to forget to carry the study phone to maintain the Bluetooth connection of the Pulsewatch system. Consequently, the storage of the smartwatch became full, resulting in the loss of newly recorded information. In the future, smartwatch apps should have a data loss prevention mechanism when the watch storage is full. If the smartwatch app notices that the user has not maintained Bluetooth connectivity, and no files are uploaded to the smartphone for more than 3 or 4 days, it should automatically start a procedure to compress all existing files into a zip file, followed by deleting the original files to free up the data storage space. With this approach, we can save approximately $80\%$ of the storage space, as the compression ratio for text files can be as high as $80\%$ [34]. If this data compression functionality is implemented in the future, even without a Bluetooth connection to the phone, the Gear S3 smartwatch could record 24 days of data, and the Galaxy Watch 3 could record 64 days of data.
When we calculated the wearing time after the clinical trial was finished, we found that although obvious signs of wearing (eg, step counts) were observed in the Samsung Health smartwatch data, our Pulsewatch did not record any data. This could be caused by several factors, including full smartwatch storage. Although our watch app was tested to continuously and automatically operate on the watch, users could still terminate the running of our Pulsewatch app. Users can terminate our Pulsewatch app if they long press the watch face and switch to another built-in watch face. They could even accidentally delete our Pulsewatch app after long press the watch face. The Samsung Tizen Wearable OS has a battery use monitor, and it often asks users to stop running smartwatch apps that drain the battery. Furthermore, it automatically asks users to enter the power saving mode when the remaining battery is <$20\%$. This power-saving mode is problematic because it terminates the operation of all third-party apps. Unless the watch is rebooted, our Pulsewatch app cannot automatically rerun again, even if users exit this mode after charging the watch. The Pulsewatch App must be active for rhythm detection, which has important implications for monitoring AF over an extended period.
In addition to wearing our Pulsewatch system, participants were also asked to wear the US Food and Drug Administration–approved, Cardea SOLO Wireless ECG Patch (Cardiac Insight). This served as the gold standard reference in phase 1 of the clinical trial to validate the accuracy of the AF monitoring algorithms for PPG. It should be noted that including a sample-level timestamp is crucial for any wearable system, especially on the reference device, as it is used to validate the accuracy of PPG data, such as peak detection comparison or heart rate variability analysis between the 2 devices. We added the sample-level timestamp for the Pulsewatch system after realizing that this capability was not enabled in the reference device when we enrolled 35 participants. Finally, we obtained the precise sample-level timestamp from the Cardiac Insight during the secondary analysis. Details of the sample-level timestamp issues are provided in Multimedia Appendix 1.
## Clinical Prospects
In this study, we detail potential issues in the design, development, and execution of a clinical trial that implements a novel digital health care system designed to monitor older stroke survivors for potential AF. The technical challenges encountered during the design and deployment process outlined in this study provide a foundational blueprint for future work in the area, both in research and clinical spaces, allowing for a more streamlined resource allocation and data management. In turn, this would lead to an improvement in patient outcomes. Our study also illustrated a successful and agile shift to internet-based recruitment in the context of the COVID-19 pandemic, providing a poignant example of how to adapt clinical trial protocols while maintaining data integrity and patient safety. Our study will enrich a diverse and inclusive pipeline of digital health and informatics professionals to address new pandemic-induced public health, medical, and scientific issues.
## Conclusions
As one of the first studies to provide a detailed design for a smartphone-smartwatch dyad for ambulatory AF monitoring, our team of engineers, programmers, clinicians, and patients successfully designed a system that has been used in a randomized clinical trial. The reported usability of our Pulsewatch system may increase the acceptability of mHealth solutions among older adult patients with cognitive impairment. Our proposed mHealth system overcomes some of the limitations of many prior devices for long-term AF monitoring. The Pulsewatch app was rated highly usable by over half of the stroke survivors in our study. All AF detection was performed solely on the smartwatch, and the smartphone served as a data transfer hub between the cloud and smartwatch. Clinicians organized the participant UIDs on the cloud and checked the collected data, including symptoms and notes logged by the participants on the Pulsewatch phone app. The Pulsewatch system successfully recorded raw data for subsequent data mining and machine-learning applications for AF detection.
## Data Availability
The data collected from the Pulsewatch study are in the analysis phase; therefore, access to the data will be available once the study is completed. Once all data analyses have been completed, we will release the data to the public on our laboratory’s website [35]. The smartwatch and smartphone apps contain patented algorithms developed by the authors; therefore, they are not available on Google App stores or any open-source code repository.
## References
1. Lippi G, Sanchis-Gomar F, Cervellin G. **Global epidemiology of atrial fibrillation: an increasing epidemic and public health challenge**. *Int J Stroke* (2021.0) **16** 217-21. DOI: 10.1177/1747493019897870
2. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, Elkind MS, Evenson KR, Ferguson JF, Gupta DK, Khan SS, Kissela BM, Knutson KL, Lee CD, Lewis TT, Liu J, Loop MS, Lutsey PL, Ma J, Mackey J, Martin SS, Matchar DB, Mussolino ME, Navaneethan SD, Perak AM, Roth GA, Samad Z, Satou GM, Schroeder EB, Shah SH, Shay CM, Stokes A, VanWagner LB, Wang N, Tsao CW. **Heart disease and stroke statistics-2021 update: a report from the American Heart Association**. *Circulation* (2021.0) **143** e254-743. DOI: 10.1161/CIR.0000000000000950
3. January CT, Wann LS, Alpert JS, Calkins H, Cigarroa JE, Cleveland Jr JC, Conti JB, Ellinor PT, Ezekowitz MD, Field ME, Murray KT, Sacco RL, Stevenson WG, Tchou PJ, Tracy CM, Yancy CW. **2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society**. *Circulation* (2014.0) **130** 2071-104. DOI: 10.1161/CIR.0000000000000040
4. Chen LY, Chung MK, Allen LA, Ezekowitz M, Furie KL, McCabe P, Noseworthy PA, Perez MV, Turakhia MP. **Atrial fibrillation burden: moving beyond atrial fibrillation as a binary entity: a scientific statement from the American Heart Association**. *Circulation* (2018.0) **137** e623-44. DOI: 10.1161/CIR.0000000000000568
5. Dilaveris PE, Kennedy HL. **Silent atrial fibrillation: epidemiology, diagnosis, and clinical impact**. *Clin Cardiol* (2017.0) **40** 413-8. DOI: 10.1002/clc.22667
6. Książczyk M, Dębska-Kozłowska A, Warchoł I, Lubiński A. **Enhancing healthcare access-smartphone apps in arrhythmia screening: viewpoint**. *JMIR Mhealth Uhealth* (2021.0) **9** e23425. DOI: 10.2196/23425
7. Agnihotri K, Charilaou P, Voruganti D, Gunasekaran K, Mehta J, Paydak H, Briasoulis A. **Impact of atrial fibrillation on in-hospital outcomes among hospitalizations for cardiac surgery: an analysis of the National Inpatient Sample**. *J Investig Med* (2022.0) **70** 899-906. DOI: 10.1136/jim-2021-001864
8. Farivar S, Abouzahra M, Ghasemaghaei M. **Wearable device adoption among older adults: a mixed-methods study**. *Int J Inf Manage* (2020.0) **55** 102209. DOI: 10.1016/j.ijinfomgt.2020.102209
9. Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, Balasubramanian V, Russo AM, Rajmane A, Cheung L, Hung G, Lee J, Kowey P, Talati N, Nag D, Gummidipundi SE, Beatty A, Hills MT, Desai S, Granger CB, Desai M, Turakhia MP. **Large-scale assessment of a smartwatch to identify atrial fibrillation**. *N Engl J Med* (2019.0) **381** 1909-17. DOI: 10.1056/NEJMoa1901183
10. Turakhia MP, Desai M, Hedlin H, Rajmane A, Talati N, Ferris T, Desai S, Nag D, Patel M, Kowey P, Rumsfeld JS, Russo AM, Hills MT, Granger CB, Mahaffey KW, Perez MV. **Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: the Apple Heart Study**. *Am Heart J* (2019.0) **207** 66-75. DOI: 10.1016/j.ahj.2018.09.002
11. Guo Y, Wang H, Zhang H, Liu T, Liang Z, Xia Y, Yan L, Xing Y, Shi H, Li S, Liu Y, Liu F, Feng M, Chen Y, Lip GY. **Mobile photoplethysmographic technology to detect atrial fibrillation**. *J Am Coll Cardiol* (2019.0) **74** 2365-75. DOI: 10.1016/j.jacc.2019.08.019
12. Guo Y, Wang H, Zhang H, Chen Y, Lip GY. **Population-based screening or targeted screening based on initial clinical risk assessment for atrial fibrillation: a report from the Huawei heart study**. *J Clin Med* (2020.0) **9** 1493. DOI: 10.3390/jcm9051493
13. Lubitz SA, Faranesh AZ, Atlas SJ, McManus DD, Singer DE, Pagoto S, Pantelopoulos A, Foulkes AS. **Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: the Fitbit heart study**. *Am Heart J* (2021.0) **238** 16-26. DOI: 10.1016/j.ahj.2021.04.003
14. Dickson EL, Ding EY, Saczynski JS, Han D, Moonis M, Fitzgibbons TP, Barton B, Chon K, McManus DD. **Smartwatch monitoring for atrial fibrillation after stroke-The Pulsewatch Study: protocol for a multiphase randomized controlled trial**. *Cardiovasc Digit Health J* (2021.0) **2** 231-41. DOI: 10.1016/j.cvdhj.2021.07.002
15. Miles R, Hamilton K. *Learning UML 2.0* (2006.0)
16. Bashar SK, Han D, Hajeb-Mohammadalipour S, Ding E, Whitcomb C, McManus DD, Chon KH. **Atrial fibrillation detection from wrist photoplethysmography signals using smartwatches**. *Sci Rep* (2019.0) **9** 15054. DOI: 10.1038/s41598-019-49092-2
17. Han D, Bashar SK, Mohagheghian F, Ding E, Whitcomb C, McManus DD, Chon KH. **Premature atrial and ventricular contraction detection using photoplethysmographic data from a smartwatch**. *Sensors (Basel)* (2020.0) **20** 5683. DOI: 10.3390/s20195683
18. Han D, Bashar SK, Lázaro J, Mohagheghian F, Peitzsch A, Nishita N, Ding E, Dickson EL, DiMezza D, Scott J, Whitcomb C, Fitzgibbons TP, McManus DD, Chon KH. **A real-time PPG peak detection method for accurate determination of heart rate during sinus rhythm and cardiac arrhythmia**. *Biosensors (Basel)* (2022.0) **12** 82. DOI: 10.3390/bios12020082
19. **Tizen Architecture**. *Tizen Developers*
20. Jackson W, Jackson W. **Smartwatch design history, concepts, terms, and installation**. *SmartWatch Design Fundamentals: WatchFace Design for Samsung Galaxy SmartWatches* (2019.0) 1-32
21. **Knox Configure for wearables**. *Samsung Knox*
22. Serrano N, Hernantes J, Gallardo G. **Mobile web apps**. *IEEE Softw* (2013.0) **30** 22-7. DOI: 10.1109/ms.2013.111
23. Morville P, Rosenfeld L. *Information Architecture for the World Wide Web: Designing Large-Scale Web Sites. 3rd edition* (2006.0)
24. Evans RS. **Electronic health records: then, now, and in the future**. *Yearb Med Inform* (2016.0) **25** S48-61. DOI: 10.15265/IYS-2016-s006
25. Ding EY, CastañedaAvila M, Tran KV, Mehawej J, Filippaios A, Paul T, Otabil EM, Noorishirazi K, Han D, Saczynski JS, Barton B, Mazor KM, Chon K, McManus DD. **Usability of a smartwatch for atrial fibrillation detection in older adults after stroke**. *Cardiovasc Digit Health J* (2022.0) **3** 126-35. DOI: 10.1016/j.cvdhj.2022.03.003
26. Brooke J, Jordan PW, Thomas B, McClelland IL, Weerdmeester B. **SUS: a 'quick and dirty' usability scale**. *Usability Evaluation in Industry* (1996.0) 189-94
27. Ding E. **Feasibility of smartwatch-based atrial fibrillation detection among older adults after stroke**. *Morningside Graduate School of Biomedical Sciences, University of Massachusetts* (2021.0)
28. Eyal N. *Hooked: How to Build Habit-Forming Products* (2014.0)
29. Chae SH, Kim Y, Lee KS, Park HS. **Development and clinical evaluation of a web-based upper limb home rehabilitation system using a smartwatch and machine learning model for chronic stroke survivors: prospective comparative study**. *JMIR Mhealth Uhealth* (2020.0) **8** e17216. DOI: 10.2196/17216
30. Lutze R, Waldhör K. **Personal health assistance for elderly people via smartwatch based motion analysis**. *Proceedings of the 2017 IEEE International Conference on Healthcare Informatics* (2017.0) 124-33. DOI: 10.1109/ichi.2017.79
31. Lutze R, Waldhör K. **A smartwatch software architecture for health hazard handling for elderly people**. *Proceedings of the 2015 International Conference on Healthcare Informatics* (2015.0) 356-61. DOI: 10.1109/ichi.2015.50
32. Lutze R, Waldhör K. **The application architecture of smartwatch apps - analysis, principles of design and organization**. *Proceedings of the 46th Jahrestagung der Gesellschaft für Informatik, Informatik von Menschen für Menschen* (2016.0) 1865-77. DOI: 10.1007/s00287-016-0954-9
33. Årsand E, Muzny M, Bradway M, Muzik J, Hartvigsen G. **Performance of the first combined smartwatch and smartphone diabetes diary application study**. *J Diabetes Sci Technol* (2015.0) **9** 556-63. DOI: 10.1177/1932296814567708
34. Bhadade US, Trivedi AI. **Lossless text compression using dictionaries**. *Int J Comput Appl* (2011.0) **13** 27-34. DOI: 10.5120/1799-1767
35. **Resources**. *Biosignal Processing and Wearable Device Lab*
|
---
title: 'Designing an App for Parents and Caregivers to Promote Cognitive and Socioemotional
Development and Well-being Among Children Aged 0 to 5 Years in Diverse Cultural
Settings: Scientific Framework'
journal: JMIR Pediatrics and Parenting
year: 2023
pmcid: PMC9972208
doi: 10.2196/38921
license: CC BY 4.0
---
# Designing an App for Parents and Caregivers to Promote Cognitive and Socioemotional Development and Well-being Among Children Aged 0 to 5 Years in Diverse Cultural Settings: Scientific Framework
## Abstract
Recent years have seen remarkable progress in our scientific understanding of early childhood social, emotional, and cognitive development, as well as our capacity to widely disseminate health information by using digital technologies. Together, these scientific and technological advances offer exciting opportunities to deliver high-quality information about early childhood development (ECD) to parents and families globally, which may ultimately lead to greater knowledge and confidence among parents and better outcomes among children (particularly in lower- and middle-income countries). With these potential benefits in mind, we set out to design, develop, implement, and evaluate a new parenting app—Thrive by Five—that will be available in 30 countries. The app will provide caregivers and families with evidence-based and culturally appropriate information about ECD, accompanied by sets of collective actions that go beyond mere tips for parenting practices. Herein, we describe this ongoing global project and discuss the components of our scientific framework for developing and prototyping the app’s content. Specifically, we describe [1] 5 domains that are used to organize the content and goals of the app’s information and associated practices; [2] 5 neurobiological systems that are relevant to ECD and can be behaviorally targeted to potentially influence social, emotional, and cognitive development; [3] our anthropological and cultural framework for learning about local contexts and appreciating decolonization perspectives; and [4] our approach to tailoring the app’s content to local contexts, which involves collaboration with in-country partner organizations and local and international subject matter experts in ECD, education, medicine, psychology, and anthropology, among others. Finally, we provide examples of the content that was incorporated in Thrive by Five when it launched globally.
## Introduction
The first 5 years of human life are a remarkable period of cognitive, social, and emotional change. Before modern neuroimaging technologies, it was an open question as to how and to what extent the brain matures during childhood. Our understanding of this phenomenon is now much clearer, with a growing literature demonstrating large-scale structural and functional changes in the brain across childhood, starting from the very first months of life [1-6]. The development of the brain and its cognitive, social, and emotional functions during early life is critical for lifelong health and well-being. Levels of cognitive, social, and emotional functioning during childhood are associated with a variety of adult social, economic, and health outcomes [7-9], and children who struggle with some of these abilities (eg, self-control) when they are young are at elevated risk for negative outcomes as adults (eg, criminality) [9,10]. The degree to which child-rearing practices influence the development of these abilities during early childhood is of great interest.
*Behavior* genetics has demonstrated complex gene-environment interactions that, beyond a simple nature-nurture dichotomy [11,12], contribute greatly to how people differ in terms of cognitive, social, and emotional functioning. Notably, potentially modifiable environmental factors, such as what parents and families do with children, have a substantial influence on these differences. For example, a recent meta-analysis of twin studies estimated that around $40\%$ of individual differences in self-control are attributable to environmental effects [13]. Twin studies have shown the contribution of the shared (family) environment to individual differences in language ability [14], empathy [15], and cognitive school readiness [16], among other traits. Epidemiologic studies have also revealed the need to protect children from harmful environments to ensure optimal cognitive, social, and emotional development [17,18]. Critically, exposure to certain harms (eg, chronic stress and abuse) might be avoided by educating parents and by equipping them and their children with protective strategies. Altogether, we now know that childhood cognitive, social, and emotional traits are malleable. Improving children’s functioning in early life via parental behaviors may optimize development and prevent poor outcomes in adulthood, ultimately resulting in lifelong health and well-being.
Modern digital technologies (eg, smartphone apps) offer a highly scalable platform for delivering health information across a range of settings (eg, low- and middle-income countries), purposes (eg, education and prevention), and health conditions [19-22]. In an increasingly web-based world, app-based technologies offer the potential to address ethnic, racial, socioeconomic, and regional disparities in access to health information and may represent an effective means of delivering information about early childhood development (ECD) to parents and caregivers globally. Realizing these opportunities hinges on investigating and interrogating the social, cultural, and political dimensions of digital technology use [23], as well as conducting detailed examinations of how users of digital technologies perceive a specific platform’s usability (eg, user-friendliness), acceptability (eg, cultural appropriateness), and feasibility (eg, ease of use in daily life) [24].
With the goal of meeting these challenges and opportunities, we partnered with a philanthropic organization—Minderoo Foundation—in 2021 to develop, implement, and evaluate an app that aims to provide parents from approximately 30 countries with science-based and culturally relevant information about ECD. At the time of writing, the app—Thrive by Five—has been launched as 5 localized, country-specific versions. A key difference between Thrive by Five and other popular parenting apps is our focus on combining scientific knowledge with a cultural and anthropologic analysis of each country’s local context (eg, approaches to child-rearing, gender roles, and the position of the child in the family; Figure 1). Moreover, rather than focusing only on 1 parent and 1 child, we broadened the scope of the app’s child-rearing tips to draw in wider family and community networks, with the goal of exposing children to a wider set of cultural and traditional practices (eg, folk stories; myths; and traditional songs, music, and dance). Accordingly, we refer to the activities in the app as collective actions.
The objective of this paper is to describe this project’s scientific framework. By scientific framework, we refer to the project’s basic conceptual and pragmatic approach, including what we are targeting (ie, cognitive, social, and emotional well-being); why these targets are of interest (ie, the empirical, scientific rationale); and, critically, how users can engage with specific practices to potentially drive their children’s development in these target areas in ways that are culturally relevant. As our approach to integrating science, culture, and anthropology within a co-design context is novel, the rationale of describing our scientific framework is to provide a road map that other projects with similar aspirations may find useful, as well as a transparent description of the process underlying the design and development of the Thrive by Five app.
**Figure 1:** *Overview of our scientific framework for developing the Thrive by Five app. We combine cultural and anthropological analyses and scientific knowledge to develop and iterate country-specific practices for parents and significant others (collective actions).*
## Scientific Framework Overview
In the following sections, we elaborate on this project’s scientific framework. First, we describe 5 conceptual domains that are used to organize the content and goals of the information about ECD and child-rearing and outline 5 neurobiological systems that can be behaviorally targeted to influence social, emotional, and cognitive development (Scientific Framework Part 1). Second, we discuss our approach to developing an understanding of each country through the cocreation of a cultural framework that summarizes various literature regarding factors that may impact child-rearing and child development (Scientific Framework Part 2). Third, we introduce the concept of collective actions as an alternative to parenting tips, emphasizing the strengths of involving wider family and community networks in child-rearing (Scientific Framework Part 3). Fourth, we discuss our iterative approach to localizing the app’s content for each country, which involves holding collaborative workshops with in-country partner organizations; subject matter experts in ECD, education, medicine, psychology, and anthropology, among other disciplines; and potential users of the app in each country (Scientific Framework Part 4). Finally, we provide examples of the content (ie, collective actions) that was included in the app when it launched internationally in 2022 (the first full version of the app was implemented in Indonesia).
## Scientific Framework Part 1—Linking Content Development to 5 Thematic Domains and 5 Neurobiological Systems
Before developing the app’s content, we agreed on 5 thematic domains that are relevant to children’s social, emotional, and cognitive development and 5 neurobiological systems that are involved in social, emotional, and cognitive development. These domains and neurobiological systems (Figure 2) guide the development of the app’s content, are used to categorize the content within the app, and provide the scientific rationale for encouraging parents to engage with the practices promoted by the app.
The thematic domains are based broadly on the Bright Tomorrows project (developed by Minderoo Foundation and Telethon Kids Institute), with the Brain and Mind Centre team mapping new domains. The domains and the broad types of content included in each domain are [1] the Cognitive Brain domain, which includes content about broad cognitive processes (eg, attention, learning, memory, visual and auditory processing, motor skills, and imagination); [2] the Social Brain domain, which includes content about social interaction and the sociocognitive processes involved in recognizing, interpreting, and responding to social cues (eg, eye gaze, joint attention, and facial expressions); [3] the Language and Communication domain, which includes content about processing, understanding, and using verbal and nonverbal language and signals (eg, gestures); [4] the Identity and Culture domain, which includes content about the development of a sense of personal, social, and community identity and the roles that culture and place play in identity development (eg, customs, festivals, and folk stories); and [5] the Physical Health domain, which includes content about physical health, growth, and development and physical protection from harm and abuse (eg, harsh discipline).
The neurobiological systems that we focus on and an outline of their relevance to early child development are shown in Figure 2. These five systems and their main functions include [1] the stress response system, which creates a hormonal response to stress (prolonged activation of the stress response system is associated with negative emotional, behavioral, and physical health outcomes); [2] the oxytocin system, which regulates social, behavioral, and emotional processes (eg, smiling, attention to eye gaze, and breastfeeding), of which many are fundamentally important for early child-caregiver bonds and other social bonds; [3] the learning system, which assigns value to objects and behaviors (in childhood, this is fundamental for motivation creation, social behaviors, and associative learning); [4] the fear-arousal-memory system, which encodes and maintains memories of fearful stimuli and the contexts in which they are experienced; and [5] the circadian system, which orchestrates the daily rhythmic timing of almost all physiological processes and behaviors (eg, sleep and wakefulness, appetite, mood, and cognitive function). Other publications provide more details about these systems and their relevance to early child development [25-38].
**Figure 2:** *The five domains for collective action and the five neurobiological systems/circuits used to guide the conceptualization and development of the app’s content.*
## Scientific Framework Part 2—Development of Cultural Frameworks to Localize Content
For each country, a cultural framework is developed collaboratively with the research team and a nominated country-specific expert. This framework is used to guide the first draft of the app’s content for each local context. The cultural framework summarizes information from a variety of published literature (eg, government reports, journal articles, and textbooks) that is relevant to child-rearing; family environments; and broader social, economic, and political factors that may influence family functioning and early child development. For each country, we follow a dedicated pro forma that covers the topics presented in Figure 3.
Concurrently, the research team prepares a literature summary that presents the strengths (eg, transgenerational family networks; the empowerment of women; and the cultural celebration of art, music, and dance) and challenges (eg, high rates of childhood mortality, obesity, and exposure to corporal punishment) of each country, which are considered when developing the app’s content. The content aims to celebrate the cultural strengths and practices of each country by highlighting how they align with the scientific evidence about childhood development while also considering the various challenges that may impede these practices and how these challenges may be mitigated. Once complete, the cultural framework and literature summary are reviewed and approved by an in-country partner organization.
**Figure 3:** *Components of the cultural framework prepared for each country.*
## Scientific Framework Part 3—Conceptualizing the App’s Content as Collective Actions and Not Just as Parenting Tips
Many available parenting apps adopt a narrow focus in their content, encouraging activities to be completed by 1 parent (typically a mother) with 1 child. Although these activities are well-meaning and are still of potential benefit, their dyadic structure is limiting. Such activities reduce exposure to a variety of social interactions with different people; lack the richness of multigenerational and extended family structures; and narrow the bounds of the complexity and variety of children’s social, emotional, and cognitive experiences. Therefore, we conceptualize the content in the app not as parenting tips but as collective actions.
These collective actions broaden the scope of the child-focused activities to include a significantly larger network of individuals in a child’s life. As well as mothers, fathers, uncles, aunts, siblings, cousins, grandmothers, and grandfathers (Figure 4), we also encourage families to bring in other trusted adults from their communities and social networks to engage in these actions. We believe that this wider circle of interactions with and around a child may drive greater gains in cognitive, social, and emotional development (by increasing the varieties of experiences, stimuli, and interactions), in addition to increasing the opportunities for a child to be exposed to and embedded within the rich tapestries of their extended family’s culture and history (eg, rituals; folk stories; myths; and traditional songs, music, and dance).
In the Thrive by Five app, the primary content (collective actions) comprises 2 components (Figure 5). First, “The Why” provides the scientific background that supports a particular activity (eg, the importance of breastfeeding for a baby’s social, cognitive, emotional, and physical development and health outcomes) [39,40]. Second, the “Activity Pop Up” provides a practical activity in which parents, siblings, grandparents, other extended family members, and trusted community members could participate with the child. “ The Why” and the “Activity Pop Up” are available both in text form and in audio form.
**Figure 4:** *Our approach to content: collective actions, not just parenting tips. As opposed to a set of child-rearing practices that encourage simplistic parent-child interactions, our concept of collective actions encourages involvement from many family members (eg, grandparents, siblings, and cousins) and trusted community members in interactions with the child. Although we have placed the child at the center of this network, we also recognize the child as an “actor,” as children often initiate interactions with others, and that these communications represent interactive loops rather than a unidirectional interaction.* **Figure 5:** *An example of a collective action, including “The Why” and “Activity Pop Up” components in English (top row) and Bahasa Indonesia (bottom row).*
## Scientific Framework Part 4—The Iterative Process of Localizing Content to Each Country
Over the course of this 3-year project, the Thrive by Five app will be implemented in 30 countries. A unique feature of this project is that for each country, we are actively considering how we can integrate cultural practices into the collective actions as a way of strengthening connections across families and communities and connections to places, cultural traditions, and values. To ensure that the app’s content is culturally acceptable, usable, relevant, and engaging, we developed a 4-stage process for developing and prototyping the country-specific collective actions (Figure 1) in partnership with local and international experts in ethnography, anthropology, ECD, medicine, psychology, and other disciplines.
The first phase of development is the conceptualization of the initial library of collective actions for a given country. This process is guided by the research team’s expertise and an examination of published research in areas that are relevant to the five domains and the five neurobiological systems that we previously described (Figure 2). The cultural framework (Figure 3) is used concurrently to highlight topics regarding local needs (eg, hygiene and distress management).
In the second phase of development and prototyping, the research team—in cooperation with a nominated in-country partner and representatives from the Minderoo Foundation—holds a series of co-design workshops with local subject matter experts (eg, educators, pediatricians, and psychologists) to examine the acceptability, feasibility, and relevance of the draft content (more details are provided elsewhere [41]). Similarly, in the third phase of development and prototyping, the research team holds a series of workshops with in-country parents, using a beta version or clickable demonstration version of the app to further examine the acceptability, usability, and relevance of the content. Based on the data that emerge from the co-design workshops, the drafted content is iteratively revised.
Finally, the last phase of development and prototyping includes the implementation of the app in the given country, after which an evaluation phase is conducted that examines the impacts of the Thrive by Five app on several factors, including parent-level confidence and self-efficacy (more details are provided elsewhere [41]). Importantly, at any one of the phases that involve communication with in-country partners, experts, and parents, ideas for subsequent collective actions may emerge.
## Results and Discussion
The international launch of the Thrive by Five app was in March 2022, marked by the implementation of the first full version of the app in Indonesia. Further, 4 other versions of the app (with localized country-specific content) have been successfully implemented in Afghanistan, Namibia, Kyrgyzstan, and Uzbekistan as of November 2022, and 5 other country-specific libraries of localized content have already been codeveloped and are awaiting implementation. As this project continues to progress, the team will codevelop 20 country-specific libraries and implement the app in 25 countries (bringing the total to 30 countries).
A series of evaluation studies will investigate the parent-level, family-level, and system-level impacts of the app on several factors, including the perceived connection between parents and children and between children and the community, parents’ confidence in their caregiving abilities, and knowledge gain with regard to positive child-rearing practices [41]. These evaluations will include a mix of quantitative and qualitative designs and a mix of country-specific investigations and larger cross-country investigations. We anticipate the first empirical reports from this program to be submitted for publication in early 2023.
We acknowledge several limitations of our approach. First, while we are co-designing each of the collective actions (collaborating with potential users and local experts) and the aspects of the app’s design (eg, illustrations) [41], several of the features and functions of the app were developed before the co-design phase. Second, in some instances, we have had strong feedback from co-design workshop participants about the relative lack of content related to religious practices. Although obviously culturally relevant, we decided that many of these suggestions would not be included in the app’s content, as we could not be confident about their relationships with the cognitive, emotional, and social outcomes of this project. Third, we recognize that while some of our team members have personal experiences with the countries for which we are developing the app, we cannot truly understand the nuances, particularities, and meanings of each country’s cultural practices (A Poulsen et al, unpublished data, 2022) [42]. In these instances, we adopt a position of cultural humility and rely more heavily (and modestly) on collaboration with our in-country partners and workshop participants. We hope that we can learn and understand enough to make each app feel authentic and relevant to the users.
Our approach to developing the Thrive by Five app—combining science with cultural and anthropological knowledge—is highly original, and we hope that it will produce a useful, relevant, and engaging resource for parents and families around the world. The first outcomes from this work are expected to be published in early 2023. In closing, this global project brings together cutting-edge knowledge from neuroscience, ECD, digital technology, and anthropology, with the major goal of empowering families around the world with new tools and practices for shaping their children’s futures.
## References
1. Gilmore JH, Knickmeyer RC, Gao W. **Imaging structural and functional brain development in early childhood**. *Nat Rev Neurosci* (2018) **19** 123-137. DOI: 10.1038/nrn.2018.1
2. Lebel C, Beaulieu C. **Longitudinal development of human brain wiring continues from childhood into adulthood**. *J Neurosci* (2011) **31** 10937-10947. DOI: 10.1523/JNEUROSCI.5302-10.2011
3. Raznahan A, Shaw P, Lalonde F, Stockman M, Wallace GL, Greenstein D, Clasen L, Gogtay N, Giedd JN. **How does your cortex grow?**. *J Neurosci* (2011) **31** 7174-7177. DOI: 10.1523/JNEUROSCI.0054-11.2011
4. Reynolds JE, Grohs MN, Dewey D, Lebel C. **Global and regional white matter development in early childhood**. *Neuroimage* (2019) **196** 49-58. DOI: 10.1016/j.neuroimage.2019.04.004
5. Knickmeyer RC, Gouttard S, Kang C, Evans D, Wilber K, Smith JK, Hamer RM, Lin W, Gerig G, Gilmore JH. **A structural MRI study of human brain development from birth to 2 years**. *J Neurosci* (2008) **28** 12176-12182. DOI: 10.1523/JNEUROSCI.3479-08.2008
6. Ouyang M, Dubois J, Yu Q, Mukherjee P, Huang H. **Delineation of early brain development from fetuses to infants with diffusion MRI and beyond**. *Neuroimage* (2019) **185** 836-850. DOI: 10.1016/j.neuroimage.2018.04.017
7. Althoff RR, Verhulst FC, Rettew DC, Hudziak JJ, van der Ende J. **Adult outcomes of childhood dysregulation: a 14-year follow-up study**. *J Am Acad Child Adolesc Psychiatry* (2010) **49** 1105-1116. DOI: 10.1016/j.jaac.2010.08.006
8. Ahmed SF, Kuhfeld M, Watts TW, Davis-Kean PE, Vandell DL. **Preschool executive function and adult outcomes: A developmental cascade model**. *Dev Psychol* (2021) **57** 2234-2249. DOI: 10.1037/dev0001270
9. Moffitt TE, Arseneault L, Belsky D, Dickson N, Hancox RJ, Harrington H, Houts R, Poulton R, Roberts BW, Ross S, Sears MR, Thomson WM, Caspi A. **A gradient of childhood self-control predicts health, wealth, and public safety**. *Proc Natl Acad Sci U S A* (2011) **108** 2693-2698. DOI: 10.1073/pnas.1010076108
10. Fergusson DM, Boden JM, Horwood LJ. **Childhood self-control and adult outcomes: results from a 30-year longitudinal study**. *J Am Acad Child Adolesc Psychiatry* (2013) **52** 709-717.e1. DOI: 10.1016/j.jaac.2013.04.008
11. Turkheimer E. **Three laws of behavior genetics and what they mean**. *Curr Dir Psychol Sci* (2000) **9** 160-164. DOI: 10.1111/1467-8721.00084
12. Chabris CF, Lee JJ, Cesarini D, Benjamin DJ, Laibson DI. **The fourth law of behavior genetics**. *Curr Dir Psychol Sci* (2015) **24** 304-312. DOI: 10.1177/0963721415580430
13. Willems YE, Boesen N, Li J, Finkenauer C, Bartels M. **The heritability of self-control: A meta-analysis**. *Neurosci Biobehav Rev* (2019) **100** 324-334. DOI: 10.1016/j.neubiorev.2019.02.012
14. Kovas Y, Hayiou-Thomas ME, Oliver B, Dale PS, Bishop DVM, Plomin R. **Genetic influences in different aspects of language development: the etiology of language skills in 4.5-year-old twins**. *Child Dev* (2005) **76** 632-651. DOI: 10.1111/j.1467-8624.2005.00868.x
15. Abramson L, Uzefovsky F, Toccaceli V, Knafo-Noam A. **The genetic and environmental origins of emotional and cognitive empathy: Review and meta-analyses of twin studies**. *Neurosci Biobehav Rev* (2020) **114** 113-133. DOI: 10.1016/j.neubiorev.2020.03.023
16. Lemelin JP, Boivin M, Forget-Dubois N, Dionne G, Séguin JR, Brendgen M, Vitaro F, Tremblay RE, Pérusse D. **The genetic-environmental etiology of cognitive school readiness and later academic achievement in early childhood**. *Child Dev* (2007) **78** 1855-1869. DOI: 10.1111/j.1467-8624.2007.01103.x
17. Lupien SJ, McEwen BS, Gunnar MR, Heim C. **Effects of stress throughout the lifespan on the brain, behaviour and cognition**. *Nat Rev Neurosci* (2009) **10** 434-445. DOI: 10.1038/nrn2639
18. Kim P, Evans GW, Angstadt M, Ho SS, Sripada CS, Swain JE, Liberzon I, Phan KL. **Effects of childhood poverty and chronic stress on emotion regulatory brain function in adulthood**. *Proc Natl Acad Sci U S A* (2013) **110** 18442-18447. DOI: 10.1073/pnas.1308240110
19. Naslund JA, Aschbrenner KA, Araya R, Marsch LA, Unützer J, Patel V, Bartels SJ. **Digital technology for treating and preventing mental disorders in low-income and middle-income countries: a narrative review of the literature**. *Lancet Psychiatry* (2017) **4** 486-500. DOI: 10.1016/S2215-0366(17)30096-2
20. Hickie IB. **The role of new technologies in monitoring the evolution of psychopathology and providing measurement-based care in young people**. *World Psychiatry* (2020) **19** 38-39. DOI: 10.1002/wps.20697
21. Buss VH, Leesong S, Barr M, Varnfield M, Harris M. **Primary prevention of cardiovascular disease and type 2 diabetes mellitus using mobile health technology: Systematic review of the literature**. *J Med Internet Res* (2020) **22** e21159. DOI: 10.2196/21159
22. Akinosun AS, Polson R, Diaz-Skeete Y, De Kock JH, Carragher L, Leslie S, Grindle M, Gorely T. **Digital technology interventions for risk factor modification in patients with cardiovascular disease: Systematic review and meta-analysis**. *JMIR Mhealth Uhealth* (2021) **9** e21061. DOI: 10.2196/21061
23. Lupton D. **Health promotion in the digital era: a critical commentary**. *Health Promot Int* (2015) **30** 174-183. DOI: 10.1093/heapro/dau091
24. Nievas-Soriano BJ, García-Duarte S, Fernández-Alonso AM, Bonillo-Perales A, Parrón-Carreño T. **Users evaluation of a Spanish eHealth pediatric website**. *Comput Methods Programs Biomed* (2021) **212** 106462. DOI: 10.1016/j.cmpb.2021.106462
25. LeGates TA, Fernandez DC, Hattar S. **Light as a central modulator of circadian rhythms, sleep and affect**. *Nat Rev Neurosci* (2014) **15** 443-454. DOI: 10.1038/nrn3743
26. Ludwig M, Armstrong WE, Tasker JG. **Dendritic release of the neuropeptides vasopressin and oxytocin**. *Neurophysiology of Neuroendocrine Neurons* (2014) 881-895
27. McLaughlin KA, Sheridan MA, Tibu F, Fox NA, Zeanah CH, Nelson CA 3rd. **Causal effects of the early caregiving environment on development of stress response systems in children**. *Proc Natl Acad Sci U S A* (2015) **112** 5637-5642. DOI: 10.1073/pnas.1423363112
28. Shahrestani S, Kemp AH, Guastella AJ. **The impact of a single administration of intranasal oxytocin on the recognition of basic emotions in humans: a meta-analysis**. *Neuropsychopharmacology* (2013) **38** 1929-1936. DOI: 10.1038/npp.2013.86
29. Tovote P, Fadok JP, Lüthi A. **Neuronal circuits for fear and anxiety**. *Nat Rev Neurosci* (2015) **16** 317-331. DOI: 10.1038/nrn3945
30. Agorastos A, Pervanidou P, Chrousos GP, Kolaitis G. **Early life stress and trauma: developmental neuroendocrine aspects of prolonged stress system dysregulation**. *Hormones (Athens)* (2018) **17** 507-520. DOI: 10.1007/s42000-018-0065-x
31. Akacem LD, Wright KP Jr, LeBourgeois MK. **Sensitivity of the circadian system to evening bright light in preschool-age children**. *Physiol Rep* (2018) **6** e13617. DOI: 10.14814/phy2.13617
32. Allada R, Bass J. **Circadian mechanisms in medicine**. *N Engl J Med* (2021) **384** 550-561. DOI: 10.1056/NEJMra1802337
33. Crouse JJ, Carpenter JS, Song YJC, Hockey SJ, Naismith SL, Grunstein RR, Scott EM, Merikangas KR, Scott J, Hickie IB. **Circadian rhythm sleep-wake disturbances and depression in young people: implications for prevention and early intervention**. *Lancet Psychiatry* (2021) **8** 813-823. DOI: 10.1016/S2215-0366(21)00034-1
34. Dedovic K, Duchesne A, Andrews J, Engert V, Pruessner JC. **The brain and the stress axis: the neural correlates of cortisol regulation in response to stress**. *Neuroimage* (2009) **47** 864-871. DOI: 10.1016/j.neuroimage.2009.05.074
35. Dias CC, Figueiredo B, Rocha M, Field T. **Reference values and changes in infant sleep-wake behaviour during the first 12 months of life: a systematic review**. *J Sleep Res* (2018) **27** e12654. DOI: 10.1111/jsr.12654
36. Feldman R, Gordon I, Influs M, Gutbir T, Ebstein RP. **Parental oxytocin and early caregiving jointly shape children's oxytocin response and social reciprocity**. *Neuropsychopharmacology* (2013) **38** 1154-1162. DOI: 10.1038/npp.2013.22
37. Guastella AJ, Mitchell PB, Dadds MR. **Oxytocin increases gaze to the eye region of human faces**. *Biol Psychiatry* (2008) **63** 3-5. DOI: 10.1016/j.biopsych.2007.06.026
38. Rilling JK, Young LJ. **The biology of mammalian parenting and its effect on offspring social development**. *Science* (2014) **345** 771-776. DOI: 10.1126/science.1252723
39. Horta BL, de Mola CL, Victora CG. **Breastfeeding and intelligence: a systematic review and meta-analysis**. *Acta Paediatr* (2015) **104** 14-19. DOI: 10.1111/apa.13139
40. Sankar MJ, Sinha B, Chowdhury R, Bhandari N, Taneja S, Martines J, Bahl R. **Optimal breastfeeding practices and infant and child mortality: a systematic review and meta-analysis**. *Acta Paediatr* (2015) **104** 3-13. DOI: 10.1111/apa.13147
41. LaMonica HM, Crouse JJ, Song YJC, Alam M, Ekambareshwar M, Loblay V, Yoon A, Cha G, Wilson C, Sweeney-Nash M, Foo N, Teo M, Perhirin M, Troy J, Hickie IB. **Developing a parenting app to support young children's socioemotional and cognitive development in culturally diverse low- and middle-income countries: Protocol for a co-design study**. *JMIR Res Protoc* (2022) **11** e39225. DOI: 10.2196/39225
42. Garner SL, Koch H, George CE, Hitchcock J, Norman G, Green G, Young P, Mahid Z. **Cross cultural team collaboration: Integrating cultural humility in mHealth development and research**. *Inform Health Soc Care* (2021) **46** 345-354. DOI: 10.1080/17538157.2021.1895168
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---
title: Holistic profiling of the venom from the Brazilian wandering spider Phoneutria
nigriventer by combining high-throughput ion channel screens with venomics
authors:
- F. C. Cardoso
- A. A. Walker
- G. F. King
- M. V. Gomez
journal: Frontiers in Molecular Biosciences
year: 2023
pmcid: PMC9972223
doi: 10.3389/fmolb.2023.1069764
license: CC BY 4.0
---
# Holistic profiling of the venom from the Brazilian wandering spider Phoneutria nigriventer by combining high-throughput ion channel screens with venomics
## Abstract
Introduction: Spider venoms are a unique source of bioactive peptides, many of which display remarkable biological stability and neuroactivity. Phoneutria nigriventer, often referred to as the Brazilian wandering spider, banana spider or “armed” spider, is endemic to South America and amongst the most dangerous venomous spiders in the world. There are 4,000 envenomation accidents with P. nigriventer each year in Brazil, which can lead to symptoms including priapism, hypertension, blurred vision, sweating, and vomiting. In addition to its clinical relevance, P. nigriventer venom contains peptides that provide therapeutic effects in a range of disease models.
Methods: *In this* study, we explored the neuroactivity and molecular diversity of P. nigriventer venom using fractionation-guided high-throughput cellular assays coupled to proteomics and multi-pharmacology activity to broaden the knowledge about this venom and its therapeutic potential and provide a proof-of-concept for an investigative pipeline to study spider-venom derived neuroactive peptides. We coupled proteomics with ion channel assays using a neuroblastoma cell line to identify venom compounds that modulate the activity of voltage-gated sodium and calcium channels, as well as the nicotinic acetylcholine receptor.
Results: *Our data* revealed that P. nigriventer venom is highly complex compared to other neurotoxin-rich venoms and contains potent modulators of voltage-gated ion channels which were classified into four families of neuroactive peptides based on their activity and structures. In addition to the reported P. nigriventer neuroactive peptides, we identified at least 27 novel cysteine-rich venom peptides for which their activity and molecular target remains to be determined.
Discussion: Our findings provide a platform for studying the bioactivity of known and novel neuroactive components in the venom of P. nigriventer and other spiders and suggest that our discovery pipeline can be used to identify ion channel-targeting venom peptides with potential as pharmacological tools and to drug leads.
## Introduction
Venomous animals are a highly adapted group of organisms whose evolutionary success excelled with the emergence of venom. Spider venoms, in particular, are rich in peptide knottins specialized in modulating, often with high potency and selectivity, voltage-gated ion channels that regulate the physiology of neuronal, muscular and cardiac systems (Cardoso and Lewis, 2018; Cardoso, 2020). Although such effects can be deleterious to envenomated animals, venom components can be tailored to selectively modulate ion channels in pathways of complex diseases such as chronic pain, motor neuron disease, and epilepsy. This has been demonstrated for numerous spider venoms (Smith et al., 2015; Cardoso and Lewis, 2018, 2019), including the venom of the infamous South American ctenid spider Phoneutria nigriventer, often referred as Brazilian wandering spider, banana spider or “armed” spider (Peigneur et al., 2018). Besides its clinical relevance due to frequent envenomation cases in Brazil, with approximately 4,000 cases per year (Isbister and Fan, 2011; Gewehr et al., 2013), P. nigriventer venom contains peptides that have therapeutic effects in a range of disease models including chronic pain (Pedron et al., 2021; Cavalli et al., 2022), Huntington’s disease (Joviano-Santos et al., 2022), glaucoma (da Silva et al., 2020) and erectile dysfunction (*Nunes da* Silva et al., 2019).
Initial studies of P. nigriventer venom employed fractionation via gel filtration and reversed-phase chromatography to separate the venom into five distinct groups of peptides based on their molecular weight and hydrophobicity properties; these groups were named PhTx1 to PhTx5 (Peigneur et al., 2018). PhTx1–4 comprise cysteine-rich peptides that are active on voltage-gated calcium (CaV), sodium (NaV) and potassium (KV) channels, while PhTx5 is comprised of short linear peptides, with a total of 34 peptides identified (Peigneur et al., 2018). Proteotranscriptomic studies of P. nigriventer venom revealed additional peptides with high similarity to those previously described, but very few have been characterised pharmacologically (Cardoso et al., 2003; Richardson et al., 2006). This represents an obstacle to the exploration of the therapeutic potential of P. nigriventer venom.
Advances in venom-peptide research have yielded high-throughput cellular screens for the discovery and pharmacological characterisation of naturally occurring molecules with activity at ion channels and receptors in physiological pathways (Cardoso et al., 2015; Cardoso et al., 2021). These methods require only a small amount of venom compared to more traditional methods and allow the identification of therapeutically relevant peptides in the early stages of the screening. Besides drug development applications, these same bioassays can assist in unravelling the bioactivity of crude and fractionated venoms from biomedically relevant venomous animals to support studies of evolution and antivenom development, but much work remains to be done in this field.
This study aimed to provide a proof-of-concept in applying high-throughput cellular screens for multiple neuronal ion channels along with proteomic studies of fractionated venom to rapidly characterise spider venoms in terms of bioactive components. It was anticipated that such a pipeline would support envenomation and evolutionary studies and the development of therapeutics from animal venoms. The venom of P. nigriventer was selected as a model system due to its medical relevance, the considerable number of therapeutically relevant peptides already uncovered in the venom, and the wide knowledge base available. Our approach enabled identification of potent modulators of voltage-gated ion channels which were classified into four families of neuroactive peptides based on their activity and structures. In addition to the previously characterised neuroactive peptides in the P. nigriventer venom, we identified 27 additional cysteine-rich venom peptides in which neuroactivities are underexplored. This work contributes to the on-going discovery and structure-function characterisation of spider-venom peptides. Moreover, our bioassay pipeline can be used to guide future research into the discovery of venom peptides that modulate the activity of ion channels, and their development as pharmacological tools and drug leads.
## Materials and methods
We applied a holistic approach combining methods in high throughput screens for ion channels, venom proteome, venom gland transcriptome and modelling of peptides as described in Figure 1.
**FIGURE 1:** *Flowchart of the venom peptide discovery pipeline applied in this study. Expanding from the traditional assay-guided fractionation, we applied HTS bioassays to characterize the pharmacology of venom peptides on multiple ion channels, followed by the identification of peptide masses and primary sequences using proteome and transcriptome. Ultimately, the three-dimensional structure of venom peptides was determined using in silico molecular modelling.*
## Cell culture
The human neuroblastoma cell line SH-SY5Y was maintained at 37°C in a humidified $5\%$ CO2 incubator in Roswell Park Memorial Institute (RPMI) medium supplemented with $15\%$ foetal bovine serum (FBS) and 2 mM L-glutamine. Replicating cells were sub-cultured every 3–4 days in a 1:5 ratio using $0.25\%$ trypsin/EDTA.
## Venom fractionation
Crude venom milked from male and female specimens of P. nigriventer was kindly provided by Prof. Marcus Vinicius Gomez from the Institute of Teaching and Research of Santa Casa de Belo Horizonte, Belo Horizonte, Brazil. Venom (lyophilised, 1 mg) was dissolved in 100 μL Milli-Q water containing $0.05\%$ trifluoroacetic acid (TFA) (Auspep, VIC, AU) and $5\%$ acetonitrile (ACN) and centrifuged at 5,000 × g for 10 min to remove particulates. Venom was fractionated by reversed-phase high performance liquid chromatography (RP-HPLC) using a C18 column (Vydac 4.6 mm × 250 mm, 5 μm, Grace Discovery Sciences, United States) with a gradient of solvent B ($90\%$ ACN in $0.045\%$ TFA) in solvent A ($0.05\%$ TFA). The gradient was $5\%$ B for 5 min, followed by $20\%$–$40\%$ solvent B over 60 min at a flow rate 0.7 mL min−1. Peaks were collected every minute, with fraction 1 eluted between 1 and 2 min and so on for the other fractions. Venom fractions were lyophilised before storage at –20°C.
## Calcium influx assays
Venom fractions were screened for neuroactivity at human (h) NaV, CaV1, CaV2 and the α7 subtype of the human nicotinic acetylcholine receptor (nAChR-α7) as previously described (Cardoso et al., 2015). Briefly, SH-SY5Y cells were plated at 40,000 cells per well in 384-well flat clear-bottom black plates (Corning, NY, United States) and cultured at 37°C in a humidified $5\%$ CO2 incubator for 48 h. Cells were loaded with 20 μL per well Calcium 4 dye (Molecular Devices) reconstituted in assay buffer containing (in mM) 140 NaCl, 11.5 glucose, 5.9 KCl, 1.4 MgCl2, 1.2 NaH2PO4, 5 NaHCO3, 1.8 CaCl2 and 10 HEPES pH 7.4 and incubated for 30 min at 37°C in a humidified $5\%$ CO2 incubator. For the hCaV1 assay, the dye was supplemented with 1 μM ω-conotoxin-CVIF (CVIF) to inhibit CaV2, and in the hCav2 assay the dye was supplemented with 10 μM nifedipine to inhibit CaV1. For the nAChR-α7 assay, the dye was supplemented with PNU-120596 (Sigma-Aldrich), a positive allosteric modulator of nAChR-α7. Venom fractions were assayed in singleton for each ion channel tested. Fluorescence responses were recorded using excitation at 470–495 nm and emission at 515–575 nm for 10 s to set the baseline, then 300 s after addition of $10\%$ venom fraction serial diluted at 1, 1:10, and 1:100, and for a further 300 s after addition of 50 μM veratridine for hNaV, 90 mM KCl and 5 mM CaCl2 for hCaV, and 30 μM choline for nAChR-α7.
## Proteomics
Venom fractions eluting between 10 and 45 min on RP-HPLC were analysed by mass spectrometry to investigate the masses and primary structures of their peptide components. Native mass determinations were carried out with $20\%$ of each fraction dried by vacuum centrifuge and resuspended in 20 μL $1\%$ formic acid (FA), followed by analysis using by liquid chromatography/tandem mass spectrometry (LC-MS/MS). For identification of primary structures, $20\%$ of each peptide fraction was reduced and alkylated by adding 40 μL of reagent composed of 4.875 mL ACN, 4.5 mL ultrapure water, 0.5 mL 1M ammonium carbonate pH 11.0, 100 μL 2-iodoethanol and 25 μL triethylphosphine, and incubating for 1 h at 37°C. Samples were speed dried in a vacuum centrifuge, and digested with 40 ng/μL trypsin in 50 mM ammonium bicarbonate pH 8.0 and $10\%$ ACN overnight at room temperature. Trypsin was inactivated by adding 50 μL solution containing $50\%$ acetonitrile and $5\%$ formic acid (FA), dried in speed vacuum centrifuge, and resuspended in $1\%$ formic acid.
LC-MS/MS samples were loaded onto a 150 mm × 0.1 mm Zorbax 300SB-C18 column (Agilent, Santa Clara, CA, United States) on a Shimadzu Nano LC system with the outflow coupled to a SCIEX 5600 Triple TOF (Framingham, MA, United States) mass spectrometer equipped with a Turbo V ion source. Peptides were eluted using a 30 min gradient of $1\%$–$40\%$ solvent B ($90\%$ ACN/$0.1\%$ FA) in solvent A ($0.1\%$ FA) at a flow rate of 0.2 mL/min. For MS1 scans, m/z was set between 350 and 2,200. Precursor ions with m/z 350–1,500, charge of +2 to +5, and signals with >100 counts/s (excluding isotopes within 2 Da) were selected for fragmentation, and MS2 scans were collected over a range of 80–1,500 m/z. Scans were obtained with an accumulation time of 250 ms and a cycle of 4 s.
A database of possible peptide sequences produced in P. nigriventer venom glands was compiled using a published venom-gland transcriptome (Diniz et al., 2018), from which open reading frames (ORFs) longer than 30 amino acids were identified and translated by TransDecoder. A list of 200 common MS contaminants was added to the translated ORFs, which was used as a sequence database to compare to mass spectral data using the Paragon algorithm in Protein Pilot 2.2 software (AB SCIEX). We report only peptides for which more than two tryptic fragments were detected with >$95\%$ confidence, or where one tryptic fragment was detected, and a secretion signal peptide was predicted by SignalP5.0.
## Molecular modelling
Venom peptides identified in this study were selected based on their cysteine-rich scaffold and bioactivity, and their three-dimensional (3D) structure were predicted using the AlphaFold 2 algorithm (Jumper et al., 2021). All 3D structures displayed were from unrelaxed models ranked 1 for each peptide prediction. 3D structures were visualised and analysed using PyMol (Pymol, 2023).
## Data analysis
Fluorescence traces from singletons were evaluated using the Maximum-Minimum or Area Under the Curve values generated after addition of ion channel activator. Data were normalised against the negative control (PSS buffer control) and positive control (ion channel activator) for each assay and corrected using the response over baseline from 1 to 5 s. No statistical analyses were required in this study.
## Screening of P. nigriventer venom fractions
Fractionation of 1 mg of P. nigriventer (Figure 2A) crude venom using RP-HPLC produced numerous peaks eluting between $20\%$ and $40\%$ solvent B, and fractions eluting between 11 and 45 min were selected for pharmacological analysis (Figure 2B). Screening using the SH-SY5Y neuroblastoma cell line revealed strong modulation of voltage-gated ion channels including both inhibition or enhancement of ion channel activity (Figure 2C). Venom fractions eluting between 18 and 34 min showed strong inhibition of CaV and NaV activity, while fractions eluting between 41 and 45 min strongly activated CaV2 channels (Figure 2C, top panel). At a dilution of 1:10, these inhibitory effects persisted for both NaV and CaV2 channels for fractions eluting at 19–20 min and 26–34 min and was absent for CaV1 channels (Figure 2C, middle panel). Fractions eluting from 21 to 25 min showed a clear preference for inhibiting only CaV2 channels (Figure 2C). Interestingly, at 1:10 dilution, channel activity enhancement was stronger on NaV channels compared to CaV2 channels, suggesting potential concentration-dependent synergistic effects of venom peptides modulating both NaV and CaV2 channels. At the highest venom dilution of 1:100, persistent inhibition of NaV channel was observed for fraction 20 (F20), while the remaining inhibitory fractions preferentially inhibited only CaV2 channels (Figure 2C, bottom panel). Channel enhancement persisted for NaV channels in fractions eluting from 41 to 45 min. No potent activity was observed against nAChR-α7 at any venom concentration tested. Overall, inhibitory activity was primarily observed for fractions eluting at shorter retention times (i.e., more hydrophilic compounds), while strong ion channel activation was induced by more hydrophobic peptides with longer RP-HPLC retention times.
**FIGURE 2:** *Fractionation and activity of P. nigriventer venom. (A)
P. nigriventer specimen displaying threat posture (photo copyright Alan Henderson, www.minibeastwildlife.com.au). (B) RP-HPLC fractionation of 1 mg P. nigriventer venom. (C) Ion channel responses calculated from the area under the curve (AUC) after addition of selective activators for fractions 10 to 45, normalized to responses in the absence of venom fractions. (D, E) Representative fluorescence traces of the intracellular calcium responses of SH-SY5Y cells evoked by KCl + CaCl2 in the presence of venom fractions 26 and 34 for CaV1, fractions 19, 26 and 34 for CaV2, and fractions 41–45 for both CaV1 and CaV2 channels. (F) Representative fluorescence traces of the intracellular calcium responses of SH-SY5Y cells evoked by veratridine and in the presence of venom fractions 19, 26 and 34 and fractions 41–45. (G) Representative fluorescence traces of the intracellular calcium responses of SH-SY5Y cells evoked by choline and in the presence of venom fractions 16 and 40 and fractions 41–45. Grey dotted line indicates the KCl + CaCl2, veratridine or choline addition.*
Fluorescent traces measured upon addition of venom fractions revealed an increase in intracellular calcium ([Ca2+]i), suggesting that these venom peptides can activate closed channels as well as enhance the responses of these channels opened using pharmacological intervention (Figures 2D–G). This was observed for CaV responses in the presence of 1 μM CVIF (CaV2 inhibitor, Figure 2D) and 10 μM nifedipine (CaV1 inhibitor, Figure 2E). In the absence of CaV inhibitors, these [Ca2+]i responses resemble the levels of CaV1 responses in Figure 2D as observed for F40–F45 applied in the NaV channels assay (Figure 2F). The activities of inhibitory fractions were mostly free from initial [Ca2+]i responses upon venom addition, except for weak inhibitors observed in F19 for NaV and F40 for nAChR-α7 (Figures 2F, G).
## Identification of peptides in P. nigriventer venom fractions
The venom of P. nigriventer has been extensively characterised in terms of composition and bioactivity (Diniz et al., 2018; Peigneur et al., 2018), including neuronal ion channel activity and proteomics, but not by using a combined approach. In this study, by combining these approaches, we were able to rapidly identify 58 peptides and proteins in the venom. Due to the complexity of previous nomenclature for P. nigriventer venom peptides, we refer to them here using both the rational nomenclature developed for spider toxins (King et al., 2008) and an identifying number (e.g., PN367) that is linked to a sequence and a list of previously used names in Supplementary Table S1. Of the 58 identified amino acid sequences, only eight ($15\%$) are peptides that have had their bioactivity reported in previous studies (Figure 3A, Supplementary Table S1) (Peigneur et al., 2018). These included the known neuroactive components μ-CNTX-Pn1a (Tx1) (Diniz et al., 2006; Martin-Moutot et al., 2006), κ-CNTX-Pn1a (Tx3-1, PhKV) (Kushmerick et al., 1999; Almeida et al., 2011), ω-CNTX-Pn1a (Tx3-2) (Cordeiro Mdo et al., 1993), Γ-CNTX-Pn1a [Tx4[5-5]] (Paiva et al., 2016), δ-CNTX-Pn1a [Tx4[6-1]] (de Lima et al., 2002; Emerich et al., 2016), δ-CNTX-Pn2c (Tx2-5a) (Yonamine et al., 2004), ω-CNTX-Pn4a (Tx3-6) (Cardoso et al., 2003; Vieira et al., 2005) and ω-CNTX Pn3a (Tx3-4) (Dos Santos et al., 2002) (Figure 3B). Even among these eight peptides, only a few venom peptides have had their molecular pharmacology characterized in detail (Peigneur et al., 2018), or their activities confirmed using recombinant peptides (Diniz et al., 2006; Paiva et al., 2016; Garcia Mendes et al., 2021).
**FIGURE 3:** *Estimated levels of peptide/protein venom components identified in fractions F17 to F45, and their respective bioactivity at NaV and CaV channels and the nAChR-α7. (A) Proportion of known and unknown venom peptides and other venom components detected in this study. (B) Venom peptides with previously reported bioactivity detected in fractions by mass spectrometry and compared to fraction bioactivity at NaV and CaV channels and the nAChR-α7. (C) Venom peptides detected in fractions classified according to their cysteine framework I to IX (Diniz et al., 2018), and compared to fraction bioactivity at NaV and CaV channels and the nAChR-α7.*
Most of the identified sequences in this study ($74\%$) represent peptides with unexplored bioactivity; 38 ($65\%$) of the 43 peptides identified have cysteine-rich scaffolds typical of spider-venom peptides (Figure 3C). Some of these venom peptides, such as PN367 and PN363, have a type I scaffold (Diniz et al., 2018) and are predicted by Alphafold 2 to fold into cystine-knot scaffolds typical of spider-venom peptides (King and Hardy, 2013) (Figure 4). Scaffolds II-VIII either form elaborated cystine-knot folds with extra disulphide bonds, or alternative structures such as for scaffolds III and IV (Figure 4). Novel peptides with high identity with other toxins and not previously described in P. nigriventer venom included: PN367 displaying identity with a *Agelena orientalis* venom peptide; PN369 displaying identity with a *Lycosa singoriensis* venom peptide, and PN365 displaying scaffold III and identity with another *Lycosa singoriensis* venom peptide (Supplementary Table S1). Additional disulphide-rich scaffolds present in P. nigriventer venom include three peptides predicted by the algorithm HMMER to form a thyroglobulin type 1 repeat domain (E < e−17 in each case), one of which has been previously reported as U24-CNTX-Pn1a; peptide PN370 which displays high identity with a peptide found in venom of the scorpion Scorpiops jendeki and is predicted by the algorithm HMMER to form into a trypsin-inhibitor-like cysteine-rich domain (E < 2e−13); and the peptide PN376 that is predicted by HMMER to form a fungal protease inhibitor domain (E < e−6) (Supplementary Table S1). Additional new scaffolds identified in this study were named following the previous suggested nomenclature (Diniz et al., 2018) as X (CXCC motif, 12 Cys residues: −C−C−C−C−CXCC−C−C−C−C−C−), XI (12 Cys residues: −C−C−C−CXC−CXC−C−CXC−C−C−), XII (11 Cys residues: −C−C−CXC−CXC−C−C−CXC−C) and XIII (10 Cys residues: −C−C−C−C−C−C−CXC−C−C−), and include the peptides PN376, PN372, PN373 and PN375, and PN370, respectively.
**FIGURE 4:** *Diversity and estimated levels of cysteine-rich scaffolds identified in highly neuroactive RP-HPLC fractions from the venom of P. nigriventer, and their predicted 3D structures. (A) Fractions 18–20 comprised high levels of scaffolds I, II and VIII represented by the 3D structures of PN367, PN105 and PN267, respectively. (B) Fractions 26 and 27 comprised high levels of scaffolds II, and IV, and an undefined scaffold represented by the 3D structures of PN321, PN350 and PN372, respectively. (C) Fraction 34 comprised high levels of scaffolds I, II, and V represented by the 3D structures of PN003, PN292 and PN028, respectively. (D) Fractions 41 and 42 comprised high levels of scaffolds IV and V represented by the 3D structures of PN381, and PN077 and PN031, respectively.*
Only $9\%$ of the identified sequences were peptides with two or fewer Cys residues (Supplementary Table S1). F17 contained a peptide (PN361) matching a C-terminally amidated peptide precursor from *Araneus ventricosus* identified in a genomic study (Kono et al., 2019). This precursor has $70\%$ sequence identify with the prohormone-1 like precursor from the honeybee *Apis mellifera* (UniProt P85798) which is believed to be cleaved to form three short peptides with neuronal activity. Another short peptide, PN366 identified in F18 and F28–F30, matches a neuropeptide in the sea slug *Aplysia californica* (UniProt P06518). Larger proteins were also detected in some fractions; for example, F18 and F31 contained a fragment at $58\%$ and $70\%$ total fraction components, respectively, matching a zinc metalloprotease from the nematode Caenorhabditis elegans (UniProt 55112) which contains a peptidase family M12A domain.
## Diversity of neuroactive peptides in P. nigriventer venom
The cysteine-rich scaffolds of venom peptides identified in this study were compared to the classification previously proposed for P. nigriventer venom peptides (Diniz et al., 2018) (Figures 3C, 4). Peptides in fractions displaying inhibitory properties corresponded to scaffolds I, II, IV, V and VIII, as well as unnamed scaffolds, while peptides in fractions with activation properties comprised mostly the scaffold V. All of these scaffolds are inhibitor cystine knot motifs, except for scaffold IV which had the highest level in F26 represented by the peptide PN350.
Neuroactive peptides with greater hydrophilicity (i.e., those with short RP-HPLC retention times) showed pharmacological properties reminiscent of known spider-derived μ-toxins (F17 and F18) and ω-toxins (i.e., inhibition of CaV1 and CaV2 channels by F19 and F20) (Figures 2C, 5A). Major components driving those bioactivities were the pharmacologically characterised peptides μ-CNTX-Pn1a, ω-CNTX-Pn1a and ω-CNTX-Pn3a, as well as additional peptides with unknown activity (Figure 4A). As the hydrophilicity of the peptides decrease (i.e., peptides with long RP-HPLC retention times), persistent CaV2 inhibition was observed with maximum inhibitory activity in F26 and F27, and with the additional peptide ω-CNTX-Pn4a detected in F24 (Figures 2C, 3B, 5B). Interestingly, venom peptides characterized as KV modulators, such as κ-CNTX-Pn1a, were detected in fractions displaying strong inhibition of calcium influx with potential μ- and ω-pharmacology (fractions 26 and 27); it was not clear if the observed bioactivity was associated to the modulation of KV channels, or to other unexplored peptides in these fractions.
**FIGURE 5:** *Venom peptide content of highly neuroactive RP-HPLC fractions from the venom of P. nigriventer. (A) Identification of the cysteine-rich peptides and proteins in fractions 17–20 displaying potent inhibition of neuronal NaV and CaV2 channels. Positively and negatively charged residues are coloured blue and orange, respectively, hydrophobic residues are green, and cysteines are highlighted in grey box. (B) Identification of the peptide and protein content of the fractions 16 and 27 displaying potent inhibition of neuronal NaV, CaV1 and CaV2 channels. (C) Identification of the peptide and protein content of the fraction 34 displaying potent inhibition of neuronal NaV, CaV1 and CaV2 channels. (D) Identification of the peptide and protein content of the fraction 34 displaying potent activation of neuronal NaV and CaV2 channels. Sequences labelled with a red asterisk (*) at the C-terminal are likely C-terminally amidated.*
Neuroactive peptides presenting more hydrophobic structures showed properties of μ and ω-peptides, but with preference for CaV2 channels as observed for fraction 34 in which the peptide Γ-Pn1a is the major component, consistent with its previously observed modulation of multiple cation channels (Paiva et al., 2016); and of δ-peptides as observed in fractions 41 to 45, in which major components included the peptides δ-Pn1a and δ-Pn2c (Figures 2C–F, and Figures 5C, D). Notably, the main components of some of the most neuroactive fractions are peptides with unexplored bioactivity, e.g., fraction 26 (Figures 2C, 4, 5).
## Pharmacological groups
Our approach allowed classification of P. nigriventer venom peptides into four major groups based on their bioactivity (Figure 6; Table 1). Group 1 is comprised of μ and ω peptides with scaffold type VIII and more hydrophilic properties as they eluted between F17 and F21. As representatives from this group, μ-CNTX-Pn1a and ω-CNTX-Pn3a have a potential “KR electrostatic trap”, a pharmacophore described in spider-venom peptides that modulate ion channels (Hu et al., 2021; Wisedchaisri et al., 2021), in their primary and tertiary structures (Figure 6A). This pharmacophore is likely composed of residues R61, K67, K70, K71, R74 and R75 in μ-CNTX-Pn1a and residues K54, K56, R59, K65, K70, R71, K73 and K74 in ω-CNTX-Pn3a. Within this group, the ω-CNTX-Pn3a homologue PN319 differs at three positions, making it an interesting candidate for further characterisation.
**FIGURE 6:** *Pharmacological groups identified in the most active venom fractions highlighting the “KR electrostatic trap” pharmacophore common to spider toxins that modulate the activity of ion channels. (A) Group 1 is represented by μ- and ω-spider-venom peptides with large and complex type VIII scaffold. (B) Group 2 is represented by κ- and ω-spider-venom peptides with type II and VII scaffolds. (C) Group 3 is represented by γ-spider-venom peptides with type V scaffold. (D) Group 4 is represented by δ-spider-venom peptides displaying a type V scaffold. K and R residues located in the C-terminal region of these peptides and grouped on a positively charged face are highlighted in red in the sequences and in red tubes in the corresponding 3D structures. Arrows shows the cysteine-bridge connection forming the cyclic peptide structures predicted for PN028 and PN031.* TABLE_PLACEHOLDER:TABLE 1 Group 2 comprises κ and ω peptides that eluted between F17 and F28, with scaffold types II and VII (Figure 6B). As representatives from this group, peptides κ-CNTX-Pn1a, ω-CNTX-Pn1a and ω-CNTX-Pn4a also contain a “KR trap” pharmacophore comprised of residues R20, K23, K34, K35 and K36 for ω-Pn1a; R21, K24, K35 and K36 for κ-Pn1a; and K42, R47, K48, K49, K51, K53 and K54 for ω-Pn4a. In this group, PN107 differs from κ-CNTX-Pn1a by only two residues and is an interesting peptide for further exploration.
Group 3 is comprised of more hydrophobic Γ peptides that eluted in F33–F36 and possess a type V scaffold (Figures 3C–D, 6C). It is represented by Γ-CNTX-Pn1a with a potential “KR trap” comprising residues K35, R41, K42 and K43. Although Γ peptides modulate N-methyl-D-aspartate (NMDA) glutamate receptors, Γ-CNTX-Pn1a has also been reported as a β-peptide that inhibits NaV channels (Paiva et al., 2016), which agrees with the results from our high-throughput ion channels assays (Figures 2C–F, 3). Interestingly, Γ-CNTX-Pn1a predicted 3D structure formed a cyclic structure in which the N-terminal cysteine formed a disulfide bridge with C-terminal cysteine (Figures 4C, 6C). These same fractions contain other ICK peptides including PN003 and PN292 with scaffold types I and II, respectively; their pharmacological targets have not been explored but they likely contribute to the strong inhibition of CaV channels by F34 (Figures 2, 3, 4).
Group 4 is composed of very hydrophobic δ peptides that elute in F40–F45 and possess a type V scaffold (Figure 2B, 6D). It is represented by δ-CNTX-Pn1a with potential “KR trap” comprising residues K43, K44, and K45 (Figure 6D). In this group we also identified δ-CNTX-Pn2c which differs not only in primary structure but also in the scaffold V tertiary structure by presenting a non-cyclic structure compared to the cyclic structure predicted for δ-CNTX-Pn1a connected by the N- and C-terminal cysteines (Figures 4D, 6D). Beyond these known peptides, this group comprised interesting unexplored peptides such as PN032 and PN023 showing δ peptide domains and differing from Γ-CNTX-Pn1a by 12 and 11 residues, respectively.
## Discussion
Spiders are one of the most speciose venomous taxa, with >50,000 characterised species (see World Spider Catalog, https://wsc.nmbe.ch/statistics/). Their venoms are rich in neuroactive peptides that target a wide range of neuronal ion channels and receptors using mechanisms distinct from those of neurotoxins from other venomous animals such as cone snails and scorpions. The exploration of venom peptides targeting ion channels and receptors provides novel opportunities for the development of pharmacological tools to understand disease mechanisms (Cardoso and Lewis, 2018; Cardoso, 2020) as well as provision of leads for development of therapeutics (King, 2011) and bioinsecticides (Smith et al., 2013).
Spiders are classified in two major groups, or infraorders (King, 2004): Mygalomorphae, or so-called “primitive spiders”, includes the family Theraphosidae, or tarantulas, which are the most well studied spider venoms due to the large-size and long lifespan (often >20 years) of these spiders. Araneomorphae, or “modern spiders,” comprise >$90\%$ of all extant spider species, including the family Ctenidae in which P. nigriventer resides. Notably, despite their much greater species diversity, araneomorph venoms are underexplored compared to mygalomorphs due to their smaller size and shorter lifespan (typically 1–2 years). Our data, and those of others (Binford et al., 2009; Zhang et al., 2010; Diniz et al., 2018; Peigneur et al., 2018), showed a great diversity of both pharmacological actions and cysteine scaffolds in araneomorph venom, which may have facilitated the highly successful araneomorph radiation. Our data also suggests Araneomorphae’s venoms may be a rich source of unique venom peptides with more diverse structures and pharmacological functions and additional biotechnological and therapeutic applications to Mygalomorphae’s venoms.
The venom from P. nigriventer comprises many exceptional peptides drug leads under development for treating a range of complex neuro disorders (Peigneur et al., 2018). These peptides have been evaluated in pre-clinical models and demonstrated interesting therapeutic efficacy in reverting or preventing conditions for which treatments are limited or unavailable. For example, ω-Pn2a and ω-Pn4a showed efficacy in treating painful neuropathies such as fibromyalgia and chronic post-ischemia pain, respectively (Pedron et al., 2021; Cavalli et al., 2022), ω-Pn4a also improved motor movement and neuroprotection in Huntington’s disease (Joviano-Santos et al., 2022). The engineered peptide PnPP-19 derived from the venom peptide δ-Pn2a was efficacious in treating glaucoma (da Silva et al., 2020) and erectile dysfunction (*Nunes da* Silva et al., 2019). In our study, these therapeutic peptides showed bioactivity at neuronal NaV and CaV channels, which greatly supports our investigative platform for the discovery of venom peptides useful for the development of efficacious drugs.
Investigative pipelines in venomic studies often focus on the elucidation of venom components based on their structures but lack clear strategies to investigate venom bioactivities (von Reumont et al., 2022). Investigations using fractionated venom (Cardoso et al., 2015; Cardoso et al., 2017; Estrada-Gomez et al., 2019; Cardoso et al., 2021) provides more defined biological functions than using crude venom due to the immense pharmacological diversity of venom, which often contains venom components with opposing activity as well as components that act synergistically (Raposo et al., 2016). Considering the large number of extant spiders and consequently the exceptionally large number of venom components available for investigation, high-throughput (HT) functional bioassays are essential for developing a holistic understanding of venom pharmacology, and they provide a complement to venomic studies.
A recent study by us using HT bioassays to investigate the ion channel targets of Australian funnel-web spider venoms recaptured current taxonomy and revealed potential drug targets to treat severely envenomated patients (Cardoso et al., 2022). In this present study, we also demonstrated the feasibility of applying HT functional bioassays to investigate spider venom components that mediated the activity of voltage-gated ion channels. We were able to capture all known venom components and associated bioactivities using a HT functional assay as well as several new unexplored venom peptides that warrant further exploration. This was achievable only by combining HT bioassays with transcriptomic and proteomic approaches. Although this pipeline provides a robust holistic overview of spider venoms, bioactive components are present in varying concentrations in each fraction, which may affect bioactivity through synergistic effects, and overlook the activity of less abundant components.
The complexity of the cysteine-rich scaffolds in P. nigriventer venom peptides unraveled in this study suggests that further exploration utilising recombinant or synthetic peptides might be challenging but essential, and these could also benefit from modern strategies utilizing HT recombinant expression or chemical synthesis (Pipkorn et al., 2002; Turchetto et al., 2017). In tandem with automated whole-cell patch-clamp electrophysiological studies, this will build a pipeline to further investigate known and new peptides in the venom of P. nigriventer and allow selection of candidates with biotechnological potential. The putative “KR trap” pharmacophores identified in those venom peptides warrants further exploration of the structure-function relationships of the diverse pharmacological groups found in the venom of P. nigriventer.
In conclusion, we demonstrated that the introduction of HT functional bioassays in venomic studies is essential to provide a more complete understanding of venom components in terms of structure and function. It also allows venom peptides to be ranked for further investigation based on their bioactivity and structural diversity, which is not possible via transcriptomic and proteomic studies alone. Furthermore, this study provides a guide to assist the exploration of neuroactive venoms from other animals, in particularly for the underexplored araneomorph spiders.
## Data availability statement
The datasets presented in this study can be found in online repositories. The name of the repository and accession number are ProteomeXchange PRIDE repository; PXD037904.
## Author contributions
Conceptualization: FC; design, conduct, and analysis of experiments: FC and AW; MG contributed with the P. nigriventer crude venom. drafting of manuscript: FC. All authors contributed to reviewing and editing of the manuscript and approved the final version for submission.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmolb.2023.1069764/full#supplementary-material
## References
1. Almeida A. P., Andrade A. B., Ferreira A. J., Pires A. C., Damasceno D. D., Alves M. N.. **Antiarrhythmogenic effects of a neurotoxin from the spider**. *Toxicon* (2011) **57** 217-224. DOI: 10.1016/j.toxicon.2010.11.013
2. Binford G. J., Bodner M. R., Cordes M. H., Baldwin K. L., Rynerson M. R., Burns S. N.. **Molecular evolution, functional variation, and proposed nomenclature of the gene family that includes sphingomyelinase D in sicariid spider venoms**. *Mol. Biol. Evol.* (2009) **26** 547-566. DOI: 10.1093/molbev/msn274
3. Cardoso F. C., Castro J., Grundy L., Schober G., Garcia-Caraballo S., Zhao T.. **A spider-venom peptide with multitarget activity on sodium and calcium channels alleviates chronic visceral pain in a model of irritable bowel syndrome**. *Pain* (2021) **162** 569-581. DOI: 10.1097/j.pain.0000000000002041
4. Cardoso F. C., Dekan Z., Rosengren K. J., Erickson A., Vetter I., Deuis J.. **Identification and characterization of ProTx-III [μ-TRTX-Tp1a], a new voltage-gated sodium channel inhibitor from venom of the tarantula**. *Mol. Pharmacol.* (2015) **88** 291-303. DOI: 10.1124/mol.115.098178
5. Cardoso F. C., Dekan Z., Smith J. J., Deuis J. R., Vetter I., Herzig V.. **Modulatory features of the novel spider toxin μ-TRTX-Df1a isolated from the venom of the spider**. *Br. J. Pharmacol.* (2017) **174** 2528-2544. DOI: 10.1111/bph.13865
6. Cardoso F. C., Lewis R. J.. **Sodium channels and pain: From toxins to therapies**. *Br. J. Pharmacol.* (2018) **175** 2138-2157. DOI: 10.1111/bph.13962
7. Cardoso F. C., Lewis R. J.. **Structure-function and therapeutic potential of spider venom-derived cysteine knot peptides targeting sodium channels**. *Front. Pharmacol.* (2019) **10** 366. DOI: 10.3389/fphar.2019.00366
8. Cardoso F. C.. **Multi-targeting sodium and calcium channels using venom peptides for the treatment of complex ion channels-related diseases**. *Biochem. Pharmacol.* (2020) **181** 114107. DOI: 10.1016/j.bcp.2020.114107
9. Cardoso F. C., Pacifico L. G., Carvalho D. C., Victoria J. M., Neves A. L., Chavez-Olortegui C.. **Molecular cloning and characterization of**. *Toxicon* (2003) **41** 755-763. DOI: 10.1016/s0041-0101(03)00011-4
10. Cardoso F. C., Pineda S. S., Herzig V., Sunagar K., Shaikh N. Y., Jin A.. **The deadly toxin arsenal of the tree-dwelling Australian funnel-web spiders**. *Int. J. Mol. Biosci.* (2022) **23** 13077
11. Cavalli J., De Assis P. M., Cristina Dalazen Goncalves E., Daniele Bobermin L., Quincozes-Santos A., Raposo N. R. B.. **Systemic, intrathecal, and intracerebroventricular antihyperalgesic effects of the calcium channel blocker CTK 01512-2 toxin in persistent pain models**. *Mol. Neurobiol.* (2022) **59** 4436-4452. DOI: 10.1007/s12035-022-02864-w
12. Cordeiro Mdo N., De Figueiredo S. G., Valentim Ado C., Diniz C. R., Von Eickstedt V. R., Gilroy J.. **Purification and amino acid sequences of six Tx3 type neurotoxins from the venom of the Brazilian 'armed' spider**. *Toxicon* (1993) **31** 35-42. DOI: 10.1016/0041-0101(93)90354-l
13. Da Silva C. N., Dourado L. F. N., De Lima M. E., Da Silva Cunha- A.. **PnPP-19 peptide as a novel drug candidate for topical glaucoma therapy through nitric oxide release**. *Transl. Vis. Sci. Technol.* (2020) **9** 33. DOI: 10.1167/tvst.9.8.33
14. De Lima M. E., Stankiewicz M., Hamon A., De Figueiredo S. G., Cordeiro M. N., Diniz C. R.. **The toxin Tx4(6-1) from the spider**. *J. Insect Physiol.* (2002) **48** 53-61. DOI: 10.1016/s0022-1910(01)00143-3
15. Diniz M. R., Theakston R. D., Crampton J. M., Nascimento Cordeiro M., Pimenta A. M., De Lima M. E.. **Functional expression and purification of recombinant Tx1, a sodium channel blocker neurotoxin from the venom of the Brazilian “armed” spider,**. *Protein Expr. Purif.* (2006) **50** 18-24. DOI: 10.1016/j.pep.2006.06.012
16. Diniz M. R. V., Paiva A. L. B., Guerra-Duarte C., Nishiyama M. Y., Mudadu M. A., Oliveira U.. **An overview of**. *PLoS One* (2018) **13** e0200628. DOI: 10.1371/journal.pone.0200628
17. Dos Santos R. G., Van Renterghem C., Martin-Moutot N., Mansuelle P., Cordeiro M. N., Diniz C. R.. *J. Biol. Chem.* (2002) **277** 13856-13862. DOI: 10.1074/jbc.M112348200
18. Emerich B. L., Ferreira R. C., Cordeiro M. N., Borges M. H., Pimenta A. M., Figueiredo S. G.. **δ-Ctenitoxin-Pn1a, a peptide from**. *Toxins (Basel)* (2016) **8** 106. DOI: 10.3390/toxins8040106
19. Estrada-Gomez S., Cardoso F. C., Vargas-Munoz L. J., Quintana-Castillo J. C., Arenas Gomez C. M., Pineda S. S.. **Venomic, transcriptomic, and bioactivity analyses of**. *Toxins (Basel)* (2019) **11** 496. DOI: 10.3390/toxins11090496
20. Garcia Mendes M. P., Carvalho Dos Santos D., Rezende M. J. S., Assis Ferreira L. C., Rigo F. K., Jose De Castro Junior C.. **Effects of intravenous administration of recombinant Phα1β toxin in a mouse model of fibromyalgia**. *Toxicon* (2021) **195** 104-110. DOI: 10.1016/j.toxicon.2021.03.012
21. Gewehr C., Oliveira S. M., Rossato M. F., Trevisan G., Dalmolin G. D., Rigo F. K.. **Mechanisms involved in the nociception triggered by the venom of the armed spider**. *PLoS Negl. Trop. Dis.* (2013) **7** e2198. DOI: 10.1371/journal.pntd.0002198
22. Hu H., Mawlawi S. E., Zhao T., Deuis J. R., Jami S., Vetter I.. **Engineering of a spider peptide via conserved structure-function traits optimizes sodium channel inhibition**. *Front. Mol. Biosci.* (2021) **8** 742457. DOI: 10.3389/fmolb.2021.742457
23. Isbister G. K., Fan H. W.. **Spider bite**. *Lancet* (2011) **378** 2039-2047. DOI: 10.1016/S0140-6736(10)62230-1
24. Joviano-Santos J. V., Valadao P. a. C., Magalhaes-Gomes M. P. S., Fernandes L. F., Diniz D. M., Machado T. C. G.. **Neuroprotective effect of CTK 01512-2 recombinant toxin at the spinal cord in a model of Huntington's disease**. *Exp. Physiol.* (2022) **107** 933-945. DOI: 10.1113/EP090327
25. Jumper J., Evans R., Pritzel A., Green T., Figurnov M., Ronneberger O.. **Highly accurate protein structure prediction with AlphaFold**. *Nature* (2021) **596** 583-589. DOI: 10.1038/s41586-021-03819-2
26. King G. F., Gentz M. C., Escoubas P., Nicholson G. M.. **A rational nomenclature for naming peptide toxins from spiders and other venomous animals**. *Toxicon* (2008) **52** 264-276. DOI: 10.1016/j.toxicon.2008.05.020
27. King G. F., Hardy M. C.. **Spider-venom peptides: Structure, pharmacology, and potential for control of insect pests**. *Annu. Rev. Entomol.* (2013) **58** 475-496. DOI: 10.1146/annurev-ento-120811-153650
28. King G. F.. **The wonderful world of spiders: Preface to the special toxicon issue on spider venoms**. *Toxicon* (2004) **43** 471-475. DOI: 10.1016/j.toxicon.2004.02.001
29. King G. F.. **Venoms as a platform for human drugs: Translating toxins into therapeutics**. *Expert Opin. Biol. Ther.* (2011) **11** 1469-1484. DOI: 10.1517/14712598.2011.621940
30. Kono N., Nakamura H., Ohtoshi R., Moran D. a. P., Shinohara A., Yoshida Y.. **Orb-weaving spider**. *Sci. Rep.* (2019) **9** 8380. DOI: 10.1038/s41598-019-44775-2
31. Kushmerick C., Kalapothakis E., Beirao P. S., Penaforte C. L., Prado V. F., Cruz J. S.. **Phoneutria nigriventer toxin Tx3-1 blocks A-type K**. *J. Neurochem.* (1999) **72** 1472-1481. DOI: 10.1046/j.1471-4159.1999.721472.x
32. Martin-Moutot N., Mansuelle P., Alcaraz G., Dos Santos R. G., Cordeiro M. N., De Lima M. E.. **Phoneutria nigriventer toxin 1: A novel, state-dependent inhibitor of neuronal sodium channels that interacts with micro conotoxin binding sites**. *Mol. Pharmacol.* (2006) **69** 1931-1937. DOI: 10.1124/mol.105.021147
33. Nunes Da Silva C., Nunes K. P., De Marco Almeida F., Silva Costa F. L., Borges P. V., Lacativa P.. **PnPP-19 peptide restores erectile function in hypertensive and diabetic animals through intravenous and topical administration**. *J. Sex. Med.* (2019) **16** 365-374. DOI: 10.1016/j.jsxm.2019.01.004
34. Paiva A. L., Matavel A., Peigneur S., Cordeiro M. N., Tytgat J., Diniz M. R.. **Differential effects of the recombinant toxin PnTx4(5-5) from the spider**. *Biochimie* (2016) **121** 326-335. DOI: 10.1016/j.biochi.2015.12.019
35. Pedron C., Antunes F. T. T., Rebelo I. N., Campos M. M., Correa A. P., Klein C. P.. *Neuropeptides* (2021) **85** 102094. DOI: 10.1016/j.npep.2020.102094
36. Peigneur S., De Lima M. E., Tytgat J.. *Toxicon* (2018) **151** 96-110. DOI: 10.1016/j.toxicon.2018.07.008
37. Pipkorn R., Boenke C., Gehrke M., Hoffmann R.. **High-throughput peptide synthesis and peptide purification strategy at the low micromol-scale using the 96-well format**. *J. Pept. Res.* (2002) **59** 105-114. DOI: 10.1034/j.1399-3011.2002.01958.x
38. Pymol (2023). The PyMOL molecular graphics system, version 2.0 schrödinger, LLC.. *The PyMOL molecular graphics system, version 2.0 schrödinger, LLC* (2023)
39. Raposo C., Bjorklund U., Kalapothakis E., Biber B., Alice Da Cruz-Hofling M., Hansson E.. **Neuropharmacological effects of**. *Neurochem. Int.* (2016) **96** 13-23. DOI: 10.1016/j.neuint.2016.04.005
40. Richardson M., Pimenta A. M. C., Bemquerer M. P., Santoro M. M., Beirao P. S. L., Lima M. E.. **Comparison of the partial proteomes of the venoms of Brazilian spiders of the genus Phoneutria**. *Comp. Biochem. Physiol. C Toxicol. Pharmacol.* (2006) **142** 173-187. DOI: 10.1016/j.cbpc.2005.09.010
41. Smith J. J., Herzig V., King G. F., Alewood P. F.. **The insecticidal potential of venom peptides**. *Cell Mol. Life Sci.* (2013) **70** 3665-3693. DOI: 10.1007/s00018-013-1315-3
42. Smith J. J., Lau C. H. Y., Herzig V., Ikonomopoulou M. P., Rash L. D., King G. F.. **Therapeutic applications of spider-venom peptides**. *Venoms to drugs: Venom as a source for the development of human therapeutics* (2015) 221-244
43. Turchetto J., Sequeira A. F., Ramond L., Peysson F., Bras J. L., Saez N. J.. **High-throughput expression of animal venom toxins in**. *Microb. Cell Fact.* (2017) **16** 6. DOI: 10.1186/s12934-016-0617-1
44. Vieira L. B., Kushmerick C., Hildebrand M. E., Garcia E., Stea A., Cordeiro M. N.. **Inhibition of high voltage-activated calcium channels by spider toxin PnTx3-6**. *J. Pharmacol. Exp. Ther.* (2005) **314** 1370-1377. DOI: 10.1124/jpet.105.087023
45. Von Reumont B. M., Anderluh G., Antunes A., Ayvazyan N., Beis D., Caliskan F.. *Modern venomics-Current insights, novel methods, and future perspectives in biological and applied animal venom research* (2022) **11**
46. Wisedchaisri G., Tonggu L., Gamal El-Din T. M., Mccord E., Zheng N., Catterall W. A.. **Structural basis for high-affinity trapping of the Na**. *Mol. Cell* (2021) **81** 38-48.e4. DOI: 10.1016/j.molcel.2020.10.039
47. Yonamine C. M., Troncone L. R., Camillo M. A.. **Blockade of neuronal nitric oxide synthase abolishes the toxic effects of Tx2-5, a lethal**. *Toxicon* (2004) **44** 169-172. DOI: 10.1016/j.toxicon.2004.05.016
48. Zhang Y., Chen J., Tang X., Wang F., Jiang L., Xiong X.. **Transcriptome analysis of the venom glands of the Chinese wolf spider**. *Zool. (Jena)* (2010) **113** 10-18. DOI: 10.1016/j.zool.2009.04.001
|
---
title: RNA sequencing least shrew (Cryptotis parva) brainstem and gut transcripts
following administration of a selective substance P neurokinin NK1 receptor agonist
and antagonist expands genomics resources for emesis research
authors:
- Kristopher J. L. Irizarry
- Weixia Zhong
- Yina Sun
- Brent A. Kronmiller
- Nissar A. Darmani
journal: Frontiers in Genetics
year: 2023
pmcid: PMC9972295
doi: 10.3389/fgene.2023.975087
license: CC BY 4.0
---
# RNA sequencing least shrew (Cryptotis parva) brainstem and gut transcripts following administration of a selective substance P neurokinin NK1 receptor agonist and antagonist expands genomics resources for emesis research
## Abstract
The least shrew is among the subset of animals that are capable of vomiting and therefore serves as a valuable research model for investigating the biochemistry, molecular biology, pharmacology, and genomics of emesis. Both nausea and vomiting are associated with a variety of illnesses (bacterial/viral infections, bulimia, exposure to toxins, gall bladder disease), conditions (pregnancy, motion sickness, emotional stress, overeating) and reactions to drugs (chemotherapeutics, opiates). The severe discomfort and intense fear associated with the stressful symptoms of nausea and emesis are the major reason for patient non-compliance when being treated with cancer chemotherapeutics. Increased understanding of the physiology, pharmacology and pathophysiology underlying vomiting and nausea can accelerate progress for developing new antiemetics. As a major animal model for emesis, expanding genomic knowledge associated with emesis in the least shrew will further enhance the laboratory utility of this model. A key question is which genes mediate emesis, and are they expressed in response to emetics/antiemetics. To elucidate the mediators of emesis, in particular emetic receptors, their downstream signaling pathways, as well as the shared emetic signals, we carried out an RNA sequencing study focused on the central and peripheral emetic loci, the brainstem and gut. Thus, we sequenced RNA extracted from brainstem and gut tissues from different groups of least shrews treated with either a neurokinin NK1 receptor selective emetic agonist, GR73632 (5 mg/kg, i.p.), its corresponding selective antagonist netupitant (5 mg/kg, i.p.), a combination of these two agents, versus their corresponding vehicle-pretreated controls and drug naïve animals. The resulting sequences were processed using a de novo transcriptome assembly and used it to identify orthologs within human, dog, mouse, and ferret gene sets. We compared the least shrew to human and a veterinary species (dog) that may be treated with vomit-inducing chemotherapeutics, and the ferret, another well-established model organism for emesis research. The mouse was included because it does not vomit. In total, we identified a final set of 16,720 least shrew orthologs. We employed comparative genomics analyses as well as gene ontology enrichment, KEGG pathway enrichment and phenotype enrichment to better understand the molecular biology of genes implicated in vomiting.
## Highlights
- Least shrew is a key model organism for investigating emesis at the biochemical level- Sequencing of Least shrew gut and brainstem provide 16,720 emesis associated transcripts- Constructed 125 emesis candidate gene set from assembled sequences- Identified 6,952 one-to-one protein orthologs across shrew, ferret, dog, mouse, and human- *Genomics data* produced in this study expands utility of Least shrew as emesis research organism
## 1 Introduction
Emesis research is important because many individuals suffer from vomiting, including human and veterinary cancer patients taking chemotherapeutics. A major research model for emesis is the least shrew, which is investigated to better understand the relationship between gut and brainstem emetic circuits. To date, very few genes in this species have been sequenced, and this has limited genomic approaches to investigate vomiting. The role of this study is to expand genomic resources through targeted RNA sequencing of gut and brain stem in response to emetic/antiemetic agents. By identifying these important transcripts/genes and mapping them to species of interest in the field of vomiting, will enhance the value and application of laboratory animal models of emesis. The least shrew appears to be an optimal emesis model because of its small size, requires small amounts of expensive emetic and antiemetic agents, needs smaller vivarium space, and exhibits sequence homology to humans and veterinary species.
Use of large animals such as cats and dogs for laboratory emesis studies are not only cost ineffective but also socially unacceptable. Smaller vomit competent animals have been validated. Though still large (0.7–2 kg each), ferrets (*Mustela putorius* furo) have often been used. Indeed, ferrets played a key role in both the identification of emetic circuits as well as in the development of serotonin 5-HT3-and substance P (SP) neurokinin NK1-receptor antagonist antiemetics (Rojas and Slusher, 2015). These antiemetics play a major role in suppression of chemotherapy-induced vomiting (CIV) in both human and veterinary patients receiving cancer chemotherapeutics (Kobrinsky, 1988; Hesketh et al., 2017). The major emetic neurotransmitters involved in CIV include serotonin, SP, and dopamine in both the brainstem emetic nuclei of dorsal vagal complex (area postrema, nucleus tractus solitarius and dorsal motor nucleus of the vagus), as well as in the gastrointestinal tract in the periphery (Darmani et al., 2009; Darmani and Ray, 2009). In the late 1980s a much smaller emesis model (70–100 g) was introduced, the house musk shrew (Suncus murinus) (Ueno et al., 1988). In the phylogenetic system shrews are closer to primates than rodents, lagomorphs, and carnivores (Colbert, 1958).
Cryptotis parva, the Least shrew, is 10–25 times smaller (adult weighing 4–6 g) than house musk shrews and is found in Central and North America. In the late 1990s our laboratory introduced the least shrew as a new model of emesis (Darmani, 1998). Since then, it has become one of the leading models for revealing diverse post-receptor intracellular emetic signals of vomiting including those following specific activation of dopamine D2-, serotonin 5-HT3-and SP NK1- receptors by their corresponding selective agonists (Zhong et al., 2014b; Zhong et al., 2016; Zhong et al., 2018; Zhong et al., 2019; Belkacemi and Darmani, 2020; Belkacemi et al., 2021). Unlike other emesis models (King, 1990), house musk shrews do not vomit in response to peripheral injection of apomorphine (a non-selective dopamine D$\frac{1}{2}$/$\frac{3}{4}$/5 receptor agonist) (Ueno et al., 1988; Jenner and Katzenschlager, 2016), whereas ferrets lack an emetic response to intraperitoneal administration of either serotonin or SP (Knox et al., 1993). In contrast, the least shrew is a more versatile model since it robustly vomits not only following intraperitoneal administration of serotonin (Darmani, 1998), dopamine (unpublished findings) or SP (Darmani et al., 2008), but also after peripheral injection of the more selective agonists of 5-HT3 (e.g., 2-methylserotonin)-, neurokinin NK1 (e.g., GR73632)- and D$\frac{2}{3}$ (e.g., quinpirole, quinelorane, respectively)-receptors. Moreover, least shrews vigorously vomit following apomorphine injection (Darmani et al., 1999; Darmani and Crim, 2005).
Except for humans, no published full nucleotide or amino acid sequences for either the NK1 receptor or its endogenous agonist SP is available for the currently used small animal models of vomiting. Using RTPCR, we have partially sequenced approximately a 700 base-pair fragment of the least shrew NK1 receptor which has $89\%$–$90\%$ overall sequence identity to humans and chimps (Darmani et al., 2013). In addition, the cDNA for the least shrew SP-producing preprotachkynin-1 (β-PPT1) was cloned and partially sequenced by us and found to be $90\%$ identical to the human sequence, with the SP-producing portion identical to humans (Dey et al., 2010).
RNA sequencing (RNA-seq) enables the construction of transcriptomes from cDNA libraries through massively parallel next-generation DNA sequencing technology (Ozsolak and Milos, 2011). RNA-seq offers advantages over array-based expression studies since unlike nucleotide arrays that require previously known transcript sequence to be encoded on the array to detect transcripts, RNA-seq allows for detection of transcripts without prior knowledge of their sequence. The sequencing data produced from the complete set of RNA molecules enables a wide array of applications including: 1) transcript sequence identification, 2) differential gene expression, 3) biological pathway analysis, and 4) enriched category tests (Han et al., 2015). De-novo transcript assembly allows for the construction of cDNA sequences without the need for a reference genome (Moreton et al., 2015). Although de novo assembly presents some challenges, such as differentiating between alternative isoforms produced from a single gene and transcripts produced from paralogous genes, the resulting data enables genomics studies to be applied to organisms for which reference genome is not available. The applications of RNA-seq are ever expanding and include investigations of single cell gene-expression, spatial transcriptomics, and RNA structure (Stark et al., 2019). This methodology has been applied to investigations of postoperative nausea and vomiting associated with sevoflourine (Hayase et al., 2016), and more recently has been used to characterize the mechanism associated with antiemetic treatments (Li et al., 2020).
RNA-sequencing has tremendous value in pharmacological research and can aide in elucidating drug targets, downstream mediators of pharmacological therapies as well as genes underlying individual differences in response to drug treatment including variation in efficacy and adverse drug events (Sa et al., 2018). Since 1950, the approval of new US drugs has declined every decade at roughly the same rate (Scannell et al., 2012). Moreover, a peak in drug development in the 1990s and early 2000s has declined to a two-decade low (Berndt et al., 2015). The potential impact of RNA-seq on the identification, development and commercialization of novel pharmacological therapies is an important application. Specifically, the information contained in genomics data sets obtained from patient transcriptome data, cell-line associated transcriptome data, and model organism transcriptome data may expedite discovery of drug-disease associations and characterization of candidate drug targets (Kwon et al., 2019).
Our rationale for applying RNA-seq to the least shrew, Cryptotis parva, was to identify the transcriptomes associated with: a) the regions of the brainstem implicated in the control of emesis, as well as b) the tissues and cells underlying emesis in the gut (Figure 1). Because the least shrew is an optimal model for investigating the pharmacology of vomiting, the construction of relevant transcripts can help guide biochemically driven, pharmacological studies. Here we report the construction and analysis of the transcriptomes identified in the brainstem and gut under treatments with an agonist and antagonist of the SP neurokinin NK1 receptor. Our genomics data sets and resulting analyses provide resources for further investigations into emetic/antiemetic pharmacology in the least shrew.
**FIGURE 1:** *Overview of RNA Sequencing Study Design. This genomics study investigated the transcripts induced in the brain stem and gut following least shrew (Cryptotis parva) treatment with an NK1R agonist (GR73632) and an NK1R antagonist (netupitant). Brain stem and gut transcripts were assembled from overlapping RNA sequencing reads and one-to-one orthologs between shrew and dog (Canis familiaris), ferret (Mustela putorius furo), mouse (Mus musculus), and human (Homo sapiens). The resulting ortholgous sequences were used to identify a set of 6,952 shared orthologs across all five species (shrew, dog, ferret, mouse, human). Images of each species were acquired from the open source image repository wikimedia.org (https://commons.wikimedia.org): dog (Afra_008.jpg), ferret (Iltisfrettchen.jpg), mouse (Lab_mouse_mg_3158.jpg), human (Bronze_anatomical_figure,_Europe_Wellcome_L0058953.jpg), shrew (Shrew1opt.jpg).*
## 2.1 Animals
Adult least shrews from the Western University of Health Sciences Animal Facilities colony were housed in groups of 5–10 on a 14:10 light:dark cycle and were fed and watered ad libitum. The experimental shrews were between 45 and 60 days old and weighed 4–6 g. Animal experiments were conducted in accordance with the principles and procedures of the National Institutes of Health Guide for the Care and Use of Laboratory Animals Protocol number R20IACUC018). All protocols were approved by the Institutional Animal Care and Use Committee of Western University of Health Sciences. All efforts were made to minimize animals’ suffering and to reduce the number of animals used in the experiments. Each shrew was used only once and following completion of each experiment the tested shrews were euthanized with $32\%$ isoflurane via inhalation.
## 2.2 Chemicals
The following drugs were used in the present study: the NK1R agonist GR73632 (Sakurada et al., 1999) was purchased from Tocris, Minneapolis, MN. The NK1 receptor antagonist netupitant was kindly provided by Helsinn HealthCare (Lugano, Switzerland). GR73632 was dissolved in distilled water and netupitant was dissolved in a 1:1:18 solution of emulphorTM, ethanol and saline. All drugs were administered at a volume of 0.1 ml/10 g of body weight.
## 2.3 Behavioral emesis studies
On the day of the experimentation least shrews were brought from the animal facility, separated into clean individual cages (20 × 18 × 21 cm) lined with wood chippings, and were allowed to adapt for at least 2 hours (h). Daily food was withheld 2 h prior to the start of the experiment but shrews were given four mealworms each prior to emetogen injection, to aid in identifying wet vomits as described previously (Darmani, 1998).
We have earlier demonstrated that a 5 mg/kg (i.p.) injection of the brain penetrating selective NK1 receptor agonist GR73632 produces a robust frequency of vomits in all tested animals (Darmani et al., 2008). We have also shown that pretreatment with the selective NK1 receptor antagonist netupitant suppresses the GR73632 (5 mg/kg, i.p.)-evoked vomiting in a dose-dependent manner with complete protection in all tested shrews at 10 mg/kg (Zhong et al., 2019). In the latter study, the 5 mg/kg dose of netupitant protected $85\%$ of shrew from vomiting. To minimize possible effects of netupitant on mRNA expression by itself, we chose its lower effective antiemetic dose (5 mg/kg) for the current drug interaction studies. One animal in the GR73632 + netupitant treatment group vomited and this animal was not included in our data analysis. Thus, different groups of shrews were treated with: i) at 0 min with netupitant vehicle (i.p.) and 30 min later with GR73632 vehicle (i.p.) ( $$n = 5$$), ii) at 0 min with netupitant vehicle (i.p.) and 30 min later with GR73632 (5 mg/kg, i.p.) ( $$n = 5$$), iii) at 0 min with netupitant (5 mg/kg, i.p.) and 30 min later with GR73632 vehicle (i.p.) ( $$n = 5$$); iv) at 0 min with netupitant (5 mg/kg, i.p) and 30 min later with GR73632 (5 mg/kg, i.p.) ( $$n = 5$$); and v) no treatment (i.e. drug naïve) ($$n = 5$$). Thirty minutes following the last injection, shrew brainstems and gut (jejunum, taken as a 1 cm length cut, 2 cm below the gastric-intestinal junction were cleaned individually with saline (37 OC)) were rapidly dissected and frozen and kept at −80 OC until analysis. At the behavioral level all netupitant-vehicle-pretreated shrews that had received GR73632 vomited. No other treatment group exhibited emesis.
## 2.4 RNA preps and sequencing
RNA samples were sequenced at the Oregon State University Center for Genome Research and Biocomputing (OSU CGRB). Tissue samples of five gut samples and five brainstem samples with three replicates each (30 total) were sent to OSU CGRB. Tissues were extracted using the Zymo Direct-zol RNA MiniPrep with TRI-Reagent kit and quantified using a Qubit fluorometer and Agilent Bioanalyzer 2,100. Extracted RNA sequencing libraries were prepared using the Wafergen RNA kit and quality controlled by qPCR. Samples were sequenced on a 100bp paired end run on an Illumina HiSeq 3,000.
## 2.5 de novo transcriptome assembly
Trimmomatic v0.33 (run with parameters: PE ILLUMINACLIP:Adapters.fa:2:30:10 SLIDINGWINDOW:4:30 LEADING:10 TRAILING:10 MINLEN:36) (Bolger et al., 2014) was used for quality control and for adapter trimming raw sequences. Raw sequences from the 15 brainstem replicates and 15 gut replicates were pooled and assembled into a single de novo transcriptome assembly using Trinity v2.4.0 (run with parameters: -seqType fq-max_memory 250G-CPU 30) (Grabherr et al., 2011). This pooled de novo transcriptome was used as the reference transcriptome for downstream expression and orthology analysis. The Trinity transcript quantification method (Trinity v2.4.0) (https://github.com/trinityrnaseq/trinityrnaseq/wiki/Trinity-Transcript-Quantification) was used to estimate transcript abundance: individual sequences from each replicate for both brainstem and gut samples were aligned using bowtie2 (Langmead and Salzberg, 2012) to the pooled de novo reference transcriptome and counted using RSEM (Li and Dewey, 2011) (run with parameters: -seqType fq-est_method RSEM-aln_method bowtie2 -trinity_mode). BAM files were manipulated with SAMtools (Li et al., 2009).
## 2.6 Orthology analysis
Orthologous transcripts were identified between the de novo assembled shrew transcriptome and dog, ferret, mouse, and human transcript sequences from the *Ensembl data* repository. Basic Local Alignment Search Tool (ncbi-blast-2.10.0+) was used to create blast databases for each species (shrew, ferret, dog, mouse, human). Each species’ set of genes was aligned via BLASTN (Altschul et al., 1990) to the assembled shrew transcriptome. Orthology was determined using a blast-back approach and custom perl scripts.
## 2.7 Identification of shrew transcripts having human/mouse/dog/ferret orthologs
We identified a non-redundant set of shrew transcript sequences which maximized the number of shrew transcript sequences across the set of human, mouse, dog, and ferret orthologs (Figure 2). Specifically, we identified the largest group of shrew transcript sequences that contained human ortholog sequences. Of the remaining shrew transcripts that did not contain human orthologs, shrew orthologs of mouse genes were identified. Shrew sequences that did not contain mouse or human orthologs were used to identify sequences orthologous to dog sequences. Finally, the shrew sequences without orthologs in human, mouse and dog were used to identify shrew orthologs to ferret. The resulting set of mapped orthologs were combined to produce the final non-redundant ortholog transcript set.
**FIGURE 2:** *Conceptual strategy for identifying Non-Redundant Orthologs Across Species. One to one orthologs between the least shrew transcript sequences and other species ranged from 10,515 (mouse) to 12,965 (human). Some transcripts were identified in one species, but not in the other species, a cumulative set of shrew transcripts containing 16,720 sequences was compiled that represents the greatest number of shrew orthologues from the four species. Another set, called “shared” transcripts (7,339) was extracted which contained a one-to-one orthologue from each of the four species. Images of each species were acquired from the open source image repository wikimedia.org (https://commons.wikimedia.org): dog (Afra_008.jpg), ferret (Iltisfrettchen.jpg), mouse (Lab_mouse_mg_3158.jpg), human (Bronze_anatomical_figure,_Europe_Wellcome_L0058953.jpg), shrew (Shrew1opt.jpg).*
## 2.8 Characterization of shrew transcript length
The set of orthologous shrew sequences was characterized using the Maria open source database server. Transcript sequences were loaded into the database and queries were performed to determine minimum, maximum, average and total sequence length. Supplementally, individual queries were performed to characterize the distribution of transcript lengths.
## 2.9 Identification of shrew transcripts implicated in emesis
A set of candidate emesis transcripts were identified from among the orthologous sequences. These candidate sequences were identified using published peer-reviewed papers that identify genes implicated in emesis. Because many genes are expressed in a variety of tissues, we leveraged the complete set of sequencing reads to build our consensus sequences. Future work will include a comprehensive analysis of the tissue specific data to better understand the role tissue specific expression plays in this process.
## 2.10 Functional genomic analysis of candidate emesis gene set
The set of candidate emesis genes were analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8. Human official gene symbols for the 128 emesis candidate genes were uploaded to the DAVID database and used to identify genes associated with human diseases contained within the Online Mendelian Inheritance in Man (OMIM) database. Additional analyses included identifying enriched a) gene ontology annotations, b) enriched KEGG pathways, c) enriched tissue expression from the UniProt Tissue Expression database, and d) enriched interacting proteins from the Database of Protein Interactions (DIP).
## 2.11 Protein coding orthologs across shrew/human/mouse/dog/ferret
One-to-one orthologs across shrew, ferret, dog, mouse, and human were identified using a relational database containing tables for shrew-to-ferret, shrew-to-dog, shrew-to-mouse, and shrew-to-human one-to-one orthologs. Queries were performed across these tables using SQL statements to identify the set of protein coding genes that were present in all five species.
## 2.12 Characterization of identity between least shrew and other species' sequences
Basic Local Alignment Search Tool (ncbi-blast-2.10.0+) was used to create blast databases for each species (shrew, ferret, dog, mouse, human). Biomart (https://www.ensembl.org/biomart/martview/) (Yates et al., 2020) was used to download the cDNA sequences from ferret, dog, mouse and human. Fasta files were used as input to create blast databases for each non-shrew species. Blastn was used to determine nucleotide identity with the following parameters: max_target_seqs 1,000 -max_hsps 1,000 -outfmt 6. Blastx was used to compare shrew transcript sequences to translated versions of the cDNA from the other four species. Statistical significance of the average identity between pairs of species (shrew-dog; shrew-ferret; shrew-mouse; shrew-human) was calculated using an online z-score calculator for two population proportions (Social Science Statistics, https://www.socscistatistics.com/tests/ztest/default2.aspx) with an alpha = 0.05. This calculation was repeated for both nucleotide identity and amino acid identity. Heatmaps showing percent identity of the subset of candidate emesis genes intersected with the 6,999 nucleotide and 6,952 amino acid conserved orthologous sequences were created with R using the heatmap package.
A set of emesis pathway genes that have been identified in the least shrew to date through biochemical analyses were compiled and used to identify the set of those pathway members from among our set of 16,720 orthologous genes. Considerable research into the biochemistry and pharmacology of emesis has used the least shrew as a model organism, and therefore the results identifying the components of the pathway have been identified in least shrews. These studies have guided our work very closely, and we have compiled a comprehensive list of 39 pathway members and compared the sequence relationships between our least shrew data and the existing transcripts publicly available for human, dog, mouse, ferret, and a shrew in the genus Sorex (Sorex araneus).
Online sequence identity search performed using Ensembl release 107 from July 2022 (https://uswest.ensembl.org/index.html). Web-based blast search was performed using Ensembl against the following species: human, mouse (CLC57BL6), dog, ferret, shrew. Best hit from each species was identified and percent identity, E-value, and transcript identifier of top hit was recorded. Individual blast results for each pathway associated gene versus each of the targeted species was downloaded. A table was compiled from the data (Howe et al., 2021). For the analysis of the emesis pathway members and identification of top scoring transcripts from human, dog, mouse, ferret, and shrew (Sorex) the Ensembl genome database (version 107) was used. The parameters for blast included a limit of reporting 100 hits, with a maximum of 100 high scoring pairs, E-value chtoff = 10, word size = 11, match score = 1, mismatch score = −3, gap opening penalty = 2, gap extension penalty = 2. Low complexity regions were filtered, and query sequences were repeat masked.
## 2.13 Phenotype enrichment of most conserved protein coding orthologs
The 6,952 conserved protein coding genes were ordered by decreasing percent identity between the shrew sequences and human sequences produced from the blastx output. The ordered set of sequences was partitioned into ten subsets, with the first most conserved 697 sequences going into the first partition and the remaining nine partitions containing 695 sequences each such that sequences in each partition were more conserved than the sequences in the subsequent partitions. The resulting deciles of the 6,952 most conserved one-to-one protein coding orthologs across shrew, ferret, dog, mouse, and human were assessed for phenotype enrichment analysis using the Model Organism Phenotype Enrichment toolkit (http://evol.nhri.org.tw/phenome2/). This analysis was performed using “human” as the species of the gene list and mouse phenotypes as the phenotypes to be analyzed. The choice to use human gene symbols was made because human gene symbols are well recognized by many analysis platforms compared to gene symbols from species such as the ferret or dog. The phenotype enrichment was initiated for each decile of the 6,952 genes versus the remaining genes in the human genome. All levels of phenotype categories were selected for the analysis corresponding to levels 2 through 16. The analysis was conducted under the alternative hypothesis that the ∼695 genes in each gene set were enriched for specific phenotypes compared to the remaining genes in the rest of the genome using Fisher’s Exact Test.
## 2.14 KEGG pathway enrichment of most conserved protein coding orthologs
The 6,952 conserved protein coding genes were ordered by decreasing percent identity between the shrew sequences and human sequences produced from the blastx output and partitioned into ten subsets, with the first containing 697 sequences and the remaining nine partitions containing 695 sequences each. KEGG pathway enrichment was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 (DAVID bioinformatics database, https://david.ncifcrf.gov/) (*Huang da* et al., 2009a; b).
## 3.1 Identification and characterization of orthologous shrew transcript sequences
RNA sequencing produced 38 billion base pairs of brainstem and 37.2 billion base pairs of gut. This data was used to assemble a de novo reference transcriptome (contigs 623,905, assembly length 241,685,294 bp, average length 387.4 bp, N50 385). To identify a set of representative gene transcripts from among these assembled fasta sequences, we selected one-to-one orthologs between the shrew sequences and human, dog, mouse, and ferret sequences. To identify the largest set of shrew orthologous transcripts, sequences were mapped to orthologs across human, mouse, dog, and ferret Supplemental File S10 (Figure 2). Shrew transcripts mapping to multiple species were associated with the human version of the gene, if possible. The remaining shrew sequences were preferentially mapped to mouse, then dog, and finally ferret orthologs that were not already identified in the human orthologs. A total of 16,720 orthologous shrew transcripts were identified among the total orthologs mapped across each species (Figure 3).
**FIGURE 3:** *Number of orthologous transcript sequences across species and shared among species. Total number of identified ortholgous sequences between the initial set of transcript fasta sequences assembled from the shrew (Cryptotis parva) brain stem and gut RNA sequencing reads and nucleotide sequences from dog (Canis familiaris), ferret (Mustela putorius furo), mouse (Mus musculus), and human (Homo sapiens). The complete set of orthologous transcript sequences identified in shrew by assembling transcripts across dog, ferret, mouse and human (illustrated in Figure 2) is shown for comparison purposes. Conserved genes and protein coding genes across all five species (shrew, dog, ferret, mouse, human).*
The distribution of nucleotide lengths across the orthologous shrew genes are shown in Figure 4. The average nucleotide length of shrew transcripts was 1,518 bp with a standard deviation of 1,146 bp. Many of the shortest transcripts we identified are under 500 bp in length. In fact, we identified 954 transcripts under 300 bp and 2,622 transcripts under 500 bp in length. For example, the shortest transcripts we identified are 201 bp long and we identified 17 transcripts that were exactly 201 bp.
**FIGURE 4:** *Distribution of nucleotide lengths across 16,720 orthologous shrew transcript sequences. The distribution of sequence lengths among the complete set of 16,720 shrew transcripts were assessed. Sequences ranged from 201 bp to 15,430 bp. Most shrew transcripts were 4,000 bp or less in length. Fewer than 500 sequences were longer than 5,000 bp.*
A representative set of six of these short 201 nucleotide shrew transcripts corresponding to ferret, dog, mouse and human orthologs were compared to the length of the corresponding orthologous transcript (Table 1). Each of these short transcripts was considerably shorter than the corresponding orthologous transcript sequence from the other species. The shortest orthologous transcript sequence was 488 bp (TPM1 from ferret, ENSMPUT00000000657) while the three other 201 nucleotide transcripts had orthologous transcripts which were 795 bp (TLCD2 from ferret, ENSMPUT00000013334), 861 bp (OTUD6A from dog, ENSCAFT00000026526) and 920 bp (NAA20 from human, ENST00000310450). The two longest orthologous transcripts mapping to the set of six short shrew transcripts were 5,000 bp (Fbxw8 from mouse, ENSMUST00000049474) and 4,704 bp (TPM1 from ferret, ENSMUST00000049474). Based on these comparisons, our short shrew transcript sequences are most likely incomplete assemblies of larger transcripts.
**TABLE 1**
| Shrew cDNA identifier | Symbol | Species | Transcript identifier | Transcript length |
| --- | --- | --- | --- | --- |
| TRINITY_DN308915_c4_g1 | Fbxw8 | mouse | ENSMUST00000049474 | 5,000 bp |
| TRINITY_DN317929_c4_g1 | TPM1 | ferret | ENSMPUT00000000657 | 4,704 bp |
| TRINITY_DN323061_c3_g2 | WIPF1 | human | ENST00000455428 | 488 bp |
| TRINITY_DN306984_c4_g3 | NAA20 | human | ENST00000310450 | 920 bp |
| TRINITY_DN326659_c1_g1 | TLCD2 | Ferret | ENSMPUT00000013334 | 795 bp |
| TRINITY_DN312853_c1_g1 | OTUD6A | dog | ENSCAFT00000026526 | 861 bp |
The largest transcript in our data set (TRINITY_DN328899_c3_g1) is 15,430 bp and encodes apolipoprotein B for which the human ortholog (UniProtKB - P04114) contains 4,563 amino acids. The second largest transcript we identified (TRINITY_DN328883_c1_g1) is 12,275 bp and encodes LDL receptor related protein 1, for which the human version of this protein (UniProtKB - Q07954) is 4,544 amino acids in length. The third largest transcript (TRINITY_DN328880_c2_g1) is 12,240 bp in length and encodes the shrew version of the 7,570 amino acid human protein dystonin (UniProtKB - Q03001). Finally, the fourth largest shrew transcript (TRINITY_DN328901_c5_g1) is 11,679 bp long and encodes an ortholog of human microtubule associated protein 1B (UniProtKB - P46821) that is 2,468 amino acids in length. These comparisons suggest that the long shrew transcript sequences correspond to comparably long orthologous transcripts from orthologous species. In order to assess how well these new shrew transcript sequences compared to previously described transcripts, several important shrew genes were aligned to assembled transcripts to show sequence comparisons.
## 3.2 Comparison of tachykinin precursor one transcript sequences
The least shrew tachykinin precursor 1 mRNA sequence was previously reported and deposited into NCBI (JUL-2016) with the accession number FJ696706. The sequence length was listed as 215 bp. The sequence we identified in this study for the tachykinin precursor is 508 bp. The two sequences were compared using pairwise blast (Supplemental File 1). The RNA-seq version of the transcript matched $100\%$ with 215 out of 215 nucleotides aligning. The e-value for this match is 1e-115.
## 3.3 Comparison of brain derived neurotrophic factor transcript sequences
Pairwise BLAST alignment between the NCBI deposited Brain Derived Neurotrophic Factor sequence and the RNA-seq version of the sequence (Supplemental File 2). Although the RNA-seq version of the gene is larger than the originally deposited sequence, the overlapping regions matched $100\%$ with no gaps. BDNF transcripts overlap 564 nucleotides for which the alignment contains 0 gaps and is $100\%$ identical.
## 3.4 Comparison of neurokinin receptor 1 transcript sequences
Pairwise BLAST alignment between the NCBI deposited Neurokinin Receptor 1 sequence (Query sequence) and the RNA-seq version of the sequence (Subject sequence). The RNA-seq version of the gene is longer than the NCBI deposited sequence. The region of overlap between the two sequences exhibits $100\%$ identity with no gaps over 602 nucleotides (Supplemental File 3).
## 3.5 Candidate genes associated with emesis
A set of 125 emesis-candidate genes were identified among the transcripts we generated in this study. The candidate set was hand curated from the RNA sequencing data and our previous studies relating to least shrew emesis and the downstream intracellular emetic signaling molecules post NK1 neurokinin receptors (Supplemental File 4).
## 3.6 Functional analysis of shrew candidate emesis genes
To better understand the biological associations within the set of shrew candidate emesis genes, functional genomics analysis was performed to identify annotations capable of providing insight into the roles of these genes. The first analysis that was performed identified the set of human diseases associated with members of these shrew candidate genes (Supplemental File 5). This analysis utilizes the Online Mendelian Inheritance in Man (OMIM) database which curates and provides resources to investigate the relationships between genes and diseases in humans.
The results include a number of diseases associated with neurological and sensory deficits including deafness, blindness, dopamine beta-hydroxylase deficiency, as well as neuropsychiatric disorders including obsessive compulsive disorder and schizophrenia. A variety of neuronal synaptic signaling genes are associated with diseases including members of the adenylate cyclase gene family, calcium voltage-gated channels, neurotransmitter receptors, neurotransmitter biosynthetic enzymes, and intracellular signaling molecules such as phospholipase and protein kinase. The discovery of these gene-disease associations in the least shrew expands the utility of the shrew as a model for psychiatric and neurological disorders, including vomiting and nausea.
In order to gain a better understanding of the functional genomic roles for these candidate emesis genes, we employed a gene ontology enrichment analysis (GOA). Because the goal of GOA is to identify constellations of genes that all share the same ontological annotations, this analysis facilitates a systematic dissection of the genes into groups associated with specific biological processes (Table 2 contains most significant results, Supplemental File six contains all significant results), molecular functions (Table 3 contains most significant results, Supplemental File seven contains all significant results) and cellular locations (Table 4).
Among the enriched biological processes, one of the most significant results is the association of 16 of the candidate emesis genes with “release of sequestered calcium ion into cytosol” (p-value = 1.10E-22) and 14 of the genes annotated with “calcium ion transport” (p-value = 5.40E-15). Other noteworthy biological processes identified in the analysis include “serotonin receptor signaling pathway” (p-value = 5.80E-15); “G-protein coupled receptor signaling pathway, coupled to cyclic nucleotide second messenger” (p-value = 6.00E-11); and “chemical synaptic transmission” (p-value = 7.20E-14). Together these results underscore the significant roles mediators of emesis play in calcium signaling and neurotransmitter transduction.
Within the category of molecular function (Table 3), our analysis identified a number of proteins implicated in calcium homeostasis and signaling including 19 genes implicated in “calmodulin binding” (p-value = 8.10E-16); 10 genes annotated as having “calmodulin-dependent protein kinase activity” (p-value = 1.20E-14); and 23 genes exhibiting “calcium ion binding” (p-value = 3.60E-09). Supplementally, proteins molecular functions related to neurotransmitter functions were also identified such as a set of eight genes that bind serotonin (p-value = 7.60E-14) and two genes classified as having “tachykinin receptor activity” (p-value = 2.10E-02). These important functions are critical to the regulation and pharmacological intervention of emesis pathways.
The cellular compartment category of gene ontology (Table 4) associates specific locations inside and outside of the cell with genes. In our analysis we identified the “phosphatidylinositol 3-kinase complex” (p-value = 3.80E-25) as a critical location where 13 genes in the emesis candidate gene set are localized. Supplementally, “sarcoplasmic reticulum membrane” and “sarcoplasmic reticulum” are highly significant with p-values of 8.50E-13 and 4.70E-11, respectively. Interestingly, 17 of our genes are associated with neuronal dendrites (p-value = 7.40E-10) and nine genes are annotated with the term “postsynaptic density” (p-value = 3.20E-05). Together, the biological process, molecular function and cellular compartment provide considerable insight into the biochemistry and neurobiology that these genes mediate.
*Although* gene ontology enrichment provides clues as to the function and location of specific molecules in our data set, such an analysis does not provide clues as to additional molecular mediators that may also be involved with the genes identified in our candidate gene set. The identification of enriched interacting proteins (Table 5) overcomes this limitation by utilizing information about protein-protein interactions and the macromolecular associations of molecules in specific complexes within the cell. This analysis looks at the set of input genes and seeks to identify clusters of genes that interact with a common partner. Such common partners may include novel pharmacological targets, or other mediators of the cellular biology under investigation.
**TABLE 5**
| Term | Count | % | p-value | Benjamini |
| --- | --- | --- | --- | --- |
| calcium/calmodulin dependent protein kinase II delta(CAMK2D) | 7 | 5.7 | 3.5e-11 | 2.6e-09 |
| calmodulin 1(CALM1) | 7 | 5.7 | 8.2e-09 | 3e-07 |
| calmodulin 3(CALM3) | 7 | 5.7 | 8.2e-09 | 3e-07 |
| calmodulin 2(CALM2) | 7 | 5.7 | 1.4e-08 | 3.4e-07 |
| calcium/calmodulin dependent protein kinase II beta(CAMK2B) | 4 | 3.3 | 1e-05 | 0.00019 |
| calcium/calmodulin dependent protein kinase II alpha(CAMK2A) | 4 | 3.3 | 1e-05 | 0.00019 |
| calcium/calmodulin dependent protein kinase II gamma(CAMK2G) | 4 | 3.3 | 2.6e-05 | 0.00037 |
| myelin basic protein(MBP) | 3 | 2.5 | 0.00058 | 0.0071 |
| troponin I3, cardiac type(TNNI3) | 3 | 2.5 | 0.00058 | 0.0071 |
| calcium/calmodulin dependent protein kinase I(CAMK1) | 3 | 2.5 | 0.00058 | 0.0071 |
| calcium/calmodulin dependent protein kinase IV(CAMK4) | 3 | 2.5 | 0.00058 | 0.0071 |
| potassium calcium-activated channel subfamily N member 2(KCNN2) | 3 | 2.5 | 0.0012 | 0.012 |
| EPH receptor A3(EPHA3) | 3 | 2.5 | 0.0019 | 0.017 |
| stromal interaction molecule 1(STIM1) | 3 | 2.5 | 0.0019 | 0.017 |
| sodium voltage-gated channel alpha subunit 5(SCN5A) | 3 | 2.5 | 0.0028 | 0.023 |
| SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily b, member 1(SMARCB1) | 3 | 2.5 | 0.0039 | 0.028 |
| calcium voltage-gated channel subunit alpha1 C(CACNA1C) | 3 | 2.5 | 0.0052 | 0.034 |
| beclin 1(BECN1) | 3 | 2.5 | 0.0052 | 0.034 |
| ORAI calcium release-activated calcium modulator 1(ORAI1) | 2 | 1.6 | 0.042 | 0.23 |
| biliverdin reductase A(BLVRA) | 2 | 1.6 | 0.042 | 0.23 |
We identified a strong calcium theme among our candidate genes which includes interacting molecules calmodulin1, calmodulin 3 (each with p-value = 8.20E-09), and calmodulin 2 (p-value = 1.40E-08). The most significant result in our analysis is calcium/calmodulin dependent protein kinase II delta (CAMK2D) for which 7 of our candidate emesis genes interact (p-value = 3.50E-11). Other interacting proteins in the analysis include members of the calcium/calmodulin dependent protein kinase II family, as well as calcium/calmodulin dependent protein kinase I (CAMK1) calcium/calmodulin dependent protein kinase IV (CAMK4). One surprising, and yet very relevant detected enriched interacting protein we detected is stromal interaction molecule 1 (STIM1) (p-value = 1.90E-03) that transitions from an inactive to an active calcium shuttle under certain conditions to facilitate movement of extracellular calcium. Taken together, these results implicate calcium as a key element in the interactions of the candidate gene set.
To better understand the tissue expression associated with the candidate emesis genes we investigated the enriched UniProt Tissue Expression Database (Table 6) which classifies genes by tissue of expression. The top result for our analysis is “brain”, for which 78 genes were associated (p-value = 1.70E-06) and “brainstem” (p-value = 2.90E-03). Our analysis also identified six genes expressed in “small intestine” (p-value = 7.00E-02).
**TABLE 6**
| Term | Count | % | p-value | Benjamini |
| --- | --- | --- | --- | --- |
| Brain | 78 | 63.9 | 1.7e-06 | 0.00018 |
| Myometrium | 3 | 2.5 | 0.0014 | 0.067 |
| Brainstem | 3 | 2.5 | 0.0029 | 0.093 |
| Substantia nigra | 4 | 3.3 | 0.0052 | 0.12 |
| Hippocampus | 9 | 7.4 | 0.009 | 0.17 |
| Corpus striatum | 2 | 1.6 | 0.031 | 0.41 |
| Osteosarcoma | 3 | 2.5 | 0.032 | 0.38 |
| Primary B-Cells | 3 | 2.5 | 0.038 | 0.39 |
| Blood | 10 | 8.2 | 0.049 | 0.43 |
| Lymphoma | 3 | 2.5 | 0.056 | 0.44 |
| Spleen | 11 | 9.0 | 0.061 | 0.44 |
| Myeloid | 2 | 1.6 | 0.067 | 0.44 |
| Lung | 25 | 20.5 | 0.069 | 0.43 |
| Small intestine | 6 | 4.9 | 0.07 | 0.41 |
| Platelet | 8 | 6.6 | 0.071 | 0.39 |
## 3.7 Identification of conserved one-to-one orthologs of ferret, dog, mouse, and human
We identified a core set of 6,952 one-to-one protein-coding orthologs across all five species, shrew, ferret, dog, mouse, and human. This set of genes provides a common framework for investigating the collective molecular biology across these diverse mammalian species. This set of genes is extremely valuable because it provides a common context for which shared biology can be investigated to uncover both the conserved aspects of biology as well as the differences between species. We performed a large-scale sequence comparison analysis to gain a better understanding of the relationship between the different species.
Our analysis investigated both the nucleotide level and amino acid level identity between the set of 6,952 protein coding conserved orthologs across shrew, dog, ferret, mouse, and human. One question we had was which of the four species were most identical to the shrew sequences. The average nucleotide identity across these 6,999 nucleotide genes and 6,952 protein-coding genes were compared and tested for statistical significance across all “pairs” of datasets, such as shrew-mouse vs shrew-human and shrew-ferret vs shrew-human. The only significant comparisons were those including shrew-mouse (average nucleotide identity 85.937 and average amino acid identity 89.962).
For example, one-tailed test of shrew-human nucleotide identity versus shrew-mouse nucleotide identity indicated $p \leq 0.00001.$ In contrast, the average nucleotide identity between the shrew-human versus shrew-ferret was so similar that the resulting p-value was 0.44038. The analysis for the average protein identity followed this same pattern with all pairs including shrew-mouse exhibiting statistical significance while none of the other comparisons rose to a level of significance given our alpha. The results indicate that the shrew is a good match to human sequences, and although it is not statistically significant, the shrew-human average amino acid identity and the shrew-ferret amino acid identity are both higher than $91\%$ but lower than $92\%$ identical based on our analysis of this data set.
## 3.8 Heat map visualization of candidate emesis gene set identity across species
When we checked the 6,999 conserved cDNA orthologs and the conserved 6,952 protein coding orthologs for members of the emesis candidate genes, we identified 84 candidate genes. The nucleotide percent identity for each of these 84 emesis candidates between the shrew and the dog, ferret, mouse, and human versions of these genes are shown in heatmap form (Figure 5). The data is ordered by the identity relationship between shrew and human in the last column. The same data is displayed for the amino acid identity in translated proteins (Figure 6).
**FIGURE 5:** *Heat Map Illustrating Identity Between Shrew cDNA and Orthologs of Emesis Candidate Genes. A set of 84 cDNAs from the emesis candidate gene set was compared at the nucleotide level between shrew and dog, ferret, mouse, and human. Results are represented in heat map format. The color key and histogram in the upper left corner provides a mapping between heat map colors and the count of cDNAs in each color category. The data is ordered by the nucleotide identity relationship between shrew cDNAs and corresponding orthologous human cDNAs in the last column.* **FIGURE 6:** *Heat Map Illustrating Identity Between Shrew Protein and Orthologs of Emesis Candidate Genes. A set of 84 cDNAs from the emesis candidate gene set was compared at the inferred amino acid level between shrew and dog, ferret, mouse and human. Results are represented in heat map format. The color key and histogram in the upper left corner provides a mapping between heat map colors and the count of cDNAs in each color category. The data is ordered by the nucleotide identity relationship between shrew cDNAs and corresponding orthologous human cDNAs in the last column.*
## 3.9 Phenotype enrichment of most conserved protein coding orthologs
We investigated the phenotype enrichment for genes belonging to the top $10\%$ most conserved 6,952 orthologs (Table 7, Supplemental File 8). This analysis sought to identify disproportionate phenotypes that were enriched in the top $10\%$ versus the rest of the genome. We identified signals from developmental, embryonic, neonatal and lethality related phenotypes. These phenotypes included mortality/aging ($$p \leq 7$$E-22); prenatal lethality ($$p \leq 1.1$$E-12); preweaning lethality ($$p \leq 1.6$$E-27); lethality during fetal growth through weaning ($$p \leq 1.0$$E-06), perinatal lethality ($$p \leq 1.2$$E-05) and embryo phenotype ($$p \leq 6.1$$E-06).
**TABLE 7**
| Enriched Phenotype | Fisher’sTest | FDR | Bonferroni |
| --- | --- | --- | --- |
| ■ mortality/aging | 2.4929999999999997e-23 | 6.98e-22 | 6.9804e-22 |
| └ abnormal survival | 2.2679999999999999e-29 | 1.588e-27 | 1.5876000000000001e-27 |
| ├ preweaning lethality | 5.123e-29 | 1.6090000000000002e-26 | 1.60862e-26 |
| │├ prenatal lethality | 1.843e-15 | 1.1e-12 | 1.10027e-12 |
| ├ perinatal lethality | 3.947e-08 | 4.131e-06 | 1.23936e-05 |
| │├ neonatal lethality | 2.874e-07 | 4.289e-05 | 0.000171578 |
| ││├ neonatal lethality, complete penetrance | 1.889e-06 | 0.0002599 | 0.001354413 |
| ■ embryo phenotype | 2.186e-07 | 3.06e-06 | 6.1208e-06 |
| ├ abnormal embryo development | 3.449e-06 | 8.048e-05 | 0.00024143 |
| │├ abnormal developmental patterning | 7.631e-05 | 0.002 | 0.02396134 |
| ││├ abnormal gastrulation | 0.0005583 | 0.019 | 0.3333051 |
| │││├ failure to gastrulate | 5.456e-05 | 0.003 | 0.03911952 |
| ├ abnormal embryo morphology | 8.526e-06 | 0.0001492 | 0.00059682 |
| │├ abnormal embryo size | 0.0004737 | 0.011 | 0.1487418 |
| ■ growth/size/body region phenotype | 1.478e-06 | 1.379e-05 | 4.1384e-05 |
| ├ abnormal prenatal growth/weight/body size | 1.531e-06 | 5.359e-05 | 0.00010717 |
| │├ abnormal embryonic growth/weight/body size | 8.16e-06 | 0.0006406 | 0.00256224 |
| ││├ abnormal embryo size | 0.0004737 | 0.017 | 0.2827989 |
| │├ abnormal prenatal body size | 7.949e-05 | 0.002 | 0.02495986 |
| │├ abnormal postnatal growth | 4.378e-05 | 0.002 | 0.01374692 |
| ││└ postnatal growth retardation | 8.227e-05 | 0.004 | 0.04911519 |
| ■ nervous system phenotype | 0.004 | 0.028 | 0.112 |
| ├ abnormal nervous system morphology | 0.0002129 | 0.003 | 0.014903 |
| │├ abnormal brain morphology | 3.144e-05 | 0.002 | 0.00987216 |
| ││├ abnormal hindbrain morphology | 9.265e-06 | 0.0006914 | 0.005531205 |
| │││├ abnormal metencephalon morphology | 3.414e-05 | 0.002 | 0.02447838 |
| ││││├ abnormal cerebellum morphology | 0.0005448 | 0.034 | 0.3437688 |
| │││││├ abnormal cerebellar cortex morphology | 0.0004864 | 0.024 | 0.2120704 |
| │││││││├ abnormal cerebellar molecular layer | 0.0001185 | 0.003 | 0.0235815 |
| ││││││││├ abnormal cerebellar lobule formation | 2.125e-05 | 0.00051 | 0.00255 |
| ││││││ ├ abnormal hippocampus neuron morphology | 2.004e-05 | 0.0006041 | 0.00398796 |
| ││││││ │└ abnormal hippocampus pyramidal cell morphology | 2.175e-06 | 0.0001305 | 0.000261 |
| ││││││ │ ├ ectopic hippocampus pyramidal cells | 1.957e-06 | 5.22e-05 | 9.3936e-05 |
| ││││││ │ ├ decreased hippocampus pyramidal cell number | 0.0001548 | 0.002 | 0.0074304 |
| │├ abnormal neuron morphology | 0.0003297 | 0.009 | 0.1035258 |
| ││├ ectopic neuron | 5.499e-06 | 0.000469 | 0.003282903 |
| │││├ ectopic hippocampus pyramidal cells | 1.957e-06 | 0.0002599 | 0.001403169 |
| │││└ ectopic Purkinje cell | 0.000458 | 0.019 | 0.328386 |
| ││├ abnormal hippocampus neuron morphology | 2.004e-05 | 0.001 | 0.01196388 |
| │││└ abnormal hippocampus pyramidal cell morphology | 2.175e-06 | 0.0002599 | 0.001559475 |
| │││ ├ ectopic hippocampus pyramidal cells | 1.957e-06 | 0.000247 | 0.001234867 |
| │││ ├ decreased hippocampus pyramidal cell number | 0.0001548 | 0.012 | 0.0976788 |
| ├ abnormal synaptic transmission | 0.000213 | 0.006 | 0.066882 |
| │├ abnormal CNS synaptic transmission | 2.046e-06 | 0.0002036 | 0.001221462 |
| ││├ abnormal glutamate-mediated receptor currents | 3.345e-06 | 0.0003124 | 0.002398365 |
| │││└ abnormal AMPA-mediated synaptic currents | 4.087e-07 | 6.447e-05 | 0.00025789 |
| │││ ├ reduced AMPA-mediated synaptic currents | 0.0006632 | 0.029 | 0.2891552 |
| ││├ abnormal long term potentiation | 0.000266 | 0.014 | 0.190722 |
| │││├ enhanced long term potentiation | 0.011 | 0.198 | 1.0 |
| │││└ reduced long term potentiation | 0.034 | 0.306 | 1.0 |
| ││├ abnormal excitatory postsynaptic potential | 0.0006 | 0.024 | 0.4302 |
| ││├ abnormal synaptic depression | 0.002 | 0.055 | 1.0 |
| │││└ abnormal long term depression | 0.0006099 | 0.035 | 0.3848469 |
| │││ ├ absent long term depression | 0.001 | 0.034 | 0.436 |
| ├ abnormal nervous system electrophysiology | 0.001 | 0.02 | 0.314 |
We also identified strong signals from phenotypes associated with brain formation and morphology. Phenotypes in this category included abnormal brain morphology ($$p \leq 0.00987$$); abnormal hindbrain morphology ($$p \leq 0.00553$$); abnormal cerebellar lobule formation ($$p \leq 0.00255$$); abnormal limbic system morphology ($$p \leq 0.00541$$); abnormal hippocampus neuron morphology ($$p \leq 0.00874$$); and decreased hippocampus pyramidal cell number ($$p \leq 0.00279$$).
Lastly a small signal from phenotypes implicated in neurotransmission was also identified in the data. Phenotypes in this category included abnormal CNS synaptic transmission ($$p \leq 0.00122$$); abnormal glutamate-mediated receptor currents ($$p \leq 0.0024$$); and abnormal AMPA-mediated synaptic currents ($$p \leq 0.00025789$$). Other phenotypes include ataxia ($$p \leq 0.01782$$) and abnormal motor coordination/balance ($$p \leq 0.05543$$ which lies just shy of significance).
## 3.10 KEGG pathway enrichment of most conserved protein coding orthologs
Similar to the phenotype enrichment, we analyzed the top $10\%$ of the 6952-protein coding conserved orthologs to identify specific cellular and biochemical pathways that were enriched with members among the genes in that dataset (Table 8). Notable pathways could be organized into roughly general categories corresponding to neurotransmitter signaling, neuronal pathways and memory related pathways, generic signaling pathways and addiction related pathways.
**TABLE 8**
| KEGG pathway | Count | p-value | Bonferroni | Benjamini |
| --- | --- | --- | --- | --- |
| hsa03040:Spliceosome | 33 | 1.93e-14 | 3.88e-12 | 3.88e-12 |
| hsa04728:Dopaminergic synapse | 30 | 1.78e-12 | 3.58e-10 | 1.79e-10 |
| hsa04720:Long-term potentiation | 18 | 1.01e-08 | 2.03e-06 | 6.77e-07 |
| hsa04713:Circadian entrainment | 21 | 2.14e-08 | 4.3e-06 | 1.08e-06 |
| hsa04120:Ubiquitin mediated proteolysis | 25 | 3.7e-08 | 7.43e-06 | 1.49e-06 |
| hsa04261:Adrenergic signaling in cardiomyocytes | 24 | 1.86e-07 | 3.73e-05 | 6.22e-06 |
| hsa04921:Oxytocin signaling pathway | 25 | 2.21e-07 | 4.43e-05 | 6.33e-06 |
| hsa03015:mRNA surveillance pathway | 19 | 3e-07 | 6.03e-05 | 7.54e-06 |
| hsa04114:Oocyte meiosis | 21 | 3.26e-07 | 6.55e-05 | 7.28e-06 |
| hsa04725:Cholinergic synapse | 21 | 3.26e-07 | 6.55e-05 | 7.28e-06 |
| hsa05216:Thyroid cancer | 11 | 6.06e-07 | 0.000122 | 1.22e-05 |
| hsa04390:Hippo signaling pathway | 22 | 1.27e-05 | 0.002542 | 0.000231 |
| hsa04730:Long-term depression | 13 | 2.62e-05 | 0.005247 | 0.000438 |
| hsa04024:cAMP signaling pathway | 25 | 3.24e-05 | 0.006495 | 0.000501 |
| hsa05210:Colorectal cancer | 13 | 3.7e-05 | 0.007405 | 0.000531 |
| hsa04310:Wnt signaling pathway | 20 | 3.8e-05 | 0.007605 | 0.000509 |
| hsa04722:Neurotrophin signaling pathway | 18 | 6.75e-05 | 0.013477 | 0.000848 |
| hsa05031:Amphetamine addiction | 13 | 7.06e-05 | 0.014091 | 0.000834 |
| hsa05211:Renal cell carcinoma | 13 | 7.06e-05 | 0.014091 | 0.000834 |
| hsa04012:ErbB signaling pathway | 15 | 7.29e-05 | 0.014555 | 0.000814 |
| hsa05200:Pathways in cancer | 38 | 8.2e-05 | 0.016339 | 0.000867 |
| hsa04916:Melanogenesis | 16 | 9.32e-05 | 0.018569 | 0.000937 |
| hsa04723:Retrograde endocannabinoid signaling | 16 | 0.000105 | 0.020833 | 0.001002 |
| hsa04724:Glutamatergic synapse | 17 | 0.000123 | 0.024519 | 0.001128 |
| hsa04360:Axon guidance | 18 | 0.000139 | 0.027464 | 0.00121 |
| hsa05213:Endometrial cancer | 11 | 0.000175 | 0.034511 | 0.001462 |
| hsa03050:Proteasome | 10 | 0.000226 | 0.044366 | 0.001814 |
| hsa04010:MAPK signaling pathway | 27 | 0.000251 | 0.04928 | 0.001942 |
| hsa03010:Ribosome | 18 | 0.00032 | 0.062394 | 0.002383 |
| hsa05034:Alcoholism | 21 | 0.000383 | 0.074084 | 0.002745 |
| hsa04520:Adherens junction | 12 | 0.000603 | 0.114137 | 0.00417 |
| hsa04071:Sphingolipid signaling pathway | 16 | 0.000714 | 0.133822 | 0.004777 |
| hsa04727:GABAergic synapse | 13 | 0.000816 | 0.151387 | 0.005281 |
| hsa04152:AMPK signaling pathway | 16 | 0.000927 | 0.170052 | 0.005808 |
| hsa04726:Serotonergic synapse | 15 | 0.000977 | 0.178303 | 0.005933 |
| hsa04550:Signaling pathways regulating pluripotency of stem cells | 17 | 0.001283 | 0.227405 | 0.00756 |
| hsa05221:Acute myeloid leukemia | 10 | 0.001436 | 0.250945 | 0.008222 |
| hsa04144:Endocytosis | 24 | 0.00156 | 0.269305 | 0.008678 |
| hsa05142:Chagas disease (American trypanosomiasis) | 14 | 0.001597 | 0.274757 | 0.008645 |
| hsa04150:mTOR signaling pathway | 10 | 0.001853 | 0.311139 | 0.00976 |
| hsa04068:FoxO signaling pathway | 16 | 0.002218 | 0.360017 | 0.011379 |
| hsa04110:Cell cycle | 15 | 0.002844 | 0.435828 | 0.014208 |
| hsa04910:Insulin signaling pathway | 16 | 0.00296 | 0.448862 | 0.014426 |
| hsa04915:Estrogen signaling pathway | 13 | 0.003103 | 0.464579 | 0.014764 |
| hsa04922:Glucagon signaling pathway | 13 | 0.003103 | 0.464579 | 0.014764 |
| hsa04660:T cell receptor signaling pathway | 13 | 0.003375 | 0.493172 | 0.01568 |
| hsa04014:Ras signaling pathway | 22 | 0.003409 | 0.496644 | 0.01548 |
| hsa04919:Thyroid hormone signaling pathway | 14 | 0.003931 | 0.546918 | 0.017439 |
| hsa05212:Pancreatic cancer | 10 | 0.004117 | 0.563651 | 0.017867 |
| hsa05214:Glioma | 10 | 0.004117 | 0.563651 | 0.017867 |
| hsa04022:cGMP-PKG signaling pathway | 17 | 0.004435 | 0.590754 | 0.01883 |
| hsa04912:GnRH signaling pathway | 12 | 0.004655 | 0.608542 | 0.019349 |
| hsa05160:Hepatitis C | 15 | 0.00538 | 0.661851 | 0.021885 |
| hsa04810:Regulation of actin cytoskeleton | 20 | 0.006965 | 0.754612 | 0.027707 |
| hsa05202:Transcriptional misregulation in cancer | 17 | 0.007546 | 0.781819 | 0.02941 |
| hsa04666:Fc gamma R-mediated phagocytosis | 11 | 0.00761 | 0.784626 | 0.029095 |
| hsa04350:TGF-beta signaling pathway | 11 | 0.00761 | 0.784626 | 0.029095 |
| hsa04141:Protein processing in endoplasmic reticulum | 17 | 0.008432 | 0.817686 | 0.031603 |
| hsa05205:Proteoglycans in cancer | 19 | 0.008973 | 0.836637 | 0.032995 |
| hsa04370:VEGF signaling pathway | 9 | 0.009282 | 0.846547 | 0.033505 |
| hsa04062:Chemokine signaling pathway | 18 | 0.009458 | 0.851923 | 0.033532 |
| hsa03013:RNA transport | 17 | 0.009916 | 0.86507 | 0.03453 |
| hsa05215:Prostate cancer | 11 | 0.010474 | 0.879535 | 0.035832 |
| hsa04721:Synaptic vesicle cycle | 9 | 0.01122 | 0.896469 | 0.037709 |
| hsa05130:Pathogenic Escherichia coli infection | 8 | 0.01148 | 0.901802 | 0.037941 |
| hsa04924:Renin secretion | 9 | 0.012293 | 0.916782 | 0.039939 |
| hsa05032:Morphine addiction | 11 | 0.013112 | 0.929555 | 0.041887 |
| hsa05100:Bacterial invasion of epithelial cells | 10 | 0.013485 | 0.934706 | 0.04239 |
Among the neurotransmitter signaling pathways are Dopaminergic synapse ($$p \leq 1.78$$E-12); Cholinergic synapse ($$p \leq 3.26$$E-07); Adrenergic signaling in cardiomyocytes ($$p \leq 1.86$$E-07); Oxytocin signaling pathway ($$p \leq 2.21$$E-07); Glutamatergic synapse ($$p \leq 1.23$$E-04); and GABAergic synapse ($$p \leq 8.16$$E-04). Within the neuronal and memory related pathways we identified Long-term potentiation ($$p \leq 1.01$$E-08); Circadian entrainment ($$p \leq 2.14$$E-08; Long-term depression ($$p \leq 2.62$$E-05); Axon guidance ($$p \leq 1.39$$E-04); Neurotrophin signaling pathway ($$p \leq 6.75$$E-05); and Synaptic vesicle cycle ($$p \leq 0.0112$$).
Among the generic pathways we detected PI3K-Akt signaling pathway ($$p \leq 0.0315$$); cAMP signaling pathway ($$p \leq 3.24$$E-05); Sphingolipid signaling pathway ($$p \leq 7.14$$E-04); HIF-1 signaling pathway ($$p \leq 0.0185$$); and Gap junction ($$p \leq 0.0276$$).
Finally, the addiction related pathways we identified included Cocaine addiction ($$p \leq 0.090$$ may indicate a trending pathway); Amphetamine addiction ($$p \leq 7.06$$E-05); Alcoholism ($$p \leq 3.83$$E-04); and Morphine addiction ($$p \leq 0.0441$$). Together, signaling processes are highly enriched for pathways that are important neurobiology signals and processes. Moreover, the phenotype and KEGG pathway analysis provide insight into these most conserved shared orthologs expressed in brainstem and gut.
## 3.11 Emesis pathway members and identification of best hit orthologous transcripts
We used the recently published (Zhong et al., 2021) the model of central and peripheral nervous system involved in emesis (Figure 7A) to identify a final set of 39 emesis receptors and signal transduction components (Figure 7B) from the initial set of 16,720 ortholgous gene sequences. These pathway components represent the currently known signal transduction components that mediate the effects of agonists and antagonists of emesis in the well-studied least shrew model. Among these pathway members include dopamine D2/D3-, opioid μ, neurokinin NK1- and serotonin 5-HT3-receptors as well as receptors for oxytocin and neuropeptide Y. Intracellular transduction machinery we identified includes members of well-known gene families such as L-type calcium channels, Protein Kinase C, Mitogen Activated Kinase C, and Phospholipase C. We identified the top match for each of our pathway members across human, dog, mouse, ferret, and shrew. These results provide valuable information about the least shrew genes mediating emesis and their corresponding orthologs in other species of interest. We identified the top scoring hit for each least shrew transcript (Supplemental File 10) we identified across human, dog, ferret, mouse, shrew (Sorex araneus).
**FIGURE 7:** *(A): The central and peripheral anatomical sites in the mediation of nausea and vomiting evoked by diverse stimuli. Following exposure to various emetics, nausea and vomiting can be generated via bidirectional interactions between brain and the gastrointestinal tract (GIT). Briefly: 1) the brainstem area postrema in the floor of the fourth ventricle lacks blood brain barrier and allows circulating emetogens direct entry from the circulating blood into the cerebrospinal fluid and the brain tissue (Wickham, 2020); 2) systemically administered emetogens can activate corresponding receptors present on peripheral vagal afferents in the gastrointestinal tract, which project sensory emetic signals to the nucleus of the solitary tract (Navari, 2014; Wickham, 2020); and 3) peripheral emetics that enter the lumen of GIT such as cytotoxic chemotherapeutics, or microbials (e.g., bacteria, viruses, fungi), as well as gastrointestinal pathologies, can cause release of local emetic neurotransmitters/hormones, which subsequently act on the corresponding receptors present on vagal afferents and/or directly stimulate the brainstem area postrema via circulating blood (Bashashati and McCallum, 2014; McKenzie et al., 2019). Besides the area postrema and the sensory vagal afferents, the nucleus of the solitary tract is also the recipient of: i) direct neural inputs from the splanchnic nerves carrying sensation caused by diseases of visceral organs (e.g., cardiac, kidney); ii) brainstem vestibular nuclei collecting signals from vestibular apparatus in inner ear and/or cerebellum, caused by stimuli related to motion sickness and opioid analgesics (Porreca and Ossipov, 2009; Smith and Laufer, 2014) and iii) the cerebral cortex and limbic system, which accept and process emotional and cognitive stimuli (Zhong et al., 2021). The nucleus of the solitary tract has efferent pathways to the dorsal motor nucleus of the vagus, which further project to the upper gastrointestinal tract to produce the vomiting reflex (Wickham, 2020). In addition, the nucleus of the solitary tract has projections to the mid- and forebrain areas for the perception of nausea (Horn et al., 2014). (B) Examples of some of the well-investigated emetic receptors include: Serotonin 5-HT3, dopamine D2/D3, substance P neurokinin NK1, L-type Calcium channel, muscarinic M1, cannabinoid CB1, neuropeptide Y2, histamine H1, opioid μ, and oxytocin receptor. Some examples of published intracellular signaling emetic cascades evoked by their corresponding receptor selective agonists in the least shrew include: i) serotonin 5-HT3 receptors activated by corresponding selective agonist 2-methyl serotonin (Zhong et al., 2014b); ii) quinpirole-evoked stimulation of dopamine D2/3 receptors (Belkacemi et al., 2021); iii) L-type selective calcium agonist FPL64176 (Zhong et al., 2018); and the selective substance P neurokinin NK1 receptor selective agonist GR73632 (Zhong et al., 2019). For simplicity, we have not shown the intracellular emetic signals such as following inhibition of phosphodiesterase four enzyme by rolipram-like drugs which elevate intracellular levels of c-AMP signaling in least shrews (Alkam et al., 2014) and ferrets (Robichaud et al., 1999). Abbreviations: PLC, phospholipase C; AP, area postrema; NTS, nucleus tractus solitarius; DMNV, dorsal motor nucleus of the vagus; GPCRs, G protein-coupled receptors; 5-HT3R, Serotonin type 3 receptor; NK1R, Substance P neurokinin one receptor; D2/3R, dopamine D2 and D3 receptors; LTCCR, L-type Ca2+ channel receptor; OXTR, oxytocin receptor, M1R, muscarinic M1 receptor; CB1R, Cannabinoid CB1 receptor; NPY2R, neuropeptide Y receptor type 2; OPRμ1, Opioid receptor μ1; H1R, Histamine one receptor; IP3R, inositol-1, 4, 5-triphosphate receptor; RyR, ryanodine receptor; DAG, diacylglycerol; PKCα/βII, protein kinase C α/βII; ERK1/2, extracellular signal-regulated protein kinase1/2; Akt, protein kinase B (PKB); CaMKIIα, Ca2+/calmodulin-dependent kinase II; mTORC1/2, mammalian targets of rapamycin complexes C1/C2.; PI3K, phosphoinositide 3-kinase; 5-HT, serotonin; SERCA, sarco/endoplasmic reticulum Ca2+-ATPase; CINV, chemotherapy-induced nausea and vomiting; Ca2+, calcium.*
## 4 Discussion
In this paper we have described the sequencing and analysis of shrew brainstem and gut transcriptomes. We identified a set of 16,720 assembled transcripts that exhibit orthology to human, mouse, dog and/or ferret sequences. This study was carried out to expand knowledge and understanding of emesis pathways. Interestingly, several mammalian species vomit, including humans, dogs, ferrets, and shrews. Other species such as mice and rats, do not vomit. In addition, among different mammalian species, the neurokinin NK1 receptor is classified as human-like (e.g., ferret, least shrew)- or rodent-like (e.g., mice, rats) (Darmani et al., 2013). The initial selective antagonists for NK1 receptors were developed against rodent-like NK1 receptors which had little affinity for the human-like receptors (Rojas and Slusher, 2015). Furthermore, the ferret and shrew have been model organisms for emetic research, therefore understanding the relationship between the genes amongst these species with differing phenotypes helps provide a context for understanding the findings that have been discovered so far. The inclusion of mouse, which does not vomit, helps provide insight into the role homology may (or may not) play in phenotypic differences observed between these two species.
Even though interspecies genomic variation may contribute to differences in the emetic phenotypes, other factors including post-translational modifications, gene expression, localization and phosphorylation status may alter emesis phenotypes. The application of this research includes both human (Hesketh, 2008) and veterinary patients, including dogs and cats (Kobrinsky et al., 1988). These species are routinely diagnosed with many of the same types of cancers and treated with similar chemotherapeutic agents. Subsequently, the inclusion of dog, ferret, human and mouse provides the necessary comparative genomics data for fully leveraging the least shrew in future emesis research.
During our analysis we characterized the length distribution of these sequences and demonstrated that the vast majority of these new shrew cDNA sequences have length of at least 1,000 bp. To assess whether our sequences showed any identity to previously deposited shrew sequences we compared three previously published shrew sequences (Brain Derived Neurotrophic Factor Transcript Sequence [GenBank: LC124902.1] (Sato et al., 2016), Neurokinin Receptor 1 Transcript Sequence [GenBank: JQ715623.1] (Darmani et al., 2013) and Tachykinin precursor one Transcript Sequence [GenBank: FJ696706.1] (Dey et al., 2010). In all three cases, our newly assembled transcript sequence was both $100\%$ identical to the deposited sequence and longer than the deposited sequence. Next, we constructed a candidate emesis gene set for further investigation in emesis related biology. We performed gene ontology enrichment on that candidate gene set which identified several calcium signaling terms as being enriched within this candidate gene set.
We also investigated enriched interacting proteins associated with these candidate emesis genes and identified additional calcium interacting molecules. Indeed, our current gene ontology enrichment data from Tables 2 (biologic process), 3 (molecular function), 4 (cellular compartment), 5 (interacting proteins with emesis), and 6 (Enriched UniProt Tissue Expression) demonstrate association of vomiting with Ca2+ and its related signals including: voltage calcium channel activity; the three isoforms of calmodulin (1, 2, and 3); calmodulin dependent protein kinase I (CAMKI), CAMK II (α, β and γ isoforms) and CAMK IV; Calcium release-activated calcium channel protein 1 (ORAI 1); stromal interaction molecule 1 (STIM1) protein; store-operated calcium entry; intracellular Ca2+-release channels (inositol triphosphate (IP3R)- and ryanodine (RYR)-receptors) present in the membrane of the sarcoplasmic/endoplasmic reticulum (SER); and L-type calcium channels (LTCCs) subunits α1, 1C, 1D and 1F (Zhong et al., 2014a; Zhong et al., 2014b; Zhong et al., 2016; Zhong et al., 2018; Zhong et al., 2019; Darmani et al., 2014).
In line with our current RNAseq enrichment findings, we have previously proposed an overview of the involvement Ca2+ mobilization in the process of vomiting evoked by diverse emetogens (Zhong et al., 2017). First, selective (e.g., agonists of neurokinin NK1 (Miyano et al., 2010)-, serotonergic 5-HT3 (Hargreaves et al., 1996) -, dopaminergic D2 (Aman et al., 2007) - receptors) and non-specific (e.g., cisplatin) emetogens evoke vomiting via an increase in cytosolic Ca2+ concentration, which subsequently initiates downstream Ca2+-activated emetic signals. Second, cisplatin, one of the oldest and most widely used cancer chemotherapeutic (Alhadeff et al., 2017), also induces nausea and vomiting via Ca2+-dependent release of multiple neurotransmitters (including serotonin (5-HT), substance P (SP), dopamine, etc.) from both central emetic loci in the dorsal vagal complex (DVC) of the brainstem as well as peripherally in the GIT (Hesketh, 2008; Darmani and Ray, 2009). The DVC contains the emetic nuclei nucleus tractus solitarius (NTS), the dorsal motor nucleus of the vagus (DMNX) and the area postrema (AP). Moreover, we have confirmed direct involvement of Ca2+ ion-channels in emesis. Indeed, the term Ca2+-induced Ca2+-release (CICR), refers to a process where an initial extracellular Ca2+ influx via activation of plasma membrane voltage-operated Ca2+ channels trigger intracellular Ca2+ release from the SER Ca2+ stores, resulting in an increase in the cytosolic concentration of Ca2+ (Homma et al., 2006; Ziviani et al., 2011). In fact, the selective L-type Ca2+ channel (LTCC) agonist FPL64176 causes extracellular influx as well as vomiting in least shrews in a dose-dependent manner (Zhong et al., 2014a; Darmani et al., 2014). Moreover, the intracellular Ca2+ releasing agent thapsigargin [a selective SER Ca2+-ATPase (SERCA) inhibitor], evokes vomiting in least shrews by increasing the cytosolic concentration of Ca2+, initially via depletion of intracellular SER Ca2+-stores followed by store-operated extracellular Ca2+ entry (SOCE) through LTCCs and other plasma membrane bound calcium channels (Solovyova and Verkhratsky, 2003; Michelangeli and East, 2011; Beltran-Parrazal et al., 2012; Zhong et al., 2016). In the latter process both STIM one and ORAI1 proteins play essential roles, since the former polymerizes to form a tube-like structure connecting the SER to ORAI or LTCC or TRCP1, which act as the mouthpiece of calcium ion-channel in the cell membrane (Avila-Medina et al., 2016; Calderon-Sanchez et al., 2020). Intracellular Ca2+ release from the SER into the cytoplasm is mediated through inositol trisphosphate receptors (IP3Rs) and ryanodine receptors (RyRs) ion-channels present in the SER membrane (Gomez-Viquez et al., 2003). By evaluating the antiemetic effects of their respective antagonists (dantrolene and 2-APB), we have demonstrated that blockade of either of these intracellular Ca2+-release channels suppress vomiting evoked by diverse emetogens including FPL64176, thapsigargin, as well as selective agonists of 5-HT3R (2-methyl-serotonin) and NK1R (GR73632) (Zhong et al., 2014b; Zhong et al., 2016; Zhong et al., 2018; Zhong et al., 2019).
Tables 4–8 also highlight the role of cAMP-PKA signaling system in vomiting such as adenylate cyclase activity, cyclic AMP (cAMP) biosynthesis, phosphodiesterase (PDE) activity and activation of protein kinase A (PKA). In mammals cAMP is synthesized by 10 adenylate cyclase isoforms (Halls and Cooper, 2017). One of the best-studied second messenger molecules downstream of selected G-protein coupled receptors is cAMP. It is an example for a transient and diffusible second messenger which is involved in signal propagation by integrating multiple intracellular signaling pathways (Gancedo, 2013). cAMP activates PKA which results in phosphorylation of downstream intracellular signals. The emetic role of cAMP has been well established, since microinjection of cAMP analogs (e.g., 8-bromocAMP) or forskolin (to enhance endogenous levels of cAMP) in the brainstem DVC emetic locus area postrema, not only increases electrical activity of local neurons, but also induces vomiting in dogs (Carpenter et al., 1988). Moreover, administration of 8-chlorocAMP in cancer patients can evoke nausea and vomiting (Propper et al., 1999). Furthermore, phosphodiesterase inhibitors (PDEI) such as rolipram prevent cAMP metabolism and consequently increase cAMP tissue levels, which leads to excessive nausea and vomiting in humans and animals including least shrews (Mori et al., 2010; Alkam et al., 2014). In fact, one major side-effect of older PDEIs is excessive nausea and vomiting which often precludes their use in the clinical setting (Vanmierlo et al., 2016). PKA-phosphorylation is associated with peak vomit frequency during both immediate- and delayed-phases of vomiting caused by either cisplatin or cyclophosphamide in the least shrew (Darmani et al., 2013; Darmani et al., 2015; Alkam et al., 2014).
Other enriched intracellular emetic signals in the current investigation include CaMKII, ERK$\frac{1}{2}$ and PKC. Indeed, time-dependent Ca2+/calmodulin kinase IIα (CaMKIIα), protein kinase Cα/βII (PKCα/βII) and extracellular signal-regulated protein kinases 1 and 2 (ERK$\frac{1}{2}$) phosphorylation in the least shrew brainstem occurs following: i) 5-HT3R-evoked vomiting caused by its selective agonist 2-methyl-5-HT (Zhong et al., 2014b), ii) FPL64176 and thapsigargin-induced emesis in the least shrew (Zhong et al., 2016; Zhong et al., 2018), as well as iii) SP neurokinin NK1R-mediated vomiting evoked by the selective NK1R agonist GR73632 in the least shrew (Zhong et al., 2019). In addition, other published evidence demonstrates that phosphorylation of protein kinase Cα/βII (PKCα/βII) and ERK$\frac{1}{2}$ in least shrew brainstem are associated with cisplatin-induced emesis. In fact, significant upregulation of ERK$\frac{1}{2}$ phosphorylation occurs with peak vomit frequency during both the immediate and delayed phases of emesis caused by cisplatin in the least shrew (Darmani et al., 2013; Darmani et al., 2015).
As part of our analysis, we identified the set of conserved one-to-one orthologs among the shrew, human, mouse, dog, and ferret which resulted in 6,952 protein coding genes. We used a BLAST method to compare the sequence identity across human, mouse, dog, and ferret to shrew and in our analysis, we identified nucleotide identity to be lowest between shrew and mouse ($85.9\%$) and similar ($88.6\%$, $88.7\%$, and $88.9\%$; respectively) between shrew and human, ferret, and dog. Our analysis of protein coding showed similar results with shrew versus mouse having the lowest percent identity ($89.9\%$) and shrew versus $91.45\%$ (dog), $91.46\%$ (ferret) and $91.89\%$ (human) based on sequences obtained from the Ensembl database (Cunningham et al., 2019; Ferret Genome assembly: https://uswest.ensembl.org/Mustela_putorius_furo/Info/Index; Dog Genome assembly; Human Genome assembly: https://uswest.ensembl.org/Homo_sapiens/Info/Index; Mouse Genome assembly: http://uswest.ensembl.org/Mus_musculus/Info/Index).
The method used to identify the 16,720 orthologous protein coding genes relied upon a reciprocal best hit algorithm to map shrew transcripts to the human, mouse, ferret, and dog. Because of the requirement for reciprocal best hits, there are likely genes that are not found in our set of 1-to-1 orthologs. Although the reciprocal best hit approach is recognized as producing lower errors than some other methods (Moreno-Hagelsieb and Latimer, 2008), it has limitations when used in large gene families with differing paralogs across species, such as mammalian species (Dalquen and Dessimoz, 2013). Inability to identify a reciprocal best hit between large gene family members does not necessarily indicate absence of an orthologous gene, but rather it indicates absence of a reciprocal best hit relationships between the pair of genes being tested. Subsequently some members of large gene families, such as the L-type calcium channels are not fully represented in our set of protein coding orthologs. The calcium channel gene family is comprised of multiple paralogous gene members (Catterall 2011). The identification of multiple gene family members, such as voltage gated calcium ion-channels and phospholipase C, may offer insight into the specific family members involved in vomiting signaling cascades.
From among 6,952 protein coding conserved orthologs we performed a phenotype enrichment to see what phenotypes were enriched among the top $10\%$ of the 6,952 genes. We identified many lethality and developmental phenotypes underscoring the role these genes play in development. Additionally, we identified phenotypes implicated in brain morphology and CNS synaptic transmission.
Finally, we performed a KEGG pathway enrichment to identify signaling pathways that were also enriched within the top $10\%$ of conserved genes within the 6,952 genes. Our results show neurotransmitter signaling and memory associated pathways. We also identified pathways implicated in circadian entrainment and addiction. The identification of memory associated pathways associated with emesis is not surprising. Substance P is a an 11-amino acid neurotransmitter peptide that is well known for its role in emesis with additional roles implicated in modulating memory pathways (Graefe and Mohiuddin, 2022). Moreover, genes implicated in addiction and circadian cycle entrainment may play roles in the regulation or induction of emesis. For example, a connection between late chronotypes and chemotherapy-induced nausea and vomiting was identified in a recent study of risk factors associated with chemotherapy induced vomiting (Lee et al., 2017).
Our work provides a valuable resource for neurobiology, pharmacology, and neurophysiology in the shrew (Supplemental File 9, complete set of 16,720 shrew transcript sequences derived from brainstem and gut tissue). Our newly produced sequences greatly expand the genomic resources available for shrew and open the least shrew up as a model for neurobiology, memory, and addiction.
Some limitations of our study include the fact that we employed the blast algorithm to determine percent identity between shrew and other species. BLAST is a local alignment tool and could therefore produce higher than average identities in some regions compared to others (or between certain sequences compared to others). Additionally, our set of 16,720 shrew transcript sequences may include incomplete sequences due to low sequencing coverage in some regions of the transcriptome Furthermore, our data is lacking transcript sequences that were not expressed in the gut and brainstem. Nevertheless, this data is valuable, and provides a substantial genomics framework for investigating emesis in the least shrew.
## 5 Conclusion
We sequenced and analyzed shrew brainstem and gut transcriptomes associated with vomiting evoked by a substance P neurokinin NK1 receptor selective agonist and its interaction with a corresponding NK1 receptor selective antagonist. We discovered a set of 16,720 assembled transcripts that exhibit orthology to human, mouse, dog and/or ferret sequences and we identified a core set of emesis pathway signal transduction components. Together, these least shrew transcripts and associated analyses provide novel genomics resources for advancing emesis research (Figure 7) in both humans and other vomit-competent animal models of emesis.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.
## Ethics statement
The animal study was reviewed and approved by Animal experiments were conducted in accordance with the principles and procedures of the National Institutes of Health Guide for the Care and Use of Laboratory Animals (Protocol number R20IACUC018). All protocols were approved by the Institutional Animal Care and Use Committee of Western University of Health Sciences.
## Author contributions
ND and KI conceived and designed the project, wrote the draft of the manuscript; constructed set of 125 gene candidate gene set; and identified the set of 39 emesis pathway gene members in the sequence data and created signal transduction pathway models. ND and WZ contributed to tissue dissection and collection; WZ and YS assisted in formatting the manuscript; BK did RNA extraction, sequencing, transcript assembly and initial reciprocal blast mapping of transcripts to orthologs across species (human, ferret, shrew, mouse, dog). KI performed the construction of 16,720 shrew orthologous transcripts from initial pair-wise species orthologs, analysis of sequence lengths across 16,720 transcripts, sequence analysis of existing shrew sequences versus the new transcript identified in this study (TAC1, BDNF, NK1R), comparative genomics analysis disease gene associations of candidate genes, gene ontology enrichment analysis and enriched interacting proteins and enriched uniport tissue expression; analysis of nucleotide and protein identity across set of 6,952 one-to-one coding orthologs; construction of heat maps; phenotype enrichment analysis; KEGG pathway enrichment analysis; construction of figures and tables for manuscript. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.975087/full#supplementary-material
## References
1. Alhadeff A. L., Holland R. A., Zheng H., Rinaman L., Grill H. J., De Jonghe B. C.. **Excitatory hindbrain-forebrain communication is required for cisplatin-induced anorexia and weight loss**. *J. Neurosci.* (2017) **37** 362-370. DOI: 10.1523/JNEUROSCI.2714-16.2016
2. Alkam T., Chebolu S., Darmani N. A.. **Cyclophosphamide causes activation of protein kinase A (PKA) in the brainstem of vomiting least shrews (Cryptotis parva)**. *Eur. J. Pharmacol.* (2014) **722** 156-164. DOI: 10.1016/j.ejphar.2013.09.080
3. Altschul S. F., Gish W., Miller W., Myers E. W., Lipman D. J.. **Basic local alignment search tool**. *J. Mol. Biol.* (1990) **215** 403-410. DOI: 10.1016/S0022-2836(05)80360-2
4. Aman T. K., Shen R. Y., Haj-Dahmane S.. **D2-like dopamine receptors depolarize dorsal raphe serotonin neurons through the activation of nonselective cationic conductance**. *J. Pharmacol. Exp. Ther.* (2007) **320** 376-385. DOI: 10.1124/jpet.106.111690
5. Avila-Medina J., Calderon-Sanchez E., Gonzalez-Rodriguez P., Monje-Quiroga F., Rosado J. A., Castellano A.. **Orai1 and TRPC1 proteins Co-localize with CaV1.2 channels to form a signal complex in vascular smooth muscle cells**. *J. Biol. Chem.* (2016) **291** 21148-21159. DOI: 10.1074/jbc.M116.742171
6. Bashashati M., McCallum R. W.. **Neurochemical mechanisms and pharmacologic strategies in managing nausea and vomiting related to cyclic vomiting syndrome and other gastrointestinal disorders**. *Eur. J. Pharmacol.* (2014) **722** 79-94. DOI: 10.1016/j.ejphar.2013.09.075
7. Belkacemi L., Darmani N. A.. **Dopamine receptors in emesis: Molecular mechanisms and potential therapeutic function**. *Pharmacol. Res.* (2020) **161** 105124. DOI: 10.1016/j.phrs.2020.105124
8. Belkacemi L., Zhong W., Darmani N. A.. **Signal transduction pathways involved in dopamine D2 receptor-evoked emesis in the least shrew (Cryptotis parva)**. *Auton. Neurosci.* (2021) **233** 102807. DOI: 10.1016/j.autneu.2021.102807
9. Beltran-Parrazal L., Fernandez-Ruiz J., Toledo R., Manzo J., Morgado-Valle C.. **Inhibition of endoplasmic reticulum Ca²⁺ ATPase in preBötzinger complex of neonatal rat does not affect respiratory rhythm generation**. *Neuroscience* (2012) **224** 116-124. DOI: 10.1016/j.neuroscience.2012.08.016
10. Berndt E. R., Nass D., Kleinrock M., Aitken M.. **Decline in economic returns from new drugs raises questions about sustaining innovations**. *Health Aff. (Millwood)* (2015) **34** 245-252. DOI: 10.1377/hlthaff.2014.1029
11. BioMart (2022). BioMart. Available at: https://www.ensembl.org/biomart/martview/
.. *BioMart* (2022)
12. Bolger A. M., Lohse M., Usadel B.. **Trimmomatic: A flexible trimmer for Illumina sequence data**. *Bioinformatics* (2014) **30** 2114-2120. DOI: 10.1093/bioinformatics/btu170
13. Calderon-Sanchez E. M., Avila-Medina J., Callejo-Garcia P., Fernandez-Velasco M., Ordonez A., Smani T.. **Role of Orai1 and L-type CaV1.2 channels in Endothelin-1 mediated coronary contraction under ischemia and reperfusion**. *Cell. Calcium* (2020) **86** 102157. DOI: 10.1016/j.ceca.2019.102157
14. Carpenter D. O., Briggs D. B., Knox A. P., Strominger N.. **Excitation of area postrema neurons by transmitters, peptides, and cyclic nucleotides**. *J. Neurophysiol.* (1988) **59** 358-369. DOI: 10.1152/jn.1988.59.2.358
15. Catterall W. A.. **Voltage-gated calcium channels**. *Cold Spring Harb. Perspect. Biol.* (2011) **3** a003947. DOI: 10.1101/cshperspect.a003947
16. Colbert E. H.. *Evolution of vertebrates* (1958) 249-261
17. Cunningham F., Achuthan P., Akanni W., Allen J., Amode M. R., Armean I. M.. **Ensembl 2019**. *Nucleic Acids Res.* (2019) **47** D745-D751. DOI: 10.1093/nar/gky1113
18. Dalquen D. A., Dessimoz C.. **Bidirectional best hits miss many orthologs in duplication-rich clades such as plants and animals**. *Genome Biol. Evol.* (2013) **5** 1800-1806. DOI: 10.1093/gbe/evt132
19. Darmani N. A., Crim J. L.. **Delta-9-tetrahydrocannabinol differentially suppresses emesis versus enhanced locomotor activity produced by chemically diverse dopamine D2/D3 receptor agonists in the least shrew (Cryptotis parva)**. *Pharmacol. Biochem. Behav.* (2005) **80** 35-44. DOI: 10.1016/j.pbb.2004.10.019
20. Darmani N. A., Crim J. L., Janoyan J. J., Abad J., Ramirez J.. **A re-evaluation of the neurotransmitter basis of chemotherapy-induced immediate and delayed vomiting: Evidence from the least shrew**. *Brain Res.* (2009) **1248** 40-58. DOI: 10.1016/j.brainres.2008.10.063
21. Darmani N. A., Dey D., Chebolu S., Amos B., Kandpal R., Alkam T.. **Cisplatin causes over-expression of tachykinin NK(1) receptors and increases ERK1/2- and PKA-phosphorylation during peak immediate- and delayed-phase emesis in the least shrew (Cryptotis parva) brainstem**. *Eur. J. Pharmacol.* (2013) **698** 161-169. DOI: 10.1016/j.ejphar.2012.09.008
22. Darmani N. A., Ray A. P.. **Evidence for a re-evaluation of the neurochemical and anatomical bases of chemotherapy-induced vomiting**. *Chem. Rev.* (2009) **109** 3158-3199. DOI: 10.1021/cr900117p
23. Darmani N. A.. **Serotonin 5-HT3 receptor antagonists prevent cisplatin-induced emesis in Cryptotis parva: A new experimental model of emesis**. *J. Neural Transm. (Vienna)* (1998) **105** 1143-1154. DOI: 10.1007/s007020050118
24. Darmani N. A., Wang Y., Abad J., Ray A. P., Thrush G. R., Ramirez J.. **Utilization of the least shrew as a rapid and selective screening model for the antiemetic potential and brain penetration of substance P and NK1 receptor antagonists**. *Brain Res.* (2008) **1214** 58-72. DOI: 10.1016/j.brainres.2008.03.077
25. Darmani N. A., Zhao W., Ahmad B.. **The role of D2 and D3 dopamine receptors in the mediation of emesis in Cryptotis parva (the least shrew)**. *J. Neural Transm. (Vienna)* (1999) **106** 1045-1061. DOI: 10.1007/s007020050222
26. Darmani N. A., Zhong W., Chebolu S., Mercadante F.. **Differential and additive suppressive effects of 5-HT3 (palonosetron)- and NK1 (netupitant)-receptor antagonists on cisplatin-induced vomiting and ERK1/2, PKA and PKC activation**. *Pharmacol. Biochem. Behav.* (2015) **131** 104-111. DOI: 10.1016/j.pbb.2015.02.010
27. Darmani N. A., Zhong W., Chebolu S., Vaezi M., Alkam T.. **Broad-spectrum antiemetic potential of the L-type calcium channel antagonist nifedipine and evidence for its additive antiemetic interaction with the 5-HT(3) receptor antagonist palonosetron in the least shrew (Cryptotis parva)**. *Eur. J. Pharmacol.* (2014) **722** 2-12. DOI: 10.1016/j.ejphar.2013.08.052
28. **DAVID bioinformatics database**. (2022)
29. Dey D., Abad J., Ray A. P., Darmani N. A.. **Differential temporal changes in brain and gut substance P mRNA expression throughout the time-course of cisplatin-induced vomiting in the least shrew (Cryptotis parva)**. *Brain Res.* (2010) **1310** 103-112. DOI: 10.1016/j.brainres.2009.11.005
30. **Dog genome assembly: CanFam3.1**. (2022)
31. **Ferret genome assembly: MusPutFur1.0**. (2022)
32. Gancedo J. M.. **Biological roles of cAMP: Variations on a theme in the different kingdoms of life**. *Biol. Rev. Camb. Philos. Soc.* (2013) **88** 645-668. DOI: 10.1111/brv.12020
33. Gomez-Viquez L., Guerrero-Serna G., Garcia U., Guerrero-Hernandez A.. **SERCA pump optimizes Ca2+ release by a mechanism independent of store filling in smooth muscle cells**. *Biophys. J.* (2003) **85** 370-380. DOI: 10.1016/S0006-3495(03)74481-6
34. Grabherr M. G., Haas B. J., Yassour M., Levin J. Z., Thompson D. A., Amit I.. **Full-length transcriptome assembly from RNA-Seq data without a reference genome**. *Nat. Biotechnol.* (2011) **29** 644-652. DOI: 10.1038/nbt.1883
35. Graefe S. B., Mohiuddin S. S.. *Biochemistry, Substance P.* (2022)
36. Halls M. L., Cooper D. M. F.. **Adenylyl cyclase signalling complexes - pharmacological challenges and opportunities**. *Pharmacol. Ther.* (2017) **172** 171-180. DOI: 10.1016/j.pharmthera.2017.01.001
37. Han Y., Gao S., Muegge K., Zhang W., Zhou B.. **Advanced applications of RNA sequencing and challenges**. *Bioinform. Biol. Insights* (2015) **9** 29-46. DOI: 10.4137/BBI.S28991
38. Hargreaves A. C., Gunthorpe M. J., Taylor C. W., Lummis S. C.. **Direct inhibition of 5-hydroxytryptamine3 receptors by antagonists of L-type Ca2+ channels**. *Mol. Pharmacol.* (1996) **50** 1284-1294. PMID: 8913360
39. Hayase T., Tachibana S., Yamakage M.. **Effect of sevoflurane anesthesia on the comprehensive mRNA expression profile of the mouse hippocampus**. *Med. Gas. Res.* (2016) **6** 70-76. DOI: 10.4103/2045-9912.184715
40. Hesketh P. J.. **Chemotherapy-induced nausea and vomiting**. *N. Engl. J. Med.* (2008) **358** 2482-2494. DOI: 10.1056/NEJMra0706547
41. Hesketh P. J., Kris M. G., Basch E., Bohlke K., Barbour S. Y., Clark-Snow R. A.. **Antiemetics: American society of clinical oncology clinical practice guideline update**. *J. Clin. Oncol.* (2017) **35** 3240-3261. DOI: 10.1200/JCO.2017.74.4789
42. Homma K., Kitamura Y., Ogawa H., Oka K.. **Serotonin induces the increase in intracellular Ca2+ that enhances neurite outgrowth in PC12 cells via activation of 5-HT3 receptors and voltage-gated calcium channels**. *J. Neurosci. Res.* (2006) **84** 316-325. DOI: 10.1002/jnr.20894
43. Horn C. C., Wallisch W. J., Homanics G., Williams J. P.. **Pathophysiological and neurochemical mechanisms of postoperative nausea and vomiting**. *Eur. J. Pharmacol.* (2014) **722** 55-66. DOI: 10.1016/j.ejphar.2013.10.037
44. Howe K. L., Achuthan P., Allen J., Allen J., Alvarez-Jarreta J., Amode M. R.. **Ensembl 2021**. *Nucleic Acids Res.* (2021) **49** D884-D891. DOI: 10.1093/nar/gkaa942
45. Huang Da W., Sherman B. T., Lempicki R. A.. **Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists**. *Nucleic Acids Res.* (2009a) **37** 1-13. DOI: 10.1093/nar/gkn923
46. Huang Da W., Sherman B. T., Lempicki R. A.. **Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources**. *Nat. Protoc.* (2009b) **4** 44-57. DOI: 10.1038/nprot.2008.211
47. **Human genome assembly: GRCh38.p13**. (2022)
48. Jenner P., Katzenschlager R.. **Apomorphine - pharmacological properties and clinical trials in Parkinson's disease**. *Park. Relat. Disord.* (2016) **33** S13-S21. DOI: 10.1016/j.parkreldis.2016.12.003
49. King G. L.. **Animal models in the study of vomiting**. *Can. J. Physiol. Pharmacol.* (1990) **68** 260-268. DOI: 10.1139/y90-040
50. Knox A. P., Strominger N. L., Battles A. H., Carpenter D. O.. **Behavioral studies of emetic sensitivity in the ferret**. *Brain Res. Bull.* (1993) **31** 477-484. DOI: 10.1016/0361-9230(93)90112-o
51. Kobrinsky N. L.. **Regulation of nausea and vomiting in cancer chemotherapy. A review with emphasis on opiate mediators**. *Am. J. Pediatr. Hematol. Oncol.* (1988) **10** 209-213. DOI: 10.1097/00043426-198823000-00005
52. Kwon O. S., Kim W., Cha H. J., Lee H.. *Arch. Pharm. Res.* (2019) **42** 879-889. DOI: 10.1007/s12272-019-01176-3
53. Langmead B., Salzberg S. L.. **Fast gapped-read alignment with Bowtie 2**. *Nat. Methods* (2012) **9** 357-359. DOI: 10.1038/nmeth.1923
54. Lee K. M., Jung D. Y., Hwang H., Kim W. H., Lee J. Y., Kim T. Y.. **Late chronotypes are associated with neoadjuvant chemotherapy-induced nausea and vomiting in women with breast cancer**. *Chronobiol. Int.* (2017) **34** 480-491. DOI: 10.1080/07420528.2017.1295978
55. Li B., Dewey C. N.. **Rsem: Accurate transcript quantification from RNA-seq data with or without a reference genome**. *BMC Bioinforma.* (2011) **12** 323. DOI: 10.1186/1471-2105-12-323
56. Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N.. **The sequence alignment/map format and SAMtools**. *Bioinformatics* (2009) **25** 2078-2079. DOI: 10.1093/bioinformatics/btp352
57. Li Y. Q., Yang Y. H., Zhang G. L., Meng Q., Feng X. D., Cheng Q. Q.. **RNA-Seq reveals inflammatory mechanisms of Xiao-Ban-Xia-Tang decoction to ameliorate cisplatin-induced emesis in a rat pica model**. *Biomed. Pharmacother.* (2020) **131** 110699. DOI: 10.1016/j.biopha.2020.110699
58. McKenzie E., Chan D., Parsafar S., Razvi Y., McFarlane T., Rico V.. **Evolution of antiemetic studies for radiation-induced nausea and vomiting within an outpatient palliative radiotherapy clinic**. *Support. Care Cancer* (2019) **27** 3245-3252. DOI: 10.1007/s00520-019-04870-6
59. Michelangeli F., East J. M.. **A diversity of SERCA Ca2+ pump inhibitors**. *Biochem. Soc. Trans.* (2011) **39** 789-797. DOI: 10.1042/BST0390789
60. Miyano K., Morioka N., Sugimoto T., Shiraishi S., Uezono Y., Nakata Y.. **Activation of the neurokinin-1 receptor in rat spinal astrocytes induces Ca2+ release from IP3-sensitive Ca2+ stores and extracellular Ca2+ influx through TRPC3**. *Neurochem. Int.* (2010) **57** 923-934. DOI: 10.1016/j.neuint.2010.09.012
61. Moreno-Hagelsieb G., Latimer K.. **Choosing BLAST options for better detection of orthologs as reciprocal best hits**. *Bioinformatics* (2008) **24** 319-324. DOI: 10.1093/bioinformatics/btm585
62. Moreton J., Izquierdo A., Emes R. D.. **Assembly, assessment, and availability of de novo generated eukaryotic transcriptomes**. *Front. Genet.* (2015) **6** 361. DOI: 10.3389/fgene.2015.00361
63. Mori F., Perez-Torres S., De Caro R., Porzionato A., Macchi V., Beleta J.. **The human area postrema and other nuclei related to the emetic reflex express cAMP phosphodiesterases 4B and 4D**. *J. Chem. Neuroanat.* (2010) **40** 36-42. DOI: 10.1016/j.jchemneu.2010.03.004
64. **Mouse genome assembly: GRCm38.p6**. (2022)
65. Navari R. M.. **Olanzapine for the prevention and treatment of chronic nausea and chemotherapy-induced nausea and vomiting**. *Eur. J. Pharmacol.* (2014) **722** 180-186. DOI: 10.1016/j.ejphar.2013.08.048
66. Ozsolak F., Milos P. M.. **RNA sequencing: Advances, challenges and opportunities**. *Nat. Rev. Genet.* (2011) **12** 87-98. DOI: 10.1038/nrg2934
67. Porreca F., Ossipov M. H.. **Nausea and vomiting side effects with opioid analgesics during treatment of chronic pain: Mechanisms, implications, and management options**. *Pain Med.* (2009) **10** 654-662. DOI: 10.1111/j.1526-4637.2009.00583.x
68. Propper D. J., Saunders M. P., Salisbury A. J., Long L., O'byrne K. J., Braybrooke J. P.. **Phase I study of the novel cyclic AMP (cAMP) analogue 8-chloro-cAMP in patients with cancer: Toxicity, hormonal, and immunological effects**. *Clin. Cancer Res.* (1999) **5** 1682-1689. PMID: 10430069
69. Robichaud A., Tattersall F. D., Choudhury I., Rodger I. W.. **Emesis induced by inhibitors of type IV cyclic nucleotide phosphodiesterase (PDE IV) in the ferret**. *Neuropharmacology* (1999) **38** 289-297. DOI: 10.1016/S0028-3908(98)00190-7
70. Rojas C., Slusher B. S.. **Mechanisms and latest clinical studies of new NK1 receptor antagonists for chemotherapy-induced nausea and vomiting: Rolapitant and NEPA (netupitant/palonosetron)**. *Cancer Treat. Rev.* (2015) **41** 904-913. DOI: 10.1016/j.ctrv.2015.09.005
71. Sa A. C. C., Sadee W., Johnson J. A.. **Whole transcriptome profiling: An RNA-seq primer and implications for pharmacogenomics research**. *Clin. Transl. Sci.* (2018) **11** 153-161. DOI: 10.1111/cts.12511
72. Sakurada C., Watanabe C., Inoue M., Tan-No K., Ando R., Kisara K.. **Spinal actions of GR73632, a novel tachykinin NK1 receptor agonist**. *Peptides* (1999) **20** 301-304. DOI: 10.1016/s0196-9781(98)00171-5
73. Sato J. J., Ohdachi S. D., Echenique-Diaz L. M., Borroto-Paez R., Begue-Quiala G., Delgado-Labanino J. L.. **Molecular phylogenetic analysis of nuclear genes suggests a Cenozoic over-water dispersal origin for the Cuban solenodon**. *Sci. Rep.* (2016) **6** 31173. DOI: 10.1038/srep31173
74. Scannell J. W., Blanckley A., Boldon H., Warrington B.. **Diagnosing the decline in pharmaceutical R&D efficiency**. *Nat. Rev. Drug Discov.* (2012) **11** 191-200. DOI: 10.1038/nrd3681
75. Smith H. S., Laufer A.. **Opioid induced nausea and vomiting**. *Eur. J. Pharmacol.* (2014) **722** 67-78. PMID: 24157979
76. **Social science Statistics**. (2022)
77. Solovyova N., Verkhratsky A.. **Neuronal endoplasmic reticulum acts as a single functional Ca2+ store shared by ryanodine and inositol-1,4,5-trisphosphate receptors as revealed by intra-ER [Ca2+] recordings in single rat sensory neurones**. *Pflugers Arch.* (2003) **446** 447-454. DOI: 10.1007/s00424-003-1094-z
78. Stark R., Grzelak M., Hadfield J.. **RNA sequencing: The teenage years**. *Nat. Rev. Genet.* (2019) **20** 631-656. DOI: 10.1038/s41576-019-0150-2
79. Ueno S., Matsuki N., Saito H.. **Suncus murinus as a new experimental model for motion sickness**. *Life Sci.* (1988) **43** 413-420. DOI: 10.1016/0024-3205(88)90520-6
80. Vanmierlo T., Creemers P., Akkerman S., Van Duinen M., Sambeth A., De Vry J.. **The PDE4 inhibitor roflumilast improves memory in rodents at non-emetic doses**. *Behav. Brain Res.* (2016) **303** 26-33. DOI: 10.1016/j.bbr.2016.01.031
81. Wickham R. J.. **Revisiting the physiology of nausea and vomiting- Challenging the paradigm**. *Support. Care Cancer* (2020) **28** 13-21. PMID: 31388745
82. Yates A. D., Achuthan P., Akanni W., Allen J., Allen J., Alvarez-Jarreta J.. **Ensembl 2020**. *Nucleic Acids Res.* (2020) **48** D682-D688. DOI: 10.1093/nar/gkz966
83. Zhong W., Chebolu S., Darmani N. A.. **Broad-spectrum antiemetic efficacy of the L-type calcium channel blocker amlodipine in the least shrew (Cryptotis parva)**. *Pharmacol. Biochem. Behav.* (2014a) **120** 124-132. DOI: 10.1016/j.pbb.2014.03.005
84. Zhong W., Chebolu S., Darmani N. A.. **Intracellular emetic signaling cascades by which the selective neurokinin type 1 receptor (NK1R) agonist GR73632 evokes vomiting in the least shrew (Cryptotis parva)**. *Neurochem. Int.* (2019) **122** 106-119. DOI: 10.1016/j.neuint.2018.11.012
85. Zhong W., Chebolu S., Darmani N. A.. **Intracellular emetic signaling evoked by the L-type Ca(2+) channel agonist FPL64176 in the least shrew (Cryptotis parva)**. *Eur. J. Pharmacol.* (2018) **834** 157-168. DOI: 10.1016/j.ejphar.2018.06.035
86. Zhong W., Chebolu S., Darmani N. A.. **Thapsigargin-induced activation of Ca(2+)-CaMKII-ERK in brainstem contributes to substance P release and induction of emesis in the least shrew**. *Neuropharmacology* (2016) **103** 195-210. DOI: 10.1016/j.neuropharm.2015.11.023
87. Zhong W., Hutchinson T. E., Chebolu S., Darmani N. A.. **Serotonin 5-HT3 receptor-mediated vomiting occurs via the activation of Ca2+/CaMKII-dependent ERK1/2 signaling in the least shrew (Cryptotis parva)**. *PLoS One* (2014b) **9** e104718. DOI: 10.1371/journal.pone.0104718
88. Zhong W., Picca A. J., Lee A. S., Darmani N. A.. **Ca(2+) signaling and emesis: Recent progress and new perspectives**. *Auton. Neurosci.* (2017) **202** 18-27. DOI: 10.1016/j.autneu.2016.07.006
89. Zhong W., Shahbaz O., Teskey G., Beever A., Kachour N., Venketaraman V.. **Mechanisms of nausea and vomiting: Current knowledge and recent advances in intracellular emetic signaling systems**. *Int. J. Mol. Sci.* (2021) **22** 5797. DOI: 10.3390/ijms22115797
90. Ziviani E., Lippi G., Bano D., Munarriz E., Guiducci S., Zoli M.. **Ryanodine receptor-2 upregulation and nicotine-mediated plasticity**. *EMBO J.* (2011) **30** 194-204. DOI: 10.1038/emboj.2010.279
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---
title: 'The Effects of Statins on Respiratory Symptoms and Pulmonary Fibrosis in COVID-19
Patients with Diabetes Mellitus: A Longitudinal Multicenter Study'
authors:
- Mohammadamin Sadeghdoust
- Farnaz Aligolighasemabadi
- Tania Dehesh
- Nima Taefehshokr
- Adel Sadeghdoust
- Katarzyna Kotfis
- Amirhossein Hashemiattar
- Amir Ravandi
- Neda Aligolighasemabadi
- Omid Vakili
- Beniamin Grabarek
- Rafał Staszkiewicz
- Marek J. Łos
- Pooneh Mokarram
- Saeid Ghavami
journal: Archivum Immunologiae et Therapiae Experimentalis
year: 2023
pmcid: PMC9972324
doi: 10.1007/s00005-023-00672-1
license: CC BY 4.0
---
# The Effects of Statins on Respiratory Symptoms and Pulmonary Fibrosis in COVID-19 Patients with Diabetes Mellitus: A Longitudinal Multicenter Study
## Abstract
The aim of this prospective cohort study was to explore the effect of statins on long-term respiratory symptoms and pulmonary fibrosis in coronavirus disease 2019 (COVID-19) patients with diabetes mellitus (DM). Patients were recruited from three tertiary hospitals, categorized into Statin or Non-statin groups, and assessed on days 0, 28, and 90 after symptoms onset to record the duration of symptoms. Pulmonary fibrosis was scored at baseline and follow-up time points by high-resolution computed tomography scans. Each group comprised 176 patients after propensity score matching. Data analysis revealed that the odds of having cough and dyspnea were significantly higher in the Non-statin group compared to the Statin group during the follow-up period. Overall, there was no significant difference in the change in pulmonary fibrosis score between groups. However, Non-statin patients with > 5 years of DM were more likely to exhibit a significantly higher fibrosis score during the follow-up period as compared to their peers in the Statin group. Our results suggest that the use of statins is associated with a lower risk of developing chronic cough and dyspnea in diabetic patients with COVID-19, and may reduce pulmonary fibrosis associated with COVID-19 in patients with long-term (> 5 years) DM.
## Introduction
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread uncontrollably around the world with considerable impacts on public health and the international economy (Peterson and Walker 2022; Walls et al. 2020). This high virulence is due to multiple mechanisms allowing SARS-CoV-2 to manipulate host immune responses, thus prolonging viral clearance periods in patients (Taefehshokr et al. 2020). The virus primarily targets the respiratory system and mainly enters respiratory cells by binding to cell surface receptor proteins such as angiotensin-converting enzyme 2 (ACE2) and neuropilin-1 (Kouhpayeh et al. 2021; Kyrou et al. 2021; Shojaei et al. 2020b; Siri et al. 2021; Walls et al. 2020). ACE2 is recognized as a non-immune receptor for SARS-CoV-2 and binds to the viral S protein receptor-binding motif at its N-terminal extracellular catalytic domain (Gawish et al. 2022; Jackson et al. 2022). Most COVID-19 patients present with mild to moderate symptoms but roughly one-sixth develop severe pneumonia, of which approximately $5\%$ ultimately develop acute respiratory distress syndrome, septic shock, or multiple organ failure (Centers for Disease Control and Prevention 2020; Peymani et al. 2021). Evidence suggests that some patients experience long-term symptoms and pulmonary fibrosis after recovery from the acute phase of COVID-19 has emerged; an undesirable pathologic phenomenon known as long COVID or post-acute COVID-19 syndrome (PCS) (Jutant et al. 2022). PCS is affected by multiple factors such as dysregulated inflammation, organ damage, and the presence of certain pre-existing conditions, including diabetes mellitus (DM) (Habibzadeh et al. 2021; Raveendran and Misra 2021).
DM is a common underlying disease in COVID-19 patients and one of the leading causes of morbidity and mortality worldwide (Centers for Disease Control and Prevention 2020, 2022; Drozdzal et al. 2021; Guo et al. 2020). COVID-19 might put DM patients at risk of hyperglycemia, which consequently might modulate immune and inflammatory responses (Lim et al. 2021). Furthermore, the dysregulated immune system and pro-inflammatory state in DM, characterized by excessive and improper cytokine responses, might predispose COVID-19 patients to severe respiratory symptoms, organ damage, and poor clinical outcomes (Hussain et al. 2020; Lim et al. 2021). Thus, DM could augment the prolonged inflammatory response in COVID-19, thereby potentially promoting pulmonary fibrosis that can lead to long-term respiratory symptoms as seen in PCS patients (Raveendran and Misra 2021). In addition, there are several common main risk factors for severe COVID-19 and idiopathic pulmonary fibrosis that include increasing age, male sex, and associated comorbidities such as DM (George et al. 2020; Lechowicz et al. 2020). Therefore, the control of DM by glucose-lowering medications and the prevention of disease complications by lipid-lowering agents such as statins need special attention in COVID-19 patients.
Statins are well-known cholesterol-lowering medications recommended by the American Diabetes Association for nearly all DM patients (Ahmadi et al. 2020; American Diabetes Association Professional Practice Committee 2022). Their principal mechanism of action is blocking the mevalonate pathway by competitively inhibiting 3-hydroxy-3-methylglutaryl coenzyme A reductase (Adhyaru and Jacobson 2018; Shojaei et al. 2020a). Beyond their lipid-lowering effects, these agents show pleiotropic immunomodulatory, anti-inflammatory, anti-fibrotic, and anti-cancer effects (Ahmadi et al. 2020; Alizadeh et al. 2017; Emami et al. 2019; Liao and Laufs 2005; Schaafsma et al. 2011a). Moreover, statins suppress HIV replication through upregulation of p21 in CD4 T cells (Elahi et al. 2016), and inhibit respiratory syncytial virus replication as well as load in mice (Gower and Graham 2001), indicating anti-viral properties. Statins could potentially limit the exaggerated inflammatory response by amplifying ACE2 expression and inhibiting Toll-like receptor nuclear factor κB and NOD-like receptors family pyrin domain containing 3 inflammasomes (Drozdzal et al. 2021; Lee et al. 2020). Recent investigations have shown strong evidence for the anti-fibrotic effects of statins in airway resident cells and improved clinical outcomes in idiopathic pulmonary fibrosis patients using statins (Kreuter et al. 2017; Schaafsma et al. 2011b; Watts et al. 2005). Moreover, statins may potentially affect COVID-19 pathogenesis via targeting autophagy and apoptosis of host cells and virulence of SARS-CoV-2 (Han et al. 2018; Peng et al. 2018). Hence, the use of statins has attracted much attention as an adjunctive therapy to mitigate dysregulated inflammation and improve the clinical outcomes of COVID-19 patients (Scheen 2021).
Based on the scale of the pandemic, the health burden of PCS and fibrotic lung disease following COVID-19 is likely to be high. At the same time, despite the scientific rationale for using statins in COVID-19 patients, the effects of statins on long-term respiratory symptoms and pulmonary fibrosis have not been characterized yet. Therefore, we followed up on our previous retrospective investigation on the impact of statins on COVID-19 (Peymani et al. 2021) and designed this prospective cohort study to specifically evaluate the effects of statins on the duration of respiratory symptoms and changes in pulmonary fibrosis using high-resolution computed tomography in COVID-19 patients with DM over a three-month follow-up period.
## Study Design and Patients
This multi-center prospective cohort study was conducted between May and December 2021 in three tertiary hospitals in Iran: The Karoon Hospital (Gotvand city), Razi Hospital (Rasht city), and Golestan Hospital (Ahvaz city). This study was conducted at the same time as the fourth and fifth waves of the COVID-19 pandemic, and based on the available data the delta variant was becoming the dominant strain in that period of time (Yavarian et al. 2022). This work was approved by the Shiraz University of Medical Sciences (IR.SUMS.REC.1399.151) and the Institutional Review Board of the relevant centers. Written informed consent was obtained from all the participants.
COVID-19 patients with diabetes who met the inclusion criteria were included in Statin or Non-statin groups and followed up for three months after initial symptoms to assess the potential effects of statins on long-term respiratory symptoms and pulmonary fibrosis. Inclusion criteria were: (a) 18 < age (years) < 85; (b) confirmed diagnosis of diabetes mellitus based on American Diabetes Association guidelines (American Diabetes Association 2021); (c) confirmed diagnosis of COVID-19 defined as a laboratory-confirmed SARS-CoV-2 infection through real-time reverse-transcriptase polymerase chain reaction; (d) presenting with at least one of the following respiratory symptoms: cough, dyspnea, chest discomfort, anosmia, ageusia, fever, sweating, fatigue, myalgia, arthralgia, or headache. Patients with chronic respiratory disease, active hepatic disease, deafness, blindness, intellectual disability, and critical cases were excluded.
## Baseline Assessment and Follow-Up
Baseline demographics, comorbidities, and blood laboratory test results were collected from the electronic medical records systems during the first visit. Initial signs, symptoms, and the presence of abnormal sounds in auscultation were also recorded. Modified Medical Research Council (mMRC) Dyspnea Scale and cough symptom score (CSS) were used to score the severity of dyspnea and cough, respectively.
All patients were offered two follow-up interviews on days 28 and 90 after presenting initial symptoms on day 0. Additionally, a clinic follow-up card was given to each patient to record the exact initiation and end date of symptoms. Also, patients with available baseline and follow-up high-resolution computed tomography scans (HRCTs) were included in HRCT analysis to evaluate pulmonary fibrosis.
## Review of HRCT Images
Pulmonary fibrosis in HRCT images was scored from 0 to 30 based on a method described by Camiciottoli et al. [ 2007]. Briefly, the total score is equal to the score for all types of lesions (ground-glass opacities = 1; linear opacities = 2; interlobular septal thickening = 3; reticulation = 4; honeycombing and bronchiectasis = 5) plus the extent score for each type of lesions (1–3 involved pulmonary segments = 1; 4–9 segments = 2; more than 9 segments = 3). All images were reviewed randomly by an expert radiologist and an experienced research assistant, who were blinded to the study groups.
## Statistical Analysis
Propensity score matching was performed through a 1:1 greedy matching algorithm to limit potential residual confounding factors. In observational studies, it is impossible to have control over confounder variables at the beginning of the study. Therefore, confounder effects should be removed by matching. Covariates in the propensity analyses included age, sex, obesity, Charlson comorbidity index, smoking status, use of insulin, diabetes duration, serum level of glycosylated hemoglobin, history of liver disease, renal disease, hypertension, cardiovascular disease, and cerebrovascular disease.
Continuous data are reported as mean and standard deviation (SD) or median and interquartile range [IQR], and categorical data are shown as numbers and percentages. The χ2 test, Student t test, and Mann–Whitney U tests were used for comparative analysis of baseline characteristics. Using the Kaplan–Meier (log-rank) test, patients in two groups were compared in terms of time to becoming symptom-free. On bivariate analysis, odds ratios along with their $95\%$ confidence intervals ($95\%$CIs) were calculated using a marginal model via generalized estimation equation. Marginal models are substitutions of repeated measurement analysis in follow-up studies when the response variable does not have a normal distribution. We also conducted a subgroup analysis to explore how statins in combination with certain factors affect pulmonary fibrosis.
IBM SPSS Statistics (IBM Corporation, version 19.0) and GraphPad Prism software version 8.0.2 (GraphPad Software, San Diego, California, USA), R version (4.1.0) were used to perform data analysis. Differences were considered statistically significant when p-values ≤ 0.05.
## Participants
A total of 652 diabetic patients with confirmed COVID-19 was assessed for participation eligibility (Fig. 1). After excluding 134 patients, 518 patients were included in the study. A hundred and seventy-six out of 263 patients in the Statin group and 206 out of 255 patients in the Non-statin group successfully attended the first and second follow-up interviews. After propensity matching, 176 patients from each group were included in the data analysis. Fig. 1Flow diagram of enrollment and follow-up of diabetic patients with COVID-19 in the statin and non-statin groups Table 1 summarizes the demographics, clinical backgrounds, and laboratory test results of patients in each group. Females with controlled diabetes were the dominant population, while hypertension was the most common comorbidity. Patients in the Statin group had significantly lower serum levels of LDL cholesterol, triglycerides, and platelets. Table 1Demographics and baseline characteristics of diabetic patients infected with SARS-CoV-2CharacteristicNon-statin ($$n = 176$$)Statin ($$n = 176$$)p-ValueFemale120 (68.2)128 (72.7)0.41Age, years61 [54–66]62 [56–66]0.21Body mass index, kg/m228.3 [3]28.7(2.8)0.16Smoker23 (13.1)20 (11.4)0.62Duration of diabetes, years4 [3–7]5 [4–7]0.12Poor-controlled diabetes*58 [33]43 (24.4)0.077Number of comorbidities2 [1–2]2 [1–2]0.15Hypertension87(49.4)99 [56]0.24Cardiovascular disease25 (14.2)36 (20.5)0.005Cerebrovascular disease9 (5.1)14 [8]0.38Chronic kidney disease24 (13.6)26 (14.8)0.43Liver disease10 (5.7)12 (6.8)0.66Charleston comorbidity index score4.4 (1.2)4.6 (1.5)0.22Laboratory tests White blood cell count; × 109/L8.1 [6.6–9.3]7.3[6.4–8.6]0.07 Neutrophil count; × 109/L6.1 [5.1–7.2]5.7 [4.9–6.6]0.034 Lymphocytes count; × 109/L1.4 [1.1–1.8]1.4 [1.1–1.8]0.81 Platelets count; × 109/L284 [235–350]270 [214–321]0.028 Haemoglobin; g/L12.1 [11.3–13]11.8 [10.8–12.7]0.061 Serum creatinine; µmol/L1.1 [0.9–1.2]1.09 [1–1.27]0.22 Triglyceride; mg/dL157 [134–182]153 [126–182]0.01 Cholesterol; mg/dL172 [155–197]168 [150–189]0.12 LDL; mg/dL95 [76–116]89 [60–104]0.001 HDL; mg/dL32 [25–40]35 [23–42]0.19 Aspartate aminotransferase; U/L25.6 [17.8–39.3]29.4 [19.3–44.6]0.17 Alanine aminotransferase; U/L19.3 [15.2–30.7]22.8 [18.1–31.9]0.19 ESR37 [26.2–51.7]32 [22–46.5]0.089 d-dimer, µg/mL0.5 [0.4–0.7]0.5 [0.4–1.1]0.18 C-reactive protein; mg/dL12.7 [11.7–35]23 [11.2–37]0.99 HbA1c7.9 [6.7–7.4]8.1 [7.5–9.5]0.082Medications AtorvastatinNA148 (84.1)NA RosuvastatinNA17 (9.7)NA SimvastatinNA8 (4.5)NA Other statinsNA3 (1.7)NA Insulin64 (36.4)60 (34.1)0.73 Metformin98 (55.7)86 (48.9)0.24 Other oral diabetes medications53 (30.1)60 (34.1)0.42 Antihypertensive of any type73 (41.5)80 (45.5)0.51 Anti-coagulant and anti-platelet62 (35.2)57 (32.4)0.65 NSAIDs69 (39.2)74 [42]0.66 Proton pump inhibitors/antacids51 [29]59 (33.5)0.42Data are presented as mean ± standard deviation, median [inter quartile range], or number (percentage)NSAIDs non-steroidal anti-inflammatory drugs; NA not applicable*HbA1c value is $7\%$ or higher
## Respiratory Symptoms
The frequency of occurring respiratory symptoms is summarized in Table 2. Cough was the most common initial symptom in both groups, followed by fever/sweating and dyspnea. Cough in the Statin group dropped from $60.2\%$ to $11.4\%$ and $5.7\%$ on days 28 and 90 of follow-up, respectively. In patients who did not receive statins, cough prevalence decreased from $51.7\%$ to $17\%$ and $5.7\%$. Similarly, there was a dramatic reduction in the presence of dyspnea and other symptoms throughout the follow-up period. Further data analysis revealed that the odds of having a cough during the follow-up period were higher in patients not using statins compared to those who did (OR: 1.35, CI $95\%$: 1.01–1.81; $$p \leq 0.046$$). In addition, patients in the Non-satin group were more likely to present with dyspnea (OR:1.42, CI $95\%$: 1.01–1.81; $$p \leq 0.046$$). However, there were no statistically significant differences in experiencing other symptoms between groups throughout the follow-up period. Table 2The frequency of initial and persistent symptoms, and the results of marginal model (GEE estimation) analysis in statin and non-statin patient groupsOnset (Day 0)Day 28Day 90GEE estimation (statin vs non-statin)SymptomsNSNSNSOR$95\%$ CI for ORp-valueFever/sweating105 (59.7)91 (51.7)43 (24.4)33 (18.8)21 (11.9)16 (9.1)1.290.94–1.780.11Fatigue89 (50.6)79 (44.9)39 (22.2)33 (18.8)12 (6.8)7 [4]1.240.91–1.710.18Myalgia/arthralgia50 (28.4)56 (31.8)24 (13.6)26 (14.8)7 [4]9 (5.1)0.860.57–1.310.49Headache48 (27.3)61 (34.7)21 (11.9)25 (14.2)10 (5.7)6 (3.4)0.840.55–1.270.41Cough106 (60.2)91 (51.7)30 [17]20 (11.4)10 (5.7)5 (2.8)1.351.01–1.810.046Dyspnea97 (55.1)81 [46]45 (25.6)36 (20.5)21 (11.9)10 (5.7)1.421.02–1.980.037Chest discomfort48 (27.3)37 [21]30 [17]14 [8]7 [4]3 (1.7)1.580.99–2.510.052Anosmia/ageusia77 (43.8)87 (49.4)35 (19.9)39 (22.2)19 (10.8)17 (9.7)1.240.91–1.710.18Abnormal sound on auscultation76 (43.2)70 (39.8)40 (22.7)25 (14.2)24 (13.6)13 (7.4)0.860.57–1.310.49Data are presented as absolute numbers and (percentages); each group contained a total of 176 patientsCI confidence interval; GEE generalized estimation equation; N non-statin group; OR odds ration; S statin group Figure 2 shows a Kaplan–*Meier analysis* of the time to a respiratory symptom-free day in each group (cough, dyspnea, chest pain). The results revealed a trend toward an earlier resolution of cough in the Statin group (HR: 0.68, $95\%$ CI: 0.48–0.94, pLog-rank: 0.016). On the other hand, there were no significant differences in time to the first symptom-free day of dyspnea or chest pain symptoms between our study groups. Fig. 2Kaplan–Meier curves showing the symptom-free percentage of patients in each group during the study The baseline and follow-up severities of cough and dyspnea are shown in Fig. 3A, B. The baseline median (M) cough CSS score of Non-statin patients was 3 (Q1 = 2, Q3 = 4), which was significantly higher than in the statin group ($M = 2$, Q1 = 2, Q3 = 3; $$p \leq 0.003$$). No other significant differences in CSS or mMRC dyspnea severity scores could be observed between groups throughout the follow-up period ($p \leq 0.05$ for all).Fig. 3Comparison of the dyspnea (A) and cough (B) severity in the statin and non-statin groups. Violin plots are showing the distribution of the Medical Research Council (mMRC) dyspnea scale and cough symptom score (CSS) at different time points. Solid and dotted lines represent the median values and quartiles, respectively. ** Significant difference ($p \leq 0.001$); ns not significant
## Pulmonary Fibrosis Scores
Fifty-one patients in the statin group and 42 in the non-statin group underwent both initial and follow-up HRCTs, which were taken 6 ± 3.2 and 51.9 ± 17.7 days after the onset of symptoms, respectively; HRCT imaging data are listed in Table 3. The most common findings were ground glass opacity, linear opacity, and reticulation in both groups. Most of the cases showed improvement in HRCT features and reduction in the involved segments over the study time course (Fig. 4). The initial median pulmonary fibrosis score was 8 for the non-statin [IQR = 6–12] as well as the statin [IQR = 6–11] group, which dropped to 5 [IQR = 0–8 and IQR = 0–6, respectively] in both groups as assessed in the follow-up HRCTs; no significant differences in pulmonary fibrosis score were observed between groups (β = 1.225, $95\%$ CI = –0.47–2.92; $$p \leq 0.15$$).Table 3Baseline and follow-up HRCT findings in statin and non-statin groupsHRCT featuresNon-statin group ($$n = 42$$)Statin group($$n = 51$$)InitialFollow-upInitialFollow-upGround glass opacity35 (83.3)20 (47.6)41 (80.4)23 (45.1)Affected segments6 [3–8]4 [2.25–6]6 [4.5–10]4 [3–6]Linear opacity33 (78.6)20 (47.6)40 (78.4)19 (37.3)Affected segments4 [3–5.5]2 [1.25–3]3.5 [2–5.75]3 [1–4]Interlobular septal thickening15 (35.7)7 (16.7)12 (23.5)4 (7.8)Affected segments2 [1–2]1.5 [1–2]2 [1–2]1 [1–2]Reticulation17 (40.5)11 (26.2)14 (27.5)7 (13.7)Affected segments1 [1–2]1 [1–1]1 [1–2]1 [1–1]Honeycombing / Bronchiectasis6 (14.3)3 (7.1)6 (11.8)4 (7.8)Affected segments2 [1–2]1 [1–1]2 [1–3.25]1 [1–1]*Pulmonary fibrosis* score8 [6–12]5 [0–8]8 [6–11]5 [0–6]Data are presented as mean ± standard deviation or median [inter quartile range]Fig. 4Representative images of axial sections of initial and follow-up HRCT scans of the lungs in COVID-19 patients with diabetes mellitus in statin (a and b) and non-statin (c and d) groups. a1 Initial HRCT image (fourth day of manifestations) of the lungs of a 52-year-old male (Statin group) complaining of dyspnea and cough. The image shows consolidation and ground glass opacity (GGO) in the anterior (red arrows) and posterior segments (blue arrows) of the upper lobes of both lungs, and the superior segment of the lower lobe of the right lung (yellow arrow). a2 The same patient came in on day 61 with a persistent cough. The follow-up HRCT showed significant regression of consolidation and GGO, without evidence of fibrotic changes. b1 Initial HRCT image (third day of manifestations) of the lungs of a 64-year-old female (Statin group) presenting with dyspnea shows an extensive airspace consolidation mainly in the posterior segments of the upper lobes in both lungs (red rectangle), diffuse GGOs, and reticular opacities as shown in the superior segment of the lower lobe of the right lung (red arrow). b2 Although follow-up HRCT images on day 43 show prominent regression of consolidation to GGOs, both lungs still have opacities. The dyspnea improved 32 days after initiation. c1 Initial HRCT image (third day of manifestations) of the lungs of a 62-year-old female (Non-statin group) presenting with cough and dyspnea revealed diffuse GGOs in both lungs associated with fine linear opacity. c2 Follow-up HRCT image on the 38th day of manifestations shows linear opacity (red arrow) and subpleural opacity in the superior segments of the bilateral lower lobes (blue arrows). The cough disappeared after two months, but the dyspnea persisted throughout the follow-up period. d1 Initial HRCT image (fifth day of manifestations) of the lungs of a 59-year-old male (Non-statin group) presenting with fever, cough, and dyspnea. Extensive bilateral GGOs (red arrows) and consolidation are seen. d2 The second HRCT obtained 82 days later shows interlobular septal thickening (red arrows) and bronchial wall thickening (blue arrow) in the anterior segment of the upper lobe of the right lung and a significant reduction of GGO in both lungs. Dyspnea and cough persisted for 59 and 55 days, respectively Further subgroup analysis was performed by categorizing participants into subsets based on shared characteristics such as the use of metformin, insulin, non-steroidal anti-inflammatory drugs, duration of DM, and the control status of DM (Table 4). These analyses revealed that Non-statin patients suffering from DM > 5 years were more likely to have a higher fibrosis score during the follow-up period (2.43 scores higher on average, SEM = 3.36) compared to Statin patients with a similar DM history ($95\%$ CI = –0.47–2.92, $$p \leq 0.041$$).Table 4Subgroup analyses of HRCTs to explore the effects of statins on pulmonary fibrosis scoreVariablesNβ$95\%$ CIp-valueNon-statinStatinUpperLowerMetforminYes24221.73–0.533.990.134No18290.33–2.232.880.801InsulinYes28191.59–1.584.760.325No24320.948–0.892.790.314NSAIDs*Yes19220.309–1.842.460.779No23291.99–0.474.460.113NSAIDs and MetforminYes1291.57–1.474.610.311No30421.221–0.8193.250.238NSAIDs and InsulinYes59–$\frac{1}{12}$–5.263.020.595No$\frac{37421}{512}$–0.3163.3410.105Diabetes duration > 5 years13142.430.124.740.041 ≤ 5 years29370.353–2.623.330.816Controlled diabetesYes13130.462–2.303.220.743No29381.530.573.640.152*Controlled diabetes is defined as HbA1c value of $7\%$ or higherNSAIDs non-steroidal anti-inflammatory drugs
## Discussion
This multicenter prospective study revealed that the use of statins is associated with lower odds of cough and dyspnea over a three-month follow-up period after the onset of COVID-19 in patients with diabetes. Moreover, patients on statins experienced substantially lower cough severity compared to non-users. Despite the improvement in severity and duration of symptoms, Statin and Non-statin patients showed no significant differences in the improvement of pulmonary fibrosis score as assessed by HRCT, with the exception of statin users suffering from DM > 5 years who exhibited significant improvement in pulmonary fibrosis as compared to non-statin patients with chronic DM. There is a paucity of prospective studies that have assessed the effects of statins on manifestations of COVID-19 or pulmonary fibrosis in DM patients, whereas retrospective studies mainly focused on assessing the mortality rate and reported controversial results. In a French nationwide observational study involving 2449 DM patients hospitalized for COVID-19, routine statin treatment was shown to be significantly associated with increased mortality (Cariou et al. 2021). In contrast, others reported that in-patient statin use was associated with a considerable reduction in the mortality rate of COVID-19 patients with DM (Lohia et al. 2021; Saeed et al. 2020). So, there appears to be a lack of consensus on the impact of statins on clinical outcomes in DM patients with COVID-19. Our current findings provide further evidence for the beneficial effects of statin use in these patients.
We observed positive effects of statins on the frequency, severity, and duration of cough. Cough is distressing to patients, causes social isolation, and increases the risk of community transmission by respiratory droplets (Dhand and Li 2020; Hulme et al. 2019). Chronic cough in PCS might result from different mechanisms such as the hematogenous spread of inflammatory mediators, the use of specific types of medications, the invasion of vagal sensory neurons by SARS-CoV-2, or a neuroinflammatory response leading to peripheral and central hypersensitivity of cough pathways (Song et al. 2021). Furthermore, pulmonary fibrosis could increase cough reflex sensitivity due to mechanical stimulation of the chest wall (Jones et al. 2011). In contrast to a cough that can persist after the flu or a common cold, chronic cough in PCS is often accompanied by other associated presentations, which could indicate a common pathological mechanism such as pulmonary fibrosis (Song et al. 2021). As evident from our results, patients in the non-statins group were more likely to have dyspnea throughout the follow-up period compared to the statin group. Hypothetically, more pronounced or sustained pulmonary fibrosis in non-statin patients (as compared to statin users) could be a possible explanation for the higher frequency of chronic cough and dyspnea in this group.
However, the analysis of follow-up CT images indicated no statistically significant differences in pulmonary fibrosis score between the overall population of the statin and non-statin groups; of note, this could be related to the relatively low number of assessed HRCTs. Interestingly, further subgroup data analysis revealed that patients with long-term (> 5 years) diabetes in the non-statin group were more likely to have a higher fibrosis score during the follow-up period compared to statin group patients with a similar DM history. This observation further supports the long-term pleiotropic effects of statins as demonstrated in other diseases, including cancer (Shojaei et al. 2020a). Although, to the best of our knowledge, no publications on the effects of statins on the progression of pulmonary fibrosis in COVID-19 patients are currently available, several clinical and basic science investigations have demonstrated that statins exert significant anti-fibrotic effects in airway resident (mesenchymal) cells and could be beneficial in the treatment of pulmonary disorders characterized by fibrosis (Kou et al. 2022; Schaafsma et al. 2011b). Statins may alleviate post-COVID pulmonary fibrosis by targeting transforming growth factor (TGF)-β signaling, a multifunctional cytokine with profibrogenic effects that is elevated during and after COVID-19 (Pawlos et al. 2021). This cytokine is associated with post-COVID-19 pulmonary fibrosis by promoting lung tissue remodeling and connective tissue deposition among fibroblasts and epithelial cells. On the other hand, statins are believed to suppress epithelial–mesenchymal transition by attenuating TGF-β signaling (Yang et al. 2013). It is also worth mentioning that the effects of statins on fibrosis could, at least in part, be related to the regulation of cellular autophagy (Ghavami et al. 2012, 2014; Shojaei et al.2020a). Indeed, several previous investigations have shown that fibrosis could be regulated via autophagy in various organs, including the lung and heart (Alizadeh et al. 2018; Ghavami et al. 2015, 2018). Recent studies revealed that pulmonary fibrosis is associated with insufficient autophagy, which lead to injury and senescence of alveolar epithelial cells, facilitates epithelial-mesenchymal transformation, and promotes fibroblasts trans-differentiation into myofibroblasts (Araya et al. 2013). Thus, the restoration of impaired autophagy can inhibit fibroblast differentiation and collagen deposition and prevent pulmonary fibrosis, and it has been shown that statins could pulmonary airway inflammation by upregulating autophagy in animal models (Gu et al. 2017). Therefore, the lower pulmonary fibrosis score after the onset of COVID-19 in long-term DM patients on statins may be due to pleiotropic anti-fibrotic effects of statins, possibly through the regulation of autophagy.
The combination of COVID-19 and diabetes could amplify the inflammatory response and contribute to a more severe disease state (Yang et al. 2020). This inflammatory condition is characterized by an increase in serum inflammatory markers, which prognosticate subsequent critical illness in COVID-19 patients. Thus, the empirical findings from our study could be attributed to the well-known anti-inflammatory and immunomodulating effects of statins that are mediated by their impact on immune cells and downregulation of plasma concentrations of inflammatory mediators such as C-reactive protein (CRP), tumor necrosis factor, interleukin (IL)-1, and IL-6 (Ahmadi et al. 2020; Kim et al. 2019; Satny et al. 2021). Baseline laboratory tests indicated that patients in the Non-statin group had significantly higher blood neutrophil and platelet counts. Neutrophils play a crucial role in COVID-19 pathogenesis, particularly in those patients with severe disease courses (Reusch et al. 2021). For example, neutrophils enhance the degranulation of primary granules and promote the release of pro-inflammatory cytokines during SARS-CoV-2 infection (Parackova et al. 2020). Additionally, identified neutrophil activators and effectors were identified as early biomarkers of severe COVID-19 (Meizlish et al. 2021). The inflammatory state is enhanced in DM patients because hyperglycemia induces neutrophils to release neutrophil extracellular traps (NETs), which in turn contribute to the cytokine storm in COVID-19 (Santos et al. 2021). Interestingly, high levels of IL-6 have been shown to induce the systemic release of NETs in other inflammatory diseases of respiratory disease such as severe asthma and chronic obstructive pulmonary disease (Lachowicz-Scroggins et al. 2019; Winslow et al. 2021), and statins may reduce IL-6 release under inflammatory conditions (Loppnow et al. 2011). The possible association between the beneficial effects of statins and IL-6 release in our patients is a subject of our future studies. We also observed a trend for higher median WBC count, hemoglobin, and CRP levels in Non-statin vs Statin patients; however, these apparent differences did not reach statistical significance. In support of our findings, a recent randomized clinical trial revealed that add-on treatment with atorvastatin in hospitalized COVID-19 patients without prior use of statins led to a significant reduction of CRP levels (Davoodi et al. 2021), indicating this might represent one of the possible anti-inflammatory mechanisms of statin therapy in our patient population.
Our research involved a relatively large study population of COVID-19 patients with DM from three hospitals across Iran. Moreover, this is the first prospective study that assessed the effects of statins on pulmonary fibrosis and long-term symptoms of COVID-19. It may provide another useful pleiotropic application of statins and hopefully further future mechanistic investigations open avenues for decreasing the post-COVID-19 effect on the pulmonary functions of DM patients.
## Conclusions
Our work revealed that the use of statins in DM patients with COVID-19 is associated with a lower risk of developing long-term cough and dyspnea. We could not confirm the significant effects of statins on pulmonary fibrosis in our general study population. However, our results do suggest that statins reduce pulmonary fibrosis associated with COVID-19 in long-term (> 5 years) DM patients. Thus, statin therapy appears to be beneficial in DM patients diagnosed with COVID-19, and our findings warrant the pursuit of randomized control trials to verify the therapeutic impact of statin use on clinical outcomes and pulmonary fibrosis in these patients.
## References
1. Adhyaru BB, Jacobson TA. **Safety and efficacy of statin therapy**. *Nat Rev Cardiol* (2018.0) **15** 757-769. DOI: 10.1038/s41569-018-0098-5
2. Ahmadi M, Amiri S, Pecic S. **Pleiotropic effects of statins: a focus on cancer**. *Biochim Biophys Acta Mol Basis Dis* (2020.0) **1866** 165968. DOI: 10.1016/j.bbadis.2020.165968
3. Alizadeh J, Zeki AA, Mirzaei N. **Mevalonate cascade inhibition by simvastatin induces the intrinsic apoptosis pathway via depletion of isoprenoids in tumor cells**. *Sci Rep* (2017.0) **7** 44841. DOI: 10.1038/srep44841
4. Alizadeh J, Glogowska A, Thliveris J. **Autophagy modulates transforming growth factor beta 1 induced epithelial to mesenchymal transition in non-small cell lung cancer cells**. *Biochim Biophys Acta Mol Cell Res* (2018.0) **1865** 749-768. DOI: 10.1016/j.bbamcr.2018.02.007
5. **2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2021**. *Diabetes Care* (2021.0) **44** S15-S33. DOI: 10.2337/dc21-s002
6. **10. Cardiovascular disease and risk management: standards of medical care in diabetes-2022**. *Diabetes Care* (2022.0) **45** S144-S174. DOI: 10.2337/dc22-er05
7. Araya J, Kojima J, Takasaka N. **Insufficient autophagy in idiopathic pulmonary fibrosis**. *Am J Physiol Lung Cell Mol Physiol* (2013.0) **304** L56-L69. DOI: 10.1152/ajplung.00213.2012
8. Camiciottoli G, Orlandi I, Bartolucci M. **Lung CT densitometry in systemic sclerosis: correlation with lung function, exercise testing, and quality of life**. *Chest* (2007.0) **131** 672-681. DOI: 10.1378/chest.06-1401
9. Cariou B, Goronflot T, Rimbert A. **Routine use of statins and increased COVID-19 related mortality in inpatients with type 2 diabetes: results from the CORONADO study**. *Diabetes Metab* (2021.0) **47** 101202. DOI: 10.1016/j.diabet.2020.10.001
10. Centers for Disease Control and Prevention (2020) Interim clinical guidance for management of patients with confirmed coronavirus disease (COVID-19). https://stacks.cdc.gov/view/cdc/89980
11. Centers for Disease Control and Prevention (2022) Underlying medical conditions associated with higher risk for severe COVID-19: information for healthcare professionals [Online]. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/underlyingconditions.html. Accessed 17/05/2022
12. Davoodi L, Jafarpour H, Oladi Z. **Atorvastatin therapy in COVID-19 adult inpatients: a double-blind, randomized controlled trial**. *Int J Cardiol Heart Vasc* (2021.0) **36** 100875. DOI: 10.1016/j.ijcha.2021.100875
13. Dhand R, Li J. **Coughs and sneezes: their role in transmission of respiratory viral infections, including SARS-CoV-2**. *Am J Respir Critical Care Med* (2020.0) **202** 651-659. DOI: 10.1164/rccm.202004-1263pp
14. Drozdzal S, Rosik J, Lechowicz K. **An update on drugs with therapeutic potential for SARS-CoV-2 (COVID-19) treatment**. *Drug Resist Updat* (2021.0) **59** 100794. DOI: 10.1016/j.drup.2021.100794
15. Elahi S, Weiss RH, Merani S. **Atorvastatin restricts HIV replication in CD4+ T cells by upregulation of p21**. *AIDS* (2016.0) **30** 171-183. DOI: 10.1097/QAD.0000000000000917
16. Emami A, Shojaei S, Da Silva Rosa SC. **Mechanisms of simvastatin myotoxicity: the role of autophagy flux inhibition**. *Eur J Pharmacol* (2019.0) **862** 172616. DOI: 10.1016/j.ejphar.2019.172616
17. Gawish R, Starkl P, Pimenov L. **ACE2 is the critical in vivo receptor for SARS-CoV-2 in a novel COVID-19 mouse model with TNF- and IFNgamma-driven immunopathology**. *Elife* (2022.0) **11** e74623. DOI: 10.7554/elife.74623
18. George PM, Wells AU, Jenkins RG. **Pulmonary fibrosis and COVID-19: the potential role for antifibrotic therapy**. *Lancet Respir Med* (2020.0) **8** 807-815. DOI: 10.1016/s2213-2600(20)30225-3
19. Ghavami S, Mutawe MM, Schaafsma D. **Geranylgeranyl transferase 1 modulates autophagy and apoptosis in human airway smooth muscle**. *Am J Physiol Lung Cell Mol Physiol* (2012.0) **302** L420-L428. DOI: 10.1152/ajplung.00312.2011
20. Ghavami S, Sharma P, Yeganeh B. **Airway mesenchymal cell death by mevalonate cascade inhibition: integration of autophagy, unfolded protein response and apoptosis focusing on Bcl2 family proteins**. *Biochim Biophys Acta* (2014.0) **1843** 1259-1271. DOI: 10.1016/j.bbamcr.2014.03.006
21. Ghavami S, Cunnington RH, Gupta S. **Autophagy is a regulator of TGF-beta1-induced fibrogenesis in primary human atrial myofibroblasts**. *Cell Death Dis* (2015.0) **6** e1696. DOI: 10.1038/cddis.2015.36
22. Ghavami S, Yeganeh B, Zeki AA. **Autophagy and the unfolded protein response promote profibrotic effects of TGF-beta1 in human lung fibroblasts**. *Am J Physiol Lung Cell Mol Physiol* (2018.0) **314** L493-L504. DOI: 10.1152/ajplung.00372.2017
23. Gower TL, Graham BS. **Antiviral activity of lovastatin against respiratory syncytial virus in vivo and in vitro**. *Antimicrob Agents Chemother* (2001.0) **45** 1231-1237. DOI: 10.1128/AAC.45.4.1231-1237.2001
24. Gu W, Cui R, Ding T. **Simvastatin alleviates airway inflammation and remodelling through up-regulation of autophagy in mouse models of asthma**. *Respirology* (2017.0) **22** 533-541. DOI: 10.1111/resp.12926
25. Guo W, Li M, Dong Y. **Diabetes is a risk factor for the progression and prognosis of COVID-19**. *Diabetes Metab Res Rev* (2020.0) **36** e3319. DOI: 10.1002/dmrr.3319
26. Habibzadeh P, Dastsooz H, Eshraghi M. **Autophagy: the potential link between SARS-CoV-2 and cancer**. *Cancers* (2021.0) **13** 5721. DOI: 10.3390/cancers13225721
27. Han F, Xiao QQ, Peng S. **Atorvastatin ameliorates LPS-induced inflammatory response by autophagy via AKT/mTOR signaling pathway**. *J Cell Biochem* (2018.0) **119** 1604-1615. DOI: 10.1002/jcb.26320
28. Hulme K, Dogan S, Parker SM. **‘Chronic cough, cause unknown’: a qualitative study of patient perspectives of chronic refractory cough**. *J Health Psychol* (2019.0) **24** 707-716. DOI: 10.1177/1359105316684204
29. Hussain A, Bhowmik B, Do Vale Moreira NC. **COVID-19 and diabetes: Knowledge in progress**. *Diabetes Res Clin Pract* (2020.0) **162** 108142. DOI: 10.1016/j.diabres.2020.108142
30. Jackson CB, Farzan M, Chen B. **Mechanisms of SARS-CoV-2 entry into cells**. *Nat Rev Mol Cell Biol* (2022.0) **23** 3-20. DOI: 10.1038/s41580-021-00418-x
31. Jones RM, Hilldrup S, Hope-Gill BD. **Mechanical induction of cough in idiopathic pulmonary fibrosis**. *Cough* (2011.0) **7** 2. DOI: 10.1186/1745-9974-7-2
32. Jutant EM, Meyrignac O, Beurnier A. **Respiratory symptoms and radiological findings in post-acute COVID-19 syndrome**. *ERJ Open Res* (2022.0) **8** 00479-2021. DOI: 10.1183/23120541.00479-2021
33. Kim SW, Kang HJ, Jhon M. **Statins and Inflammation: New therapeutic opportunities in psychiatry**. *Front Psychiatry* (2019.0) **10** 103. DOI: 10.3389/fpsyt.2019.00103
34. Kou L, Kou P, Luo G. **Progress of statin therapy in the treatment of idiopathic pulmonary fibrosis**. *Oxid Med Cell Longev* (2022.0) **2022** 6197219. DOI: 10.1155/2022/6197219
35. Kouhpayeh HR, Tabasi F, Dehvari M. **Association between angiotensinogen (AGT), angiotensin-converting enzyme (ACE) and angiotensin-II receptor 1 (AGTR1) polymorphisms and COVID-19 infection in the southeast of Iran: a preliminary case–control study**. *Transl Med Commun* (2021.0) **6** 26. DOI: 10.1186/s41231-021-00106-0
36. Kreuter M, Bonella F, Maher TM. **Effect of statins on disease-related outcomes in patients with idiopathic pulmonary fibrosis**. *Thorax* (2017.0) **72** 148-153. DOI: 10.1136/thoraxjnl-2016-208819
37. Kyrou I, Randeva HS, Spandidos DA. **Not only ACE2-the quest for additional host cell mediators of SARS-CoV-2 infection: neuropilin-1 (NRP1) as a novel SARS-CoV-2 host cell entry mediator implicated in COVID-19**. *Signal Transduct Target Ther* (2021.0) **6** 21. DOI: 10.1038/s41392-020-00460-9
38. Lachowicz-Scroggins ME, Dunican EM, Charbit AR. **Extracellular DNA, neutrophil extracellular traps, and inflammasome activation in severe asthma**. *Am J Respir Crit Care Med* (2019.0) **199** 1076-1085. DOI: 10.1164/rccm.201810-1869OC
39. Lechowicz K, Drożdżal S, Machaj F. **COVID-19: the potential treatment of pulmonary fibrosis associated with SARS-CoV-2 infection**. *J Clin Med* (2020.0) **9** 1917. DOI: 10.3390/jcm9061917
40. Lee KCH, Sewa DW, Phua GC. **Potential role of statins in COVID-19**. *Int J Infect Dis* (2020.0) **96** 615-617. DOI: 10.1016/j.ijid.2020.05.115
41. Liao JK, Laufs U. **Pleiotropic effects of statins**. *Annu Rev Pharmacol Toxicol* (2005.0) **45** 89-118. DOI: 10.1146/annurev.pharmtox.45.120403.095748
42. Lim S, Bae JH, Kwon HS. **COVID-19 and diabetes mellitus: from pathophysiology to clinical management**. *Nat Rev Endocrinol* (2021.0) **17** 11-30. DOI: 10.1038/s41574-020-00435-4
43. Lohia P, Kapur S, Benjaram S. **Statins and clinical outcomes in hospitalized COVID-19 patients with and without diabetes mellitus: a retrospective cohort study with propensity score matching**. *Cardiovasc Diabetol* (2021.0) **20** 140. DOI: 10.1186/s12933-021-01336-0
44. Loppnow H, Zhang L, Buerke M. **Statins potently reduce the cytokine-mediated IL-6 release in SMC/MNC cocultures**. *J Cell Mol Med* (2011.0) **15** 994-1004. DOI: 10.1111/j.1582-4934.2010.01036.x
45. Meizlish ML, Pine AB, Bishai JD. **A neutrophil activation signature predicts critical illness and mortality in COVID-19**. *Blood Adv* (2021.0) **5** 1164-1177. DOI: 10.1182/bloodadvances.2020003568
46. Parackova Z, Zentsova I, Bloomfield M. **Disharmonic inflammatory signatures in COVID-19: Augmented neutrophils' but impaired monocytes' and dendritic cells' responsiveness**. *Cells* (2020.0) **9** 2206. DOI: 10.3390/cells9102206
47. Pawlos A, Niedzielski M, Gorzelak-Pabiś P. **COVID-19: Direct and Indirect mechanisms of statins**. *Int J Mol Sci* (2021.0) **22** 4177. DOI: 10.3390/ijms22084177
48. Peng S, Xu LW, Che XY. **Atorvastatin inhibits inflammatory response, attenuates lipid deposition, and improves the stability of vulnerable atherosclerotic plaques by modulating autophagy**. *Front Pharmacol* (2018.0) **9** 438. DOI: 10.3389/fphar.2018.00438
49. Peterson CL, Walker C. **Universal health care and political economy, neoliberalism and effects of COVID-19: a view of systems and complexity**. *J Eval Clin Pract* (2022.0) **28** 338-340. DOI: 10.1111/jep.13631
50. Peymani P, Dehesh T, Aligolighasemabadi F. **Statins in patients with COVID-19: a retrospective cohort study in Iranian COVID-19 patients**. *Transl Med Commun* (2021.0) **6** 3. DOI: 10.1186/s41231-021-00082-5
51. Raveendran A, Misra A. **Post COVID-19 syndrome (“Long COVID”) and diabetes: challenges in diagnosis and management**. *Diabetes Metab Syndr* (2021.0) **15** 102235. DOI: 10.1016/j.dsx.2021.102235
52. Reusch N, De Domenico E, Bonaguro L. **Neutrophils in COVID-19**. *Front Immunol* (2021.0) **12** 652470. DOI: 10.3389/fimmu.2021.652470
53. Saeed O, Castagna F, Agalliu I. **Statin use and in-hospital mortality in patients with diabetes mellitus and COVID-19**. *J Am Heart Assoc* (2020.0) **9** e018475. DOI: 10.1161/JAHA.120.018475
54. Santos A, Magro DO, Evangelista-Poderoso R. **Diabetes, obesity, and insulin resistance in COVID-19: molecular interrelationship and therapeutic implications**. *Diabetol Metab Syndr* (2021.0) **13** 23. DOI: 10.1186/s13098-021-00639-2
55. Satny M, Hubacek JA, Vrablik M. **Statins and inflammation**. *Curr Atheroscler Rep* (2021.0) **23** 80. DOI: 10.1007/s11883-021-00977-6
56. Schaafsma D, Dueck G, Ghavami S. **The mevalonate cascade as a target to suppress extracellular matrix synthesis by human airway smooth muscle**. *Am J Respir Cell Mol Biol* (2011.0) **44** 394-403. DOI: 10.1165/rcmb.2010-0052oc
57. Schaafsma D, Mcneill KD, Mutawe MM. **Simvastatin inhibits TGFbeta1-induced fibronectin in human airway fibroblasts**. *Respir Res* (2011.0) **12** 113. DOI: 10.1186/1465-9921-12-113
58. Scheen AJ. **Statins and clinical outcomes with COVID-19: meta-analyses of observational studies**. *Diabetes Metab* (2021.0) **47** 101220. DOI: 10.1016/j.diabet.2020.101220
59. Shojaei S, Koleini N, Samiei E. **Simvastatin increases temozolomide-induced cell death by targeting the fusion of autophagosomes and lysosomes**. *FEBS J* (2020.0) **287** 1005-1034. DOI: 10.1111/febs.15069
60. Shojaei S, Suresh M, Klionsky DJ. **Autophagy and SARS-CoV-2 infection: a possible smart targeting of the autophagy pathway**. *Virulence* (2020.0) **11** 805-810. DOI: 10.1080/21505594.2020.1780088
61. Siri M, Dastghaib S, Zamani M. **Autophagy, unfolded protein response, and neuropilin-1 cross-talk in SARS-CoV-2 infection: what can be learned from other coronaviruses**. *Int J Mol Sci* (2021.0) **22** 5992. DOI: 10.3390/ijms22115992
62. Song WJ, Hui CKM, Hull JH. **Confronting COVID-19-associated cough and the post-COVID syndrome: role of viral neurotropism, neuroinflammation, and neuroimmune responses**. *Lancet Respir Med* (2021.0) **9** 533-544. DOI: 10.1016/s2213-2600(21)00125-9
63. Taefehshokr N, Taefehshokr S, Hemmat N. **Covid-19: perspectives on innate immune evasion**. *Front Immunol* (2020.0) **11** 580641. DOI: 10.3389/fimmu.2020.580641
64. Walls AC, Park YJ, Tortorici MA. **Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein**. *Cell* (2020.0) **181** 281-292.e6. DOI: 10.1016/j.cell.2020.02.058
65. Watts KL, Sampson EM, Schultz GS. **Simvastatin inhibits growth factor expression and modulates profibrogenic markers in lung fibroblasts**. *Am J Respir Cell Mol Biol* (2005.0) **32** 290-300. DOI: 10.1165/rcmb.2004-0127oc
66. Winslow S, Odqvist L, Diver S. **Multi-omics links IL-6 trans-signalling with neutrophil extracellular trap formation and Haemophilus infection in COPD**. *Eur Respir J* (2021.0) **58** 2003312. DOI: 10.1183/13993003.03312-2020
67. Yang T, Chen M, Sun T. **Simvastatin attenuates TGF-β1-induced epithelial–mesenchymal transition in human alveolar epithelial cells**. *Cell Physiol Biochem* (2013.0) **31** 863-874. DOI: 10.1159/000350104
68. Yang J, Zheng Y, Gou X. **Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis**. *Int J Infect Dis* (2020.0) **94** 91-95. DOI: 10.1016/j.ijid.2020.03.017
69. Yavarian J, Nejati A, Salimi V. **Whole genome sequencing of SARS-CoV2 strains circulating in Iran during five waves of pandemic**. *PLoS ONE* (2022.0) **17** e0267847. DOI: 10.1371/journal.pone.0267847
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---
title: The clinical and epidemiological characteristics of a series of patients living
with HIV admitted for COVID-19 in a district hospital
authors:
- Ayanda Trevor Mnguni
- Denzil Schietekat
- Nabilah Ebrahim
- Nawhaal Sonday
- Nicholas Boliter
- Neshaad Schrueder
- Shiraaz Gabriels
- Lovemore N. Sigwadhi
- Annalise E. Zemlin
- Zivanai C. Chapanduka
- Veranyuy Ngah
- Anteneh Yalew
- Thumeka Jalavu
- Ibtisam Abdullah
- Jacques L. Tamuzi
- Yamanya Tembo
- Mary-Ann Davies
- Rene English
- Peter S. Nyasulu
journal: BMC Infectious Diseases
year: 2023
pmcid: PMC9972337
doi: 10.1186/s12879-023-08004-6
license: CC BY 4.0
---
# The clinical and epidemiological characteristics of a series of patients living with HIV admitted for COVID-19 in a district hospital
## Abstract
### Background
The coronavirus disease 2019 (COVID-19) pandemic continues to evolve. Globally, COVID-19 continues to strain even the most resilient healthcare systems, with Omicron being the latest variant. We made a thorough search for literature describing the effects of the COVID-19 in a high human immunodeficiency virus (HIV)/tuberculosis (TB) burden district-level hospital setting. We found scanty literature.
### Methods
A retrospective observational study was conducted at Khayelitsha District Hospital in Cape Town, South Africa (SA) over the period March 2020–December 2021. We included confirmed COVID-19 cases with HIV infection aged from 18 years and above. Analysis was performed to identify predictors of mortality or hospital discharge among people living with HIV (PLWH). Predictors investigated include CD4 count, antiretroviral therapy (ART), TB, non-communicable diseases, haematological, and biochemical parameters.
### Findings
This cohort of PLWH with SARS-CoV-2 infection had a median (IQR) age of 46 (37–54) years, male sex distribution of $29.1\%$, and a median (IQR) CD4 count of 267 (141–457) cells/mm3. Of 255 patients, 195 ($76\%$) patients were discharged, 60 ($24\%$) patients died. One hundred and sixty-nine patients ($88\%$) were on ART with 73($28\%$) patients having acquired immunodeficiency syndrome (AIDS). After multivariable analysis, smoking (risk ratio [RR]: 2.86 (1.75–4.69)), neutrophilia [RR]: 1.024 (1.01–1.03), and glycated haemoglobin A1 (HbA1c) [RR]: 1.01 (1.007–1.01) were associated with mortality.
### Conclusion
The district hospital had a high COVID-19 mortality rate among PLWH. Easy-to-access biomarkers such as CRP, neutrophilia, and HbA1c may play a significant role in informing clinical management to prevent high mortality due to COVID-19 in PLWH at the district-level hospitals.
## Background
Globally, COVID-19 continues to test even the resilient healthcare systems, with the Omicron being the latest variant in circulation. As of March 25th, 2022, the World Health Organization (WHO) had reported over 476 million confirmed cases of COVID-19, including over 6 million deaths, and approximately 11 billion vaccine doses had been administered [1]. South Africa, a middle-income country, is experiencing epidemics of noncommunicable diseases and chronic infectious diseases such as the human immunodeficiency virus (HIV) and tuberculosis (TB). Around eight million of South Africa's 60 million people are HIV-positive, accounting for one-fifth of all HIV-positive people worldwide [2, 3]. A high proportion of people newly diagnosed in South Africa are at an advanced stage of HIV infection (defined as a CD4 count < 200 cells/mm3), by which point the immune system is extremely compromised [3, 4]. Lastly, South Africa recorded $0.7\%$ of TB prevalence for all ages, among them 301,000 are new cases of TB [5].
A systematic review including twenty-two studies conducted in developed and low and middle countries showed that HIV remains a significant risk factor for SARS-CoV-2 infection and is linked to a higher risk of death from COVID-19 [6]. Similarly, a meta-analysis found that active TB was more common in COVID-19/HIV co-infected people than in COVID-19 infected people [7]. On the other hand, COVID-19 risk was high among current HIV/TB co-infected cases [7]. According to one study, patients with SARS-CoV-2 infection who also had HIV and TB had altered T-cell functions and were at higher risk of developing severe disease [8]. Furthermore, COVID-19 could be accelerated in HIV patients with compromised immunity [7, 9]. SARS-CoV-2 and HIV may both decrease lymphocyte and CD4 counts. Corticosteroids given as management for moderate to severe disease for SARS-CoV-2 may predispose to TB [7, 10]. Co-infection with SARS-CoV-2 and tuberculosis may exacerbate the pathologies associated with each pathogen [11]. Co-infection of macrophages can increase the production of pro- and anti-inflammatory cytokines, playing a significant role in pathogenesis of COVID-19 [11]. COVID-19 pneumonia, on the other hand, may hasten the progression of tuberculosis [12, 13]. The combination of a weakened virus-induced cytokine response caused by a severe/dysfunctional immune system and in vitro activity of certain antiretroviral drugs (tenofovir and lopinavir-ritonavir) on coronaviruses is thought to be associated with a reduction in the severity COVID-19 in PLWH [14–16]. However, PLWH were $30\%$ more likely to die after admission to hospital with COVID-19, regardless of age, gender, severity at presentation, or co-morbidities [17]. Diabetes, high blood pressure, and male sex were all associated with an increased risk of death [18]. These comorbidities are caused by ART-related inflammation and ongoing immune dysregulation, which may influence COVID-19 disease severity, the durability of protective antiviral responses, and vaccine responsiveness [19–22]. According to a recent non-peer reviewed systematic review, COVID-19 vaccines had lower immunogenicity and antigenicity among PLWH, than among HIV negative people [23]. Furthermore, PLWH require more attention during the COVID-19 pandemic because the emergence of Omicron in Southern Africa has raised the question of whether this heavily mutated variant is the result of the HIV pandemic, a common cause of immunodeficiency in the region [3].
South Africa has a dual healthcare system with both public and privately funded facilities. The publicly funded district healthcare system serves approximately $84\%$ of the population [24]. The district-level healthcare system, which is often understaffed, along with the primary healthcare system, are the primary points of contact for COVID-19 patients. Experiences from district-level hospitals with high HIV/TB burdens provide a unique opportunity to study the effects of the COVID-19 pandemic at the 'grass roots' level, which will inform the success or failure of public health interventions. The purpose of this study was to describe the clinical features of COVID-19 patients in a district hospital setting in a population with a high TB/HIV prevalence.
## Study design
This was a retrospective observational study describing the epidemiological and clinical characteristics of COVID-19 patients admitted at Khayelitsha District Hospital, Cape Town, South Africa from March 2020–December 2021.
## Case definition and management
A COVID-19 case was defined as PLWH with a positive antigen or reverse transcriptase polymerase chain reaction (RT-PCR) assay (Abbott Panbio Covid-19 antigen test or Cepheid Xpert Xpress CoV-2 plus test) for SARS-CoV-2 who were admitted to hospital. For those diagnosed with HIV, ELISA (BIOMERIEUX MINI VIDAS duo kit) test was performed on all patient during admissions if HIV status was not known. In terms of management and treatment, all patients were given oxygen and corticosteroid therapy. The management pillars were based on the recovery trial results, which showed that COVID-19 patients who were oxygen dependent and on corticosteroid therapy had a better outcome than those who were not on corticosteroid therapy. Because the clinical and radiological findings were consistent with hypoxemic pneumonia, all patients were started on empiric bacterial treatment with Ceftriaxone and Azithromycin in accordance with national and international guidelines for community acquired pneumonia. Once COVID-19 was confirmed by antigen testing or RT-PCR, empiric antibiotic therapy was thereafter immediately discontinued.
## Study population
We included all consecutive COVID-19 cases with HIV infection, 18 years and older who required hospital admission from March 2020 until December 2021. The main indication for hospitalization was anyone with COVID-19 pneumonia requiring oxygen therapy. Patients were prospectively followed up until completed hospital course (either discharge, transfer to tertiary or field hospital or death) at censoring. A total of 255 PLWH were included in this study.
## Setting
Khayelitsha District Hospital, located in Mandela Park, Khayelitsha, is a 330-bed district hospital that opened in 2012. Khayelitsha township is Southeast of Cape Town (Fig. 1). Most of the population ($98.6\%$) is black African [25]. Khayelitsha was created by the apartheid government via the forced removals of the Group Areas Act. It is isolated, located on sand dunes, and has a substantial risk of flooding. It is entirely residential, with no designated commercial or industrial areas [26]. Most of the population ($55.6\%$) lives in informal housing [27]. Khayelitsha has South Africa's highest concentration of poverty and unemployment [27]. Furthermore, with $37.0\%$ TB burden and $47.6\%$ HIV/TB co-infection, Khayelitsha has the worst health indicators in the Cape Town metropolitan area [28]. Similarly, Khayelitsha has the highest rates of mortality for stroke, hypertension, and diabetes mellitus [29, 30]. Overpopulation, poverty, trauma, mental health, communicable, and noncommunicable diseases have all posed significant challenges to Khayelitsha Hospital's ability to serve the community. No fewer than ten nearby primary care clinics refer patients to the hospital. The population of *Khayelitsha is* estimated to be close to 500 000 people [31], but this is a gross underestimation due to the community's continued growth. In this population, $51.1\%$ are females, $48.8\%$ are between the ages of 25 and 65, only $4.9\%$ completed high school, and $30.7\%$ finished secondary school [27]. The unemployment rate is estimated to be $38.32\%$ [27]. The first case of COVID-19 in a South African township occurred in Khayelitsha. Overtime medical admissions at Khayelitsha District Hospital have risen steadily over the last five years, averaging 5500 per year with a mortality rate of less than $10\%$.Fig. 1Map of the district of Khayelitsha in Cape Town
## Data collection
The patients’ demographic, clinical, baseline laboratory results and outcome data were collected from digital application-based registries. Additional data were captured from electronic medical records. Furthermore, the presence of comorbidities including HIV/AIDS hypertension, diabetes, overweight or obesity (defined as a body mass index (BMI > 25 or > 30 kg/m2 respectively) or as documented by treating clinicians as the BMI was not captured. Other comorbidities were cardiac disease, chronic kidney disease, and active or previous history of TB were also included in the data collection process. Baseline arterial blood gas and laboratory values including severity indices were captured. These included the partial pressure of arterial oxygen to fraction of inspired oxygen (P/F Ratio), the white cell count (WCC), the neutrophil to lymphocyte ratio (N/L Ratio), serum creatinine (Cr), HbA1c and the CRP and CD4 cell counts. Viral load measurements were captured if they were performed up to a year prior to admission. The main outcome of the study was death or discharge. Patients whose outcome data was unknown were excluded from the analyses. Data were captured retrospectively based on clinical notes at the bedside, which were securely stored electronically, and clinical data were entered remotely onto a Research Electronic Data Capture (REDCap®) database; laboratory results were imported into the database. Data were checked by the ‘data entry supervisor.’
## Ethical approval
Ethical approval for the study was obtained from the Stellenbosch University health research ethics committee (Ethics Reference Number: N$\frac{20}{05}$/020_COVID-19).
## Statistical analysis
Continuous variables were expressed as median with inter-quartile range for skewed data. Categorical variables were expressed using frequencies and percentages. A multivariable model was developed for demographics, comorbidities, clinical symptoms, haematological, and biochemical parameters using variables strongly associated with mortality or survival outcomes at univariable analysis. The comparison between mortality and survival used the Pearson χ2 test or Fisher exact test where appropriate for categorical variables, and the Wilcoxon's rank-sum test for continuous variables. Robust Poisson regression was used to assess significant associations between demographic, laboratory results, and mortality. Factors associated with death at $p \leq 0.15$ in unadjusted univariable robust Poisson regression were included in a multivariable model to identify predictor variables associated with death. Due to the high mortality, around $24\%$, the logistic regression overestimated the effect measure with large standard errors resulting in wide confidence intervals, therefore robust Poisson regression was used. Adjusted risk ratios and their $95\%$ CIs were used as a measure of association. Receiver Operating Characteristic curve (ROC) analysis was performed to evaluate the diagnostic performance of various haematological and biochemical parameters to discriminate between severe cases in terms of survival and non-survival. Schoenfeld residuals and cox proportional hazards test were used to assess the proportional hazards assumption [32]. Kaplan-Meir survival curve was plotted, and the log-rank test was used to compare the two groups [32]. All statistical analyses were performed using Stata (V.16, Stata Corp, College Station, Texas, USA) and R (V, 4.1.0, R Core Team) with R Studio (V.1.3, R Studio Team) statistical software.
## Characteristics of PLWH with SARS-COV-2
A total of 255 PLWH were admitted during the first and the second waves. The cohort was comprised mainly of females ($70.9\%$) and mostly above 50 years old ($38.8\%$), with $55.2\%$ ($$n = 32$$) of all patients who died being 50 years and older (Table 1). Among PLWH, the main pre-existing co-morbidities were hypertension ($43.1\%$), diabetes mellitus ($29.4\%$), chronic kidney disease ($7.1\%$), TB ($12.9\%$), and acute kidney injury ($16.9\%$) (Table 1). The presenting clinical signs and symptoms suggestive of COVID-19 included fever ($38.8\%$), cough ($75.7\%$), sore throat ($11.0\%$), and myalgia ($22.4\%$) (Table 1). Most of this PLWH cohort ($88.5\%$) were on ARV therapy. Among them, $36.9\%$ of this cohort had severe immunosuppression with CD4 < 200mm3 with only $21\%$ having a CD4 ≥ 500 mm3 (Table 1). The median oxygen saturation and PaO2 were $93\%$ (86–97) and 8 kPa (6.8–9.9), respectively (Table 1). Higher median CRP was observed among those who died 20.4 (12.4–28.5) as compared to those discharged 13.9 (7.0–20.7) characterised this PLWH cohort and, high median (IQR) HbA1c was a predictor of mortality with $12\%$ (10.35–$13.35\%$) (Table 1). The case fatality rate in the study population was $23.5\%$ ($\frac{60}{255}$). Around sixty-seven percent ($\frac{150}{225}$) had known HIV viral load and seventy-eight ($\frac{117}{150}$) percent had undetectable HIV viral load (< 50 copies/ml).Table 1Comparison of COVID-19 characteristics among PLWH who died or survivedVariableLevelTotal ($$n = 255$$), n (%)Discharge ($$n = 195$$), n (%)Death ($$n = 60$$), n (%)p^AgeMedian46 (37–54)45 (36–52)51 (44–59) < 0.001Age categories < 4077 (30.2)68 (34.9)9 (15.0)0.00340–5083 (32.6)64 (32.8)19 (31.7) > 5095 (37.2)63 (32.3)32 (53.3)SexMale74 (29.1)50 (25.8)24 (40.0)0.034Female180 (70.9)144 (74.2)36 (60.0)SmokerNo242 (94.9)190 (97.4)52 (86.7)0.003Yes13 (5.1)5 (2.6)8 (13.3)HypertensionNo145 (56.9)115 (59.0)30 (50.0)0.220Yes110 (43.1)80 (41.0)30 (50.0)Diabetes MellitusNo180 (70.6)141 (72.3)39 (65.0)0.280Yes75 (29.4)54 (27.7)21 (35.0)Chronic Kidney DiseaseNo237 (92.9)183 (93.8)54 (90.0)0.390Yes18 (7.1)12 (6.2)6 (10.0)TBNo222 (87.1)172 (88.2)50 (83.3)0.330Yes33 (12.9)23 (11.8)10 (16.7)Congestive Heart FailureNo248 (97.3)191 (97.9)57 (95.0)0.360Yes7 (2.7)4 (2.1)3 (5.0)Acute kidney injuryNo212 (83.1)170 (87.2)42 (70.0)0.002Yes43 (16.9)25 (12.8)18 (30.0)ARDSNo244 (95.7)187 (95.9)57 (95.0)0.720Yes11 (4.3)8 (4.1)3 (5.0)ShockNo248 (97.3)190 (97.4)58 (96.7)0.670Yes7 (2.7)5 (2.6)2 (3.3)CoughNo62 (24.3)46 (23.6)16 (26.7)0.630Yes193 (75.7)149 (76.4)44 (73.3)FeverNo153 (61.2)115 (60.2)38 (64.4)0.560Yes97 (38.8)76 (39.8)21 (35.6)Sore ThroatNo226 (89.0)171 (87.7)55 (93.2)0.230Yes28 (11.0)24 (12.3)4 (6.8)MyalgiaNo198 (77.6)150 (76.9)48 (80.0)0.620Yes57 (22.4)45 (23.1)12 (20.0)ARV therapyNo22 (11.5)17 (11.6)5 (11.1)1.000Yes169 (88.5)129 (88.4)40 (88.9)Viral loadUndetectable (< 50 copies)117 (78.0)87 (79.1)23 (20.9)0.593Undetectable (< 50 copies)33 (22.0)30 (75.0)10 (25.0)Unknown/missing1058520*CD4 categories < 20073 (36.9)48 (32.0)25 (52.1)0.021200–49983 (41.9)65 (43.3)18 (37.5)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge$$\end{document}≥50042 (21.2)37 (24.7)5 (10.4)*CD4 CountMedian (IQR)267 (141–457)296.5 (174–498)188 (72.5–337)0.004Oxygen saturationMedian (IQR)93 (86–97)94 (90–98)86.5 (74.5–93) < 0.001PaO2Median (IQR)8 (6.8–9.9)8.1 (6.9–9.9)7.3 (6.1–11.2)0.160FiO2Median (IQR)21 (21–21)21 (21–21)21 (21–40)0.016HGTMedian (IQR)7.4 (6–11.9)7.3 (5.7–12.7)7.9 (6.8–11.8)0.100CreatinineMedian (IQR)74 (58–116)71 (55–100)101 (71–262) < 0.001CRPMedian (IQR)145 (76–223)139 (70–207)204 (124–285) < 0.001White Cell CountMedian (IQR)8.64 (6.56–11.16)7.93 (6.35–10.59)9.42 (7.8–13.32)0.002LymphocytesMedian (IQR)1.65 (1.22–2.20)1.68 (1.27–2.22)1.37 (0.9–1.855)0.031NeutrophilsMedian (IQR)5.62 (4.12–8.22)5.34 (3.97–7.70)7.3 (5.60–9.35)0.002PlateletsMedian (IQR)280 (221–374)287 (223–374)273 (207–369)0.480HbA1cMedian (IQR)12.8 (11.2–14)12.9 (11.3–14.1)12.0 (10.4–13.4)0.038ARDS acute respiratory distress syndrome, ARV antiretroviral therapy, CD4 cluster of differentiation 4, CRP: C-reactive protein, IQR: interquartile range, FiO2 fraction of inspired oxygen, HGT Hemo Glucose test, PaO2 partial pressure of oxygen, TB Tuberculosis^Median (IQR), *CD4 counts: At least six months prior to SARS-CoV-2 infection
## Association of demographic, haematological, and biochemical parameters with mortality among PLWH with SARS-CoV-2 infection
Table 2 shows the association between demographic, haematological, and biochemical parameters with survival among PLWH. Creatinine (1.001, $95\%$ CI: 1.001–1.002, $p \leq 0.001$), Neutrophils (1.02, $95\%$ CI: 1.01–1.03; $p \leq 0.001$), HbA1c (1.01, $95\%$ CI: 1.007–1.010; $p \leq 0.001$), and CRP (1.003, $95\%$ CI: 1.001–1.005, $p \leq 0.001$) were significantly associated with the risk of mortality. In multivariable analysis smoking, neutrophils, HbA1c and CRP were all significantly associated with increased risk of mortality (aRR 4.17: 1.50–10.01); (aRR: 1.03, $95\%$ CI: 1.02–1.05; $p \leq 0.001$); (aRR: 1.01, $95\%$ CI: 1.001–1.02; $$p \leq 0.021$$); (aRR; 1.002, $95\%$ CI: 1.002–1.01; $p \leq 0.032$).Table 2Univariate and multivariable level analysis of factors associated with COVID-19 mortality among PLWHCharacteristicRR ($95\%$ CI)pARR ($95\%$ CI)pAge categories < 401 40–491.96 (0.94–4.07)0.0722.24 (0.76–6.64)0.143 ≥ 502.88 (1.46–5.67)0.0022.32 (0.67–8.14)0.186Sex: Female1.62 (1.04–2.52)0.0322.00 (0.85–4.67)0.111Smoker2.86 (1.75–4.69) < 0.0014.17 (1.50–11.61)0.006Hypertension1.32 (0.85–2.05)0.2200.93 (0.40–2.19)0.869Diabetes Mellitus1.29 (0.82–2.04)0.2711.04 (0.51–2.16)0.904Chronic Kidney Disease1.46 (0.73–2.93)0.283TB1.35 (0.76–2.38)0.309Viral Load: Detectable1.18 (0.65–2.16)0.588Congestive Heart Failure1.87 (0.77–4.52)0.168Acute kidney injury2.11 (1.36–3.30)0.0010.71 (0.33–1.57)0.401ARDS1.17 (0.43–3.15)0.760Shock1.22 (0.37–4.03)0.742Cough0.88 (0.54–1.45)0.624Fever0.87 (0.55–1.39)0.565Sore Throat0.59 (0.23–1.50)0.265Myalgia0.868 (0.50–1.52)0.621ARV therapy1.04 (0.46–2.36)0.922*CD4: 200–4991 < 2001.58 (0.94–2.65)0.0851.27 (0.62–2.61)0.509 ≥ 5000.55 (0.22–1.38)0.2020.35 (0.09–1.29)0.114pa021.012 (0.96–1.07)0.665HGT0.999 (0.973–1.03)0.944CRP1.003 (1.001–1.005) < 0.0011.002 (1.002–1.01)0.032Creatinine1.001 (1.001–1.002) < 0.0011.00 (0.99–1.00)0.187WCC1.05 (1.01–1.08)0.005Neutrophils1.024 (1.01–1.03) < 0.0011.03 (1.02–1.06) < 0.001HbA1c1.01 (1.007–1.01) < 0.0011.01 (1.001–1.02)0.011Platelets0.999 (0.997–1.00)0.477Lymphocytes0.882 (0.58–1.34)0.557ARDS Acute respiratory distress syndrome, ARV antiretroviral, CD4 cluster of differentiation 4, CRP: C-reactive protein, HbA1c Glycated haemoglobin A1, HGT haemo glucose test, PaO2 partial pressure of oxygen, TB tuberculosis, WCC white cell count*CD4 counts: At least six months prior to SARS-CoV-2 infection
## ROC curves and cut-offs
As the adjusted RR was significant for neutrophils and CRP at the borderline, we determined the optimal cut-offs to predict non-survival and test performance of these two parameters using ROC curves. ROC curves were drawn with sensitivity as the horizontal coordinate and the 1‑specificity as the vertical coordinate to predict COVID-19 severity and mortality among PLWH admitted to the hospital. The proposed optimum cut-off points for neutrophils derived from ROC analysis was ≥ 5.6 × 109/L with sensitivity = $76\%$ and specificity = $56\%$ (Table 3 and Fig. 2). An optimal cut-off of 202 mg/L rendered $52\%$ sensitivity and $74\%$ specificity for the CRP (Table 3 and Fig. 3). The area under the ROC curves (AUC) for the neutrophils and CRP were 0.66 and 0.65, respectively (Table 3). However, the performance of both was suboptimal to use as a predictive marker on their own. The combination of the two variables and their predictive value on the roc curve changed slightly to AUC = 0.67.Table 3Optimal cut-off, sensitivity, specificity, and AUC for CRP, and NeutrophilsAnalyteDirectionOptimal cut pointSensitivitySpecificityAUCCRP (mg/L) ≥ 2020.520.740.65Neutrophils (× 109/L) ≥ 5.60.760.560.66Fig. 2ROC curve for Neutrophils between death and discharged COVID-19 cases with HIV infectionFig. 3ROC curve for CRP between death and discharged COVID-19 cases with HIV infection
## Kaplan–Meier survival estimates between males and females
The rate of death seemed to be higher among male patients during the whole duration of the hospitalization. However, Poisson regression analysis was used to compare male and female mortality, no significant difference was found ($$p \leq 0.160$$) (Fig. 4). The median stay was 7 (IQR: 3–12) days for females compared to 5 (IQR: 3–9.5) days for males. The overall median stay was 6 (IQR: 3–10) days. ( Fig. 4). The plots of the scaled Schoenfeld residuals of each covariate against log-time were used to determine whether the proportional hazards assumption was violated (Fig. 5). The Schoenfeld residuals test revealed that a proportional hazard assumption had the same effect on male and female survival rates ($$p \leq 0.3905$$) (Fig. 5).Fig. 4Kaplan–Meier plot for overall survival among COVID-19 males and females with HIV co-infectionFig. 5Plots of the scaled Schoenfeld residuals of each covariate between males and females
## Discussion
This retrospective cohort study of 255 PLWH with SARS-CoV-2 infection focused on hospitalized individuals at a district hospital in Cape Town. Most of these patients were stable on ART and over one-third of them died. The median (IQR) age was 46 (37–54) years, with $70.9\%$ of the population being female. PLWH who died were older than 50 years old, females, smokers, had acute kidney injury, CD4 less than 200 mm3, hypoxemic, high CRP, and HbA1c. Creatinine, Neutrophils, HbA1c, and CRP were significantly associated with the risk of mortality in univariable analysis. Smoking, neutrophils, HbA1c, and CRP were all significantly associated with an increased risk of mortality in a multivariable analysis. CRP and neutrophil performance were both suboptimal for use as survival predictors. The high mortality experienced among PLWH was consistent with a systematic review and meta-analysis of twenty-two studies involving 20,982,498 PLWH, which found that HIV was associated with a significantly higher risk of SARS-CoV-2 infection (RR 1.24, 95 percent CI 1.05–1.46) [6]. The overall pooled RR of COVID-19 mortality associated with HIV was 1.78 ($95\%$CI 1.21–2.60), implying that HIV-positive patients have an $80\%$ increased risk of death when compared to people who do not have HIV/AIDS [6]. Similarly, a review revealed that the RR of severe COVID-19 in PLWH was significant only in Africa (RR = 1.14, $95\%$ CI = 1.05–1.24), while the relative risk of mortality was 1.5 ($95\%$ CI = 1.45–2.03) globally [33]. Although the RR of severe COVID-19 in PLWH appeared to be lower, the studies included in this review were conducted in often well-resourced and skilled tertiary, academic, and intensive care unit (ICU) hospital settings. The high COVID-19 mortality rate among PLWH should be considered in the context of the devastation brought on by the COVID-19 pandemic in a limited resource district hospital which is already overwhelmed by the high burden of HIV, TB, and other non-communicable diseases. In addition to this, the low CD4 count, high AIDS rate, and a high prevalence of multiple co-morbidities among PLWH with COVID-19, seem to have a key role in the high COVID-19 mortality in a district hospital setting. A recent study conducted among PLWH in Cape Town revealed that PLWH with CD4 count < 200 cells/µl were associated with COVID-19 death (aHR vs people living without HIV, 2.36 [$95\%$ CI, 1.47–3.78]; aHR vs PLWH with CD4 count ≥ 350 cells/µl, 1.97 [$95\%$ CI, 1.14–3.40]) [34]. Compared with HIV disease stage 1 (CD4 counts > 500 cells/ml), hospitalization rates were $29\%$ higher for stages 2 (CD4 counts 200–499 cells/ml) and $69\%$ higher for stage 4 CD4 counts < 200 cells/ml) [35]. The unadjusted hazard ratio of COVID-19 death was 1.07 ($95\%$ CI 0.88–1.32) for HIV-positive vs. HIV-negative, and 2.14 ($95\%$ CI 1.70–2.20) after adjustment for age, sex, and other comorbidities [34]. This finding was consistent with our findings, as CD4 counts of less than 200 cells/ml were associated with COVID-19 among PLWH in unadjusted RR. The interplay between the inverse relationship between the CD4 count, HIV viral load, the access and adherence to ARV therapy play a crucial role in the determinant of severity of HIV infection and associated outcomes.
As this study was conducted in a high HIV/TB setting, emphasis is also placed on the interactions between COVID-19, HIV, and TB. This cohort found $12.9\%$ cases of TB coinfection with COVID-19 among PLWH. However, the records did not specify whether these cases were active or had previous TB. Knowing that a study performed in an urban township of Cape Town observed a very high ($88.0\%$) latent TB infection (LTBI) prevalence rate among PLWH [36] and SARS-CoV-2 infection decreases TB–specific CD4 + T cell response [8], many concerns have been raised about the possibility that COVID-19 could reactivate latent TB in a high TB-endemic setting such as Khayelitsha district. The reactivation of TB could be explained by cytokines and chemokines dysregulation, higher consumption of CD4 + and CD8 + T-cells, decrease in regulatory T-cells, and an altered innate immune environment leading to a cytokine storm and worsen tissue damage [37–39]. A study showed poor outcome in mortality rate among COVID-19/HIV/TB co-infection compared to COVID-19/TB [7]. Even though, our study found no significant link between COVID-19 mortality and HIV/TB cases, a large cohort study conducted in Cape Town found an association between COVID-19 mortality and previous and active TB [34]. Our findings should be interpreted in the context of inadequate TB screening in COVID-19 patients admitted to the district hospital.
Our study also found out that raised neutrophil count, CRP, HbA1c, and smoking were associated with worse outcomes after confounding adjustment. This is substantial as those biomarkers may play a vital role in preventing worse outcomes among COVID-19 in PLWH. A multivariable analysis showed that raised neutrophil count was an independent risk factor for mortality. This finding is similar to the previous study which revealed that a higher neutrophil count in COVID-19 patients was found to be associated with a higher mortality rate among PLWH with SARS-CoV-2 infection [40]. This could be explained by emergency release of granulocyte colony-stimulating factor with activation of immature neutrophils, neutrophil maturation, neutrophil degranulation, and release of neutrophil extracellular traps (NETs) [41, 42]. Neutrophil activation and degranulation result in release of neutrophil-activation markers including resistin, lipocalin-2, hepatocyte growth factor and interleukin-8 which causes significant collateral damage. The immune response of natural killer cells and T lymphocytes contributes to the formation of NETs and the activation of the complement system (C5 and C3) [42]. The result is the development of microvascular thrombosis, which leads to organ damage with an increased risk of severe COVID-19 and mortality [41, 42]. The early elevation of activated immature neutrophils, G-CSF and neutrophil-activation markers were suggested as early predictors of severe COVID-19 infection and increased mortality. Additionally, alteration to gene expression of neutrophils with a prominence of immature neutrophils markers was found in severe COVID-19 patients [43]. While access to these tests is not feasible in limited-resource countries the use of neutrophil count could be used as an affordable predictor variable of COVID-19 severity and mortality among PLWH. Importantly, our analysis revealed a significantly higher CRP in the non-survivor group compared to the survivor group. The CRP is an acute phase reactant which functions as a reliable biomarker and is recommended as a predictor of COVID-19 severity and mortality. Our findings were in the line with studies including PLWH and SARS-COV-2, conducted in the same settings which showed significantly high CRP levels among non-survivors [8, 40]. The cut-offHbA1c level of ≥ $6.5\%$ is defined as diabetes mellitus. A study revealed that high HbA1c level is associated with inflammation, hypercoagulability, and low SaO2 in COVID-19 patients, and the mortality rate is higher in patients with diabetes mellitus [44, 45]. In our study, HbA1c was high in both discharged and death cases and the difference in HGT was not statistically significant between the two groups. The plausible explanation for the high HbA1c among COVID-19 cases with HIV could be more severe inflammatory process and more intense coagulation disorder associated with poor prognosis [45]. Another explanation could be the high HbA1c found in public sector primary health care facilities in Cape Town as a study showed $78.9\%$ of type 2 diabetes mellitus patients had HbA1c outside of the target range [46]. As high HbA1c may not be a reliable biomarker in this context, our findings were focused on the neutrophils and CRP. Even though, the performance of both was suboptimal to use as a prediction marker, high neutrophils and CRP are consistent with previous studies showing higher diagnostic accuracy and AUC of ROC analysis [47, 48]. Lastly, our study found that smoking was an independent factor of mortality PLWH with SARS-CoV-2 infection. A systematic review of fifty‐seven studies found that current smokers are at reduced risk of SARS-CoV-2 infection, while former smokers appeared to be at increased risk of hospitalization, increased disease severity and mortality from COVID-19 [49]. We could not find a plausible explanation for this association. However, a study conducted on smoking status found that the response rate was low among current smoking ($10\%$) compared to $58.8\%$ among former smokers [50]. This could overestimate the effect of smoking status on COVID-19 outcome. Even though our study did not identify current and former smoking status, the association between smoking and COVID-19 mortality among PLWH should be taken with caution.
This study has several strengths. This is the first study to our knowledge conducted in a South African district-level hospital involving COVID-19 patients with HIV infection. Based on the study findings, COVID-19 management among PLWH at the district-level may face multiple challenges due to limited resources. As primary healthcare systems are often poorly funded, overcrowded, short-staffed, and are unable to provide efficient care. This study has provided an overview of COVID-19 severity and mortality risk factors among vulnerable and specific population of PLWH in district-level hospital. Additionally, significant COVID-19 challenges faced by the facility included the lack of infrastructure within the facility with a daily bed occupancy rate to deal with the separation of COVID-19 patients from Persons Under Investigation (PUIs) and general medical admissions. This study showed that easily available biomarkers in a district hospital setting are associated with COVID-19 mortality. These biomarkers could be used by clinicians at district-level hospitals to risk stratify and motivate for higher level of care at their tertiary referral hospitals. This study had several limitations among which is its retrospective design nature. The retrospective cohort study design limited our ability to gather data about factors that may influence the risk of mortality such as the HIV viral load, ART regimens, active and previous TB, estimated glomerular filtration rate (eGFR), and smoking status. Another study flaw was that transferred cases were not followed up with the Western Cape Provincial Health Data Centre (PHDC) to learn about their outcomes. This was the source of missing and incomplete data. The other limitation of the study might be that it is a single centre study but also underpowered due to sample size used, hence our results might be limited to the population of Khayelitsha and not generalized to other similar populations.
## Conclusion
This study showed that the mortality rate of COVID-19 patients co-infected with HIV was high at district hospital level. An increase in neutrophils, HbA1c, and CRP were all significantly associated with risk of mortality. As such COVID-19 diagnosis and management among PLWH at the district-level should include easily accessible, reliable, and affordable assessment for these biomarkers to support the front-line clinicians for an effective risk stratification of patients at an increased of poor outcome and subsequent early transfer to tertiary level hospital for advanced care and minimise loss of life in such patients.
## References
1. 1.WHO. WHO Coronavirus (COVID-19) Dashboard-Overview, 2022. https://covid19.who.int/
2. 2.UNAIDS. South Africa. https://www.unaids.org/en/regionscountries/countries/southafrica
3. Freer J, Mudaly V. **HIV, and covid-19 in South Africa**. *BMJ* (2022.0) **376** e069807. DOI: 10.1136/bmj-2021-069807
4. Carmona S, Bor J, Nattey C. **Persistent high burden of advanced HIV disease among patients seeking care in South Africa’s national HIV program: data from a nationwide laboratory cohort**. *Clin Infect Dis* (2018.0) **66** S111-S117. DOI: 10.1093/cid/ciy045
5. 5.National Department of Health. The First National TB Prevalence Survey|South Africa, 2018. https://www.samrc.ac.za/media-release/first-south-african-national-tb-prevalence-survey-gives-clearer-picture-epidemic
6. Ssentongo P, Heilbrunn ES, Ssentongo AE, Advani S, Chinchilli VM, Nunez JJ, Du P. **Epidemiology, and outcomes of COVID-19 in HIV-infected individuals: a systematic review and meta-analysis**. *Sci Rep* (2021.0) **11** 6283. DOI: 10.1038/s41598-021-85359-3
7. Tamuzi JL, Ayele BT, Shumba CS. **Implications of COVID-19 in high burden countries for HIV/TB: a systematic review of evidence**. *BMC Infect Dis* (2020.0) **20** 744. DOI: 10.1186/s12879-020-05450-4
8. Riou C, du Bruyn E, Stek C. **Relationship of SARS-CoV-2-specific CD4 response to COVID-19 severity and impact of HIV-1 and tuberculosis coinfection**. *J Clin Invest* (2021.0) **131** e149125. DOI: 10.1172/JCI149125
9. Soriano V, Barreiro P. **Impact of new coronavirus epidemics on HIV-infected patients**. *AIDS Rev* (2020.0) **22** 57-58. DOI: 10.24875/AIDSRev.M20000031
10. Liu W, Fontanet A, Zhang PH. **Pulmonary tuberculosis and SARS**. *China Emerg Infect Dis* (2006.0) **12** 707-709. DOI: 10.3201/eid1204.050264
11. Tapela K, Ochieng' Olwal C, Quaye O. **Parallels in the pathogenesis of SARS-CoV-2 and**. *Future Microbiol.* (2020.0) **15** 1691-1695. DOI: 10.2217/fmb-2020-0179
12. Tadolini M, Codecasa LR, García-García JM. **Active tuberculosis, sequelae, and COVID-19 co-infection: first cohort of 49 cases**. *Eur Respir J* (2020.0) **56** 2001398. DOI: 10.1183/13993003.01398-2020
13. Wilkinson RJ. **Tuberculosis and type 2 diabetes mellitus: an inflammatory danger signal in the time of coronavirus disease 2019**. *Clin Infect Dis* (2021.0) **72** 79-81. DOI: 10.1093/cid/ciaa747
14. Laurence J. **Why aren't people living with hiv at higher risk for developing severe coronavirus disease 2019 (COVID-19)?**. *AIDS Patient Care STDS* (2020.0) **34** 247-248. DOI: 10.1089/apc.2020.29005.com
15. Del Amo J, Polo R, Moreno S, Dı’az A, Martı’nez E, Arribas JR. **Antiretrovirals and risk of COVID-19 diagnosis and hospitalization in HIV-Positive persons**. *Epidemiol* (2020.0) **31** e49-e51. DOI: 10.1097/EDE.0000000000001235
16. Mascolo S, Romanelli A, Carleo MA, Esposito V. **Could HIV infection alter the clinical course of SARS-CoV-2 infection? When less is better**. *J Med Virol* (2020.0) **92** 1777-1778. DOI: 10.1002/jmv.25881
17. 17.WHO. Clinical features and prognostic factors of COVID-19 in people living with HIV hospitalized with suspected or confirmed SARS-CoV-2 infection, 2021. https://www.who.int/publications/i/item/WHO-2019-nCoV-Clinical-HIV-2021.1
18. 18.Bertagnolio S, Thwin S, Silva R, Ford N, Baggaley R, Vitoria M, Jassat W, Doherty M. Clinical characteristics, and prognostic factors in people living with HIV hospitalized with COVID-19: findings from the WHO Global Clinical Platform. In: 11th International AIDS Society Conference on HIV Science, 2021; pp. 18–21.
19. Deeks SG, Phillips AN. **HIV infection, antiretroviral treatment, ageing, and non-AIDS related morbidity**. *BMJ* (2009.0) **338** a3172. DOI: 10.1136/bmj.a3172
20. Pallikkuth S, Parmigiani A, Silva SY. **Impaired peripheral blood T-follicular helper cell function in HIV-infected nonresponders to the 2009 H1N1/09 vaccine**. *Blood* (2012.0) **120** 985-993. DOI: 10.1182/blood-2011-12-396648
21. Moir S, Fauci AS. **B-cell responses to HIV infection**. *Immunol Rev* (2017.0) **275** 33-48. DOI: 10.1111/imr.12502
22. Alrubayyi A, Gea-Mallorquí E, Touizer E. **Characterization of humoral and SARS-CoV-2 specific T cell responses in people living with HIV**. *Nat Commun* (2021.0) **12** 5839. DOI: 10.1038/s41467-021-26137-7
23. 23.Tamuzi JL, Muyaya LM, Mitra A, Nyasulu P. Systematic review and meta-analysis of COVID-19 vaccines safety, tolerability, and efficacy among HIV-infected patients. 10.1101/2022.01.11.22269049v1
24. Naidoo S. **The South African national health insurance: a revolution in health-care delivery!**. *J Public Health (Oxf)* (2012.0) **34** 149-150. DOI: 10.1093/pubmed/fds008
25. 25.City of Cape Town. 2011 Census Suburb Khayelitsha. Strategic development information and GIS Department (SDI&GIS), Cape Town, 2013. http://www.capetown.gov.za/en/stats/2011CensusSuburbs/2011_Census_CT_Suburb_Khayelitsha_Profile.
26. Maylam P. **The rise and decline of urban apartheid in South Africa**. *Afr Aff* (1990.0) **89** 57-84. DOI: 10.1093/oxfordjournals.afraf.a098280
27. 27.City of Cape Town. Census Suburb Khayelitsha-2011, 2013. https://resource.capetown.gov.za/documentcentre/Documents/Maps%20and%20statistics/2011_Census_CT_Suburb_Khayelitsha_Profile.pdf
28. Mahtab S, Coetzee D. **Influence of HIV, and other risk factors on tuberculosis**. *S Afr Med J* (2017.0) **107** 428-434. DOI: 10.7196/SAMJ.2017.v107i5.11271
29. Groenewald P, Bradshaw D, Daniels J, Zinyakatira N, Matzopoulos R, Bourne D, Shaikh N, Naledi T. **Local-level mortality surveillance in resource-limited settings: a case study of Cape Town highlights disparities in health**. *Bull World Health Organ* (2010.0) **88** 444-451. DOI: 10.2471/BLT.09.069435
30. Smit W, de Lannoy A, Dover RV, Lambert EV, Levitt N, Watson V. **Making unhealthy places: The built environment and non-communicable diseases in Khayelitsha**. *Cape Town Health Place* (2016.0) **39** 196-203. DOI: 10.1016/j.healthplace.2016.04.006
31. 31.Western Cape Government. Population data, 2020. https://www.westerncape.gov.za/assets/departments/health/h_102_2020_covid-19_population_data.pdf
32. Lalla U, Koegelenberg CFN, Allwood BW, Sigwadhi LN, Irusen EM, Zemlin AE, Masha TE, Erasmus RT, Chapanduka ZC, Prozesky H, Taljaard J, Parker A, Decloedt EH, Retief F, Jalavu TP, Ngah VD, Yalew A, Tamuzi JL, Baines N, McAllister M, Zumla A, Nyasulu PS. **Comparison of patients with severe COVID-19 admitted to an intensive care unit in South Africa during the first and second wave of the COVID-19 pandemic**. *Afr J Thorac Crit Care Med.* (2021.0). DOI: 10.7196/AJTCCM.2021.v27i4.185
33. Oyelade T, Alqahtani JS, Hjazi AM, Li A, Kamila A, Raya RP. **Global and regional prevalence and outcomes of COVID-19 in people living with HIV: a systematic review and meta-analysis**. *Trop Med Infect Dis* (2022.0) **7** 22. DOI: 10.3390/tropicalmed7020022
34. 34.Western Cape Department of Health in collaboration with the National Institute for Communicable Diseases, South Africa. Risk Factors for Coronavirus Disease 2019 (COVID-19) Death in a Population Cohort Study from the Western Cape Province, South Africa [published correction appears in Clin Infect Dis. 2022 Mar 17:]. Clin Infect Dis. 2021;73(7): e2005-e2015.
35. Tesoriero JM, Swain CE, Pierce JL. **COVID-19 Outcomes Among Persons Living with or Without Diagnosed HIV Infection in New York State**. *JAMA Netw Open* (2021.0) **4** e2037069. DOI: 10.1001/jamanetworkopen.2020.37069
36. Wood R, Lawn SD, Johnstone-Robertson S, Bekker LG. **Tuberculosis control has failed in South Africa–time to reappraise strategy**. *S Afr Med J* (2011.0) **101** 111-114. DOI: 10.7196/samj.4587
37. Grifoni A, Weiskopf D, Ramirez SI, Mateus J, Dan JM, Moderbacher CR, Rawlings SA, Sutherland A, Premkumar L, Jadi RS, Marrama D, de Silva AM, Frazier A, Carlin AF, Greenbaum JA, Peters B, Krammer F, Smith DM, Crotty S, Sette A. **Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals**. *Cell* (2020.0) **181** 1489-1501.e15. DOI: 10.1016/j.cell.2020.05.015
38. Rydyznski Moderbacher C, Ramirez SI, Dan JM, Grifoni A, Hastie KM, Weiskopf D, Belanger S, Abbott RK, Kim C, Choi J, Kato Y, Crotty EG, Kim C, Rawlings SA, Mateus J, Tse LPV, Frazier A, Baric R, Peters B, Greenbaum J, Ollmann Saphire E, Smith DM, Sette A, Crotty S. **Antigen-specific adaptive immunity to SARS-CoV-2 in acute COVID-19 and associations with age and disease severity**. *Cell* (2020.0) **183** 996-1012.e19. DOI: 10.1016/j.cell.2020.09.038
39. Tan LY, Komarasamy TV, Rmt BV. **Hyperinflammatory immune response and COVID-19: a double-edged sword**. *Front Immunol* (2021.0) **12** 742941. DOI: 10.3389/fimmu.2021.742941
40. Parker A, Koegelenberg CFN, Moolla MS. **High HIV prevalence in an early cohort of hospital admissions with COVID-19 in Cape Town**. *South Africa S Afr Med J* (2020.0) **110** 982-987. DOI: 10.7196/SAMJ.2020.v110i10.15067
41. Meizlish ML, Pine AB, Bishai JD. **A neutrophil activation signature predicts critical illness and mortality in COVID-19**. *Blood Adv* (2021.0) **5** 1164-1177. DOI: 10.1182/bloodadvances.2020003568
42. Nappi F, Giacinto O, Ellouze O. **Association between COVID-19 diagnosis and coronary artery thrombosis: a narrative review**. *Biomedicines* (2022.0) **10** 702. DOI: 10.3390/biomedicines10030702
43. Aschenbrenner AC, Mouktaroudi M, Krämer B. **Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients**. *Genome Med* (2021.0) **13** 7. DOI: 10.1186/s13073-020-00823-5
44. Wang Z, Du Z, Zhu F. **Glycosylated hemoglobin is associated with systemic inflammation, hypercoagulability, and prognosis of COVID-19 patients**. *Diabetes Res Clin Pract* (2020.0) **164** 108214. DOI: 10.1016/j.diabres.2020.108214
45. Unluguzel Ustun G, Keskin A, Aci R, Arslanbek Erdem M, Ari M. **Association between Hb A1c and Severity of COVID-19 Patients**. *Hemoglobin* (2021.0) **45** 124-128. DOI: 10.1080/03630269.2021.1926278
46. Monanabela KB, van Huyssteen M, Coetzee R. **Describing medicine therapy management of type 2 diabetes mellitus at primary health care facilities in Cape Town**. *Health SA* (2019.0) **24** 1051. PMID: 31934403
47. Xu JB, Xu C, Zhang RB, Wu M, Pan CK, Li XJ, Wang Q, Zeng FF, Zhu S. **Associations of procalcitonin, C-reaction protein and neutrophil-to-lymphocyte ratio with mortality in hospitalized COVID-19 patients in China**. *Sci Rep* (2020.0) **10** 15058. DOI: 10.1038/s41598-020-72164-7
48. Xiao LN, Ran X, Zhong YX, Li SS. **Clinical value of blood markers to assess the severity of coronavirus disease 2019**. *BMC Infect Dis* (2021.0) **21** 921. DOI: 10.1186/s12879-021-06623-5
49. Simons D, Shahab L, Brown J, Perski O. **The association of smoking status with SARS-CoV-2 infection, hospitalization, and mortality from COVID-19: a living rapid evidence review with Bayesian meta-analyses (version 7)**. *Addiction* (2021.0) **116** 1319-1368. DOI: 10.1111/add.15276
50. Merkely B, Szabó AJ, Kosztin A, Berényi E, Sebestyén A, Lengyel C. **Novel coronavirus epidemic in the Hungarian population, a cross-sectional nationwide survey to support the exit policy in Hungary**. *GeroScience* (2020.0) **42** 1063-1074. DOI: 10.1007/s11357-020-00226-9
|
---
title: 'Enhanced and Stem-Cell-Compatible
Effects of Nature-Inspired
Antimicrobial Nanotopography and Antimicrobial Peptides to Combat
Implant-Associated Infection'
authors:
- Mohd Irill Ishak
- Marcus Eales
- Laila Damiati
- Xiayi Liu
- Joshua Jenkins
- Matthew J. Dalby
- Angela H. Nobbs
- Maxim G. Ryadnov
- Bo Su
journal: ACS Applied Nano Materials
year: 2023
pmcid: PMC9972347
doi: 10.1021/acsanm.2c04913
license: CC BY 4.0
---
# Enhanced and Stem-Cell-Compatible
Effects of Nature-Inspired
Antimicrobial Nanotopography and Antimicrobial Peptides to Combat
Implant-Associated Infection
## Abstract
Nature-inspired antimicrobial surfaces and antimicrobial peptides (AMPs) have emerged as promising strategies to combat implant-associated infections. In this study, a bioinspired antimicrobial peptide was functionalized onto a nanospike (NS) surface by physical adsorption with the aim that its gradual release into the local environment would enhance inhibition of bacterial growth. Peptide adsorbed on a control flat surface exhibited different release kinetics compared to the nanotopography, but both surfaces showed excellent antibacterial properties. Functionalization with peptide at micromolar concentrations inhibited *Escherichia coli* growth on the flat surface, *Staphylococcus aureus* growth on the NS surface, and *Staphylococcus epidermidis* growth on both the flat and NS surfaces. Based on these data, we propose an enhanced antibacterial mechanism whereby AMPs can render bacterial cell membranes more susceptible to nanospikes, and the membrane deformation induced by nanospikes can increase the surface area for AMPs membrane insertion. Combined, these effects enhance bactericidal activity. Since functionalized nanostructures are highly biocompatible with stem cells, they make promising candidates for next generation antibacterial implant surfaces.
## Introduction
Titanium dental and orthopedic implants are an essential component of modern medical treatments. Dental implants replace missing teeth resulting from trauma or periodontal disease, while orthopedic implants replace joints such as the hip and knee as a treatment for chronic diseases such as osteoarthritis.1−4 The use of these implants has increased rapidly due to aging populations and rising obesity levels.3 Bacterial infection is one of the most common causes of premature implant failure, and the most prevalent microbes associated with orthopedic implant infections are Gram-positive staphylococci, particularly *Staphylococcus aureus* and Staphylococcus epidermidis, which account for $80\%$ of all implant infections, and the Gram-negative bacteria Pseudomonas aeruginosa, Klebsiella pneumoniae, and Escherichia coli.5 The subsequent revision surgery required upon implant failure has serious potential ramifications for the patient and places a significant burden on the healthcare infrastructure, and with increasing bacterial antimicrobial resistance, the infections are becoming more difficult to treat.6,7 Alternative strategies to combat implant-associated infections are therefore of great clinical need.
Nanotopographies exhibiting antifouling or bactericidal properties have been observed across the natural world from lotus leaves to shark skin and butterfly wings.8 Previous studies have succeeded in growing analogous nanotopographies on titanium, and some have reported bactericidal effects, especially against motile and Gram-negative species, such as P. aeruginosa and E. coli.9,10 This presents the opportunity to generate novel antimicrobial implant materials by exploiting nature-inspired nanotopographies. Similarly, as antimicrobial resistance levels are escalating against the last-resort antibiotics and newest-generation drugs, alternatives are being pursued such as antimicrobial peptides (AMPs). AMPs are part of the innate immune system of bacteria, archaea, protists, fungi, plants, and animals. AMPs have been shown to exhibit bactericidal activity against multidrug resistant bacteria, highlighting their potential as promising alternatives to current antimicrobials that have become redundant due to the rise in resistance.11,12 When the amino acid sequence of an AMP is known, it may be synthesized using approaches such as solid phase peptide synthesis (SPPS), and its activity may be improved by amino acid modification.13 The AMP used in this study is a specifically designed adaptation of the naturally occurring AMP cecropin B (CecB),14 first isolated from the haemolymph of the giant silk moth, Hyalophora cecropia. CecB has known activity against Gram-negative bacteria, such as P. aeruginosa and E. coli, but little or no activity reported against Gram-positive bacteria, such as S. aureus.15,16 To increase the spectrum of bactericidal activity for CecB, modifications were designed by Pfeil et al. resulting in a chopped cecropin mutant (ChoM), which proved to be effective against both Gram-positive and Gram-negative bacteria.16 *In this* work, we fabricated nature-inspired nanospike (NS) surfaces using pure titanium and explored the capacity for their antibacterial effects to be enhanced by the incorporation of ChoM, with the goal of generating an antibacterial and biocompatible biomaterial that is effective against both Gram-negative and Gram-positive bacteria.
## Formation of Titanium Oxide Nanospikes on
Pure Titanium Substrate via Alkaline Hydrothermal Method
Titanium (Grade 1) disks were polished to a mirror shine to obtain <10 nm roughness across the disk to optimize NS orientation during the subsequent alkaline hydrothermal growth. The disks were polished on a Struers TegraPol-15 with silicon carbide grinding paper (Struers) at increasing grit levels from 80 to 4000 on MD Fuga pads (Struers) at 30 N and 300 RPM for 4 min each. To obtain a mirror shine, the disks were polished with MD Chem pads (Struers) at 35 N and 150 RPM and $10\%$ hydrogen peroxide (Acros Organics) in colloidal silica suspension (Struers) for 15 min. The disks were cleaned by sonication (Grant XUB5) for 15 min in deionized water, preheated to 40 °C, and immersed in absolute ethanol (Merck) for 10 min before blow-drying with compressed air.
Polished titanium disks [24] were slotted into custom-made PTFE holders to ensure the disks remained upright and placed into a 125 mL PTFE cup. The cup was then inserted into an acid digestion vessel (Parr Instrument Company-Model 4748) containing 52 mL of 1 M NaOH (Fisher). The vessel was tightly sealed and placed in a preheated oven (Gallenkamp Plus II) for 2 h at 240 °C. After the alkaline hydrothermal treatment, the acid digestion vessel was removed from the oven and left to cool to room temperature. The disks were then removed from the holders and soaked in deionized water and absolute ethanol for 10 min each. The disks were finally placed on ceramic blocks and left to dry overnight.
After the alkaline hydrothermal treatment, the disks were initially heated at 300 °C (temperature ramp of 10 °C/min) for 1 h using a chamber furnace (Elite Thermal Systems Ltd., Model-BMF $\frac{11}{7}$) to ensure the nanospikes fixed to the titanium disk surface. When cooled, the disks were immersed in 0.6 M HCl (Fisher) for 1 h where the sodium in the nanospikes was exchanged with the hydrogen in the HCl to form hydrogen titanate. The disks were then rinsed with deionized water and absolute ethanol for 10 min each and air dried. The final step involved placing the disks in the chamber furnace for calcination for 2 h at 600 °C where the hydrogen titanate nanospikes were converted into TiO2. The disks were cooled and stored in a sterile, enclosed plastic Petri dish until use (Figure 1).
**Figure 1:** *Schematic illustration
of the fabrication of TiO2 nanospikes
(NS) and functionalization with ChoM. The inset shows the ChoM peptide
sequence.*
## Scanning Electron Microscopy (SEM)
Samples were prepared for SEM by sputter coating (Emitech K757X) the surface with a conductive metal layer of ∼6 nm thick consisting of $20\%$ palladium and $80\%$ gold. The samples were imaged on a FEI Quanta 200 scanning electron microscope at various magnifications.
## X-ray Photoelectron Spectroscopy (XPS)
The surface elemental composition of mirror polished pure titanium disks and NS disks was analyzed by XPS in ultrahigh vacuum (UHV) setup equipped with a high-resolution specs PHOIBOS 150 2D-DLD elevated pressure energy analyzer equipped with differential pumping system. A monochromatic Al Kα X-Ray source was used with a photon energy of 1486.6 eV and anode operating energy of 15 kV. The base pressure was ∼2 × 10–10 bar. A survey scan (settings of 0.5 eV steps, 0.1 s dwell time, epass 40, and range between 1100 and −10 eV) was initially performed to determine the elemental peaks in the sample. Ti2p, O1s, C1s, and N1s peaks were observed. Peaks were fitted using the CasaXPS software.
## Bacterial Interactions with Nanospikes
Mueller–Hinton broth cultures (10 mL) were incubated for 16 h at 37 °C and 220 RPM. These were then subcultured into 20 mL of prewarmed broth in a 50 mL conical flask to an optical density at 600 nm (OD600) of 0.1 and further incubated at 37 °C and 220 RPM until the start of exponential phase growth (usually after 1.5–3 h). Bacterial suspensions were then adjusted to the desired cell density of 106 CFU/mL in MH broth. Details of the strains can be found in Table S1.
Prior to bacterial inoculation, disks were immersed in absolute ethanol for 10 min within a sterile, plastic Petri dish, thoroughly washed with 0.01 M Tris–HCl, and then air dried within a flow hood (Brassaire). Once dried, the disks were stored in sterile Petri dishes until utilized.
## Live/Dead Staining
BacLight live/dead staining (Invitrogen) was used to investigate the membrane integrity of adherent bacteria on the NS surfaces. The surface of each disk was inoculated with 40 μL of bacterial suspension, incubated at 37 °C for 3 h, and then gently washed with Tris–HCl. SYTO9/Propidium iodide was prepared according to manufacturer’s instructions and 40 μL applied to each surface and left for 15 min in the dark at ambient room temperature. Disks were then washed twice with Tris–HCl to remove excess stain. The disks were placed onto a glass slide and covered with a glass cover slip and imaged under a fluorescence microscope at wavelengths 450–490 and 515–560 nm. The relative numbers of bacterial cells with intact membranes (fluorescing green) and membrane-compromised cells (fluorescing red) were quantified using Image J (NIH) software.
## BacTiter-Glo
Aliquots (40 μL) of bacterial suspension were applied to the surface of flat and NS disks within a white, opaque 24-well plate (Perkin Elmer) and incubated within a humidity chamber at 37 °C for 0.5–3 h. BacTiter-Glo reagent (40 μL) was added to the bacterial suspension and the luminescence was measured in a plate reader (Tecan Infinite F200 Pro) with automatic attenuation and 1000 ms integration time.
## RealTime-Glo
RealTime-Glo assay (Promega) was used since it allows continuous monitoring of the metabolic activity of mammalian or bacterial cells and has particularly good sensitivity for Gram-positive bacteria.17 Bacterial suspensions (1 mL) were mixed with 1 μL of MT cell viability substrate and 1 μL of NanoLuc enzyme, and incubated in the dark for 1 h at 37 °C and 220 RPM. Bacterial suspensions (40 μL) were then applied to disks within a white, opaque 24-well plate, which was then sealed with transparent film (Greiner Bio-one EasySeal plate sealer) to ensure sterility and to prevent the surfaces from drying out. The plate was placed in a preheated (37 °C) plate reader (Tecan infinite F200 Pro) and luminescence recorded every 10 min for up to 18 h with 1000 ms integration time, wait time of 0.1 s, and settle time of 150 ms.
## ChoM Biofunctionalization
Aliquots of ChoM (KWKVFKKIEKMIRNIRNKIVK-am) at three different concentrations (25, 50, and 100 μM, 40 μL) were applied to disks under sterile conditions (within a flow hood) until visually dried (typically around 3 h). The disks were stored at 4 °C until required (Figure 1).
## ChoM Release Quantification Using Nanodrop
The release of ChoM from flat and NS disks was quantified using the Nanodrop (SimpliNano) at 280 nm. Deionized water (40 μL) was applied to the surface of each disk and incubated at 37 °C for a determined time duration. Aliquots (2 μL) were transferred at periodic intervals to the Nanodrop for A280 measurements. At the determined time interval, the broth was removed from the disk and the disks were left to dry before being processed for SEM.
## Cell Culture
Human mesenchymal stem cells (hMSCs) (Promocell) were cultured in Dulbecco’s modified eagle medium (DMEM) (Sigma-Aldrich) supplemented with $1\%$ penicillin/streptomycin (Invitrogen), $1\%$ (v/v) l-glutamine (200 mM, Gibco), $1\%$ sodium pyruvate (Sigma-Aldrich), $1\%$ nonessential amino acids (Sigma-Aldrich), and $2\%$ antibiotics (6.74 U/mL penicillin–streptomycin, 0.2 μg/mL fungizone; Sigma), and $10\%$ foetal bovine serum (FBS) (Invitrogen) at 37 °C in $5\%$ CO2. No cells beyond passage 4 were used. Seeding on titanium disks was done in 24-well plates at 104 cells/disk, with $5\%$ FBS. The culture medium was replenished every 3 days for up to 28 days. For Geimsa staining studies, osteogenic medium (MERCK, Germany) was used as a positive control.
## AlamarBlue Assay
AlamarBlue solution (Bio-Rad) was mixed 1:10 in DMEM, and 900 μL of it was applied to each titanium disk before incubating for 6 h at 37 °C in $5\%$ CO2. Aliquots (200 μL) were transferred in triplicate into a 96-well plate and analyzed with a Thermo Scientific Multiskan FC. Absorbance was measured at A1 = 570 and A2 = 600 nm.
## Immunofluorescence Staining
hMSCs were seeded onto the disks at a cell density of 3000 cells/cm2 and incubated for 3 days. Then, the samples were washed with PBS (Sigma-Aldrich), fixed for 15 min at 37 °C with $3.7\%$ v/v formaldehyde/PBS, permeabilized, and stained for vinculin using monoclonal antivinculin antibody (1:100 dilution) (Sigma-Aldrich), and phalloidin-rhodamine actin diluted 1:500 in PBS/BSA. The antibody was removed, and the cells were washed three times for 5 min in PBS/$0.5\%$ v/v Tween. A secondary antibody (horse anti-mouse IgG, biotinylated, Vector Labs, U.K., Z0715) diluted 1:50 PBS/BSA was added for 1 h at 37 °C. The antibody was removed, and the cells were washed three times for 5 min in PBS/$0.5\%$ v/v Tween. Streptavidin-FITC (Vector Laboratories, U.K., SA-5001) was diluted in 1:50 PBS/BSA and incubated for 30 min at 4 °C. Disks were rinsed three times for 5 min in PBS/$0.5\%$ v/v Tween. Visualization was via a fluorescence microscope (Zeiss Axiovert 200 M, 10× magnification, NA 0.5). Comparisons of staining intensity between surfaces were analyzed using Image J software version 1.42q.
## Giemsa Staining
For the histological analysis of cells on titanium surfaces, Giemsa staining was used. After cell fixation (as above), the cells were stained with Giemsa stock solution (MERCK, Germany) for 1 min and washed thoroughly with distilled water. The samples were air dried and observed using a Zeiss immunofluorescence microscope at $\frac{495}{519}$ nm and under normal light. This staining method allowed visualization of the cell nuclei and cytoplasm, providing information on cell morphology and organization on the titanium surfaces.
## Statistical Analyses
All statistical analyses were performed using GraphPad Prism V9. Data were analyzed by ANOVA with the Tukey HSD post hoc test, and p-values <0.05 were considered significant. Unless otherwise stated, values given are mean ± standard deviation and are representative of three experimental replicates ($$n = 3$$) performed in duplicate.
## Functionalization of Titanium Surfaces with
ChoM
Flat titanium (control) and NS surfaces were functionalized with ChoM through physical adsorption, whereby 40 μL of ChoM in deionized water was left to dry onto the surfaces. Peptide was adsorbed onto the surfaces at increasing micromolar concentrations above its MIC values to compensate for the potential loss of the peptide from the surface and to establish an optimal concentration for surface functionalization (25 M, 50 M, and 100 μM). SEM was used to assess the homogeneity of initial peptide coatings on the titanium surfaces and over a 3-h elution period (Figure 2A,B,E). The flat surfaces showed more homogenous or larger peptide deposits with only some of the coating remaining visible 30 min into the elution period. By contrast, the peptide coverage on the NS surfaces appeared to lack homogenous distribution with some NS areas covered with the peptide material more visibly than others. Much of the peptide coating on the NS surfaces remained visible after a 10-min elution period. Only the tips of the nanospikes could be visualized where the coating was thick. The peptide functionalized surfaces were further characterized using XPS to confirm peptide presence by detecting the nitrogen peak at 399 eV (Figure 2C). The peak corresponds to the amine group and was detected on both flat and NS functionalized surfaces, confirming the presence of ChoM. It was also found that the relative atomic percentage of amine groups on the flat surfaces was significantly higher than on the NS surfaces, suggesting that more peptide was retained on the flat surfaces compared to on the NS surfaces. Our XPS analysis also confirmed that the nanospikes consisted of pure titanium dioxide and a small amount of metal hydroxides (Supplementary Figure 1). No sodium from the sodium titanate was present following the ion exchange treatment with HCl before annealing, but the presence of −OH groups on the nanospikes was expected.
**Figure 2:** *Characterization of ChoM
functionalization of titanium surfaces.
SEM image of nonfunctionalized (A) flat titanium disk and (B) NS disk
(inset scale bar is 1 μm). (C) XPS spectra of the functionalized
(50 μM total peptide) and nonfunctionalized flat and NS surfaces.
(D) Release profiles from flat and NS surfaces over 3 h as determined
by absorbance at 280 nm. (E) SEM images of ChoM (50 μM total
peptide) coating on flat titanium (top) and NS (bottom) surfaces following
elution into MH broth over a 3-h period. White and black arrows on
Flat + 50 μm ChoM after 30 min show the bare disk and the coated
area, respectively. Inset scale bar of NS + 50 μm ChoM after
3 h is 500 nm. Data are presented as mean ± SD, n = 2.*
The ChoM release profiles for each surface across the concentration range were determined by measuring absorbance of the eluate at 280 nm. This wavelength is used to detect aromatic residues in peptides, of which in ChoM, there are two (tryptophan and phenylalanine). For the flat surfaces, there was a rapid release of peptide into the MH broth within the first 30 min and once reached, the maximum absorbance signal was maintained over the 3-h period (Figure 2D). Based on these data and the SEM observations, it was estimated that >$90\%$ of the AMPs were released from the flat surface after 3 h. In comparison, the NS surface generally showed a lower overall level of peptide release but increases still occurred beyond 1 h in a dose-dependent manner, indicating a more gradual peptide release after the initial 10-min period. The exception to this was for the NS disk functionalized with 25 μM ChoM where no gradual increase in absorbance was observed after the initial 10-min release.
## Bactericidal Activity of Peptide-Functionalized
Surfaces
Having demonstrated the release of ChoM into the local environment, the next step was to determine the antibacterial properties of the functionalized NS surface. The control used in this study was the nonfunctionalized flat surface.
After the first hour of incubation with E. coli, there was a $70\%$ reduction in viable bacteria on the NS surface when compared to the flat surfaces. Cell numbers increased on both nonfunctionalized surfaces up to 3 h, indicating some degree of bacterial growth, but the significant difference in viability was maintained, confirming the antibacterial properties of the NS surface alone. When functionalized with 25 μM ChoM, the flat surface showed significant antibacterial activity after 1 h of incubation compared to the control surface and bacterial growth continued at a limited rate after 3 h. When the flat surface was functionalized with peptide at the higher concentrations, total inhibition of E. coli growth was observed over the 3-h incubation period (Figure 3A).
**Figure 3:** *Viability of bacteria on different functionalized
NS surfaces.
The antibacterial activity of titanium surfaces functionalized with
0–100 μM ChoM was assessed against (A) E. coli over 3 h using BacTiter-Glo or (B,C) using
RealTime-Glo against (B) S. aureus over
8 h or (C) S. epidermidis over 18 h.
Data are presented as mean ± SD. *P < 0.05
compared to the control as determined by one-way ANOVA with Tukey
HSD post hoc test; n = 3.*
There was a significant reduction in bacterial growth for the NS-25 μM surface relative to the control and NS surfaces after 1 h, but no significant difference was found when compared to the Flat-25 μM surface. The bacteria were still able to proliferate on the NS-25 μM surface over the 3-h period but at a significantly slower rate compared to the nonfunctionalized surface. After 3 h, there was a $75\%$ reduction in cell viability for the functionalized NS surface compared to the nonfunctionalized surface. In contrast to the flat surface, when the NS surface was functionalized with higher peptide concentrations (i.e., 100 μM), E. coli still managed to grow after 3 h although at a much slower rate.
These studies were extended to assess the antibacterial performance of the NS surface functionalized with 100 μM ChoM against Gram-positive species, specifically S. aureus and S. epidermidis. These studies were run using the RealTime-Glo assay until growth of the population started to decline, which was after approximately 8 or 18 h, respectively (Figure 3B,C). On the nonfunctionalized flat titanium surface, the luminescence signal generated by S. aureus increased over 8 h, reaching a maximum of 1.2 × 105 RLU. As before, the presence of nanospikes significantly impaired S. aureus growth relative to control, with a maximum RLU of 7 × 104 reached after 8 h. Following functionalization, a slower level of growth was seen on the flat surface compared to the nonfunctionalized control, reaching a plateau between 6and 7 h at a RLU of 3.0 × 1044. By contrast, functionalization of the nanospikes with ChoM ablated S. aureus growth over the 8 h period. S. epidermidis displayed an even greater susceptibility to the nanostructures in the presence of ChoM. Upon incubation on the NS, Flat-ChoM, and NS-ChoM surfaces, no growth was detected over the 18-h period, whereas S. epidermidis growth on the flat, nonfunctionalized control reached an RLU of 2.3 × 104 after 18 h.
## Biocompatibility of Peptide-Functionalized
Surfaces
Antibacterial strategies tend to also be detrimental to eukaryotic as well as prokaryotic cells.18 Therefore, an additional aspect of these studies was to determine any reduction in biocompatibility and osteogenic potential of the NS surface following functionalization with ChoM. This is an important consideration for the intended application as an implant material as host cell adhesion to the implant material is required for long-term infection control in winning the “race to the surface. ”19 To determine if ChoM functionalization of the nanospikes affected human mesenchymal stem cell (hMSC) adherence to the titanium surfaces, immunofluorescent staining was used to visualize focal adhesion formation. Cell elongation and a well-organized cytoskeleton were visualized on the flat titanium disks in the presence or absence of ChoM (Figure 4A). Numerous focal adhesion points could be seen (in red and highlighted with white arrows), indicating attachment to the titanium surfaces. These adhesion points were observed along the leading edges of the cells, highlighting the movement of the cell in multiple trajectories across the surface. Lamellipodia and filopodia were also present as the cells spread on the surface. On the NS surface, smaller hMSCs were observed with less evidence of cell spreading or motility across the surface (Figure 4B). These images suggest that after 3 days, the hMSCs preferred to attach and spread on flat titanium rather than the nanospikes. The presence of the ChoM coating had no discernible effect on these interactions. This slower adhesion has been observed before on high aspect-ratio nanotopographies,20 but it is important that the hMSCs did adhere and the ChoM did not have further detrimental effects.
**Figure 4:** *Immunofluorescent staining of hMSCs after 3 days on different surfaces.
hMSCs were seeded onto (A) nonfunctionalized flat, (B) nonfunctionalized
NS, (C) ChoM-coated flat, and (D) ChoM-coated NS surfaces at 3000
cells/cm2 and incubated for 3 days at 37 °C in 5%
CO2. Cells were washed, fixed, and then stained for vinculin
(green) and actin (red). Nuclei were stained with DAPI (blue). White
arrows indicate actin focal points.*
We also utilized Giemsa staining to visualize and assess the morphology and confluency of the hMSCs on the tested surfaces after the 28-day incubation period. Osteogenic medium was used as a positive control to provide an environment to support hMSC adhesion and proliferation on both flat titanium and NS surfaces (Figure 5A,D). After 28 days on the nonfunctionalized flat titanium (Figure 5B), the hMSCs were clearly visible with the nuclei stained dark blue/purple and the cytoplasm a light blue. The cells had formed a dense layer with a range of morphologies, and the majority of cells demonstrated spreading and motility across the surface with elongated and stretched membranes. The morphology was similar to hMSCs grown in osteogenic medium (Figure 5A). The presence of ChoM did not adversely affect growth (Figure 5C).
**Figure 5:** *Brightfield imaging of
Giemsa-stained hMSCs on different surfaces.
hMSCs were seeded onto (A) nonfunctionalized flat (positive control),
(B) nonfunctionalized flat, (C) ChoM-coated flat, (D) nonfunctionalized
NS (positive control), (E) nonfunctionalized NS, and (F) ChoM-coated
NS titanium disks. The disks were seeded with 3000 cells/cm2 and incubated for 28 days at 37 °C in 5% CO2. Positive
control disks (A,D) were incubated in osteogenic medium. Cells were
washed, fixed, and then stained with Giemsa stain. Red brackets highlight
individual cells. Scale bar, 200 μm.*
Visualization of cells on the nanospikes with the light microscope was challenging due to the underlying color of the disks (Figure 5D–F), which was not seen on the flat surface. There was also high intradisk and interdisk variability in color and patterning. Nonetheless, a dense coverage of hMSCs was seen across the nonfunctionalized NS surface with cell growth, stretching, and motility evident for the majority of cells (Figure 5E). There was no observable difference in morphology compared to the cells grown in osteogenic medium (Figure 5D), and cells were comparable when grown on NS disks with the ChoM peptide coating (Figure 5F). Together, these results suggest that the hMSCs could adhere, grow, and spread across the nanospikes, as well as on the flat surface, regardless of the presence of ChoM. In all instances, however, no significant osteogenic differentiation occurred during the 28-day time period.
To investigate the biocompatibility of the surfaces over the extended 28 day period, the alamarBlue assay was used. This assay quantitatively determines the viability of mammalian cells by measuring the innate reducing power of a cell. Living cells take up and reduce resazurin into resorufin, which fluoresces red. On day 3, hMSC viability was around $60\%$ on both the flat and NS surfaces with or without ChoM functionalization (Figure 6A). A similar trend was seen at days 7, 14, and 21 with cells on all surfaces exhibiting viability values of ∼$100\%$ (Figure 6B–D). In 28 days, the longest timepoint was tested, and the viability dropped slightly for all the surfaces to approximately $70\%$ (Figure 6E). Again, there was no statistically significant difference between the surfaces ± ChoM, indicating that the peptide did not cause any cytotoxicity. These results demonstrate that while initial hMSC attachment dynamics were slower on the NS surface compared to flat, both surfaces ultimately exhibited comparable biocompatibility, at least over a 28-day period, and were not adversely affected by the ChoM peptide coating.
**Figure 6:** *hMSC viability following incubation over 28
days on different surfaces.
hMSCs were seeded onto flat and NS surfaces ± 100 μM ChoM
and incubated at 37 °C in 5% CO2 for (A) 3 days, (B)
7 days, (C) 14 days, (D) 21 days, and (E) 28 days. AlamarBlue solution
was then added to each well and incubated for 6 h at 37 °C in
5% CO2, and the absorbance measured at 570 and 600 nm.
Data are presented as mean ± SD; n = 4 in quadruplicate.*
## Discussion
To the best of our knowledge, this is the first report on enhancing the antibacterial properties of a TiO2 nanotopography by biomimetic. Prior efforts to enhance the antibacterial performance of nanospikes have been through the use of incorporated metal ions like copper, zinc, silver, and magnesium.21−25 *In this* study, we chose to functionalize the titanium surfaces with AMPs, which unlike metal ions are biodegradable and nontoxic to human cells. Similar to antimicrobial metal ions, resistance toward AMPs by bacteria is limited.26
## Release Kinetics of ChoM
Physical adsorption was chosen as the method of functionalization because it is a simple technique with no special surface treatment needed, thereby avoiding the use of linkers such as glutaraldehyde that are potentially cytotoxic.27 Physical adsorption methods allow the free release of AMPs into the local environment from the surface, enabling the peptides to exert effective, short-term antimicrobial activity. In these studies, ChoM was rapidly released within 10 min from the flat titanium surface and reached a higher concentration than achieved with the NS surface over 3 h. However, SEM images of the disks indicated that while only a residual peptide coating was left on the flat surface after 3 h, significant peptide remained on the NS surface, implying that ChoM release from the nanospikes could be maintained for a much longer period. This was supported by subsequent studies exploring the antibacterial properties of the surfaces, in which the functionalized NS surface was more effective against S. aureus than the equivalent flat surface over 8 h.
Adsorption and desorption processes are dependent upon factors, such as surface chemistry, physicochemical properties of the solvent and the surface, surface area, and topography.28,29 Given the differences observed with the flat mirror polished titanium surface and NS surface, it is anticipated that ChoM release dynamics were influenced by one or more of these properties (Table S2). Wettability is the most significant difference between the two surfaces where the flat surface is slightly hydrophilic with a contact angle of 80°, while the NS surface has high wetting with a contact angle of less than 10°. The superhydrophilicity of the NS surface could be due to an increase in surface roughness and total surface area, which together lead to a higher overall surface energy.30,31 Roughness promotes the spread of the liquid, while the large surface area of the nanotopography further enhances the surface wetting.32 The volume of ChoM that was used to functionalize the surfaces was limited by the maximum volume that could be pipetted onto the NS surface. Thus, the differences in surface wettability meant that the application of the same volume of ChoM led to differences in the coverage of the peptide coating on the two surfaces. A small, peptide-dense area formed on the flat surface, while the peptide solution on the superhydrophilic NS surface covered the entire surface (Figure 7). This variation in ChoM distribution explains why SEM showed the coating on the NS surface to be much thinner (<500 nm) than that on the flat surface and why XPS spectra confirmed significantly more amine groups on the flat surface compared to the NS surface.
**Figure 7:** *Schematic
of the differences in ChoM coating found on (A) flat
control and (B) NS surfaces.*
SEM and XPS data suggest that the NS architecture directly influenced the adsorption and release kinetics of ChoM. The presence of nanospikes increases the total surface area of the titanium disk and provides a reservoir (Table S2). Consequently, most of the peptides were physically absorbed to the nanospikes and not readily released upon activation by growth medium or water (Figure 2E). This contrasts with the flat surface where most of the peptides were activated and released unimpeded into the suspension within a short period of time.
## Antimicrobial Activity of Functionalized ChoM
It was initially anticipated that functionalization with ChoM would enhance the antibacterial properties of the NS surface. However, against E. coli, the efficacy of ChoM, even at 25 μM, was so high that the presence of this peptide alone was sufficient to kill the bacterial population over a 3-h period, independent of the nanospikes. However, after 3 h, it was only on the functionalized NS surface that slight bacterial growth was detected. Pyne et al. proposed that a monolayer pore-forming peptide could bind to a bacterial cell membrane in either the S-state (inactive state) or M-state (pore formation state) and that the antibacterial activity is folding-dependent. The peptides could also exfoliate the bacterial cell membrane without inserting into it, thus causing a more rapid and extensive membrane rupture.14 Thus, it is possible that the slight variation seen in antibacterial performance of the functionalized flat versus NS surfaces reflects the fact that ChoM residues that remained adsorbed to the nanospikes were not in a fully active conformation compared to the released form. Nonetheless, with longer incubation periods when testing the surfaces against Gram-positive species, there was evidence of the nanospikes and ChoM working together for enhanced bacterial killing. Growth of S. epidermidis was ablated over a 18-h period by either nanospikes or ChoM alone. However, for S. aureus, the presence of nanospikes or ChoM could only suppress growth for up to 3.5–4 h. By contrast, the combination of nanospikes and ChoM ablated S. aureus growth for the entire 8-h period. To more readily assess the contribution of nanospikes and ChoM on bacterial viability when alone or in combination, these data were also expressed as percentage change in bacterial viability for the NS, Flat + ChoM (100 μM), or NS + ChoM (100 μM) surfaces at the end of each assay compared to the flat control surface (Table S3). As predicted, the high susceptibility of E. coli to ChoM effectively masked any potential enhanced effect with nanospikes, with a >$98\%$ reduction in E. coli viability seen on either the Flat + ChoM or NS + ChoM surface. A similar situation was observed for S. epidermidis with either nanospikes or ChoM alone capable of reducing bacterial viability by >$97\%$. By contrast, for S. aureus, nanospikes alone could reduce bacterial viability by $39\%$ while ChoM alone resulted in a $73\%$ reduction in viability. Combined, the reduction in S. aureus viability exceeded $99\%$, clearly demonstrating the additive effects of nanospikes with ChoM. This additive effect against S. aureus is likely important as it is Gram-positive pathogens such as these that are typically introduced by surgery, i.e., that are in the “race to the surface. ”19 The combined action of nanospikes and ChoM is similar to Bright et al., who reported that exposure of bacterial cells incubated with nanospikes prior to vancomycin treatment mediated synergistic effects.33 Specifically, the interaction between bacteria and nanospikes caused intracellular reactive oxygen species (ROS) generation and bacterial upregulation of catalase in response. In the presence of vancomycin, however, upregulation of catalase was impaired, leading to elevated oxidative damage and resultant bacterial cell lysis.
In proposing a potential mechanism for the additive antibacterial effects of nanospikes and ChoM, it is likely that the prolonged release of ChoM from the nanospikes relative to the flat surface is a factor, but it is also important to consider the nanospike–bacterium interface. Evidence from FIB-SEM has shown that adherent bacteria on nanospikes will undergo membrane stretching,34 which in turn induces the formation of ROS.17,33,34 ChoM mediates its antimicrobial effects by intercalating with phospholipid head groups within the lipid bilayer of the bacterial cell membrane. This leads to thinning and exfoliation of the lipid bilayer and ultimately cell lysis.16 For Gram-negative bacteria, it is expected that within the first 10 min of incubation, ChoM that is released from the coating starts to irreversibly perturb the outer lipid bilayer. This thinning and pore formation within the outer membrane then makes any surviving bacteria more susceptible to the effects of the nanospikes upon bacterial adhesion to the surface. Since the outermost layer of the Gram-positive bacterial cell envelope comprises a thick layer of peptidoglycan, ChoM cannot readily access the underlying phospholipid bilayer. However, on the functionalized NS surface, we propose that stretching of the adherent Gram-positive cell envelope by the nanospikes serves to expose the cytoplasmic membrane to ChoM and so enhance its bactericidal efficacy (Figure 8). This is a similar principle to a study in which cells became more susceptible to nanopillars after their membrane integrity had been compromised with microwave radiation.35
**Figure 8:** *Schematic
of proposed mechanisms of additive antibacterial effects
between nanospikes and AMPs against Gram-negative and Gram-positive
bacteria. Top insets show ChoM (orange cylinder) exfoliating the cell
envelope of Gram-negative (left) and Gram-positive (right) bacteria.
ChoM can rapidly start to compromise the outer and then inner cell
membranes of Gram-negative bacteria, inducing cell lysis. For those
bacteria that survive this initial challenge, the compromised cell
membranes make them more susceptible to the sharp nanospikes, which
can result in further membrane thinning and potential rupturing and
penetration (bottom left inset). The presence of the thick peptidoglycan
layer in Gram-positive bacteria prevents easy access of ChoM to the
underlying cell membrane. However, bacterial contact with the nanospikes
can stretch and compromise the peptidoglycan layer, thereby generating
an opening for ChoM to access and disrupt the cytoplasmic membrane
(bottom right inset). *The insets are not drawn to scale.*
It is important to note that while debris resulting from damage to bacterial cells, such as proteins, may adsorb onto the NS surface, these constituents are unlikely to exhibit long-term stability and can be expected to degrade over time.36 Moreover, for the in vivo application of this functionalized NS surface, both hMSCs and microbial cells will be present simultaneously. The ultimate goal for the enhanced antimicrobial performance of the functionalized nanospikes is therefore to restrict microbial attachment and growth to a sufficient level that allows hMSCs to win the “race to the surface.” Once attachment and spreading of hMSCs is underway, microbial infection will be inhibited.
## Biocompatibility of ChoM-Functionalized Nanospikes
In this study, hMSCs initially favored attachment to the flat titanium surface compared to the NS surface. This could be related to the diameter of the nanospikes as reported previously.20,37,38 *In a* study comparing nanospikes of different diameters, Goreham et al. reported that nanospikes with a 16 nm diameter encouraged adhesion of MG63 and 3T3 cells compared to nanospikes with 38 or 68 nm diameters.39 Sjöström et al. also reported that more adhesion and bone matrix formed on nanospikes with a smaller diameter (28 nm) compared to larger ones (41 and 56 nm).40 Nonetheless, over a longer time period, the NS surface was found to be of comparable biocompatibility as the flat surface. Greater levels of cellular adhesion on the nanospikes were observed over time, likely due to the deposition of serous and extracellular matrix (ECM) proteins onto the nanospikes, making the surface more favorable for hMSC attachment. Critically, as seen from our Giemsa and immunofluorescent staining results, functionalization with ChoM did not compromise the biocompatibility of the surfaces. This aligns with Pfeil et al., in which ChoM at a concentration of 250 μM was found to have no adverse effects on blood cells.16 Similarly, PEEK substrates that were functionalized with AMP and osteogenic growth peptide (OGP) showed resistance toward bacterial infection, stabilized bone homeostasis, and facilitated osteogenesis in vivo after 14 days.41 More recently, Gao et al. reported that TiO2 nanospikes functionalized with cationic polymers can kill both Gram-negative and Gram-positive bacteria and inhibit biofilm formation for up to 14 days. They also reported that the residual hydroxyl group on the titanium substrate promoted deposition of hydroxyapatite in Kokubo’s simulated body fluid, which was important for orthopedic and dental applications.42
## Conclusions
In summary, this study has shown that a nature-inspired NS surface with a high density of high aspect ratio nanospikes can exhibit promising antibacterial effects against E. coli, S. aureus, and S. epidermidis. Moreover, the antimicrobial activity of the NS surface can be enhanced by functionalization with a bioinspired AMP, ChoM. For Gram-negative bacteria, we propose that these enhanced effects result from membrane stretching and deformation of the bacterial cell envelope by nanospikes following initial disruption of the outer cell membrane by ChoM. For Gram-positive bacteria, such as S. aureus, the thick peptidoglycan layer of the cell envelope renders them less susceptible to the immediate effects of ChoM. In this instance, the additive effects come from interactions between adherent bacteria and nanospikes that cause the cell wall to experience deformation, which in turn generates openings for the ChoM to access the underlying cytoplasmic membrane. Crucially, when considering their potential as novel implant materials, both the functionalized and nonfunctionalized surfaces were found to be highly biocompatible. Taken together, this research highlights the potential to generate biocompatible titanium surfaces with enhanced antibacterial activity comprising both physical and chemical mechanisms of action. Such an approach could be exploited to develop next-generation implants to combat bacterial infections, and thus maximize the longevity of medical implants and improve the wellbeing of millions of patients worldwide.
## References
1. Bahraminasab M., Farahmand F.. **State of the Art Review on Design and Manufacture of Hybrid Biomedical Materials: Hip and Knee Prostheses**. *Proc. Inst. Mech. Eng., Part H* (2017) **231** 785-813. DOI: 10.1177/0954411917705911
2. Cordeiro J. M., Barão V. A. R.. **Is There Scientific Evidence Favoring the Substitution of Commercially Pure Titanium with Titanium Alloys for the Manufacture of Dental Implants?**. *Mater. Sci. Eng., C* (2017) **71** 1201-1215. DOI: 10.1016/j.msec.2016.10.025
3. Harms S., Larson R., Sahmoun A. E., Beal J. R.. **Obesity Increases the Likelihood of Total Joint Replacement Surgery among Younger Adults**. *Int. Orthop.* (2007) **31** 23-26. DOI: 10.1007/s00264-006-0130-y
4. Ishak M. I., Liu X., Jenkins J., Nobbs A. H., Su B.. **Protruding Nanostructured Surfaces for Antimicrobial and Osteogenic Titanium Implants**. *Coatings* (2020) **10** 756. DOI: 10.3390/coatings10080756
5. Ribeiro M., Monteiro F. J., Ferraz M. P.. **Infection of Orthopedic Implants with Emphasis on Bacterial Adhesion Process and Techniques Used in Studying Bacterial-Material Interactions**. *Biomatter* (2012) **2** 176-194. DOI: 10.4161/biom.22905
6. Kallala R. F., Vanhegan I. S., Ibrahim M. S., Sarmah S., Haddad F. S.. **Financial Analysis of Revision Knee Surgery Based on NHS Tariffs and Hospital Costs: Does It Pay to Provide a Revision Service?**. *Bone Joint J.* (2015) **97** 197-201. DOI: 10.1302/0301-620X.97B2.33707
7. Badarudeen S., Shu A. C., Ong K. L., Baykal D., Lau E., Malkani A. L.. **Complications after Revision Total Hip Arthroplasty in the Medicare Population**. *J. Arthroplasty* (2017) **32** 1954-1958. DOI: 10.1016/j.arth.2017.01.037
8. Bixler G. D., Bhushan B.. **Biofouling: Lessons from Nature**. *Philos. Trans. R. Soc., A* (2012) **1967** 2381-2417. DOI: 10.1098/rsta.2011.0502
9. Elbourne A., Crawford R. J., Ivanova E. P.. **Nano-Structured Antimicrobial Surfaces: From Nature to Synthetic Analogues**. *J. Colloid
Interface Sci.* (2017) **508** 603-616. DOI: 10.1016/j.jcis.2017.07.021
10. Damiati L. A., Tsimbouri M. P., Hernandez V. L., Jayawarna V., Ginty M., Childs P., Xiao Y., Burgess K., Wells J., Sprott M. R., Meek R. M. D., Li P., Oreffo R. O. C., Nobbs A., Ramage G., Su B., Salmeron-Sanchez M., Dalby M. J.. **Materials-Driven Fibronectin Assembly on Nanoscale Topography Enhances Mesenchymal Stem Cell Adhesion, Protecting Cells from Bacterial Virulence Factors and Preventing Biofilm Formation**. *Biomaterials* (2022) **280**. DOI: 10.1016/j.biomaterials.2021.121263
11. Hazam P. K., Goyal R., Ramakrishnan V.. **Peptide Based Antimicrobials: Design Strategies and Therapeutic Potential**. *Prog.
Biophys. Mol. Biol.* (2019) **142** 10-22. DOI: 10.1016/j.pbiomolbio.2018.08.006
12. Eales M. G., Ferrari E., Goddard A. D., Lancaster L., Sanderson P., Miller C.. **Mechanistic and Phenotypic Studies of Bicarinalin, BP100 and Colistin Action on Acinetobacter Baumannii**. *Res. Microbiol.* (2018) **169** 296-302. DOI: 10.1016/j.resmic.2018.04.005
13. Alencar-Silva T., Braga M. C., Santana G. O. S., Saldanha-Araujo F., Pogue R., Dias S. C., Franco O. L., Carvalho J. L.. **Breaking the Frontiers of Cosmetology with Antimicrobial Peptides**. *Biotechnol. Adv.* (2018) **36** 2019-2031. DOI: 10.1016/j.biotechadv.2018.08.005
14. Pyne A., Pfeil M.-P., Bennett I., Ravi J., Iavicoli P., Lamarre B., Roethke A., Ray S., Jiang H., Bella A.. **Engineering Monolayer Poration for Rapid Exfoliation of Microbial Membranes**. *Chem. Sci.* (2017) **8** 1105-1115. DOI: 10.1039/c6sc02925f
15. Wang B., Liu H., Wang Z., Shi S., Nan K., Xu Q., Ye Z., Chen H.. **A Self-Defensive Antibacterial Coating Acting through the Bacteria-Triggered Release of a Hydrophobic Antibiotic from Layer-by-Layer Films**. *J. Mater. Chem. B* (2017) **5** 1498-1506. DOI: 10.1039/c6tb02614a
16. Pfeil M.-P., Pyne A. L. B., Losasso V., Ravi J., Lamarre B., Faruqui N., Alkassem H., Hammond K., Judge P. J., Winn M.. **Tuneable Poration: Host Defense Peptides as Sequence Probes for Antimicrobial Mechanisms**. *Sci. Rep.* (2018) **8** 14926. DOI: 10.1038/s41598-018-33289-y
17. Jenkins J., Mantell J., Neal C., Gholinia A., Verkade P., Nobbs A. H., Su B.. **Antibacterial Effects of Nanopillar Surfaces Are Mediated by Cell Impedance, Penetration and Induction of Oxidative Stress**. *Nat. Commun.* (2020) **11** 1626. DOI: 10.1038/s41467-020-15471-x
18. Mas-Moruno C., Su B., Dalby M. J.. **Multifunctional Coatings and Nanotopographies: Toward Cell Instructive and Antibacterial Implants**. *Adv. Healthcare Mater.* (2019) **8**. DOI: 10.1002/adhm.201801103
19. Busscher H. J., Van Der Mei H. C., Subbiahdoss G., Jutte P. C., Van Den
Dungen J. J. A. M., Zaat S. A. J., Schultz M. J., Grainger D. W.. **Biomaterial-Associated Infection: Locating the Finish Line in the Race for the Surface**. *Sci. Transl. Med.* (2012) **4** 153rv10. DOI: 10.1126/scitranslmed.3004528
20. Tsimbouri P. M., Fisher L., Holloway N., Sjostrom T., Nobbs A. H., Meek R. M. D., Su B., Dalby M. J.. **Osteogenic and Bactericidal Surfaces from Hydrothermal Titania Nanowires on Titanium Substrates**. *Sci. Rep.* (2016) **6** 36857. DOI: 10.1038/srep36857
21. Wang B., Wu Z., Wang S., Wang S., Niu Q., Wu Y., Jia F., Bian A., Xie L., Qiao H., Chang X., Lin H., Zhang H., Huang Y.. **Mg/Cu-Doped TiO2 Nanotube Array: A Novel Dual-Function System with Self-Antibacterial Activity and Excellent Cell Compatibility**. *Mater. Sci. Eng.,
C* (2021) **128**. DOI: 10.1016/j.msec.2021.112322
22. Zhang L., Zhang J., Dai F., Han Y.. **Cytocompatibility and Antibacterial Activity of Nanostructured H2Ti5O11· H2O Outlayered Zn-Doped TiO2 Coatings on Ti for Percutaneous Implants**. *Sci. Rep.* (2017) **7** 13951. DOI: 10.1038/s41598-017-13954-4
23. Besinis A., Hadi S. D., Le H. R., Tredwin C., Handy R. D.. **Antibacterial Activity and Biofilm Inhibition by Surface Modified Titanium Alloy Medical Implants Following Application of Silver. Titanium Dioxide and Hydroxyapatite Nanocoatings**. *Nanotoxicology* (2017) **11** 327-338. DOI: 10.1080/17435390.2017.1299890
24. Gao F., Pang H., Xu S., Lu Q.. **Copper-Based Nanostructures: Promising Antibacterial Agents and Photocatalysts**. *Chem. Commun.* (2009) **24** 3571-3573. DOI: 10.1039/b904801d
25. Wu R., Zhang H., Pan J., Zhu H., Ma Y., Cui W., Santos H. A., Pan G.. **Spatio-design of Multidimensional Prickly Zn-doped CuO Nanoparticle for Efficient Bacterial Killing**. *Adv. Mater. Interfaces* (2016) **3**. DOI: 10.1002/admi.201600472
26. Assoni L., Milani B., Carvalho M. R., Nepomuceno L. N., Waz N. T., Guerra M. E. S., Converso T. R., Darrieux M.. **Resistance Mechanisms to Antimicrobial Peptides in Gram-Positive Bacteria**. *Front. Microbiol.* (2020) **11**. DOI: 10.3389/fmicb.2020.593215
27. Xu H., Zhang G., Xu K., Wang L., Yu L., Xing M. M. Q., Qiu X.. **Mussel-Inspired Dual-Functional PEG Hydrogel Inducing Mineralization and Inhibiting Infection in Maxillary Bone Reconstruction**. *Mater. Sci. Eng., C* (2018) **90** 379-386. DOI: 10.1016/j.msec.2018.04.066
28. Sevilla P., Gil J., Aparicio C.. **Relevant Properties for Immobilizing Short Peptides on Biosurfaces**. *IRBM* (2017) **38** 256-265. DOI: 10.1016/j.irbm.2017.06.003
29. Richert L., Variola F., Rosei F., Wuest J. D., Nanci A.. **Adsorption of Proteins on Nanoporous Ti Surfaces**. *Surf.
Sci.* (2010) **604** 1445-1451. DOI: 10.1016/j.susc.2010.05.007
30. Hazell G., Fisher L. E., Murray W. A., Nobbs A. H., Su B.. **Bioinspired Bactericidal Surfaces with Polymer Nanocone Arrays**. *J. Colloid Interface Sci.* (2018) **528** 389-399. DOI: 10.1016/j.jcis.2018.05.096
31. Ishak M. I., Dobryden I., Claesson P. M., Briscoe W. H., Su B.. **Friction at Nanopillared Polymer Surfaces beyond Amontons’ Laws: Stick-Slip Amplitude Coefficient (SSAC) and Multiparametric Nanotribological Properties**. *J. Colloid Interface Sci.* (2020) **583** 414-424. DOI: 10.1016/j.jcis.2020.09.038
32. Zhang B., Wang J., Zhang X.. **Effects of the Hierarchical Structure of Rough Solid Surfaces on the Wetting of Microdroplets**. *Langmuir* (2013) **29** 6652-6658. DOI: 10.1021/la400800u
33. Bright R., Hayles A., Wood J., Palms D., Brown T., Barker D., Vasilev K.. **Surfaces Containing Sharp Nanostructures Enhance Antibiotic Efficacy**. *Nano Lett.* (2022) **22** 6724-6731. DOI: 10.1021/acs.nanolett.2c02182
34. Ishak M. I., Jenkins J., Kulkarni S., Keller T. F., Briscoe W. H., Nobbs A. H., Su B.. **Insights into Complex Nanopillar-Bacteria Interactions: Roles of Nanotopography and Bacterial Surface Proteins**. *J. Colloid Interface Sci.* (2021) **604** 91-103. DOI: 10.1016/j.jcis.2021.06.173
35. Pogodin S., Hasan J., Baulin V. A., Webb H. K., Truong V. K., Nguyen T. H. P., Boshkovikj V., Fluke C. J., Watson G. S., Watson J. A.. **Biophysical Model of Bacterial Cell Interactions with Nanopatterned Cicada Wing Surfaces**. *Biophys.
J.* (2013) **104** 835-840. DOI: 10.1016/j.bpj.2012.12.046
36. Nguyen D. H. K., Loebbe C., Linklater D. P., Xu X., Vrancken N., Katkus T., Juodkazis S., Maclaughlin S., Baulin V., Crawford R. J., Ivanova E. P.. **The Idiosyncratic Self-Cleaning Cycle of Bacteria on Regularly Arrayed Mechano-Bactericidal Nanostructures**. *Nanoscale* (2019) **11** 16455-16462. DOI: 10.1039/C9NR05923G
37. Diu T., Faruqui N., Sjöström T., Lamarre B., Jenkinson H. F., Su B., Ryadnov M. G.. **Cicada-Inspired Cell-Instructive Nanopatterned Arrays**. *Sci.
Rep.* (2014) **4** 7122. DOI: 10.1038/srep07122
38. Loye A. M., Kinser E. R., Bensouda S., Shayan M., Davis R., Wang R., Chen Z., Schwarz U. D., Schroers J., Kyriakides T. R.. **Regulation of Mesenchymal Stem Cell Differentiation by Nanopatterning of Bulk Metallic Glass**. *Sci.
Rep.* (2018) **8** 8758. DOI: 10.1038/s41598-018-27098-6
39. Goreham R. V., Mierczynska A., Smith L. E., Sedev R., Vasilev K.. **Small Surface Nanotopography Encourages Fibroblast and Osteoblast Cell Adhesion**. *RSC Adv.* (2013) **3** 10309-10317. DOI: 10.1039/C3RA23193C
40. Sjöström T., Nobbs A. H., Su B.. **Bactericidal Nanospike Surfaces via Thermal Oxidation of Ti Alloy Substrates**. *Mater.
Lett.* (2016) **167** 22-26. DOI: 10.1016/j.matlet.2015.12.140
41. Li M., Bai J., Tao H., Hao L., Yin W., Ren X., Gao A., Li N., Wang M., Fang S.. **Rational Integration of Defense and Repair Synergy on PEEK Osteoimplants via Biomimetic Peptide Clicking Strategy**. *Bioact. Mater.* (2022) **8** 309-324. DOI: 10.1016/j.bioactmat.2021.07.002
42. Gao Q., Feng T., Huang D., Liu P., Lin P., Wu Y., Ye Z., Ji J., Li P., Huang W.. **Antibacterial and Hydroxyapatite-Forming Coating for Biomedical Implants Based on Polypeptide-Functionalized Titania Nanospikes**. *Biomater. Sci.* (2020) **8** 278-289. DOI: 10.1039/c9bm01396b
|
---
title: 'Semi-quantitative assessment of environmental tobacco smoke exposure and its
association with the development of oral squamous cell carcinoma: A pilot study'
authors:
- Susanne Wolfer
- Henning Schliephake
- Thomas Asendorf
- Antonie Spillner
- Philipp Kauffmann
journal: Tobacco Induced Diseases
year: 2023
pmcid: PMC9972359
doi: 10.18332/tid/159378
license: CC BY 4.0
---
# Semi-quantitative assessment of environmental tobacco smoke exposure and its association with the development of oral squamous cell carcinoma: A pilot study
## Abstract
### INTRODUCTION
Two known major risk factors for oral squamous cell carcinoma are smoking and alcohol consumption. Environmental tobacco smoke (also known as secondhand smoke) has been proven to be associated with the occurrence of lung and breast carcinoma. This study aimed to assess exposure to environmental tobacco smoke and its association with the development of oral squamous cell carcinomas.
### METHODS
Using a standardized questionnaire, 165 cases and 167 controls were asked about their demographic data and risk behaviors, including environmental tobacco smoke exposure. An environmental tobacco smoke score (ETS-score) was developed to semi-quantitatively record the previous exposure to ETS. Statistical analyses were performed with χ2 test or Fishers exact test, and with ANOVA or Welch’s t-test as appropriate. An analysis was done using multiple logistic regression.
### RESULTS
Cases had a significantly increased previous exposure to environmental tobacco smoke compared to the controls (ETS-score: 36.69 ± 26.34 vs 13.92 ± 12.44; $p \leq 0.0001$). Comparing only the groups without additional active risk factors, exposure to environmental tobacco smoke was associated with a more than threefold higher likelihood of oral squamous cell carcinoma (OR=3.47; $95\%$ CI: 1.31–10.55). Statistically significant differences in ETS-score were found for different tumor locations ($$p \leq 0.0012$$) and different histopathological gradings ($$p \leq 0.0399$$). A multiple logistic regression analysis confirmed exposure to environmental tobacco smoke as an independent risk factor for the development of oral squamous cell carcinomas ($p \leq 0.0001$).
### CONCLUSIONS
Environmental tobacco smoke is an important but yet underestimated risk factor for the development of oral squamous cell carcinomas. Further studies are needed to confirm the results, including the usefulness of the developed environmental tobacco smoke score for exposure.
## INTRODUCTION
Lip and oral squamous cell carcinoma (OSCC) are widespread malignancies worldwide, causing many deaths (approximately 170000 annually). Two of the known major risk factors are smoking and alcohol consumption1,2. However, many patients with OSCC have shown no risk behavior3-6. Environmental tobacco smoke (ETS) (also known as secondhand smoke) has been considered a health risk factor for a number of years7. Efforts are being made worldwide to adequately prevent secondhand smoking, for example, through non-smoker protection laws8. ETS has been proven to be associated with lung and breast carcinoma9,10. Many other diseases, such as diabetes mellitus and cardiovascular diseases, are thought to be associated with secondhand smoke7,11,12. There are only a few reports that have examined the impact of ETS on the development of OSCC13,14.
The recording of active smoke exposure using pack-years is routinely noted in clinical practice today. However, it is difficult to quantify the exposure to ETS specifically for each individual. *In* general, exposure to ETS is rarely or not recorded, and only categorically evaluated13-15. Quantitative laboratory tests are available, but these can only record the current exposure to active tobacco use or the actual exposure to passive smoking. These tests do not appear to be suitable for everyday clinical routine and for assessing long-term ETS exposure16-18.
Passive smoke exposure is usually recorded in case-control studies7. The evaluation is carried out with categorical values by determining an odds ratio, which cannot consider individual values. Therefore, a comprehensive overview of the exposure and a comparison of different groups can be difficult. With numerical values, it would be possible to classify the exposure to passive smoking more precisely. An investigation of the influence of ETS as a possible cause of oral squamous cell carcinoma is particularly interesting in the patient group of non-smokers and non-drinkers.
This study aimed to establish an ETS-score for the semi-quantitative assessment of exposure to ETS and the possible association of ETS-score levels with OSCC.
## METHODS
The study was designed as a case-control study. Patients with a history of OSCC were recruited as cases from the regular tumor follow-up between February 2020 to June 2021, using the following inclusion criteria: 1) age ≥18 years; 2) histologically confirmed OSCC; 3) tumor location of the oral tongue as the anterior two-thirds of the tongue, gingiva of the upper jaw, gingiva of the lower jaw, floor of the mouth, palate, buccal mucosa; 4) no immunosuppression; 5) no other malignancy; and 6) capable of giving informed consent and not supervised. Exclusion criteria were: 1) age <18 years; 2) no histologically confirmed OSCC; 3) hypo-, naso- and oropharyngeal location, tumor location outside the oral cavity; 4) immunosuppression; 5) other history of malignancy; and 6) not able to give consent, supervised. Age- and sex-matched inpatients and outpatients without a history of OSCC, aged ≥18 years, without immunosuppression or malignancy, and capable of giving informed consent, served as case controls. The clinical data for the cases were extracted from the clinical records, including the date of diagnosis, location of the OSCC, histology with tumor size, lymph node and metastatic (TNM) stage, residual tumor status (R status), histological tumor grading, nodal extracapsular spread, the incidence of recurrence (census 31 January 2022). A total of 165 cases and 167 controls were included in this study. Details regarding age and sex distribution, among other variables such as marital status and risk behavior, are given in Table 1 and show that there were no significant differences between cases and controls.
**Table 1**
| Characteristics | Cases N=165 n (%) | Controls N=167 n (%) | p |
| --- | --- | --- | --- |
| Age (years), mean ± std | 62.16 ± 11.44 | 63.99 ± 10.94 | 0.1327 |
| Sex | n=165 | n=167 | 0.0786 |
| Male | 93 (56.36) | 78 (46.71) | |
| Female | 72 (43.64) | 89 (53.29) | |
| Marital status | n=164 | n=167 | 0.0583 |
| Single | 26 (15.85) | 14 (8.38) | |
| Married | 109 (66.46) | 126 (75.45) | |
| Divorced | 12 (7.32) | 6 (3.59) | |
| Widowed | 14 (8.54) | 13 (7.78) | |
| Permanent partner | 3 (1.83) | 8 (4.79) | |
| Risk behavior | n=164 | n=167 | <0.0001 |
| NSND | 37 (22.42) | 60 (35.93) | |
| SND and NSD | 67 (40.60) | 86 (51.50) | |
| SD | 61 (36.97) | 21 (12.57) | |
| Pack-years, | n=154 | n=159 | <0.0001 |
| mean ± std | 24.95 ± 26.97 | 7.78 ± 11.75 | |
| ETS history, n | n=163 | n=167 | 0.0011* |
| ETS+ | 145 | 124 | |
| ETS- | 18 | 43 | |
| ETS-score, | n=163 | n=167 | <0.0001 |
| mean ± std | 36.69 ± 26.34 | 13.92 ± 12.44 | |
## Exposure to ETS
After the patient’s written consent to participate in this one-time survey, a standardized questionnaire was completed by the participants with the supervision of one single trained interviewer to exclude an inter-interviewer variation. Data on demographic parameters such as age, sex, and relationship status at diagnosis were recorded. Exposure to ETS was specifically asked for and registered separately for ETS at work and ETS at home. Any exposure to tobacco smoke that was not caused by the participant’s smoking behavior but by other people at work or at home was classified as environmental tobacco smoke exposure. To explore ETS exposure at home, the smoking habits of spouses or life partners and parents were asked and whether this had resulted in participants’ exposure to ETS. Scores were attributed as follows: 1) duration of ETS in years, and 2) frequency of exposure to ETS recorded as ETS rate (never=0, occasionally=0.5, constantly=1). Occasionally means now and then, irregularly; constantly means every day, regularly. Data were used to calculate a simple and easy to use composite score that reflected exposure to environmental tobacco smoke using the following formula: For example: constant ETS exposure at home for 10 years and an occasional ETS exposure at work for 20 years results in an ETS-score of (10×1) + (20×0.5) = 20. A high score represents high exposure to ETS, and vice versa.
## Risk behavior
Current and previous risk behavior (tobacco smoking and alcohol consumption) and information about the amount and duration of smoking and alcohol consumption was recorded, as well as possible periods of cessation. The intensity of active smoking was expressed in pack-years [cigarette packs per day (20 cigarettes/pack) × years smoked). Patients were divided into NSND (non-smoker–non-drinker, without any risk behavior), SND (smoker and non-drinker), NSD (non-smoker and drinker) with one risk behavior, and SD (smoker and drinker) as participants with two risk behaviors. Participants who had never smoked were considered non-smokers, and participants who regularly consumed alcohol every day were considered drinkers.
## Statistical analysis
Baseline characteristics were compared by Welch’s t-test/ANOVA or Fisher’s exact test/ test, as appropriate. Group comparisons of the ETS-score were performed using either Welch’s t-test or ANOVA for more than two groups. Mean ± standard deviation (std) values are reported for numerical variables. Risk differences between cases and controls depending on exposure to ETS were assessed by odds ratio, and in a multiple logistic regression analysis for covariates age, sex, pack-years and smoker/drinker category using generalized linear models. The two-sided significance level was set to $5\%$ for all statistical tests and $95\%$ confidence intervals were reported for values of interest.
## RESULTS
There were clear differences in risk behavior and in ETS exposure between the cases and controls. Cases were more frequent SDs ($36.97\%$ vs $12.57\%$; $p \leq 0.0001$) and had a higher number of pack-years (24.95 ± 26.97 vs 7.78 ± 11.75; $p \leq 0.0001$). The ETS-score shows a significantly increased exposure in the cases compared to the controls (36.69 ± 26.34 vs 13.92 ± 12.44; $p \leq 0.0001$) (Table 1 and Figure 1).
**Figure 1:** *ETS-scores of cases (N=163) and controls (N=167); Cases have significantly higher ETS exposure than controls, February 2020 to June 2021*
The unadjusted exposure to ETS at home and at work (in years) was higher in the cases than in the controls (23.15 ± 21.15 vs 11.56 ± 13.72 at home; 16.67 ± 17.29 vs 6.44 ± 11.63 at work; $p \leq 0.0001$). Also, significantly more cases reported exposure to ETS at both places, i.e. home and work ($39.63\%$ vs $14.97\%$; $p \leq 0.0001$). The unadjusted odds ratio (OR) of ETS (no exposure vs exposure) increased from 1.95 ($95\%$ CI: 1.23–3.12) at home and 2.67 ($95\%$ CI: 1.71–4.21) at work to as much as 3.70 ($95\%$ CI: 2.20–6.38) with exposure to ETS at both places (Table 2).
**Table 2**
| Exposure | Cases N=164 n (%) | Controls N=167 n (%) | p | OR (95% CI) |
| --- | --- | --- | --- | --- |
| Exposure to ETS | Exposure to ETS | Exposure to ETS | Exposure to ETS | Exposure to ETS |
| At home | n=164 | n=167 | 0.0039 | 1.95 (1.23–3.12) |
| Yes | 119 (72.56) | 96 (57.49) | | |
| No | 45 (27.43) | 71 (42.51) | | |
| At work | n= 162 | n=167 | <0.0001 | 2.67 (1.71–4.21) |
| Yes | 91 (55.49) | 53 (31.74) | | |
| No | 73 (44.51) | 114 (68.26) | | |
| At home and work | n=162 | n=167 | <0.0001 | 3.7 (2.20–6.38) |
| Yes | 65 (39.63) | 25 (14.97) | | |
| No | 99 (60.37) | 142 (85.03) | | |
| ETS at home | n=164 | n=167 | <0.0001 | |
| Never | 45 (27.16) | 71 (42.51) | | |
| Occasionally | 16 (9.25) | 36 (21.56) | | |
| Constantly | 103 (63.58) | 60 (35.93) | | |
| ETS at home (years), mean ± std | 23.15 ± 21.15 | 11.56 ± 13.72 | <0.0001 | |
| ETS at work | n=164 | n=167 | <0.0001 | |
| Never | 73 (44.51) | 114 (68.26) | | |
| Occasionally | 16 (9.76) | 20 (11.98) | | |
| Constantly | 75 (45.73) | 33 (19.76) | | |
| ETS at work (years), mean ± std | 16.67 ± 17.29 | 6.44 ± 11.63 | <0.0001 | |
| ETS from smoking partner | n=146 | n=166 | 0.0143 | |
| Never | 104 (71.23) | 138 (83.13) | | |
| Occasionally | 7 (4.85) | 10 (6.02) | | |
| Constantly | 35 (23.97) | 18 (10.84) | | |
ETS exposure was also significantly different between cases and controls within the various risk groups, which is shown in detail in Supplementary file Table S1. The group of NSNDs recorded less exposure to ETS than participants with positive risk behavior. In the cases, there was a clear increase in the ETS-score with increasing risk behavior (23.79 vs 36.41 vs 44.61; $p \leq 0.0067$) (Figure 2). Among the NSND cases, ETS exposure was associated with three-fold odds of developing an OSCC than among the NSND controls (OR=3.47; $95\%$ CI: 1.31–10.55) (Figure 3 and Supplementary file Table S2).
**Figure 2:** *ETS-score regarding the different risk behavior; With increasing risk behavior there is an increasing ETS exposure in the cases (N=163) compared to the controls (N=167), February 2020 to June 2021* **Figure 3:** *Odds ratios for the different risk behavior groups; note the high odds ratio for the NSND group compared to the other risk groups (cases: N=163; controls: N=167), February 2020 to June 2021*
The amount of ETS experienced by the cases also appeared to have an influence on the tumor characteristics. In this study, different ETS-scores were found for the different tumor locations ($$p \leq 0.0012$$) (Supplementary file Figure S1). A significant difference was also found in the histopathological grading ($$p \leq 0.0399$$) (Figure 4). The amount of ETS appeared to have no effect on the T and N stages, and the recurrence ($p \leq 0.05$). Likewise, no difference could be determined as to whether the patients had a nodal status with or without extracapsular spread ($p \leq 0.05$) (Supplementary file Table S3).
**Figure 4:** *ETS-score regarding the histopathological grading (cases: N=137); There are increasing ETS-scores for increasing grading classifications, February 2020 to June 2021*
The multiple logistic regression analysis confirmed the independent influence of the ETS on the development of OSCC ($p \leq 0.0001$). With an average marginal effect of 0.01, it was approximately shown that with an increase of the ETS-score by 1 point, the risk of OSCC increases by $1\%$ in our study (Table 3).
**Table 3**
| Variable | AME* | 95% CI | p |
| --- | --- | --- | --- |
| Age | -0.0026 | -0.0067 – 0.0016 | 0.2241 |
| Gender | 0.0318 | -0.0646 – 0.1282 | 0.5182 |
| Pack-years | 0.0064 | 0.0027 – 0.0100 | 0.0006 |
| ETS-score | 0.0102 | 0.0079 – 0.0125 | <0.0001 |
| NSD/SND risk group | -0.1721 | -0.2795 – -0.0646 | 0.0017 |
| SD risk group | -0.0393 | -0.2079 – -0.1294 | 0.6482 |
## DISCUSSION
In our study, the cases had a significantly increased exposure to ETS compared to the controls. Comparing just the groups without additional active risk factors (NSND groups), a history of ETS exposure was three times more likely in the cases than in the controls.
ETS has been reported as an independent risk factor for the development of OSCC in Chinese women13, where a multiplicative interaction between passive smoking and exposure to cooking oil fumes was found, with OR values ranging 1.52–2.38, in line with our results. He et al.13 had recorded the ETS categorically. With the ETS-score it is possible to record a numerical value for each individual. Other case-control studies report a dose-response relationship for the degree of ETS exposure and describe secondhand smoke as an independent predictor of recurrence and survival in patients with head and neck cancer14,15. Zhang et al.14 found an increased OR for heavy ETS exposure (exposure at home and at work) in comparison to moderate ETS exposure (exposure only at home or at work) in their study group. This was also true when only NSND patients were analyzed. This is very much in line with our results, where the OR is also higher with exposure at both places. In contrast to the results of Idris et al.15, we could not determine any difference in the occurrence of recurrences in our patients with and without exposure to ETS. However, Zhang et al.14 and Idris et al.15 had evaluated patients with head and neck squamous cell carcinoma and in one study, only about half of the participants were patients with OSCC, and in the other study, no information was given on the percentage of patients with oral cancer. Again, both use a categorical assessment of the ETS exposure, and levels were graded as no, moderate, or high, exposure with ETS occurrence at home, at work, or at both. Information on the duration of exposure was not recorded. However, we believe it matters how long (in years) the exposure lasted. Therefore, we included the exposure time in years in the calculation of the ETS-score. Also, it makes a difference whether the exposure to ETS occurred constantly or occasionally. Therefore, we developed the ETS-score based on these three studies13-15, taking into account the place of exposure, the duration (in years), and the exposure rate (none, occasionally, constantly). In this way, the ETS exposure is to be recorded as precisely as possible with the ETS-score, and at the same time, the exposure to ETS can be represented numerically. The ETS score is certainly an approximation, but as a numerical value, it is better to use for statistical evaluations. Furthermore, the amount of exposure is clearly visible with the ETS-score, which allows the sorting of low after high as a type of quantitative measure. The increasing ETS-score with increasing risk behavior of the patients in our study shows that a good differentiation is possible. Zhang et al.14 also described the influence of passive smoking in active smokers, since they probably spend more time with other smokers and inhaled the side stream smoke of the other smokers in addition to mainstream smoke. They describe an elevated prevalence of ETS with an increased number of pack-years. Also, Dahlstrom et al.5 had described an increase of ETS exposure from NSND to ever smokers and ever drinkers, which was reported in percentages at home or at work, or at both. So we can confirm these results. However, we can additionally represent this as a visible and comparable value with the ETS-score.
For our study group, we could show with the multiple logistic regression analysis that ETS was significantly higher in the cases than in the controls, indicating that ETS may be an independent risk factor for the development of OSCC. Furthermore, we found an increased risk by $1\%$ with an increasing ETS-score by 1 point. This, to our knowledge, has never been reported in the literature, and shows the good functionality of the developed ETS-score in evaluating the exposure to ETS.
Moyses et al.19 did not regard ETS as a major risk factor for OSCC1, mentioning the difficulty in measurement of lifetime ETS load and that data about the ETS exposure are not routinely recorded in clinical practice. With the ETS score, this is possible and the required data can be collected easily and clinically practicably, comparable to collecting the data for the calculation of the pack-years.
Different lifestyle risk factors for oral cancer were discussed by Petti20 but ETS was not included in that extensive review. NSNDs are a particularly important patient group when considering the effects of ETS exposure. A significant proportion of patients with OSCC are NSND, with the majority women3-5,21,22. It was discussed, that NSND differ in carcinogenesis mechanisms typically associated with smoking, and that there are other genetic alterations or not yet investigated environmental causes involved21. In this study, we investigated the ETS exposure as an environmental cause. The proportion of women in the NSND cases is high ($86.5\%$; $\frac{32}{37}$). When comparing the ETS-scores, however, no difference in sex could be determined in any of the risk groups or in the cases and controls. This means that exposure to ETS is similar in both sexes in our study groups. This is in line with the results by Zhang et al.14 who also saw no difference in sex and age. Other studies report that there is a high level of secondhand smoke exposure in women due to smoking spouses5,9,23. We also found a significantly higher exposure to ETS from the spouse among the cases compared to the controls, regardless of gender and risk profile, and thus, we partially support these statements. Due to the small number in the subgroups, a separate analysis of the sole NSND groups and female participants has not been carried out.
NSND were also particularly considered when examining cases of breast cancer and lung cancer. ETS is described as an important risk factor for lung cancer in female non-smokers9,24. Non-smoking women who live with smoking men have a $24\%$ increased risk to develop lung cancer compared to non-smoking women who were not exposed to ETS24. As an explanation, the exposure to ETS which contains many carcinogens, was given9,23.
It is well known that tobacco smoke causes a variety of cancers. However, it is not only the airways, which represent the direct path of the smoke during active inhalation, that are affected. In addition to lung, laryngeal, pharyngeal and OSCC, carcinomas of the esophagus and also of the liver, bladder, cervix, kidneys and pancreas that are very distant from the respiratory tract have been described as being tobacco-related20,22,25. More than 60 carcinogens have been identified within tobacco smoke20. The carcinogens of tobacco smoke are thought to cause direct and indirect DNA mutations, and indirect DNA damage which disturbs the cellular processes22 and switches tumor suppressor genes or oncogenes, on or off, and related to the development of OSCC26. Thus, normal keratinocytes can transform into malignantly growing keratinocytes26. Increased DNA adducts were found in smoking patients with OSCC, and protein adduct measurement can distinguish non-smokers exposed to ETS from those not exposed25. Furthermore, an animal study analyzed the tongue epithelial response to cigarette smoke exposure, and concluded that cigarette smoke exposure induces the risk of oral cancer development27. It is, therefore, conceivable that the carcinogens taken within ETS can also cause OSCC in NSND, for which the mechanisms of formation with different lifestyle factors are still largely unknown, and furthermore, the carcinogens can increase the risk of OSCC in active smokers15,28. The ETS-score in our study indicates increased exposure to ETS, associated especially with cancers of the floor of the mouth and lower jaw gingival, which due to the their location can result in an increased accumulation of carcinogens in the saliva-collecting regions. If one compares the ETS-scores of the different risk groups with those of the different locations, there may also be a connection between the increased risk burden in SD and floor of the mouth, and in the NSND and the tongue. It has been described that floor of the mouth carcinoma is more likely to occur in smokers, including women who smoke, and tongue carcinoma more in non-smokers5,28. Recently, different studies report that tobacco smoke alters the structure of the oral microbiome and shifts it to dysbiosis. The composition of the oral bacterial and fungal species in the saliva changes, and therefore changes occur which alter the cell and tissue re-modeling, the suppression of apoptosis, and the secretion of carcinogenic toxins29-31. However, the reason for the emergence of OSCC in NSNDs is still largely unknown. ETS exposure could be one possible explanation of what is likely a multifactorial and complex process.
## Limitations
A statistical evaluation of the ETS-score and the localization divided according to the various risk groups was not carried out in this study due to the small number within the subgroups. This requires further studies with a much larger number of participants. A further limitation of this study is that a case-control study cannot really demonstrate cause and effect. For that, large cohort studies are required. But even with cohort studies, the amount of ETS exposure is very difficult to measure and must last for many years. Case-control studies are very common and widely used because of their practicability to clarify these questions about the influence of ETS in the development of different cancers9,10. Another limitation of the study is that we do not record the ETS exposure from free-time activities, and from public places, for example. This ETS exposure is certainly also a part of the ETS load that should not be underestimated. Due to this even more irregular exposure, it is even more difficult to capture, both categorically and numerically. With the ETS-score, however, at least the exposure occurring at the main sites (home and workplace) can be sufficiently quantified. It must also be mentioned that the ETS exposure was recorded by a questionnaire survey, which is based on recall of past circumstances of the participants, and has the potential to be incorrect. In the literature, the possible misclassification of the smoking status by questionnaires as a possible bias is discussed and the assessment of the tobacco smoke exposure by objective biomarkers like cotinine is mentioned17,32. Urinary cotinine as a major metabolite of nicotine that can be used as a short-term biomarker with half-life of only 18h, which is reported to have a strong correlation with self-reported exposure to tobacco smoke17. But for the assessment of the total exposure to ETS, a period over many years is necessary, therefore the use of the urinary cotinine level is impractical16. In the literature, we found that self-reported exposure to ETS is to be regarded as valid both in the short-term and the long-term14. Furthermore, the potential bias due to smoker misclassification was assessed as unlikely to be responsible for the increased health risk observed in studies on ETS17. We have provided a possible instrument to record numerically ETS exposure of patients. It represents only an approximation, as does the calculation of the pack-years, but the ETS-score differentiates the exposure to ETS well enough, is a numerical value for statistical use, and is easy to obtain for the participants and the investigators. This makes it suitable for everyday clinical use. Further studies with much larger number of participants must are needed to confirm the practicability of the ETS score and to validate the results of this pilot study.
## CONCLUSIONS
ETS is an important, yet underestimated, risk factor for developing oral squamous cell carcinoma. The risk increases with increasing additional risk behavior. The presented ETS-score has shown to be a sensitive measure for semi-quantitative assessment of ETS exposure. Further studies are needed to confirm the results and validate the usefulness of the ETS-score as a numerical measurement of environmental tobacco smoke exposure. This ETS-score could be used as an easy-to-use instrument in everyday clinical practice for all known diseases but also for the detection of other diseases possibly associated with ETS.
## CONFLICTS OF INTEREST
The authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none was reported.
## FUNDING
The open access fee was partially funded by the Open Access Publication Funds of the Göttingen University.
## ETHICAL APPROVAL AND INFORMED CONSENT
Ethical approval was obtained from the Institutional Ethics Committee of University Medical Center Göttingen (Approval number: $\frac{1}{1}$/20; Date: 19 February 2020). Participants provided informed consent.
## DATA AVAILABILITY
The data supporting this research are available from the authors on reasonable request.
## AUTHORS’ CONTRIBUTIONS
Research concept and design: WS and SH. Data collection: WS and SA. Data analysis and interpretation: WS, AT and KP. Writing of the manuscript: WS and KP. Critical revision of the manuscript: all authors. Final approval of the manuscript: all authors.
## PROVENANCE AND PEER REVIEW
Not commissioned; externally peer reviewed.
## References
1. Sung H, Ferlay J, Siegel RL. **Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries**. *CA Cancer J Clin* (2021) **71** 209-249. DOI: 10.3322/caac.21660
2. Shaw R, Beasley N. **Aetiology and risk factors for head and neck cancer: United Kingdom National Multidisciplinary Guidelines**. *J Laryngol Otol* (2016) **130** S9-S12. DOI: 10.1017/S0022215116000360
3. Kruse AL, Bredell M, Grätz KW. **Oral squamous cell carcinoma in non-smoking and non-drinking patients**. *Head Neck Oncol* (2010) **2** 24. DOI: 10.1186/1758-3284-2-24
4. Koo K, Barrowman R, McCullough M, Iseli T, Wiesenfeld D. **Non-smoking non-drinking elderly females: a clinically distinct subgroup of oral squamous cell carcinoma patients**. *Int J Oral Maxillofac Surg* (2013) **42** 929-933. DOI: 10.1016/j.ijom.2013.04.010
5. Dahlstrom KR, Little JA, Zafereo ME, Lung M, Wei Q, Sturgis EM. **Squamous cell carcinoma of the head and neck in never smoker-never drinkers: A descriptive epidemiologic study**. *Head Neck* (2008) **30** 75-84. DOI: 10.1002/hed.20664
6. Farshadpour F, Hordijk GJ, Koole R, Slootweg PJ. **Non-smoking and non-drinking patients with head and neck squamous cell carcinoma: a distinct population**. *Oral Dis* (2007) **13** 239-243. DOI: 10.1111/j.1601-0825.2006.01274.x
7. Sikorska-Jaroszyńska MHJ, Mielnik - Błaszczak M, Krawczyk D, Nasiłowska-Barud A, Błaszczak J. **Passive smoking as an environmental health risk factor**. *Ann Agric Environ Med* (2012) **19** 547-550. PMID: 23020054
8. Leon ME, Peruga A, McNeill A. **European Code against Cancer, 4th Edition: Tobacco and cancer**. *Cancer Epidemiol* (2015) **39** S20-S33. DOI: 10.1016/j.canep.2015.06.001
9. Ni X, Xu N, Wang Q. **Meta-Analysis and Systematic Review in Environmental Tobacco Smoke Risk of Female Lung Cancer by Research Type**. *Int J Environ Res Public Health* (2018) **15** 1348. DOI: 10.3390/ijerph15071348
10. Luo J, Margolis KL, Wactawski-Wende J. **Association of active and passive smoking with risk of breast cancer among postmenopausal women: a prospective cohort study**. *BMJ* (2011) **342** d1016. DOI: 10.1136/bmj.d1016
11. DiGiacomo SI, Jazayeri MA, Barua RS, Ambrose JA. **Environmental Tobacco Smoke and Cardiovascular Disease**. *Int J Environ Res Public Health* (2018) **16** 96. DOI: 10.3390/ijerph16010096
12. Wang Y, Ji J, Liu YJ, Deng X, He QQ. **Passive Smoking and Risk of Type 2 Diabetes: A MetaAnalysis of Prospective Cohort Studies**. *PLoS One* (2013) **8** e69915. DOI: 10.1371/journal.pone.0069915
13. He B, Chen F, Yan L. **Independent and joint exposure to passive smoking and cooking oil fumes on oral cancer in Chinese women: a hospital-based case-control study**. *Acta Otolaryngol* (2016) **136** 1074-1078. DOI: 10.1080/00016489.2016.1185539
14. Zhang ZF, Morgenstern H, Spitz MR. **Environmental tobacco smoking, mutagen sensitivity, and head and neck squamous cell carcinoma**. *Cancer Epidemiol Biomarkers Prev* (2000) **9** 1043-1049. PMID: 11045786
15. Idris S, Baqays A, Isaac A, Chau JKM, Calhoun KH, Seikaly H. **The effect of second hand smoke in patients with squamous cell carcinoma of the head and neck**. *J Otolaryngol Head Neck Surg* (2019) **48** 33. DOI: 10.1186/s40463-019-0357-4
16. Kolb S, Brückner U, Nowak D, Radon K. **Quantification of ETS exposure in hospitality workers who have never smoked**. *Environ Health* (2010) **9** 49. DOI: 10.1186/1476-069X-9-49
17. Riboli E, Haley NJ, Trédaniel J, Saracci R, Preston-Martin S, Trichopoulos D. **Misclassification of smoking status among women in relation to exposure to environmental tobacco smoke**. *Eur Respir J* (1995) **8** 285-290. DOI: 10.1183/09031936.95.08020285
18. Braverman Bronstein A, Lomelín Gascón J, Eugenio González CI, Barrientos-Gutierrez T. **Environmental Tobacco Exposure and Urinary Cotinine Levels in Smoking and Nonsmoking Adolescents**. *Nicotine Tob Res* (2018) **20** 523-526. DOI: 10.1093/ntr/ntx124
19. Moyses RA, López RVM, Cury PM. **Significant differences in demographic, clinical, and pathological features in relation to smoking and alcohol consumption among 1,633 head and neck cancer patients**. *Clinics (Sao Paulo)* (2013) **68** 738-744. DOI: 10.6061/clinics/2013(06)03
20. Petti S. **Lifestyle risk factors for oral cancer**. *Oral Oncol* (2009) **45** 340-350. DOI: 10.1016/j.oraloncology.2008.05.018
21. Durr ML, Li D, Wang SJ. **Oral cavity squamous cell carcinoma in never smokers: analysis of clinicopathologic characteristics and survival**. *Am J Otolaryngol* (2013) **34** 388-393. DOI: 10.1016/j.amjoto.2013.01.017
22. Bradley G, Magalhaes MA, Hyrcza M. **Mutational signatures in oral cancer indicate a complex role for tobacco smoke carcinogens**. *Oral Dis* (2018) **24** 682-684. DOI: 10.1111/odi.12665
23. Samet JM, Avila-Tang E, Boffetta P. **Lung Cancer in Never Smokers: Clinical Epidemiology and Environmental Risk Factors**. *Clin Cancer Res* (2009) **15** 5626-5645. DOI: 10.1158/1078-0432.CCR-09-0376
24. Kligerman S, White C. **Epidemiology of Lung Cancer in Women: Risk Factors, Survival, and Screening**. *AJR Am J Roentgenol* (2011) **196** 287-295. DOI: 10.2214/AJR.10.5412
25. Phillips DH. **Smoking-related DNA and protein adducts in human tissues**. *Carcinogenesis* (2002) **23** 1979-2004. DOI: 10.1093/carcin/23.12.1979
26. Scully C, Bagan J. **Oral squamous cell carcinoma overview**. *Oral Oncol* (2009) **45** 301-308. DOI: 10.1016/j.oraloncology.2009.01.004
27. Prasetyaningtyas N, Jatiatmaja NA, Radithia D. **The Response of the Tongue Epithelial on Cigarette Smoke Exposure as a Risk Factor for Oral Cancer Development**. *Eur J Dent* (2021) **15** 320-324. DOI: 10.1055/s-0040-1721312
28. Wolfer S, Kunzler A, Foos T, Ernst C, Leha A, Schultze-Mosgau S. **Gender and risk-taking behaviors influence the clinical presentation of oral squamous cell carcinoma**. *Clin Exp Dent Res* (2022) **8** 141-151. DOI: 10.1002/cre2.523
29. Kadam S, Vandana M, Patwardhan S, Kaushik KS. **Looking beyond the smokescreen: can the oral microbiome be a tool or target in the management of tobacco-associated oral cancer?**. *Ecancermedicalscience* (2021) **15** 1179. DOI: 10.3332/ecancer.2021.1179
30. Kushwaha SS, Multani RK, Kushwaha NS. **Saliva as a Potential Diagnostic Tool to Evaluate Relationship between Oral Microbiome and Potentially Malignant Disorders for Prevention of Malignant Transformation**. *Asian Pac J Cancer Prev* (2021) **22** 125-129. DOI: 10.31557/APJCP.2021.22.1.125
31. Chattopadhyay I, Verma M, Panda M. **Role of Oral Microbiome Signatures in Diagnosis and Prognosis of Oral Cancer**. *Technol Cancer Res Treat* (2019) **18** 1533033819867354. DOI: 10.1177/1533033819867354
32. Nilsson R. **Environmental Tobacco Smoke Revisited: The Reliability of the Data Used for Risk Assessment**. *Risk Anal* (2001) **21** 737-760. DOI: 10.1111/0272-4332.214147
|
---
title: Applying Human-Centered Design in Global Mental Health to Improve Reach Among
Underserved Populations in the United States and India
authors:
- Candace J. Black
- Jenna M. Berent
- Udita Joshi
- Azaz Khan
- Lila Chamlagai
- Ritu Shrivastava
- Bhuwan Gautam
- Abdikadir Negeye
- Abdi Nor Iftin
- Halimo Ali
- Alethea Desrosiers
- Anant Bhan
- Sunand Bhattacharya
- John A. Naslund
- Theresa S. Betancourt
journal: 'Global Health: Science and Practice'
year: 2023
pmcid: PMC9972370
doi: 10.9745/GHSP-D-22-00312
license: CC BY 4.0
---
# Applying Human-Centered Design in Global Mental Health to Improve Reach Among Underserved Populations in the United States and India
## Abstract
We demonstrate how 2 global mental health research programs engaged end users to design tailored, culturally informed digital tools used to support the delivery of evidence-based interventions.
### Key Findings
Engaging end users in the development of mental health intervention delivery tools ensured that the design features reflected the perspectives and needs of those who would use them. Technology may increase the reach of mental health interventions and offers several potential benefits, including supporting capacity-building in low-resource settings; improving technical literacy in underserved communities; and achieving difficult scale-up processes.
### Key Implications
When developing mHealth tools with underserved populations or those with lower technical literacy, researchers and practitioners should build supportive mechanisms within the development process to help users engage in the design and evaluation of each product’s features. Policymakers, national stakeholders, researchers, and practitioners can potentially reduce mental health treatment gaps by recruiting and training lay health workers using task-sharing models.
## ABSTRACT
### Introduction:
Human-centered design (HCD) refers to a diverse suite of interactive processes that engage end users in the development of a desired outcome. We showcase how 2 global mental health research teams applied HCD to develop mobile health tools, each directed at reducing treatment gaps in underserved populations.
### Case Study 1:
Refugees face higher risks for mental health problems, yet these communities face structural and cultural barriers that reduce access to and use of services. To address these challenges, the Research Program on Children and Adversity at the Boston College School of Social Work, in partnership with resettled refugee communities in the northeastern United States, used codesign methodology to digitally adapt delivery of the Family Strengthening Intervention for Refugees—a program designed to improve mental health and family functioning among resettled families. We describe how codesign methods support the development of more feasible, acceptable, and sustainable interventions.
### Case Study 2:
Sangath, an NGO in India focused on mental health services research, in partnership with Harvard Medical School, designed and evaluated a digital training program for community health workers to deliver an evidence-based, brief psychological treatment for depression as part of primary care in Madhya Pradesh, India. We describe how HCD was applied to program development and discuss our approach to scaling up training and capacity-building to deliver evidence-based treatment for depression in primary care.
### Implications:
HCD involves a variety of techniques that can be flexibly adapted to engage end users in the conceptualization, implementation, scale-up, and sustainment of global mental health interventions. Community solutions generated using HCD offer important benefits for key stakeholders. We encourage widespread adoption of HCD within global mental health policy, research, and practice, especially for addressing mental health disparities with underserved populations.
## INTRODUCTION
Global mental health is a relatively new field directed at achieving mental health equity worldwide.1 Driven by the perspective that mental health is a fundamental human right,2 global mental health research has sought to reduce the mental health treatment gap—a measure of unmet need in mental health—especially in low- and middle-income countries where it is most pervasive.
In this article, we describe how 2 global mental health research teams have implemented research that targets the care gap—a similar but more holistic concept than the treatment gap that acknowledges the psychosocial and physical health care needs of people with mental health problems. We first introduce human-centered design (HCD) methodology and describe how this particular approach dovetails with the mission and values of global mental health research. We then provide 2 case studies to illustrate how HCD has been applied to enhance the implementation and delivery of evidence-based mental health interventions in the United States and India. The benefits and challenges, lessons learned, and implications for research and practice are discussed.
Global mental health arose from the confluence of several transformational shifts in the social and biomedical sciences centered around self-determination, human dignity, and human rights.3 For example, the etiology of mental health disorders evolved from a purely biomedical perspective to one that encompassed social, environmental, and institutional contexts, resulting in a fundamental shift toward more humanistic and holistic mental health interventions. These movements deconstructed the standard model of mental health care, which relied on institutionalization and in which the majority of service provision was conducted by psychiatrists. Instead, the new paradigm incorporated rehabilitation and community care, expanded the range of acceptable qualifications to engage in mental health treatment, and empowered a new generation of mental health stakeholders to become nonspecialist providers, including community health workers (CHWs), educators, caregivers, and the people affected by mental disorders themselves.
The field of global mental health made its debut with a 2007 Lancet Global Mental Health Group call to action to address global mental health disparities by scaling up mental health care, especially in low- and middle-income countries.4 Several global initiatives emerged to support significant developments in nonspecialist workforce development, establish standards of care, collect data for tracking progress, and strengthen governance. However, these investments lacked impact on a global scale. Ten years after the initial call to action, the Lancet Commission on Global Mental Health and Sustainable Development was established to align global mental health with sustainable development, thereby leveraging the ambitious vision and widespread momentum of the United Nations Sustainable Development Goals to improve mental health and reduce the overall disease burden worldwide.2 The Commission identified 4 features of global mental health interventions that should be prioritized for scale-up efforts (Box).
## HCD IN GLOBAL MENTAL HEALTH RESEARCH PROGRAMS
We selected 2 global mental health research programs to illustrate how the priorities recommended by the Lancet Commission (Box) are implemented in real-world settings. Both case studies describe efforts to digitally adapt components of evidence-based mental health interventions or their implementation. Both interventions were developed with strong input from the communities in which they have been implemented, and both of them are delivered by nonspecialist CHWs. Importantly, both teams engaged end users in the digital adaptations by using principles of HCD—a powerful and versatile framework for problem-solving and innovation that can be used to address mental health disparities and inequitable access to essential mental health services.5 Although HCD approaches vary, core features involve engaging relevant stakeholders and centering their needs in the design process.6 HCD has long been recognized for its potential to improve user satisfaction of digital products and services. More recent applications in mental health interventions have shown promise for enhancing effectiveness, efficiency, accessibility, and sustainability of evidence-based treatments.7 These positive outcomes are facilitated by HCD’s application of systems thinking, which recognizes interrelations among individuals, groups, and societies. This holistic approach restructures common power dynamics toward equity, engendering trust and collaboration and supporting stakeholder ownership of solutions.8 The first case study describes the development of a mobile app for resettled refugee families involved in a mental health intervention being tested in the United States. We illustrate how design thinking can be applied at early stages of app development and describe the process of codesigning digital imagery for the app. We also highlight challenges faced as the early stages of codesign occurred during the COVID pandemic. The second case study describes the development, implementation, and evaluation of a digital program for training CHWs to deliver an evidence-based, brief psychological treatment for depression in routine primary care settings in Madhya Pradesh, India. This case study is interesting as an advanced stage demonstration of how HCD was applied in each stage of development, adaptation, and testing; formal evaluation; and implementation and scale-up.
## Background
Refugee families often experience traumatic stress from persecution, war, and displacement, followed by resettlement and acculturation stress.9 Elevated risk for developing mental health problems is compounded by cultural and systemic barriers to accessing mental health services.
The Somali Bantu, an ethnic minority originating in sub-Saharan Africa, faced enslavement and marginalization beginning in the 19th century. Civil war erupted in Somalia in 1991, resulting in forced migration to refugee camps in Kenya, where they were deprived of education, jobs, and statehood. In 2004, more than 13,000 Somali Bantu were resettled in the United States.10 In the 1990s, more than 100,000 Lhotshampas Bhutanese were evicted from Bhutan following ethnic cleansing and forced to resettle in refugee camps in Nepal characterized by challenging living conditions and myriad diseases. Starting in 2007, more than 110,000 Bhutanese refugees began resettling in the United States.11 The World Health Organization Mental Health Gap Action Programme recommends brief psychological interventions as a first-line treatment for depression.15 However, in low- and- middle-income countries like India, delivery of brief psychological interventions is remarkably challenging because of a scarcity of specialist providers to deliver these treatments, supervise care, or train additional therapists.16,17 These concerns are further exacerbated in rural areas, where upwards of $90\%$ of individuals living with depression and other common mental disorders do not receive adequate care.18 Consequently, mental health capacity-building is a priority in India and for extending global mental health efforts more broadly.19 Approaches like task-sharing support capacity-building of health workers20,21 who can effectively deliver a brief psychological intervention. Thus, task-sharing holds potential to address the care gap for depression22,23; however, a key bottleneck to effective implementation is the need to train these health workers to deliver these psychological treatments and to ensure that this workforce achieves the necessary clinical competencies to deliver high-quality depression care.24,25
Traditional health worker training methods, like in-person training, are most common both in India and globally. However, the need for expert trainers, access to training facilities, and frequently required extensive travel for participants pose financial and logistical barriers to scalability.16 Increasing access to and use of digital technologies among health workers may offer opportunities to leverage these technologies to expand capacity remotely.26 For instance, digital technology can enhance in-person training programs for health workers in low-resource settings by offering remote support, allowing asynchronous access to training materials and content, and enabling learning tracking and monitoring progress.26,27 To build on this promise, this case study involved the systematic and iterative development of a digital training program to scale up efforts to build capacity of CHWs to deliver depression care in rural India.28
## Program Development and Adaptation
The Research Program on Children and Adversity (RPCA), at the Boston College School of Social Work, used a community-based participatory research (CBPR) approach to adapt a mental health intervention for locally resettled Somali Bantu and Lhotshampas Bhutanese refugees in the northeastern United States. The intervention was based on the Family Strengthening Intervention (FSI), which was adapted from the Family-Based Preventive Intervention, and that promotes healthy parenting and mental health in children of HIV/AIDS-affected caregivers in Rwanda.12 RPCA researchers cultivated relationships with resettled refugees and collaborated with them on CPBR activities to adapt FSI content to address acculturative stress, leverage community and family resilience, and reduce mental health disparities. The resulting Family Strengthening Intervention for Refugees (FSI-R) follows a home-visiting model, using trained lay CHWs to provide families with psychoeducation, coaching, and skill-building tools through 10 weekly modules. Randomized pilot testing demonstrated feasibility and acceptability and showed promise for reducing traumatic stress and depressive symptoms, as well as conduct problems among youth.13 CBPR emphasizes equity among community members, researchers, and stakeholders,14 identifying community members as integral team members throughout the research and implementation process. Somali Bantu and Bhutanese community members participated in needs assessments, served on community advisory boards, and were employed as research assistants, CHWs, and supervisors. FSI-R development incorporated community feedback about community-based protective resources such as support from mutual assistance organizations, cultural and religious organizations, and culturally relevant resources to emphasize the unique needs, strengths, and challenges of resettled communities.9 FSI-R delivery used paper-based manuals. However, exit interviews with pilot CHWs revealed that families and CHWs could benefit from digitized FSI-R materials to support navigation while also increasing engagement and learning through graphics, culturally relevant imagery, embedded videos, and hyperlinks to additional resources. Boston College supported a multidisciplinary team of designers, student programmers, public health researchers, and industry professionals to guide the HCD and adaptation process of the FSI-R manual into 2 digital FSI-R apps: a CHW-facing version supporting training and delivery and a family-facing version supporting engagement and learning.
Design thinking, including problem analysis, consultations with staff and community advisory boards, and user interface/user experience (UI/UX) testing supported app development. The UI/UX testing incorporated a multimethod approach to better understanding the usability, functionality, and features of the app, including: “think aloud” methodology, whereby the participant is given a set of instructional actions to perform in the app and speaks their thought processes aloud while performing them; video recording of the participant’s movements and navigation throughout the app to better understand speed and ease of use; and an open-ended semistructured interview to elicit additional feedback and user thoughts. All data, including audio and visual recordings, were coded and analyzed for themes. The findings revealed navigational pain points, features that were not well-liked, and symbols or icons that did not present clearly. For example, participants identified difficulty in discerning which icons or images could be clicked on to yield additional information, which resulted in the addition of design features to distinguish interactive icons. The process also highlighted features and functions that were particularly helpful, clear, and well received. For instance, participants revealed that they benefited from the contents on the home screen and that they were able to navigate efficiently throughout the app with the use of arrows.
In addition to strengthening UI/UX features of the app, we also applied HCD to incorporate the cultural beliefs of participating communities using visually recognizable elements. We felt that the codesign process was important for this technology-driven intervention for several reasons. First, codesign aligns with RPCA’s CBPR approach and emphasis on intervening “for refugees, by refugees.” Codesign encourages a sense of ownership in the creation of solutions. Second, codesign brings users closer to the visual interface and its functioning by graphically incorporating culturally familiar features that would further support learning about FSI-R concepts. Lastly, codesign can help to reduce “tech anxiety,” which is especially relevant when users lack experience with digital tools.
The development of the digital training program began with a core team of researchers who had previously supported the development and evaluation of the Healthy Activity Program (HAP), an evidence-based, brief psychological treatment for depression based on behavioral activation and adapted for use in Goa, India.29 The study employed HCD and involved collaboration with accredited social health activists (ASHAs), a cadre of trained CHWs who work with the National Health Mission to deliver essential routine primary care services to rural areas and promote community utilization of the existing health services.30,31 With ASHAs as the target audience of our training efforts, it was critical to ensure that these health workers were engaged throughout the development and pilot testing of the digital training program, practices that are integral to HCD. We used an iterative design approach to develop the digital training program, initiated first through formative research activities with ASHAs in the form of focus group discussions and design workshops to better understand their perspectives and feedback on the training content and potential for using digital technology in its delivery.20 Our work was also guided by prior digital mental health initiatives,32 and informed by the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) framework33 with a focus on learners’ engagement as well as cultural and contextual adaptation.34 Our systematic approach, illustrated in Figure 2, involved 5 key steps.
**FIGURE 2:** *Iterative Process of Codesign Consultation for Development of the Training Program For a Brief Psychological Intervention for Depression in Rural IndiaAbbreviations: ADDIE, Analysis, Design, Development, Implementation, and Evaluation; ASHA, accredited social health activist; HAP, Healthy Activity Program.*
1. Create blueprint of training program. We conducted a careful review of the original HAP treatment manuals so that the outline of the digital training content would retain fidelity to the core components of the manuals. This involved multiple rounds of expert review with psychologists and counselors with experience delivering psychological treatments and training health workers in settings in India.
2. Develop training program content. Prior formative research activities in which ASHAs expressed their preference for video-based content and use of visuals to effectively reflect realistic scenarios guided us in the development of digital content and representation of the local culture and context.20 For content development, we engaged 4–5 ASHAs (age range: 18–40 years) in a brainstorming session to discuss the character and stories for role-play, colloquial language, and terminologies. Based on the discussions, HAP content was adapted and converted into scripts, which included local montages, translation into local language, and simplification of complex terms. The content also included scripted role-plays of ASHAs delivering HAP to patients in health care and community settings. The same group of ASHAs reviewed the translation of the materials and ensured that the content was relevant for use in the target setting. Throughout this process of digital training content development and script creation, counselors with expertise in the delivery of HAP reviewed the content to ensure ongoing fidelity to the evidence-based HAP intervention.
3. Digitize training program content. Based on the ASHAs’ preference for video-based content, we worked with professional video production companies to develop short films that included lectures by experienced counselors on the key skills for delivery of HAP and role-play scenarios illustrating application of the skills. The video-based content was supplemented with PowerPoint-based lecture videos and reading materials, which were simpler for our team to develop and also easier to modify as needed, as well as culturally relevant graphics. This process involved multiple rounds of editing and review from our team to ensure the quality of the digital content and fidelity to the original evidence-based HAP program. The ASHAs involved in Step 2 iteratively tested the digital content. Their feedback and comments primarily focused on the cultural adaptation of the content by including local images, places, and culturally relevant phrases.20 4. Develop a learning management system (LMS) and upload content. We worked closely and simultaneously with ASHAs while developing the digital content with an IT agency to develop, test, and finalize the digital LMS platform that housed the training program. The LMS app was chosen based on consultation with ASHA workers in the formative phase of the study, its compatibility with most smartphones (i.e., Android operating system, which is the platform ASHAs are most comfortable with) available in the target setting in rural India, and its ability to function in the event of poor bandwidth and low connectivity.20 The LMS consisted of an Android mobile app for easy access to the digital training with offline capabilities to allow access in settings with low bandwidth. The mobile app was also customized using graphics, a user-friendly interface, and text translated into Hindi (Figure 3). We tested the feasibility of the digital training app with a group of volunteers (aged 18–40 years) to address any potential challenges with accessing the training content or technical concerns that may impede navigation within the mobile app. This step allowed for further modification and improvement to the mobile app interface. The key findings from user testing were: [1] the LMS should be simple with basic features and not have complicated, multiple options to navigate the platform; and [2] the interface should have offline accessibility to learn the content to avoid buffering of videos and slow performance due to poor Internet connectivity.
**FIGURE 3:** *Sample Graphics and Screenshots of the Digital Training Program For a Brief Psychological Intervention for Depression in Rural India*
5. Pilot test the digital training program. Next, we pilot-tested the digital training program with 33 ASHAs (mean age 34.1, standard deviation=6.75) who had an average of 6.98 years in their current role (standard deviation=4.01) and education level of eighth standard and above.35 This step involved user testing the digital training content on the Sangath learning app with ASHAs from primary care facilities in Sehore district, Madhya Pradesh, to replicate a real-world training environment. Smartphones were provided to ASHAs with the downloaded Sangath app, including a SIM card with enough *Internet data* to access the course and complete the course activities. In alignment with HCD practices to involve end users in the design process, we prioritized ongoing engagement with ASHAs, including in field testing. Through this engagement, we discovered the need to address digital literacy concerns among participants, given that many ASHAs in our user group had no prior experience using a smartphone or completing online training. Therefore, in response to the direct needs of our target population group, we developed a brief digital orientation session to support participants and their use of the smartphone, including instruction in the basic operating features of the device, navigation of the app, and completion of various course activities. These participant insights, which were critical in informing our approach, were also captured through focus group discussions with participants as part of a pilot study.35
## Ethical Approval
Community members were recruited into community co-design teams (CCDTs) to enhance cultural relevance of graphic design features in the family-facing app. Ethics approval was obtained from the Boston College Institutional Review Board. All participants gave their informed consent before joining CCDTs.
All study procedures for this study were approved by institutional review boards at Sangath and Harvard Medical School. Additional Health Ministry Screening Committee approval was obtained from its secretariat at the Indian Council of Medical Research, Government of India.
## Implementation
FSI-R staff recruited 8 adults and youth from each resettled community via a description posted to social media and shared via word-of-mouth. Eligibility criteria included identifying as part of a resettled Bhutanese or Somali Bantu community and aged older than 13 years. The Somali Bantu CCDT was composed of up to 3 male and female adults and up to 4 adolescent girls, while the Bhutanese CCDT was composed of up to 2 male adults and up to 5 adolescent boys and girls. Participants were compensated with US$15 gift cards per session and given a certificate of appreciation upon completion. A community volunteer facilitated each session. RPCA staff included the program manager, a postdoctoral researcher, and a paid student intern.
Due to the COVID pandemic, sessions were conducted virtually using an interactive Google Slides deck and Zoom videoconferencing software. Participants joined by computer or mobile phone and were encouraged to join by video to increase participation. Sessions were 1.5 hours weekly over 10 weeks. RPCA staff led an orientation to introduce the goals of codesign, materials and technology, ground rules, and practice examples. Participant contributions were integrated directly into the shared Google Slides deck for immediate viewing and live interaction. Following each session, a graphic designer used Procreate to create a composite that reflected sample images, text descriptions, and CCDT feedback.
The codesign protocol included the following 6 iterative steps, adapted for remote application. Over a Zoom video conference call, the facilitator presented a Google slide with a written description of an FSI-R concept (e.g., “family strengths”). Any clarifying questions were answered. Facilitators indicated the next several minutes (e.g., 2–5 minutes) would be allotted for CCDT members to independently brainstorm how the FSI-R concept could be best represented visually. CCDT members were asked to choose imagery that would be widely understood by families participating in the FSI-R by thinking about their own experiences, cultural history, and/or values and how the concept might look for a family in their community. On the shared Google slide deck, participants added their thoughts from the brainstorming session directly to an Ideas slide using drawings, Internet images, personal photos, or verbal descriptions. Where bandwidth issues prevented direct interaction with Google slides, facilitators documented ideas following verbal guidance from CCDT members. Once each participant had added their idea(s), facilitators asked each participant to verbally share their rationale for choosing an image. This discussion also allowed facilitators to probe for additional details as needed to ensure key features are represented visually. Where possible, additional images were retrieved and detailed notes were recorded on comments about the concept. Each idea for a visual representation was then placed on virtual notecards on a new Google slide so that all images could be viewed at once. Several star-shaped indicators placed on the slide were selected by participants and moved next to their preferred image. Where bandwidth issues prevented direct interaction, facilitators assisted participants by moving the star indicator next to their preferred image expressed verbally. Participants were asked to consider perspectives of community members characterized by diverse genders, ages, and experiences when choosing an image. The image with the most votes was selected for further discussion and refinements. Occasionally, more than 1 image was highly voted and, where possible, multiple images were retained for further discussion and refinements. Facilitators asked community members what they liked and disliked about the image and whether they would change anything about the image. Detailed notes documented these discussions. The notes and images collected during CCDT meetings were provided to a graphic designer who created an illustrated prototype. Facilitators also provided additional context to guide graphic designers where needed. As these exchanges were also remote due to the COVID pandemic, materials were provided as shared files where facilitators and graphic designers could edit documents in real time. In addition to email, phone, and virtual conference contact with graphic designers, facilitators also provided guidance via comments applied directly in the shared file. The completed prototype was presented to the team during a subsequent meeting or by email, and members were asked for feedback on the prototype. Any additional refinements suggested by the team were provided to the graphic designer, who made adjustments for final use of the image.
This process was repeated for selected main concepts from the 10 FSI-R modules. Figure 1 displays the alignment of the codesign protocol with the 3 major phases of HCD. The majority of the work completed by following this protocol was directed at the ideation phase.
**FIGURE 1:** *Codesign Protocol Alignment With Inspiration, Ideation, and Implementation Phases of Human-Centered Design*
Our pilot study demonstrated the feasibility, acceptability, and preliminary effectiveness of digital technology for training CHWs to acquire the skills and competencies needed to deliver HAP.35 We also evaluated the effectiveness and cost-effectiveness of the digital training in a randomized controlled noninferiority trial in Madhya Pradesh, India.36 In the trial, we randomized 340 CHWs from Sehore district to receive either conventional face-to-face training or 2 forms of digital training, which included digital training alone or digital training enhanced with remote coaching support.37 Based on our observations from the pilot study, we included the remote coaching support to promote participant engagement and improve successful completion of the training program. The primary outcome of the trial was the change in health workers’ competency to deliver depression care, measured through a validated multiple-choice exam-style competency assessment tool developed as part of the project38 and adapted to the local context and language through further engagement of our target group of ASHAs.39 In addition, throughout the study, we also collected process indicators, such as the proportion of health workers who successfully completed the training, data metrics related to course progression, digital literacy barriers to navigate the digital content, and any technical challenges that affected the learners’ experience. We also recognized that these training sessions may result in increased workload of health workers. Hence, to explore this potential impact, we collected measures of health worker well-being, such as stress, burnout, and job satisfaction, along with their knowledge, attitudes, and behaviors toward mental health. Since the completion of the trial, we have achieved more than $90\%$ training completion rates among participating health workers, and preliminary analyses have demonstrated that the digital training appeared to be equivalent to conventional in-person face-to-face training when enhanced with remote coaching support.
## Strengths
Design features shaped by early-stage testing. Early UI/UX testing ensured that the app’s design features aligned with perceptions and expectations of end users. For instance, Think Aloud testing revealed how these features were interpreted while navigating the app, which helped identify both strengths and areas for improvement. Conducting UI/UX testing at the earliest stages of app development ensured that all subsequent iterations of the app reflected this feedback.
High-quality, culturally appropriate imagery developed through codesign. Our CCDTs were composed of participants recruited from within each resettled refugee community involved in the FSI-R. By conducting codesign with these community members, we were able to develop high-quality images that reflected the cultural values and history of each community. These images will be embedded in the family-facing version of the mobile app, accompanying data-light text descriptions of key ideas covered in each module of the intervention. For instance, the family narrative is a key component of the FSI-R, which asks participants to develop a timeline and narrative of their family’s experiences. Codesign supported development of culturally relevant icons to accompany an example timeline with events commonly experienced by resettled families. Additionally, codesign enabled the team to develop visual imagery to accompany more abstract ideas featured in many of the FSI-R modules, such as “family challenges” and “family strengths.” Representation of diverse perspectives and promotion of equity. Engaging communities in the development of interventions, including tools and auxiliary materials, empowers community agency and autonomy. HCD creates a shared learning environment beneficial for researchers and community members. Our CCDTs included diverse ages and genders, and, in the case of 1 community, diverse caste members. With multiple perspectives, discussions explicitly assessed how images might be made more gender-sensitive and inclusive.
Community buy-in and mutual learning. Rooting intervention research in CBPR and HCD methods is a bottom-up approach based on mutual learning between communities and researchers. CCDT discussions involved rich cultural histories, practices, and values, as well as lived experiences of team members, enabling the research team to challenge their own biases and produce a more meaningful, better-quality product. The community also benefits from resources and connections with educational institutions, empowered youth, and increased social capital (B. Gautam, oral personal communication, June 2022).
Improved feedback. Each iterative feedback loop of our codesign method offered opportunities for input, discussion of concepts, clarification of ideas, retrieval of diverse source material, anonymous voting for images, and opportunities to tailor images through multiple feedback loops. Emphasizing inclusion and openness to member ideas meant that final illustrations accurately represented cultural perspectives.
## Challenges
Time constraints. CCDT discussions generated multiple culturally rich suggestions for imagery and often delved into the histories, beliefs, and values of each community. Occasionally, our efforts to gather information so that all voices were heard posed challenges given the allotted time available for the project. Ultimately, we added sessions to achieve adequate coverage of the topics. Including strategic planning to solidify the scope of work, allotting time in the schedule accordingly, and using a timer could facilitate more timely progress.
Technological limitations. CCDT members brought varying levels of experience with technology, and some were learning these technologies for the first time, reflecting the low digital literacy and digital divide experienced by some members of marginalized communities. The orientation session was essential for training CCDTs on the use of these technologies, and ongoing technical support was provided, as needed. Furthermore, Internet connectivity was occasionally a challenge. When necessary, members adapted by turning off video or joining by phone. Moreover, recruiting and conducting the sessions by digital means essentially excluded certain demographics, such as older generations or individuals without WiFi access. Although we asked participants explicitly to consider diverse perspectives when generating imagery ideas as part of the protocol, this approach likely did not fully capture the depth and breadth of alternative views. In asking participants how we might engage more community members, we received feedback suggesting that in-person sessions would likely alleviate technological challenges, increase diversity further, and improve participation.
Recruitment and retention. Communities varied in their availability and willingness to participate. For one community, we lacked youth perspectives for several sessions, and some members stopped joining. We partially attribute attendance challenges to the digital format because, before the pandemic, in-person gatherings had greater and more consistent attendance.
## Scale-Up
Building on our application of HCD to support the development and evaluation of the digital training platform and as part of broader efforts to scale up interventions for depression, our initiative EMPOWER offers a feasible approach to rapidly scale up the delivery of psychological treatments by providing a sustainable platform to efficiently train CHWs to deliver quality-assured mental health care.40 Importantly, drawing on HCD principles, our development and pilot testing of the digital training program involved close collaboration and engagement of ASHAs, our target group of CHWs. Such efforts have ensured that we were able to incorporate their recommendations and perspectives regarding the training content, delivery format, and utility in their daily work routines—all key insights necessary for supporting implementation and scale-up of the training. A key consideration with the use of HCD practices in our context in rural *India is* that we engaged a group of ASHAs, who were all women, in the design of a training program. These health workers represent the backbone of the primary health care system, experience high work-related burden, and typically have few opportunities for decision-making in their work due to the combination of gender inequality and their location at the bottom of the health system hierarchy. This made our efforts to engage and work with this group of health workers especially important; it helped to amplify their voices and plan for eventual implementation.
Our first step toward launching the EMPOWER initiative was to extend this training to 43 CHWs of Jhagadia, a tribal block in selected primary health care settings of Gujarat, another state in India. The training was delivered in the Gujarati language to CHWs with the help of an online LMS nested within a TeCHO+ (Technology Enabled Community Health Operations) platform already in widespread use by the health system across the state. Preliminary results from the pilot study are promising and echo the prior results.35 Another component of this initiative involves training more than 1,000 CHWs in 3 districts of Madhya Pradesh, including Narmadapuram, Vidisha, and Raisen, who serve a population of approximately 1.4 million persons. This effort is currently being implemented in partnership with the state government’s health department.
## SUMMARY AND CONCLUSIONS
We presented 2 unique case studies demonstrating the use of HCD in global mental health research in India and the United States. The first case study described early-stage application of HCD, particularly using codesign methodology, to digitally adapt a supportive intervention for resettled refugees. The second case study characterized later-stage application of HCD, engaging end users in the design, testing, and scale-up of a digital training program for a brief psychological intervention for depression. Both case studies draw from projects that reflect the mission and values of the global mental health field and current research priorities identified by the Lancet Commission and other global initiatives, including using task-sharing as the foundation of mental health care delivery, adopting digital tools to facilitate intervention delivery, and implementing community-based interventions. These characteristics are especially beneficial when communities lack an adequate workforce to meet their mental health burden and may help reduce cultural barriers, such as stigma, for those who need treatment.
In line with guidance on best practices for HCD, both programs employed professional designers to develop the media used in each digital application and used participatory design approaches involving end users and iterative feedback loops.41 For both case studies, we reveal how HCD can be used to address implementation barriers that contribute to inequities, particularly with regard to digital literacy and equitable access. These strategies included providing devices, adding features for offline use, and, key to the success of both case studies, training for digital use.42 Furthermore, design thinking enabled both teams to ensure that their mobile application user interface was user-friendly by presenting material with imagery, animations, and short videos alongside data-light educational information and practice exercises.43 Each case study highlighted unique lessons learned. The first case study described how codesign supported community engagement in the development of graphics for a digital application for refugee families participating in the FSI-R in the United States. In particular, this approach improved community buy-in and satisfaction with the components of the interface developed using codesign methodology. We were also able to describe solutions to practical challenges that arose, such as building in support for helping participants acquire new technical skills and managing time constraints when conducting community-based research.
The second case described how HCD principles supported the development, testing, and scale-up of a digital training application for a brief psychological intervention for depression in rural India. Especially notable were its findings that CHWs completed digital training at exceptionally high rates (>$90\%$) and, importantly, that it was as effective as face-to-face training when paired with remote coaching support. These successful findings position the digital training program to be rapidly scaled to train over a thousand CHWs serving over a million people.
These case studies demonstrate the value of employing HCD in research with underserved populations. HCD clearly aligns with the goals of the global mental health field. We demonstrate here how this union between HCD and global mental health can improve user engagement, satisfaction, effectiveness, and scalability. Given these benefits, policymakers, funders, national stakeholders, and others involved in global mental health research should encourage or require participation of end users at all stages of program development, implementation, and evaluation.
## References
1. Holeman I, Kane D; I. H. **Human-centered design for global health equity**. *Inf Technol Dev.* (2020) **26** 477-505. DOI: 10.1080/02681102.2019.1667289
2. Patel V, Saxena S, Lund C. **The Lancet Commission on global mental health and sustainable development**. *Lancet.* (2018) **392** 1553-1598. DOI: 10.1016/S0140-6736(18)31612-X
3. Buchanan R. **Human dignity and human rights: Thoughts on the principles of human-centered design**. *Des Issues.* (2001) **17** 35-39. DOI: 10.1162/074793601750357178
4. Chisholm D, Flisher AJ, Lund C. **Scale up services for mental disorders: a call for action**. *Lancet.* (2007) **370** 1241-1252. DOI: 10.1016/S0140-6736(07)61242-2
5. 5.I.D.E.O.
Field Guide to Human-Centered Design.
IDEO; 2015.. (2015)
6. Baker FW, Moukhliss S. **Concretising design thinking: a content analysis of systematic and extended literature reviews on design thinking and human-centered design**. *Rev Educ.* (2020) **8** 305-333. DOI: 10.1002/rev3.3186
7. 7.ISO 9241-210:2019. Ergonomics of Human-System Interaction–Part 210: Human-Centred Design for Interactive Systems.
International Organization for Standardization; 2019. Accessed February 3, 2023. https://www.iso.org/standard/77520.html. (2019)
8. Wheelock A, Bechtel C, Leff B. **Human-centered design and trust in medicine**. *JAMA.* (2020) **324** 2369-2370. DOI: 10.1001/jama.2020.21080
9. Betancourt TS, Frounfelker R, Mishra T, Hussein A, Falzarano R. **Addressing health disparities in the mental health of refugee children and adolescents through community-based participatory research: a study in 2 communities**. *Am J Public Health.* (2015) **105**. DOI: 10.2105/AJPH.2014.302504
10. Besteman C. **Translating race across time and space: the creation of Somali Bantu ethnicity**. *Identities (Yverdon).* (2012) **19** 285-302. DOI: 10.1080/1070289X.2012.681862
11. Das Shrestha D. *Resettlement of Bhutanese refugees surpasses 100,000 mark.* (2015)
12. Betancourt TS, Ng LC, Kirk CM. **Family-based promotion of mental health in children affected by HIV: a pilot randomized controlled trial**. *J Child Psychol Psychiatry.* (2017) **58** 922-930. DOI: 10.1111/jcpp.12729
13. Betancourt TS, Berent JM, Freeman J. **Family-based mental health promotion for Somali Bantu and Bhutanese refugees: feasibility and acceptability trial**. *J Adolesc Health.* (2020) **66** 336-344. DOI: 10.1016/j.jadohealth.2019.08.023
14. Israel BA, Coombe CM, Cheezum RR. **Community-based participatory research: a capacity-building approach for policy advocacy aimed at eliminating health disparities**. *Am J Public Health.* (2010) **100** 2094-2102. DOI: 10.2105/AJPH.2009.170506
15. 15.World Health Organization (WHO). mhGAP Intervention Guide for Mental, Neurological and Substance Use Disorders in Non-Specialized Health Settings: Mental Health Gap Action Programme Version 2.0.
WHO; 2016. Accessed January 26, 2023. https://www.who.int/publications/i/item/9789241549790. (2016)
16. Fairburn CG, Patel V. **The global dissemination of psychological treatments: a road map for research and practice**. *Am J Psychiatry.* (2014) **171** 495-498. DOI: 10.1176/appi.ajp.2013.13111546
17. Kazdin AE. **Addressing the treatment gap: a key challenge for extending evidence-based psychosocial interventions**. *Behav Res Ther.* (2017) **88** 7-18. DOI: 10.1016/j.brat.2016.06.004
18. **The burden of mental disorders across the states of India: the Global Burden of Disease Study 1990–2017**. *Lancet Psychiatry.* (2020) **7** 148-161. DOI: 10.1016/S2215-0366(19)30475-4
19. Endale T, Qureshi O, Ryan GK. **Barriers and drivers to capacity-building in global mental health projects**. *Int J Ment Health Syst.* (2020) **14** 89. DOI: 10.1186/s13033-020-00420-4
20. Muke SS, Shrivastava RD, Mitchell L. **Acceptability and feasibility of digital technology for training community health workers to deliver brief psychological treatment for depression in rural India**. *Asian J Psychiatr.* (2019) **45** 99-106. DOI: 10.1016/j.ajp.2019.09.006
21. Raviola G, Naslund JA, Smith SL, Patel V. **Innovative models in mental health delivery systems: task sharing care with non-specialist providers to close the mental health treatment gap**. *Curr Psychiatry Rep.* (2019) **21** 44. DOI: 10.1007/s11920-019-1028-x
22. Hoeft TJ, Fortney JC, Patel V, Unützer J. **Task-sharing approaches to improve mental health care in rural and other low-resource settings: a systematic review**. *J Rural Health.* (2018) **34** 48-62. DOI: 10.1111/jrh.12229
23. Singla DR, Kohrt BA, Murray LK, Anand A, Chorpita BF, Patel V. **Psychological treatments for the world: lessons from low- and middle-income countries**. *Annu Rev Clin Psychol.* (2017) **13** 149-181. DOI: 10.1146/annurev-clinpsy-032816-045217
24. Barnett ML, Gonzalez A, Miranda J, Chavira DA, Lau AS. **Mobilizing community health workers to address mental health disparities for underserved populations: a systematic review**. *Adm Policy Ment Health.* (2018) **45** 195-211. DOI: 10.1007/s10488-017-0815-0
25. Padmanathan P, De Silva MJ. **The acceptability and feasibility of task-sharing for mental healthcare in low and middle income countries: a systematic review**. *Soc Sci Med.* (2013) **97** 82-86. DOI: 10.1016/j.socscimed.2013.08.004
26. Naslund JA, Shidhaye R, Patel V. **Digital technology for building capacity of non-specialist health workers for task-sharing and scaling up mental health care globally**. *Harv Rev Psychiatry.* (2019) **27** 181-192. DOI: 10.1097/HRP.0000000000000217
27. Rahman A, Akhtar P, Hamdani SU. **Using technology to scale-up training and supervision of community health workers in the psychosocial management of perinatal depression: a non-inferiority, randomized controlled trial**. *Glob Ment Health (Camb).* (2019) **6** e8. DOI: 10.1017/gmh.2019.7
28. Khan A, Shrivastava R, Tugnawat D. **Design and development of a digital program for training non-specialist health workers to deliver an evidence-based psychological treatment for depression in primary care in India**. *J Technol Behav Sci.* (2020) **5** 402-415. DOI: 10.1007/s41347-020-00154-7
29. Patel RM, Misra R, Raj S, Balasubramanyam A. **Effectiveness of a group-based culturally tailored lifestyle intervention program on changes in risk factors for Type 2 diabetes among Asian Indians in the United States**. *J Diabetes Res.* (2017) **2017** 1-13. DOI: 10.1155/2017/2751980
30. Scott K, George AS, Ved RR. **Taking stock of 10 years of published research on the ASHA programme: examining India’s national community health worker programme from a health systems perspective**. *Health Res Policy Syst.* (2019) **17** 29. DOI: 10.1186/s12961-019-0427-0
31. Mane Abhay B, Khandekar Sanjay V. **Strengthening primary health care through Asha workers: a novel approach in India**. *Prim Health Care.* (2014) **4** 2167-1079
32. Gonsalves PP, Hodgson ES, Kumar A. **Design and development of the “POD adventures” smartphone game: a blended problem-solving intervention for adolescent mental health in India**. *Front Public Health.* (2019) **7** 238. DOI: 10.3389/fpubh.2019.00238
33. Peterson C. **Bringing ADDIE to life: instructional design at its best**. *J Educ Multimed Hypermedia.* (2003) **12** 227-241
34. Yardley L, Morrison L, Bradbury K, Muller I. **The person-based approach to intervention development: application to digital health-related behavior change interventions**. *J Med Internet Res.* (2015) **17** e30. DOI: 10.2196/jmir.4055
35. Muke SS, Tugnawat D, Joshi U. **Digital training for non-specialist health workers to deliver a brief psychological treatment for depression in primary care in India: findings from a randomized pilot study**. *Int J Environ Res Public Health.* (2020) **17** 6368. DOI: 10.3390/ijerph17176368
36. Joshi U, Naslund JA, Anand A. **Assessing costs of developing a digital program for training community health workers to deliver treatment for depression: a case study in rural India**. *Psychiatry Res.* (2022) **307** 114299. DOI: 10.1016/j.psychres.2021.114299
37. Naslund JA, Tugnawat D, Anand A. **Digital training for non-specialist health workers to deliver a brief psychological treatment for depression in India: protocol for a three-arm randomized controlled trial**. *Contemp Clin Trials.* (2021) **102** 106267. DOI: 10.1016/j.cct.2021.106267
38. Restivo JL, Mitchell L, Joshi U. **Assessing health worker competence to deliver a brief psychological treatment for depression: development and validation of a scalable measure**. *J Behav Cogn Ther.* (2020) **30** 253-266. DOI: 10.1016/j.jbct.2020.10.001
39. Joshi U, Haney JR, Sequeira M. **Adaptation and translation of a scalable measure for assessing health worker competence to deliver a brief psychological intervention: a case study from central India of its comparison to a performance-based measure**. *Psychiatr Q.* (2022). DOI: 10.1007/s11126-022-10007-6
40. Patel V, Naslund JA, Wood S. **EMPOWER: toward the global dissemination of psychosocial interventions**. *Focus (Am Psychiatr Publ).* (2022) **20** 301-306. DOI: 10.1176/appi.focus.20220042
41. Vial S, Boudhraâ S, Dumont M. **Human-centered design approaches in digital mental health interventions: exploratory mapping review**. *JMIR Ment Health.* (2022) **9** e35591. DOI: 10.2196/35591
42. Stiles-Shields C, Cummings C, Montague E, Plevinsky JM, Psihogios AM, Williams KDA. **A call to action: using and extending human-centered design methodologies to improve mental and behavioral health equity**. *Front Digit Health.* (2022) **4** 848052. DOI: 10.3389/fdgth.2022.848052
43. Kumar M, Macharia P, Nyongesa V. **Human-centered design exploration with Kenyan health workers on proposed digital mental health screening and intervention training development: thematic analysis of user preferences and needs**. *Digit Health.* (2022) 8. DOI: 10.1177/20552076221090035
|
---
title: 'Protocol for the INFORMED (Individualised Patient Care and Treatment for Maternal
Diabetes) Study: a randomised controlled trial embedded within routine care'
authors:
- Cassy F Dingena
- Anvesha Mahendra
- Melvin J Holmes
- Naomi S Clement
- Eleanor M Scott
- Michael A Zulyniak
journal: BMJ Open
year: 2023
pmcid: PMC9972421
doi: 10.1136/bmjopen-2022-065388
license: CC BY 4.0
---
# Protocol for the INFORMED (Individualised Patient Care and Treatment for Maternal Diabetes) Study: a randomised controlled trial embedded within routine care
## Abstract
### Introduction
Diabetes in pregnancy presents a unique physiological challenge to manage glycaemia while maintaining adequate nourishment for the growing fetus. Women with diabetes who become pregnant are at greater risk of adverse maternal and newborn outcomes, compared with women without diabetes. Evidence suggests that control of (postprandial) glycaemia is key to manage maternal and offspring health but it is not yet clear [1] how diet and lifestyle moderate these shifts across the full duration of pregnancy or [2] what aspects of maternal and offspring health are associated with dysglycaemia.
### Methods and analysis
To investigate these gaps, a cross-over randomised clinical trial has been embedded within routine clinical care. Seventy-six pregnant women in their first trimester with type 1 or type 2 diabetes (with or without medication) attending their routine antenatal appointments at National Health Service (NHS) Leeds Teaching Hospitals will be recruited. Following informed consent, data on women’s health, glycaemia, pregnancy and delivery will be shared by the NHS with researchers. At each visit in the first (10–12 weeks), second (18–20 weeks) and third (28–34 weeks) trimester, participants will be asked for consent to: [1] lifestyle and diet questionnaires, [2] blood for research purposes and [3] analysis of urine collected at clinical visits. Additionally, participants will be asked to consume two blinded meals in duplicate in second and third trimester. Glycaemia will be assessed by continuous glucose monitoring as part of routine care. The primary outcome is the effect of experimental meals (high vs low protein) on postprandial glycaemia. Secondary outcomes include [1] the association between dysglycaemia and maternal and newborn health, and [2] the association between maternal metabolic profiles in early pregnancy with dysglycaemia in later pregnancy.
### Ethics and dissemination
The Leeds East Research Ethics Committee and NHS (REC: 21/NE/0196) approved the study. Results will be published in peer-reviewed journals and disseminated to participants and the wider public.
### Trial registration number
ISRCTN57579163.
## Background and scope
Pregnancy naturally induces a state of mild insulin resistance (IR) to shuttle more nutrients to the growing baby; however, in women with diabetes in pregnancy (DIP), excessive IR and persistent hyperglycaemia increases the risk of adverse pregnancy outcomes.1–4 Globally, the prevalence of DIP is on the rise, affecting ~$17\%$ of all pregnancies.4 5 Compared with women without diabetes, women with DIP are at elevated risk of pre-eclampsia, preterm delivery and mortality, while their offspring are at increased risk of unhealthy weight (<2.5 kg or >4.5 kg), dysglycaemia, injuries at birth, and higher risk of type 2 diabetes mellitus (T2DM) and cardiovascular disease in later life.1 5 6 Postprandial glycaemic control is important for healthy pregnancy outcomes.1 6 Evidence supports a healthy diet and lifestyle—that includes whole grains, fruits and vegetables, and regular physical activity—as the cornerstone for managing DIP, which is effective in $70\%$–$85\%$ of women with DIP.7–9 National Institute for Health Care Excellence (NICE) UK guidelines primarily focus on improving carbohydrate quality by including more low glycaemic index (GI) foods as part of a balanced diet including whole grains, fruits and vegetables to manage glycaemia during pregnancy.7 Although low GI diets do support the management of mean glucose levels, their effect on reducing episodes of hypoglycaemia and hyperglycaemia and ability to reduce maternal and offspring risk of complications is not clearly established.10 Alternatively, emerging evidence in preclinical and human studies suggests that the amount of maternal protein intake can improve management of dysglycaemia in DIP,11 but its effect on metabolism and 24-hour dysglycaemia in pregnancy is unknown. Finally, some women find it challenging to consistently follow a balanced diet, due to barriers such as availability, accessibility and affordability of healthy foods, lack of time and cooking skills7 9 therefore, a cost-effective nutritious meal replacement may be useful for supporting healthy eating habits. Most recently, we noted that ‘morning’ is when pregnant women with diabetes struggle most to manage glucose levels within a healthy range,10 suggesting that breakfast may be a particularly important point in the day to offer support for managing dysglycaemia.
Continuous glucose monitors (CGMs) are becoming routinely used in the UK National Health Service (NHS) in perinatal clinical settings for women with DIP.1 The unobtrusive patches record an individual’s glucose every 5 min for up to 14 days and offer quantitative information to identify interstitial glucose deviations over a 24-hour period. By measuring glucose continuously over hours and days, a more complete representation of dysglycaemia can be modelled and offer novel insight regarding the parameters that drive and associate with dysglycemia, and their impact on maternal and offspring health.11 Previous studies12 13 have uncovered new associations and identified novel points of interest for managing dysglycaemia during pregnancy and mediating health risks. However, our ability to inform new strategies to manage these new areas of concern are limited by our understanding of the contribution of biological, lifestyle, and environment exposures on dysglycaemia in early, mid, and late pregnancy and their moderating effect on maternal and offspring health. To address this gap in current understanding, this study aims to investigate the effect of breakfast meal replacements and dietary protein on glucose variability in pregnancy in women with pre-existing type 1 or type 2 diabetes.
## Aim and objectives
Our overall aim is to investigate postprandial CGM profiles throughout the course of the pregnancy and how they are associated with personal (lifestyle) characteristics and physiological parameters. Our primary research objective is to assess the effect of easy-to-prepare meals and dietary protein on dysglycaemia over the course of pregnancy. Secondary research objectives include [1] the association between dysglycaemia and maternal and newborn health, and [2] the association between maternal metabolic profiles in early pregnancy with dysglycaemia in later pregnancy.
## Participants
Women with type 1 diabetes mellitus (T1DM) and T2DM during pregnancy in their first trimester will be recruited from the DIP antenatal clinics at Leeds Teaching Hospitals NHS Trust (LTHT). Women will be approached by their direct clinical care team and given a study information flyer and invited to contact the research team (via phone or email) if they are interested to participate or if they wish to discuss the study in more detail. Women expressing interest at the end of the initial meeting will be emailed a participant information sheet and a web link to secure electronic informed consent. Once a secure electronic signature is provided, the participant’s eligibility will be assessed according to study inclusion and exclusion criteria.
## Sample size using power calculation
CGM data provide numerous metrics to offer unique insight into variations and deviations of glucose levels over time—area under the curve (AUC), mean glucose, coefficient of variation of glucose, mean amplitude of glucose excursions (MAGE) and time in range (TIR).14 Additionally, there is no current evidence regarding the effect of diet composition and glycaemic load on metrics of CGM in pregnant women with diabetes throughout the duration of pregnancy. Therefore, we have elected to focus on AUC as the primary metric because it is easily interpretable and commonly used to quantify postprandial glycaemia. Evidence from Fabricatore et al15 demonstrated a significant association ($p \leq 0.05$) between self-reported GI and measures of CGM (including AUC, mean glucose and % time hyperglycaemic) in a clinical trial of 21 women and 5 men with type 2 diabetes. Assuming similar effect sizes between GI (per unit) and AUC glucose (β=0.36 mg/dL/min; R2=0.38), mean glucose (β=0.02 mol/L; R2=0.38) and time spent >10 mol/L blood glucose (β=$0.41\%$; R2=0.36), we will have sufficient power (power=0.90) to detect a significant pairwise effect of GI on these parameters with 63 participants. Another study by Law et al suggested that this will provide sufficient power (power=0.90) to compare subgroups of participants (stratified by body mass index (BMI), age, type of diabetes) and test for significant differences (of a minimum effect size) in AUC glucose (±61 mmol/L/min), mean glucose (±0.5 mmol/L) and % time hyperglycaemic (±$3.7\%$).16 Finally, given the comparable proportions of women reported to be of white *European versus* non-white European ancestry ($57\%$ vs $43\%$) or diet versus diet+medication ($46\%$ vs $54\%$), we also anticipate having adequate power to compare these confounders of glycaemic response. To account for attrition, we will increase our recruitment target by $20\%$ above our calculated, suggesting a target sample size of 76 recruited women. We have allocated ~6 months to recruit 76 women (at 10–12 weeks’ gestation) and ~15 months for study completion (ie, final delivery). All power analyses were performed using G*Power (V.3.1).17
## Study participation inclusion and exclusion criteria
All pregnant women over the age of 18 years, with pre-existing T1DM or T2DM, in their first trimester and a singleton pregnancy, will be considered for the study. Women who develop diabetes in pregnancy (i.e., gestational diabetes) will not be eligible for the study because they would not be offered CGM until 26–28 weeks’ gestation. Exclusion criteria include: [1] inability to understand English sufficiently to read the participant information sheet and provide consent (online supplemental materials A–C); [2] mental and/or psychological disorders that undermine informed consent; [3] cancer, digestive tract disorders; [4] lack of internet access on a computer or tablet at home. Detailed exclusion criteria are shown in table 1 (study screening questionnaires are provided in online supplemental materials D and E).
**Table 1**
| Inclusion criteria | Exclusion criteria |
| --- | --- |
| Women aged 18–45 years | Women under 18 or above 45 years of age |
| Singleton pregnancy | Multiple pregnancy |
| Women in the first trimester of pregnancy | Fetal congenital abnormality |
| Previously diagnosed with type 1 or type 2 diabetes mellitus | No diagnosis of diabetes |
| | Diagnosis of gestational diabetes |
| | Significant coexistent medical condition (e.g., overt diabetes complications, cancer, gut mobility or digestion disorder) |
| | Significant psychological (e.g., anorexia, bulimia) and/or mental disorders which undermine informed consent |
| | Dietary allergies or intolerance for the experimental meals |
| | Lack of internet access on a computer or tablet at home |
| | Unable to understand written English and provide informed consent |
## Data collection stages and procedures
The study will proceed in three stages at the St James’s Hospital (LTHT) with the dietary intervention and interviews conducted remotely (e.g., participant’s home) (figure 1). All pregnant women with pre-existing T1DM or T2DM are scheduled for regular NHS clinical visits every 2 weeks throughout the pregnancy. Each woman has an assigned diabetes midwife who caseloads her pregnancy and liaises with the rest of the clinical care team. Due to COVID-19 and intermittent lockdowns, pregnant women only attend face-to-face meetings at the clinic when due for a scan (a dating scan at 10–12 weeks, an anomaly scan at 18–20 weeks and growth scans at 26–28, 32–34 and 36 weeks of pregnancy) unless deviated due to complications or early delivery. All women with T1DM and T2DM are currently offered CGM as part of their clinical care. The CGM data are automatically uploaded to a secure, remote clinical database, where it can be securely accessed and downloaded by the clinical team and authorised researchers.
**Figure 1:** *Study flow chart. Participant will be monitored and in contact with the clinical and research team through the study, starting at 10–12 weeks’ gestation. At each clinical visit, routine data will be collected from each participant as standard of care (e.g., anthropometrics, blood samples and CGM). This information will be supplemented with lifestyle information (e.g., diet and sleep) collected directly from the participant via electronic and internet questionnaires. The maternal and offspring data available for analysis at each time point are listed below the timeline. The interventions will be delivered at two time points (18–20 and 34–36 weeks). CGM, continuous glucose monitoring; NHS, National Health Service.*
The study will require patient consent [1] permitting secure access to routinely collected clinical details regarding maternal and offspring health at each clinical visit and delivery (i.e., height, weight, blood pressure, HbA1c, lipids), CGM data and delivery outcomes with approved study researchers; [2] permitting researchers to use the residual urine from routine clinically collected samples for metabolite analysis; [3] to conduct online and interview questionnaires to assess diet and lifestyle during each trimester at ~10–12, ~18–20, and ~28–34 weeks; and [4] a 10 mL blood sample to be taken with routine clinical bloods at visits ~10–12, ~18–20, and ~28–34 weeks for subsequent metabolomic and genetic analysis. Each participant will be contacted three times for a phone or video chat (participant preference) for ≤30 min each within 2 weeks of each clinical appointment.
## Patient and public involvement
Patients or the public have not been involved in the design of this pilot and feasibility study. However, upon completion of the study, participants will be invited to provide insight and comments regarding the study itself, the burden enrolment and intervention, and identifying other research priorities relevant to the health condition that the researchers can integrate into future studies. They will also be asked if they consent to follow-up discussions and are keen to be updated on study results and publication material. These points will be invaluable for guiding future work in this area: First call between 10 and 12 weeks of pregnancy: a member of the research team will provide details of the study with the participant and answer any questions. The participants will be instructed to record their dietary intake for 3 days, including 2 weekdays and 1 weekend, using the myFood24 app. The myFood24 is a validated online food diary system created to analyse nutritional intake, which has previously been used in pregnancies complicated by diabetes.18 Details on the participant’s recent physical activity levels (Par-Q for pregnancy)19 and sleep quality (Leeds Sleep Evaluation)20 will be recorded by interview (online supplemental materials F and G), while patterns and habitual mealtimes will be collected by myFood24.21 The dietary meal intervention in the study will also be discussed.
Second call between 18 and 20 weeks of pregnancy: participants’ compliance in the study and details on physical activity, sleep and habitual mealtimes will be re-recorded. A reminder and summary of when and how to use the myFood24 app will be given. The participant will also be instructed about how and when their study intervention’s first set of experimental meals should be consumed.
Third call between 28 and 34 weeks of pregnancy: participants’ compliance and information transfer as in the second call will be repeated. The participant will also be instructed about how and when their study intervention’s second set of experimental meals should be consumed. Subsequently, the participant will be thanked for their participation in the study and not contacted again after this point.
## Nested cross-over dietary meal intervention within the study
Shortly after their clinical visit at ~18–20 weeks and ~28–34 weeks, participants will be asked to consume standardised breakfast meal replacements in their own home, at breakfast time for 4 days. These will be two different experimental meals (A and B; matched for 400 kcal and 13 g of fat) consumed under free-living conditions. The experimental meals will appear and taste similar but differ in protein quantity which will alter how quickly the glucose in each meal is absorbed into the blood.22 One experimental meal will have 20 g of vegan and gluten-free protein powder added, which slows gastric emptying and glucose absorption into the blood to a rate that is comparable with commonly consumed whole-grain breakfast cereals (eg, steel-cut or rolled oats; GI ≈40). The other experimental meal will have no added protein. The experimental meals (labelled A or B) with a drink shaker are stored in a box by the research team in a COVID-19-safe laboratory. Each participant will be assigned to one of six random orders to consume the experimental breakfast meals (AABB, ABAB, BBAA, BABA, ABBA, BAAB) over 4 days using an online randomiser (https://www.random.org/integer-sets/). This will be done by block randomisation to assign 12–13 participants to each of the six possible orders of meal consumption, which the participant will follow for both sets of meals (i.e., ~18–20 and ~28–34 weeks). The participant will only need to pour the powder into the shaker, add cold water to the line marked on the cup (500 mL), and consume within 5 min.
The powder is a nutritionally complete meal replacement in drink form; every meal contains a balance of protein, carbohydrates, essential fats, fibre, plus all 26 essential vitamins and minerals, and phytonutrients. Additionally, the product is low in sugar, lactose-free, contains no nuts or palm oil, and has a long shelf life. The powder is commercially available and is produced in facilities that meet highest quality standards. This product was chosen to minimise time burden for participants and it is free from many commonly avoided food items (i.e., lactose, nuts, gluten and meat).
Participants will be asked to avoid consuming other foods and drinks (aside from water) for 2 hours, after which they may consume food as usual. However, they are freely permitted to measure their own blood glucose levels at any point and will be advised to manage any hyperglycaemic or hypoglycaemic events, even if this means eating or drinking within 2 hours of the meal. Participants will be asked to inform the research team of any events by email as soon as possible.
## Data management
The research team will assign unique random screening IDs at the recruitment phase. The study ID will be used to pseudonymise (using personal participant identification numbers) and harmonise the data shared by the NHS clinical database (clinical records) with the data collected by the research team at the University of Leeds (ie, questionnaires, metabolite and genetic data). Only the clinical team and authorised members of the research team will be able to link the study ID to the participant. All personally identifiable information will be stored in a password-protected and encrypted database in a secure area.
Clinical data: standard care to measure and collect maternal anthropometric, glycaemic, medication, lipid levels, and blood pressure information during routine hospital visits and during and after labour. Offspring anthropometry measures are taken by the direct healthcare team, which is part of routine care. The women will be asked to give consent for the research team to access their clinical records to obtain these data.
CGM: standard clinical care in T1DM and T2DM pregnancies; women will be asked to give consent for the research team to access their CGM data. Of the numerous metrics provided by CGM—that is, AUC, mean glucose, coefficient of variation of glucose, MAGE and TIR—AUC will be the primary metric for the study.
Urine samples: standard clinical care; we will ask for up to 2.5 mL of any urine not required for clinical analysis to be saved for research use. These samples will be stored for subsequent metabolic analysis. All samples will be stored at the University of Leeds in designated Human Tissue Act-approved and compliant facilities.
Blood samples: standard clinical care requires blood samples for analysis. At the time of routine collection at each clinical visit, an additional 10 mL blood will be collected for this study. These blood samples will be stored for subsequent metabolic and genomic analysis (relevant to nutrition/diabetes/pregnancy and fetal growth). All samples will be stored at the University of Leeds in designated and secure facilities.
Lifestyle questionnaires: at three time points across pregnancy (~10–12, ~18–20 and ~28–34 weeks’ gestation), a designated member of the research team will call (phone or video) the participant to complete the questionnaires on physical activity, sleep quality/patterns and habitual mealtime habits.
Dietary records: at two time points across pregnancy (~18–20 and ~28–32 weeks of gestation), each participant will be asked to record their diet for 3 days (2 week days, 1 weekend day) using a validated online semiquantitative food frequency questionnaire (myFood24), which estimates dietary intake data (i.e., macronutrients, micronutrients and vitamins for up to 220 nutrients) according to McCance and Widdowson (seventh edition) and branded items that offer nutritional data.23 Briefly, the data are provided to researchers as a spreadsheet with anonymised identifiers for each participant that can be directly imported into R for analysis. The performance of myFood24 and telephone-based 24-hour dietary recall is in agreement (interclass correlation 0.4–0.5). 23
## Outcomes of interest
The primary outcome of interest is CGM glucose data, with AUC glucose as the primary CGM metric of interest. Secondary outcomes of interest are associations between metrics of dysglycaemia during pregnancy with maternal outcomes (e.g., gestational weight gain, pre-eclampsia, hypertension, mode of birth, birth trauma, preterm delivery and metabolism) and infant outcomes (e.g., birth weight, height, preterm delivery, mortality, birth trauma, hypoglycaemia, congenital malformation, head and abdominal circumference, perinatal morbidity), and the moderating effects of genetics, metabolism, and diet and lifestyle. All secondary analyses are considered exploratory.
## Statistical analysis considerations
All standard CGM metrics will be calculated, with AUC glucose as the primary CGM metric (mean±SD). The primary analysis will be the effect of dietary protein on AUC glucose for the 24-hour window immediately after each study meal. The analysis will be constructed as pairwise linear model with study meal (0=low protein, 1=high protein) regressed against 24-hour mean AUC glucose and adjusted for study parameters (e.g., randomised meal order) and participant covariates (e.g., maternal age, ethnicity, parity, BMI, gestational age, physical activity and sleep quality). Statistical significance will be set at $p \leq 0.05$, where a $p \leq 0.05$ for study meal will suggest a significant effect of dietary protein on 24-hour postprandial AUC glucose. The direction and significance of covariates will be investigated to identify study and participant mediators of the association. Statistical analysis will be conducted in R studio and SPSS (Ver. 29+).
Secondary research objectives include [1] the association between dysglycaemia and maternal and newborn health, and [2] the association between maternal diet in early pregnancy with dysglycaemia during pregnancy. These analyses will also be performed using regression models adjusted for covariates, with the assessment of early diet on longitudinal changes in dysglycaemia also adjusted for time points of AUC (i.e., mixed-model). The association between early maternal diet and AUC will be performed using three distinct dietary metrics (calculated from myFood24): 1. Overall diet quality The association between diet quality and dysglycaemia will be assessed using an overall diet quality score.24 This scoring method has been modified and used previously to assess maternal diet quality in a multiethnic prospective birth cohort.25 The modified Alternative Healthy Eating Index (mAHEI) score is calculated using the following method; an individual will receive 10 points for each of the following food categories when they consume above or below a threshold of: ≥5 servings of vegetables, ≥4 servings of fruits, ≥1 serving of nuts or soy proteins, ≥3 servings of whole grains, a ratio of ≥4 servings of fish to 1 serving of meat and eggs, and ≤0.5 servings of less-healthy foods (i.e., fried foods and processed meats). Intermediate intake is scored proportionally between 0 and 10. The maximum mAHEI score is 60; the higher the score, the more healthful the participant’s diet.
2. Macronutrient Daily macronutrient consumption (total carbohydrates, proteins and fats) and markers of GI quality (fibre, sugars) will be adjusted for energy and regressed against CGM measures of dysglycaemia. Doing so will allow for the contribution of individual macronutrients on glycaemic measures to be evaluated.
3. Cardinal foods Partial least squares will be used to identify foods that are more commonly observed in participants with favourable or unfavourable glycaemic control (identified above/below median for glycaemic measures). These foods will then be investigated for their association with measures of dysglycaemia using a regression model.
## Quality control
All participants will receive standard clinical care as per NICE guidance, which will minimise researcher bias. The primary outcome measures are based on laboratory measurements and predetermined cut-off values, which the researchers will not be able to influence. We do not foresee significant researcher bias in collecting antenatal and perinatal outcome data because these will be obtained by clinical staff who are independent of the study outcome and from the participants’ medical records. We do not foresee any significant researcher bias in collecting lifestyle records, as standardised and validated questionnaires will be used to obtain this information. Furthermore, all participants will be asked to consume the two standard meals but the order of their consumption will be randomised. We do not foresee any conflict of interests. The data collected will not be used for informing clinical care decisions of specific cases; all the women will continue with their usual clinical care pathway for the duration of their pregnancy, and all women will be free to terminate their participation in the study at any time with no effect on their quality of care.
## Ethics approval
This study has been reviewed and approved by the Leeds East Research Ethics Committee at the University of Leeds (21/NE/0196).
## Dissemination
There is no formal interim analysis planned except the ongoing evaluation of the recruitment numbers. Results will be disseminated in peer-reviewed scientific journals, conference presentations, and publication on (social) media and in newsletters, to inform the participants and wider public.
## Discussion
The INFORMED clinical trial is double-blinded cross-over randomised clinical trial to evaluate the effect of dietary protein within experimental meals on dysglycaemia in women with pre-existing type 1 or type 2 diabetes. Additionally, we will explore [1] the association between dysglycaemia and maternal and newborn health, and [2] the association between maternal metabolic profiles in early pregnancy with dysglycaemia in later pregnancy, which may provide insights into novel precision therapies for women with DIP.26 The identification of dietary mediators of glucose variability will aid in the development of more efficacious and appropriate strategies to control glucose levels and minimise maternal and offspring risks in women with DIP.12 13 26
## Patient consent for publication
Not required.
## References
1. Macintosh MCM, Fleming KM, Bailey JA. **Perinatal mortality and congenital anomalies in babies of women with type 1 or type 2 diabetes in England, Wales, and Northern Ireland: population based study**. *BMJ* (2006) **333** 177. DOI: 10.1136/bmj.38856.692986.AE
2. Murphy HR, Howgate C, O’Keefe J. **Characteristics and outcomes of pregnant women with type 1 or type 2 diabetes: a 5-year national population-based cohort study**. *Lancet Diabetes Endocrinol* (2021) **9** 153-64. DOI: 10.1016/S2213-8587(20)30406-X
3. Symonds ME, Ramsay MM. *Maternal-fetal nutrition during pregnancy and lactation* (2010). DOI: 10.1017/CBO9780511674792
4. Lapolla A, Metzger BE. *Gestational Diabetes: A Decade After the HAPO Study* (2020). DOI: 10.1159/isbn.978-3-318-06612-8
5. 5International Diabetes Federation. IDF diabetes atlas. Brussels, Belgium, 2019.. *IDF diabetes atlas* (2019)
6. Tennant PWG, Glinianaia SV, Bilous RW. **Pre-Existing diabetes, maternal glycated haemoglobin, and the risks of fetal and infant death: a population-based study**. *Diabetologia* (2014) **57** 285-94. DOI: 10.1007/s00125-013-3108-5
7. 7National Institute for Clinical E. Diabetes in pregnancy: management from preconception to the postnatal period. NICE guidelines [NG3]. 2015.. *Diabetes in pregnancy: management from preconception to the postnatal period. NICE guidelines [NG3]* (2015)
8. Smith R, Ridout A, Livingstone A. **Motivational interviewing to increase physical activity in women with gestational diabetes**. *British Journal of Midwifery* (2021) **29** 550-6. DOI: 10.12968/bjom.2021.29.10.550
9. American Diabetes A. **13. management of diabetes in pregnancy**. *Diabetes Care* (2017) **40** S114-9. DOI: 10.2337/dc17-S016
10. Dingena CF, Holmes MJ, Campbell MD. **Observational assessments of the relationship of dietary and pharmacological treatment on continuous measures of dysglycemia over 24 hours in women with gestational diabetes**. *Front Endocrinol (Lausanne)* (2023) **14**. DOI: 10.3389/fendo.2023.1065985
11. Yu Q, Aris IM, Tan KH. **Application and utility of continuous glucose monitoring in pregnancy: a systematic review**. *Front Endocrinol (Lausanne)* (2019) **10**. DOI: 10.3389/fendo.2019.00697
12. Perichart-Perera O, Balas-Nakash M, Rodríguez-Cano A. **Low glycemic index carbohydrates versus all types of carbohydrates for treating diabetes in pregnancy: a randomized clinical trial to evaluate the effect of glycemic control**. *Int J Endocrinol* (2012) **2012**. DOI: 10.1155/2012/296017
13. Moses RG, Luebcke M, Davis WS. **Effect of a low-glycemic-index diet during pregnancy on obstetric outcomes**. *Am J Clin Nutr* (2006) **84** 807-12. DOI: 10.1093/ajcn/84.4.807
14. Scott EM, Murphy HR, Kristensen KH. **Continuous glucose monitoring metrics and birth weight: informing management of type 1 diabetes throughout pregnancy**. *Diabetes Care* (2022) **45** 1724-34. DOI: 10.2337/dc22-0078
15. Fabricatore AN, Ebbeling CB, Wadden TA. **Continuous glucose monitoring to assess the ecologic validity of dietary glycemic index and glycemic load**. *Am J Clin Nutr* (2011) **94** 1519-24. DOI: 10.3945/ajcn.111.020354
16. Law GR, Alnaji A, Alrefaii L. **Suboptimal nocturnal glucose control is associated with large for gestational age in treated gestational diabetes mellitus**. *Diabetes Care* (2019) **42** 810-5. DOI: 10.2337/dc18-2212
17. Faul F, Erdfelder E, Buchner A. **Statistical power analyses using G*power 3.1: tests for correlation and regression analyses**. *Behav Res Methods* (2009) **41** 1149-60. DOI: 10.3758/BRM.41.4.1149
18. Morris MA, Hutchinson J, Gianfrancesco C. **Relationship of the frequency, distribution, and content of meals/snacks to glycaemic control in gestational diabetes: the myfood24 GDM pilot study**. *Nutrients* (2019) **12**. DOI: 10.3390/nu12010003
19. Charlesworth S, Foulds HJA, Burr JF. **Evidence-based risk assessment and recommendations for physical activity clearance: pregnancy**. *Appl Physiol Nutr Metab* (2011) **36** S33-48. DOI: 10.1139/h11-061
20. Parrott AC, Hindmarch I. **The Leeds sleep evaluation questionnaire in psychopharmacological investigations-a review**. *Psychopharmacology (Berl)* (1980) **71** 173-9. DOI: 10.1007/BF00434408
21. Gianfrancesco C, Darwin Z, McGowan L. **Exploring the feasibility of use of an online dietary assessment tool (myfood24) in women with gestational diabetes**. *Nutrients* (2018) **10**. DOI: 10.3390/nu10091147
22. Meng H, Matthan NR, Ausman LM. **Effect of macronutrients and fiber on postprandial glycemic responses and meal glycemic index and glycemic load value determinations**. *Am J Clin Nutr* (2017) **105** 842-53. DOI: 10.3945/ajcn.116.144162
23. Wark PA, Hardie LJ, Frost GS. **Validity of an online 24-h recall tool (myfood24) for dietary assessment in population studies: comparison with biomarkers and standard interviews**. *BMC Med* (2018) **16**. DOI: 10.1186/s12916-018-1113-8
24. Dehghan M, del Cerro S, Zhang X. **Validation of a semi-quantitative food frequency questionnaire for Argentinean adults**. *PLoS One* (2012) **7**. DOI: 10.1371/journal.pone.0037958
25. de Souza RJ, Zulyniak MA, Desai D. **Harmonization of food-frequency questionnaires and dietary pattern analysis in 4 ethnically diverse birth cohorts**. *J Nutr* (2016) **146** 2343-50. DOI: 10.3945/jn.116.236729
26. Schaefer-Graf U, Napoli A, Nolan CJ. **Diabetes in pregnancy: a new decade of challenges ahead**. *Diabetologia* (2018) **61** 1012-21. DOI: 10.1007/s00125-018-4545-y
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---
title: Does problem-based learning improve patient empowerment and cardiac risk factors
in patients with coronary heart disease in a Swedish primary care setting? A long-term
prospective, randomised, parallel single randomised trial (COR-PRIM)
authors:
- Christina Andreae
- Pia Tingström
- Staffan Nilsson
- Tiny Jaarsma
- Nadine Karlsson
- Anita Kärner Köhler
journal: BMJ Open
year: 2023
pmcid: PMC9972427
doi: 10.1136/bmjopen-2022-065230
license: CC BY 4.0
---
# Does problem-based learning improve patient empowerment and cardiac risk factors in patients with coronary heart disease in a Swedish primary care setting? A long-term prospective, randomised, parallel single randomised trial (COR-PRIM)
## Abstract
### Objectives
To investigate long-term effects of a 1-year problem-based learning (PBL) on self-management and cardiac risk factors in patients with coronary heart disease (CHD).
### Design
A prospective, randomised, parallel single centre trial.
### Settings
Primary care settings in Sweden.
### Participants
157 patients with stable CHD completed the study. Subjects with reading and writing impairments, mental illness or expected survival less than 1 year were excluded.
### Intervention
Participants were randomised and assigned to receive either PBL (intervention) or home-sent patient information (control group). In this study, participants were followed up at baseline, 1, 3 and 5 years.
### Primary and secondary outcomes
Primary outcome was patient empowerment (Swedish Coronary Empowerment Scale, SWE-CES) and secondary outcomes General Self-Efficacy Scale (GSES), self-rated health status (EQ-VAS), high-density lipoprotein cholesterol (HDL-C), body mass index (BMI), weight and smoking. Outcomes were adjusted for sociodemographic factors.
### Results
The PBL intervention group resulted in a significant improved change in SWE-CES over the 5-year period (mean (M), 39.39; $95\%$ CI 37.88 to 40.89) compared with the baseline (M 36.54; $95\%$ CI 35.40 to 37.66). PBL intervention group increased HDL-C level (M 1.39; $95\%$ CI 1.28 to 1.50) compared with baseline (M 1.24; $95\%$ CI 1.15 to 1.33) and for EQ-VAS (M 77.33; $95\%$ CI 73.21 to 81.45) compared with baseline (M 68.13; $95\%$ CI 63.66 to 72.59) while these outcomes remained unchanged in the control group. There were no significant differences in BMI, weight or scores on GSES, neither between nor within groups over time. The overall proportion of smokers was significantly higher in the control group than in the experimental group.
### Conclusion
One-year PBL intervention had positive effect on patient empowerment, health status and HDL-C at a 5-year follow-up compared with the control group. PBL education aiming to improve patient empowerment in cardiac rehabilitation should account for sociodemographic factors.
### Trial registration number
NCT01462799.
## Introduction
Nearly 126 million people globally suffer from coronary heart disease (CHD), the leading cause of death worldwide.1 2 Important treatment goals include slowing down the underlying atherosclerosis process by targeting hypertension, high cholesterol, diabetes, overweight, tobacco use, alcohol consumption, physical inactivity and poor diet.3 Cardiac rehabilitation in postmyocardial infarction improve lifestyle habits such as physical activity and dietary intake, and has also shown positive effects on blood lipids, blood pressure and smoking.4–6 *In a* small randomised trial, cardiac rehabilitation programme for patients with myocardial infarction or who had undergone coronary artery by-pass graft surgery (CABG), focusing on stress management, physical exercise and dietary intake, reduced morbidity and hospitalisation for CHD.7 Attendance in cardiac rehabilitation programme has long-term effects on survival among patients who have undergone CABG with reduced 10-year all-cause mortality.8 Cardiac rehabilitation enables patients to make lifestyle changes that are important for maintaining health. Cardiac rehabilitation consists of multidisciplinary interventions where patients are offered tailored education and counselling with the goals of improving health behaviour for sustainable secondary prevention.3 Several studies have investigated the effect of interventions such as nurse telephone follow-up or group education, and described the effects on illness perception, self-efficacy, behaviour change and cardiac risk factors.9–11 The effects of several programme on cardiac risk factors or behaviour were found from 3 months up to 2 years. Patients’ attendance of cardiac rehabilitation appears to be low, and risk factors remain or even deteriorate from the first to second cardiac event.12–14 For example, persistent smoking is common after discharge from hospital, and intentions to quit smoking in the near future remain low. This regards also overweight and physical activity after a cardiac event, many patients do not lose weight and nearly half of patients perform no physical activity and do not make changes to their physical status.13 Thus, primary care has great challenges motivating patients to achieve healthy lifestyles.
Empowering patients to take control over the disease, and making them aware of factors that affect illness, have positively impacted on health outcomes in chronic diseases.15 Problem-based learning (PBL) is a method that empowers patients to become aware of how to reduce risk factors in chronic diseases.16 PBL is a cognitive educational model that promotes self-learning where critical and reflective thinking are important components of learning outcomes and enhanced self-management. PBL is process-oriented, meaning that learning skills are developed through active, creative and cognitive processes. Previous knowledge is placed in the light of real situations where knowledge is developed in a context together with others, all led by a trained PBL tutor.17 A focus group intervention with problem solving intervention in a cardiac population showed that an 8-week focus group sessions resulted in significant improvements of stress management, dietary intake and physical activity.18 The first results from a 1-year follow-up in a Coronary Heart Disease in Primary Care Study (COR-PRIM) showed no differences in patient empowerment or self-efficacy between PBL intervention and patients who received home-sent patient information (controls). However, significant differences in secondary outcomes—that is, body mass index (BMI), body weight and high-density lipoprotein cholesterol (HDL-C)—were found between the groups, with the PBL group performing favourably.19 Although cardiac rehabilitation in Sweden has recently been assessed to be high-quality20 participation remains low. Only a few interventions incorporating a holistic view of health compromising psychological and social aspects of health behaviours have been performed on the group level. PBL may empower self-management. This study is elaborated in accordance with the COR-PRIM study basic aim, which was to discover whether PBL provided in primary healthcare for 1 year has long-term effects on patient empowerment and self-care, assessed at baseline and, at 1, 3 and 5 years after randomisation. In this 5-year and final assessment, we wanted to identify if the findings in the 1-year follow-up remained or changed.19 *By this* performance, this article is examining the sustainability of the effects by PBL, which to our knowledge has not been performed before.
Thus, the purpose of this study was to investigate the long-term effects on patient empowerment, self-efficacy, health status and cardiac risk factors in patients with CHD of a 1-year PBL intervention in primary care, compared with home-sent patient information.
## Design, sample and procedure
The COR-PRIM study was a prospective, randomised, parallel and single-centre trial designed to investigate whether 1 year of a PBL programme had long-term effects on patient empowerment, self-efficacy and cardiac risk factors.17 The study protocol is registered at ClinicalTrial.gov, with the registration number NCT01462799. Patients diagnosed with CHD verified by percutaneous coronary intervention (PCI) or coronary artery by-pass surgery (CABG) and CABG+PCI or myocardial infarction within 6–12 months prior to the intervention and who had previously completed cardiac rehabilitation were eligible for the study. Additional criteria were that patients were stable in their heart disease, pharmacologically optimised in the last month before inclusion and, if applicable had completed cardiac school in the clinic. Patients with impaired ability to communicate and read in Swedish, verified psychiatric illness or short expected survival (of less than 1 year) were excluded.17 The recruitment started in November 2011 and assessments of primary and secondary outcomes were collected until November 2019. Nurses at outpatient cardiac clinics identified eligible patients through medical records. These patients were then invited to participate in the study at a regular nurse-led cardiac follow-up visit. Patients received oral and written study information from a research assistant, and written consent was obtained from those interested in participating (online supplemental material 1).
## The study randomisation and intervention
In COR-PRIM, a total of 157 patients were randomised and assigned to either PBL or home-sent patient information (control group). The randomisation procedure was based on block randomisation with 18 study numbers based on a minimum of 12–18 study participants. Block randomisation was performed using sealed and unmarked envelopes. An assistant, blinded to the randomisation and located outside the research setting, randomly allocated patients to either the PBL group (experimental) or home-sent patient information (control group) at a 1:1 ratio.17 Nurses who performed the PBL intervention were not blinded, as the intervention was based on PBL, which is different from regular care. The PBL intervention consisted of group learning sessions in primary care led by nurses who had advanced training in leading PBL. Tutoring groups consisted of 6–9 participants and participated in 13 sessions (2 hours per meeting) over 1 year. This was based on the multiple of areas to discuss for lifestyles changes in CHD. For further information about the PBL intervention, see online supplemental material 2. Lifestyles changes require work over time and to be feasible in the patient’s life, a long training is required. The PBL model has been validated as feasible in clinical settings.21 Patients randomised to the control group received home-sent patient information reflecting a cognitive intervention, but did not participate in critical or reflective learning. Group effects were assessed after 11 sessions over 1 year.17
## Patient and public involvement
There were no patient or public involvement in the study.
## Sample size
Sample size was calculated based on the primary outcome variable, that is, patient empowerment. The expected means for the Swedish Coronary Empowerment Scale (SWE-CES) were 30 and 36 for the control group and the PBL group, respectively. With an estimated significance level of α=$5\%$ and a power of 1−β=$80\%$, the sample size resulted in a minimum of 63 participants in each group.17 Analyses conducted for the 5-year follow-up period was $$n = 72$$ (PBL) and $$n = 71$$ (control group) (figure 1).
**Figure 1:** *Flow chart on study participation.*
## Primary and secondary outcome measures
Patient empowerment was the study’s primary outcome and was assessed at baseline, 1, 3 and 5 years (ie, baseline, time 1, time 3 and time 5, respectively). Secondary outcomes included general self-efficacy while five secondary outcomes were added post hoc and these were health status, HDL-C, BMI, weight and smoking and were assessed at baseline, 1, 3 and 5 years.
## Sociodemographic variables and covariates
Sociodemographic variables consisting of age, sex, education level, marital status, place of residence, employment and smoking were determined by self-reported questionnaires. The covariates consisted of age, sex, education level and marital status.
## Self-reported measures
The SWE-CES measures how patients achieve goals, overcome barriers for goals achievement and use the strategies necessary to make self-care choices. The strategies include dimensions of coping in managing disease, stress and dissatisfaction and also readiness to make health changes. The instrument contains 10 items that have 5 response alternatives ranged by Likert type options from 1 to 5. The total score ranges between 10 and 50, where high scores imply high level of patient empowerment. Four subscales measure different aspects of patient empowerment.22 *In this* study, the total scores of the SWE-CES scale were used. Internal consistency reliability indicated acceptable values with Cronbach’salpha 0.751.23 Self-efficacy was measured by using the General Self-Efficacy Scale (GSES). The instrument consists of 10 items that measure a person’s belief in their own ability to implement behavioural changes in order to reduce risk factors for unhealthy lifestyles. Items are rated on a four-point Likert type scale ranging from 1 (not at all true) to 4 (exactly true). The total score ranges from 10 to 40, where a high total score indicates higher general self-efficacy.24 Self-efficacy has been used to evaluate interventions to strengthen the self-care capacity of patients with various chronic diseases such as diabetes25 26 and patients with CHD.27–29 It has been translated and psychometrically evaluated among a general Swedish population.30 *In this* study, internal consistency reliability indicated acceptable values with Cronbach’s alpha 0.914.
## Health status
Self-rated health was assessed using EuroQol Visual Analogue Scale (EQ-VAS). The scale represents patients’ overall health status and consists of a score between 0 and 100, where 0 indicates worst imaginable state of health and 100 the best imaginable state of health.31 EuroQol Visul Analogue Scale (EQ-VAS) has shown acceptable construct validity across populations.32
## Cardiac risk factors
HDL-C was assessed using blood samples collected and analysed according to normal clinical routines. Anthropometric measurements of body size included length, weight and BMI. Length and weight were measured in light cloths and with shoes removed. BMI is a widely used clinical measure and is recommended as indicator for defining obesity in adults. It is, furthermore, a reliable anthropometric measure in predicting metabolic syndromes.1 33 BMI was calculated by dividing weight by the square of height in metres using the formula (weight (kg)/height (m2)).34 Tobacco use was self-reported at each measure point.
## Statistical analysis
The descriptive statistics included mean SD or frequencies percent (%). Independent sample t-tests or χ2 tests were used to analyses differences in sociodemographic and data characteristics between groups. Because the occasions (baseline to 5 years) are nested within individuals, we employed a two-level mixed linear model with occasions (‘time’ at level 1) that are nested within individuals (level 2) to analyse continuous outcomes. The random part of the variance components model is individuals. The normality of continuous study outcomes was assessed using Shapiro-Wilk test. If the outcomes were non-normally distributed, they were log transformed prior to the analysis. Smoking is a binary outcomes and was analysed with a logistic mixed model. The statistical analysis of each study outcome was performed using a mixed linear model with treatment group, age and sex as fixed variables, with occasions nested within individuals, and an interaction of time by treatment group. The interaction (combined effect) between treatment group and time was tested using the likelihood ratio test. In the presence of an interaction, the analysis was stratified by treatment group, and a mixed model with occasions nested within individuals was performed separately for each group. These stratified analyses were adjusted in two models. Model 1 was adjusted for age and sex, and model 2 was, furthermore, adjusted for educational level and marital status. The level of significance used was $p \leq 0.05.$ When the interaction term was not significant at $5\%$ but was significant at $10\%$, a sensitivity analysis was performed by exploring the results of the analysis stratified by intervention group (PBL and control). The statistical analysis was performed in SPSS V.27 and Stata V.16.0.
## Sample
Of the initial 446 invited participants, 289 were excluded. Of the latter, 23 did not fulfil the inclusion criteria, 246 declined to participate and 20 did not respond to the invitation. The final study group consisted of 157 participants, of whom 79 were assigned to the PBL group and 78 assigned to the control group. At the 3-year follow-up, there were five drop-outs, two participants (one from the control group and one from the PBL group) had not completed the study for personal reasons, and three participants had died (two from the control group and one from the PBL group), leaving $$n = 77$$ participants (PBL) and $$n = 75$$ (control group) at the 3-year follow-up. At the 5-year follow-up, there were nine drop-outs, two participants (one from the control group and one from the PBL group) had advanced in their illness, and seven participants had died (three from the control group and four from the PBL group). There was no statistically significant difference in the drop-out rate between treatment groups as tested by χ2 test ($$p \leq 0.980$$).
Of the total sample of 157 participants, the mean age was 68.7 years±8.5 and the majority were men $$n = 122$$, $77.7\%$ (table 1). Almost half of the participants lived in suburban areas, and the remaining lived in small towns or the countryside. In total, 113 participants were retired and the remaining were in employment. Almost half of the participants had suffered from myocardial infarction, which occurred at a mean of 284 days before the start of the study.
**Table 1**
| Unnamed: 0 | Total sample(N=157) | PBL(n=79) | Control(n=78) | P value |
| --- | --- | --- | --- | --- |
| Age year, mean (SD) | 68.7 (8.5) | 68.5 (9.2) | 68.9 (7.7) | 0.78* |
| Sex, n (%) | | | | |
| Male | 122 (77.7) | 60 (75.9) | 62 (79.5) | |
| Female | 35 (22.3) | 19 (24.1) | 16 (20.5) | 0.70† |
| Marital status, n (%) | | | | |
| Cohabiting | 115 (74.2) | 60 (75.9) | 55 (72.4) | |
| Living alone | 40 (25.8) | 19 (24.1) | 21 (27.6) | 0.71† |
| Education level, n (%) | | | | |
| Compulsory | 84 (54.2) | 46 (58.2) | 38 (50.0) | |
| Upper secondary | 31 (20.0) | 16 (20.3) | 25 (19.7) | |
| University | 38 (24.5) | 17 (21.5) | 21 (27.6) | 0.43† |
| SWE-CES total score, mean (SD) | 36.84 (5.37) | 36.54 (4.94) | 37.15 (5.81) | 0.49* |
| GSES total score, mean (SD) | 31.30 (5.13) | 31.20 (5.44) | 31.41 (4.82) | 0.80* |
| EQ-VAS, mean (SD) | 70.21 (17.9) | 68.13 (19.5) | 72.44 (15.8) | 0.05* |
| HDL-C, mmol/L, mean (SD) | 1.27 (0.44) | 1.24 (0.39) | 1.30 (0.48) | 0.42* |
| BMI, kg/m2, mean (SD) | 27.10 (4.39) | 27.12 (3.91) | 27.07 (4.85) | 0.95* |
| Smoking, n (%) | 19 (12.1) | 9 (11.4) | 10 (12.8) | 0.81† |
At baseline, the majority had no symptoms of angina pectoris. Less than half of the participants had hyperlipidaemia. Additional characteristics are available from a previous study.19 The distribution for continuous outcomes for the PBL and control groups at the 1-year, 3-year and 5-year follow-ups is illustrated with a summary of data including minimum, first quartile, median, third quartile and maximum in boxplots (figure 2). The results of the statistical analysis of study outcomes over time and by intervention are summarised below (table 2). Results of the stratified analysis adjusted for age and sex only gave results similar to the model also adjusted for educational level and marital status. Therefore, table 2 presents only results adjusted for age, sex, educational level and marital status.
## Patient empowerment
There was a statistically significant change over time of patient SWE-CES (patient empowerment) from baseline to time 5 ($$p \leq 0.025$$) in the total group of participants (table 2).
Additional findings of post hoc sensitivity analysis with interaction test significant at $10\%$ was found for SWE-CES. The interaction between time and group was ($$p \leq 0.086$$). This implies that the analysis of SWE-CES stratified by group showed a significant increase in the PBL group as a part of the sensitivity analysis. SWE-CES increased significantly at time 3 ($M = 38.34$±5.76, $$p \leq 0.023$$) and time 5 ($M = 39.39$±5.23, $p \leq 0.001$) (table 2) compared with the baseline, independently of positive effects of covariates low education ($$p \leq 0.041$$); it did not, however, change significantly over time in the control group. Being a woman did, however, negatively effect SWE-CES ($$p \leq 0.010$$).
## Self-rated health
We observed a statistically significant change of EQ-VAS (self-rated health status) from baseline to time 1 ($$p \leq 0.038$$). The interaction between time and group was significant at the level of $5\%$ ($$p \leq 0.022$$). Therefore, the analysis was stratified by group. EQ-VAS increased significantly at time 1 ($M = 74.64$±18.05, $$p \leq 0.026$$), time 3 ($M = 78.27$±14.84, $$p \leq 0.007$$) and time 5 ($M = 77.33$±15.52, $$p \leq 0.031$$) in the PBL group compared with baseline, independently of negative effects of covariates age ($$p \leq 0.018$$), while positive effects of low education increased EQ-VAS by 10 units compared with high education ($$p \leq 0.011$$). No significant changes were observed in the control group at time 1 ($M = 75.30$±16.36, $$p \leq 0.501$$), time 3 ($M = 71.89$±18.33, $$p \leq 0.195$$) and time 5 ($M = 72.06$±17.19, $$p \leq 0.137$$) compared with baseline (table 2). The covariate living alone showed a negative effect on EQ-VAS by −7.5 units compared with living together ($$p \leq 0.039$$).
## High density lipoprotein cholesterol
There was a statistically significant change of HDL-C from baseline to time 5 ($$p \leq 0.036$$) (table 2). The interaction between time and group was significant at alpha level $5\%$ ($$p \leq 0.016$$). Therefore, the analysis was stratified by group. HDL-C increased significantly at time 1 ($M = 1.37$ mmol/L±0.43, $$p \leq 0.003$$), time 3 ($M = 1.42$ mmol/L±0.55, $p \leq 0.001$) and time 5 ($M = 1.39$ mmol/L±0.41, $p \leq 0.001$) in the PBL group compared with baseline, independently of the positive effects of covariates sex (woman) ($$p \leq 0.002$$) and negative effects of low education compared high education ($$p \leq 0.035$$). There was no significant change in the control group at time 1 ($M = 1.23$±0.36, $$p \leq 0.781$$), time 3 ($M = 1.27$±0.37, $$p \leq 0.655$$) or time 5 ($M = 1.27$±0.42, $$p \leq 0.459$$) compared with baseline (table 2). However, being a woman was positively associated with HDL-C ($$p \leq 0.026$$).
## BMI and weight
There was no statistically significant main effect (time or group) for BMI (table 2). There was a significant interaction between time and group at the significance level $10\%$ ($$p \leq 0.061$$). Therefore, the analysis was stratified by group. The analysis stratified by group showed a slight decrease from baseline ($M = 27.12$±3.91) after 1 year ($M = 26.83$±3.93, $$p \leq 0.076$$), but that did not remain over time. No significant changes over time were observed for the control group (table 2). For weight, there was a significant interaction between time and group at alpha level of $5\%$ ($$p \leq 0.047$$). Therefore, the analysis was stratified by group. No significant differences were observed between time 1 and 5 in the PBL group compared with baseline. However, the mean weight of patients in the control group at time one increased significantly compared with baseline ($$p \leq 0.045$$) (table 2).
## Self-efficacy
For GSES (self-efficacy) there was no significant group effect or change over time (table 2).
## Smoking
There was no significant interaction between time and intervention ($$p \leq 0.787$$), but there was a significant group effect ($$p \leq 0.029$$) for smoking, showing that the overall proportion of smokers in the control group was statistically significantly higher than the proportion of smokers in the PBL group (table 2).
## Discussion
We investigated the long-term effects of a 1-year PBL intervention in primary care on patient empowerment, self-efficacy, health status and cardiac risk factors in patients with CHD. PBL improved patient empowerment, health status and HDL-C over the 5 years follow-up compared with control group. However, PBL did not result in changes in BMI, weight, smoking or self-efficacy.
We observed significant changes from PBL in patient empowerment and health status over the 5-year follow-up compared with the 1-year follow-up study.19 It seems possible that it might take a long time to adapt to longstanding behavioural changes. As many studies in this area often end within a year9 10 35 our study indicates that long-term follow-up is needed to understand the effects on patient behavioural outcomes of interventions using social cognitive theories. Other research shows the need for long-term data on effectiveness of patient education about how to lessen risk factors after CHD.36 Cardiac rehabilitation reduces risk factors but may also improve health over time.37 A national prospective cohort study with nearly 4500 participants showed that cardiac rehabilitation had positive effects on quality of life up to 1 year after hospital discharge compared with non-participants.38 In our study, health status improved up to 1 year, and also after 3 and 5 years compared with the control group. PBL allows patients to actively decide what is important to discuss in cardiac rehabilitation, suggesting that PBL might promote control over the disease, leading to better health.
We found that HDL-C improved over the 5 years follow-up. Similar results have been reported in short term follow-up studies on cardiac risk factors using combined education and written information intensive health education sessions and individual support.39 40 These studies share a focus on education, but we have used PBL in group sessions in a period of 1 year. PBL activates the participants’ problem-solving skills, which is not possible with home-sent patient information. Increased patient empowerment can lead to lifestyle changes as well as decisions to make no changes at all. Improvements in patient empowerment in our study indicate a clinically relevant change in for example HDL-C that also consisted as clinically relevant after 5 years follow-up. Our findings confirm that cardiac rehabilitation focusing on behavioural strategies may be beneficial in maintaining healthy levels of HDL-C.
Multidisciplinary weigh loss behavioural interventions in cardiac rehabilitation may be effective in the short run.41 We did not observe any changes in BMI between or within groups over time. In contrast, a meta-analysis study involving almost 20 000 participants showed that internet-based education significantly reduced cardiac risk factors regarding blood pressure, blood lipids, weight and physical inactivity, with changes lasting up to 1 year.35 We did not include physical activity which could have been valuable to gain a deeper understanding of the non-significant results of BMI and weight over the 5 years follow-up.
A cognitive nurse-led intervention designed to improve physical activity by using repeated telephone calls and text messaging consultations improved BMI at a 6-month follow-up compared with controls.42 Our study was also nurse led, but the intervention did not include extra consultations, telephone calls or text message for achieving lifestyle goals. Digital aids with continuous support may be helpful when new health promotion activities are being started.43 44 The results from the Euroaspire V study reported a high prevalence of persistent smokers after a CHD event.13 *In this* study, PBL did not result in a successful lifestyle change in smoking during the follow-up. Even though few participants were smokers in our study, there was a trend, although not a significant one, of participants in the control group being smokers more often than those in the PBL group. Higher age, low level of education and living alone were also significantly associated with smoking. As reported from other research, these sociodemographic aspects could be considered in stratifying for cardiac risk factors.45 We observed no long-term effects of PBL on self-efficacy. This finding is in contrast to Su et al, who reported significant improvements in self-efficacy from an eHealth cardiac rehabilitation intervention.46 Both studies included cardiac patients and used social cognitive theory methods. However, the inconsistency can probably be explained by the facts that participants in our study were older, several lived alone and the majority had compulsory education. We accounted for these factors, suggesting that future studies may need to take sociodemographic factors into account in studies designed for cardiac rehabilitation interventions.
The COR-PRIM study has some study limitations and strengths that we would like to address. We aimed to include a representative sample of patients with CHD. To enable this, the study sample was based on rigorous inclusion criteria. Participants with CHD that was verified by coronary intervention with PCI, CABG or previous myocardial infarction. Participants were stable in their CHD and were optimally pharmacologically treated in the last month before inclusion. If applicable, patients should have completed cardiac school at the hospital. With these inclusion criteria, we consider that our sample is representative of a population being diagnosed and treated for CHD. However, the majority in this study were retired and therefore the results need to be interpreted with caution according to a younger population with CHD.
Most of the participants in this study were men and the majority were living in suburban areas. This should be considered as limitation to the generalisability of the results of the study. To mitigate the unequal distribution of men and woman, analysis was adjusted for sex.
Another aspect is the high number of participants that did not want to be included. This can be partly due to the reluctance to be in a study and having to schedule meetings and follow-up visits. We have seen this recently in another study in a similar patient group47 in which the lack of time and other commitments (eg, travel or taking care of grandchildren) were presented as reasons of non-participation. This may have consequences of the generalisability of the study results.
Moreover, the poor uptake of the PBL programme could be due to the programme design involving 13 sessions for 2 hours over 1 year. This may be regarded as challenging to manage especially if patients reside far from the hospital and not having economic compensation, which was a reason for non-participation in a similar PBL programme including 10 sessions. Of 800 screened patients with rheumatoid arthritis nearly 600 patients did not participate and stated, for example, that they did not want to even if they fit the inclusion criterions.48 However, patients abandon patient education even if there are few patient education sessions. This was found in the context of diabetes education for 3 days. Only $24\%$ of the patients attended the education.49 One way to improve barriers to the uptake of the PBL programme is to offer a digital PBL programme. Digital programme enables people to participate despite living in sub-urban areas who do not have practical or economic resources to travel for a PBL programme. The advantage of digital PBL programme is also that selected parts of the programme could be included as prerecorded modules. This could make the programme more flexible and accessible to a broader group of patients with CHD, for example, for those of employable age. We believe that PBL as a pedagogy, closely offered in a digital way to the patients may be a future option.
All participants had similar opportunities to undergo traditional cardiac school for 1 day. Cardiac school may have influenced participants attitude and knowledge and the results in this study. However, the primary outcome in this study was patient empowerment, and completion of cardiac school before the intervention might have low influence on patient empowerment.
It is a challenge to manage problems arising due to losst to follow-up and deaths while performing a study with long-term follow-up design. During a longitudinal study, participants are exposed by several things for example social media, television (TV) campaigns and articles. This affects both control and intervention participants. However, participants in PBL group have learnt to appraise patient information as more or less evident. Publicity in various media can influence the attitude towards important secondary preventive factors such as treatment with statins.50 It is, therefore, important that the PBL group are supported with an evidence-based approach. Furthermore, participants received a strategy during the 1 year intervention to reflect on public information presented in, for example, newspapers and TV. They also felt empowered in a new way to discuss treatment and self-care in the group.51 An increased patient empowerment (SWE-CES) in patients may not result in adherence to guidelines. Most of the patients were retired and cohabiting, which may imply that lifestyles were influenced by partners.52 They were invited to some of the PBL sessions to take part in discussions about their own questions with healthcare professionals, but we did not follow-up the spouses after the intervention. However, the PBL-intervention was performed for 1 year, and we believe this is a strength, as the education became a part of the patients’ lives.
Patients were recruited 6–12 months after the cardiac event on the basis that many continue to smoke, live with hypertension and elevated cholesterol levels about 6 months after starting the medication,13 which indicate that patients returned to old habits as before the cardiac event. Another clinical observation was that many patients wait for the visit to the cardiologist after discharge from the hospital. However, this is delayed with several months due to heavy workload, leaving the patients to themselves. Thus, we suggest that our intervention fills a gap during the rehabilitation process. A major strength and novelty of this study is that it was performed in primary care after the hospital-based rehabilitation programme. This fact also explains why it was so long time after the event.
A major strength of our study is the long-term follow-up, which is very much needed in interventions aiming at cardiac preventive behavioural changes. Another strength is that the intervention was led by pedagogical trained specialist nurses who worked in primary care.
Patient education led by healthcare professionals skilled in the chosen educational model improve patients’ knowledge, empowerment and management of the disease.53 A further advantage is that patients could choose to participate in group education, but few chose to participate, a digital PBL education may therefore be a future option.
## Conclusion
One-year PBL intervention had positive effect on patient empowerment, health status and HDL-C compared with the control group but did not result in improvements in other cardiac risk factors or self-efficacy. Covariates age, sex, education and marital status emerge both as healthy and cardiac risk factors.
## Data availability statement
Data are available on reasonable request.
## Patient consent for publication
Consent obtained directly from patient(s).
## Ethics approval
This study involves human participants and was approved by Regional Ethics Committee of Linköping, Ref. No. Dnr $\frac{2010}{128}$-31 Participants gave informed consent to participate in the study before taking part.
## References
1. Timmis A, Townsend N, Gale CP. **European Society of cardiology: cardiovascular disease statistics 2019**. *Eur Heart J* (2020.0) **41** 12-85. DOI: 10.1093/eurheartj/ehz859
2. **Global health estimates 2020: deaths by cause, age, sex, by country and by region, 2000-2019**. (2020.0)
3. Ambrosetti M, Abreu A, Corra U. **Secondary prevention through comprehensive cardiovascular rehabilitation: from knowledge to implementation update. A position paper from the secondary prevention and rehabilitation section of the European association of preventive cardiology**. *Eur J Prev Cardiol* (2020.0) **2020**. DOI: 10.1177/2047487320913379
4. Novaković M, Novak T, Vižintin Cuderman T. **Exercise capacity improvement after cardiac rehabilitation following myocardial infarction and its association with long-term cardiovascular events**. *Eur J Cardiovasc Nurs* (2022.0) **21** 76-84. DOI: 10.1093/eurjcn/zvab015
5. Novaković M, Rajkovič U, Košuta D. **Effects of cardiac rehabilitation and diet counselling on adherence to the Mediterranean lifestyle in patients after myocardial infarction**. *Nutrients* (2022.0) **14**. DOI: 10.3390/nu14194048
6. Magalhães S, Viamonte S, Miguel Ribeiro M. **Long-term effects of a cardiac rehabilitation program in the control of cardiovascular risk factors**. *Rev Port Cardiol* (2013.0) **32** 191-9. DOI: 10.1016/j.repc.2012.08.005
7. Plüss CE, Billing E, Held C. **Long-term effects of an expanded cardiac rehabilitation programme after myocardial infarction or coronary artery bypass surgery: a five-year follow-up of a randomized controlled study**. *Clin Rehabil* (2011.0) **25** 79-87. DOI: 10.1177/0269215510376006
8. Pack QR, Goel K, Lahr BD. **Participation in cardiac rehabilitation and survival after coronary artery bypass graft surgery: a community-based study**. *Circulation* (2013.0) **128** 590-7. DOI: 10.1161/CIRCULATIONAHA.112.001365
9. Mills M, Loney P, Jamieson E. **A primary care cardiovascular risk reduction clinic in Canada was more effective and no more expensive than usual on-demand primary care -- a randomised controlled trial**. *Health Soc Care Community* (2010.0) **18** 30-40. DOI: 10.1111/j.1365-2524.2009.00872.x
10. Storm V, Dörenkämper J, Reinwand DA. **Effectiveness of a web-based computer-tailored multiple-lifestyle intervention for people interested in reducing their cardiovascular risk: a randomized controlled trial**. *J Med Internet Res* (2016.0) **18**. DOI: 10.2196/jmir.5147
11. Goyer L, Dufour R, Janelle C. **Randomized controlled trial on the long-term efficacy of a multifaceted, interdisciplinary lifestyle intervention in reducing cardiovascular risk and improving lifestyle in patients at risk of cardiovascular disease**. *J Behav Med* (2013.0) **36** 212-24. DOI: 10.1007/s10865-012-9407-3
12. Ferrières J, Lautsch D, Ambegaonkar BM. **Use of guideline-recommended management in established coronary heart disease in the observational DYSIS II study**. *Int J Cardiol* (2018.0) **270** 21-7. DOI: 10.1016/j.ijcard.2018.06.008
13. Kotseva K, De Backer G, De Bacquer D. **Lifestyle and impact on cardiovascular risk factor control in coronary patients across 27 countries: results from the European Society of cardiology ESC-EORP EUROASPIRE V registry**. *Eur J Prev Cardiol* (2019.0) **26** 824-35. DOI: 10.1177/2047487318825350
14. Olsen SJ, Schirmer H, Bønaa KH. **Cardiac rehabilitation after percutaneous coronary intervention: results from a nationwide survey**. *Eur J Cardiovasc Nurs* (2018.0) **17** 273-9. DOI: 10.1177/1474515117737766
15. Gómez-Velasco DV, Almeda-Valdes P, Martagón AJ. **Empowerment of patients with type 2 diabetes: current perspectives**. *Diabetes Metab Syndr Obes* (2019.0) **12** 1311-21. DOI: 10.2147/DMSO.S174910
16. Williams B, Pace AE. **Problem based learning in chronic disease management: a review of the research**. *Patient Educ Couns* (2009.0) **77** 14-9. DOI: 10.1016/j.pec.2009.03.004
17. Kärner A, Nilsson S, Jaarsma T. **The effect of problem-based learning in patient education after an event of coronary heart disease -- a randomised study in primary health care: design and methodology of the COR-PRIM study**. *BMC Fam Pract* (2012.0) **13**. DOI: 10.1186/1471-2296-13-110
18. Lilly CL, Bryant LL, Leary JM. **Evaluation of the effectiveness of a problem-solving intervention addressing barriers to cardiovascular disease prevention behaviors in 3 underserved populations: Colorado, North Carolina, West Virginia, 2009**. *Prev Chronic Dis* (2014.0) **11**. DOI: 10.5888/pcd11.130249
19. Köhler AK, Jaarsma T, Tingström P. **The effect of problem-based learning after coronary heart disease - a randomised study in primary health care (COR-PRIM)**. *BMC Cardiovasc Disord* (2020.0) **20** 370. DOI: 10.1186/s12872-020-01647-2
20. Ögmundsdottir Michelsen H, Sjölin I, Schlyter M. **Cardiac rehabilitation after acute myocardial infarction in Sweden-evaluation of programme characteristics and adherence to European guidelines: the perfect cardiac rehabilitation (perfect-CR) study**. *Eur J Prev Cardiol* (2020.0) **27** 18-27. DOI: 10.1177/2047487319865729
21. Tingström P, Kamwendo K, Göransson A. **Validation and feasibility of problem-based learning in rehabilitation of patients with coronary artery disease**. *Patient Educ Couns* (2002.0) **47** 337-45. DOI: 10.1016/s0738-3991(02)00007-1
22. Leksell J, Funnell M, Sandberg G. **Psychometric properties of the Swedish diabetes empowerment scale**. *Scand J Caring Sci* (2007.0) **21** 247-52. DOI: 10.1111/j.1471-6712.2007.00463.x
23. Zumbo BD, Gadermann AM, Zeisser C. **Ordinal versions of coefficients alpha and theta for likert rating scales**. *J Mod App Stat Meth* (2007.0) **6** 21-9. DOI: 10.22237/jmasm/1177992180
24. Löve J, Moore CD, Hensing G. **Validation of the Swedish translation of the general self-efficacy scale**. *Qual Life Res* (2012.0) **21** 1249-53. DOI: 10.1007/s11136-011-0030-5
25. Tanimura C, Aoto H, Kobayashi N. **Effects of a self-efficacy theory-based training program for Peers of patients with type 2 diabetes**. *Yonago Acta Med* (2020.0) **63** 282-93. DOI: 10.33160/yam.2020.11.006
26. Tan CCL, Cheng KKF, Hwang SW. **Effect of a diabetes self-efficacy enhancing program on older adults with type 2 diabetes: a randomized controlled trial**. *Clin Nurs Res* (2020.0) **29** 293-303. DOI: 10.1177/1054773818792480
27. Tang CY, Blackstock FC, OʼHalloran PD. **A standard form of cardiac rehabilitation can improve self-reported positive behavior changes in the short- to medium-term**. *J Cardiopulm Rehabil Prev* (2019.0) **39** 175-80. DOI: 10.1097/HCR.0000000000000371
28. Borzou SR, Amiri S, Salavati M. **Effects of the first phase of cardiac rehabilitation training on self-efficacy among patients undergoing coronary artery bypass graft surgery**. *JTHC* (2018.0) **13** 126-31. DOI: 10.18502/jthc.v13i3.132
29. Madueño-Caro AJ, Pardos-Lafarga M, Muñoz-Ayllón M. **Cardiac rehabilitation in primary care. impact of an intervention on perceived self-efficacy**. *J Functional Morphol* (2019.0) **4** 32. DOI: 10.3390/jfmk4020032
30. Lenz ER, Shortridge-Baggett LM. *Self efficacy in nursing. research and measurement perspectives* (2002.0)
31. **EQ-5D-Y user guide**
32. Cheng LJ, Tan R-Y, Luo N. **Measurement properties of the EQ vas around the globe: a systematic review and meta-regression analysis**. *Value Health* (2021.0) **24** 1223-33. DOI: 10.1016/j.jval.2021.02.003
33. Gurka MJ, Filipp SL, Musani SK. **Use of BMI as the marker of adiposity in a metabolic syndrome severity score: derivation and validation in predicting long-term disease outcomes**. *Metabolism* (2018.0) **83** 68-74. DOI: 10.1016/j.metabol.2018.01.015
34. Gibson RS. *Principles of nutritional assessment* (2005.0)
35. Beishuizen CRL, Stephan BCM, van Gool WA. **Web-based interventions targeting cardiovascular risk factors in middle-aged and older people: a systematic review and meta-analysis**. *J Med Internet Res* (2016.0) **18**. DOI: 10.2196/jmir.5218
36. Melamed RJ, Tillmann A, Kufleitner H-E. **Evaluating the efficacy of an education and treatment program for patients with coronary heart disease**. *Dtsch Arztebl Int* (2014.0) **111** 802-8. DOI: 10.3238/arztebl.2014.0802
37. Hansen VB, Maindal HT. **Cardiac rehabilitation with a nurse case manager (goheart) across local and regional health authorities improves risk factors, self-care and psychosocial outcomes. A one-year follow-up study**. *JRSM Cardiovasc Dis* (2014.0) **3**. DOI: 10.1177/2048004014555922
38. Hurdus B, Munyombwe T, Dondo TB. **Association of cardiac rehabilitation and health-related quality of life following acute myocardial infarction**. *Heart* (2020.0) **106** 1726-31. DOI: 10.1136/heartjnl-2020-316920
39. Dehghani A, Kumar Bhasin S, Dwivedi S. **Influence of comprehensive life style intervention in patients of CHD**. *Glob J Health Sci* (2015.0) **7** 6-16. DOI: 10.5539/gjhs.v7n7p6
40. Salas-Salvadó J, Díaz-López A, Ruiz-Canela M. **Effect of a lifestyle intervention program with energy-restricted Mediterranean diet and exercise on weight loss and cardiovascular risk factors: one-year results of the PREDIMED-plus trial**. *Diabetes Care* (2019.0) **42** 777-88. DOI: 10.2337/dc18-0836
41. Gallagher R, Kirkness A, Armari E. **Weight management issues and strategies for people with high cardiovascular risk undertaking an Australian weight loss program: a focus group study**. *Nurs Health Sci* (2012.0) **14** 18-24. DOI: 10.1111/j.1442-2018.2011.00651.x
42. Alsaleh E, Windle R, Blake H. **Behavioural intervention to increase physical activity in adults with coronary heart disease in Jordan**. *BMC Public Health* (2016.0) **16**. DOI: 10.1186/s12889-016-3313-5
43. Karyotaki E, Ebert DD, Donkin L. **Do guided internet-based interventions result in clinically relevant changes for patients with depression? An individual participant data meta-analysis**. *Clin Psychol Rev* (2018.0) **63** 80-92. DOI: 10.1016/j.cpr.2018.06.007
44. Johansson P, Westas M, Andersson G. **An Internet-based cognitive behavioral therapy program adapted to patients with cardiovascular disease and depression: randomized controlled trial**. *JMIR Ment Health* (2019.0) **6**. DOI: 10.2196/14648
45. Safford MM, Reshetnyak E, Sterling MR. **Number of social determinants of health and fatal and nonfatal incident coronary heart disease in the REGARDS study**. *Circulation* (2021.0) **143** 244-53. DOI: 10.1161/CIRCULATIONAHA.120.048026
46. Su JJ, Yu D-F. **Effects of a nurse-led ehealth cardiac rehabilitation programme on health outcomes of patients with coronary heart disease: a randomised controlled trial**. *Int J Nurs Stud* (2021.0) **122** 104040. DOI: 10.1016/j.ijnurstu.2021.104040
47. Poli A, Kelfve S, Klompstra L. **Prediction of (non) participation of older people in digital health research: exergame intervention study**. *J Med Internet Res* (2020.0) **22**. DOI: 10.2196/17884
48. Arvidsson S, Bergman S, Arvidsson B. **Effects of a self-care promoting problem-based learning programme in people with rheumatic diseases: a randomized controlled study**. *J Adv Nurs* (2013.0) **69** 1500-14. DOI: 10.1111/jan.12008
49. Gucciardi E, DeMelo M, Offenheim A. **Patient factors associated with attrition from a self-management education programme**. *J Eval Clin Pract* (2007.0) **13** 913-9. DOI: 10.1111/j.1365-2753.2006.00773.x
50. Nielsen SF, Nordestgaard BG. **Negative statin-related news stories decrease statin persistence and increase myocardial infarction and cardiovascular mortality: a nationwide prospective cohort study**. *Eur Heart J* (2016.0) **37** 908-16. DOI: 10.1093/eurheartj/ehv641
51. Hjelmfors L, Abrandt Dahlgren M, Kärner A. **Problem-based learning used in the context of cardiac rehabilitation: different scenes and different roles**. *Studies in Continuing Education* (2014.0) **36** 218-32. DOI: 10.1080/0158037X.2014.904779
52. Köhler AK, Nilsson S, Jaarsma T. **Health beliefs about lifestyle habits differ between patients and spouses 1 year after a cardiac event-a qualitative analysis based on the health belief model**. *Scand J Caring Sci* (2017.0) **31** 332-41. DOI: 10.1111/scs.12351
53. Horder M, Andersen S. *Patient education–a health technology assessment* (2009.0)
|
---
title: 'Maternal and child outcomes for pregnant women with pre-existing multiple
long-term conditions: protocol for an observational study in the UK'
authors:
- Siang Ing Lee
- Holly Hope
- Dermot O’Reilly
- Lisa Kent
- Gillian Santorelli
- Anuradhaa Subramanian
- Ngawai Moss
- Amaya Azcoaga-Lorenzo
- Adeniyi Francis Fagbamigbe
- Catherine Nelson-Piercy
- Christopher Yau
- Colin McCowan
- Jonathan Ian Kennedy
- Katherine Phillips
- Megha Singh
- Mohamed Mhereeg
- Neil Cockburn
- Peter Brocklehurst
- Rachel Plachcinski
- Richard D Riley
- Shakila Thangaratinam
- Sinead Brophy
- Sudasing Pathirannehelage Buddhika Hemali Sudasinghe
- Utkarsh Agrawal
- Zoe Vowles
- Kathryn Mary Abel
- Krishnarajah Nirantharakumar
- Mairead Black
- Kelly-Ann Eastwood
- Francesca Crowe
journal: BMJ Open
year: 2023
pmcid: PMC9972454
doi: 10.1136/bmjopen-2022-068718
license: CC BY 4.0
---
# Maternal and child outcomes for pregnant women with pre-existing multiple long-term conditions: protocol for an observational study in the UK
## Abstract
### Introduction
One in five pregnant women has multiple pre-existing long-term conditions in the UK. Studies have shown that maternal multiple long-term conditions are associated with adverse outcomes. This observational study aims to compare maternal and child outcomes for pregnant women with multiple long-term conditions to those without multiple long-term conditions (0 or 1 long-term conditions).
### Methods and analysis
Pregnant women aged 15–49 years old with a conception date between 2000 and 2019 in the UK will be included with follow-up till 2019. The data source will be routine health records from all four UK nations (Clinical Practice Research Datalink (England), Secure Anonymised Information Linkage (Wales), Scotland routine health records and Northern Ireland Maternity System) and the Born in Bradford birth cohort. The exposure of two or more pre-existing, long-term physical or mental health conditions will be defined from a list of health conditions predetermined by women and clinicians. The association of maternal multiple long-term conditions with (a) antenatal, (b) peripartum, (c) postnatal and long-term and (d) mental health outcomes, for both women and their children will be examined. Outcomes of interest will be guided by a core outcome set. Comparisons will be made between pregnant women with and without multiple long-term conditions using modified Poisson and Cox regression. Generalised estimating equation will account for the clustering effect of women who had more than one pregnancy episode. Where appropriate, multiple imputation with chained equation will be used for missing data. Federated analysis will be conducted for each dataset and results will be pooled using random-effects meta-analyses.
### Ethics and dissemination
Approval has been obtained from the respective data sources in each UK nation. Study findings will be submitted for publications in peer-reviewed journals and presented at key conferences.
## Introduction
Maternal single long-term conditions such as cardiac conditions, chronic kidney disease and epilepsy are associated with adverse pregnancy outcomes.1–4 *This is* likely to be compounded when the pregnant woman has two or more long-term physical or mental health conditions (multimorbidity). Some conditions may need different treatments from different healthcare teams, thereby increasing the treatment burden and complexity of care.5 Recent evidence has shown that maternal multiple long-term conditions are associated with adverse outcomes for women and their children, such as severe maternal morbidity and mortality, pre-eclampsia, emergency caesarean birth, preterm birth and low birth weight.6–8 In the UK 2016–2018 national maternal mortality report, $90\%$ of women who died during or up to a year after pregnancy had multiple health or social problems.9 Currently, one in five pregnant women has multiple long-term conditions prior to pregnancy in the UK.10 The number of pregnant women with pre-existing multiple long-term conditions is likely to increase as women are getting pregnant later in life and with higher body weight.11–14 *As this* becomes an increasingly important issue, information on pregnancy, maternal and child outcomes is crucial for women and their healthcare professionals to make informed decisions on preconception and pregnancy care planning. However, there remains a lack of evidence to guide care pathways for pregnant women with multiple long-term conditions.8 15 *Healthcare is* free in the UK and over $98\%$ of the population are registered at a general practice (akin to family practice in other countries).16 General practices not only provide primary and community healthcare, but they also serve as the main point of contact for referrals to specialist clinical services and provide the majority of prescribing outside of a hospital setting.16 In the UK, pregnant women are recommended to have their booking appointment before 10 weeks gestation.17 *This is* the pregnant woman’s first midwife or doctor appointment, where they undergo health and social care assessment of needs and risks for her pregnancy.18 Over $97\%$ of births occur in healthcare settings in England and Wales.19 Therefore, routine health records in primary and secondary care in the UK offer a rich data source for observational studies of pregnant women and their children.
This observational study aims to compare outcomes for women with multiple long-term conditions to those without multiple long-term conditions. Outcomes studied will include those for women and their children. Datasets from routine health records from all four UK nations (England, Wales, Scotland and Northern Ireland) will be used. In addition, the Born in Bradford birth cohort from a deprived, ethnically diverse city in the UK, will also be used.20 The four research objectives are to examine the association between maternal pre-existing multiple long-term conditions with: (a) antenatal, (b) peripartum, (c) postnatal and long-term outcomes and (d) mental health outcomes. The findings from each research objective will be published in a separate paper.
## Study design
This is a cohort observational study using data from routine healthcare records and a birth cohort in the UK.
## Study population and eligibility criteria
The study population will consist of women aged 15–49 years old at conception, with pregnancies beginning between 1 January 2000 and 31 December 2019 in the UK. Date of conception (pregnancy start date) will be defined as the first day of the last menstrual period or gestational day 0. To ensure sufficient quality data, eligible women must have health records that meet the standard data quality checks as defined by each data source and 1 year’s worth of health records prior to index pregnancy.
## Data sources
Table 1 presents the five data sources that will be used. Each UK devolved nation is represented by a population based routine health record dataset, with good national coverage for Wales, Scotland and Northern Ireland and a representative sample for England.16 The exposure status will be determined from primary care records for Clinical Practice Research Datalink (CPRD) and Secure Anonymised Information Linkage (SAIL), with CPRD GOLD representing $5\%$ of UK general practices,21 and SAIL covering $80\%$ of *Welsh* general practices.22 For Scotland’s linked routine records and Northern Ireland Maternity System (NIMATS), the exposure status will be determined from hospital and prescribing records.
**Table 1**
| Name of data source | Country | Population: pregnant women | Exposure: maternal multiple long-term conditions status | Outcomes: pregnant women | Outcomes: children |
| --- | --- | --- | --- | --- | --- |
| Clinical Practice Research Datalink (CPRD)16 | England | Pregnancy register (primary care) | Primary care routine health records | Primary care records, hospital admissions, death registration | Mother–baby linked data: primary care records, hospital admissions, death registration |
| Secure Anonymised Information Linkage (SAIL)22 | Wales | Births from National Community Child Health Dataset | Primary care routine health records | Primary care records, hospital admissions, death registration | Mother–baby linked data: primary care records, hospital admissions, death registration |
| Scotland routine health records | Scotland | Scottish Maternity Records, pregnancy-related hospital admissions | Hospital admissions, psychiatric admissions, accident and emergency attendances, prescriptions | Hospital admissions, psychiatric admissions, accident and emergency attendances, death registration | Mother–baby linked data: hospital admissions, psychiatric admissions, accident and emergency attendances, death registration |
| Northern Ireland Maternity System (NIMATS)43 | Northern Ireland | Maternity booking (first antenatal) appointment records, birth related hospital admissions | Maternity booking (first antenatal) appointment records, birth related hospital admissions, prescriptions | Hospital admissions | Mother–baby linked data: hospital admissions |
| Born in Bradford44 | Bradford, England | Birth cohort ofover 13 500 children born from around 12 500 mothers at the Bradford Royal Infirmary between March 2007 and June 2011 | Primary care routine health records | Data from birth cohort: clinical dataData from linked health records: maternity, primary care, hospital admissions | Data from birth cohort: offspring developmental, clinical and education dataData from linked health records: primary care, hospital admissions |
CPRD and SAIL’s primary care data offer the opportunity to study outcomes that may not be captured in secondary care. For instance, vomiting in pregnancy, miscarriage and neurodevelopmental conditions in children. The Scottish dataset provides detailed information on the different types of hospital attendances, including psychiatric admissions and accident and emergency attendances. NIMATS’s unique first antenatal visit dataset is a good source of pre-pregnancy clinical data not available in other datasets.
As routine health records were not collected for research purposes, it is prone to missing data. Therefore, we have also included Born in Bradford, a regional birth cohort (2007–2011) where data were collected systematically and longitudinally from pregnancy, childhood through to adult life.
## Exposure
The exposed group will consist of pregnant women with multiple long-term conditions. Measurements of multiple long-term conditions are variable in existing literature.23 24 Currently only Bateman et al’s Maternal Comorbidity Index has been developed specifically for obstetric research.25 26 It consists of 20 health conditions and included conditions arising in pregnancy such as gestational hypertension, pre-eclampsia and placenta praevia.26 This limits the ability to study the impact of pre-existing long-term conditions on maternal and child health and the implication for long-term condition management preconception.8 *In this* study, we shall define multiple long-term conditions as two or more long-term physical or mental health conditions that pre-existed before pregnancy. Pregnancy related complications will not be included as they will be studied as outcomes. Multiple long-term conditions will be defined from a list of 79 health conditions previously described in our epidemiological work (Box 1) and will be measured with simple count.10 This list was compiled from existing multimorbidity literature9 24 27 and a workshop with our multidisciplinary research advisory group, including patient representatives and clinicians.10 Selection of health conditions were based on: (a) prevalence; (b) potential to impact on pregnancy outcomes; (c) considered important by women and (d) recorded in the study datasets.10 The phenome definitions for these health conditions have previously been described in our epidemiological work.10 For health conditions that are transient and episodic in nature (eg, asthma, eczema, depression and anxiety), we will only include the condition if it is active, which we have defined as requiring a doctors’ consultation or medical prescription in the 12 months preceding pregnancy.10 *Sensitivity analysis* will be performed defining maternal multiple long-term conditions with a different list of health conditions by D’Arcy and Knight.28 Exposure will be ascertained by the presence of diagnostic or prescriptions codes, including Read (to identify exposures in primary care data) and International Classification of Disease 10th version (ICD-10, secondary care).
## Cancers
1. All cancers
## Cardiovascular disease
2. Hypertension 3. Ischaemic heart disease and myocardial infarction 4. Heart failure 5. Stroke
6. Atrial fibrillation 7. Congenital heart disease 8. Valvular heart disease (mitral, aortic, mixed) 9. Cardiomyopathy
## Dermatology
10. Eczema 11. Psoriasis 12. Autoimmune skin disease 13. Other dermatological conditions
## Ear, nose, throat
14. Profound deafness 15. Allergic rhinitis and allergic conjunctivitis
## Eye
16. Inflammatory eye disease 17. Cataract 18. Diabetic eye disease 19. Severe blindness
20. Retinal detachment
## Gastroenterology
21. Irritable bowel syndrome 22. Inflammatory bowel disease 23. Coeliac disease 24. Chronic liver disease
25. Peptic ulcer 26. Gall stones
## Gynaecology
27. Polycystic ovarian syndrome 28. Endometriosis 29. Fibroids 30. Infertility
## Haematology
31. History of venous thromboembolism 32. Primary thrombocytopenia 33. Haemophilia 34. Sickle cell anaemia
35. Pernicious anaemia
## Mental health
36. Depression 37. Anxiety 38. Severe mental illness 39. Eating disorder
40. History of alcohol use disorder (misuse/dependence) 41. History of substance misuse 42. Others Mental health outcomes cover the antenatal and postnatal period and will be considered up to 12 months after birth. This is to account for possible delay in women presenting to clinicians and reaching a formal diagnosis. We will consider both: (a) incident and (b) recurrent mental health outcomes, where incident means a woman enters the analysis with no prior record of the specific mental health outcome. A perinatal mental health event is indicated by a primary care visit or hospital admission and includes mental health outcomes of concern in the antenatal and postnatal period (eg, depression, psychosis, post-traumatic stress disorder, self-harm and suicide attempts). Comparing the mental health event rates of pregnant women who have and have not got mental health conditions as part of their multiple long-term conditions will allow us to delineate the contribution of mental and physical morbidity to perinatal mental health outcomes. Children’s mental ill health will also be considered (eg, depression and anxiety).
## Neurodevelopmental conditions
43. Neurodevelopmental conditions
## Rheumatology
44. Systemic lupus erythematosus 45. Spondylarthritis 46. Inflammatory arthritis 47. Ehler’s Danlos Syndrome (EDS) type 3 (hypermobile EDS)
## Orthopaedic
48. Scoliosis 49. Vertebral disorder 50. Chronic back pain 51. Osteoporosis
52. Osteoarthritis
## Neurology
53. Migraine 54. Other chronic headache (including cluster headache, tension headache) 55. Epilepsy 56. Multiple sclerosis
57. Spina bifida 58. Idiopathic intracranial hypertension 59. Peripheral neuropathy 60. Other neurological conditions/musculoskeletal disorders
## Respiratory
61. Asthma 62. Chronic obstructive pulmonary disease 63. Obstructive sleep apnoea 64. Pulmonary fibrosis, interstitial lung disease
65. Pulmonary hypertension 66. Bronchiectasis 67. Cystic fibrosis 68. Sarcoidosis
## Renal
69. Chronic kidney disease 70. Urinary tract stones
## Endocrine
71. Diabetes mellitus 72. Thyroid disorder 73. Pituitary disorder 74. Adrenal benign tumour
75. Hyperparathyroidism
## Other
76. HIV infection/AIDS 77. Turner’s syndrome 78. Marfan’s syndrome 79. Solid organ transplant
## Multiple long-term conditions versus no multiple long-term conditions
Comparisons will be made with the following exposure group: The selection of which combinations and clusters of long-term conditions to study will be based on how common they are and their clinical relevance, following consultation with patient representatives and clinicians in our research team. Pregnant women with no multiple long-term conditions (ie, no or single long-term conditions) will be the common comparator group.
## Multiple long-term conditions with and without mental illness
In addition, we will also compare the outcomes for pregnant women who have mental health conditions as part of their multiple long-term conditions against pregnant women with multiple long-term conditions who do not have mental health conditions.
## Outcomes
The outcomes will be grouped into the following four categories based on the research objectives: (a) antenatal, (b) peripartum, (c) postnatal and long-term outcomes and (d) mental health outcomes. Examples of outcomes are provided as follows, based on existing core outcome sets for pregnancy and childbirth.29 30 The definitive list of outcomes will be confirmed once the development work for a core outcome set for studies of pregnant women with multiple long-term conditions is completed.31 Outcomes will be ascertained from the study datasets (1 January 2000 to 31 December 2019) using clinical codes, such as Read, ICD-10 and Operating Procedures Codes Classification of Interventions and Procedures.
## Antenatal
Antenatal outcomes occur from conception to before the onset of childbirth. Examples for women include miscarriage, gestational hypertension, pre-eclampsia, gestational diabetes, venous thromboembolism, placenta abruption and antenatal hospital admissions. Examples for children include fetal growth restriction.
## Peripartum
Peripartum outcomes occur during and immediately after childbirth. This category will also include survival outcomes for women and children. Examples for women include mode of birth (spontaneous vaginal birth, birth with forceps/ventouse, caesarean birth), postpartum haemorrhage, severe maternal morbidity, admission to intensive care and maternal death. Examples for children include preterm birth, small for gestational age, admission to neonatal unit, stillbirth, perinatal death and neonatal death.
## Postnatal and long-term
Postnatal outcomes occur in the 42 days after birth,32 while long-term outcomes are beyond the peripartum and postpartum period. For women this would include functional outcomes such as incontinence. For children, we will use mother–baby linked primary and secondary care data to study postnatal and long-term outcomes such as congenital anomalies, neurodevelopmental conditions (eg, autism, attention deficit hyperactive disorder and learning difficulty), cerebral palsy and chronic lung disease. The length of follow-up will depend on the availability of data in the routine health records. For example, CPRD has a median follow-up of 5 years.16 We will also examine postpartum readmission for mother and child.
## Covariates
Analyses will adjust for the following covariates. Additional covariates may be added for individual outcomes based on the literature. For example, in analyses of mental health outcomes there will be additional covariates. For the mother, we will include history of any mental illness, for the child we will include maternal history of any mental and/or neurodevelopmental conditions.
Where data for antenatal exposures are available (eg, from NIMATS and Born in Bradford’s booking appointments), additional analyses may be conducted where appropriate.
## Maternal age
We shall explore whether the association between maternal age and the outcomes are linear. Where this is not the case and to aid clinical interpretability, we will categorise maternal age at conception into 5-yearly age bands.
## Parity/gravidity
The variable used will depend on availability in study datasets. Where both variables are available, both will be reported with preference given to parity (the number of times a woman gave birth at gestation ≥24 weeks); and sensitivity analysis will be conducted using gravidity (the number of times a woman has been pregnant).
## Ethnicity
Maternal ethnicity will be categorised based on the variables available and to allow for harmonisation across the datasets: Asian, black, mixed, other and white. Where data permits, we may use more granular categories of ethnicity. Where numbers are too small and risk identifying individuals, such as in NIMATS, we may collapse the categories to white and non-white.
## Social deprivation
The patient level Index of Multiple Deprivation specific to each nation will be used and categorised into quintiles.
## Body mass index
We shall include the latest available pre-pregnancy body mass index for the pregnant women. Where booking data is available before 16 weeks gestation, this will be used (eg, in NIMATS). Body mass index will be considered a covariate instead of a health condition. The WHO’s classification of obesity will be used to categorise body mass index: <18.5 kg/m2, 18.5 to 24.9 kg/m2, 25.0 to 29.9 kg/m2, 30.0 to 34.9 kg/m2, 35.0 to 39.9 kg/m2 and 40+ kg/m2.33 Categories may be combined where numbers are too small.
## Smoking
We shall include the latest available pre-pregnancy smoking status for the pregnant women. Smoking status will be categorised as: non-smoker, ex-smoker and smoker.
## Year (pregnancy start date)
Data quality and clinical guidelines may vary by year. Its effect on outcomes will be accounted for by adjusting for year of conception in the analysis.
## Statistical analysis
We anticipate analyses will commence in June 2023 with study completion by June 2024. Baseline characteristics of the study population and outcomes will be described with summary statistics. Modified Poisson regression will be performed to estimate the relative risks of study outcomes. Cox regression will be performed for longer-term outcomes. The unit of analysis will be the pregnancy episode.
A federated analysis approach will be used as data governance arrangements do not allow pooling of the data across the four nations. Each dataset will be analysed separately following a common study protocol. A common data model will be established and implemented across the dataset, building on our previous work harmonising the phenome definitions for exposure conditions.10 The effect sizes will be pooled using random-effects meta-analyses with inverse variance weighting for the primary care and secondary care datasets, respectively.34 Where rare combinations of health conditions and outcomes may lead to identification of an individual or at the prespecified minimum count allowed by each data source, we will suppress the output.
## Pregnant women with more than one pregnancy episode
An individual may have more than one pregnancy over the study period. The pregnancy episodes of the same woman will not be independent of each other. The severity of the exposure variable (pre-existing multiple long-term conditions) may increase in later pregnancy episodes as the pregnant women accumulates more long-term health conditions. If a woman had an adverse pregnancy outcome, she is more at risk of the same adverse outcome in subsequent pregnancy episodes. We shall account for this clustering effect of women with more than one pregnancy episode during the study period using the Generalised Estimating Equation in the regression analyses.
## Multiple pregnancies
The main analysis will be limited to singleton pregnancies. Outcomes for pregnant women with multiple long-term conditions and multiple pregnancies (ie, twins and higher order pregnancies) will be analysed as a separate cohort.
## Missing data
Where exposure and outcome conditions are identified based on diagnostic codes, the absence of the code will be considered as an absence of the condition. The level and types of missingness of covariates will be reviewed and where appropriate will be addressed with representing missing data as a separate category or multiple imputation with chain equation (MICE). For variables required to compute an outcome, missing values will be imputed using MICE. Example of these variables include birth weight, gestational age and baby’s sex to determine preterm birth and small for gestational age. For each outcome, the statistical analyses will be performed on the imputed datasets and the estimates will be pooled with Rubin’s rule.
## Sensitivity analyses
We shall conduct sensitivity analyses using (a) complete case analysis, (b) varying definitions of maternal multiple long-term conditions exposure using D’Arcy and Knight’s core exposure set28 and (c) in primiparous women. The latter is to account for the fact that some long-term conditions can arise from complications from a previous pregnancy.
## Patient and public involvement
The research question was informed by discussions with our patient and public involvement (PPI) advisory group and our PPI coinvestigators (NM and RP).
The selection of outcomes is guided by our ongoing work developing a core outcome set for studies of pregnant women with multiple long-term conditions, where patients are key stakeholders.31 Our PPI advisory group and PPI coinvestigators will be involved in interpreting the study findings, producing lay summaries and infographics, and disseminating the study findings through their network.
## Ethics approval
CPRD: CPRD has broad National Research Ethics Service Committee ethics approval for purely observational research using the primary care data and established data linkages. The study has been reviewed and approved by CPRD’s Independent Scientific Advisory Committee (reference: 20_181R).
SAIL: In accordance with UK Health Research Authority guidance, ethical approval is not mandatory for studies using only anonymised data. The study has been approved by SAIL Information Governance Review Panel.
Scotland dataset: The study has been approved by the National Health Service Scotland Public Benefit and Privacy Panel for Health and Social Care (HSC-PBPP) and The University Teaching and Research Ethics Committee (UTREC) from the University of St Andrews.
NIMATS: The study has been approved by the Honest Broker Service Governance Board.
Born in Bradford: Ethics approval was granted by Bradford National Health Service Research Ethics Committee (ref 07/H$\frac{1302}{112}$) for the Born in Bradford cohort.
The proposed study is purely observational and will use anonymised research data. The study will not involve participant recruitment. Therefore, consent to participate is not required.
## Consent for publication
This is not applicable as the manuscript is a study protocol. In the proposed study, we will use deidentified study data, therefore consent for publication will not be required.
## Dissemination
Study findings will be submitted for publications in peer-reviewed journals and presented at key conferences for health and social care professionals involved in the care of pregnant women with multiple long-term conditions and their children. We will also organise dissemination events to share our findings with the public, service users, clinicians and researchers.
## Discussion
MuM-PreDiCT is a consortium across all four nations of the UK studying multiple long-term conditions in pregnancy. As part of MuM-PreDiCT’s programme of work, we outlined the protocol for an observational study of maternal and child outcomes for pregnant women with multiple long-term conditions, using routine health records and a birth cohort in the UK.
## Comparison with current literature
A recent systematic review found seven observational studies on the association of pre-pregnancy multiple long-term conditions with adverse maternal outcomes.8 The review found that pre-pregnancy multiple long-term conditions were associated with severe maternal morbidity, hypertensive disorders of pregnancy and acute healthcare use in the perinatal period.8 Most studies were conducted in the USA.8 Authors of the review commented that many studies included conditions arising in pregnancy in defining multiple long-term conditions, making it difficult to examine the impact of chronic conditions on maternal health.8 This proposed study will be based in the UK and will use a broad range of long-term conditions selected by women and clinicians to define multiple long-term conditions. Pregnancy related conditions and complications will be treated as study outcomes and will not be included in the exposure’s definition. We will also study outcomes across all stages of pregnancy and outcomes for both women and their children.
## Strengths and limitations
This proposed study will use routine health records from all four nations of the UK (England, Scotland, Wales and Northern Ireland). The available data sources consist of anonymised patient records from primary and secondary care, community prescription data, and maternity care data from routine booking appointments (first antenatal appointment offered universally and as the gateway to access maternity care in the UK).
Rich data will also be available from a birth cohort from Bradford, an ethnically diverse population in England. Beyond examining maternal outcomes, linked mother–baby data and the birth cohort data will allow for the exploration of child outcomes. The key strength of this proposed study therefore is the generalisability of study findings to the UK population. Observing similar effect sizes across the different datasets will also increase the confidence in the study findings. Conversely, discrepancy in findings will stimulate further exploration of the datasets which may generate new knowledge.
As this is an observational study using anonymised routine health records, key limitations include missing data, misclassification bias due to inaccurate clinical coding and residual confounding.
Maternal multimorbidity will be quantified with simple counts. A systematic review of comorbidity indices used in maternal health research found three indices: Maternal Comorbidity Index, Charlson comorbidity index and Elixhauser comorbidity index.25 Only the Maternal Comorbidity Index was developed from pregnant and postpartum women.25 It was developed using hospital data with 20 maternal comorbidities but it included pregnancy related complications and factors such as multiple gestation, gestational diabetes and hypertension disorder of pregnancy.25 26 In contrast, the list of health conditions we will use to define maternal pre-existing multimorbidity is more comprehensive and included leading causes of indirect maternal death (eg, epilepsy) and mental health conditions.
Nevertheless, when using simple counts to quantify multiple long-term conditions, the severity of each health conditions will not be captured. The dose–response relationship will only be reflected in the total number of pre-existing long-term conditions. For example, we will not be able to distinguish the outcomes for a pregnant woman with diet controlled diabetes and mild asthma from a pregnant woman with insulin dependent diabetes and brittle asthma. However, pregnant women with severe conditions are more likely to receive intense specialist care than pregnant women with mild conditions. As the number of pregnant women with greater disease severity is likely to be smaller than those with milder condition, adverse pregnancy outcomes may be underestimated.
Exposure and outcome events are only captured in routine health records when the pregnant women have presented to primary or secondary care and therefore the true prevalence and incidence may be underestimated. Health conditions that are managed conservatively in primary care, such as depression, anxiety and miscarriage, may not be captured in secondary care datasets. Events such as termination of pregnancy that occurred outside of the traditional healthcare settings may also be underestimated.35 Similarly, antenatal hospital admission data may not reflect the full burden of additional antenatal appointments or acute care attendances, as care accessed through other routes may not be captured.
Body mass index, which encompasses underweight and obese categories, will be studied as a covariate instead of being counted as part of multimorbidity. There is much debate around whether obesity should be considered a disease36 or a risk factor for other long-term conditions such as cardiometabolic conditions and cancers.37–39 *What is* clear is pre-pregnancy maternal obesity is associated with adverse pregnancy outcome and dedicated care guideline has been established to manage this risk.40 41
## Clinical implications
Current obstetric guidelines for pregnant women with medical conditions are focused on specific and single health conditions. There are currently no guidelines for the management of pregnant women with multiple long-term conditions in the UK. The heterogeneity of multiple long-term conditions means an all-encompassing guideline for every possible combination of long-term conditions would not be possible. Indeed the English national guideline for multimorbidity focuses on general approaches such as coordinated and holistic care, improving quality of life by reducing treatment burden and shared decision making between patients and clinicians.42 A guideline for multiple long-term conditions (multimorbidity) in pregnancy is likely to follow the same principles but with additional focus on the maternity care aspect.
The basis of shared decision making is the provision of evidence based information. As observed in the systematic review, there is currently a lack of evidence on the consequences of pregnancy for women with multiple long-term conditions.8 Our PPI advisory group and preliminary findings from our core outcome set development work have highlighted how women valued having information to help them mentally prepare to face potential adverse pregnancy outcomes. The output from this study will therefore provide valuable information for women to make informed decision with their clinicians about family planning and their preconception, pregnancy and postpartum care. It will also provide valuable information to guide the future design of care pathway for women with multiple long-term conditions.
## Patient consent for publication
Not applicable.
## References
1. Eggleton EJ, McMurrugh KJ, Aiken CE. **Maternal pregnancy outcomes in women with cardiomyopathy: a systematic review and meta-analysis**. *Am J Obstet Gynecol* (2022.0) **227** 582-92. DOI: 10.1016/j.ajog.2022.05.039
2. Roos-Hesselink J, Baris L, Johnson M. **Pregnancy outcomes in women with cardiovascular disease: evolving trends over 10 years in the ESC registry of pregnancy and cardiac disease (ROPAC)**. *Eur Heart J* (2019.0) **40** 3848-55. DOI: 10.1093/eurheartj/ehz136
3. Wiles K, Webster P, Seed PT. **The impact of chronic kidney disease stages 3-5 on pregnancy outcomes**. *Nephrol Dial Transplant* (2021.0) **36** 2008-17. DOI: 10.1093/ndt/gfaa247
4. Viale L, Allotey J, Cheong-See F. **Epilepsy in pregnancy and reproductive outcomes: a systematic review and meta-analysis**. *Lancet* (2015.0) **386** 1845-52. DOI: 10.1016/S0140-6736(15)00045-8
5. **Multimorbidity: technical series on safer primary care**. (2016.0)
6. Admon LK, Winkelman TNA, Heisler M. **Obstetric outcomes and delivery-related health care utilization and costs among pregnant women with multiple chronic conditions**. *Prev Chronic Dis* (2018.0) **15**. DOI: 10.5888/pcd15.170397
7. D’Arcy R, Knight M, Mackillop L. **A retrospective audit of the socio-demographic characteristics and pregnancy outcomes for all women with multiple medical problems giving birth at A tertiary hospital in the UK in 2016**. *BJOG: An International Journal of Obstetrics and Gynaecology* (2019.0) **126** 128. DOI: 10.1016/j.preghy.2018.08.430
8. Brown HK, McKnight A, Aker A. **Association between pre-pregnancy multimorbidity and adverse maternal outcomes: a systematic review**. *J Multimorb Comorb* (2022.0) **12** 26335565221096584. DOI: 10.1177/26335565221096584
9. Knight M, Bunch K, Tuffnell D. *Saving lives, improving mothers’ care - lessons learned to inform maternity care from the UK and ireland confidential enquiries into maternal deaths and morbidity 2016-18* (2020.0)
10. Lee SI, Azcoaga-Lorenzo A, Agrawal U. **Epidemiology of pre-existing multimorbidity in pregnant women in the UK in 2018: a population-based cross-sectional study**. *BMC Pregnancy Childbirth* (2022.0) **22**. DOI: 10.1186/s12884-022-04442-3
11. **Childbearing for women born in different years, england and wales: 2020**. (2022.0)
12. **SF2.3: age of mothers at childbirth and age-specific fertility**. (2022.0)
13. Chen C, Xu X, Yan Y. **Estimated global overweight and obesity burden in pregnant women based on panel data model**. *PLoS One* (2018.0) **13**. DOI: 10.1371/journal.pone.0202183
14. Wang MC, Freaney PM, Perak AM. **Trends in prepregnancy obesity and association with adverse pregnancy outcomes in the united states, 2013 to 2018**. *J Am Heart Assoc* (2021.0) **10**. DOI: 10.1161/JAHA.120.020717
15. Beeson JG, Homer CSE, Morgan C. **Multiple morbidities in pregnancy: time for research, innovation, and action**. *PLoS Med* (2018.0) **15**. DOI: 10.1371/journal.pmed.1002665
16. Herrett E, Gallagher AM, Bhaskaran K. **Data resource profile: clinical practice research datalink (CPRD)**. *Int J Epidemiol* (2015.0) **44** 827-36. DOI: 10.1093/ije/dyv098
17. **Clinical knowledge summaries: antenatal care - uncomplicated pregnancy**. (2021.0)
18. Hatherall B, Morris J, Jamal F. **Timing of the initiation of antenatal care: an exploratory qualitative study of women and service providers in east london**. *Midwifery* (2016.0) **36** 1-7. DOI: 10.1016/j.midw.2016.02.017
19. **Birth characteristics in england and wales: 2020**. (2022.0)
20. Raynor P. **Born in bradford, a cohort study of babies born in bradford, and their parents: protocol for the recruitment phase**. *BMC Public Health* (2008.0) **8**. DOI: 10.1186/1471-2458-8-327
21. **Release notes: CPRD GOLD may 2022**. (2022.0)
22. **10 years of spearheading data privacy and research utility**
23. Ho IS-S, Azcoaga-Lorenzo A, Akbari A. **Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies**. *BMJ Open* (2022.0) **12**. DOI: 10.1136/bmjopen-2021-057017
24. Ho I-S, Azcoaga-Lorenzo A, Akbari A. **Examining variation in the measurement of multimorbidity in research: a systematic review of 566 studies**. *Lancet Public Health* (2021.0) **6** e587-97. DOI: 10.1016/S2468-2667(21)00107-9
25. Aoyama K, D’Souza R, Inada E. **Measurement properties of comorbidity indices in maternal health research: a systematic review**. *BMC Pregnancy Childbirth* (2017.0) **17**. DOI: 10.1186/s12884-017-1558-3
26. Bateman BT, Mhyre JM, Hernandez-Diaz S. **Development of a comorbidity index for use in obstetric patients**. *Obstet Gynecol* (2013.0) **122** 957-65. DOI: 10.1097/AOG.0b013e3182a603bb
27. Kuan V, Denaxas S, Gonzalez-Izquierdo A. **A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National health service**. *Lancet Digit Health* (2019.0) **1** e63-77. DOI: 10.1016/S2589-7500(19)30012-3
28. D’Arcy R, Knight M. **List of health conditions to define multiple long-term conditions in pregnancy**
29. Devane D, Begley CM, Clarke M. **Evaluating maternity care: a core set of outcome measures**. *Birth* (2007.0) **34** 164-72. DOI: 10.1111/j.1523-536X.2006.00145.x
30. Nijagal MA, Wissig S, Stowell C. **Standardized outcome measures for pregnancy and childbirth, an ICHOM proposal**. *BMC Health Serv Res* (2018.0) **18**. DOI: 10.1186/s12913-018-3732-3
31. Lee SI, Eastwood K-A, Moss N. **Protocol for the development of a core outcome set for studies of pregnant women with pre-existing multimorbidity**. *BMJ Open* (2021.0) **11**. DOI: 10.1136/bmjopen-2020-044919
32. **WHO technical consultation on postpartum and postnatal care**. (2010.0)
33. **Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World health organization technical report series 2000;894:i-xii, 1-253**. (2001.0)
34. Borenstein M, Hedges LV, Higgins JPT. **A basic introduction to fixed-effect and random-effects models for meta-analysis**. *Res Synth Methods* (2010.0) **1** 97-111. DOI: 10.1002/jrsm.12
35. Minassian C, Williams R, Meeraus WH. **Methods to generate and validate a pregnancy register in the UK clinical practice research datalink primary care database**. *Pharmacoepidemiol Drug Saf* (2019.0) **28** 923-33. DOI: 10.1002/pds.4811
36. Burki T. **European commission classifies obesity as a chronic disease**. *Lancet Diabetes Endocrinol* (2021.0) **9** 418. DOI: 10.1016/S2213-8587(21)00145-5
37. Abdullah A, Peeters A, de Courten M. **The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies**. *Diabetes Res Clin Pract* (2010.0) **89** 309-19. DOI: 10.1016/j.diabres.2010.04.012
38. Bhaskaran K, Douglas I, Forbes H. **Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5·24 million UK adults**. *Lancet* (2014.0) **384** 755-65. DOI: 10.1016/S0140-6736(14)60892-8
39. Powell-Wiley TM, Poirier P, Burke LE. **Obesity and cardiovascular disease: a scientific statement from the American heart association**. *Circulation* (2021.0) **143** e984-1010. DOI: 10.1161/CIR.0000000000000973
40. D’Souza R, Horyn I, Pavalagantharajah S. **Maternal body mass index and pregnancy outcomes: a systematic review and metaanalysis**. *Am J Obstet Gynecol MFM* (2019.0) **1** 100041. DOI: 10.1016/j.ajogmf.2019.100041
41. **Care of women with obesity in pregnancy (green-top guideline no. 72)**. (2018.0)
42. **NICE guideline [NG56]: multimorbidity: clinical assessment and management**. (2016.0)
43. **Northern ireland regional maternity system (NIMATS**. (2021.0)
44. **About us 2021**
|
---
title: 'Xanthine oxidase inhibition and white matter hyperintensity progression following
ischaemic stroke and transient ischaemic attack (XILO-FIST): a multicentre, double-blinded,
randomised, placebo-controlled trial'
authors:
- Jesse Dawson
- Michele Robertson
- David Alexander Dickie
- Phillip Bath
- Kirsten Forbes
- Terence Quinn
- Niall M. Broomfield
- Krishna Dani
- Alex Doney
- Graeme Houston
- Kennedy R. Lees
- Keith W. Muir
- Allan Struthers
- Matthew Walters
- Mark Barber
- Ajay Bhalla
- Alan Cameron
- Alexander Dyker
- Paul Guyler
- Ahamad Hassan
- Mark T. Kearney
- Breffni Keegan
- Sekaran Lakshmanan
- Mary Joan Macleod
- Marc Randall
- Louise Shaw
- Ganesh Subramanian
- David Werring
- Alex McConnachie
journal: eClinicalMedicine
year: 2023
pmcid: PMC9972492
doi: 10.1016/j.eclinm.2023.101863
license: CC BY 4.0
---
# Xanthine oxidase inhibition and white matter hyperintensity progression following ischaemic stroke and transient ischaemic attack (XILO-FIST): a multicentre, double-blinded, randomised, placebo-controlled trial
## Body
Research in contextEvidence before this studyWe searched PubMed and Embase between 1st October 2019 and October 31st 2022. These dates were chosen as we have recently published a systematic review which covered dates from inception to 30th September 2019. In people with stroke, allopurinol has been shown to reduce markers of inflammation, endothelial function and blood pressure in small studies. Meta-analysis of randomised trials suggests allopurinol may lower cardiovascular event rate in people with established cardiovascular disease but this was not confirmed for people with ischaemic heart disease in the recent *Allopurinol versus* usual care in UK patients with ischaemic heart disease (ALL-HEART) study. Added value of this studyOur study confirms a small effect of allopurinol on blood pressure. However, this effect, or that of other putative modes of action, were insufficient to modify a marker of cerebral small vessel disease and recurrent stroke risk. Implications of all the available evidenceAllopurinol did not reduce white matter hyperintensities and is unlikely to reduce risk of stroke or cognitive decline in unselected people with ischaemic stroke and transient ischaemic attack. Allopurinol has a small effect on blood pressure, which is unlikely to be important in unselected people, but may be larger in people with hyperuricaemia.
## Summary
### Background
People who experience an ischaemic stroke are at risk of recurrent vascular events, progression of cerebrovascular disease, and cognitive decline. We assessed whether allopurinol, a xanthine oxidase inhibitor, reduced white matter hyperintensity (WMH) progression and blood pressure (BP) following ischaemic stroke or transient ischaemic attack (TIA).
### Methods
In this multicentre, prospective, randomised, double-blinded, placebo-controlled trial conducted in 22 stroke units in the United Kingdom, we randomly assigned participants within 30-days of ischaemic stroke or TIA to receive oral allopurinol 300 mg twice daily or placebo for 104 weeks. All participants had brain MRI performed at baseline and week 104 and ambulatory blood pressure monitoring at baseline, week 4 and week 104. The primary outcome was the WMH Rotterdam Progression Score (RPS) at week 104. Analyses were by intention to treat. Participants who received at least one dose of allopurinol or placebo were included in the safety analysis. This trial is registered with ClinicalTrials.gov, NCT02122718.
### Findings
Between 25th May 2015 and the 29th November 2018, 464 participants were enrolled (232 per group). A total of 372 (189 with placebo and 183 with allopurinol) attended for week 104 MRI and were included in analysis of the primary outcome. The RPS at week 104 was 1.3 (SD 1.8) with allopurinol and 1.5 (SD 1.9) with placebo (between group difference −0.17, $95\%$ CI −0.52 to 0.17, $$p \leq 0.33$$). Serious adverse events were reported in 73 ($32\%$) participants with allopurinol and in 64 ($28\%$) with placebo. There was one potentially treatment related death in the allopurinol group.
### Interpretation
Allopurinol use did not reduce WMH progression in people with recent ischaemic stroke or TIA and is unlikely to reduce the risk of stroke in unselected people.
### Funding
The $\frac{10.13039}{501100000274}$British Heart Foundation and the $\frac{10.13039}{501100000364}$UK Stroke Association.
## Evidence before this study
We searched PubMed and Embase between 1st October 2019 and October 31st 2022. These dates were chosen as we have recently published a systematic review which covered dates from inception to 30th September 2019. In people with stroke, allopurinol has been shown to reduce markers of inflammation, endothelial function and blood pressure in small studies. Meta-analysis of randomised trials suggests allopurinol may lower cardiovascular event rate in people with established cardiovascular disease but this was not confirmed for people with ischaemic heart disease in the recent *Allopurinol versus* usual care in UK patients with ischaemic heart disease (ALL-HEART) study.
## Added value of this study
Our study confirms a small effect of allopurinol on blood pressure. However, this effect, or that of other putative modes of action, were insufficient to modify a marker of cerebral small vessel disease and recurrent stroke risk.
## Implications of all the available evidence
Allopurinol did not reduce white matter hyperintensities and is unlikely to reduce risk of stroke or cognitive decline in unselected people with ischaemic stroke and transient ischaemic attack. Allopurinol has a small effect on blood pressure, which is unlikely to be important in unselected people, but may be larger in people with hyperuricaemia.
## Introduction
People who have an ischaemic stroke are at risk of cognitive decline and recurrent vascular events.1,2 *Higher serum* uric acid (UA) levels are associated with vascular cognitive impairment,3 increased risk of first and recurrent stroke4 and a worse outcome after ischaemic stroke.5 Mendelian randomisation studies demonstrate that higher genetically predicted serum uric acid level is associated with increased risk of coronary artery disease and ischaemic stroke and that this is, in part, mediated by the relationship between serum uric acid and blood pressure (BP).6 Allopurinol, the most used urate-lowering drug in people with gout, has been shown to reduce markers of inflammation,7 augmentation index, progression of carotid intima-media thickness and BP8 in people with stroke and to increase cerebral nitric oxide bioavailability in people with diabetes.9 Allopurinol also reduces blood pressure in hyperuricemic adolescents with hypertension and may have additional urate-independent effects.10 Meta-analysis of randomised trials suggests allopurinol may lower cardiovascular event rate in high-risk individuals.6 White matter hyperintensities of presumed vascular origin (WMH) are a marker of cerebral small vessel disease and are present in as many as $90\%$ of people with ischaemic stroke.11 The degree of WMH burden and progression over time are associated with higher rates of stroke, death, and cognitive and physical decline.12 BP reduction may reduce WMH progression.13,14 We hypothesized that allopurinol may reduce WMH progression in people with recent stroke by lowering BP, and through additional effects on vascular stiffness and function. If this were the case, this would raise the possibility that allopurinol may reduce cognitive decline and stroke recurrence after stroke.
The Xanthine oxidase Inhibition for improvement of Long-term Outcomes Following Ischaemic Stroke and Transient ischaemic attack (XILO-FIST) trial aimed to determine whether allopurinol reduces WMH progression and BP in people with recent ischaemic stoke.
## Study design
XILO-FIST was a multicentre, prospective, randomised, double-blinded, placebo-controlled trial performed in 22 sites in the UK. Further details regarding the design of the trial have been published previously and the protocol is available on-line.15 The study was approved by the NHS Research Ethics Committee (REC number 14/WS/0113) and by the UK Medicine and Health Regulatory Agency. Written informed consent was obtained from all participants. The study was conducted according to the Declaration of Helsinki 2013. The study reporting followed Consolidated Standards of Reporting Trials (CONSORT) guidance. The study included a cardiac Magnetic Resonance Imaging (MRI) sub-study, which is not reported here.
## Participants
Study participants were adults aged greater than 50 years with a history of ischaemic stroke or transient ischaemic attack (TIA) within the past 4 weeks. Potential participants were identified during in-patient stay in an acute stroke unit or in a cerebrovascular out-patient clinic. Diagnosis was confirmed by a stroke physician. All subtypes of ischaemic stroke and TIA were included. Full inclusion and exclusion criteria are provided in Appendix Table 1.
## Randomisation and masking
Participants were randomised (1:1) following completion of the run-in phase to receive either allopurinol or matching placebo orally for 104 weeks. Randomisation was carried out using a bespoke study web portal and was performed by the Robertson Centre for Biostatistics at the University of Glasgow. Twenty percent of participants were allocated to treatments by simple randomisation, with the remaining $80\%$ allocated by a minimisation algorithm which included presence of WMH at baseline and cardiac sub-study eligibility as minimisation factors. Changes in serum uric acid concentration would have compromised allocation concealment so this was not measured as part of the study.
## Procedures
The study comprised a 4-week run in phase and a 104-week treatment phase. The run-in phase comprised an enrolment visit on day 0 and a baseline assessment visit at 4 weeks. In order to successfully complete the run-in phase and proceed to randomisation, participants must have completed baseline data collection and have undergone brain MRI. No study medication was given during the run-in phase.
A summary of study procedures is given in Appendix Table 2. The baseline assessment visit included assessment of brachial sphygmomanometer BP, ambulatory blood pressure monitoring (ABPM), electrocardiography, brain MRI, and assessment of cognitive function using the Montreal Cognitive Assessment (MoCA, version 7.3) and a multidomain neuropsychological battery.
After randomisation, participants were dispensed study medication and were followed up at weeks 4, 13, 26, 52, 78 and 104. At all visits, participants were assessed for adverse events, brachial BP was measured, safety blood tests were performed, and study medication was dispensed. In addition, ABPM was performed at the week 4 visit and a MoCA was performed at week 52. At the week 104 visit, measurement of brachial BP, ABPM, electrocardiography, brain MRI, and assessment of cognitive function were performed, and study medication was stopped. A telephone follow-up was performed one week later to assess for adverse events.
During the first 4 weeks after randomisation, a single 300 mg daily dose of oral allopurinol or placebo was prescribed. All participants underwent dose titration to allopurinol 300 mg twice daily or placebo unless estimated Glomerular Filtration Rate (eGFR) was <60 mL/min, where once daily dosing was continued. The total treatment duration was 104 weeks. Dose modification (a reduction from 300 mg twice daily to 300 mg once daily) occurred if renal function declined to an eGFR of <50 mL/min or in the event of side effects. Dosing was stopped if renal function declined to an eGFR of <30 mL/min.
Brain MRI was performed using 1.5 or 3 T MRI. The protocol required the same MRI scanner and same sequence parameters to be used for the baseline and follow-up scans. Study sequences include T1 weighted imaging, T2-weighted imaging, fluid attenuated inversion recovery (FLAIR), diffusion weighted imaging and susceptibility weighted imaging. Isotropic T1, T2 and FLAIR imaging were performed where possible. Typical sequence parameters are given in Appendix Table 3.
All scans were reviewed blinded to treatment allocation. The Standards for Reporting Vascular changes on Neuroimaging (STRIVE) recommendations were followed during image review.16 Accordingly, WMH of presumed vascular origin were defined as hyperintense lesions on FLAIR in the white matter that were not due to the index stroke, did not have a hyperintense rim, and were not confluent with areas of cortical infarction. All visual rating scales were assessed independently by two trained observers (JD, KD, or DD). Where there was any level of disagreement on a score, this was resolved in an adjudication meeting between the two reviewers and a consensus score applied.
Fazekas and Scheltens scale scores were calculated for each baseline and week 104 scan.17,18 A Rotterdam progression score (RPS) and Schmidt's progression score were calculated by simultaneous side by side review of the baseline and week 104 scans.19,20 This was done with random ordering of baseline and follow-up scans. Once review was complete, ordering was un-blinded to allow determination of progression score.
Volumetric assessment of WMH volume was performed. First, the white matter volume was estimated using atlas-based segmentation.21 A probability map of white matter created from 313 volunteers aged 18–96 years was used22 and registered to each scan using non-linear (diffeomorphic) registration to provide an initial estimate of white matter in each participant.23,24 Hyperintense outliers were identified on FLAIR by transforming each voxel to a standard (z) score. Voxels with z ≥ 1.5 and within the estimated white matter volume were initially defined as WMH. Final WMH estimates were defined by 3D Gaussian smoothing to reduce noise and account for partial volumes around WMH edges. Automatic WMH estimates were visually checked, and infarcts masked by a trained image analyst following STRIVE guidelines.16 New brain infarction was assessed by side-to-side review of baseline and follow-up MRI scans by 1 reviewer (JD). FLAIR and DWI images were reviewed. Areas of new cortical infarction or new lacunar infarction were classed as new brain infarction.
Twenty four hour ABPM was performed at baseline, week 4 and week 104 unless contraindicated. A Spacelabs Ultralight Ambulatory Blood Pressure Monitor was used. This was set to take readings every 30 minutes during daytime (0800 h–2159 h) and every 60 minutes during night-time (2200 h–0759 h). ABPM was not performed in participants with significant arm weakness who would be unable to remove the device in the event of discomfort or other problems.
## Outcomes
The primary outcome was WMH progression measured using the RPS. Secondary outcomes were Schmidt's progression score, change in WMH volume at week 104, change in Fazekas' score at week 104, change in Scheltens' score at week 104, new brain infarction at week 104 MRI, RPS with those who died/became too frail to undergo repeat imaging assigned worst score, change in mean day-time systolic BP (SBP) at week 4, change in mean day-time diastolic BP (DBP) at week 4, change in mean day-time SBP at week 104, change in mean day-time DBP at week 104 and change in MoCA score. We also assessed in-clinic BP at week 4 and week 104 as an exploratory outcome.
## Protocol amendments and additional changes due to the COVID-19 pandemic
A summary of all protocol amendments is given in Appendix Table 4. On the 23rd of March 2020 the government in the United Kingdom issued a stay-at-home order in response to the COVID-19 pandemic. All participating sites suspended clinical research activity, unless it was related to COVID-19 or unless there was a specific participant safety issue. Several sites implemented similar changes in the weeks prior to this date. At this point, enrolment and visits up to week 52 had completed but there were 90 participants still under follow-up. An amendment was approved to allow participants to continue study medication for a maximum of an additional 6-months in the hope that follow-up could be completed after the first wave of infections had passed. The amendment also allowed for telephone visits to be conducted to obtain study data if a face-to-face visit was not possible. In addition, ABPM was no longer performed at the week 104 visit.
## Statistical analysis
We assumed that $90\%$ of participants would have evidence of WMH at baseline and that approximately $64\%$ would progress by one point or more on the RPS and that the mean progression score in the placebo group would be 1.293 at week 104 based on data from the Leukoaraiosis and Disability Study.25 We calculated, based on a Wilcoxon-Mann-Whitney test, that a sample size of 192 participants per group would give $80\%$ power to detect a $30\%$ relative reduction in progression score at a $5\%$ significance level (nQuery Advisor® v7.0). This was chosen as a conservative minimally important difference as it is less than the previously reported difference seen with BP reduction.26 Further detail on the assumptions used of the sample size calculation are contained in the study protocol. We planned to randomize 232 participants per group to allow for withdrawals and for participants who would be unable to return for the week 104 MRI. We also calculated that 101 participants per group would be required to give $80\%$ power at a $5\%$ significance level to detect the previously reported 3.3 mmHg difference in change in SBP26 (assumed SD 8.3) at week 4.
All analyses were carried out according to the intention-to-treat (ITT) principle. Additional analyses were to be carried out using a per-protocol (PP) population. This excluded participants where there was an eligibility violation, participants who had more than 90 days of total treatment interruption and participants from one site where a serious breach of good clinical practice (GCP) was detected. The safety analysis set included all participants who received at least one dose of study medication.
The primary outcome was assessed by a linear regression model which adjusted for minimisation variables, site (as a random factor), and baseline characteristics associated with WMH progression (age, baseline National Institute of Health Stroke Scale score, baseline clinical SBP and Scheltens total score). Secondary outcomes were assessed by the same method except for progression on Schmidt's Progression score and presence of new brain infarction which were analysed by a Chi-squared test and logistic regression to adjust for minimisation variables. A p value of <0.05 was used for statistical significance. We pre-specified three sub-group analyses. These were by age, baseline uric acid level defined by the median and whether participation was completed before the introduction of Covid restrictions. We also performed a sensitivity analysis for MRI outcomes which included only those participants who had baseline and week 104 imaging performed on the same scanner, with the same sequence parameters, and no other quality issues deemed to affect interpretation.
The trial is registered in clinicaltrials.gov (registration number NCT02122718) and was adopted by the UK National Institute of Health Stroke Research Network and the Scottish Stroke Research Network.
The trial was overseen by a Trial Steering Committee (TSC) which met at least annually and comprised an independent chair, three other independent members, a participant representative, the Chief Investigator, and trial statistician. An independent Data Monitoring Committee (IDMC) met at least annually to review unblinded data. This comprised 4 independent members. The day-to-day running of the trial was overseen by the Trial Management Group at the University of Glasgow chaired by the Chief Investigator. Details of committee members are given in Appendix X.
## Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. JD, AM and MR have had access to all study data. JD had final responsibility for the submission of the manuscript. All authors agreed to submission.
## Results
A total of 538 participants were consented and entered the run-in phase between 25th May 2015 and the 29th November 2018. Of these, 74 withdrew prior to randomisation, leaving 464 participants who were randomised (232 per group). Of these, 372 (189 with placebo and 183 with allopurinol) attended for week 104 MRI (see trial profile, Fig. 1). There were no significant protocol deviations that affected the rights, safety, or well-being of participants or the scientific integrity of the study with the exception of at one site which enrolled fewer than 20 participants. This site was found to be in serious breach of GCP, which was reported to UK Medicine and Health Regulatory Agency. No participant came to harm from this breach. The safety analysis set included 460 participants who received at least one dose of medication. The per protocol analysis set included 379 participants. Reasons for exclusion from the per-protocol population were eligibility violation ($$n = 3$$), treatment interruption for more than 90 days ($$n = 71$$) and enrolment from the site with a serious breach of GCP. The trial was subject to a routine inspection by the UK Medicine and Health Regulatory Agency in January 2019. There were no critical findings. Fig. 1Trial profile. Figure shows the number of participants who were eligible for participation and gave consent followed by details on randomisation, follow-up and withdrawal. ∗Included in assessment of the primary outcome. MRI = magnetic resonance imaging. GCP = good clinical practice.
Baseline demographics are shown in Table 1. Baseline measures of WMH are shown in Table 2. Groups were well matched at baseline. Enrollment by site is shown in Appendix Table 5.Table 1Baseline characteristics. Allopurinol ($$n = 232$$)Placebo ($$n = 232$$)Age years, mean (SD)65.8 (8.9)65.6 (8.6)Male gender, n (%)154 ($66.4\%$)165 ($71.1\%$)Female gender, n (%)78 ($33.6\%$)67 ($28.9\%$)Ethnicity, n (%) White231 ($99.6\%$)228 ($98.3\%$) Multiple/mixed ethnic01 ($0.4\%$) Black1 ($0.4\%$)2 ($0.9\%$) Other01 ($0.4\%$)Current smoker, n (%)47 ($20.3\%$)48 ($20.7\%$)Body mass index, mean (SD)28.1 ($4.7\%$)28.8 ($5.4\%$)Systolic blood pressure, mmHg, mean (SD)136.0 (17.7)136.6 (17.2)Diastolic blood pressure, mmHg, mean (SD)78.5 (10.6)79.9 (10.4)Myocardial infarction, n (%)20 ($8.6\%$)21 ($9.1\%$)Stroke, n (%)17 ($7.3\%$)24 ($10.3\%$)Transient ischaemic attack, n (%)21 ($9.1\%$)25 ($10.8\%$)Peripheral vascular disease, n (%)12 ($5.2\%$)15 ($6.5\%$)Hypertension, n (%)121 ($52.2\%$)122 ($52.6\%$)Diabetes, n (%)48 ($20.7\%$)51 ($22.0\%$)Dyslipidemia, n (%)83 ($35.8\%$)80 ($34.5\%$)Gout, n (%)a4 ($1.8\%$)1 ($0.4\%$)Qualifying event, n (%) Ischemic stroke215 ($92.7\%$)217 ($93.5\%$) Transient ischaemic attack17 ($7.3\%$)15 ($6.5\%$)Time from index event to randomization, days mean (SD)41.2 (11.6)41.5 (9.4)Stroke class, n (%) Total anterior circulation stroke7 ($3.0\%$)7 ($3.0\%$) Partial anterior circulation stroke68 ($29.3\%$)66 ($28.4\%$) Lacunar stroke103 ($44.4\%$)91 ($39.2\%$) Posterior circulation stroke54 ($23.3\%$)67 ($28.9\%$) Amaurosis fugax01 ($0.4\%$)Lipid lowering therapy, n (%)226 ($97.4\%$)225 ($97.0\%$)Antithrombotic therapy, n (%)227 ($97.8\%$)226 ($97.4\%$)Blood pressure lowering therapy, n (%)205 ($88.4\%$)205 ($88.4\%$)National Institute of Health Stroke Scale score, mean (SD)1.4 (1.7)1.6 (2.0)Modified Rankin Scale Score of 0–1128 ($55.2\%$)134 ($57.7\%$)Estimated Glomerular Filtration Rate, ml/min/1.73 m2, mean (SD)85.5 (19.8)86.3 (19.8)Serum uric acid, μmol/l, mean (SD)342.2 (84.2)328.5 (87.5)aGout data were only reported on $$n = 228$$ with allopurinol and 229 with placebo. SD = standard deviation. Table 2Baseline measures of white matter hyperintensities. Allopurinol ($$n = 232$$)Placebo ($$n = 232$$)Fazekas total score, mean (SD)2.7 (1.2)2.7 (1.3)Scheltens total score, mean (SD)12.5 (6.0)12.7 (7.1)White matter hyperintensity volume, mls, mean (SD)16.7 (15.0)18.2 (18.8)Fazekas periventricular hyperintensities score, n (%) Caps or pencil thin lining161 ($69.4\%$)158 ($68.1\%$) Smooth halo around ventricles45 ($19.4\%$)50 ($21.6\%$) Irregular halo26 ($11.2\%$)24 ($10.3\%$)Fazekas deep white matter score n (%) Absent8 ($3.4\%$)11 ($4.7\%$) Multiple focal lesions163 ($70.3\%$)154 ($66.4\%$) Early confluent lesions48 ($20.7\%$)48 ($20.7\%$) Confluent lesions13 ($5.6\%$)19 ($8.2\%$)Scheltens periventricular hyperintensity score, mean (SD)3.6 (1.1)3.7 (1.2)Scheltens white matter hyperintensity score, mean (SD)6.7 (4.3)6.5 (4.4)Scheltens basal ganglia score, mean (SD)0.9 (1.1)1.1 (1.5)Scheltens infratentorial fossa score, mean (SD)1.3 (1.4)1.4 (1.7)SD = standard deviation.
A total of 83 participants stopped study medication (52 ($22.4\%$) with allopurinol and 31 ($13.4\%$) with placebo). This was due to a related adverse event in 33 treated with allopurinol and 21 with placebo. The median time (IQR) to treatment discontinuation was 181 (54–368) days with allopurinol and 109 (43–208) days with placebo. The mean change in serum uric acid level at week 104 was - 92.6 μmol/l (SD 135.1) in the allopurinol group and - 13.8 μmol/l (SD 101.4) in the placebo group.
Four sites installed new MRI scanners during the study period meaning that their week 104 scans were performed on a different scanner to the baseline scan ($$n = 32$$).
The RPS was 1.3 (SD 1.8) with allopurinol and 1.5 (SD 1.9) with placebo, between group difference −0.17, $95\%$ CI −0.52 to 0.17, $$p \leq 0.33.$$ There was no significant difference in the Schmidt's Progression Score, in WMH volume, or in the RPS where those who died or became too frail to undergo MRI were assigned the highest score (Table 3). There was also no difference in the odds of new brain infarction at week 104 (Table 3). There was no significant difference in change in Fazekas' score but change in Scheltens' score was lower with allopurinol (Table 3).Table 3Imaging outcomes. OutcomeChange/n (%) with allopurinolChange/n (%) with placeboBetween group difference$95\%$ CIP valueRPS score, mean (SD)1.3 (1.8)1.5 (1.9)−0.17−0.52 to 0.170.33WMH volume (log), mean (SD)0.1 (0.3)0.1 (0.3)0.01−0.04 to 0.070.61Schmidt's progression score, n (%)107 ($58.5\%$)107 ($56.6\%$)OR 1.080.72–1.630.72Schelten's score, mean (SD)0.8 (2.8)1.3 (3.1)−0.68−1.28 to −0.080.026Fazekas score, mean (SD)0.0 (0.6)0.1 (0.7)−0.07−0.19 to 0.060.29New infarction, n (%)28 ($15.3\%$)23 ($12.4\%$)OR 1.310.72–2.370.38CI = confidence interval. RPS = Rotterdam progression scale. WMH = white matter hyperintensity. A negative value denotes a change in favour of allopurinol. The sample size for these analyses was $$n = 189$$ for placebo and $$n = 183$$ for allopurinol. OR = odds ratio for progression. All reported outcomes are adjusted for site, age, baseline National Institute of Health Stroke Scale score, baseline clinical systolic blood pressure and Schelten's total score. SD = standard deviation.
At week 4 SBP fell with allopurinol (−2.4 mmHg $95\%$ CI $95\%$ CI −4.0 to −0.8, $$p \leq 0.0029$$) but was unchanged with placebo (0.9 mmHg, $95\%$ CI −0.6 to 2.4, $$p \leq 0.24$$). The between-group difference was −3.3 mmHg (negative value in favor of allopurinol, $95\%$ CI −5.5 to −1.1, $$p \leq 0.0034$$). The change in daytime SBP at week 104 was similar but the difference was not significant. There was no significant difference in daytime DBP at week 4 or week 104 (Table 4).Table 4BP outcomes. OutcomeChange with AllopurinolChange with PlaceboBetween group difference$95\%$ CIP valueABPM SBP week 4, mean (SD)−2.3 (12.9)0.8 (10.7)−3.33−5.55 to −1.110.0034ABPM SBP week 104, mean (SD)−2.3 (14.2)0.1 (13.4)−2.95−6.0 to 0.100.058ABPM DBP week 4, mean (SD)−1.2 (7.4)−0.3 (5.6)−1.17−2.47 to 0.130.076ABPM DBP week 104, mean (SD)−1.3 (8.5)−1.1 (8.5)−0.84−2.65 to 0.960.36CI = confidence interval. ABPM = ambulatory blood pressure monitor. SD = standard deviation. SBP = systolic blood pressure. DBP = diastolic blood pressure. A negative value for the between group difference denotes a change in favour of allopurinol. The sample size for the ABPM analyses was $$n = 182$$ for placebo and $$n = 170$$ for allopurinol at week 4 and $$n = 126$$ for placebo and $$n = 109$$ for allopurinol at week 104. All reported outcomes are adjusted for site, age, baseline National Institute of Health Stroke Scale score, baseline clinical systolic blood pressure and Schelten's total score.
The change in MOCA score at week 104 was 0.2 (SD 2.7) with allopurinol and 0.4 (SD 2.8) with placebo, between group difference 0.00, $95\%$ CI −0.49 to 0.49, $$p \leq 0.99.$$
Findings were consistent for all primary and secondary endpoints in the per protocol analysis and the sensitivity analyses (Appendix Table 6). Results were consistent for all outcomes across the age, pre or post COVID-19 and uric acid sub-groups subgroups with no significant treatment interactions (Appendix Table 7). Although the treatment interaction as not significant, there was no significant difference in the week 4 change in SBP in people with baseline uric acid below the median level but there was a significant difference in favor of allopurinol in people with baseline uric acid above the median level (between group difference −4.9 mmHg, $95\%$ CI −8.8 to −1.0, $$p \leq 0.014$$) (Appendix Table 7).
No suspected unexpected serious adverse reactions were reported. A total of 239 serious adverse events (127 with allopurinol, 112 with placebo) were reported in 137 ($30\%$) participants (73 ($32\%$) with allopurinol and 64 ($28\%$) with placebo) (Table 5). There was one fatal serious adverse event of aplastic anemia in a participant randomised to allopurinol. Eleven participants had a suspected drug rash with allopurinol and 3 with placebo. None of the rash events were reported as serious. There were four SAEs reported as possibly or probably related to study drug in the allopurinol group. There were 5 deaths in allopurinol treated participants (three cardiovascular and two non-cardiovascular) and three deaths in placebo treated participants (two cardiovascular and one non-cardiovascular).Table 5Safety data. Serious adverse eventAll participants ($$n = 460$$)Allopurinol ($$n = 231$$)Placebo ($$n = 229$$)At least one SAE137 ($29.8\%$)73 ($31.6\%$)64 ($28.0\%$)Nervous system disorders51 ($11.1\%$)31 ($13.4\%$)20 ($8.7\%$)Infections and infestations29 ($6.3\%$)14 ($6.1\%$)15 ($6.6\%$)Cardiac disorders23 ($5\%$)10 ($4.3\%$)13 ($5.7\%$)Injury, poisoning and procedural complications14 ($3.0\%$)7 ($3.0\%$)7 ($3.1\%$)Neoplasms, benign, malignant and unspecified12 ($2.6\%$)8 ($3.5\%$)4 ($1.7\%$)Surgical and medical procedures11 ($2.4\%$)4 ($1.7\%$)7 ($3.1\%$)Vascular disorders11 ($2.4\%$)6 ($2.6\%$)5 ($2.2\%$)Gastrointestinal disorders9 ($2.0\%$)4 ($1.7\%$)5 ($2.2\%$)Renal and urinary disorders8 ($1.7\%$)6 ($2.6\%$)2 ($0.9\%$)Metabolism and nutrition disorders7 ($1.5\%$)2 ($0.9\%$)5 ($2.2\%$)Respiratory, thoracic and mediastinal disorders7 ($1.5\%$)2 ($0.9\%$)5 ($2.2\%$)General disorders and administration site conditions6 ($1.3\%$)4 ($1.7\%$)2 ($0.9\%$)Musculoskeletal and connective tissue disorders5 ($1.1\%$)3 ($1.3\%$)2 ($0.9\%$)Psychiatric disorders5 ($1.1\%$)4 ($1.7\%$)1 ($0.4\%$)Hepatobiliary disorders3 ($0.7\%$)1 ($0.4\%$)2 ($0.9\%$)Skin and subcutaneous tissue disorders2 ($0.4\%$)2 ($0.9\%$)0Blood and lymphatic system disorders1 ($0.2\%$)1 ($0.4\%$)0Ear and labyrinth disorders1 ($0.2\%$)1 ($0.4\%$)0Eye disorders1 ($0.2\%$)1 ($0.4\%$)0Investigations1 ($0.2\%$)1 ($0.4\%$)0Adverse events in each MedDRA system. All values are n (%). SAE = serious adverse event.
## Discussion
Two years of allopurinol treatment did not reduce progression of brain WMH when initiated within 4 weeks of ischaemic stroke or TIA in people aged greater than 50 years. Systolic BP was lower following 4 weeks of allopurinol treatment and although the between group difference in systolic BP was not statistically significantly different at week 104, it was of similar magnitude and this may reflect type two error.
The change in WMH volume in our study was similar to that seen in other studies which included people with stroke.13,27 In the PROGRESS MRI sub-study,13 SBP was 11.2 mmHg lower and WMH volume 1.6 cm3 lower with BP treatment. In the PRoFESS MRI substudy,27 there was no difference in WMH volume with BP treatment but the between group difference in SBP was 3 mmHg. Studies in other populations consistently show a reduction in WMH volume in favour of intensive BP control and there is a strong relationship between the intergroup BP difference and the difference in change in WMH volume28 during prospective follow up. It is likely that the BP difference obtained in our study was insufficient to lead to a difference in WMH volume over a two-year period of follow up.
Allopurinol is reported to have effects on the cardiovascular system which are independent of BP reduction, and which could be associated with WMH progression.29 However, our data support neither a BP dependent nor independent effect of allopurinol on WMH. There was a statistically significant difference in the change in Schelten's score. However, this difference was not apparent in any other measure, including volumetric analysis, so may be a chance finding. While many studies show potentially beneficial effects of xanthine oxidase inhibition on the cardiovascular system, it is important to note that the xanthine oxidase system is part of a complex pro-oxidant and anti-oxidant system. Xanthine oxidase inhibition may inhibit reduction of nitrite and nitrate back to NO.30 In addition, metabolism of allopurinol to oxypurinol, which occurs rapidly in the plasma, can generate hydrogen peroxide and oxidative stress.
The recently reported *Allopurinol versus* usual care in UK patients with ischaemic heart disease (ALL-HEART) study found no difference in the rate of a composite primary outcome of non-fatal myocardial infarction, non-fatal stroke, or cardiovascular death (or in any secondary outcome) in approximately 6000 participants with ischaemic heart disease.31 There was also no suggestion of benefit in people in the highest tertile of serum uric acid levels.
The observed reduction in BP following allopurinol treatment was small but was greater in people with higher baseline serum uric acid. Broadly this is consistent with results of a recent systematic review and meta-analysis of randomised trials,6 and results of studies in hyperuricemic adolescents where large reductions in BP were seen with allopurinol.10,32 However, it is in contrast to the recent cross-over SURPHER trial,33 where no difference between allopurinol or placebo in 24-h average SBP or ABPM was seen. One important difference with the SURPHER trial is that we used a higher dose of 300 mg twice daily, which has been shown to have a greater effect on endothelial function.34 Meta-regression analysis suggests a greater fall in BP with a higher baseline serum UA level and it is likely that older adults with prolonged hyperuricemia become insensitive to the large effect of UA reduction seen in younger adults.6 This means it is likely that high doses of allopurinol are needed to see even a small BP effect. Previous studies may have been underpowered to detect small differences in BP, particularly if baseline serum UA is low, participants are well treated with other medications, and lower doses of allopurinol are used. We believe the most informative measure of the effect of allopurinol on BP is the week 4 change. This is because BP treatment is highly likely to be modified to achieve BP targets in the longer-term, which could mask any effect of allopurinol on BP. This is less likely to confound change at earlier timepoints. This may partly explain why the two-year between group difference in change in SBP did not reach statistical significance, although it was similar to the week 4 data. This may also reflect less precision due to a lower sample size for the week 104 analysis, due to the Covid pandemic, and the greater standard deviation for the change at week 104.
Allopurinol has important potential side effects. We saw adverse events at a rate in keeping with other secondary prevention trials. The study had an independent data monitoring committee who regularly reviewed all efficacy and safety data. Importantly, the number of serious adverse events reported to be possibly or probably related to study drug were in the allopurinol group was 4.
The strengths of our study include blinding to treatment allocation, use of central randomisation and wide inclusion criteria which should increase generalizability. The trial was rigorously monitored and was subject to a routine regulatory inspection by the regulatory authority. We also used a high dose of allopurinol.
Our trial has limitations. We anticipated that some participants would be unable to attend follow up due to death or illness. Our sample size calculation was based on 384 participants having a 2-year MRI performed but only 372 attended. Unfortunately, 12 participants did not attend for final follow up and we were unable to perform ABPM in 90 people at the week 104 visit as a direct consequence of the Covid pandemic. In addition, 52 participants in the allopurinol group stopped taking study medication. This withdrawal rate from treatment rate of $22.4\%$ in allopurinol treated participants is in line with other secondary prevention trials. In the Insulin Resistnace Intervention Trial of pioglitazone use in people with stroke or TIA and insulin resistance, it was $40\%$.35 In the Effects of fluoxetine on functional outcomes after acute stroke (FOCUS) trial, approximately $\frac{2}{3}$ of participants took medication for 150 days or more.36 *Although this* will reduce study power, it is unlikely this led to type 2 error given the absence of any evidence of a between group difference in WMH volume and no suggestion of important differences in the per-protocol and sensitivity analyses. Our study included a majority of male and Caucasian participants. In addition we included a heterogenous sample of people with ischaemic stroke and TIA and we did not specifically select people with small vessel disease or by degree of white matter hyperintensity burden. We did not adjust for multiple testing on assessment of our secondary endpoints.
In the XILO-FIST trial we found no evidence of an effect of allopurinol on white matter hyperintensity progression or on new brain infarction but a small reduction in SBP at 4 weeks, which was broadly sustained at 2 years. The change in BP with allopurinol may be greatest in people with higher serum uric acid levels and further study should aim to assess the clinical importance of this in people with stroke. It is unlikely it will reduce progression of white matter hyperintensities in a clinically important way in an unselected population of people with ischaemic stroke or TIA.
## Contributors
JD, NB, MRW, KD, KM, KL, AD, GH, SK, AM, TQ, MK, AS were involved in design of the study. MR and AM performed statistical analysis. JD, DD, KF, KD, TQ, MB, DW, AB, AD, AC, AH, MM, BK, AB, LS, GS, PG and SL acquired study data. JD drafted the manuscript. PB chaired the trial steering committee. All authors provided critical comment and contributed to the design of the study. JD, MR and AM have accessed and verified the underlying data.
## Data sharing statement
Study data, including brain MRI will be shared with the Virtual International Stroke Trials Archive after publication of the primary manuscript. Study data including anonymized individual level participant data will be shared with academic investigators or health care professionals following review and approval of a proposal and subject to a data sharing agreement (contact [email protected]).
## Declaration of interests
JD has received honoraria from Pfizer, Daiichi Sankyo, Medtronic, Astra Zeneca, Bristol Myers Squibb, and Bayer unrelated to this trial.
PMB is Stroke Association Professor of Stroke Medicine and an Emeritus NIHR Senior Investigator. He has received consulting fees from CoMInd, DiaMedica, Roche and Phagenesis. He is co-chair of the World Stroke Organisation Industry Committee. He has received equipment for research studies from Phagenesis. He reports stock options in DiaMedica and CoMind and was a member of the Data Safety Monitoring Committee for the European Carotid Surgery Trial-2. All reported declarations are unrelated to this research.
KWM has received consulting fees from Boehringer Ingelheim, Biogen, Abbvie and honoraria from Boehringer Ingelheim unrelated to the trial; trial support from Boehringer Ingelheim, the NIHR, the Stroke Association, Innovate UK and the British Heart Foundation unrelated to the trial. He was a member of the data monitoring committee for the ARREST trial, unrelated to this research.
AC has received research grants from $\frac{10.13039}{100004319}$Pfizer and honoraria from BMS, Pfizer, AstraZeneca and Boeheringer Ingelheim unrelated to this trial.
MK has received honoraria from Astra Zeneca and research funding from the $\frac{10.13039}{501100000274}$British Heart Foundation unrelated to this research.
AS holds a patent for the use of xanthine oxidase inhibition for the treatment of angina pectoris.
KD has received conference support from $\frac{10.13039}{100004336}$Novartis and honoraria from Allegan unrelated to this research.
DD received payment for image analysis in this study and has received payment for image analysis from MicroTransponder Inc unrelated to this research.
LS is a member of the executive committee of the British and Irish Association of Stroke Physicians. She is a member of stroke specialist advisory committee of the Joint Royal College and Training Board in the UK.
DW has received consulting fees and honoraria from Bayer, Alnylam, Portola and NovoNordisk unrelated to this research. He is chair of the IDMC for the OXHARP trial. He is president-elect of British and Irish Association of Stroke Physicians. He is Chair of Association of British Neurologists Stroke Advisory Group. He serves on the Editorial Board of Practical Neurology, European Journal of Neurology and International Journal of Stroke. He is Chair of UK Stroke Forum. He is member of NICE AI in Stroke Diagnosis Guideline Committee. He is Chief Investigator for the OPTIMAS and Prohibit-ICH trials. He serves on the steering committee and co-investigator for LACI-2, TICH-3, RECAST-3. He serves on the steering committee and is co-investigator for RESTART, TICH-2.
The other authors declare they have no competing interests.
## Supplementary data
Supplementary material
## References
1. Li L., Yiin G.S., Geraghty O.C.. **Incidence, outcome, risk factors, and long-term prognosis of cryptogenic transient ischaemic attack and ischaemic stroke: a population-based study**. *Lancet Neurol* (2015) **14** 903-913. PMID: 26227434
2. Craig L., Hoo Z.L., Yan T.Z., Wardlaw J., Quinn T.J.. **Prevalence of dementia in ischaemic or mixed stroke populations: systematic review and meta-analysis**. *J Neurol Neurosurg Psychiatry* (2022) **93** 180-187. PMID: 34782389
3. Khan A.A., Quinn T.J., Hewitt J., Fan Y., Dawson J.. **Serum uric acid level and association with cognitive impairment and dementia: systematic review and meta-analysis**. *Age (Dordr)* (2016) **38** 16. PMID: 26820749
4. Weir C.J., Muir S.W., Walters M.R., Lees K.R.. **Serum urate as an independent predictor of poor outcome and future vascular events after acute stroke**. *Stroke* (2003) **34** 1951-1956. PMID: 12843346
5. Dawson J., Lees K.R., Weir C.J.. **Baseline serum urate and 90-day functional outcomes following acute ischemic stroke**. *Cerebrovasc Dis* (2009) **28** 202-203. PMID: 19571532
6. Gill D., Cameron A.C., Burgess S.. **Urate, blood pressure, and cardiovascular disease: evidence from mendelian randomization and meta-analysis of clinical trials**. *Hypertension* (2021) **77** 383-392. PMID: 33356394
7. Muir S.W., Harrow C., Dawson J.. **Allopurinol use yields potentially beneficial effects on inflammatory indices in those with recent ischemic stroke: a randomized, double-blind, placebo-controlled trial**. *Stroke* (2008) **39** 3303-3307. PMID: 18845806
8. Higgins P., Walters M.R., Murray H.M.. **Allopurinol reduces brachial and central blood pressure, and carotid intima-media thickness progression after ischaemic stroke and transient ischaemic attack: a randomised controlled trial**. *Heart* (2014) **100** 1085-1092. PMID: 24790069
9. Dawson J., Quinn T., Harrow C.. **Allopurinol and nitric oxide activity in the cerebral circulation of those with diabetes: a randomized trial**. *Diabetes Care* (2009) **32** 135-137. PMID: 18945924
10. Feig D.I., Soletsky B., Johnson R.J.. **Effect of allopurinol on blood pressure of adolescents with newly diagnosed essential hypertension: a randomized trial**. *JAMA* (2008) **300** 924-932. PMID: 18728266
11. Fu J.H., Lu C.Z., Hong Z., Dong Q., Luo Y., Wong K.S.. **Extent of white matter lesions is related to acute subcortical infarcts and predicts further stroke risk in patients with first ever ischaemic stroke**. *J Neurol Neurosurg Psychiatry* (2005) **76** 793-796. PMID: 15897500
12. Debette S., Markus H.S.. **The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis**. *BMJ* (2010) **341** c3666. PMID: 20660506
13. Dufouil C., Chalmers J., Coskun O.. **Effects of blood pressure lowering on cerebral white matter hyperintensities in patients with stroke: the PROGRESS (perindopril protection against recurrent stroke study) magnetic resonance imaging substudy**. *Circulation* (2005) **112** 1644-1650. PMID: 16145004
14. Nasrallah I.M., Pajewski N.M., Auchus A.P.. **Association of intensive vs standard blood pressure control with cerebral white matter lesions**. *JAMA* (2019) **322** 524-534. PMID: 31408137
15. Dawson J., Broomfield N., Dani K.. **Xanthine oxidase inhibition for the improvement of long-term outcomes following ischaemic stroke and transient ischaemic attack (XILO-FIST) - protocol for a randomised double blind placebo-controlled clinical trial**. *Eur Stroke J* (2018) **3** 281-290. PMID: 30246149
16. Wardlaw J.M., Smith E.E., Biessels G.J.. **Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration**. *Lancet Neurol* (2013) **12** 822-838. PMID: 23867200
17. Fazekas F., Chawluk J.B., Alavi A., Hurtig H.I., Zimmerman R.A.. **MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging**. *AJR Am J Roentgenol* (1987) **149** 351-356. PMID: 3496763
18. Scheltens P., Barkhof F., Leys D.. **A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging**. *J Neurol Sci* (1993) **114** 7-12. PMID: 8433101
19. Prins N.D., van Straaten E.C., van Dijk E.J.. **Measuring progression of cerebral white matter lesions on MRI: visual rating and volumetrics**. *Neurology* (2004) **62** 1533-1539. PMID: 15136677
20. Schmidt R., Fazekas F., Kapeller P., Schmidt H., Hartung H.P.. **MRI white matter hyperintensities: three-year follow-up of the Austrian Stroke Prevention Study**. *Neurology* (1999) **53** 132-139. PMID: 10408549
21. Avants B.B., Epstein C.L., Grossman M., Gee J.C.. **Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain**. *Med Image Anal* (2008) **12** 26-41. PMID: 17659998
22. Tustison N.J., Cook P.A., Klein A.. **Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements**. *Neuroimage* (2014) **99** 166-179. PMID: 24879923
23. Avants B.B., Tustison N.J., Wu J., Cook P.A., Gee J.C.. **An open source multivariate framework for n-tissue segmentation with evaluation on public data**. *Neuroinformatics* (2011) **9** 381-400. PMID: 21373993
24. Zhang Y., Brady M., Smith S.. **Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm**. *IEEE Trans Med Imaging* (2001) **20** 45-57. PMID: 11293691
25. Schmidt R., Berghold A., Jokinen H.. **White matter lesion progression in LADIS: frequency, clinical effects, and sample size calculations**. *Stroke* (2012) **43** 2643-2647. PMID: 22879094
26. Agarwal V., Hans N., Messerli F.H.. **Effect of allopurinol on blood pressure: a systematic review and meta-analysis**. *J Clin Hypertens (Greenwich)* (2013) **15** 435-442. PMID: 23730993
27. Weber R., Weimar C., Blatchford J.. **Telmisartan on top of antihypertensive treatment does not prevent progression of cerebral white matter lesions in the prevention regimen for effectively avoiding second strokes (PRoFESS) MRI substudy**. *Stroke* (2012) **43** 2336-2342. PMID: 22738922
28. Lai Y., Jiang C., Du X.. **Effect of intensive blood pressure control on the prevention of white matter hyperintensity: systematic review and meta-analysis of randomized trials**. *J Clin Hypertens (Greenwich)* (2020) **22** 1968-1973. PMID: 33459521
29. Ng K.P., Stringer S.J., Jesky M.D.. **Allopurinol is an independent determinant of improved arterial stiffness in chronic kidney disease: a cross-sectional study**. *PLoS One* (2014) **9** e91961. PMID: 24632580
30. Godber B.L., Doel J.J., Sapkota G.P.. **Reduction of nitrite to nitric oxide catalyzed by xanthine oxidoreductase**. *J Biol Chem* (2000) **275** 7757-7763. PMID: 10713088
31. Mackenzie I.S., Hawkey C.J., Ford I.. **Allopurinol versus usual care in UK patients with ischaemic heart disease (ALL-HEART): a multicentre, prospective, randomised, open-label, blinded-endpoint trial**. *Lancet* (2022) **400** 1195-1205. PMID: 36216006
32. Soletsky B., Feig D.I.. **Uric acid reduction rectifies prehypertension in obese adolescents**. *Hypertension* (2012) **60** 1148-1156. PMID: 23006736
33. Gaffo A.L., Calhoun D.A., Rahn E.J.. **Effect of serum urate lowering with allopurinol on blood pressure in young adults: a randomized, controlled, crossover trial**. *Arthritis Rheumatol* (2021) **73** 1514-1522. PMID: 33779064
34. George J., Carr E., Davies J., Belch J.J., Struthers A.. **High-dose allopurinol improves endothelial function by profoundly reducing vascular oxidative stress and not by lowering uric acid**. *Circulation* (2006) **114** 2508-2516. PMID: 17130343
35. Kernan W.N., Viscoli C.M., Furie K.L.. **Pioglitazone after ischemic stroke or transient ischemic attack**. *N Engl J Med* (2016) **374** 1321-1331. PMID: 26886418
36. **Effects of fluoxetine on functional outcomes after acute stroke (FOCUS): a pragmatic, double-blind, randomised, controlled trial**. *Lancet* (2019) **393** 265-274. PMID: 30528472
|
---
title: Proprioceptors-enriched neuronal cultures from induced pluripotent stem cells
from Friedreich ataxia patients show altered transcriptomic and proteomic profiles,
abnormal neurite extension, and impaired electrophysiological properties
authors:
- Chiara Dionisi
- Marine Chazalon
- Myriam Rai
- Céline Keime
- Virginie Imbault
- David Communi
- Hélène Puccio
- Serge N Schiffmann
- Massimo Pandolfo
journal: Brain Communications
year: 2023
pmcid: PMC9972525
doi: 10.1093/braincomms/fcad007
license: CC BY 4.0
---
# Proprioceptors-enriched neuronal cultures from induced pluripotent stem cells from Friedreich ataxia patients show altered transcriptomic and proteomic profiles, abnormal neurite extension, and impaired electrophysiological properties
## Abstract
Friedreich ataxia is an autosomal recessive multisystem disorder with prominent neurological manifestations and cardiac involvement. The disease is caused by large GAA expansions in the first intron of the FXN gene, encoding the mitochondrial protein frataxin, resulting in downregulation of gene expression and reduced synthesis of frataxin. The selective loss of proprioceptive neurons is a hallmark of Friedreich ataxia, but the cause of the specific vulnerability of these cells is still unknown. We herein perform an in vitro characterization of human induced pluripotent stem cell-derived sensory neuronal cultures highly enriched for primary proprioceptive neurons. We employ neurons differentiated from healthy donors, *Friedreich ataxia* patients and *Friedreich ataxia* sibling isogenic control lines. The analysis of the transcriptomic and proteomic profile suggests an impairment of cytoskeleton organization at the growth cone, neurite extension and, at later stages of maturation, synaptic plasticity. Alterations in the spiking profile of tonic neurons are also observed at the electrophysiological analysis of mature neurons. Despite the reversal of the repressive epigenetic state at the FXN locus and the restoration of FXN expression, isogenic control neurons retain many features of *Friedreich ataxia* neurons. Our study suggests the existence of abnormalities affecting proprioceptors in Friedreich ataxia, particularly their ability to extend towards their targets and transmit proper synaptic signals. It also highlights the need for further investigations to better understand the mechanistic link between FXN silencing and proprioceptive degeneration in Friedreich ataxia.
Dionisi, Chazalon et al., report the identification of defects in axonal extension and synaptic transmission in sensory neurons in Friedreich ataxia. Pathological features are not fully reverted after removal of the GAA expansion mutation. Further investigations are needed to clarify the effects of FXN silencing in proprioceptive neurons.
## Graphical Abstract
Graphical Abstract
## Introduction
FRDA is the most common autosomal recessive inherited ataxia in Caucasians, accounting for half of the inherited degenerative ataxias and for three-quarters of those with onset before age 25. The disease is caused by a GAA expansion mutation in the first intron of the FXN gene on chromosome 9q21.11.1 The mutation causes frataxin deficiency by promoting chromatin condensation and disrupting gene transcription.2 *Frataxin is* thought to be a component of the protein complex that assembles Fe-S clusters in the mitochondrial matrix.3 Thus, mitochondrial dysfunction and ROS production are proposed to be key features of the pathophysiology of FRDA.4 Symptoms include progressive afferent and cerebellar ataxia, dysarthria, pyramidal weakness, skeletal abnormalities (scoliosis and pes cavus), cardiomyopathy, insulin resistance and beta cell dysfunction.5,6 FRDA is characterized by severe loss of PPNs of the DRGs,7 key components of the sensory system that provide information about body position in space and movement.8 PPNs pathology is early event in FRDA, already present before symptoms onset. Both a developmental deficit and a progressive degeneration are thought to occur, but the relative contribution of these two components is still unknown, as is the cause of the specific vulnerability of these neurons in FRDA. Indeed, frataxin is a widely expressed protein and the reason for the involvement of a limited set of cells and organs in FRDA is still an open question. Several animal models of the disease have been developed so far, allowing a better understanding of the disease pathophysiology and enabling pre-clinical testing of potential therapeutics.9–12 However, the expanded GAA repeat causing FRDA originated from the poly-A segment connecting the two halves of a primate-specific Alu element, so the repeat is not present in the genome of other species. Together with the lethality of systemic Fxn KO, this has been a constrain in the generation of mouse models of FRDA. Some of the current models are conditional KOs, which have no GAA repeat expansion and have complete loss of frataxin in targeted tissues, which undergo relentless degeneration and cell death. In other models, expanded GAA repeat have been inserted into the *Fxn* gene or are present in large transgenes containing the human FXN locus on a mouse Fxn−/− background. Despite low levels of frataxin, the GAA-carrying models show mild or no phenotype, possibly because mice can tolerate much lower levels of the protein than humans. Limitations of mouse models make cell models derived from FRDA patients even more relevant for the study of FRDA pathogenesis. While easily accessible cells as PBMCs and skin fibroblasts have provided some valuable information, these cell types are not affected in the disease, indicating that they can tolerate low frataxin levels without triggering pathological cascades leading to cell dysfunction and death. Conversely, directly obtaining from patients specifically vulnerable cells, as neurons and cardiomyocytes, is nearly impossible. This limitation can be circumvented with the use of hiPSCs. Not only hiPSCs have the potential to generate every cell type of the human body, their differentiation process can at least partially model cellular development. Moreover, the recent development of FRDA sibling ISO CT lines generated by removal of the GAA expansion mutation by means of genome editing approaches, has enabled the direct assessment of the effects induced by the presence or absence of frataxin expression in genetically matched cell lines,13,14 reducing the variability due to the use of hiPSCs with different genetic backgrounds.
So far, most studies on hiPSCs-derived neurons in FRDA have utilized differentiation protocols predominantly generating neurons with an immature dorsal cortical phenotype, which do not model specifically vulnerable cells in the disease. Only recently, with the development of a few protocols for the generation of mixed populations of DRG neurons from hiPSCs, a few studies about the possible alterations existing in FRDA sensory neurons have started to emerge.15,16 We previously developed a protocol for the rapid differentiation of PPN-enriched cultures from hiPSCs.17 *In this* study, we exploited that system for the in-depth characterization of sensory neurons derived from healthy donors (CT) and FRDA patients as well as from FRDA sibling ISO CTs.13–15 We started from the epigenetic analysis of the FXN locus and with the investigation of the transcriptomic and proteomic profile of developing or fully differentiated neurons. The analysis was implemented with the characterization of the morphological features of neuronal cultures and with the assessment of the electrophysiological properties of mature neurons. The inclusion of the ISO CT lines helped us to evaluate the possible effects induced by the specific removal of the GAA expansion mutation responsible for the disease. Our study provides new and more specific insights into the pathological features of PPNs in FRDA and highlights the need for a further investigation of the role of GAA expansion mutation and frataxin deficiency in PPNs degeneration in FRDA.
## Induced pluripotent stem cell culture and neuronal differentiation
hiPSCs were obtained by reprogramming of human fibroblasts or PBMCs from two healthy donors (CT: HEL46.11, male; HEL24.3, male) and five FRDA patients (FRDA). The following FRDA lines were used: HEL135.2 (male; $\frac{980}{1180}$ GAA repeats); ULBi004FA4 (female; $\frac{500}{750}$ GAA repeats); ULBi005FA1 (male; $\frac{879}{1080}$ GAA repeats); 4259.11 (male; $\frac{550}{830}$ GAA repeats); 4676.2 (male; $\frac{700}{700}$ GAA repeats). Three Isogenic Controls (ISO CT: E35, 4259.11, 4676.2) were also included in the study. The ISO CT lines 4259.3C7 and 4676.2D3 were derived from the FRDA lines 4259.11, 4676.2, respectively, with the direct excision of the GAA expansion mutation by CRISPR-Cas9, targeting a site 334 bp upstream and a site 896 bp downstream of the GAA expansion.13,14 The ISO CT line E35 was derived from another FRDA line (line GM03816: female, $\frac{223}{490}$ GAA repeats; not included in the study), by homologous recombination with a correction vector plasmid, FXN-HdAV, containing 19 kb of the healthy human FXN gene.15 Participating individuals provided written informed consent according to the respective Institutional Review Board or Ethic Committee.
The 10 lines utilized in the study were indicated as follows: CT1: HEL46.11; CT2: HEL24.2; FA1: HEL135.2; FA2: ULBi004FA4; FA3: ULBi005FA1; FA4: 4259.11; FA5: 4676.2; IcFA4: 4259.3C7; IcFA5: 4676.2D3; IcFAg: E35.
Human iPSCs were cultured under feeder-free conditions, in Essential 8 Medium (E8, Thermo Fisher Scientific, Cat. N. A1517001) or mTeSR-1 Medium (Stemcell Technologies, Cat. N. 85850) on Matrigel-coated tissue culture plates (Corning, Cat. No. 356231; 0.05 mg/ml Matrigel solution in DMEM/F12 medium). Cells were fed daily and passaged every 3 days using 0.5 mM EDTA.
Neuronal differentiation was performed as previously described.17 Briefly, hiPSCs were plated as single cells on Matrigel treated dishes (0.5 mg/ml Matrigel solution in DMEM/F12 Medium) in E8/mTeSR-1 Medium supplemented with 10 μM ROCK Inhibitor (Y-27632 dihydrochloride, Sigma, Cat. Y0503). The day after seeding (0 DIV), the spent medium was replaced with fresh medium without Y-27632, and cells were allowed to proliferate for other 24 hours, reaching a 60–$80\%$ confluency. To initiate sensory differentiation (1 DIV), cells were treated with the following factors: 100 nM LDN193189 and 10 μM SB431542 were added from 1 DIV to 5 DIV, 3 μM CHIR99021 was added from 2 DIV to 7 DIV, 10 μM DAPT and 9 μM SU5402 were added from 2 DIV to 8 FIV. From 1 to 8 DIV, cells were fed daily, and medium was gradually switched from E6 Medium (Thermo Fisher, Cat. N. A1516401) to N2-A Medium with a $25\%$ increment every 2 days. N2-A medium consisted of Neurobasal-A Medium (Thermo Fisher, Cat. N. 10888022), supplemented with $1\%$ N2 Supplement (Thermo Fisher, Cat. N. 17502001) and $1\%$ GlutaMAX (Thermo Fisher, Cat. N. 35050061).
Starting on 9 DIV, cells were fed in N2-B medium, consisting of Neurobasal-A medium supplemented with $1\%$ N2 Supplement, $1\%$ B27 Supplement (Thermo Fisher, Cat. N. 17504001), $1\%$ GlutaMAX and $1\%$ MEM Non-Essential Amino Acids (Thermo Fisher, Cat. 11140050). Medium was replaced every 2 days. 40 ng/ml NT3 and 5 ng/ml BDNF were added from 9 DIV until the last day of differentiation (19-21 DIV), while 5 ng/ml Nerve Growth Factor (NGF) and 5 ng/ml Glial-Derived Neurotrophic Factor (GDNF) were added on 9 and 10 DIV. Small molecule inhibitors and neurotrophic factors utilized in the study were obtained from STEMCELL Technologies.
## Chromatin immunoprecipitation
Investigation of the epigenetic marks of H3 at FXN locus was performed by ChIP in fully differentiated neurons obtained from nine hiPSC lines (CT 1-2, FRDA 1-5, IcFA4, IcFA5). Assays were carried out using the Abcam ChIP Kit (ab500), following manufacturer’s instructions. Briefly, differentiated neurons at 20 DIV were fixed with $1.1\%$ formaldehyde and chromatin from lysed nuclei was sheared for 10 min using a 30 sec ON—30 sec OFF sonication cycle in Bioruptor (Diagenode), keeping a temperature of +4°C. Sheared chromatin was immunoprecipitated with antibodies specific for each marker, keeping the samples in incubation overnight at +4°C, in constant rotation. Antibody-antigen complexes were recovered with unblocked Protein A Sepharose beads for 60 min at +4 °C. Finally, quantitative PCR (qPCR) was performed on the eluted DNA using multiple primer pairs to amplify distinct regions within the 5′ end of the FXN gene. qPCR was carried out on a QuantStudio3 Real-Time PCR System (Thermo Fisher) using Power SYBR Green Master Mix (Thermo Fisher, Cat. No. 4367659). Primer sets utilized were obtained from Chan et al.18 and are listed in Supplementary Table 1.
Antibodies used in the assay included: Histone H3 (2 μl/106 cells; Abcam, ab1791); Histone H3 Acetyl K9 (2 μl/106 cells; Abcam, Ab4441); Histone H3 Trimethyl K9 (2 μl/106 cells; Abcam, ab8898); Histone H3 Acetyl K27 (2 μl/106 cells; Abcam, ab4729); Histone H3 Trimethyl K27 (5 μl/106 cells; Abcam, ab6002). 106 cells were used for each marker.
Signals from the immunoprecipitated DNA were calculated as percentage of input and normalized to signals from histone H3 antibody. Results were expressed independently for the acetylated and tri-methylated isoforms of H3K9 and H3K27, and then as ratio between the two, for each region of investigation. ChIP experiments were performed in two biological replicates per cell line. Statistical analysis was performed using GraphPad Prism v9.2.0 software, dividing the samples in three groups: CT, FRDA and ISO CT lines. Statistical analysis of data was performed using a two-way ANOVA followed by Bonferroni’s test for multiple comparisons (*$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$).
## Transcriptome analysis
Transcriptomic analysis was performed by bulk RNA-sequencing for all lines available at three different stages of differentiation: iPSCs, developing neurons (9 DIV) and mature neurons (20 DIV), for a total of 30 samples processed. The 10 lines available were organized in three groups: CT (CT1, CT2), FRDA (FRDA1-5) and ISO CT (IcFAg, IcFA4, IcFA5).
For each sample, RNA was extracted and purified with the RNeasy Mini RNA Kit (Qiagen, Cat. N. 74104), following manufacturer’s instructions. RNA quality was examined on NanoDrop ND-1000 Spectrophotometer (Isogen) and only samples with $\frac{260}{280}$ and $\frac{260}{230}$ absorbance ratios of ∼2.0 were utilized for the analysis.
Total RNA was kept at −80°C until use. RNA concentration and quality were evaluated a second time prior to library generation using an Agilent Fragment Analyzer automated CE system (Advanced Analytical Technologies, Inc., UK), following manufacturer’s procedure. RNA libraries were generated starting from 1 μg of total RNA, using TruSeq Stranded mRNA Sample Preparation Kit (Illumina). *The* generated libraries were sequenced on the Illumina HiSeq4000 system as single-end 50-base reads, following Illumina’s instructions. Reads were preprocessed to remove adapter, polyA and low-quality sequences (Phred quality score below 20). Reads shorter than 40 bases were discarded for further analysis. This process was performed using bowtie v2.2.8 aligner. Reads mapping to rRNA sequences were removed for further analysis.
Reads were mapped onto the hg38 assembly of Homo sapiens genome using STAR v2.5.3a. Read coverage over genes in all samples was computed using geneBodyCoverage from RSeQC v2.6.4. Gene expression quantification was performed for uniquely aligned reads using HTSeq-count v0.6.1p1, with gene annotations from Ensembl release 102 and ‘union’ mode. Only non-ambiguously assigned reads were retained for further analysis. PCA was performed to show the main sources of variance in the analysed data using R software v3.3.2.
*Differential* gene expression analysis was performed using the method proposed by Love et al.19 and implemented in the Bioconductor Package DESeq2 v1.16.1. Genes with no P-value in the resulting file corresponded to gene with high Cook’s distance and were filtered out. P-values were adjusted to multiple testing using the Benjamini and Hochberg method. Genes with no adjusted P-value in the resulting file corresponded to gene filtered out in the independent filtering step. Independent filtering based on the mean of normalized counts was performed to filter out genes with little or no chance of showing significant evidence of differential expression, resulting in increased power of detection.
Significantly DEGs were selected using the following thresholds: absolute Log2(Fold-Change) > 1 and adjusted P-value < 0.1. Data were represented as Normalized Read Counts in the log2 scale, using GraphPad Prism v9.2.0 software. GO enrichment analysis was performed for the biological functions of identified genes using the Panther classification system (Panther v.14.0). P-values were log10-transformed, and their sign inverted for plotting. Only enriched pathways with P-value < 0.01 were considered significant and represented.
## Mass spectrometry-based proteomic analysis
Sample collection and preparation. For proteomic analysis, differentiated neurons from all lines available were collected at 20 DIV, washed twice in cold PBS supplemented with Protease and Phosphatase Inhibitors at $0.1\%$ and spun at 300 g for 3 min. After the last washing, the supernatant was discarded, and cell pellets were stored at −80°C until use. For proteomic sample preparation, cell pellets were extracted using 500 µl chlorhydrate of guanidine 8 M and homogenized in a Bullet Blender (Next Advance) for 5 min, at speed 8, with 100 µl of zirconium beads 1 mm, keeping a constant temperature of +4°C. After centrifugation for 15 min at 16 000 g, at +4°C, the supernatant was collected, and protein concentration was measured with filter paper dye-binding assay. 100 µg of proteins from each sample were incubated for 1 h at +4°C with 25 mM dithiothreitol (DTE), followed by incubation with 71 mM iodoacetamide (IAA) for 1 h at +4°C, in the dark. The solution was diluted two times in H2O and proteins were precipitated by adding four volumes of cold acetone. After 1 h of incubation at −20°C, samples were spun for 20 min at 13 000 rpm, at +4°C. Proteins were incubated with 4 µg of Trypsin Mass Spectrometry Grade (Promega) in 25 mM NH4HCO3, overnight at +37°C. Digestion was stopped by adding formic acid (HCOOH) at a final concentration of $0.2\%$. Peptides were then purified using Oasis HLB 30 cc (Waters) according to manufacturer’s instructions. Peptides were evaporated at 60°C and resuspended in 10 µl of $0.1\%$ HCOOH in H2O. The peptide concentration was determined using Pierce Quantitative Fluorometric Peptide Assay (Thermo Fisher, Cat. No. 23290). All samples were spiked with iRT peptides (Biognosys) at the concentration of $\frac{1}{10.}$
DDA for generation of a cell-specific spectral library. For the generation of spectral libraries, samples from CT, FRDA and ISO CT lines were pooled and processed with the same protocol. 300 µg of peptides were injected into a column C18 (5 µm, 2.1 × 250 mm2; Vydac) at 200 µl/min using a gradient of 5–$50\%$ acetonitrile/$0.1\%$ TFA. 70 fractions were collected (fraction/1.5 min) and concatenated in 15 fractions (Fr1–Fr16–Fr31–Fr46–Fr61; Fr2–Fr17–Fr32–Fr47–Fr62; Fr3–Fr18–Fr33–Fr48–Fr63; etc.). Fractions were purified using Oasis HLB 30 cc (Waters) according to manufacturer’s instructions. Peptides were evaporated at 60°C and resuspended in 10 µl of $0.1\%$ HCOOH in H2O. Peptide concentration was determined using Pierce Quantitative Fluorometric Peptide Assay. All samples were spiked with iRT peptides (Biognosys) at the concentration of $\frac{1}{10.}$ 8 µg of peptides of each fraction were injected into a Triple TOF 5600 mass spectrometer (Sciex, Concord, Canada) interfaced to an Eksignet NanoLC Ultra 2D HPLC System (Eksignet, Dublin, CA) using Data-Dependent-Acquisition (DDA). MS1 spectra were collected in the range of 400–1250 m/z for 250 ms. The 20 most intense precursors with charge state 2–4 were selected for fragmentation, and MS2 spectra were collected in the range of 50–2000 m/z for 100 ms; precursor ions were excluded for reselection for 12 s. Combined data were searched using ProteinPilot 4.5 (Sciex) and the Paragon Algorithm (Sciex). Data were searched against the human SwissProt database (Jan 2021).
DIA (SWATH-MS) and Differential Expression Analysis. 1 µg of peptides was injected using SWATH-MS acquisition on a Triple TOF 5600 mass spectrometer (Sciex, Concord, Canada) interfaced to an Eksigent NanoLC Ultra 2D HPLC System (Eksignet, Dublin, CA). Peptides were injected on a separation column (Eksigent ChromXP C18, 150 mm, 3 µm, 120A) using a two steps acetonitrile gradient (5–$25\%$ ACN/$0.1\%$ HCOOH for 98 min, then 25–$60\%$ ACN/$0.1\%$ HCOOH for 60 min) and were sprayed online in the mass spectrometer. Swath acquisitions were performed using 71 windows of variable effective isolation width to cover a mass range of 400–1250 m/z. SWATH MS2 spectra were collected from 50 to 2000 m/z. The collision energy for each window was determined according to the calculation for a charge 2+ ion centred upon the window with a spread of 15. An accumulation time of 45 ms was used for all fragment-ion scans in high-sensitivity mode and for the survey scans in high-resolution mode acquired at the beginning of each cycle, resulting in a duty cycle of ∼2.3 s. Spectra were aligned using SWATH 2.0 in the PeakView v2.2 Software (Sciex) against the cell-specific spectral library (generated from the search result, allowing no modifications; 5123 protein entries). iRT peptides (Biognosys) were used for retention time calibration. Data were processed in PeakView using a XIC extraction window of 30 min and XIC width of 30 ppm. Peak areas from peptides with >$99\%$ confidence and <$1\%$ Global False Discovery Rate (FDR) were extracted using MarkerView v1.2.1 (SCIEX). Normalized peak intensity values for extracted proteins were analysed by Student’s t-test. Proteins with an absolute Log2(Fold Change) > 1 and P-value < 0.01 were considered as differentially expressed in this study. Data were represented as Log2(Mean Peak Intensity). Functional interactions of differentially expressed proteins were predicted using the Search Tool for Retrieval of Interacting Genes (STRING-DB) database. Gene ontology (GO) enrichment analysis was performed for the biological functions of identified proteins. FDR values were log10-transformed, and their sign inverted for plotting. Only enriched pathways with adjusted P-value (FDR) < 0.01 were considered significant and represented.
## Neurite outgrowth
For the analysis of neurite outgrowth, developing neurons at 9 DIV were replated into sterile cloning cylinders (O.D. 8 × H 8 mm2; Merck, Cat. No. CLS31668), located in the middle of Matrigel-coated glass coverslips (22 mm diameter, Cat. No. 631.0159, WDR), into six-well plates. Neurons were seeded at a density of 2500 cells/cylinder and were kept in culture accordingly to the differentiation protocol. The day after seeding, the cylinder was gently removed, allowing the radial extension of neurites by developing neurons. At 20 DIV, neurons were fixed with $3.7\%$ paraformaldehyde in PBS for 10 min, permeabilized with ice cold $0.1\%$ Triton X-100 in PBS and incubated in blocking buffer ($5\%$ Normal Donkey Serum; Abcam, Cat. N. ab7475) for 30 min at room temperature. Nuclear bodies and neurites were stained overnight at +4 °C with an anti-tubulin III antibody (1:1000; Abcam, Cat. No. ab18207), followed by incubation with Donkey Anti-Rabbit IgG Alexa Fluor 488 (1:1000, Abcam, Cat. No. ab150073). Images were acquired using ZEISS Axio Zoom. V16 Microscope and processed using Zeiss ZEN 2.6 Blue Microscopy Software.
To quantify axonal growth, we adapted the Sholl method of concentric rings to our cultures. Prior to processing, images were modified with ImageJ v1.5.3 (National Institutes of Health, Bethesda, MD) to eliminate fluorescence background and artefacts. For application of the Sholl analysis, we employed the ShollAnalysis ImageJ plug-in, which counts the number of intersections of neurites as a function of distance from the cell soma or explant. Immunofluorescent images were imported into ImageJ and converted in greyscale, the size scale was set accordingly, and the brightness/contrast threshold was selected manually to remove the thinner and shorter neurites emerging from the clusters. Sholl analysis was performed by selecting the centre of the neuronal body cluster as the centre of outgrowth (start radius = 0 inch) and using a step size of 0.5 inch and an end radius of 8 inch. These parameters were chosen to divide the image into concentric annuli at a radial distance of 1000 μm from each other. Intersections were counted from the border of the neuronal body cluster (set as 0 μm) outwards. Results were inputted into GraphPad Prism 9.2.0 software and statistical analysis was performed between CT and FRDA groups at each individual distance from the body cluster (two-way ANOVA with Bonferroni’s test for multiple comparisons; *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$).
## Electrophysiological analysis
Individual culture slides from CT (CT1 and 2), FRDA (FRDA 1, 2 and 5) and ISO CT (IcFA4, IcFA5) at 19–21 DIV were treated as described in our previous report.17 Neuronal cultures were maintained immersed in a thermoregulated chamber, continuously superfused with an oxygenated artificial CSF (aCSF) containing (in mM): NaCl 127, KCl 2.5, NaH2PO4 1.25, MgCl2 1, NaHCO3 26, D-glucose 10, CaCl2 2, bubbled with $95\%$ O2 and $5\%$ CO2 (pH of 7.3, 300–316 mOsm, rate of 1.5–2 ml/min, temperature of 30°C). Patch clamp experiments were performed in whole cell configuration with a solution containing biocytin $0.5\%$ (Sigma-Aldrich, Cat. B4261) and (in mM): KMeSO4 125, KCl 12, CaCl2 0.022, MgCl2 4, HEPES 10, EGTA 0.1, Na2-phosphocreatine 5, Mg2-ATP 4, Na2-GTP 0.5 (pH of 7.2, 292 mOsm). First, passive properties [capacitance (Cm, pF), membrane resistance (Rm, MΩ), membrane time constant (τ, ms)] and series resistances were extracted in voltage-clamp mode, as previously shown.17 Then, in current-clamp mode, cell excitability was investigated by setting the resting membrane potential at −60 mV and injecting 1 sec depolarizing steps in 10 pA increments (from 0 to 100 pA). Signals were sampled at 10 kHz with a gain of 2 mV/pA. Firing frequency was calculated as number of automatically detected APs over 1 sec step duration, with voltage threshold set at 0 mV for each depolarizing step. Spikes with a peak amplitude below 0 mV were not included in the estimation of firing frequency and neurons were considered to be in accommodation from that point on. The neuron RMP was derived from the averaged off-line values of potential fluctuations during the entire step at 0 pA of injected current. The liquid injection potential of the solution, equal to 6.6 mV, was not subtracted in the calculation of RMP. For cells showing spontaneous AP firing, the RMP was measured manually by the value of neuron potential at 0.02 sec before the first action potential without any current injection. Series resistance was not compensated during recordings.
Series resistances averaged for each neuron group were not significantly different. For single AP recorded neurons, series resistances were 29.0 ± 2.6 MΩ in CT ($$n = 11$$), 35.3 ± 2.9 MΩ in FA ($$n = 10$$) and 31.9 ± 3.2 MΩ in IC neurons ($$n = 5$$); P value = 0.2707. Series resistances for burst neurons were 33.2 ± 4.3 MΩ in CT ($$n = 3$$), 28.5 ± 3.9 MΩ in FA ($$n = 6$$) and 32.4 ± 7.6 MΩ in IC neurons ($$n = 3$$); P value = 0.7580. For tonic neurons, series resistances were 33.0 ± 1.5 MΩ in CT ($$n = 22$$); 36.6 ± 1.5 MΩ in FA ($$n = 26$$) and 36.4 ± 1.4 MΩ in IC ($$n = 17$$). P value = 0.1849 (one-way ANOVA). If access resistance between the beginning and the end of recording changed more than $25\%$, neurons were not included in the analysis. Passive properties and neuronal excitability were analysed by IgorPro 6.3 software (WaveMetrics, Portland, USA) using Patcher’sPower Tools, NeuroMatic plugins and Microsoft Excel software. For analysis of passive properties, mean values of neurons from same culture replicates were used (between 1 and 10 neurons per replicate). Data are represented as mean of culture replicates ± SEM. For analysis of active properties of tonic neurons, mean values of neurons with homologous firing patterns from same culture replicates were used (between 1 and 10 neurons per replicate). Data are represented as mean of culture replicates ± SEM. When possible, recorded neurons were identified through biocytin-TrKC co-expression, as previously described.17 Series resistances averaged for each neuron group were not significantly different. For single AP recorded neurons, series resistances were 29.0 ± 2.6 MΩ in CT ($$n = 11$$), 35.3 ± 2.9 MΩ in FA ($$n = 10$$) and 31.9 ± 3.2 MΩ in IC neurons ($$n = 5$$); P value = 0.2707. Series resistances for burst neurons were 33.2 ± 4.3 MΩ in CT ($$n = 3$$), 28.5 ± 3.9 MΩ in FA ($$n = 6$$) and 32.4 ± 7.6 MΩ in IC neurons ($$n = 3$$); P value = 0.7580. For tonic neurons, series resistances were 33.0 ± 1.5 MΩ in CT ($$n = 22$$); 36.6 ± 1.5 MΩ in FA ($$n = 26$$) and 36.4 ± 1.4 MΩ in IC ($$n = 17$$). P value = 0.1849 (one-way ANOVA).
If access resistance changed more than $25\%$ between the beginning and the end of the recording, the neuron was discarded. Analyses of passive properties and excitability of recorded neurons were performed with IgorPro 6.3 software (WaveMetrics, Portland, USA) using Patcher’sPower Tools, NeuroMatic plugins and Microsoft Excel software. For analysis of passive properties, mean values of neurons from same culture replicates were used (between 1 and 10 neurons per replicate). Data are represented as mean of culture replicates ± SEM. For analysis of active properties of tonic neurons, mean values of neurons with homologous firing patterns from same culture replicates were used (between 1 and 10 neurons per replicate). Data are represented as mean of culture replicates ± SEM. When possible, identity of recorded neurons was assessed for each group with biocytin-TRKC double immunostaining, as previously described.17
## Immunoblotting
Western *Blot analysis* of frataxin expression was performed on mature neuronal cultures at 20 DIV, in all CT and FRDA lines available, along with IcFA4 and IcFA5. For protein extraction, neurons were harvested, washed twice in PBS followed by centrifugation at 200× g for 3 min, and cell pellet was solubilized in chilled 1 × PTR Extraction Buffer (Abcam, Cat. No. ab193970) in water, supplemented with 1 × Extraction Enhancer Buffer (Abcam, Cat. No. 193971) and protease inhibitors (Roche, cOmplete, Mini Protease Inhibitor Cocktail, Cat. No. 11836153001). Samples were incubated on ice for 1 hour, followed by centrifugation at 18.000×g for 20 min at 4°C. The supernatant was collected and stored at −80°C until use. Protein concentration was determined with Pierce BCA Protein Assay Kit (Thermo Fisher, Cat. N. 23225), following manufacturer’s instructions. Protein separation was performed by SDS-PAGE in $12\%$ polyacrylamide gels. Proteins were denatured in Laemmli buffer (4 × Laemmli Sample Buffer, Bio-Rad, Cat. No. 1610747) for 10 min at 95 °C. 20 μg of total protein were loaded per sample and electrophoresis was performed in Tris-glycine SDS running buffer at 100 mV for 1.5–2 hours. Proteins were then transferred onto 0.45 μm pore-sized nitrocellulose membranes at 100 V for 1 hour at 95 °C, using a wet blotting device. Following transfer, membranes were blocked for 5 min with EveryBlot Blocking Buffer (Bio-Rad, Cat. No. 12010020) and incubated with primary antibodies diluted in TBST/Blocking Buffer (1:1), overnight at 4°C with gentle shaking. Membranes were rinsed three times for 5 min with TBST and incubated with secondary antibodies diluted in TBST/Blocking Buffer for 1 hour at room temperature on a shaking platform in the dark. After two washes in TBST and one wash in TBS of 5 min each, blots were dried at room temperature and wrapped in Whatman filter paper and in aluminium foil. Fluorescent signals were captured using the c600 AzureBiosystems detector and cSeries software. Images were converted in greyscale and band intensity quantified with ImageJ v1.5.3 software. Experiments were performed in duplicate, and data analysed with GraphPad Prism v9.2.0 software (two-way ANOVA with Bonferroni’s test for multiple comparisons; *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$).
The following antibodies were used: Frataxin (1:20; Abcam, Cat. No. ab110328), GAPDH-Loading Control (1:5000; Abcam, Cat. No. ab9485), Goat anti-Mouse IgG (H + L), DyLight 800 4×PEG (1:10 000, Thermo Fisher, Cat. N. SA535521), Goat anti-Rabbit IgG (H + L) and DyLight 680 (1:15 000, Thermo Fisher, Cat. No. 35568). Precision Plus Protein Kaleidoscope Standards (Bio-Rad, Cat. No.1610375) were used as control.
## Statistical analysis
Where not specifically indicated, data were visualized and tested for significance using GraphPad Prism v9.2.0 software. In the comparisons between CT, FRDA and ISO CT groups for Chromatin Immunoprecipitation and Sholl analysis, data were analysed with a two-way ANOVA followed by Bonferroni’s test for multiple comparisons. Statistical analysis of passive properties and resting membrane potential from culture replicates was performed with one-way ANOVA followed by Tukey’s test for multiple comparisons. Statistical analysis of active firing properties of recorded neurons from different culture replicates was performed with two-way ANOVA followed by Tukey’s test for multiple comparisons. Significance is defined as *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$ for all analyses.
## Data availability
The authors declare that all data supporting the findings of this study are available within the article or in the supplementary data files. The dataset generated in the study and processed data are available from the corresponding author upon reasonable request.
## Epigenetic analysis of FXN locus in differentiated neurons
Previous studies showed reduced histone acetylation and increased tri-methylation of H3K9 and H3K27 in the FXN locus as hallmarks of FRDA.20 We used ChIP to assess these key histone modifications in fully differentiated sensory neuronal cultures (Fig. 1) obtained from nine hiPSC lines (two CTs, five FRDA and the ISO CTs lines IcFA4 and IcFA5), in a region spanning from the first exon of the gene to 880 bp downstream of the GAA expansion18 (Fig. 1C). Statistically significant differences between CT and FRDA lines were observed in the level of acetylation of H3K9 and K3K27 in the coding region (exon1) and in the proximal intronic region (Fig. 1A and B). Also, increased trimethylation for H3K9 and H3K27 was observed in FRDA lines, but that was mainly in the regions upstream and downstream of the GAA repeats (Fig. 1A and B, middle). ISO CT lines showed a partial recovery of the altered epigenetic marks. The direct comparison of IcFA4 and IcFA5 lines (Supplementary Figure with their sibling FRDA lines (Supplementary Fig. 1) indicated a partial recovery in the level of acetylation of H3K9 and a reduced level of trimethylation of both H3K9 and H3K27 in different regions of the gene. No significant differences were observed instead for H3acK27 between FRDA and ISO CT neurons (Fig.1B, up). When we looked at the combined effects of histone acetylation and trimethylation, the recovery of the epigenetic signatures in the isogenic control lines was more evident. Thus, it appears that different mechanisms contribute to gene silencing in FRDA, depending on the distance from the expanded GAA repeats, and that the removal of the GAA repeats can lead to a reversal of the repressive marks in the ISO CTs. In mammals, trimethylation of H3K9 and H3K27 depends on different effectors: H3K9 methylation depends on the histone methyltransferase SUV39H1 and its interaction with the HP1 protein, while H3K27 is methylated by the action of the Polycomb repressive complex 2 (PRC2) and the methyltransferase EZH2.21 Looking at the epigenetic profile of FXN in our cultures, the action of PRC2 seemed to extend to the entire locus, while the action of SUV39H1 looked limited to the regions immediately upstream and downstream of the GAA repeats. An inhibition of both systems seemed to follow the excision of the GAA expansion mutation in ISO CT neurons.
**Figure 1:** *Epigenomic analysis of FXN locus. (A, B) Investigation of Histone H3 post-translational modifications of FXN 5′-end at exon 1 (Ex1), intronic region (intron 1) upstream (IntP1, IntP2, IntP3) and downstream (IntP4, IntP5) of GAA expansion. Chromatin from differentiated neurons was immunoprecipitated using antibodies specific for the Acetylated (ac) and Trimethylated (3m) isoform of Lysine 9 (K9) and Lysine 27 (K27) of H3. Eluted DNA was amplified by qPCR. ChIP-qPCR data were obtained with the percentage of input method and normalized to Histone H3 signals. Results were indicated for each marker, as well as for the ratio between the acetylated and trimethylated isoforms of K9 (A) and K27 (B). ChIP was performed on two independent chromatin preparations for each line included in the study (CT: n = 2; FRDA: n = 5; ISO CT: n = 2). Dots represent the mean of two biological replicates per cell line. Horizontal bars represent mean values for each group of investigation (CT, FRDA and ISO CT lines). For ISO CT group, IcFA4 and IcFA5 were used: in these cell lines, regions immediately upstream and downstream of GAA expansion were removed and no amplification was detected for IntP3, IntP4 and IntP5. Statistical analysis was performed using a two-way ANOVA followed by Bonferroni’s test for multiple comparisons (adjusted P-value: *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001). The vertical broken line shows the location of the GAA repeats. (C) Schematic representation of the FXN region investigated by ChIP, from the promoter (Pr) to exon 1 and intron 1. Primer sets used for qPCR are indicated as dashes. Putative regulatory non-coding elements (L2, MIRb, MER, MER1, Alu) located in that region of the gene are also represented.*
Of note, increased markers of gene silencing (constant trend of reduced acetylation and increased trimethylation of H3K9 and H3K27) were also observed in CT lines in the regions flanking the GAA repeats (Fig. 1A and B). This agrees with the DNA methylation profile at the FXN locus highlighted in different cellular models, including hiPSC-derived sensory neurons:22,23 in fact, although a higher rate of DNA methylation was observed in FRDA cells, a sort of physiological increase was observed also in non-affected cells upstream of the GAA repeats.
Overall, our data confirmed the presence of repressive histone marks in FRDA compared to CT sensory neurons along with the possibility to partially revert those marks through the removal of the GAA expansion mutation. This reversal was sufficient to restore the expression of frataxin protein to CT levels (Supplementary Figure of transcriptomic alterations in FRDA and ISO CT lines).
Bulk RNA sequencing was performed for CT, FRDA and ISO CT lines at three different stages of differentiation: iPSCs; developing neurons, prior to complete differentiation supported by treatment with neurotrophic factors, and fully mature neurons (Fig. 2 and Supplementary Figure. All available lines were included in the study (two CT, five FRDA and three ISO CT lines). We separately compared FRDA versus CT, FRDA versus ISO CT and ISO CT versus CT lines. Differentially expressed genes (DEGs) were selected using a cut-off of absolute Log2(Fold Change) > 1 and adjusted P-value < 0.1. For each comparison, the level of expression of DEGs was then investigated also in the other group, in order to have a complete view of gene expression for all groups. Results were expressed as normalized read counts the in log2 scale (Figs. 2 and 3 and Supplementary Figs. 3–5).
**Figure 2:** *Transcriptome profiling of FRDA differentiating cultures. (A) Principal Component Analysis (PCA) of transcriptome profile at different stages of differentiations (iPSCs, Developing Neurons and Mature Neurons) for all lines, coloured by cluster membership. (B) Venn Diagrams of differentially expressed coding genes in the comparison of FRDA or ISO CT with CT lines, at each stage of differentiation. Common DEGs are indicated under the corresponding diagram (n = 2 for CT; n = 5 for FRDA; n = 3 for ISO CT).* **Figure 3:** *Heatmaps representing differentially expressed coding genes between FRDA and CT. [|Log2(Fold Change)| > 1; adjusted P-value < 0.1] is shown for each stage of differentiation (from left to right: iPSCs; developing neurons; mature neurons). For each gene, the log2(Fold Change; FC) between FRDA and CT is shown on the right side of the corresponding heatmap. For each comparison, the transcriptional level of identified DEGs was represented also for the ISO CT group, allowing the evaluation of DEG expression after removal of the GAA expansion mutation. Colour scale represents normalized read count values in the log2 scale.*
Principal Component Analysis (PCA) revealed well separated clusters for the three stages of differentiation, confirming the ability of all lines to respond similarly to the differentiation protocol. A major difference occurred between iPSCs and the other two stages (PC1 = $55.76\%$), while developing and mature neuron clusters were closer (PC2 = $11\%$), suggesting that only minor changes occurred in the final stage of differentiation (Fig. 2A).
PCA performed at each differentiation stage revealed only minor differences between FRDA and CT lines, as indicated by the proximity of the two groups and the low PC values (iPSC: PC1 = $31.37\%$, PC2 = $23.91\%$; developing neurons: PC1 = $25.88\%$, PC2 = $19.4\%$ and mature neurons: PC1 = $31.73\%$, PC2 = $20.45\%$). It was not possible to define a clear clustering for the ISO CT lines, with IcFA4 and IcFA5 being unable to significantly diverge from their related FRDA lines (FA4 and FA5, respectively). DEGs were only detected between FRDA and CT or ISO CT and CT lines with no clear reversal of the transcriptional profile between ISO CT and FRDA lines for most identified DEGs (Fig. 2B). Accordingly, ISO CT and FRDA groups shared most DEGs in their comparison to CT lines (Fig. 2B; Supplementary Fig. 3). Overall, the number of identified DEGs was quite low, allowing a deeper assessment of their biological function (Supplementary Table 2).
It is important to mention that we did not use purified PPN cultures. While we previously showed17 that our differentiation protocol mostly generates PPNs, it also generates mechanoreceptive neurons (25–$30\%$), which share with PPNs an immediate common progenitor and several markers. It is also likely that our cultures show some minor variability in terms of the specific subtypes of mechanoreceptor and PPN populations known to be present in DRGs. However, by comparing our transcriptomic data with available data from transcriptional profiling of mouse (which may have species-specific differences) and human DRG neurons,24–28 we confirmed the presence of PPN markers in finally differentiated cultures without any statistically significant difference between CT, FRDA and ISO CT lines (Supplementary Fig. 3A). A medium-high level of expression for mechanoreceptive-specific markers was also observed, while expression of nociceptive-specific markers was very low (Supplementary Fig. 3B). Thus, we can assume that differences among CT, FRDA and ISO CT cultures were not due to the presence of different proportions of DRG neuronal subtypes. We can also assume that transcriptomics differences in FRDA lines most likely reflect gene expression differences in PPNs, which were the most abundant category of neurons in the differentiated cultures and are the earliest and most severely affected DRG neurons in FRDA.
Downregulation of FXN expression was detected in FRDA compared to CT lines at all stages, along with an upregulation of gene expression in ISO CT lines (Fig. 3), in agreement with previous studies,13,15,16 and with the reversal of the epigenetic profile and recovery of FXN biosynthesis observed in this study.
Most DEGs were detected in immature neurons, supporting the existence of an important developmental component in FRDA. Some of them persisted in fully mature neurons (Fig. 3). However, we cannot exclude that some of the differences observed for developing neurons were due to the persistence of cell type heterogeneity at this stage when sensory neuronal cultures were probably not yet completely defined.
Gene ontology analysis for upregulated genes in FRDA developing and mature neurons indicated the involvement of stress activated pathways, MAPK cascade regulation, response to increased oxygen levels, as well as negative regulation of cellular chemotaxis, dysregulation of cell adhesion and activation of catabolic activities (Fig. 4A). We identified specifically involved genes in protein ubiquitination and degradation (MINDY, AGBL1, KHNYN),29,30 and genes which are known to be upregulated in the presence of peripheral axonal injury (GADD45A, GADD45G, NNAT, RTN4R).31–34 Interestingly, some of the upregulated genes encoding for adhesion molecules were members of the clustered protocadherin family of homophilic cell-adhesion molecules (PCDHA9, PCDHA11, PCDHB8, PCDHGA10) (Fig. 4B and C). They belong to gene clusters which are associated in tandem on chromosome 5, in a unique genomic organization, and their regulated expression is critical for the establishment and function of specific cell-cell connections in the brain and in the spinal cord.35
**Figure 4:** *Biological processes of differentially expressed genes. (A) Gene ontology analysis for coding upregulated (left) or downregulated genes (right) in FRDA developing and mature neurons in the comparison with CT cells. Only GO terms that were significantly overrepresented (Panther classification system; P-value < 0.01) are shown. (B, C) Balloon plots for upregulated (B) or downregulated (C) genes in FRDA neurons, classified by their biological functions. The log2(Fold change) expression of selected genes is represented for FRDA (n = 5) and ISO CT (n = 3) lines in their comparison to CTs (n = 2) for all stages of differentiation (iPSCs, developing and mature neurons).*
We also detected the upregulation of genes involved in oxidation-reduction processes (SDR42E1, CBR1, PEX11G, TXNRD2, ACOXL, VAT1L), although they were not those previously associated with FRDA36,37 (Supplementary Table 3) and others encoding mitochondrial proteins (Fig. 4B). The latter included NDUFA4L2, which encodes a subunit of NADH dehydrogenase (Complex I); ANTKMT, participates in the regulation of mitochondrial ATP synthesis coupled proton transport; ECHS1, participating in mitochondrial fatty acid β-oxidation; and MTLN, which encodes the protein Mitoregulin, involved in several processes, including fatty acid-beta oxidation and regulation of the mitochondrial membrane potential. However, these changes could not be clearly linked to the expected deficiency in Fe-S proteins in FRDA. We also did not detect any change in the expression level of genes encoding Fe-S proteins or involved in the control of iron homeostasis (Supplementary Table 3).
*Downregulated* genes in FRDA mainly encoded proteins involved in axon development and guidance, actin polymerization and depolymerization, cell-junction assembly and synaptic plasticity (Fig. 4A). For some of these genes, mostly involved in cytoskeleton organization and axon guidance (COBL, TUBA4A, KIF1C, TLN2, DOK5, TSPAN2 etc.), differences were already evident and even more statistically significant in developing than in mature neurons. For others, instead, mainly related to synaptic plasticity (SYNJ2, SV2C, CBLN1, UTS2, etc.), there was further downregulation in mature neurons (Fig. 2B). We also confirmed17 the downregulation of two specific proprioceptive markers, the transcription factor RUNX3 and the Ca2+-binding protein parvalbumin (PVALB) (Fig. 4C). Of note, RUNX3 is known to be essential for target-specific axon pathfinding of TRKC+ DRG proprioceptive neurons.38,39 Finally, some of the identified DEGs encoded subunits of ion channel neurotransmitter receptors known to be expressed in proprioceptive neurons. We observed a massive upregulation of GRIN3A along with a downregulation of GRIN1 and GABRA5 in FRDA neurons. GRIN3A and GRIN1 are involved in glutamate neurotransmission, while GABRA5 encodes a GABA-A receptor subunit (Fig. 4C). Proprioceptive afferents express these receptors at the presynaptic level.25,40 GRIN3A and GRIN1 are both located on chromosome 9 (9q31.1 for GRIN3A and 9q34.3, close to telomeric region, for GRIN1) and encode distinct subunits of the NMDA family of ionotropic glutamate receptors. A correct balance between different subunits is critical for a proper function of NMDA tetrameric receptors, which are involved in a number of physiological and pathological processes in the nervous system, including synaptic plasticity, refinement of synaptic connections and excitotoxicity:41 GluN3A, the subunit encoded by GRIN3A, is usually expressed at lower levels compared to GluN1 and GluN2 (encoded by GRIN1 and GRIN2, respectively) and exhibits atypical biophysical properties, such as a reduced permeability to Ca2+ and a lower sensitivity to Mg2+ block at hyperpolarized potentials.42 *It is* plausible to hypothesize that the concomitant upregulation of GRIN3A and downregulation of GRIN1 at the presynaptic level could be associated with an alteration of the excitatory signalling in affected neurons.
Although our analyses mainly focused on developing and mature neurons, significant alterations were already evident at the iPSC stage (Fig. 2B), for example, for genes that could play important roles in neuron development (CEND1, DCC, SEZ6L, EPHB3, GDA etc.), in the regulation of cytoskeletal structures (MAP2, FHOD3, ACTA1, WASHC1, ACTC1 etc.) and in the cellular response to reactive oxygen species (SLC8A1, SDR42E1, ACOXL, VAT1L, SCD5 etc.).
For most identified DEGs, we could not observe a clear recovery in ISO CT lines, whose transcriptional profile resembled that of FRDA cells. To confirm that this lack of recovery was not due to intragroup variability for FRDA lines, we also performed a one-to-one comparison between IcFA4 and IcFA5 with their sibling FRDA lines (Supplementary Fig. 4): also in this case, a partial reversal was observed only for a few genes, without any clear association between the presence or absence of the GAA expansion mutation and the transcriptional profile of sibling lines.
Finally, an important finding of our analysis was the detection of numerous aberrantly expressed non-coding RNAs (ncRNAs) (Supplementary Fig. 6). Some of them were either massively upregulated (RPS4XP22, RCN1P2, H19 etc.) or downregulated (AP000763.2, AC098847.1, ZBTB8OSP2, RHOXF1-AS1 etc.) in FRDA and ISO CT lines at all stages of differentiation. As these elements participate to gene expression control at the epigenetic, transcriptional and post-transcriptional level, their aberrant expression might impact PPNs development and physiology in FRDA.43,44
## Proteome profiling of mature neurons
We performed a quantitative proteomic analysis of mature neurons from all available CT, FRDA and ISO CT lines. As for transcriptomics, we separately compared FRDA versus CT, FRDA versus ISO CT and ISO CT versus CT neurons and then assessed the expression of DEPs also in the third group to have a direct comparison of protein expression. Protein abundance was estimated using the normalized Mean Peak Intensity of peptide spectra, and differentially expressed proteins were selected using a cut-off of absolute Log2(Fold Change) > 1 and a P-value < 0.01 (Fig. 4).
DEPs were detected in all cases. We obtained 50 proteins that were significantly differentially expressed between FRDA and CT cells, 105 DEPs between ISO CT and CT neurons, and 83 DEPs between FRDA and ISO CTs (Fig. 5, Supplementary Fig. 7).
**Figure 5:** *Heatmaps of differentially expressed proteins in the comparison between FRDA and CT (left), FRDA and ISO CT (middle), ISO CT and CT (right) neurons. The log2(Fold Change) between the two groups analysed (FRDA/CT, FRDA/ISO CT or ISO CT/CT) is shown for each protein on the left side of the corresponding heatmap. For each comparison, the level of expression of identified proteins was represented also for the third group, allowing a direct comparison among all lines. Colour scale represents mean peak intensity of protein spectra in the log2 scale. Exact values of mean peak intensities for each protein are indicated in the heatmaps (n = 2 for CT, n = 5 for FRDA, n = 3 for ISO CT).*
Proteomic analysis of differentiated cultures focused on the biological functions and interactions among the detected DEPs. Top enriched pathways were mainly related to neuron projection development, cell junction organization and assembly, and synaptic plasticity (Fig. 6). Accordingly, proteins were mainly localized in the distal axon at the growth cone and at synapses (Fig. 6).
**Figure 6:** *Biological processes and cellular localization of differentially expressed proteins. Gene Ontology (GO) analysis of biological processes (left) and cellular compartment (right) for differentially expressed proteins in FRDA and ISO CT neurons. Only GO terms that were significantly overrepresented [False Discovery Rate (FDR) < 0.01] are shown.*
Most DEPs identified for FRDA and ISO CT neurons are associated in a tight network and take part in the same biological functions (Fig. 7). Though transcriptomics and proteomics data indicate involvement of the same processes and pathways, there is no full correspondence between DEGs and DEPs. This may be due to post-translational modifications and changes in the degradation rate for specific proteins, in addition to the lower sensitivity of proteomics in comparison to RNA-seq.
**Figure 7:** *Protein network of differentially expressed proteins. Known interactions (STRING-DB) and biological processes of upregulated and downregulated proteins in FRDA and ISO CT neurons are shown.*
Proteomic analysis identified proteins involved in cytoskeleton organization and assembly (DNM3, STMN2, NEFL, MAPT, PALM etc.), some of which are specifically related to actin filaments, major components of the growth cone (GSN, ARPC1B, BRK1 etc.), others are associated with cell adhesion (N2CAM, ALCAM, L1CAM, CNTN1 etc.), and axon guidance (ABLIM1, NPTN, PRKCA, PLXNA4) (Figs 4C and 5B). In FRDA and ISO CT, there were lower protein levels of neurofilament light chain (NFL, encoded by NEFL), increasingly used as a biomarker of neuroaxonal damage in many neurological conditions, including FRDA,45,46 despite no difference at the transcriptomic level [FRDA versus CT neurons: log2(Fold change)= 0.48, adj P-value = 0.89; ISO CT versus CT neurons: log2(Fold change)= −0.36, adj P-value = 0.99], suggesting loss from degenerating axons or post-transcriptional downregulation of this protein.
DEPs involved in vesicular trafficking (SH3GLB2, DNAJC6, SEC24A, TMCC3 etc.), synaptic vesicle recycling (SYT1, SYT7, SYNGR3 etc.) and in synaptic organization and assembly (HOMER1, SNAP25, S100B, etc.) were also detected (Figs 6 and 7).
As for transcriptomics, we also observed a dysregulation of proteins involved in oxidation-reduction processes (CAT, CCS, GSTO1, PCK2, LDHA, COX20), in fatty acid metabolism (ILVBL, SCAP, FABP7, ACADSB etc.) and in autophagy or catabolic processes (VPS36, ATG4B, MTMR14, etc.), localized either in the cytosol or in mitochondria. Other aberrantly expressed mitochondrial proteins included ribosomal proteins (MRPL18, MRPL45, RPL23L), two components of the mitochondrial respiratory chain, NDUFV3 and NDUFA4 of complex I and IV, respectively, and other proteins taking part to the regulation of the mitochondrial matrix volume and mitochondrial transmembrane potential (CCDC51, CCDC58, GBAS/NIPSNAP2, VPS13C).47,48 Finally, some of the identified proteins were specifically involved in chromatin remodelling and epigenetic gene control (LMNA, NAP1L1, SMARCAD1, NCAPG, CTR9 etc.), in regulation of transcription and response to DNA damage (HIRA, HMGA2, RFC1, SUPT16H etc.) and in RNA processing (RPS5, EIF2AK2, INTS2, SF3B6 etc.) ( Figs 6 and 7). It is important to note that this classification is not exhaustive, as most DEPs are involved in more than one process. A complete list of DEPs with their biological functions is provided in Supplementary Table 4.
Although a significant number of DEPs was identified between FRDA and ISO CT lines, they have known interactions with other DEPs identified between CT and FRDA or ISO CT neurons or both, suggesting that they may be part of a wide homeostatic response to changes originally triggered by the presence of the GAA expansion mutation and persisting in the isogenic controls. Moreover, when we performed a one-to-one comparison of sibling FRDA and ISO CT lines for all identified DEPs, we observed that only a few were differentially expressed between sibling lines (Supplementary Fig. 8).
## Irregular neurite extension in FRDA neurons
Since a significant fraction of DEGs and DEPs between FRDA and CT neurons was associated with cytoskeletal organization and axon development, we tested if these differences affected neurite extension in our cultures (Fig. 8). We isolated the neuronal bodies of developing neurons in the centre of a plate, allowing the radial extension of their axons in the presence of neurotrophic factors for 12 days. Both FRDA and CT neurons showed the ability to extend long neurites, reaching a maximum external radius of 4000 μm from the edge of soma cluster (Fig. 8A). Direct measurement of single neurite length was hindered by the high density of neurites in culture. However, some irregularities were observed for FRDA neurons, whose longest axons looked tortuous, sometimes with complex twisting. Statistically significant differences between FRDA and CT neurons at the Sholl analysis49 were observed at 3000 μm of radial distance from the body cluster (two-way ANOVA with Bonferroni’s test for multiple comparisons; adjusted P-value = 0.002), indicating the inability of FRDA neurites to reach the same distances of CTs, probably because of their irregular trajectories (Fig. 8B). These morphological alterations seem to support the existence of deficits in axon development in FRDA neurons.
**Figure 8:** *Morphological analysis of neurite outgrowth. (A) Representative images of proprioceptive enriched cultures from CT (up) and FRDA (down) lines at 20 DIV labelled with Tubulin β-III. Neuronal cell bodies were isolated in a cluster, allowing radial neurite extension. Scale bar: 1000 μm. (B) Schematic representation of morphometric Sholl analysis. Neuron reconstruction and analysis were performed using the ShollAnalysis plug-in of ImageJ v1.5.3 software (up). The number of intersections between neurites and concentric spheres centred in the neuronal body cluster was determined at various distances, starting from the edge of cluster (0 μm) with 1000 μm increments (down). Each dot in the plot corresponds to a biological replicate (n = 3 for CT; n = 3 for FRDA). Horizontal bars represent mean values of CT and FRDA biological replicates at each distance from cell cluster (mean ± SD). Statistical analysis was performed with two-way ANOVA followed by Bonferroni’s test for multiple comparisons (adjusted P-value: **P < 0.01).*
## Investigation of electrophysiological properties of mature neurons
We performed whole-cell patch-clamp recordings to investigate the passive and active electrical properties of differentiated neurons at 19-21 DIV, when they seemed to have already reached a stage of functional maturation, as we previously shown.17 Recorded pseudo-unipolar neurons in CT, FRDA and ISO CT cultures were marked with a biocytin-TrKC double immunolabelling (Fig. 9E). TrKC was chosen for its abundant and clear expression in culture and because it is the only common marker which is equally expressed among the different subtypes of proprioceptive neurons. It is important to mention, however, that some TrKB+/TrKC+ double positive mechanoreceptive neurons might be present in culture, even if they should only represent a small percentage of finally differentiated cultures.
**Figure 9:** *Electrophysiological properties of differentiated neurons. Electrophysiological properties of mature differentiated neurons at 19-21 DIV in CT, FRDA, and ISO CT cultures. (A) Representative traces of current-clamp recordings at 20 pA of injected current showing three different firing patterns: single action potential (AP) irrespective of the current injected (up), short duration burst of APs (middle) or tonic AP firing (bottom). (B, C) Scatter dot plots representing mean values of Capacitance, Membrane Resistance (Rm), Membrane Time Constant (Tau) (B) and Resting Membrane Potential (RMP) (C) for single AP, burst and tonic neurons in CT, FRDA, and ISO CT culture replicates. Dots represent mean values for neurons from independent culture replicates (between 1 and 10 neurons per replicate). Horizontal bars represent mean values of culture replicates for each group (mean ± SE). No significant differences were observed between the three groups for any of the analysed properties (one-way ANOVA with Tukey’s test for multiple comparisons; ns). (D) Percentages of cells showing the three different firing patterns in differentiated CT (left), FRDA (middle) and ISO CT (right) cultures. They were almost equally represented in the three groups (Chi-square test, confidence interval 99%, ns). n = 36 for CT, n = 42 for FRDA, n = 27 for ISO CT. (E) Representative image of neuron identified with a 63 × water immersion objective and infrared CCD camera during recordings, followed by representative fluorescent images of biocytin-filled TRKC+ neurons in CT, FA and ISO CT cultures (scale bars = 10 μm).*
We could detect the three different firing patterns usually observed for DRG neurons, including proprioceptors25,50–53 (Fig. 9A), which were almost equally represented in CT, FRDA and ISO CT neurons (Fig. 9D). Some neurons exhibited a rapid adaptation and generated a single action potential (AP) independently of the intensity of injected currents (Fig. 9A, up); others displayed a burst of APs followed by accommodation upon sustained stimulation (Fig. 9A, middle) and a third type of neuron, the most represented in culture, exhibited a tonic firing pattern, followed by accommodation upon increased current stimulations (Fig. 9A, bottom). These different behaviours likely corresponded to rapidly (single AP and burst) and slowly adapting (tonic) neurons, both of which have been detected in muscle spindle and Golgi tendon organs.25 *The analysis* of passive properties for culture replicates of neurons with different firing patterns revealed the absence of significant differences between CT, FRDA and ISO CT neurons (Fig. 9B). Single AP neurons showed a capacitance (Cp) of 31.4 ± 5.5 pF in CT, 31.7 ± 5.4 pF in FRDA, 24.3 ± 3.3 pF in ISO CT ($$P \leq 0.5967$$), a membrane resistance (Rm) of 1617.9 ± 419.5 MΩ in CT, 1233.3 ± 153.8 MΩ in FRDA, 1149.2 ± 176.7 MΩ in ISO CT ($$P \leq 0.4400$$) and a membrane time constant (Tau, τ) of 47.7 ± 7.9 ms in CT, 37.8 ± 6.8 ms in FRDA, 28.6 ± 7.1 ms in ISO CT ($$P \leq 0.3051$$).
For burst neurons, Cp was 19.5 ± 0.4 pF in CT, 31.8 ± 2.7 pF in FRDA, 31.3 ± 11.0 pF in ISO CT ($$P \leq 0.2758$$), Rm was 1730.7 ± 591.2 MΩ in CT, 1362.3 ± 199.8 MΩ in FRDA, 1331.6 ± 359.3 MΩ in ISO CT ($$P \leq 0.7043$$), τ was 33.2 ± 11.3 ms in CT, 44.9 ± 7.9 ms in FRDA and 36.4 ± 2.1 ms in ISO CT ($$P \leq 0.6192$$).
Finally, tonic neurons exhibited a Cp of 22.3 ± 1.1 pF in CT, 25.5 ± 2.0 pF in FRDA, 25.5 ± 3.7 pF in IC ($$P \leq 0.5342$$), a Rm of 2849.2 ± 372.5 MΩ in CT, 2119.1 ± 223.8 MΩ in FRDA, 2042.2 ± 254.5 MΩ in ISO CT ($$P \leq 0.1367$$), a τ of 58.8 ± 6.9 ms in CT, 51.1 ± 3.3 pF in FRDA and 53.5 ± 12.1 pF in ISO CT ($$P \leq 0.7561$$).
Results suggested that the presence of the GAA expansion mutation was not causing any significant alteration of neuronal intrinsic electrical passive properties in our cultures.
The RMP was also evaluated for each firing pattern (Fig. 9C). Also in this case, no significant differences were observed among CT, FRDA and ISO CT culture replicates: single AP neurons showed a RMP of −56.9 ± 0.8 mV in CT, −57.4 ± 2.2 mV in FRDA, −65.7 ± 4.3 mV in ISO CT ($$P \leq 0.1016$$); for burst neurons the RMP was of −47.5 ± 6.1 mV in CT, −55.7 ± 2.3 mV in FRDA, −53.7 ± 8.9 mV in IC ($$P \leq 0.5103$$) and finally, the RMP for tonic neurons was −54.9 ± 2.2 mV in CT, −53.0 ± 1.3 mV in FRDA and −53.5 ± 1.6 mV in ISO CT ($$P \leq 0.7173$$).
We next focused on the characterization of the active firing properties of tonic neurons in CT, FRDA and ISO CT lines (Fig. 10). Those neurons exhibited either a spontaneous activity ($\frac{13}{20}$ tonic CT, $\frac{13}{24}$ tonic FRDA and $\frac{7}{16}$ tonic ISO CT neurons) or a low threshold of AP generation [Rheobase = 10 ± 0 pA CT versus 15.45 ± 2.473 pA FRDA versus 10 ± 0 pA ISO CT (Mean ± SEM)], accordingly to what is usually observed for proprioceptive neurons. Most of these neurons started to show accommodation at about 30–50 pA of injected currents.
**Figure 10:** *Firing properties of tonic differentiated neurons. Firing responsiveness to depolarizing 1 sec current steps in CT, FRDA and ISO CT tonic neurons. (A) (i) Current-frequency scatter dot plot with connecting lines for individual culture replicates of CT tonic neurons (magenta) in response to current steps from 0 to 50 pA. Each line corresponds to a single culture replicate (between 1 and 10 neurons per replicate). CT neurons exhibited accommodation starting at 30 to 50 pA of injected currents. (ii) Representative traces of current-clamp recordings in CT neurons in response to 0 (left trace), 20 (middle trace) and 70 pA (right trace) of injected current. (B) (i) Pie chart indicating the number of FRDA neurons with two different tonic firing patterns and the number of culture replicates in which they were observed (Type 1: rapid accommodation lighter; Type 2: slower accommodation darker) and (ii) representative traces of current-clamp recordings for each type (at 0, 20 and 70 pA of injected current). (iii) Current-frequency scatter dot plot with connecting lines for individual culture replicates for tonic FRDA neurons of either Type 1 or Type 2 in response to depolarizing current steps. Each dot corresponds to mean ± SEM of replicates for increasing injected currents (between 1 and 3 neurons per replicate). All culture replicates are represented. (iv) Comparison of firing frequencies between Type 1 (lighter squares; n = 8) and (v) Type 2 (darker squares; n = 4) FRDA neurons and CT neurons (circles; n = 5). Each dot represents the mean firing rate observed at a defined injected current for recorded neurons of the same type from different culture replicates. Type 1 FRDA neurons showed no significant differences in AP frequency compared to CT neurons and accommodation at 30–50 pA. Type 2 FRDA tonic neurons displayed a slower accommodation and a significant reduction of AP frequency compared to CT neurons (two-way ANOVA with Tukey’s test for multiple comparisons; ns for Type 1 neurons; ****P < 0.0001 for Type 2 neurons). Results are expressed as mean of replicates ± SEM for each injected current. (C) (i) Pie chart indicating the number of ISO CT neurons with two different tonic firing patterns and the number of culture replicates in which they were observed (Type 1: rapid accommodation; Type 2: slow accommodation) and (ii) representative traces of current-clamp recordings for each type. (iii) Current-frequency scatter dot plot with connecting lines for culture replicates of tonic ISO CT neurons of either Type 1 or Type 2 in response to depolarizing current steps. Each dot corresponds to mean ± SEM of replicates for increasing injected currents (between 1 and 4 neurons per replicate). All culture replicates are represented. (iv) Comparison of firing frequencies between Type 1 (lighter squares; n = 5) and (v) darker squares Type 2 (; n = 3) ISO CT neurons and CT neurons (circles; n = 5). Each dot represents the mean firing rate observed at a defined injected current for recorded neurons of the same type from different culture replicates. Type 1 ISO CT tonic neurons showed no significant differences in AP frequency compared to CT neurons and accommodation at 30–50 pA. Type 2 ISO CT tonic neurons showed no significant differences in AP frequency compared to CT neurons (two-way ANOVA with Tukey’s test for multiple comparisons; ns) but displayed a slower accommodation. Results are expressed as mean of replicates ± SEM for each injected current.*
Nevertheless, there were still some alterations in the spiking profile of tonic FRDA (Fig. 10B) and ISO CT neurons (Fig. 1C) as compared to CT neurons (Fig. 10A). Indeed, in contrast to tonic CT neurons that all accommodated rapidly (between 30 and 50 pA of depolarizing current step injection), two different behaviours were observed in FRDA culture replicates, that we defined as ‘Type 1’ and ‘Type 2’: Type 1 FRDA neurons (15 neurons in 8 replicates) showed no significant differences in AP frequency compared to CT neurons and accommodation at 30–50 pA (Fig. 10Biv). Type 2 FRDA neurons (9 neurons in 4 replicates), instead, showed no accommodation until 100 pA of injected current and a significant reduction of AP frequency compared to CT neurons in response to the same level of injected currents (30.5 ± 6.0 Hz in CT, 11.0 ± 2.2 Hz in FRDA for 30 pA of injected current; P-value < 0.0001; two-way ANOVA with Tukey’s test for multiple comparisons) (Fig. 10Bv). Two different patterns were observed also in ISO CT culture replicates, defined again as Type 1 (nine neurons in five replicates) and Type 2 (seven neurons in three replicates). As for Type 1 FRDA neurons, Type 1 ISO CT neurons showed no differences compared to CTs (Fig. 8Civ), whereas Type 2 ISO CT neurons showed delayed accommodation, though maintaining a similar AP frequency profile than CT neurons in response to increasing injected currents up to 50 pA (30.5 ± 6.0 Hz in CT, 27.3 ± 3.4 Hz in ISO CT for 30 pA of injected current, P-value = 0.96; two-way ANOVA with Tukey’s test for multiple comparisons) (Fig. 10Cv).
The different behaviours observed for FRDA and ISO CT neurons in their comparison to CTs suggested an alteration in the expression or function (kinetics or sensitivity) of ion channels involved in firing frequency regulation and firing pattern determination, with particular regard to voltage-gated ion channels. However, the complexity of the proprioceptive system, which involves different classes of ion channels with diverse sensitivities to mechanical, chemical and electrical stimuli, does not allow any clear conclusions at this stage and calls for a deeper investigation of the observed differences.25,54–56
## Discussion
We herein present an in-depth characterization of hiPSC-derived sensory neuronal cultures highly enriched for PPNs, whose abnormal development and degeneration are hallmarks of FRDA.
We attempted to address the question of the high sensitivity of PPNs in FRDA by in vitro characterization of FRDA, CT and ISO CT hiPSC-derived sensory neurons at the genetic, transcriptomic, proteomic, morphological and electrophysiological level. The inclusion of FRDA sibling ISO CT lines also helped us to address the question if the removal of the expanded GAA repeats would be sufficient to fully correct the phenotype of FRDA cells.
As for primary fibroblasts, lymphoblasts, tissues from FRDA patients and animal models of the disease,9,20,57 we confirmed the epigenetic repression of the FXN locus in mature sensory neurons with the contribution of different effectors depending on the distance from the GAA repeat expansion. While reduced acetylation of H3K9 and H3K27 was prominent in the coding region and proximal intronic site, increased H3K9 and H3K27 trimethylation prevailed in the regions flanking the GAA expansion mutation, implying the action of both SUV39H1/HP1 and PRC2. Our observations are in line with the DNA methylation profile at the FXN locus.22,23 We also suggest the existence of a physiological silencing of the intronic region flanking the GAA repeats in CT neurons, as indicated by the combined reduced acetylation and increased trimethylation of H3K9 and H3K27 in that site. The removal of the GAA expansion mutation in ISO CT neurons was only able to induce a partial reversal of the repressive marks, which was, however, sufficient to restore frataxin expression.
The transcriptomic and proteomic analyses of our cultures indicated a dysregulation of pathways involved in the organization of axonal cytoskeleton at the growth cone, in neurite extension and axon guidance, and, mostly at later stages of maturation, in synaptic plasticity and chemical transmission. Numerous altered markers were already present in immature neurons, supporting the existence of a developmental component in FRDA. These findings were strengthened by the defects observed at the morphological levels in FRDA cells. Differentiating neurons showed the ability to extend very long processes in cultures, but FRDA neurites exhibited a more complex and tortuous course.
Our study also revealed signs of oxidative and mitochondrial stress, although the involved factors were not the same as reported in other FRDA models10–16,58–60 such as SOD2, NRF2 and iron regulatory proteins, which were not differentially expressed in our cultures. However, our study did not assess post-translational modifications affecting the functional properties of these proteins, including the presence of Fe-S clusters in some of them. This aspect can be assessed in future studies.
Finally, the electrophysiological analysis of our cultures detected irregular firing properties in tonic FRDA and ISO CT neurons, although only FRDA neurons also exhibited a reduction of their firing frequencies in comparison to CTs, with a partial recovery observed in ISO CT cells. A common feature was, instead, the delayed accommodation in response to sustained depolarizing stimuli: this could be the consequence of alterations in the expression or in the kinetics of inactivation of sodium channels or in the potassium conductance through the membrane, both of which are involved in the regulation of adaptation to continuous stimulations in those neurons. However, the electrical response of proprioceptive neurons to stimulation is complex and involves different types of mechanosensitive and voltage-gated ion channels.25,54–56 The complexity of these events makes the interpretation of our results difficult at this stage. A deeper investigation of the different channels and interactions involved in proprioceptive signalling is needed.
Taken together, our results suggest that in FRDA, PPNs might not be able to properly reach and innervate their targets in muscles or in the spinal cord. Since proper targeting is critical for neuronal survival, this could consequently lead to proprioceptive degeneration. Our findings are in line with other recent studies involving the usage of hiPSC-derived mixed sensory neuronal cultures or DRG organoids:15,16 affected sensory neurons showed a dysregulation of pathways related to axonogenesis and chemical synaptic transmission, while DRG organoids derived from FRDA patients displayed a severe impairment in axonal spreading in vitro, along with the inability to form proper contacts with intrafusal fibres in DRG-muscle cell co-cultures.
Interestingly, a recovery of the pathological features in DRG organoids was observed only after the excision of the entire FXN intron 1 and not only the region flanking the GAA expansion mutation. However, in that case the limited excision of the GAA repeat did not lead to full recovery of frataxin levels, while in our ISO CT lines frataxin levels were comparable to CT lines. We suspect that the persisting differences may represent non-completely erased homeostatic responses to frataxin deficiency in the original FRDA lines, as supported by similar changes in non-coding RNAs in FRDA and ISO CT lines. Many of these transcripts were either massively upregulated or downregulated both in FRDA and ISO CT lines at all stages of differentiation. A deeper investigation of these regulatory factors may help in elucidating pathogenic processes and homeostatic responses in FRDA.
Our study also confirmed changes in RUNX3 and TRKC-NT3 signalling, the main intrinsic and extrinsic factors in PPN survival and specification. RUNX3, which we found downregulated in FRDA PPNs, plays a critical role in proprioceptive pathfinding,38,39 while the TRKC-NT3 signalling regulates many pathways that promote neurite outgrowth and synaptic plasticity,60 which we found altered in our study. This is supported by the observation that animal models of RUNX3 and TRKC deficiency resemble many pathological features observed in FRDA.39,61,62 In conclusion, our analysis led to the identification of a significant number of differentially expressed genes and proteins that could play a critical role in the determination of the pathological features of FRDA. However, further and more detailed investigations are needed to highlight the contribution and the specific role of these elements. At this stage, in fact, it is difficult to address which of these markers or biological processes are the result of the disease and which could be the cause. These studies may also help addressing some unsolved questions in FRDA63 from the relatively high levels of frataxin in FRDA patients compared to other loss-of-function disorders, to the equivocal role of ROS production as main cause of cellular dysfunction and death and to the high sensitivity of a limited number of cell types despite the extensive distribution and expression of frataxin in the body.
## Funding
This work was supported by a Friedreich Ataxia Research Alliance (FARA) grant to MP. CD received a doctoral fellowship from the Belgian National Scientific Research Funds (FNRS).
## Competing interests
The authors report no competing interests.
## Supplementary material
Supplementary material is available at Brain Communications online.
## References
1. Campuzano V, Montermini L, Moltò MD. **Friedreich’s ataxia: Autosomal recessive disease caused by an intronic GAA triplet repeat expansion**. *Science* (1996) **271** 1423-1427. PMID: 8596916
2. Kumari D, Usdin K. **Is Friedreich ataxia an epigenetic disorder?**. *Clin Epigenet* (2012) **4** 2
3. Yoon T, Cowan JA. **Frataxin-mediated iron delivery to ferrochelatase in the final step of heme biosynthesis**. *J Biol Chem* (2004) **279** 25943-25946. PMID: 15123683
4. Campuzano V, Montermini L, Lutz Y. **Frataxin is reduced in Friedreich ataxia patients and is associated with mitochondrial membranes**. *Hum Mol Genet* (1997) **6** 1771-1780. PMID: 9302253
5. Koeppen AH. **Friedreich's ataxia: Pathology, pathogenesis, and molecular genetics**. *J Neurol Sci* (2011) **303** 1-12. PMID: 21315377
6. Koeppen AH. **Nikolaus Friedreich and degenerative atrophy of the dorsal columns of the spinal cord**. *J Neurochem* (2013) **126** S4-10
7. Koeppen AH, Morral JA, Davis AN. **The dorsal root ganglion in Friedreich’s ataxia**. *Acta Neuropathol* (2009) **118** 763-776. PMID: 19727777
8. Emery EC, Ernfors P, Wood JN. *The Oxford handbook of the neurobiology of pain* (2018) 127-155
9. Perdomini M, Hick A, Puccio H, Pook MA. **Animal and cellular models of Friedreich ataxia**. *J Neurochem* (2013) **126** S65-S79
10. Puccio H, Simon D, Cossee M. **Mouse models for Friedreich ataxia exhibit cardiomyopathy, sensory nerve defect and Fe-S enzyme deficiency followed by intramitochondrial iron deposits**. *Nat Genet* (2001) **27** 181-186. PMID: 11175786
11. Al-Mahdawi S, Pinto RM, Varshney D. **GAA Repeat expansion mutation mouse models of Friedreich ataxia exhibit oxidative stress leading to progressive neuronal and cardiac pathology**. *Genomics* (2006) **88** 580-590. PMID: 16919418
12. Simon D, Seznec H, Gansmuller A. **Friedreich ataxia mouse models with progressive cerebellar and sensory ataxia reveal autophagic neurodegeneration in dorsal root ganglia**. *J Neurosci* (2004) **24** 1987-1995. PMID: 14985441
13. Li J, Rozwadowska N, Clark A, Fil D, Napierala JS, Napierala M. **Excision of the expanded GAA repeats corrects cardiomyopathy phenotypes of iPSC-derived Friedreich’s ataxia cardiomyocytes**. *Stem Cell Res* (2019) **40** 101529. PMID: 31446150
14. Li Y, Polak U, Bhalla AD. **Excision of expanded GAA repeats alleviates the molecular phenotype of Friedreich’s ataxia**. *Mol Ther* (2015) **23** 1055-1065. PMID: 25758173
15. Lai JI, Nachun D, Petrosyan L. **Transcriptional profiling of isogenic Friedreich ataxia neurons and effect of an HDAC inhibitor on disease signatures**. *J Biol Chem* (2019) **294** 1846-1859. PMID: 30552117
16. Mazzara PG, Muggeo S, Luoni M. **Frataxin gene editing rescues Friedreich’s ataxia pathology in dorsal root ganglia organoid-derived sensory neurons**. *Nat Commun.* (2020) **11** 4178. PMID: 32826895
17. Dionisi C, Rai M, Chazalon M, Schiffmann SN, Pandolfo M. **Primary proprioceptive neurons from human induced pluripotent stem cells: A cell model for afferent ataxias**. *Sci Rep* (2020) **10** 7752. PMID: 32385372
18. Chan PK, Torres R, Yandim C. **Heterochromatinization induced by GAA-repeat hyperexpansion in Friedreich’s ataxia can be reduced upon HDAC inhibition by vitamin B3**. *Hum Mol Genet* (2013) **22** 2662-2675. PMID: 23474817
19. Love MI, Huber W, Anders S. **Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2**. *Genome Biol* (2014) **15** 550. PMID: 25516281
20. Sandi C, Sandi M, Virmouni SA, Al-Mahdawi S, Pook MA. **Epigenetic-based therapies for Friedreich ataxia**. *Front Genet.* (2014) **5** 165. PMID: 24917884
21. Millán-Zambrano G, Burton A, Bannister AJ, Schneider R. **Histone post-translational modifications—Cause and consequence of genome function**. *Nat Rev Genet* (2022) **23** 563-580. PMID: 35338361
22. Rodden LN, Chutake YK, Gilliam K. **Methylated and unmethylated epialleles support variegated epigenetic silencing in Friedreich ataxia**. *Hum Mol Gen* (2021) **29** 3818-3829. PMID: 33432325
23. Rodden LN, Gilliam KM, Lam C. **DNA Methylation in Friedreich ataxia silences expression of frataxin isoform E**. *Sci Rep* (2022) **12** 5031. PMID: 35322126
24. Wu H, Petitpré C, Fontanet P. **Distinct subtypes of proprioceptive dorsal root ganglion neurons regulate adaptive proprioception in mice**. *Nat Commun* (2021) **12** 1026. PMID: 33589589
25. Oliver KM, Florez-Paz DM, Badea TC, Mentis GZ, Menon V, de Nooij JC. **Molecular correlates of muscle spindle and Golgi tendon organ afferents**. *Nat Commun* (2021) **12** 1451. PMID: 33649316
26. Li CL, Li KC, Wu D. **Somatosensory neuron types identified by high-coverage single-cell RNA-sequencing and functional heterogeneity**. *Cell Res* (2016) **26** 83-102. PMID: 26691752
27. Usoskin D, Furlan A, Islam S. **Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing**. *Nat Neurosci* (2015) **18** 145-153. PMID: 25420068
28. Nguyen MQ, von Buchholtz LJ, Reker AN, Ryba NJ, Davidson S. **Single-nucleus transcriptomic analysis of human dorsal root ganglion neurons**. *Elife* (2021) **10** e71752. PMID: 34825887
29. Ihara M, Yamasaki N, Hagiwara A. **Sept4, a component of presynaptic scaffold and Lewy bodies, is required for the suppression of alpha-synuclein neurotoxicity**. *Neuron* (2007) **53** 519-533. PMID: 17296554
30. Rehman SAA, Kristariyanto YA, Choi SY. **MINDY-1 is a member of an evolutionarily conserved and structurally distinct new family of deubiquitinating enzymes**. *Mol Cell* (2016) **63** 146-155. PMID: 27292798
31. Wahane S, Halawani D, Zhou X, Zou H. **Epigenetic regulation of axon regeneration and glial activation in injury responses**. *Front Genet* (2019) **10** 640. PMID: 31354788
32. Ehmsen JT, Kawaguchi R, Kaval RD. **GADD45A is a protective modifier of neurogenic skeletal muscle atrophy**. *JCI Insight* (2021) **6** e149381. PMID: 34128833
33. Joseph RM. **Neuronatin gene: Imprinted and misfolded studies in lafora disease, diabetes and cancer may implicate NNAT-aggregates as a common downstream participant in neuronal loss**. *Genomics* (2014) **103** 183-188. PMID: 24345642
34. Wang X, Zhou T, Maynard GD. **Nogo receptor decoy promotes recovery and corticospinal growth in non-human primate spinal cord injury**. *Brain* (2020) **143** 1697-1713. PMID: 32375169
35. Pancho A, Aerts T, Mitsogiannis MD, Seuntjens E. **Protocadherins at the crossroad of signaling pathways**. *Front Mol Neurosci* (2020) **13** 117. PMID: 32694982
36. Rötig A, de Lonlay P, Chrétien D. **Aconitase and mitochondrial iron-sulphur protein deficiency in Friedreich ataxia**. *Nat Genet* (1997) **17** 215-217. PMID: 9326946
37. Michael S, Petrocine SV, Qian J. **Iron and iron-responsive proteins in the cardiomyopathy of Friedreich’s ataxia**. *Cerebellum* (2006) **5** 257-267. PMID: 17134988
38. Levanon D, Bettoun D, Harris-Cerruti C. **The Runx3 transcription factor regulates development and survival of TrkC dorsal root ganglia neurons**. *EMBO J* (2002) **21** 3454-3463. PMID: 12093746
39. Inoue KI, Ozaki S, Shiga T. **Runx3 controls the axonal projection of proprioceptive dorsal root ganglion neurons**. *Nat Neurosci* (2002) **5** 946-954. PMID: 12352981
40. Bewick GS, Banks RW. **Mechanotransduction in the muscle spindle**. *Pflugers Arch* (2015) **467** 175-190. PMID: 24888691
41. Paoletti P, Bellone C, Zhou Q. **NMDA Receptor subunit diversity: Impact on receptor properties, synaptic plasticity and disease**. *Nat Rev Neurosci* (2013) **14** 383-400. PMID: 23686171
42. Pérez-Otaño I, Larsen RS, Wesseling JF. **Emerging roles of GluN3-containing NMDA receptors in the CNS**. *Nat Rev Neurosci* (2016) **17** 623-635. PMID: 27558536
43. Statello L, Guo CJ, Chen LL, Huarte M. **Gene regulation by non-coding RNAs and its biological function**. *Nat Rev* (2021) **22** 96-118
44. Qureshi IA, Mehler MF. **Emerging roles of non-coding RNAs in brain evolution, development, plasticity and disease**. *Nat Rev Neurosci* (2012) **13** 528-541. PMID: 22814587
45. Bomont P. **The dazzling rise of neurofilaments: Physiological functions and roles as biomarkers**. *Curr Opin Cell Biol* (2021) **68** 181-191. PMID: 33454158
46. Clay A, Obrochta KM, Soon RK, Russell CB, Lynch DR. **Neurofilament light chain as a potential biomarker of disease status in Friedreich ataxia**. *J Neurol* (2020) **267** 2594-2598. PMID: 32385683
47. Paggio A, Checchetto V, Campo A. **Identification of an ATP-sensitive potassium channel in mitochondria**. *Nature* (2019) **572** 609-613. PMID: 31435016
48. Lesage S, Drouet V, Majounie E. **Loss of VPS13C function in autosomal-recessive parkinsonism causes mitochondrial dysfunction and increases PINK1/Parkin-dependent mitophagy**. *Am J Hum Genet* (2016) **98** 500-513. PMID: 26942284
49. Binley KE, Ng WS, Tribble JR, Song B, Morgan JE. **Sholl analysis: A quantitative comparison of semi-automated methods**. *J Neurosci Methods* (2014) **225** 65-70. PMID: 24485871
50. De Moraes ER, Kushmerick C, Naves LA. **Morphological and functional diversity of first-order somatosensory neurons**. *Biophys Rev* (2017) **9** 847-856. PMID: 28889335
51. Yu YQ, Chen XF, Yang Y, Yang F, Chen J. **Electrophysiological identification of tonic and phasic neurons in sensory dorsal root ganglion and their distinct implications in inflammatory pain**. *Physiol Res* (2014) **63** 793-799. PMID: 25157654
52. Körner J, Lampert A. **Functional subgroups of rat and human sensory neurons: A systematic review of electrophysiological properties**. *Pflugers Arch* (2022) **474** 367-385. PMID: 35031856
53. Woo SH, Lukacs V, De Nooij JC. **Piezo2 is the principal mechanotransduction channel for proprioception**. *Nat Neurosci.* (2015) **18** 1756-1762. PMID: 26551544
54. Takahashi K. **Mechanosensitive ion channels**. *AIMS Biophys.* (2016) **3** 63-74
55. Bewick GS, Banks RW. **Mechanotransduction channels in proprioceptive sensory nerve terminals: Still an open question?**. *Curr Opinion Physiol* (2021) **20** 90-104
56. Schulz DJ, Temporal S, Barry DM, Garcia ML. **Mechanisms of voltage-gated ion channel regulation: From gene expression to localization**. *Cell Mol Life Sci* (2008) **65** 2215-2231. PMID: 18408887
57. Yandim C, Natisvili T, Festenstein R. **Gene regulation and epigenetics in Friedreich’s ataxia**. *J Neurochem* (2013) **126** S21-S42
58. Schulz JB, Dehmer T, Schöls L. **Oxidative stress in patients with Friedreich ataxia**. *Neurology* (2000) **55** 1719-1721. PMID: 11113228
59. Hick A, Wattenhofer-Donzé M, Chintawar S. **Neurons and cardiomyocytes derived from induced pluripotent stem cells as a model for mitochondrial defects in Friedreich’s ataxia**. *Dis Mod Mech* (2013) **6** 608-621
60. Huang EJ, Reichardt LF. **TRKC receptors: Roles in neuronal signal transduction**. *Annu Rev Biochem* (2003) **72** 609-642. PMID: 12676795
61. Huang EJ, Reichardt LF. **Neurotrophins: Roles in neuronal development and function**. *Annu Rev Neurosci* (2001) **24** 677-736. PMID: 11520916
62. Klein R, Silos-Santiago I, Smeyne RJ. **Disruption of the neurotrophin-3 receptor gene trkC eliminates Ia muscle afferents and results in abnormal movements**. *Nature* (1994) **368** 249-251. PMID: 8145824
63. Lynch DR, Deutsch EC, Wilson RB, Tennekoon G. **Unanswered questions in Friedreich ataxia**. *J Child Neurol* (2013) **27** 1223-1229
|
---
title: PIMT regulates hepatic gluconeogenesis in mice
authors:
- Bandish Kapadia
- Soma Behera
- Sireesh T. Kumar
- Tapan Shah
- Rebecca Kristina Edwin
- Phanithi Prakash Babu
- Partha Chakrabarti
- Kishore V.L. Parsa
- Parimal Misra
journal: iScience
year: 2023
pmcid: PMC9972567
doi: 10.1016/j.isci.2023.106120
license: CC BY 4.0
---
# PIMT regulates hepatic gluconeogenesis in mice
## Summary
The physiological and metabolic functions of PIMT/TGS1, a third-generation transcriptional apparatus protein, in glucose homeostasis sustenance are unclear. Here, we observed that the expression of PIMT was upregulated in the livers of short-term fasted and obese mice. Lentiviruses expressing Tgs1-specific shRNA or cDNA were injected into wild-type mice. Gene expression, hepatic glucose output, glucose tolerance, and insulin sensitivity were evaluated in mice and primary hepatocytes. Genetic modulation of PIMT exerted a direct positive impact on the gluconeogenic gene expression program and hepatic glucose output. Molecular studies utilizing cultured cells, in vivo models, genetic manipulation, and PKA pharmacological inhibition establish that PKA regulates PIMT at post-transcriptional/translational and post-translational levels. PKA enhanced 3′UTR-mediated translation of TGS1 mRNA and phosphorylated PIMT at Ser656, increasing Ep300-mediated gluconeogenic transcriptional activity. The PKA-PIMT-Ep300 signaling module and associated PIMT regulation may serve as a key driver of gluconeogenesis, positioning PIMT as a critical hepatic glucose sensor.
## Graphical abstract
## Highlights
•PIMT protein levels are enhanced upon fasting•Hepatic expression of PIMT is increased in HFD and obese mice•PIMT is a PKA substrate and phosphorylation of PIMT augments its gluconeogenic ability•Depletion of PIMT improves glucose clearance capacity and enhances insulin sensitivity
## Abstract
Hepatology; Human metabolism; Molecular biology
## Introduction
In mammals, the transition between fasting and fed state is accompanied by complex hepatic gene expression changes. The liver is the central hub for coordinating the fasting-feeding transitions, given its role in maintaining blood glucose levels via processing the dietary intake and maintaining whole-body nutritional/energy balance. Several extracellular factors tightly coordinated this hepatic switch on/off mechanism, thereby controlling insulin and glucagon levels. Elucidating the complex metabolic changes associated with fasting and feeding and their transcriptional underpinnings is crucial for understanding physiology and metabolic dysfunctions such as insulin resistance.
During fasting, evolutionarily conserved glucagon stimulates GPCR-induced cAMP-activated PKA-driven transcriptional signatures in hepatocytes, augments the expression of critical gluconeogenic regulatory genes, including phosphoenolpyruvate carboxykinase 1 (PCK1) and glucose-6-phosphatase catalytic subunit 1 (G6PC1). This promoter-regulated rate-limiting gluconeogenic enzyme expression is vital for homeostasis and provides an efficient adaptive mechanism to the external clues.1,2,3 Several factors, such as a high energy-rich diet, disrupt glucose-sensitive metabolic switches leading to metabolic alterations and the development of T2D. In eukaryotes, numerous nuclear receptors and transcription factors, stimulated by different hormones, regulate the gene expression to sustain hepatic glucose production.1,3 The lack of a direct association between specific transcription factors and upstream regulatory nodes led to the discovery of second-generation transcriptional regulators, the co-regulators. By converging signals to the transcription factors/nuclear receptors via a multi-leveled signaling cascade, co-regulators function as essential signaling integrators for coordinating broad gluconeogenic transcriptional programs.4,5 Such multi-protein regulatory complexes fine-tune gluconeogenic gene expression patterns and thus confer the second level of specificity in the transcriptional regulation,6,7 an event typically hijacked in metabolic syndrome. Although significant progress has been made in understanding the complex relationships between hormonal signaling, whole-body metabolism, and liver machinery for glucose production, the lack of a unique convergence signaling node guiding the multi-protein co-regulator complex for integrating the diverse extracellular signaling cues remains to be elucidated.
PIMT/TGS1 (PRIP Interacting protein with Methyl Transferase domain/Trimethyl guanosine synthase I) was first reported from Prof. Janardan Reddy’s lab using PRIP (peroxisome proliferators-activated receptor (PPARγ)-interacting protein) as a bait in a yeast two-hybrid study.8 A detailed in vitro proteome interaction of the protein revealed that PIMT interacts with several co-activators encompassing HAT complex and mediator complex proteins. This comprehensive report revealed differential modulation of co-activator-driven transcriptional complexes by PIMT, proposing the protein acts as a key node in switching the HAT complex with the Mediator complex for enhanced mRNA synthesis.9 Besides the transcriptional regulation, protein also participates in evolutionarily conserved hypermethylation of small non-coding RNA. Studies from Drosophila10 and mice models establish that PIMT is essential for development.11 Previous work from our lab established that PIMT is an essential component of nuclear receptor-driven transcriptional regulations.12,13 Furthermore, kinase-dependent phosphorylation of PIMT in the recruitment of transcriptional co-regulators has positioned PIMT as a critical player in hormonal signaling.13 Indeed, inflammation-induced PIMT expression in skeletal muscle hampers insulin signaling leading to insulin resistance via the transcriptional downregulation of MEF2A and GLUT4 and attenuation of Akt phosphorylation.12 *Our previous* studies also showed that ERK/hyperthyroidism-induced phosphorylation of PIMT at Ser298 was required for enhanced gluconeogenesis, suggesting that PIMT may be a driver of pre-insulin resistance conditions.13 Recently, PIMT was reported to be a key player regulating β-cell mass and function.14 Although PIMT was known to play an essential role under pathological relevant conditions, the importance of PIMT in controlling glucose homeostasis during short-term physiological fasting and hepatic insulin resistance conditions remained elusive. In the current study, we observed that the expression of PIMT was upregulated in a post-transcriptional/translational-dependent manner upon short-term fasting. Furthermore, glucagon-induced PKA regulated PIMT activity/levels at the gene expression level along with its transcriptional activity. Notably, depletion of PIMT in pathological relevant insulin resistance animal models displayed significant improvement in hepatic insulin sensitivity positioning PIMT as a druggable target.
## Fasting responsive signaling pathway regulates hepatic PIMT expression
To gain a deeper understanding of the role of PIMT in glucose homeostasis, we investigated the hepatic expression of PIMT in obese and short-term fasted mice. We observed that the levels of PIMT protein were increased in the livers of two obesity-associated T2D mouse models: ob/ob mice (Figures 1A and 1B) and wild-type (C57BL/6) mice fed with the high-fat diet (HFD) (Figures 1D and 1E) compared with the wild-type and control diet-fed mice, respectively. Consistent with our previous report,12 the mRNA level of Tgs1 was enhanced in the diabetic mice models (Figures 1C and 1F). Importantly, the phosphorylation of CREB, an established downstream mediator of the Glucagon-cAMP-PKA axis,15 was found to be enhanced (Figures 1A and 1D). Furthermore, protein levels of PHLPP1, a Ser/Thr phosphatase, earlier reported by us to inhibit insulin signaling cascade in the skeletal muscle, were also modestly increased in the livers of ob/ob and HFD-fed mice (Figures 1A, 1B, 1D, and 1E).16 PIMT protein abundance and the levels of fasting-sensitive pCREB and G6Pase were increased in mice subjected to 8h fasting (short-term fasting) (Figures 1G and 1H), however, PHLPP1 levels remained largely unchanged. While the transcript levels of Pck1 and G6pc1 were elevated in short-term fasted mice as anticipated, the mRNA level of Tgs1 was unaltered (Figure 1I). To further study the impact of nutritional status and hormonal dependency on PIMT expression, mice were subjected to fasting followed by re-feeding. As expected, 8h fasting induced the expression of Pck1 and G6pc1 (Figure S1) but not PHLPP1. Consistent with our previous observations, PIMT protein levels, but not mRNA levels, were enhanced upon fasting and were normalized upon 8 h of re-feeding (Figures S1A–S1C). Thus, the induction of PIMT is a relatively early event suggesting that glucagon-cAMP-PKA signaling regulates its hepatic level. Figure 1Hepatic expression of PIMT is upregulated upon nutritional stress(A) *Immunoblot analysis* indicated antibodies in the liver lysate of wild-type (WT) or ob/ob mice ($$n = 5$$).(B) Quantitative densitometry evaluation of Figure 1A. Values were normalized with the corresponding loading control, Vinculin.(C) qPCR analysis of the indicated genes in the liver lysate of WT and ob/ob mice ($$n = 5$$). Values were normalized using 18S as a reference gene ($$n = 5$$).(D) *Immunoblot analysis* indicated antibodies in the liver lysate of mice fed on a chow diet (CD, $$n = 5$$) or high-fat diet (HFD, $$n = 6$$).(E) Quantitative densitometry evaluation of Figure 1D. Values were normalized with the corresponding loading control, actin.(F) qPCR analysis of the indicated genes in the liver lysate of CD ($$n = 5$$) and HFD mice ($$n = 6$$). Values were normalized using 18S as a reference gene.(G) *Immunoblot analysis* indicated antibodies in the liver lysate of wild-type fed, or 8h fasted mice ($$n = 5$$).(H) Quantitative densitometry evaluation of Figure 1G. Values were normalized with the corresponding loading control, Vinculin.(I) qPCR analysis of the indicated genes in the liver lysate of wild-type fed or 8h fasted mice ($$n = 5$$). Values were normalized using 18S as a reference gene. Statistical analysis was performed using unpaired Student’s t test (two-tailed) ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$, ∗∗∗∗$p \leq 0.001$ versus the corresponding controls.
## Hepatic PKA signaling augments PIMT protein levels
Alterations in the PIMT protein levels during fasting prompted us to examine whether glucagon-cAMP-induced PKA signaling impacts PIMT protein levels. For this, the catalytic alpha subunit of the PKA holoenzyme complex (PKAc) was overexpressed in HepG2 cells in a dose-dependent manner, and levels of PIMT protein were examined. A dose-dependent increase of PKAc enhanced pCREB levels (Figure 2A). More importantly, PIMT protein levels, but not mRNA levels, were enhanced in a PKA-dose-dependent fashion (Figures 2A–2C). mRNA levels of gluconeogenic genes, PCK1, and G6PC1 were also augmented in a dose-dependent manner (Figure 2C). Supporting our findings, PIMT protein level but not its transcript levels were increased in HepG2 cells treated with increasing concentrations of Forskolin, an adenyl cyclase activator (Figures S2A–S2C). Finally, corroborating the findings that intact PKA activity is responsible for PIMT protein induction, we treated the cells with two independent PKA inhibitors, H89 and Rp-8-Br-cAMPs (RP), and examined PIMT protein levels by immunoblotting. We noted that the inhibition of PKA activity significantly depleted endogenous PIMT protein but not mRNA levels (Figures 2D–2F and S2D–S2F). Transcript levels of PCK1 and G6PC1 served as the internal controls (Figures 2F and S2F). Taken together, these results indicate that fasting-induced PKA signaling is required to induce PIMT protein levels during fasting. Figure 2Hepatic PKA regulates PIMT protein expression(A) HepG2 cells were transfected with an increasing concentration of active catalytic subunit of PKA (PKAc). Post 48h of transfection, cells were lysed, and immunoblots assessed the protein levels of PIMT.(B) Densitometry quantification of Figure 2A. Values were normalized with the corresponding loading control, Vinculin. Data are representative of 3 independent experiments and are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. ∗∗∗∗$p \leq 0.001$ compared to pcDNA3.1 transfected cells, ap<0.05, dp<0.001 vs PKAc (4 μg) transfected cells.(C) qPCR analysis of the indicated genes upon PKAc overexpression in HepG2 cells. Values were normalized using 18S as a reference gene. Data are representative of 3 independent experiments and are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$, ∗∗∗∗$p \leq 0.001$ compared to pcDNA3.1 transfected cells, ap<0.05, bp<0.01, dp<0.001 vs PKAc (4 μg) transfected cells.(D) HepG2 cells were treated with increasing concentrations of Rp-Br-cAMPs (RP). Post 8h of treatment, cells were lysed, and immunoblots assessed the protein levels of PIMT.(E) Densitometry quantification of Figure 2D. Values were normalized with the corresponding loading control, Vinculin. Data are representative of 3 independent experiments and are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. ∗∗∗∗$p \leq 0.001$ compared to DMSO treated cells, bp<0.01, dp<0.001 vs RP (50 μM) treated cells.(F) qPCR analysis of the indicated genes upon RP treatment in HepG2 cells for 8h. Values were normalized using 18S as a reference gene. Data are representative of 3 independent experiments and are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$, ∗∗∗∗$p \leq 0.001$ compared to DMSO treated cells, cp<0.005, dp<0.001 vs RP (50 μM) treated cells.(G) HepG2 cells were treated with increasing concentration of RP for 4h followed by treatment with MG132 for 4h (10 μM). Post-treatment cells were lysed and probed for endogenous PIMT protein levels.(H) Densitometry quantification of Figure 2G. Values were normalized with the corresponding loading control, Vinculin. Data are representative of 3 independent experiments and are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. ∗∗∗∗$p \leq 0.001$ compared to DMSO treated cells, ns: non-significant.(I) qPCR of the sucrose gradient fractions from fed and fasting liver lysates of mice. The values were normalized with extracellular Luciferase RNA ($$n = 4$$).
We next investigated the molecular events underlying the PKA-regulated PIMT expression. The increase in the protein levels of PIMT was not associated with elevated mRNA levels, suggesting that the alteration was likely independent of its transcriptional regulation. Therefore, we explored whether PIMT protein stability is affected by PKA signaling. Thus, we overexpressed SFB-PIMT in HepG2 cells and altered PKA activity to study its impact on the overall protein levels. Surprisingly, treatment with two independent PKA inhibitors (H89 and RP) or PKA activator Forskolin (FSK) showed minimal impact on overall flag antibody signals (Figure S3A). Next, we treated HepG2 cells with a proteasomal degradation complex inhibitor, MG132. We observed that the PIMT protein levels were not impacted (Figures 2G and 2H), indicating that the PKA signaling does not alter PIMT protein stability. Next, we questioned whether PKA signaling affects the post-transcriptional regulation of PIMT. Here, we transfected HepG2 cells with a luciferase reporter construct fused with the 3′UTR of TGS1 at its 3′ end, followed by treatment with PKA modulators. Post-treatment, cells were lysed, and luciferase activity was measured. Treatment with PKA inhibitors significantly reduced the luciferase readout, while exposure to Forskolin enhanced the luciferase expression (Figure S3B). To ascertain that the modulation of PIMT expression is regulated at 3′UTR in a PKA-dependent manner, we co-expressed PKAc and luciferase reporter constructs in HepG2 cells. We observed that the activity was upregulated in a dose-dependent manner (Figure S3C). Empty Luciferase reporter vector was used as the internal control. To examine further, we performed sucrose gradient density fractionation of the livers of fasted mice. Consistently, Tgs1 mRNA levels were observed to be enriched in actively translating ribosomal fractions (Figure 2I), suggesting that PKA signaling may potentiate PIMT levels by enhancing the translation of Tgs1.
## PIMT regulated gluconeogenesis in vivo and in vitro
Our earlier findings that PIMT was readily recruited to the TRE-GRE region of PCK1 promoter compared to the PPRE region in HepG2 cells13 prompted us to evaluate whether PIMT is recruited to cAMP-PKA signaling controlled CRE sites of Pck1 promoter (Figure S4A). Using chromatin immunoprecipitation (ChIP) followed by PCR, we found that PIMT was indeed recruited to CRE-region of Pck1 promoter in lean mice (Figure S4B). More importantly, ChIP-qPCR analysis showed significant enrichment of PIMT at the CRE region but not at GRE-TRE and PPRE sites in the Pck1 promoter (Figure S4C). Having observed that fasting/PKA signaling enhances PIMT protein level and enhances recruitment to the CRE region of Pck1 promoter, we next investigated the impact of PIMT on PKA-regulated hepatic gluconeogenesis. Freshly isolated mouse primary hepatocytes were infected with lentiviral particles expressing two independent short hairpin RNA (shRNA) against Tgs1. shSCR (nonspecific, NT) infected cells were used as the control. Post 48h of infections, cells were treated with either glucagon or Forskolin, and hepatic glucose output was evaluated. As shown in Figure 3A, depletion of PIMT significantly reduced both basal (DMSO treated samples) and glucagon/forskolin-induced hepatic glucose output. Similar experiments with insulin and metformin treatments (anti-hyperglycemic drugs) showed overall no additional significant effect on hepatic glucose output (Figure 3B).Figure 3Hepatic PIMT expression is essential for fasting-inducing glucose production(A and B) Primary hepatocytes isolated from three different female mice were infected with lentiviruses expressing shRNA against Tgs1 (two independent shRNA). shSCR (nonspecific control) was used as the internal control. Post 48h of infections, cells were cultured in glucose production media and indicated treatments, followed by an estimation of glucose released in the media. Data are representative of 3 independent experiments and are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗$p \leq 0.05$, ∗∗∗∗$p \leq 0.001$ compared to control infected cells.(C) C57BL/6 male mice were tail-vein injected with lentivirus expressing shRNA against Tgs1 (two independent shRNA) ($$n = 5$$). shSCR tail-vein injections were used as the internal control. Post 7 days of injection, mice fasted for 8h. Post-fasting, glucose was quantified from tail vein. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$ compared to control fasting mice.(D) Oral Glucose tolerance test in C57BL/6J male mice expressing shRNA against Tgs1 (two independent shRNA) in the liver ($$n = 4$$).(E) *The area* under the curve for Figure 3D. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗∗∗$p \leq 0.005$, ∗∗∗∗$p \leq 0.001$ compared to NT-infected mice.(F) *Immunoblot analysis* of the liver lysates using the indicated antibodies.(G) Densitometric quantification of Figure 3F. Values were normalized with the corresponding loading control, Vinculin. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$ compared to NT-infected mice.(H) Pyruvate tolerance test in C57BL/6J mice expressing shRNA against Tgs1 (two independent shRNA) in the liver ($$n = 4$$).(I) *The area* under the curve for Figure 3H. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗∗∗$p \leq 0.005$, ∗∗∗∗$p \leq 0.001$ compared to NT-infected mice.(J) *Immunoblot analysis* of the liver lysates using the indicated antibodies.(K) Densitometric quantification of Figure 3J. Values were normalized with the corresponding loading control, actin. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$, ∗∗∗∗$p \leq 0.001$ compared to NT-infected mice.
Furthermore, to explore the potential role of PIMT in hepatic glucose metabolism, lentivirus preparations of Tgs1 shRNA or scramble shRNA were administered into C57BL/6 mice through tail vein injection.17 Subsequently, mice were nutritionally deprived for 8h, and the expression of gluconeogenic genes was assessed by qPCR. As shown in Figure S4D, the expression of Pck1 and G6pc1 was depleted in both fed and fasting conditions in Tgs1 shRNA-injected mice compared to shSCR-injected mice (NT). More importantly, fasting glucose levels were lower in PIMT shRNA-injected mice than in nonspecific control-injected mice (Figure 3C). After the oral glucose challenge (OGTT), systemic glucose disposal was significantly enhanced in mice injected with shRNA against Tgs1 compared to mice receiving the shSCR injection (Figures 3D and 3E). To confirm the impact on insulin sensitivity upon PIMT depletion, we assessed the phosphorylation status of Akt and its upstream regulator p85 in the liver lysates of shPIMT-injected mice (Figures 3F and 3G). The expression level of p85 and the phosphorylation status of Akt were significantly enhanced upon acute knockdown of Tgs1 in the liver. More importantly, the expression of PHLPP1, a well-documented phosphatase of Akt, was attenuated. Consistent with our previous observations,12 the expression of GLUT4 was also upregulated in PIMT-depleted hepatocytes. Expression of G6Pase served as an internal control for the fasting condition (Figures 3F and 3G). Acute depletion of PIMT also hampered hepatic gluconeogenic activity with decreased fasting glucose levels in the pyruvate tolerance test (PTT) Figures 3H and 3I. PIMT exerts this biological function by attenuating the expression of essential gluconeogenic genes, namely PEPCK, G6Pase, and PGC1α (Figures 3J and 3K). Collectively, these findings suggest that PIMT regulates systemic glucose tolerance mainly by governing hepatic glucose production in coordination with hepatic insulin sensitivity.
The observation that transient knockdown of PIMT suppresses hepatic gluconeogenic activity led us to wonder whether PIMT overexpression impacts systematic glucose clearance. To address this question, ectopic expression of PIMT was achieved by tail vein injection of lentiviral particles expressing PIMT. Reciprocal to our previous observations, systematic glucose disposal after OGTT was significantly enhanced in PIMT overexpressing mice (Figures S5A and S5B). Consistently, the protein level of p85 and the phosphorylation status of Akt were reduced considerably (Figures S5C and S5D). Expression of PHLPP1 was noted to be enhanced while the protein levels of glucose transporter GLUT4 were reduced. Expression levels of G6Pase were also noted to be robustly enhanced upon PIMT overexpression (Figures S5C and S5D). Similarly, upon the challenge of mice with PTT, the glucose production was significantly enhanced (Figures S5E and S5F) with increased expression of key gluconeogenic markers; PCK1, G6Pase, and PGC1α (Figures S5G and S5H). The above observations unambiguously establish PIMT as a potent regulator of hepatic glucose metabolism.
## PKA phosphorylates PIMT
Given the strong impact of PIMT expression on liver glucose metabolism, we reasoned that PIMT activity might also be directly regulated by PKA-mediated phosphorylation. We performed computational analysis to identify the potential phospho-acceptor sites of human PIMT. Such analysis revealed two PKA consensus phosphorylation sites (Ser656, Ser851) (Figures S6A and S6B). Besides, the Ser656 phospho-acceptor site of PIMT was evolutionarily conserved from yeast (S. cerevisiae), while Ser851 was reported to be present only in higher vertebrates (Figures S6C and S6D). More importantly, both the sites showed high score prediction using multiple phosphosite evaluators (Figure S6E, Motifscan, DISPHOS, Netphos, and Netphos kinase).
We performed in vitro kinase assay using purified GST fused fragment PIMT-C (330–853).13 In vitro kinase with purified PKA revealed that PIMT-C was robustly phosphorylated. Consistent with our previous report,13 both ERK1 and ERK2 failed to phosphorylate PIMT-C (Figure 4A). Phosphorylation of GST-PIMT-C by HeLa nuclear extract (HNE) served as the positive control. To evaluate whether PKA phosphorylates PIMT under cellular conditions, we overexpressed human PIMT in primary hepatocytes followed by treatment with PKA activator forskolin alone or in combination with PKA inhibitors H89 or RP. Post-treatment cells were lysed, and PKA substrates were enriched using the PKA-substrate antibody. The enriched samples were separated on SDS-PAGE and probed with indicated antibodies. As shown in Figure 4B, the treatment of cells with Forskolin enhanced the phosphorylation level of PIMT by ∼ 2-fold (Figure 4C). Co-treatment with either H89 or RP robustly reduced the phospho-signals of PIMT. GCN5 was used as an internal control for the assay.18 To evaluate whether PIMT is phosphorylated under in vivo conditions, transiently infected PIMT overexpressing mice were fasted for 8h and subjected to immunoprecipitation with PKA substrate antibodies. As shown in Figure 4D, the enrichment of PIMT-V5 was enhanced upon fasting (Figure 4E). Furthermore, recombinant PKA phosphorylated WT, mutant M1 (S656A), and M2 (S851A) but failed to phosphorylate double mutant confirming the authenticity of PKA phosphorylation sites (Figure 4F). As Ser851 residue is not conserved between humans and mice, the site was not further studied. Furthermore, mutation of evolutionary conserved Ser656 to alanine of human PIMT reduced PKA-dependent phospho-signals in forskolin-induced primary hepatocytes by ∼ 2-fold (Figures 4G and 4H). Likewise, phosphoSer656 PIMT signals were significantly reduced in Ser656Ala PIMT overexpressing mice upon fasting (Figures 4I and 4J). Importantly, we also observed that the phosphorylation of endogenous mouse PIMT was enhanced ∼2-fold in the liver lysates of fasting mice (Figures 4K and 4L). The phosphorylation status of GCN5 was minimally modified and served as the internal control (Figure 4G). Thus, we concluded that fasting-induced PKA activity enhanced the phosphorylation of PIMT at Ser656.Figure 4PIMT is phosphorylated by PKA at Ser656(A) Sepharose bound GST fused PIMT-C was subjected to kinase reaction in the presence of HeLa nuclear lysate, constitutively active purified MAPKS (ERK1 and ERK2), or PKA.(B) Primary Hepatocytes infected with lentivirus expressing PIMT-V5 were exposed to Forskolin (10 μM) with or without H89 (20 μM) or RP (20 μM) for 1h and then subjected to IP with PKA substrate antibody followed by immunoblot with indicated antibodies.(C) Densitometric quantification of Figure 4B. The phosphorylation signals were normalized with their corresponding input. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. ∗∗∗∗$p \leq 0.001$ compared to DMSO treated cells, ap<0.001 compared to Forskolin treated cells.(D) Mice were infected with lentivirus expressing PIMT-V5 ($$n = 3$$). Post 7 days of infection, mice were fasted for 8h and liver lysates were subjected to IP with PKA substrate antibody followed by immunoblots with anti-V5.(E) Densitometric quantification of Figure 4D. The phosphorylation signals were normalized with the corresponding input signals. Numerical data are expressed as mean ± SD. Statistical analysis was performed using unpaired Student’s t-test (two-tailed) ∗∗∗$p \leq 0.005$, versus the corresponding input.(F) Glutathione Sepharose beads bound GST-PIMT-C (W and mutants) were subjected to kinase assay with active and purified PKA. Double mutations at the PKA recognition site (RxxS, Ser656, and Ser851) abolished the phosphorylation of PIMT.(G) Primary hepatocytes isolated from female mice infected with either PIMT (W) or PIMTS656A were treated with Forskolin for 4h and then subjected to IP with PKA substrate antibody followed by immunoblots with the defined antibodies ($$n = 4$$).(H) Densitometric quantification of Figure 4G. The phosphorylation signals were normalized with the corresponding input signals. Numerical data are expressed as mean ± SD. Statistical analysis was performed using unpaired Student’s t-test (two-tailed) ∗∗∗$p \leq 0.005$, versus the corresponding input.(I) C57BL/6 male mice were tail-vein injected with lentivirus expressing PIMT (wt or S656A) ($$n = 3$$). Post 7 days of injection, mice fasted for 8h. Liver lysates were subjected to IP with PKA substrate antibody followed by immunoblots with the defined antibodies.(J) Densitometric quantification of Figure 4G. The phosphorylation signals were normalized with the corresponding input signals. Numerical data are expressed as mean ± SD. Statistical analysis was performed using unpaired Student’s t-test (two-tailed) ∗∗∗$p \leq 0.005$, versus the corresponding input.(K) 8h fasted liver lysates subjected to IP with PKA substrate antibody followed by immunoblots with the defined antibodies ($$n = 3$$).(L) Densitometric quantification of Figure 4I. The phosphorylation signals were normalized with the corresponding input signals. Statistical analysis was performed using unpaired Student’s t-test (two-tailed) ∗∗$p \leq 0.01$, versus the corresponding input.(M) HepG2 cells were transfected with the pGL3-PEPCK promoter and PIMT (W or mutants) encoding constructs with or without PKAc. Post-transfection cells were lysed, and luciferase readout was measured. The values were normalized with corresponding Renilla luciferase activity and expressed relative to PEPCK-Luc (unphosphorylated) (column 1), set to 1. Data are representative of 5 independent experiments and expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. dp<0.001 vs PECK-Luc (unphosphorylated) ∗∗∗∗$p \leq 0.001$ compared to PEPCK-Luc with PKAc transfected cells. γp<0.05, α $p \leq 0.005$ compared to PEPCK-Luc + PIMT (W) without PKAc, Dp<0.001 compared to PEPCK-Luc + PIMT (W) + PKAc, PIMT-W: PIMT wild type, PIMT-A: PIMT S656A mutant, PIMT-D: PIMT S656D mutant.
Having established that PIMT is a bonafide substrate of PKA, we next evaluated the functional relevance of the phosphorylation event. We transiently transfected cells with PEPCK-Luc and PIMT (wild-type and mutants) in the presence or absence of the catalytic subunit of the PKA complex (Figure 4M). The PEPCK-Luc activity was significantly stimulated by overexpression of either PKAc or PIMT (wt). Combined overexpression of PIMT and PKA displayed an inductive effect on luciferase activity. However, mutation of Ser656 to alanine significantly reduced both basal and PKAc-induced PEPCK-luciferase readout in HepG2 cells (Figure 4M). Supporting our previous findings,13 mutation of Ser656 to aspartate (which mimics phosphorylated PIMT Ser656 residue) induced PEPCK-Luc reporter activity comparable to wild-type PIMT, both under basal and PKAc-stimulated conditions (Figure 4M). Based on the above observations, it is likely that PKA-mediated PIMT phosphorylation may enhance its ability to promote PCK1 transcription and thus may play a vital role in controlling the gluconeogenic profile.
## PKA-mediated PIMT phosphorylation is a hepatic hyperglycemic driver in vivo
We previously reported that ectopic expression of PIMT augments hepatic glucose output in primary rat hepatocytes in MAPK/ERK-dependent manner.13 To evaluate the impact of PKA-mediated phospho-modification of PIMT in regulating hepatic gluconeogenesis, we infected primary mouse hepatocytes with lentiviral particles expressing either wild-type PIMT or its mutants. GFP-infected cells were used as the internal control. After 48h of infection, cells were treated with either glucagon, Forskolin, insulin, or metformin and were subjected to glucose output assay. Consistent with our previous report,13 PIMT wild-type (PIMT-W) overexpression significantly enhanced hepatic glucose output (Figure S7A). However, PIMTS656A (PIMT-A) failed to augment basal hepatic glucose production and robustly suppressed glucagon or forskolin-induced hepatic glucose output. Interestingly, ectopic expression of PIMTS656D (PIMT-D) robustly increased the glucose output compared to both GFP-infected cells and PIMT wild-type infected cells. More importantly, treatment with either glucagon or Forskolin further augmented hepatic glucose production upon PIMT wild-type infection. Interestingly, the overexpression of phospho-mimetic PIMT robustly enhanced glucagon and forskolin-mediated glucose production. Remarkably, the phospho-mimetic mutant displayed significantly enhanced glucose production compared to wild-type PIMT upon stimulation with glucagon and Forskolin (Figure S7A). Similarly, cells treated with insulin showed a significant reduction in hepatic glucose output upon PIMT overexpression, which was further reduced upon PIMTS656A overexpression. Intriguingly, treatment with metformin abolished the effect of PIMT (wild-type) overexpression on hepatic glucose production. Curiously, the phospho-mimetic mutant overexpressing cells displayed robust glucose output even under the influence of metformin and insulin (Figure S7B). Taken together, the above findings indicate that PIMT phosphorylation at Ser656 represents a major crosstalk event between glucagon, insulin, and metformin-controlled regulatory signaling cascades.
Next, we verified the importance of Ser656 phosphorylation in the PIMT-mediated regulation of gluconeogenic genes by qPCR. Ectopic expression of PIMT (PIMT-W) significantly enhanced Pck1, and G6pc1 expression along with Phlpp1, while the expression of Slc2a4 was significantly blunted. Ser656Ala mutation of PIMT (PIMT-A) completely abolished the ability of PIMT to modulate the expression of the aforementioned genes. In contrast, overexpression of the phospho-mimetic mutant of PIMT (PIMT-D) retained the impact of PIMT on gluconeogenic gene expression (Figure S7C). We also tested fasting glucose levels in PIMT overexpressed mice. As anticipated, mere overexpression of PIMT significantly enhanced the circulatory basal glucose level compared to GFP-infected mice upon short-term fasting. Additionally, mutation of Ser656 to alanine impaired the ability of PIMT to increase the fasting glucose, while the phospho-mimetic mutant of PIMT retained its competence to augment basal glucose level in mice (Figure S7D). Collectively the presence of a negative charge on Ser656 residue is imperative in regulating PIMT-driven liver glucose homeostasis.
To gain insights into the physiological significance of PKA-mediated PIMT phosphorylation on systematic glucose control, we subjected PIMT (wild-type and mutants) overexpressing mice to OGTT. Surprisingly, the hypoactivation state of PIMT (Ser656Ala) in the liver of lean mice abolished the PIMT-mediated repressive impact on systematic glucose clearance and improved insulin sensitivity (Figures 5A and 5B). In contrast, the hyperactivation state of PIMT (Ser656Asp) retained the hyperglycemic properties of PIMT-driven gluconeogenesis (Figures 5A and 5B). Characterizing the effect on insulin signaling cascade, S656A mutation blunted PIMT-dependent suppression of the glucose transporter GLUT4 and p85 expression and its downstream phosphorylation of Akt (Figures 5C and 5D). On the other hand, expression of PHLPP1 and G6pase dwindled compared to PIMT (w) overexpressing mice (Figures 5C and 5D). In contrast to the S656A mutation, S656D decreased GLUT4 and p85 expression and phosphorylated Akt levels, almost comparable to PIMT (wt). Protein levels of PHLPP1 and G6Pase were similar to PIMT (wt) overexpression (Figures 5C and 5D). Additionally, animals with hepatic PIMTS656A expression exhibited significantly reduced hyperglycemic response to pyruvate, while expression of aspartate mutant promoted glucose de novo biosynthesis from pyruvate (Figure 5E). Mechanistically, the hypophosphorylation status of PIMT failed to induce the gluconeogenic drivers of de novo synthesis of glucose (Figures 5E and 5F). In contrast, the hyperphosphorylation status of PIMT resulted in impaired glucose tolerance (Figures 5F and 5G). These results further demonstrate that PKA-dependent hyperactivation of PIMT acts as a critical driver in the dysregulation of mice’s glucose homeostasis. Figure 5Phosphorylation of PIMT hampers liver glucose homeostasis(A) Oral Glucose tolerance test (OGTT) in C57BL/6J mice expressing GFP or PIMT (W or mutants) in the liver ($$n = 5$$).(B) *The area* under the curve for Figure 5A. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. dp<0.001 compared to GFP-infected mice, Dp<0.001 compared to PIMT-W-infected mice.(C) *Immunoblot analysis* of the liver lysates using the indicated antibodies.(D) Densitometric quantification of Figure 5C. Values were normalized with the corresponding loading control, actin. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. ap<0.05, bp<0.01, cp<0.005, dp<0.001 compared to GFP-infected mice, Ap<0.05, Bp<0.01, Cp<0.005 compared to PIMT-W-infected mice.(E) Pyruvate tolerance test (PTT) in C57BL/6J mice expressing GFP or PIMT (W or mutants) in the liver.(F) *The area* under the curve for Figure 5E. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. dp<0.001 compared to GFP-infected mice, Dp<0.001 compared to PIMT-W-infected mice.(G) *Immunoblot analysis* of the liver lysates using the indicated antibodies.(H) Densitometric quantification of Figure 5G. Values were normalized with the corresponding loading control, Vinculin. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. bp<0.01, cp<0.005, dp<0.001 compared to GFP infected mice, Cp<0.005, Dp<0.001 compared to PIMT-W-infected mice. PIMT-W: PIMT wild type, PIMT-A: PIMT S656A mutant, PIMT-D: PIMT S656D mutant.
## PKA regulates PIMT-Ep300 transactivation activity through phosphorylation
To determine the molecular mechanisms underlying PKA-mediated and PIMT-dependent enhancement of de novo glucose synthesis at the chromatin level, we investigated the possibility of Ep300 and CBP in coordinating PIMT-orchestrated gene regulation. Ep300 and CBP are crucial regulators of hepatic homeostasis through their acetyltransferase and transcriptional co-activator activities. During fasting, cAMP-PKA mediated phosphorylation of CREB at Ser133 recruits Ep300/CBP to CRE-containing genes, including PCK1 and G6PC1.19,20 We previously reported that PIMT interacts and colocalizes with CBP and Ep300 in the nucleus.9 To evaluate the association between PIMT, Ep300, and CBP in PKA-driven gluconeogenic program, we transfected HepG2 cells with a luciferase reporter driven by PEPCK promoter together with either Ep300 or CBP and PIMT (wild-type and mutants) in the presence or absence of PKAc. PEPCK Luciferase activity was observed to be significantly enhanced upon the transfection of either Ep300 or PIMT wild-type and PIMTS656D, but not with PIMTS656A (Figure 6A). The luciferase activity was further intensified in the presence of PKAc. Interestingly, co-expression of Ep300 and PIMT further amplified (∼3-fold more than PIMT (wt) alone and ∼5-fold more than Ep300 alone) the luciferase readings; furthermore, the activity of PEPCK promoter was noted to be robustly enhanced in the presence of PKAc, suggesting that cAMP-PKA mediated cascade exerts a positive impact on Ep300-PIMT driven gene regulation. However, overexpression of the phospho-deficient mutant PIMT in conjunction with Ep300 displayed a marked reduction in the luciferase readout. More importantly, the phospho-mimetic mutant of PIMT induced a significant increase in promoter activity compared to Ep300-PIMT (wt) in the presence and the absence of PKAc (Figure 6A). However, to our surprise, when we performed similar experiments with CBP, there was no positive impact on PEPCK promoter activity upon co-expression of CBP or PIMT either in the presence or absence of PKAc (Figure S8), suggesting that PKA-mediated phosphorylation of PIMT may enhance Ep300, but not CBP, driven hepatic gluconeogenesis. In support of this hypothesis, we treated primary hepatocytes overexpressing PIMT-V5 and evaluated its complex formation with acetyltransferases. As shown in Figure 6B, exposure to Forskolin facilitated the complex formation between Ep300 and PIMT while interaction with CBP was minimal altered (Figures 6B and 6C). Furthermore, we found that the mutation of PIMT S656A markedly reduced the immunoprecipitation of Ep300. In contrast, phosphomimic mutation further enhanced the co-immunoprecipitation, confirming the effect of phosphorylation on PIMT-Ep300 interaction in lean mice. The interaction of CBP was minimally altered upon PIMT mutations (Figures 6D and 6E). To further ascertain our observations at endogenous protein levels, we performed co-immunoprecipitation with either Ep300 or CBP in the fasted mice liver lysates. As expected, there was a significant enrichment of PIMT upon Ep300 pull down in lysates from the liver of fasted mice. However, the endogenous interaction of PIMT and CBP was least affected by the nutritional challenge (Figures 6F and 6G). These results demonstrate that phosphorylation at Ser656 of PIMT is an important event leading to enhanced tethering with Ep300 in regulating hepatic gluconeogenesis. Figure 6PIMT enhanced Ep300 coactivation activity(A) HepG2 cells were transfected with pGL3-PEPCK-Luc promoter along with Ep300, PIMT (W and mutants) in the presence or absence of PKAc. Post 36h of transfection, cells were lysed, and luciferase signals were quantified. Renilla luciferase signals were used as an internal control. The values were normalized with corresponding Renilla luciferase activity and expressed relative to PEPCK-Luc (unphosphorylated) (column 1), set to 1. Data are representative of 5 independent experiments and expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. ap<0.05, dp<0.001 vs PECK-Luc (unphosphorylated) αp<0.001 compared to PEPCK-Luc + PIMT + PKAc transfected cells, Dp<0.001 compared to PEPCK-Luc + PIMT (W) without PKAc, #$p \leq 0.001$ compared to PEPCK-Luc + PIMT (W)+ Ep300 + PKAC.(B) Primary hepatocytes isolated from female mice infected with either PIMT (w) or GFP were treated with Forskolin for 4h and then subjected to IP with anti-V5 antibody followed by immunoblots with the defined antibodies ($$n = 4$$).(C) Densitometric quantification of Figure 6B. The signals were normalized with the corresponding input signals and neutralized with the PIMT-V5. DMSO-treated samples were set to 1. Data are representative of 3 independent experiments and expressed as mean ± SD. Statistical analysis was performed using unpaired Student’s t-test (two-tailed) ∗∗∗∗$p \leq 0.001$, versus the DMSO, treated cells.(D) Primary hepatocytes isolated from female mice infected with either PIMT (W or mutants) ($$n = 3$$). Post-infection (48h), cells were lysed and subjected to IP with anti-V5 antibody followed by immunoblots with the defined antibodies.(E) Densitometric quantification of Figure 6D. The signals were normalized with the corresponding input signals and neutralized with the PIMT-V5 (W) set to 1. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Bonferroni’s post hoc test. ∗$p \leq 0.05$, ∗∗∗∗$p \leq 0.001$ compared to PIMT(W) infected cells.(F) 8h fasted liver lysates were subjected to IP with anti-CBP or anti-Ep300 antibodies followed by immunoblots with the defined antibodies ($$n = 3$$).(G) Densitometric quantification of Figure 6F. The interaction signals were normalized with the corresponding enriched protein signals. Statistical analysis was performed using unpaired Student’s t-test (two-tailed) ∗∗$p \leq 0.01$, versus the fed liver lysates.
## Suppression of PIMT ameliorates diabetes
We also examined the phosphorylation status of PIMT at Ser656 in the liver of diabetic mice. Phosphorylation of PIMT (Figures S9A–S9D) and the expression of PIMT (Figures 1A–1F) were enhanced in the liver of ob/ob mice and wild-type fed on HFD. The interaction of PIMT with Ep300, but not CBP, was also increased in the liver of diabetic mice (Figures S9E–S9H). Such changes (PIMT phosphorylation and its interaction with Ep300) are consistent with the enhancement of gluconeogenesis which prompted us to evaluate whether PIMT contributes to promoting hepatic gluconeogenesis in these animals. To test this hypothesis, we injected lentiviral particles expressing two independent shRNA against Tgs1 in diabetic mice. shSCR (NT) served as an internal control. Post-injection, mice were sacrificed, and the expression of gluconeogenic genes was quantified by qPCR. Consistent with our previous observation, depletion of PIMT significantly reduced Pck1, G6pc1, and Phlpp1 transcript levels, while the expression of Slc2a4 was enhanced (Figures S10A and S10B). Next, we performed ChIP assays to study whether PIMT was directly recruited to the Pck1 promoter. Consistent with our earlier publication and above mention data, PIMT abundantly accumulated on the Pck1 promoter. Strengthening our observations, accumulation of PIMT was also observed on G6pc, Phlpp1, and Slc2a4 in the liver of insulin-resistant mice models (Figures S10C and S10D), compared to their corresponding control mice. More importantly, suppression of PIMT activity significantly lowered the fasting blood glucose in the diabetic mice models (Figures S10E and S10F), demonstrating that the regulatory function of PIMT in the expression of a specific subset of genes, especially encoding the gluconeogenic pathway, is vital in driving hyperglycemic conditions in insulin resistance.
Having observed a robust decrease in gluconeogenic genes followed by a significant decrease in blood glucose level, we next challenged the mice with pyruvate with a presumption that PIMT depletion will remarkably reduce the de novo glucose synthesis. As anticipated, the acute suppression of PIMT significantly hampered the non-carbohydrate conversion to glucose in the liver of diabetic mice models (Figures 7A, 7B, 7E, and 7F). In support of this, levels of gluconeogenic proteins PEPCK, G6Pase, and PGC1α were diminished in PIMT depleted liver lysates (Figures 7C, 7D, 7H, and 7H). To further ascertain the pathological impact of hepatic PIMT on glucose homeostasis in insulin-resistant mice, we challenged the mice with oral glucose (OGTT). OGTT represents one of the most widely accepted tests to determine the glucose-intolerant capacity and diabetes in genetically engineered or diet-induced mouse models. In harmony with the above-described observations, shRNA-mediated depletion of PIMT significantly improved the glucose clearance capacity from the peripheral circulation in diabetic mice models (Figures 8A, 8B, 8E, and 8F). Furthermore, the levels of the insulin signaling component, p85, were upregulated, followed by enhanced activity of Akt (pAkt) (Figures 8C, 8D, 8G, and 8H). In contrast, the expression of PHLPP1, which hampers the insulin signaling cascade, was reduced in the obesity mice models compared to shSCR-infected mice. Additionally, the expression of glucose transporter was also found to be upregulated. In accordance with our observations, the expression of G6Pase was reduced upon PIMT depletion (Figures 8C, 8D, 8G, and 8H). Collectively, the data establish that the disruption in PIMT’s activity (either expression or phosphorylation) attenuates gluconeogenesis and ameliorates hyperglycemia by improving hepatic insulin sensitivity and suppressing glucose biosynthesis from non-carbohydrate sources. Figure 7Hepatic PIMT depletion improves blood glucose levels in diabetic mice(A and E) Pyruvate tolerance test in HFD (A) or OB (E) mice expressing shSCR (NT) or two independent shRNA against Tgs1 in the liver ($$n = 4$$).(B and F) *The area* under the curve for Figures 7A and B or Figures 7E and F. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗∗∗$p \leq 0.005$ compared to NT-infected mice.(C and G) *Immunoblot analysis* of the liver lysates using the indicated antibodies.(D and H) Densitometric quantification of Figures 7C and 7G. Values were normalized with the corresponding loading control, actin. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$, ∗∗∗∗$p \leq 0.001$ compared to NT-infected mice. Figure 8Hepatic PIMT depletion hampers de novo glucose synthesis in diabetic mice(A and E) Oral glucose tolerance test in HFD (A) or OB (E) mice expressing shSCR (NT) or two independent shRNA against Tgs1 in the liver.(B and F) *The area* under the curve for Figures 7A and B or Figures 7E and F. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗∗∗$p \leq 0.005$ compared to NT-infected mice.(C and G) *Immunoblot analysis* of the liver lysates using the indicated antibodies.(D and H) Densitometric quantification of Figures 7C and 7G. Values were normalized with the corresponding loading control, actin. Numerical data are expressed as mean ± SD. Statistical analysis was performed using one-way ANOVA followed by Dunnett’s post hoc test. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$, ∗∗∗∗$p \leq 0.001$ compared to NT-infected mice.
## Discussion
Our work collectively establishes a unique governing network that delineates the complex obesogenic diet-induced insulin resistance, providing feasibility and mechanistic basis for therapeutic implications for reversing over-nutrition-associated metabolic syndrome. Our studies identify the PKA-PIMT phosphorylation switch in the hepatocytes as a regulatory mechanism to ensure robust glucose production. Dysregulation of the PKA-PIMT axis, low-grade chronic inflammation, and a high-fat/western diet perpetuates insulin resistance development. Interestingly, this signaling intermediate, PIMT, may be targeted to reverse or attenuate diet-associated insulin resistance.
Short-term fasting has a dominant role in sustaining serum glucose levels, an essential physiological process that is hijacked in metabolic diseases, including Type 2 Diabetes Mellitus. The importance of glucagon in the fasting-associated glucose secretion and pathogenesis of diabetes is well characterized. Increased glucagon signaling leads to dysregulated glucose homeostasis, whereas a decrease in glucagon action improves glycemic index in diabetes independent of insulin sensitivity.21,22,23,24,25,26 Transcriptional dysregulation of the hepatic gluconeogenic signature genes due to changes in the hormonal level in the portal vein is a hallmark of hepatic insulin resistance.2,27 PCK1 and G6Pase are the rate-limiting enzymes of de novo glucose synthesis and, therefore, robustly regulated at the transcript level by a plethora of transcription factors/nuclear receptors and co-regulators. Despite this deep understanding of the complex regulatory network, the limited application in clinics has motivated the research community to identify novel broad actionable targets. Even though several protein players were recently shown to play an essential role in fueling gluconeogenic transcription, we have focused on the third generation of transcriptional regulators, PIMT.
Our previous study showed that pPIMT (Ser298) is critical for enhancing Med1-mediated hepatic gluconeogenesis.13 Modulating PIMT expression (either upregulation or downregulation) in primary hepatocytes directly impacted the gluconeogenic gene expression and, thus, glucose production.13 Similarly, overexpression of PIMT also enhanced hepatic gluconeogenic gene expression in the fasted state and glycemia after fasting or pyruvate administration. Likewise, depletion of PIMT in the liver of mice revealed reduced glycemia or pyruvate clearance due to a significant reduction in hepatic gluconeogenic expression. In addition, assessing the impact of PIMT expression on the insulin signaling cascade, we noted that PIMT negatively regulates insulin sensitizers in part by promoting the expression of PHLPP1, an established intracellular Akt,28 and AMPK inhibitor.16 Thus, PIMT has been proposed to function as a negative regulator of insulin signaling, consequently promoting hepatic gluconeogenesis, prompting us to evaluate its expression in insulin resistance pathological models.
Consistently, PIMT expression (at protein and mRNA levels) was noted to be upregulated in nutritionally burdened and in ob/ob mice. Surprisingly, short-term fasting elevated endogenous hepatic PIMT protein levels with minimal impact on its transcript. Molecular dissection utilizing molecular, cellular, and in vivo approaches to such observations revealed that fasting-induced PKA signaling enhances the translational capacity of the Tgs1mRNA to enhance the protein content. Although the exact underlying mechanism is still unknown, it appears that the translation of TGS1mRNA was preferentially enhanced upon PKA activation. This is intriguing because PIMT regulates a panel of gluconeogenic genes at the transcript level; therefore, our results show that PIMT being regulated at the post-transcriptional level by fasting-induced signaling may provide a quick activation for the transcriptional machinery during the glucose shortage. However, further studies will be necessary to demonstrate the cellular partners involved in the selective up-regulation of PIMT mRNA for enhanced translation during short-term fasting in hepatocytes.
The impact of the post-transcriptional/translation regulation of PKA signaling on PIMT expression was only evident in wild-type mice upon fasting but not in mice with insulin resistance. This prompted us to evaluate whether the PKA pathway regulates PIMT at the post-translational level. In this regard, using conventional tools, we established that PIMT is a PKA substrate and PKA phosphorylates PIMT at Ser656 residue. Ectopic expression of PIMTS656D displayed an enhanced glycemic index under both fasting and pyruvate clearance. However, forced expression of PIMTS656A in the liver failed to elicit any alterations in the glucose levels compared to PIMT (wt and asp mutant), revealing that PKA-mediated PIMT phosphorylation enhances the PIMT’s transcriptional functionality. Thus, the phosphorylation of PIMT at Ser656 by PKA stimulation may represent an additional mechanism whereby fasting/PKA signaling maintains basal gluconeogenic program in the liver and favor quick response during serum glucose depletion.19 The dual regulation of PIMT in hepatocytes by PKA signaling seems logical, given that protein is critical in pathophysiological conditions for glucose homeostasis12,13,14 (Figure 9).Figure 9PKAc-driven PIMT activity regulates hepatic glucose metabolismProtein levels of PIMT are elevated in the liver upon short-term fasting and in HFD & ob/ob mice. The activity of PIMT is finely regulated in the liver in response to changes in nutritional availability. During fasting, glucagon-induced PKAc enhances the protein levels of PIMT in hepatocytes. Notably, PKAc also phosphorylates PIMT which augments the expression of gluconeogenic genes resulting in enhanced glucose production. Curtailing the expression of PIMT or hampering the PKAc-PIMT axis alleviates the glucose burden in obese and diabetic rodents.
The predominant regulation of gluconeogenesis by fasting/PKA signaling via the CREB-CBP-CRCT2 complex is a crucial signaling pathway.15,29 Wondisford and colleagues' recent work suggests that Ep300 is perhaps responsible for basal hepatic gluconeogenesis and can be active even in the postprandial state when insulin levels are high.19 Likewise, PIMT was noted to be recruited to the Pck1promoter under basal conditions in primary mouse hepatocytes and liver. This observation provoked to study the impact of CBP and/or Ep300 on PKA-PIMT-driven PCK1 expression. Our experiments suggest that PIMT works cooperatively with Ep300, but not with CBP, to activate the gluconeogenic program in fasting. *In* generalizing the interactions between CBP, Ep300, and PIMT in the overall regulation of hepatic glucose output, we have observed that PIMT-Ep300 can promote PKA-driven hepatic glucose synthesis, whereas CBP-PIMT was minimally modified. However, it will be interesting to study CBP-CRTC2-PIMT crosstalk, if any, in liver glucose metabolism.9,30 Data obtained in this study are in striking contrast with our previous publication,9 further justifying the dynamic impact of PIMT in regulating co-activator-driven transcriptional regulation in a context-dependent manner.
Our findings add an additional level of complexity to the regulation of de novo glucose biosynthesis. This raises several questions. Does re-feeding/insulin signal impact PIMT expression and/or transcriptional activity? *What is* the impact of clinically relevant anti-hyperglycemic drugs such as metformin on PIMT’s activity? Moreover, can any generalization be made on the fluidity of PIMT’s covalent phospho-modification in the regulation of hepatic gluconeogenesis? Regarding the first question, our preliminary study does direct that insulin/feeding has a direct impact on PIMT protein levels, in part, by regulating its 3′UTR activity (Figure S12). However, a more detailed analysis is required for a holistic understanding of the PIMT expression and nutritional status. As for the second question, we are currently pursuing additional research to assess the molecular underpinnings of anti-hyperglycemic drugs such as insulin and metformin on PIMT’s activity. Initial observations from PIMT overexpressing primary hepatocytes revealed a significant decrease in hepatic glucose output upon insulin or metformin treatment. However, to our surprise, ectopic expression of PIMTS656Ddisplayed a trivial decrease in glucose production compared to WT protein, revealing that PKA-mediated phosphorylation may hamper the anti-hyperglycemic effect on glucose production. Concerning PIMT post-translational phospho-modifications, we have observed that phosphorylation at Ser298 and Ser656 are critical for PIMT functionality under ERK12,13 and PKA signaling, respectively. Integrating these observations, the effect of each phosphorylation event on hepatic gluconeogenesis will depend upon the external clues and the dominant co-regulator contributing to the maintenance of gluconeogenesis.
Depleting PIMT in the liver of mice fed on an obesogenic diet and type 2 diabetes mellitus suppressed gluconeogenic gene expression and improved glycemic index. Furthermore, ectopic expression of PIMTS656A failed to alter the glycemic index in wild-type mice, compared to PIMT-wt-expressing mice. This observation suggests that the inhibition of PIMT or culminating PKA-PIMT phosphorylation may ameliorate diabetes. Disruption of the PKA-PIMT-Ep300 axis (which is achieved by PIMT depletion) also suppressed gluconeogenesis, suggesting that this module is a promising pharmacological target for treating obesity and type 2 diabetes.
## Limitations of the study
We observed a significant reduction in the expression of the gluconeogenic genes in primary hepatocytes upon PIMT depletion. Consistently, circulatory glucose levels were noted to be reduced in diabetic mice models. Although the function of the PIMT in the regulation of gluconeogenic genes is clear, the intramolecular machinery regulating the PIMT expression is still not well established. Also, our observation that starvation-induced PKA signaling augments the PIMT protein synthesis requires deeper dissection of the molecules associated with increasing the Tgs1 transcript load on the translational apparatus machinery. Furthermore, the current study falls short of evaluating the direct inputs of Insulin and Metformin-induced signaling in regulating PIMT-driven hepatic glucose regulation.
## Key resources table
REAGENT/RESOURCESOURCEIDENTIFIERAntibodiesAnti-PCK1SantaCruz Technologiessc-271029; RRID: AB_106110383Anti-G6PaseSantaCruz Technologiessc-25840; RRID: AB_2107514Anti-PCG1αSantaCruz Technologiessc-518025; RRID: AB_2890187Anti-PIMTAbcamab70559; RRID: AB_1269767Anti-PHLPP1aBethylA300-661A; RRID: AB_2299551Anti-pCREBCell signaling technology#9198; RRID: AB_2561044Anti-PKAcCell signaling technology#4782; RRID: AB_2170170Anti-PKAc substrateCell signaling technology#9624; RRID: AB_331817Anti-Phospho Akt Sert473Cell signaling technology#4060; RRID: AB_2315049Anti-AktCell signaling technology#4685; RRID: AB_2225340Anti-P85Cell signaling technology#4257; RRID: AB_659889Anti-GLUT4Novus BiologicalsNBP1-49533Anti-CBPCell signaling technology#7425; RRID: AB_10949975Anti-Ep300SantaCruz Technologiessc-48343; RRID: AB_628075Anti-V5ThermoFisherR960-25; RRID: AB_2556564Anti-FlagMillipore SigmaF1804-50UG; RRID: AB_262044Anti-ActinSantaCruz Technologiessc-47778; RRID: AB_626632Anti-VinculinMillipore SigmaV9264-100UL; RRID: AB_10603627Anti-Mouse HRPSantaCruz Technologiessc-516102; RRID: AB_2687626Anti-Rabbit HRPSantaCruz Technologiessc-2357; RRID: AB-628497Chemicals, peptides, and recombinant proteinsSuperSignal™ West Pico PLUS Chemiluminescent SubstrateThermo Fisher Scientific34579Rp-8-Br-cAMPSMillipore SigmaB2432DMEMLonzaBE12-604FFetal bovine serumHimediaRM9955Power Sybr green master mixThermo Fisher Scientific4368577PEIPolyscience Inc23966H-89 dihydrochloride hydrateMillipore sigmaB1427MG-132, Ready Made SolutionMillipore sigmaM7449-1ML$60\%$ high fat dietResearch DietsD12492RIPA bufferCell Signaling Technology9806TRIzol RNA isolation reagentThermo Fisher Scientific15596026MK2206Selleck ChemS1078U0126Selleck ChemS1102Care Touch Blood Glucose MeterAmazonCT210Recombinant Human Active ERK1 ProteinRnD Systems1879-KS-010Recombinant Human Active ERK2 ProteinRnD Systems1230-KS-010Recombinant Human Active PKA C beta ProteinRnD Systems268-KS-010Critical commercial assaysHigh-Capacity cDNA Reverse Transcription KitThermo Fisher Scientific4368813Glucose colorimetric assay kitCayman10009582Site directed Mutagenesis kit QuikChange IIAgilient200523Experimental models: Organisms/strainsMouse: WT C57BL6/JIndian Institute of Chemical Biology, KolkataIn bred; RRID: MGI:2177676Mouse: Lepob (ob/ob)Jackson LaboratoriesPurchased; RRID: MGI:5816460Experimental models: Cell linesHepG2ATCCHB-8065; RRID: CVCL_0027HEK293TATCCCRL-3216; RRID: CVCL_0063Primary Hepatocytes`Freshly isolatedIn houseOligonucleotidesPIMT_Fwd: GCCATCGACAGGTCAGGTATIntegrated DNA Technologies IDTN/APIMT_Rev; TGAACAGGATGTGCTTGCTCN/APCK1_Fwd: AAGGAAAACGCCTTGAACCTN/APCK1_Rev: GTAAGGGAGGTCGGTGTTGAN/AMouse CRE-Fwd: ACCGTGCTGACCATGGCTATN/AMouse CRE-Rev: TGTGTTCCCAGAGGGAAGGCN/AMouse GRE-Fwd: CCAGCTAACTCAGCAGGTACAGAN/AMouse GRE-Rev: GGTGGCTGCTGGTTGTCAAN/AMouse PPRE-Fwd: CTCTCTCCCATTGACTTCTCACTCACN/AMouse PPRE-Rev: GTGGCACTTGAGCAACAAGACCN/ARecombinant DNApDonR201 (Wt and mutants)PCR cloning, GW technologiespcDNA-PIMT-Flag as template13SFB-PIMT (Wt and mutants)GW cloningBehera et al. 201831pLenti-PIMT (wt and mutants)GW cloningThermo Fisher (pLenti 6.3 kit)Catalog number: V53306pLenti-PIMT-3'UTRThis studyHuman 293T cDNA was used as the templatepGL3-PEPCK promoterIn lab cloningKapadia et al. 201313pLKO.1-shTsg1Sigma AldrichTRCN0000090626, TRCN0000338134psPAX2Addgene12260; RRID: Addgene_12260pMD2.GAddgene12259; RRID: Addgene_12259GST-PIMT-N [1-334]In lab cloningKapadia et al. 201313GST-PIMT [330-853] (Wt and Mutants)In lab cloningKapadia et al. 201313pCMVb-Ep300Addgene10717pcDNA3β-FLAG-CBP-HAAddgene32908; RRID: Addgene_32908pCalpha EV (PKA catalytic subunit Calpha)Addgene15310; RRID: Addgene_15310SoftwaresGraphPad Prism 6GraphPad Softwarehttps://www.graphpad.com/scientific-software/prism/; RRID: SCR_002798EndNote™ X8EndnoteProduct Details | EndNote; RRID: SCR_014001LI-COR Image Studio SoftwareLicor BiosciencesDownload Free Image Studio Lite for Western Blot Quantification (licor.com); RRID: SCR_015795
## Lead contact
Information and requests for resources and reagents should be directed to and fulfilled by the lead contact, Parimal Misra ([email protected]).
## Materials availability
All materials used in this study are either commercially available or through collaboration, as indicated.
## Mice
C57BL/6 (B6) mice were fed a high-fat diet ($60\%$ fat calories, $20\%$ protein calories, and $20\%$ carbohydrate calories; Research Diets) or a normal chow diet ad libitum. In most assays, 8 weeks old mice (male and female) were fed with HFD for 12 weeks. All mice used in this study were maintained at 22°C in a $\frac{12}{12}$ h light-dark cycle in a specific pathogen-free facility and given free access to food and water, and studies were conducted during the light cycle. Additionally, for lentivirus-mediated gain and loss of function, aged-matched wild-type and obese mice (loss of function study) were injected intravenously through tail vein with the indicated particles in figure legends. Post 4 days of injection, mice received booster injection. HFD-fed mice (12 weeks fed) were injected (intravenous) with lentivirus expressing shRNA against Tgs1 or scramble, followed by a booster injection on the 4th day. After 7 days of the injection, mice fasted for 8h (OGTT) or 16h (PTT) studies. For the HFD study, mice were on a high-fat diet during the complete study.
## Study approval
All animal procedures were done according to the Animal Ethics Committee guidelines of the University of Hyderabad, Hyderabad (No: UH/IAEC/PB/2020–$\frac{1}{40}$) and Indian Institute of Chemical Biology, Jadavpur, Kolkata 700032(No: IICB/AEC/Meeting/July/$\frac{2020}{10}$). All animals were randomly assigned cohorts when used.
## Glucose tolerance and pyruvate tolerance tests
For glucose tolerance tests, mice orally received one dose of glucose (2 g/kg body weight) after 8h of fasting. For pyruvate tolerance tests, mice were fasted for 16h and then i.p. injected with pyruvate (2g/kg body weight). Blood was drawn from the tail vein, and glucose levels were quantified with a hand-held glucometer (Care Touch Blood Glucose Meter).
## Isolation of primary hepatocytes
Primary hepatocytes were isolated as described previously.13 Briefly, mice were infused with a calcium-free HEPES-phosphate buffer A (Calcium and magnesium-free PBS containing 0.2 μM EGTA pH 7.4) via the vena cava for ∼5 min. Observing the color of the liver changing to beige or light brown color, collagenase-containing buffer B (PBS with one mM magnesium and one mM calcium, 0.5mg/mL collagenase H) was perfused into the liver. For ∼5 min. Noticing the crack on the surface of the liver, perfusion was stopped immediately, and the liver was excised into ice-cold buffer A. Cells from digested livers were teased out, suspended in Buffer A, filtered through 100 μm cell strainer, and centrifuged at 100 × g for 5 min at 4°C. The pellets were washed with Buffer B (no collagenase) twice. The cells were cultured in complete DMEM with $20\%$ FBS) on collagen-coated plates. The media was refreshed after overnight incubation.
## Glucose output assay
After serum-starved for 6h, freshly isolated primary hepatocytes were washed twice and then exposed to glucose-free media (phenol-red and glucose-free DMEM containing 20mM sodium lactate, 2mM sodium pyruvate). Cell supernatants were collected 6h later and were subjected to glucose measurement using a Glucose (GO) assay kit (GAG0-20, Sigma, St. Louis, MO, USA). Cells were lysed, and protein concentration was determined using the Bradford reagent. The glucose output was normalized to cellular protein concentration and was expressed as arbitrary units.
## Cell culture, plasmids, transfection, and reagents
HEK-293T and HepG2 were procured from ATCC and maintained routinely in DMEM supplemented with fetal bovine serum (FBS) at 37oC with $5\%$ CO2. Reagents were procured from Sigma unless otherwise mentioned. Cells were regularly passaged as per the manufacturer's guidelines. Exponentially growing HepG2 cells were treated with reagents at different concentrations and different time points, as mentioned in the corresponding figure legends. According to the manufacturer's instruction, the expression constructs SFB (S protein/FLAG/Streptavidin-binding peptide)-tagged PIMT and pLenti6.3-PIMT-V5 were cloned using Gateway recombination cloning technology (Thermo scientific). Expression construct of PKA catalytic subunit (Plasmid #15310), Ep300 (Plasmid #10717), CBP (Plasmid #10718) were procured from Addgene. pLKO.1- shPIMT and pLKO.1-SCR were procured from the Sigma RNAi consortium. PIMTS656Aand PIMTS656D mutants were generated using a Quick-change site- directed mutagenesis kit as per the manufacturer's instructions. Transient transfection with different expression constructs was performed using Polyethylenimine (Polysciences) following the manufacturer's instructions. Post 24 h of transfection, cells were harvested and processed for the downstream experiments.
HEK-293 T cells were seeded at ∼$50\%$ confluency for lentiviral production and transfected the following day with psPAX2 (Addgene #12260) and pMD2G (Addgene #12259) along with pLKO.1-SCR and pLKO.1-shPIMT (mouse) constructs. Virus containing medium was collected post 48 h and 72 h of transfection and was concentrated using Amicon Ultra−15,100 kDa centrifugal filters as per the manufacturer's instructions. Lentiviral particles expressing PIMT, and its mutant were generated using a Virapower lentiviral expression kit with transfection in 293FT cells. Viral particles were collected post 72h of injection and were concentrated using centricon as described above. The lentiviral particles used for the complete study expresses shRNA under the human U6 promoter.
For the promoter-luciferase assay, each transfection mix contained 700ng of reporter PEPCK promoter, 300ng of CBP/Ep300, 200ng of PKAc, and 100ng of renilla luciferase expression vectors along with 300ng of PIMT or PIMTS656A or PIMTS6565Dexpression constructs. Cells were lysed 30h after transfection with passive lysis buffer (Promega, Madison, WI, USA) and equal amounts of lysate and luciferase assay buffer (20mM tricine, 1.07mM magnesium carbonate, decahydrate, 0.1mM EDTA, 2.67mM MgSO4, 33.3mM DTT, 270μM co-enzyme A, 470μM d-Luciferin, 530μM ATP) were mixed. Luminescence values were recorded in Sirius Luminometer (Berthold Detection Systems GmbH, Pforzheim, Baden-Württemberg, Germany). The values were normalized to the co-transfected renilla luciferase activity (renilla luciferase assay buffer: 4μM coelenterazine, 50μM phenyl benzothiazole, 25mM sodium pyrophosphate, 15mM EDTA, 10mM sodium acetate, 500mM sodium sulfate, and 500mM sodium chloride). For 3'UTR Luciferase assay, 700 ng pLenti empty luciferase construct or pLenti3/UTR PIMT luciferase construct and 100ng of renilla luciferase expression vector were transfected in HepG2 cells. Where indicated, cells were co-transfected with the PKAc construct. Post transfections cells were treated with indicated reagents at the specified concentration (see figure legends), and luciferase was estimated using Sirius Luminometer (Berthold Detection Systems GmbH, Pforzheim, Baden-Württemberg, Germany).
## Cell lysis, western blotting, and immunoprecipitation
Post-treatment cells were lysed with ice-cold 1X RIPA lysis buffer (50 mM TRIS pH 7.5, 150 mM NaCl, $0.1\%$ SDS, $0.5\%$ sodium deoxycholate, $1\%$ Triton X-100, 1 mM EDTA, and 1 mM EGTA, 1 mM sodium orthovanadate, 1 mM sodium fluoride, 1× protease inhibitor (Sigma-Aldrich), phosphatase inhibitor cocktails #2 and #3 (Sigma-Aldrich), and 1 mM PMSF). Cells lysate was quantified using Bradford reagents, and an equal amount of protein was separated on an SDS-PAGE and probed with the antibodies described in key resources table. Densitometry analyses were performed using Image Studio (Licor Biosciences) and presented as target band signal intensity ratio to GAPDH/Actin/Vinculin band signal intensity. For immunoprecipitation, precleared 300 μg of cell lysates were incubated with either PKA substrate antibody (2 μg, CST, USA) or PIMT antibody (2 μg, Abcam, USA) or Ep300 (1μg, SCBT, USA) or CBP (2μg. CST, USA) bound to protein A/G beads for 2h at 4 °C. Post incubation beads were washed with RIPA lysis buffer followed by boiling with laemmli Buffer. The enriched samples were separated and probed with defined antibodies. According to the manufacturer's instructions, densitometry analysis was performed using freely available Image Studio Lite image processing software (Licor Biosciences) and presented as a target band signal intensity ratio to corresponding input samples.
## Chromatin immunoprecipitation
Experimental Liver samples were fixed with $1\%$ formaldehyde for 20 min followed by 0.125 M glycine to arrest the cross-linking. The tissues were disaggregated with a Tissue tearor (BioSpec Products, Bartlesville, OK), and chromatin was isolated by adding lysis buffer,13 followed by disruption with a Dounce homogenizer. Lysates were sonicated, and the DNA sheared to an average length of 300–500 bp. Genomic DNA (input) was prepared by treating aliquots of chromatin with RNase, proteinase K, and heat for de-crosslinking, followed by ethanol precipitation. Pellets were re-suspended, and the resulting DNA was quantified with a Nanodrop spectrophotometer. Chromatin regions of interest were enriched using an antibody against PIMT (Abcam, USA). Complexes were washed, eluted from the beads with SDS buffer, and subjected to RNase, proteinase-K treatment, and heat for de-crosslinking at 65°C. ChIP DNA was purified by phenol-chloroform extraction and ethanol precipitation. qPCR reactions were carried out in triplicate on specific genomic regions using power SYBR Green mix (Thermo scientific). The resulting signals were normalized to the input DNA. Primer sequences are submitted in key resources table.
## Polysome profile
Small pieces of the liver (50–100 mg) were homogenized on ice in 5 volumes of polysome buffer (25 mM Tris (HCl), pH 7.4, 10 mM MgCl2, 25 mM NaCl, $0.05\%$ Triton X-100, 0.14M sucrose) containing 100 μg/mL heparin. Nuclei and mitochondria were pelleted by microcentrifugation at 4°C for 10 min. The resulting cytosol preparation was diluted with an equal volume of polysome buffer. 300μL of precleared lysate was fractionated using sucrose gradient (10–$50\%$ sucrose) at 35,000 r.p.m. 3 hours at 4 °C using Beckman Coulter SW41 rotor. Polysomal lysis buffer consisted of 20 mM Tris-HCL, 100 mM KCL, 5mM MgCl2, $1\%$Triton, and sodium deoxycholate 0.25 g. RNA was isolated using Trizol.
## Real-time PCR
Total RNA was isolated using Tri reagent (Sigma). For qPCR, reverse transcription was performed as described earlier.13 The mRNA expression was normalized to reference genes (as mentioned in Figure legends), with the values for control arbitrarily set to 1. Primer sequences are submitted in key resources table.
## In vitro, cellular, and in vivo phosphorylation of PIMT
In vitro kinase reactions with GST-PIMT fragments were carried out as reported earlier.13 In brief, GST-PIMT fragments were incubated with catalytical active PKA as described earlier.31 For cellular phosphorylation assay, freshly isolated primary hepatocytes were infected with lentiviral particles expressing pLenti-6.3-PIMT-V5 (wt) or pLenti-6.3-PIMTS565A-V5. Post infections (30h), cells were treated with PKA modulators. Cells were lysed in RIPA lysis buffer.32 PKA substrates were immunoprecipitated using Anti-PKA substrate (Cell signaling, CST) bound to protein A/G magnetic beads (Genescript, NJ USA). After 2h incubation at 4°C, the beads were washed three times with lysis buffer and subsequently were reconstituted in *Lamelli lysis* buffer and separated on 4–$12\%$ SDS-PAGE. The separated proteins were transferred to the PVDF membrane and probed by Anti-V5 (Invitrogen, 1:10,000) followed by Anti- GCN5 (1:1000, SCBT). Nonspecific Anti-rabbit IgG (Santacruz Biotechnology Inc., Santacruz, CA, USA) was used as the negative control. For in vivo phosphorylation, fasting liver lysates were incubated with PKA substrate antibody for 2h, resolved on 4–$12\%$ SDS-PAGE, and probed with anti-PIMT antibody (1:1000)
## Quantification and statistical analysis
Values were expressed as mean ± S.D. For comparison between 2 groups, the unpaired Student's t-test was used. One-way ANOVA followed by Bonferroni's post hoc analysis or Dunnett's post hoc analysis compared more than 2 groups. $p \leq 0.05$ was considered significant.
## Supplemental information
Document S1. Figures S1–S11
## Data and code availability
•All immunoblot analyses were performed with Image Studio Lite as indicated in the STAR Methods sections. No new code is generated in this study.•*All data* generated or analyzed during this study are included in the present article.•Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon reasonable request.
## Author contributions
Conceptualization, supervision, and project administration (BK, KVLP, and PM) and funding acquisition (KVLP and PM); investigation, resources and data curation (BK, SB, and RKE); animal experiments and supervision (BK, PM, KVLP, STK, PPB, and PC); data analysis (BK, SB, TS) and original manuscript draft preparation (BK, KVLP, and PM). All authors read and approved the final article.
## Declaration of interests
The authors declare no competing interests.
## Inclusion and diversity
We support inclusive, diverse, and equitable conduct of research.
## References
1. Desvergne B., Michalik L., Wahli W.. **Transcriptional regulation of metabolism**. *Physiol. Rev.* (2006) **86** 465-514. DOI: 10.1152/physrev.00025.2005
2. Petersen M.C., Vatner D.F., Shulman G.I.. **Regulation of hepatic glucose metabolism in health and disease**. *Nat. Rev. Endocrinol.* (2017) **13** 572-587. DOI: 10.1038/nrendo.2017.80
3. Hatting M., Tavares C.D.J., Sharabi K., Rines A.K., Puigserver P.. **Insulin regulation of gluconeogenesis**. *Ann. N. Y. Acad. Sci.* (2018) **1411** 21-35. DOI: 10.1111/nyas.13435
4. Mouchiroud L., Eichner L.J., Shaw R.J., Auwerx J.. **Transcriptional coregulators: fine-tuning metabolism**. *Cell Metabol.* (2014) **20** 26-40. DOI: 10.1016/j.cmet.2014.03.027
5. Sever R., Glass C.K.. **Signaling by nuclear receptors**. *Cold Spring Harbor Perspect. Biol.* (2013) **5**. DOI: 10.1101/cshperspect.a016709
6. Goldstein I., Hager G.L.. **The three ds of transcription activation by glucagon: direct, delayed, and dynamic**. *Endocrinology* (2018) **159** 206-216. DOI: 10.1210/en.2017-00521
7. Lin J., Handschin C., Spiegelman B.M.. **Metabolic control through the PGC-1 family of transcription coactivators**. *Cell Metabol.* (2005) **1** 361-370. DOI: 10.1016/j.cmet.2005.05.004
8. Zhu Y., Qi C., Cao W.Q., Yeldandi A.V., Rao M.S., Reddy J.K.. **Cloning and characterization of PIMT, a protein with a methyltransferase domain, which interacts with and enhances nuclear receptor coactivator PRIP function**. *Proc. Natl. Acad. Sci. USA* (2001) **98** 10380-10385. DOI: 10.1073/pnas.181347498
9. Misra P., Qi C., Yu S., Shah S.H., Cao W.Q., Rao M.S., Thimmapaya B., Zhu Y., Reddy J.K.. **Interaction of PIMT with transcriptional coactivators CBP, p300, and PBP differential role in transcriptional regulation**. *J. Biol. Chem.* (2002) **277** 20011-20019. DOI: 10.1074/jbc.M201739200
10. Komonyi O., Pápai G., Enunlu I., Muratoglu S., Pankotai T., Kopitova D., Maróy P., Udvardy A., Boros I.. **DTL, the Drosophila homolog of PIMT/Tgs1 nuclear receptor coactivator-interacting protein/RNA methyltransferase, has an essential role in development**. *J. Biol. Chem.* (2005) **280** 12397-12404. DOI: 10.1074/jbc.M409251200
11. Jia Y., Viswakarma N., Crawford S.E., Sarkar J., Sambasiva Rao M., Karpus W.J., Kanwar Y.S., Zhu Y.J., Reddy J.K.. **Early embryonic lethality of mice with disrupted transcription cofactor PIMT/NCOA6IP/Tgs1 gene**. *Mech. Dev.* (2012) **129** 193-207. DOI: 10.1016/j.mod.2012.08.002
12. Kain V., Kapadia B., Viswakarma N., Seshadri S., Prajapati B., Jena P.K., Teja Meda C.L., Subramanian M., Kaimal Suraj S., Kumar S.T.. **Co-activator binding protein PIMT mediates TNF-alpha induced insulin resistance in skeletal muscle via the transcriptional down-regulation of MEF2A and GLUT4**. *Sci. Rep.* (2015) **5**. DOI: 10.1038/srep15197
13. Kapadia B., Viswakarma N., Parsa K.V.L., Kain V., Behera S., Suraj S.K., Babu P.P., Kar A., Panda S., Zhu Y.J.. **ERK2-mediated phosphorylation of transcriptional coactivator binding protein PIMT/NCoA6IP at Ser298 augments hepatic gluconeogenesis**. *PLoS One* (2013) **8**. DOI: 10.1371/journal.pone.0083787
14. Blandino-Rosano M., Romaguera Llacer P., Lin A., Reddy J.K., Bernal-Mizrachi E.. **Trimethylguanosine synthase 1 is a novel regulator of pancreatic beta-cell mass and function**. *J. Biol. Chem.* (2022) **298**. DOI: 10.1016/j.jbc.2022.101592
15. Herzig S., Long F., Jhala U.S., Hedrick S., Quinn R., Bauer A., Rudolph D., Schutz G., Yoon C., Puigserver P.. **CREB regulates hepatic gluconeogenesis through the coactivator PGC-1**. *Nature* (2001) **413** 179-183. DOI: 10.1038/35093131
16. Behera S., Kapadia B., Kain V., Alamuru-Yellapragada N.P., Murunikkara V., Kumar S.T., Babu P.P., Seshadri S., Shivarudraiah P., Hiriyan J.. **ERK1/2 activated PHLPP1 induces skeletal muscle ER stress through the inhibition of a novel substrate AMPK**. *Biochim. Biophys. Acta, Mol. Basis Dis.* (2018) **1864** 1702-1716. DOI: 10.1016/j.bbadis.2018.02.019
17. Mao Z., Feng M., Li Z., Zhou M., Xu L., Pan K., Wang S., Su W., Zhang W.. **ETV5 regulates hepatic fatty acid metabolism through PPAR signaling pathway**. *Diabetes* (2021) **70** 214-226. DOI: 10.2337/db20-0619
18. Sakai M., Tujimura-Hayakawa T., Yagi T., Yano H., Mitsushima M., Unoki-Kubota H., Kaburagi Y., Inoue H., Kido Y., Kasuga M., Matsumoto M.. **The GCN5-CITED2-PKA signalling module controls hepatic glucose metabolism through a cAMP-induced substrate switch**. *Nat. Commun.* (2016) **7**. DOI: 10.1038/ncomms13147
19. He L., Naik K., Meng S., Cao J., Sidhaye A.R., Ma A., Radovick S., Wondisford F.E.. **Transcriptional co-activator p300 maintains basal hepatic gluconeogenesis**. *J. Biol. Chem.* (2012) **287** 32069-32077. DOI: 10.1074/jbc.M112.385864
20. Zhou X.Y., Shibusawa N., Naik K., Porras D., Temple K., Ou H., Kaihara K., Roe M.W., Brady M.J., Wondisford F.E.. **Insulin regulation of hepatic gluconeogenesis through phosphorylation of CREB-binding protein**. *Nat. Med.* (2004) **10** 633-637. DOI: 10.1038/nm1050
21. Conarello S.L., Jiang G., Mu J., Li Z., Woods J., Zycband E., Ronan J., Liu F., Roy R.S., Zhu L.. **Glucagon receptor knockout mice are resistant to diet-induced obesity and streptozotocin-mediated beta cell loss and hyperglycaemia**. *Diabetologia* (2007) **50** 142-150. DOI: 10.1007/s00125-006-0481-3
22. D'Alessio D.. **The role of dysregulated glucagon secretion in type 2 diabetes**. *Diabetes Obes. Metabol.* (2011) **13** 126-132. DOI: 10.1111/j.1463-1326.2011.01449.x
23. Dunning B.E., Gerich J.E.. **The role of alpha-cell dysregulation in fasting and postprandial hyperglycemia in type 2 diabetes and therapeutic implications**. *Endocr. Rev.* (2007) **28** 253-283. DOI: 10.1210/er.2006-0026
24. Baron A.D., Schaeffer L., Shragg P., Kolterman O.G.. **Role of hyperglucagonemia in maintenance of increased rates of hepatic glucose output in type II diabetics**. *Diabetes* (1987) **36** 274-283. DOI: 10.2337/diab.36.3.274
25. Gelling R.W., Du X.Q., Dichmann D.S., Romer J., Huang H., Cui L., Obici S., Tang B., Holst J.J., Fledelius C.. **Lower blood glucose, hyperglucagonemia, and pancreatic alpha cell hyperplasia in glucagon receptor knockout mice**. *Proc. Natl. Acad. Sci. USA* (2003) **100** 1438-1443. DOI: 10.1073/pnas.0237106100
26. Liang H., Nie J., Van Skike C.E., Valentine J.M., Orr M.E.. **Mammalian target of rapamycin at the crossroad between alzheimer's disease and diabetes**. *Adv. Exp. Med. Biol.* (2019) **1128** 185-225. DOI: 10.1007/978-981-13-3540-2_10
27. Bideyan L., Nagari R., Tontonoz P.. **Hepatic transcriptional responses to fasting and feeding**. *Genes Dev.* (2021) **35** 635-657. DOI: 10.1101/gad.348340.121
28. Brognard J., Newton A.C.. **PHLiPPing the switch on Akt and protein kinase C signaling**. *Trends Endocrinol. Metabol.* (2008) **19** 223-230. DOI: 10.1016/j.tem.2008.04.001
29. Han H.S., Kwon Y., Koo S.H.. **Role of CRTC2 in metabolic homeostasis: key regulator of whole-body energy metabolism?**. *Diabetes Metab. J.* (2020) **44** 498-508. DOI: 10.4093/dmj.2019.0200
30. Luo Q., Viste K., Urday-Zaa J.C., Senthil Kumar G., Tsai W.W., Talai A., Mayo K.E., Montminy M., Radhakrishnan I.. **Mechanism of CREB recognition and coactivation by the CREB-regulated transcriptional coactivator CRTC2**. *Proc. Natl. Acad. Sci. USA* (2012) **109** 20865-20870. DOI: 10.1073/pnas.1219028109
31. Misra P., Owuor E.D., Li W., Yu S., Qi C., Meyer K., Zhu Y.J., Rao M.S., Kong A.N.T., Reddy J.K.. **Phosphorylation of transcriptional coactivator peroxisome proliferator-activated receptor (PPAR)-binding protein (PBP). Stimulation of transcriptional regulation by mitogen-activated protein kinase**. *J. Biol. Chem.* (2002) **277** 48745-48754. DOI: 10.1074/jbc.M208829200
32. Kapadia B., Nanaji N.M., Bhalla K., Bhandary B., Lapidus R., Beheshti A., Evens A.M., Gartenhaus R.B.. **Fatty Acid Synthase induced S6Kinase facilitates USP11-eIF4B complex formation for sustained oncogenic translation in DLBCL**. *Nat. Commun.* (2018) **9** 829. DOI: 10.1038/s41467-018-03028-y
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---
title: Long-term Temporal Stability of Peripheral Blood DNA Methylation Profiles in
Patients With Inflammatory Bowel Disease
authors:
- Vincent Joustra
- Andrew Y.F. Li Yim
- Ishtu Hageman
- Evgeni Levin
- Alex Adams
- Jack Satsangi
- Wouter J. de Jonge
- Peter Henneman
- Geert D’Haens
journal: Cellular and Molecular Gastroenterology and Hepatology
year: 2022
pmcid: PMC9972576
doi: 10.1016/j.jcmgh.2022.12.011
license: CC BY 4.0
---
# Long-term Temporal Stability of Peripheral Blood DNA Methylation Profiles in Patients With Inflammatory Bowel Disease
## Body
SummaryThis work provides insight into the long-term intra-individual stability of the peripheral blood DNA methylome in patients with inflammatory bowel disease, a key aspect of predictive biomarker development. The data could serve to pre-select stable biomarkers to increase the probability of independent validation. In addition, the marked stability HLA-associated cytosine-phosphate-guanines have potential implications in understanding disease pathogenesis.
Crohn’s disease (CD) and ulcerative colitis (UC) are chronic relapsing and remitting inflammatory bowel diseases (IBDs) characterized by a wide variety of phenotypic manifestations.1 Although the etiology of IBD remains unknown, it is thought to arise as a result of a complex interplay between the host and microbial composition, triggered by environmental factors, such as tobacco smoking or diet.2, 3, 4 Accordingly, much effort has been invested in understanding the interaction between host and environment, which is thought to be mediated by the epigenome.5 The epigenome represents the set of mitotically heritable modifications that can affect gene transcriptions without altering the primary DNA sequence.6 DNA methylation, one of the most studied epigenetic mechanisms, involves the attachment of methyl groups to cytosine-phosphate-guanine (CpG) nucleotide sequences on the DNA. This covalent attachment is mitotically heritable and can, under certain conditions, regulate gene expression, thereby altering cellular behavior.7 Over the past decade, multiple epigenome-wide association studies (EWAS) have sought to characterize, classify, and predict IBD and its various phenotypes using DNA methylation.8, 9, 10, 11, 12, 13 However, most EWAS in IBD to date have been cross-sectional in design, reporting aberrant DNA methylation signatures in peripheral blood leukocytes (PBLs) and/or mucosal tissue,9,11,12,14, 15, 16, 17, 18, 19, 20 with only a single longitudinal study in mucosal tissue12 and PBLs.21 Previous literature has shown that the intra-individual variability of DNA methylation is most prominent during the early stages of life, which gradually diminishes and presents a more stable phenotype after 5 years of life.22,23 Nonetheless, the influence of aging on genome-wide DNA methylation has been well-described in monozygotic twins24,25 and unrelated healthy populations,26,27 demonstrating a global decrease in methylation as individuals age, as well as site-specific increases in methylation in CpG-rich areas, both of which are thought to result from dynamic external and internal environmental changes.28, 29, 30 As epigenetics, and thus DNA methylation, is cell-type specific, observed differences found in heterogeneous populations such as PBLs or tissue might reflect differences in the cellular composition.31, 32, 33 Nonetheless, age-related differences were found in more homogeneous populations, such as purified T-cells and monocytes.15,34,35 Despite the strong effects of age on DNA methylation, a high correlation between baseline and follow-up methylation data in pediatric IBD mucosal tissue has been observed.12 In contrast, IBD-associated differences in blood have shown to largely revert back to patterns observed in non-IBD controls during follow-up as the result of treatment and normalization of C-reactive protein (CRP).21 *It is* noteworthy that these studies focused only on a subset of IBD-associated CpGs, and did not report on the long-term stability of all CpG probes located on the Illumina HumanMethylation EPIC BeadChip array. Although temporal stability and intra-individual variability in PBL-derived DNA methylation has been investigated in adult healthy individuals36, 37, 38, 39 and patients with systemic lupus erythematosus,40 no such study has been conducted in patients with IBD.
There is widespread interest in the application of epigenetic markers in personalization of treatment.41 If epigenetic biomarkers are to be used as pathognomonic for IBD or its (sub)phenotypes, the features of interest would need to remain stable throughout the duration of the disease and, hence, over time, without being affected by various internal/external exposures. In addition, for biomarker development, loci that are time-stable reduce the number of false-positive findings, thereby increasing the probability of independent replication. Furthermore, selection of time-stable epigenetic biomarkers would help overcome current practical barriers in sample collection at specific time frames, thereby facilitating the use of larger samples sizes with similar phenotypes needed to enhance predictive power. We therefore sought to identify CpG positions that present stable DNA methylation in PBLs obtained from a well-generalizable cohort of adult patients with IBD with a 7-year median time between collected DNA samples.
## Abstract
### Background & Aims
There is great current interest in the potential application of DNA methylation alterations in peripheral blood leukocytes (PBLs) as biomarkers of susceptibility, progression, and treatment response in inflammatory bowel disease (IBD). However, the intra-individual stability of PBL methylation in IBD has not been characterized. Here, we studied the long-term stability of all probes located on the Illumina HumanMethylation EPIC BeadChip array.
### Methods
We followed a cohort of 46 adult patients with IBD (36 Crohn’s disease [CD], 10 ulcerative colitis [UC]; median age, 44 years; interquartile range [IQR] 27–56 years; $50\%$ female) that received standard care follow-up at the Amsterdam University Medical Centers. Paired PBL samples were collected at 2 time points with a median of 7 years (range, 2–9 years) in between. Differential methylation and intra-class correlation (ICC) analyses were used to identify time-associated differences and temporally stable CpGs, respectively.
### Results
Around $60\%$ of all EPIC array loci presented poor intra-individual stability (ICC <0.50); 78.114 (≈$9\%$) showed good (ICC, 0.75–0.89), and 41.274 (≈$5\%$) showed excellent (ICC ≥0.90) stability, between both measured time points. Focusing on previously identified consistently differentially methylated positions indicated that 22 CD-, 11 UC-, and 24 IBD-associated loci demonstrated high stability (ICC ≥0.75) over time; of these, we observed a marked stability of CpG loci associated to the HLA genes.
### Conclusions
Our data provide insight into the long-term stability of the PBL DNA methylome within an IBD context, facilitating the selection of biologically relevant and robust IBD-associated epigenetic biomarkers with increased potential for independent validation. These data also have potential implications in understanding disease pathogenesis.
## Patient Demographics
A total of 46 adult patients with IBD (36 CD, 10 UC) with a median age of 44 years (interquartile range [IQR], 27–56 years) and median disease duration of 12 years (IQR, 7-21 years) were included. Gender, surgical history, disease location, and disease behavior were balanced within this cohort (Table 1). Notably, 32 patients ($69.6\%$) were previously treated with an anti-tumor necrosis factor, prior to T1 sampling. Between T1 and T2 during regular IBD care follow-up, 10 patients ($21.7\%$) underwent IBD-related surgery, 24 patients ($52.2\%$) were treated with vedolizumab, and 14 patients ($30.4\%$) were treated with ustekinumab, reflecting the tertiary referral population seen at the Amsterdam University Medical Centers (UMC). No significant differences in median CRP ($$P \leq .97$$) or leukocyte count ($$P \leq .85$$) between T1 and T2 were observed (Figure 1, B–C).Table 1Baseline CharacteristicsBaseline characteristicsCD ($$n = 36$$)UC ($$n = 10$$)Total ($$n = 46$$)Female18 [50]5 [50]23 [50]Age, y42 (27–53)52 (36–61)44 (27–56)Disease duration, y11 (6–20)13 (7–22)12 (7–21)Ethnic background Caucasian30 (83.3)9 [90]39 (84.8)Disease location CD Ileal disease (L1)14 (38.9)–14 (38.9) Colonic disease (L2)4 (11.1)–4 (11.1) Ileocolonic disease (L3)18 [50]–18 [50]Disease location UC Left-sided UC, distal to splenic flexure (E2)3 [30]3 [30] Extensive, proximal to splenic flexure (E3)7 [70]7 [70]Disease behavior Non-stricturing/penetrating (B1)9 [25]–9 [25] Stricturing (B2)13 (36.1)–13 (36.1) Penetrating (B3)14 (38.9)–14 (38.9) Perianal disease (p)12 (36.1)–12 (36.1)Previous IBD-related surgery19 (52.8)1 [10]20 (43.5)IBD-related surgery between T1 and T210 (27.8)–10 (21.7)Previous medical treatment Immunomodulator (AZA, 6MP, 6TG, MTX)29 (80.6)7 [70]36 (78.3) Anti-TNF (IFX and/or ADA)28 (77.8)4 [40]32 (69.6) Ustekinumab1 (2.8)–1 (2.2)Treatment between T1 and T2 Immunomodulator (AZA, 6MP, 6TG, MTX)14 (38.9)3 [30]17 [37] Anti-TNF (IFX and/or ADA)16 (44.4)7 [70]23 [50] Vedolizumab17 (47.2)7 [70]24 (52.2) Ustekinumab13 (36.1)1 [10]14 (30.4)CRP T1, mg/L3.2 (1.4–8.0)2 (0.4–5.8)2.6 (1.2–6.6)CRP T2, mg/L3 (1.6–5.3)2.5 (0.8–7.7)3.0 (1.4–5.3)Leukocyte count T1, ∗10ˆ97.1 (6.3–9.1)5.4 (4.7–6.3)6.7 (5.4–8.3)Leukocyte count T2, ∗10ˆ97.1 (5.7–8.9)5.8 (5.2–7.6)7.1 (5.3–8.5)Smoking Active8 (22.2)3 [30]11 (23.9) Non-smoker28 (77.8)7 [70]35 (76.1)Note: Data are presented as number (%) or median (interquartile range).CD, Crohn’s disease; CRP, C-reactive protein; IBD, inflammatory bowel disease; UC, ulcerative colitis. Figure 1Patient characteristics over time. ( A), Visualization of the number of years between both samplings per patient. Visualization of the CRP (mg/L) (B) and leukocyte count [109] (C) between both time points, where connected samples were obtained from the same patient annotated with the mean difference and P-value.
## Time-associated Differential Methylation Expectedly Associates With Age-related CpGs
We first investigated the differences in methylation between both time points, identifying 194,391 (≈$23\%$) differentially methylated positions (DMPs) when comparing T1 and T2 at a false-discovery rate (FDR)-adjusted P-value of <.05 (Figure 2, A–C), which we termed time-associated DMPs. As our sample of interest was derived from peripheral blood, we investigated whether differences in the cellular composition were observable. Comparing the predicted blood cell composition yielded significant increase of the B- ($$P \leq .017$$) and CD4+ T-cells ($$P \leq .013$$) over time, whereas the neutrophils present a significant decrease ($$P \leq .02$$) (Figure 2, D).Figure 2Time-variant methylated positions. ( A), A volcano plot depicting the mean difference in methylation between the 2 time points on the x-axis and the –log10(P-value) on the y-axis. ( B), Heatmap visualizing the percentage methylation for the 25 most hyper- and 25 most hypo methylated DMPs. ( C), Manhattan plot showing the chromosomal distribution of all Illumina HumanMethylation EPIC array probes. Each dot represent a single CpG locus; dots above the black line are statistically significantly different between T1 and T2 (FDR-adjusted P-value ≤.05). ( D), Estimated blood cell distribution stratified by time. Dashed lines connect samples obtained from the same donor. Statistical significance was calculated using a Mann-Whitney U test. ( E), Volcano plot colored for age-associated CpGs. ( F), Gene set enrichment analysis barcode plot representing the overrepresentation of the age-related CpGs among the time-associated DMPs.
Expectedly, the time-associated differences were enriched for age-associated CpGs, which have been defined as the “epigenetic clocks” from Horvath,42 Hannum,43 Levine,44 and Knight45 (Figure 2, E). Furthermore, for these specific epigenetic clock CpGs, we observed a general hypomethylated pattern at T2 relative to T1 CpG sites (Figure 2, F), suggesting that the observed differences in DNA methylation are enriched for age-related differences. Functional enrichment analyses of the time-associated DMPs displayed several cancer-associated pathways (Figure 3).Figure 3Functional enrichments analyses using Gene Ontology-term and Kyoto Encyclopedia of Genes and Genomes pathways for the time-associated DMPs.
## Time-invariant, Stable Methylated Probes are Enriched in Genes Involved in Cell Adhesion
To identify CpGs that were consistently methylated at both time points, we performed intra-class correlation (ICC) analysis, which indicated that the majority of the CpGs (517.576 probes or around $60\%$) present poor intra-individual stability over time (ICC <0.50) (Table 2). Conversely, 119.388 CpGs (≈$14\%$) displayed a statistically significant high ICC (≥0.75), which we termed stably methylated positions (SMPs). Expectedly, CpGs with high ICC values typically presented less difference in mean methylation (Figure 4). We reasoned that probes that were associated with sites known to harbor genetic variants, both intentional and unintentional,46 should present the highest stability, as the genome of an individual typically does not change over the course of 7 years. Indeed, splitting the data by modality suggested that the CpG sites associated with known germline variants, namely those that were included for quality control purposes, presented high (>0.9) ICC values (Figure 5, A and Table 2). Of the SMPs with ICC values over 0.9, 15.766 SMPs (around $2\%$) presented no indication that they bind predicted or potential genetic variants, which we classified as hyper-stable methylated positions (HSMPs) (Table 3 and Figure 5, B). Functional enrichment analyses of the SMPs and HSMPs indicated significant enrichment of genes involved in cell-cell signaling, adhesion and neurogenesis (Figures 6 and 7).Table 2ICC ScoresPoor (ICC <0.5)Moderate (ICC, 0.5–0.74)Good (ICC, 0.75–0.89)Excellent (0.9≥ ICC)QC GV00059Annotated GV1046241411539576Predicted GV18583250292219924873Methylation4885311687905437615766Note: An overview of the ICC values classified using the system presented by Koo and Li.47 QC GV = Quality control probes that bind genetic variant only. Annotated GV = Methylation probes that are annotated to bind genetic variants at the CpG of interest. Predicted GV = Methylation probes that are annotated to bind genetic variants that were annotated by Gaphunter to be caused by genetic variants. Methylation = Methylation probes for which we have no evidence that they bind genetic variants. GV, Genetic variants; ICC, interclass correlation. Figure 4Difference in methylation according to ICC values. The x-axis represents the mean difference in methylation relative to the ICC on the y-axis. Note that the shape is slight conical, with the mean difference in methylation decreasing as the ICC increases. This becomes slightly more visible when binning the ICC values by the poor (ICC < 0.5), moderate (0.5 ≤ ICC < 0.75), good (0.75 ≤ ICC < 0.9), and excellent (0.9 ≤ ICC) ICC classification, shown below. Figure 5Time-invariant methylated positions. ( A), The ICCs stratified by probe type, with QC GV representing the aforementioned quality control probes that bind GVs, the potential GV representing probes that were annotated with a genetic variant, and predicted GV representing probes with a methylation signal typically found when driven by a genetic variant. Red dashed lines represent the classification boundaries introduced by Koo and Li,47 with blocks representing poor (ICC < 0.5), moderate (0.5 ≤ ICC < 0.75), good (0.75 ≤ ICC < 0.9), and excellent (0.9 ≥ ICC). ( B), Jittered visualisation of the 11 probes that present a time in variant difference that is as stable as the aforementioned quality control probes with the percentage methylation on the y-axis and the time point on the x-axis. Dashed lines connect samples obtained from the same donor. The cross-bar visualization represents the mean and standard error of the mean. Table 3Hyper-stable Methylated Positions (ICC ≥0.9)CGIDCoordinateICCP-valueICCP-adjustedICCDMP-valueDMP-adjustedDMGenecg04998153chr1:1018233310.9901530462.24E−404.64E−380.002350766.917048378.960490142LINC01307 (body)cg06231783chr11:14750480.9903186421.53E−403.24E−38−0.002016075.913436831.958698404BRSK2 (body)cg22113540chr16:101255210.9903575451.40E−402.98E−38−0.005125811.726564998.852281861GRIN2A (body)cg04546413chr19:292181010.9920920451.64E−424.48E−40−0.008758714.737190388.859028126cg05896524chr21:476046540.9910113742.90E−416.71E−39−0.00862793.708898078.841027778C21orf56 (TSS1500)cg20540428chr3:730456860.9903485811.43E−403.03E−38−0.005080004.778207203.884339751PPP4R2 (TSS1500)cg22243260chr3:1269460360.9919614182.37E−426.31E−40−0.000283585.989690923.995366819cg16885113chr6:296485070.9901736752.14E−404.44E−380.009565754.695688826.832438074cg18280909chr6:297233010.9907601295.38E−411.21E−380.009797484.631735087.788308313cg09271603chrX:244828850.9900917592.57E−405.29E−380.002551285.901311297.952513118PDK3 (TSS1500)cg08222221chrX:1395896170.9903607591.39E−402.95E−38−0.005243195.737019022.858927936CGID, Illumina CpG identifier; Coordinate, Genomic coordinate of the CpG on the human genome (build hg19); DM, mean difference in percentage methylation; ICC, intraclass correclation coefficient; Gene, associated gene as well as the location in the gene; P-valueICC, P-value associated with the intraclass correlation coefficient; P-adjustedICC, Benjamini-Hochberg-adjusted P-value associated with the intraclass correlation coefficient; P-valueDM, P-value associated with the mean difference in percentage methylation; P-adjustedDM, Benjamini-Hochberg-adjusted P-value associated with the mean difference in percentage methylation. Figure 6Functional enrichments analyses using Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathways for the SMPs. Figure 7Functional enrichments analyses using Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathways for the HSMPs.
## Stability Analysis of Previous IBD-associated DMPs, HLA, and IBD-susceptibility genes
We next investigated whether previously reported IBD-associated DMPs were found to be invariant over time. To do so, we evaluated ICC values of 255 CD-associated, 103 UC-associated, and 221 IBD-associated consistent DMPs identified in our systematic review and meta-analysis on CD-, UC-, and IBD-associated differential methylation,48 which included a total of 552 samples (177 CD, 132 UC, and 243 HC) from 4 different EWAS.14,17, 18, 19 Focusing on the stability of these DMPs in this cohort, we show that the majority (151 or $59.2\%$ CD-associated, 73 or $70.9\%$ UC-associated, and 156 or $70.6\%$ IBD-associated) present poor to moderate stability, indicating that the methylation status of these DMPs are affected by age or other exposures over time (Figure 8, A; Supplementary Table 1). Nonetheless, 22 CD-associated ($12.4\%$), 11 UC-associated ($8.3\%$), and 24 IBD-associated ($9.9\%$) loci show good to excellent stability over time, providing evidence that these CD-, UC-, or IBD-associated DMPs are unaffected by aging or the exposures over time (Figure 8, A; Supplementary Table 1).Figure 8IBD-associated SMPs. ( A), The ICCs of consistent DMPs identified through meta-analyses of 4 EWAS14,17, 18, 19 by Joustra et al48 stratified by comparison (CD vs HC; UC vs HC; and IBD vs HC). Black dashed lines represent the classification boundaries introduced by Koo and Li,47 with blocks representing poor (ICC < 0.5), moderate (0.5 ≤ ICC < 0.75), good (0.75 ≤ ICC < 0.9), and excellent (0.9 ≥ ICC). ( B), Jittered visualization of the IBD-associated DMPs as reported on by Adams et al18 and Ventham et al,17 as well as the CRP-independent probes reported on by Somineni et al (C).21 The percentage methylation is plotted on the y-axis and the time point on the x-axis. Dashed lines connect samples obtained from the same donor. The cross-bar visualization represents the mean and standard error of the mean.
Among the many IBD-associated DMPs, we specifically zoomed in on VMP1 (cg12054453 and cg16936953) as well as RPS6KA2 (cg17501210), as they were identified in multiple IBD-EWAS,17,18,21 as well as shown found to be among the most significant IBD-associated DMPs in our meta-analysis.48 We observed moderate consistency over time, with noticeable overall hypermethylation at T2 relative to T1 for the aforementioned 3 CpGs (Figure 8, B).
In addition to our meta-analysis, we also interrogated the CpGs that were CD-associated but CRP-independent, as reported on by Somineni et al.21 Interrogation thereof using our cohort revealed that cg25112191 (RORC), cg13707793 (CXXC5), cg21049840 (GPR183), cg06460200 (GPR183), cg06366627 (DIDO1), and cg15860510 (ESP8L3) presented poor consistency (ICC <0.5); cg04570316 (GMNN), cg02240291 (SMARCD3), cg00092736 (ESPNL), and cg00092736 (ESPNL) presented moderate consistency (ICC, 0.5–0.74); and cg09171692 (RORC) and cg02055816 (SMARCD3) presented good consistency (ICC, 0.75–0.89) between both time points (Figure 8, C).
We next interrogated the stability of all CpG loci associated with several well-known GWAS-identified IBD risk genes involved in IBD pathogenesis, namely ATG16L1, NOD2, IL23R, CARD9, FUT2, TYK2, and TNFSF15,49,50 as well as specific IBD-associated major histocompatibility complex encoding HLA genes previously reported on in GWAS studies, namely HLA-DRB1, HLA-DQB1, HLA-DQA1, HLA-DPA1, HLA-DPB1, HLA-A, HLA-B, and HLA-C.49,51, 52, 53, 54 Comparing all IBD risk genes, we noticed that the HLA genes presented the highest stability, all of which had a median ICC score over 0.5, whereas the majority of CpGs that annotate to non-HLA IBD risk genes had poor ICC values (<0.5) (Figure 9, A). Nonetheless, for each of these non-HLA IBD risk genes, we identified highly stable methylated positions, several of which located to transcription start sites or first exons (Figure 10 and Supplementary Table 2), implicating potential regulatory function. Figure 9IBD risk genes. ( A), Visualisation of the ICCs for all CpGs annotated to IBD-associated GWAS genes. Box plots show the overall median stability within each gene. Red dashed lines represent the classification boundaries introduced by Koo and Li,47 with blocks representing poor (ICC < 0.5), moderate (0.5 ≤ ICC < 0.75), good (0.75 ≤ ICC < 0.9), and excellent (0.9 ≥ ICC). ( B), ICC values of all CpG loci of class I and II HLA genes. The potential GV representing probes that were annotated with a genetic variant, predicted GV representing probes that presented a methylation signal typically found when driven by a GV, and methylation representing probes for which we have no evidence that they hybridize with any GV. Visualizations of the ICC values of all Illumina CpGs annotated to HLA-C (C), HLA-DPB1 (D), and HLA-DPA1 (E), relative to their position on each gene and grouped as potential GV (pink), predicted GV (green), or methylation (blue). Dots below represent known genetic variants as reported by Goyette53 for CD vs healthy controls (pink) and UC vs healthy controls (turquoise).Figure 10Visualization of the ICC values of all Illumina CpGs annotated to IBD GWAS risk genes, relative to their position on each gene. Dots represent individual CpG loci, dots above the red dashed line are considered SMPs (ICC ≥0.75), whereas dots above the blue dashed line are HSMPs (ICC ≥0.9).
As DNA methylation measured using deamination technologies cannot distinguish DNA methylation from genetic variants located at the CpG of interest,46 we investigated whether such technical artefacts were found among the HLA SMPs by interrogating the dbSNP (v151) database for catalogued variants, as well as by investigating for a typical clustered methylation signal when probes hybridize with genetic variants (GVs) using Gaphunter.55 Notably, most of the high ICC values were found for CpGs that presented some type of clustering typical of GVs but were not necessarily catalogued in dbSNP (v151) (Figure 9, B). In addition, HLA class II genes (HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, and HLA-DRB1) appeared to have a larger proportion of highly stable probes compared with HLA class I genes (HLA-A, HLA-B, and HLA-C) (Figure 9, B). Besides technical artefacts, DNA methylation itself can be affected by GVs that occur in the vicinity.56 As such, we cross-referenced our observations with a previous large-scale genotyping study of the HLA region in both patients with CD and UC.52 We indeed found multiple probes within the vicinity (<1 Kb) of CD- or UC-associated HLA alleles, many of which did not appear to have an annotated dbSNP identifier, indicating that the observed differences were either unannotated GVs, or CpGs whose methylation status is strictly controlled by neighboring IBD-associated GVs. Notably, several highly stable probes (ICC ≥0.75) found to be annotated to HLA-C, HLA-DPB1, and HLA-DPA1 were located far away (>1 Kb) from any of the IBD-associated GVs, did not associate with catalogued GVs, nor were identified as a potential GV by Gaphunter (Figure 9, C–E; Figure 11; and Supplementary Table 3), suggesting by default strong methylation stability over time. Figure 11Visualization of the ICC values of all Illumina CpGs annotated to HLA-A (A), HLA-B (B), HLA-DQA1 (C), HLA-DQB1 (D), and HLA-DRB1 (E) relative to their position on each gene and grouped as potential GV (pink), predicted GV (green), or methylation (blue).Dots below represent known genetic variants as reported by Goyette53 for CD vs healthy controls (pink) and UC vs healthy controls (turquoise).
## Discussion
Biomarker research often involves samples taken prior to or within a strictly pre-defined timeframe to the outcome of interest to mitigate the number of additional variables. In this study, we performed long-term longitudinal stability analyses of the PBL DNA methylome obtained from a cohort of adult patients with IBD (36 CD and 10 UC) that were collected at 2 time points separated by a median of 7 years reflective of a real-life tertiary referral population.
Our observations indicate that the majority of all loci (≈$60\%$) measured on the Illumina HumanMethylation EPIC BeadChip array present notable intra-individual variation in methylation over time (ICC <0.5), which is enriched for age-associated CpGs. Nonetheless, not all time-associated DMPs in our cohort were previously reported as age-associated CpGs. Although we observed no significant differences in CRP and leukocyte count between both time points, other external or environmental exposures, such as smoking, dietary alterations, therapy failure or switch, IBD-related surgery, or disease progression might have altered methylation status contributing to the observed time-associated differences. By contrast, 119.388 (≈$14\%$) and 41.274 (≈$5\%$) loci presented a highly stable pattern across both time points, with ICC values ≥0.75 and ≥0.90, respectively. Such loci retained their degree of methylation even after the aforementioned known IBD-associated and unknown external exposures as well as differences in cellular composition, suggesting time stability.
Previous studies investigating DNA methylation stability in PBLs of healthy adults using both the Illumina HumanMethylation EPIC BeadChip array, as well as its predecessor the Illumina HumanMethylation 450k BeadChip array, presented similar observations. In these studies, $16.9\%$ to $23\%$ of the CpG loci presented a moderate/good (ICC, 0.50–0.79), whereas $8.3\%$ to $12.9\%$ of the CpG loci presented a good/excellent (ICC ≥0.8) stability over a span of 1 to 6 years.37,39 Focusing on IBD, when interrogating 255 CD-associated, 103 UC-associated, and 221 IBD-associated DMPs identified in our own meta-analysis48 of 4 IBD EWAS,14,17, 18, 19 we observed that the majority presented poor to moderate stability, suggesting that the aforementioned IBD-associated loci might also be affected by exposures over time that might or might not be related with IBD. Although interesting, such time-variant probes should be interpreted with care when used as predictive biomarkers, given their association with exposures that occurred during both time points. To that end, our data could be used as a resource to preselect time-invariant CpG loci before independent validation when performing IBD-associated EWAS,57 thereby increasing the potential to identify replicable predictive biomarkers better reflecting the underlying biology of IBD. In addition, such an approach would enable a larger pool of samples to be used as samples need not to be obtained within the same age range when performing DNA methylation studies on IBD and its phenotypes.
When specifically interrogating the IBD-associated probes cg12054453 (VMP1), cg16936953 (VMP1), and cg17501210 (RPS6KA2), we find moderate consistency over time with a noticeable hypermethylation at T2 compared with T1. Similar analyses performed on the CD-associated yet CRP-independent probes reported by Somineni et al21 showed that the majority of these CpGs did not present long-term stability in our cohort. Differences between our observations and that of Somineni might be attributable to differences between adult and paediatric cohorts or might simply reflect non-inflammatory changes in methylation that occur over time. By contrast, cg09171692 (RORC) and cg02055816 (SMARCD3) presented high ICC values (0.76 and 0.87, respectively), indicating good stability in our cohort, irrespective of CRP or non-inflammatory exposures. Notably, both genes have previously been associated with IBD in multiple studies21,58,59 and therefore, show promise as stable IBD-associated loci.
Given the complex, multifactorial nature of IBD,2 focussing on the interplay between genetic variation and DNA methylation rather than single gene mutations alone might prove more useful in understanding its molecular etiology. Previous EWAS of mucosal tissue60 and peripheral blood61 both demonstrated differential methylation between IBD and controls for well-known GWAS-identified IBD-associated risk genes, suggesting differential methylation of key risk genes to affect disease susceptibility. Our observations corroborate this hypothesis, showing highly stable methylation for particular CpG loci within these risk genes. Interestingly, several of these HSMPs were located in or near to the transcription start sites, potentially regulating gene transcription by maintaining the aberrant phenotype.18 There has been extensive interest in (epi)genetic alterations of the highly polymorphic HLA region related to IBD pathogenesis, most consistently reported for HLA class II genes involved in the presentation of bacterial antigens to CD4+ T-cells.14,18,49,51, 52, 53, 54,62,63 Specifically, genetic variation of classical HLA genes has been suggested to play a role in the aberrant response to the dysbiotic microbiome observed in IBD,51 with particular impacts for the response to biological treatment64, 65, 66 and the formation of anti-drug antibodies.54 However, translation of the results into clinical practice has proven to be difficult due to the high number of polymorphisms of HLA αβ heterodimers and strong linkage disequilibrium.51 In our study, we observed multiple HSMPs in HLA genes, suggesting that the DNA methylation profile of these genes is very stable over time. Our results corroborate with previous array data showing highly significant correlations between CpG loci on several HLA class II genes of neonates compared with toddlers ($r = 0.83$) and adults ($r = 0.88$) with type 1 diabetes.67 Although further interrogation of these HSMPs indicates that multiple CpGs might be actual genetic variants, we also find multiple HSMPs that are not genetic variants. Nonetheless, several of such epigenetic HSMPs do occur within the vicinity of known IBD-associated HLA-variants, providing evidence that particular HLA-alleles might impart a strong, stabilizing effect on the epigenome.
## Strengths and Limitations
To our knowledge, we are the first to assess the stability of the DNA methylome obtained from PBLs of patients with IBD with a median 7-year follow-up period in a real-life disease exposure setting. This study is explorative in nature, using a moderate sample size without prior power calculation. Nonetheless, we note that studies of similar design have been conducted with a similar sample size.37,40,57 Although we can be reasonably confident in identifying the time-invariant aspect of the SMPs, we cannot fully eliminate the possibility that the SMPs would remain stable in a more diverse IBD cohort, as the typical markers of inflammation (CRP and leukocyte count) were hardly different between both time points.
## Conclusion
We observe considerable variability in DNA methylation measurements taken from PBL at 2 different time points separated by a median of 7 years. By contrast, around $14\%$ of all CpG loci could be considered highly stable even after IBD-specific exposures during the 2 points. Focusing on these CpG loci during biomarker discovery might result in the identification of biologically relevant and more robust IBD-associated epigenetic biomarkers with an increased probability of independent replication.
## Patient Selection
We performed a single-center, longitudinal EWAS, where we collected PBL samples from adult patients with IBD at the Amsterdam UMC. The interval between the time of sampling ranged from 2 to 9 years with a median of 7 (Figure 1, A). All included patients were historically diagnosed with either CD or UC on the basis of a combination of clinical symptoms and endoscopic inflammation as confirmed by histology per the current guidelines.68,69 In addition, all patients received standard care follow-up. No additional inclusion or exclusion criteria were used as the goal was to collect a cohort of patients with IBD that reflected the overall IBD population at the Amsterdam UMC. This study was approved by the medical ethics committee of the academic medical hospital (METC NL24572.018.08 and NL53989.018.15), and written informed consent was obtained from all subjects prior to sampling.
## Sample Collection and DNA Methylation Analysis
Whole peripheral blood samples were collected in a 6-mL EDTA tube and stored at −80 ºC until further processing. Genomic DNA was isolated using the QIAsymphony, whereupon the quantity of the DNA was assessed using the FLUOstar OMEGA and quality of the high-molecular weight DNA on a $0.8\%$ agarose gel. Genomic DNA was bisulfite converted using the Zymo EZ DNA Methylation kit, randomized per plate to limit batch effects, and analyzed on the Illumina HumanMethylation EPIC BeadChip array at the Core Facility Genomics, Amsterdam UMC, Amsterdam, the Netherlands.
## Statistical Analysis of Clinical Data
Baseline characteristics of all included patients were summarized using descriptive statistics. Categorical variables are presented as percentages and continuous variables as median annotated with the IQR. Differences in CRP and leukocyte count levels between T1 and T2 were calculated using the Wilcoxon signed ranks test. Analyses of clinical data were performed in IBM SPSS statistics version 26 and methylation analyses in the R statistical environment version 4.2.1.
## Time-dependent DNA Methylation Data Analyses
For differential methylation analyses, raw DNA methylation data were imported into the R statistical environment using the Bioconductor minfi70 package (version 1.36), whereupon the raw signal intensities were normalized using functional normalization71 and converted into methylation ratios. Differential methylation analyses was performed using limma72 (version 3.46) and eBayes73 regressing against time point (T2 vs T1), gender, smoking behavior, disease, and blood cell distribution. Statistical significance was defined as an FDR-adjusted P-value <.05. In addition to identifying time-associated differences in methylation, we also investigated differences in methylation associated with CRP and leukocyte count. Blood cell estimations were performed using the IDOL predictor CpGs as reference.74 Time-associated DMPs were investigated for their association with age by performing gene set enrichment analyses using the age-associated CpGs reported by Horvath,42 Hannum,43 Levine,44 and Knight.45 Visualizations were generated using ggplot275 (version 3.3.5) and gghighlight (version 0.3.2).
## Time-independent DNA Methylation Data Analyses
For the time stability analyses, raw DNA methylation data were imported using ewastools to retain the 89 quality control probes that bind GVs. Methylation probes that might bind GVs were identified on the basis of the minfi-provided annotation files, which we termed as potential GVs. Additional GV-binding probes were estimated using the Gaphunter tool55 as implemented in minfi, which we termed the predicted GVs. Moreover, as opposed to the differential methylation analysis, for the time stability analyses, we did not perform normalization nor did we correct for any other potential confounders (eg, gender, smoking behavior, disease, and blood cell distribution) to identify truly stable signals. Stability analyses were conducted using ICC analyses, where ICC estimates and their $95\%$ confidence intervals were calculated using the irr package implemented in R. Specifically, a 2-way mixed, single measures, consistency analysis was performed.76 Visualizations were generated using ggplot275 (version 3.3.5) and gghighlight (version 0.3.2).
## Gene Ontology Enrichment and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Analyses
Functional enrichment analyses genes annotated to both stable and unstable methylated probes was performed using GOmeth77 as implemented in missMethyl.33 *The* gene ontology terms were grouped according to biological process, cellular component, and molecular function, and an FDR corrected P-value below.05 indicates a statistical significant difference.
## Supplementary Material
Supplementary Table 1 Supplementary Table 2 Supplementary Table 3
## CRediT Authorship Contributions
Vincent Wilhelmus Joustra, MD (Conceptualization: Equal; Data curation: Supporting; Formal analysis: Supporting; Methodology: Lead; Project administration: Lead; Visualization: Supporting; Writing – original draft: Lead) Andrew Y.F. Li Yim, PhD (Conceptualization: Equal; Formal analysis: Lead; Methodology: Equal; Supervision: Supporting; Visualization: Lead; Writing – original draft: Supporting; Writing – review & editing: Supporting) Ishtu Hageman, MD (Data curation: Supporting; Methodology: Supporting; Writing – review & editing: Supporting) Evgeni Levin, PhD (Conceptualization: Supporting; Formal analysis: Supporting; Methodology: Supporting; Writing – review & editing: Supporting)
Alex Adams, MBChB, PhD (Conceptualization: Supporting; Formal analysis: Supporting; Methodology: Supporting; Writing – review & editing: Supporting) Jack Satsangi, DPhil, FRCP (Conceptualization: Supporting; Methodology: Supporting; Supervision: Equal; Writing – review & editing: Supporting) Wouter J. de Jonge, PhD (Conceptualization: Supporting; Data curation: Supporting; Funding acquisition: Equal; Methodology: Supporting; Resources: Equal; Supervision: Supporting; Writing – review & editing: Supporting) Peter Henneman, PhD (Conceptualization: Supporting; Formal analysis: Supporting; Methodology: Supporting; Supervision: Supporting; Writing – review & editing: Supporting)
Geert D’Haens, MD, PhD (Conceptualization: Equal; Data curation: Supporting; Funding acquisition: Lead; Methodology: Supporting; Supervision: Equal; Writing – review & editing: Supporting)
## References
1. Ng S.C., Shi H.Y., Hamidi N.. **Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies**. *Lancet* (2017) **390** 2769-2778. PMID: 29050646
2. de Souza H.S.P., Fiocchi C., Iliopoulos D.. **The IBD interactome: an integrated view of aetiology, pathogenesis and therapy**. *Nat Rev Gastroenterol Hepatol* (2017) **14** 739-749. PMID: 28831186
3. de Souza H.S., Fiocchi C.. **Immunopathogenesis of IBD: current state of the art**. *Nat Rev Gastroenterol Hepatol* (2016) **13** 13-27. PMID: 26627550
4. Carreras-Torres R., Ibanez-Sanz G., Obon-Santacana M.. **Identifying environmental risk factors for inflammatory bowel diseases: a Mendelian randomization study**. *Sci Rep* (2020) **10**
5. Ventham N.T., Kennedy N.A., Nimmo E.R., Satsangi J.. **Beyond gene discovery in inflammatory bowel disease: the emerging role of epigenetics**. *Gastroenterology* (2013) **145** 293-308. PMID: 23751777
6. Riggs A.D., Russo V.E.A., Martienssen R.A.. (1996)
7. Deaton A.M., Bird A.. **CpG islands and the regulation of transcription**. *Genes Dev* (2011) **25** 1010-1022. PMID: 21576262
8. Jung G., Hernandez-Illan E., Moreira L.. **Epigenetics of colorectal cancer: biomarker and therapeutic potential**. *Nat Rev Gastroenterol Hepatol* (2020) **17** 111-130. PMID: 31900466
9. Samarani S., Dupont-Lucas C., Marcil V.. **CpG methylation in TGFbeta1 and IL-6 genes as surrogate biomarkers for diagnosis of IBD in children**. *Inflamm Bowel Dis* (2020) **26** 1572-1578. PMID: 32407484
10. Tao W., Concepcion A.N., Vianen M.. **Multiomics and machine learning accurately predict clinical response to adalimumab and etanercept therapy in patients with rheumatoid arthritis**. *Arthritis Rheumatol* (2021) **73** 212-222. PMID: 32909363
11. Moret-Tatay I., Cerrillo E., Saez-Gonzalez E.. **Identification of epigenetic methylation signatures with clinical value in Crohn's disease**. *Clin Transl Gastroenterol* (2019) **10**
12. Howell K.J., Kraiczy J., Nayak K.M.. **DNA methylation and transcription patterns in intestinal epithelial cells from pediatric patients with inflammatory bowel diseases differentiate disease subtypes and associate with outcome**. *Gastroenterology* (2018) **154** 585-598. PMID: 29031501
13. Kalla R., Adams A.T., Vatn S.. **Epigenetic alterations at diagnosis predict susceptibility, prognosis and treatment escalation in inflammatory bowel disease-IBD character**. *Gut* (2017) **66** A24-A25
14. Li Yim A.Y.F., Duijvis N.W., Zhao J.. **Peripheral blood methylation profiling of female Crohn’s disease patients**. *Clin Epigenetics* (2016) **8** 65. PMID: 27279921
15. Gasparetto M., Payne F., Nayak K.. **Transcription and DNA methylation patterns of blood-derived CD8(+) T cells are associated with age and inflammatory bowel disease but do not predict prognosis**. *Gastroenterology* (2021) **160** 232-244.e7. PMID: 32814113
16. McDermott E., Ryan E.J., Tosetto M.. **DNA methylation profiling in inflammatory bowel disease provides new insights into disease pathogenesis**. *J Crohns Colitis* (2016) **10** 77-86. PMID: 26419460
17. Ventham N.T., Kennedy N.A., Adams A.T.. **IBD BIOM consortium; IBD CHARACTER consortium. Integrative epigenome-wide analysis demonstrates that DNA methylation may mediate genetic risk in inflammatory bowel disease**. *Nat Comm* (2016) **7**
18. Adams A.T., Kennedy N.A., Hansen R.. **Two-stage genome-wide methylation profiling in childhood-onset Crohnʼs disease implicates epigenetic alterations at the VMP1/MIR21 and HLA Loci**. *Inflamm Bowel Dis* (2014) **20** 1784-1793. PMID: 25144570
19. Harris R.A., Nagy-Szakal D., Pedersen N.. **Genome-wide peripheral blood leukocyte DNA methylation microarrays identified a single association with inflammatory bowel diseases**. *Inflamm Bowel Dis* (2012) **18** 2334-2341. PMID: 22467598
20. Hasler R., Feng Z., Backdahl L.. **A functional methylome map of ulcerative colitis**. *Genome Res* (2012) **22** 2130-2137. PMID: 22826509
21. Somineni H.K., Venkateswaran S., Kilaru V.. **Blood-derived DNA methylation signatures of Crohn disease and severity of intestinal inflammation**. *Gastroenterology* (2019) **156** 2254-2265.e3. PMID: 30779925
22. Perez R.F., Santamarina P., Tejedor J.R.. **Longitudinal genome-wide DNA methylation analysis uncovers persistent early-life DNA methylation changes**. *J Transl Med* (2019) **17** 15. PMID: 30626398
23. Nagy C., Turecki G.. **Sensitive periods in epigenetics: bringing us closer to complex behavioral phenotypes**. *Epigenomics* (2012) **4** 445-457. PMID: 22920183
24. Planterose Jimenez B., Liu F., Caliebe A.. **Equivalent DNA methylation variation between monozygotic co-twins and unrelated individuals reveals universal epigenetic inter-individual dissimilarity**. *Genome Biol* (2021) **22** 18. PMID: 33402197
25. Fraga M.F., Ballestar E., Paz M.F.. **Epigenetic differences arise during the lifetime of monozygotic twins**. *Proc Natl Acad Sci U S A* (2005) **102** 10604-10609. PMID: 16009939
26. Salameh Y., Bejaoui Y., El Hajj N.. **DNA methylation biomarkers in aging and age-related diseases**. *Front Genet* (2020) **11** 171. PMID: 32211026
27. Dobbs K.R., Embury P., Koech E.. **Age-related differences in monocyte DNA methylation and immune function in healthy Kenyan adults and children**. *Immun Ageing* (2021) **18** 11. PMID: 33685492
28. Gentilini D., Mari D., Castaldi D.. **Role of epigenetics in human aging and longevity: genome-wide DNA methylation profile in centenarians and centenarians' offspring**. *Age (Dordr)* (2013) **35** 1961-1973. PMID: 22923132
29. Fraga M.F., Agrelo R., Esteller M.. **Cross-talk between aging and cancer: the epigenetic language**. *Ann N Y Acad Sci* (2007) **1100** 60-74. PMID: 17460165
30. Johnson A.A., Akman K., Calimport S.R.. **The role of DNA methylation in aging, rejuvenation, and age-related disease**. *Rejuvenation Res* (2012) **15** 483-494. PMID: 23098078
31. Houseman E.A., Kelsey K.T., Wiencke J.K.. **Cell-composition effects in the analysis of DNA methylation array data: a mathematical perspective**. *BMC Bioinformatics* (2015) **16** 95. PMID: 25887114
32. Bartlett A.H., Liang J.W., Sandoval-Sierra J.V.. **Longitudinal study of leukocyte DNA methylation and biomarkers for cancer risk in older adults**. *Biomark Res* (2019) **7** 10. PMID: 31149338
33. Phipson B., Maksimovic J., Oshlack A.. **missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform**. *Bioinformatics* (2016) **32** 286-288. PMID: 26424855
34. Reynolds L.M., Taylor J.R., Ding J.. **Age-related variations in the methylome associated with gene expression in human monocytes and T cells**. *Nat Commun* (2014) **5** 5366. PMID: 25404168
35. Hearps A.C., Martin G.E., Angelovich T.A.. **Aging is associated with chronic innate immune activation and dysregulation of monocyte phenotype and function**. *Aging Cell* (2012) **11** 867-875. PMID: 22708967
36. Flanagan J.M., Brook M.N., Orr N.. **Temporal stability and determinants of white blood cell DNA methylation in the breakthrough generations study**. *Cancer Epidemiol Biomarkers Prev* (2015) **24** 221-229. PMID: 25371448
37. Zaimi I., Pei D., Koestler D.C.. **Variation in DNA methylation of human blood over a 1-year period using the Illumina MethylationEPIC array**. *Epigenetics* (2018) **13** 1056-1071. PMID: 30270718
38. Forest M., O’Donnell K.J., Voisin G.. **Agreement in DNA methylation levels from the Illumina 450K array across batches, tissues, and time**. *Epigenetics* (2018) **13** 19-32. PMID: 29381404
39. Shvetsov Y.B., Song M.A., Cai Q.. **Intraindividual variation and short-term temporal trend in DNA methylation of human blood**. *Cancer Epidemiol Biomarkers Prev* (2015) **24** 490-497. PMID: 25538225
40. Coit P., Ortiz-Fernandez L., Lewis E.E.. **A longitudinal and transancestral analysis of DNA methylation patterns and disease activity in lupus patients**. *JCI Insight* (2020) **5**
41. Leite M.L., de Oliveira K.B.S., Cunha V.A.. **Epigenetic therapies in the precision medicine era**. *Advanced Therapeutics* (2020) **3**
42. Horvath S.. **DNA methylation age of human tissues and cell types**. *Genome Biol* (2013) **14** R115. PMID: 24138928
43. Hannum G., Guinney J., Zhao L.. **Genome-wide methylation profiles reveal quantitative views of human aging rates**. *Mol Cell* (2013) **49** 359-367. PMID: 23177740
44. Levine M.E., Lu A.T., Quach A.. **An epigenetic biomarker of aging for lifespan and healthspan**. *Aging (Albany NY)* (2018) **10** 573-591. PMID: 29676998
45. Knight A.K., Craig J.M., Theda C.. **An epigenetic clock for gestational age at birth based on blood methylation data**. *Genome Biol* (2016) **17** 206. PMID: 27717399
46. Daca-Roszak P., Pfeifer A., Zebracka-Gala J.. **Impact of SNPs on methylation readouts by Illumina Infinium HumanMethylation450 BeadChip Array: implications for comparative population studies**. *BMC Genomics* (2015) **16** 1003. PMID: 26607064
47. Koo T.K., Li M.Y.. **A guideline of selecting and reporting intraclass correlation coefficients for reliability research**. *J Chiropr Med* (2016) **15** 155-163. PMID: 27330520
48. Joustra V., Hageman I.L., Satsangi J.. **Systematic review and meta-analysis of peripheral blood DNA methylation studies in inflammatory bowel disease**. *J Crohns Colitis* (2022)
49. Momozawa Y., Dmitrieva J., Theatre E.. **IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes**. *Nat Commun* (2018) **9** 2427. PMID: 29930244
50. Yamazaki K., McGovern D., Ragoussis J.. **Single nucleotide polymorphisms in TNFSF15 confer susceptibility to Crohn’s disease**. *Hum Mol Genet* (2005) **14** 3499-3506. PMID: 16221758
51. Ashton J.J., Latham K., Beattie R.M.. **Review article: the genetics of the human leucocyte antigen region in inflammatory bowel disease**. *Aliment Pharmacol Ther* (2019) **50** 885-900. PMID: 31518029
52. Goyette P., Boucher G., Mallon D.. **International Inflammatory Bowel Disease Genetics Consortium; Australia and New Zealand IBDGC; Belgium IBD Genetics Consortium; Italian Group for IBD Genetic Consortium; NIDDK Inflammatory Bowel Disease Genetics Consortium; United Kingdom IBDGC; Wellcome Trust Case Control Consortium; Quebec IBD Genetics Consortium. High-density mapping of the MHC identifies a shared role for HLA-DRB1∗01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis**. *Nat Genet* (2015) **47** 172-179. PMID: 25559196
53. Verstockt B., Smith K.G., Lee J.C.. **Genome-wide association studies in Crohn's disease: past, present and future**. *Clin Transl Immunology* (2018) **7**
54. Sazonovs A., Kennedy N.A., Moutsianas L.. **HLA-DQA1∗05 carriage associated with development of anti-drug antibodies to infliximab and adalimumab in patients with Crohn’s disease**. *Gastroenterology* (2020) **158** 189-199. PMID: 31600487
55. Andrews S.V., Ladd-Acosta C., Feinberg A.P.. **Gap hunting” to characterize clustered probe signals in Illumina methylation array data**. *Epigenetics & Chromatin* (2016) **9** 56. PMID: 27980682
56. Villicana S., Bell J.T.. **Genetic impacts on DNA methylation: research findings and future perspectives**. *Genome Biology* (2021) **22** 127. PMID: 33931130
57. Sugden K., Hannon E.J., Arseneault L.. **Patterns of reliability: assessing the reproducibility and integrity of DNA methylation measurement**. *Patterns (N Y)* (2020) **1**
58. Bogaert S., Laukens D., Peeters H.. **Differential mucosal expression of Th17-related genes between the inflamed colon and ileum of patients with inflammatory bowel disease**. *BMC Immunol* (2010) **11** 61. PMID: 21144017
59. Sipos F., Galamb O., Wichmann B.. **Peripheral blood based discrimination of ulcerative colitis and Crohn’s disease from non-IBD colitis by genome-wide gene expression profiling**. *Dis Markers* (2011) **30** 1-17. PMID: 21508504
60. Cooke J., Zhang H., Greger L.. **Mucosal genome-wide methylation changes in inflammatory bowel disease**. *Inflamm Bowel Dis* (2012) **18** 2128-2137. PMID: 22419656
61. Nimmo E.R., Prendergast J.G., Aldhous M.C.. **Genome-wide methylation profiling in Crohnʼs disease identifies altered epigenetic regulation of key host defense mechanisms including the Th17 pathway**. *Inflamm Bowel Dis* (2012) **18** 889-899. PMID: 22021194
62. Agliata I., Fernandez-Jimenez N., Goldsmith C.. **The DNA methylome of inflammatory bowel disease (IBD) reflects intrinsic and extrinsic factors in intestinal mucosal cells**. *Epigenetics* (2020) **15** 1068-1082. PMID: 32281463
63. Ahmad T., Marshall S.E., Jewell D.. **Genetics of inflammatory bowel disease: the role of the HLA complex**. *World J Gastroenterol* (2006) **12** 3628-3635. PMID: 16773677
64. van Vugt L.J., van den Reek J., Hannink G.. **Association of HLA-C∗06:02 status with differential response to ustekinumab in patients with psoriasis: a systematic review and meta-analysis**. *JAMA Dermatol* (2019) **155** 708-715. PMID: 30994858
65. Dand N., Duckworth M., Baudry D.. **HLA-C∗06:02 genotype is a predictive biomarker of biologic treatment response in psoriasis**. *J Allergy Clin Immunol* (2019) **143** 2120-2130. PMID: 30578879
66. Hirose W., Harigai M., Amano K.. **Impact of the HLA-DRB1 shared epitope on responses to treatment with tofacitinib or abatacept in patients with rheumatoid arthritis**. *Arthritis Res Ther* (2021) **23** 228. PMID: 34465391
67. Kindt A.S.D., Fuerst R.W., Knoop J.. **Allele-specific methylation of type 1 diabetes susceptibility genes**. *J Autoimmun* (2018) **89** 63-74. PMID: 29224923
68. Gionchetti P., Dignass A., Danese S.. **European evidence-based consensus on the diagnosis and management of Crohn’s disease 2016: part 2: surgical management and special situations**. *J Crohns Colitis* (2017) **11** 135-149. PMID: 27660342
69. Lamb C.A., Kennedy N.A., Raine T.. **British Society of Gastroenterology consensus guidelines on the management of inflammatory bowel disease in adults**. *Gut* (2019) **68** s1-s106. PMID: 31562236
70. Aryee M.J., Jaffe A.E., Corrada-Bravo H.. **Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays**. *Bioinformatics* (2014) **30** 1363-1369. PMID: 24478339
71. Fortin J.P., Labbe A., Lemire M.. **Functional normalization of 450k methylation array data improves replication in large cancer studies**. *Genome Biol* (2014) **15** 503. PMID: 25599564
72. Ritchie M.E., Phipson B., Wu D.. **limma powers differential expression analyses for RNA-sequencing and microarray studies**. *Nucleic Acids Res* (2015) **43** e47. PMID: 25605792
73. Smyth G.K.. **Linear models and empirical bayes methods for assessing differential expression in microarray experiments**. *Stat Appl Genet Mol Biol* (2004) **3**
74. Salas L.A., Koestler D.C., Butler R.A.. **An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray**. *Genome Biol* (2018) **19** 64. PMID: 29843789
75. Wickham H.. (2016)
76. Shrout P.E., Fleiss J.L.. **Intraclass correlations: uses in assessing rater reliability**. *Psychol Bull* (1979) **86** 420-428. PMID: 18839484
77. Maksimovic J., Oshlack A., Phipson B.. **Gene set enrichment analysis for genome-wide DNA methylation data**. *Genome Biol* (2021) **22** 173. PMID: 34103055
|
---
title: Insulin-like growth factor binding protein 5b of Trachinotus ovatus and its
heparin-binding motif play a critical role in host antibacterial immune responses
via NF-κB pathway
authors:
- Hehe Du
- Yongcan Zhou
- Xiangyu Du
- Panpan Zhang
- Zhenjie Cao
- Yun Sun
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9972581
doi: 10.3389/fimmu.2023.1126843
license: CC BY 4.0
---
# Insulin-like growth factor binding protein 5b of Trachinotus ovatus and its heparin-binding motif play a critical role in host antibacterial immune responses via NF-κB pathway
## Abstract
### Introduction
Insulin-like growth factor binding protein 5 (IGFBP5) exerts an essential biological role in many processes, including apoptosis, cellular differentiation, growth, and immune responses. However, compared to mammalians, our knowledge of IGFBP5 in teleosts remains limited.
### Methods
In this study, TroIGFBP5b, an IGFBP5 homologue from golden pompano (Trachinotus ovatus) was identified. Quantitative real-time PCR (qRT-PCR) was used to check its mRNA expression level in healthy condition and after stimulation. In vivo overexpression and RNAi knockdown method were performed to evaluate the antibacterial profile. We constructed a mutant in which HBM was deleted to better understand the mechanism of its role in antibacterial immunity. Subcellular localization and nuclear translocation were verified by immunoblotting. Further, proliferation of head kidney lymphocytes (HKLs) and phagocytic activity of head kidney macrophages (HKMs) were detected through CCK-8 assay and flow cytometry. Immunofluorescence microscopy assay (IFA) and dual luciferase reporter (DLR) assay were used to evaluate the activity in nuclear factor-κB (NF-κβ) pathway.
### Results
The TroIGFBP5b mRNA expression level was upregulated after bacterial stimulation. In vivo, TroIGFBP5b overexpression significantly improved the antibacterial immunity of fish. In contrast, TroIGFBP5b knockdown significantly decreased this ability. Subcellular localization results showed that TroIGFBP5b and TroIGFBP5b-δHBM were both present in the cytoplasm of GPS cells. After stimulation, TroIGFBP5b-δHBM lost the ability to transfer from the cytoplasm to the nucleus. In addition, rTroIGFBP5b promoted the proliferation of HKLs and phagocytosis of HKMs, whereas rTroIGFBP5b-δHBM, suppressed these facilitation effects. Moreover, the in vivo antibacterial ability of TroIGFBP5b was suppressed and the effects of promoting expression of proinflammatory cytokines in immune tissues were nearly lost after HBM deletion. Furthermore, TroIGFBP5b induced NF-κβ promoter activity and promoted nuclear translocation of p65, while these effects were inhibited when the HBM was deleted.
### Discussion
Taken together, our results suggest that TroIGFBP5b plays an important role in golden pompano antibacterial immunity and activation of the NF-κβ signalling pathway, providing the first evidence that the HBM of TroIGFBP5b plays a critical role in these processes in teleosts.
## Introduction
The insulin-like growth factor (IGF) system is mainly composed of IGF-I/II, IGF receptors, and IGF-binding proteins (IGFBPs) [1]. IGFBPs act as IGF carriers and regulate their biological distribution [2]. The IGF signaling pathway has been proven to be crucial for the onset and progression of numerous diseases as well as the control of cellular activities [1, 3]. Given the extensive evidence regarding the significance of IGF, the IGFBP family, which was identified and designated IGFBP1 to IGFBP6, has attracted much attention in recent years [4, 5]. IGFBP5 belongs to one of the most diverse groups in the biologically active IGFBP family [6]. It was first found in human bone extracts [7]. Since then, IGFBP5 has been cloned from a wide range of species and has the highest level of sequence similarity among the IGFBP family (8–10).
In mammals, IGFBP5 plays a variety of functions in cellular activities and has been reported to be associated with many diseases [1, 4]. Studies found that the expression of IGFBP5 may stimulate retinal pigment epithelium (RPE) cell fibrosis, leading to the progression of proliferative vitreoretinopathy [11, 12]. It was discovered that the expression of IGFBP5 was downregulated in kidney renal papillary renal cell carcinoma patients, could strengthen tissue regeneration, and had an anti-inflammatory effect by maintaining immune homeostasis (13–15).
Overall, IGFBP5 remains poorly understood in teleosts compared with mammals. To date, few studies have focused on the immune response of the IGF system in fish, and even fewer ones have focused on the function of IGFBP5. Several IGFBP5 sequences in fine flounder (Paralichthys adspersus), zebrafish (Danio rerio), grass carp (Ctenopharyngodon idella), salmon, and rainbow trout (Oncorhynchus mykiss) have been cloned and characterized (8, 16–19). These studies mainly focused on the aspects of evolution, function in growth, muscle or embryonic development, and hormonal regulation [20, 21].
IGFBP5 contains a nuclear localization signal (NLS) in the C-terminal domain which is suggested to help IGFBP5 translocate to the nucleus and activate many transcription factors in the nucleus of cells involved in immune and inflammatory reactions [22, 23]. Recently, many studies have focused on IGFBP5 nuclear trafficking and demonstrated that its subcellular compartmentalization affects its functions—for example, NLS-mutated IGFBP5 is mainly located in the cytoplasm, and it can enhance proliferation and migration [24]. IGFBP5 induces Egr-1 and binds to each other in the nucleus, resulting in the promotion of fibrotic gene transcription [25]. Moreover, according to some reports, the NLS domain contains a heparin-binding motif (HBM; 206KRKQCK211) that appears to be key in determining the various functions of IGFBP5 (26–29).
Vibrio harveyi is the main threat to the large-scale farming of Trachinotus ovatus, an important commercial fish in China [30, 31]. In the present study, TroIGFBP5b was cloned and identified, and its different expression patterns were examined. To assess whether HBM deficiency affects its subcellular location and influences its function, we generated a mutant containing a truncated form of TroIGFBP5b in which the HBM motif of the NLS domain was deleted. The findings provide insight into the mechanisms underlying the immune function of TroIGFBP5b.
## Fish and cells
T. ovatus (average weight 18.5 g) from Hainan Province was temporarily reared for 1 week before the experiments. Golden pompano snout (GPS) cells, kindly provided by Professor Qin, were cultured in L-15 medium [containing $10\%$ fetal bovine serum (FBS, Gibco), 100 U/ml penicillin, and 100 U/ml streptomycin] at 26°C [32]. Human embryonic kidney (293T) cells were incubated in Dulbecco’s modified Eagle’s medium with $10\%$ FBS and incubated at 37°C ($5\%$ CO2 incubator).
## Pathogenic bacteria and challenge experiment
V. harveyi that was isolated by our laboratory from golden pompano was used as the pathogen and cultured in Luria–Bertani (LB) medium (containing 100 μg/ml ampicillin, Amp) at 30°C [31]. The suspension was diluted to 3 × 107 colony-forming units (CFU)/ml when the OD600 value reached approximately 0.6. The fish were intraperitoneally injected with 0.1 ml of the suspension, and the same volume of phosphate-buffered saline (PBS) was injected as the control. The liver, spleen, and head kidney were collected at 6, 9, 12, and 24 h post-infection (hpi). Three separate samples were prepared as well.
## Quantitative real-time PCR
Total RNA of tissues and cells was extracted using E.A.N.A. Total RNA Extraction Kit (OMEGA, USA) and digested with DNase (OMEGA, USA). cDNAs were synthesized using Eastep® RT Master Mix Kit (Promega, USA). qRT-PCR was performed to quantify the target gene mRNA level using SYBR ExScript qRT-PCR Kit (Promega, China). Beta-2-microglobulin was used as the housekeeping gene, and data were analyzed by the 2-ΔΔCT method [33]. The primers used are listed in Supplementary Table S1.
## Gene cloning and analysis
The full open reading frame of the TroIGFBP5b sequence was amplified with primers TroIGFBP5b-F1/TroIGFBP5b-R1 by using T. ovatus liver cDNA as the template. The TroIGFBP5b sequence was blasted at the National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov/blast). The structural domain was predicted at SMART online (http://smart.embl-heidelberg.de/). The three-dimensional (3D) structure prediction of TroIGFBP5b was carried out on the SWISS-MODEL website, and the visualization of the predicted protein 3D structure was achieved using PyMOL software. The phylogenetic tree constructed by MEGA 7.0 employed the neighbor-joining (NJ) method.
## Plasmid construction and small interfering RNA synthesis
The TroIGFBP5b sequence, except for the signal peptide (SP) domain, was amplified with primer TroIGFBP5b-F2/TroIGFBP5b-R1, using the pEASY-T-TroIGFBP5b plasmid as a template by PCR, and named TroIGFBP5b-ΔSP. Subsequently, two HBM-deleted mutants (215RKGFFKRKQCKPSRGRKR232 to 215RKGFFPSRGRKR226, delete 220KRKQCK225) of TroIGFBP5b were amplified with full-length TroIGFBP5b or TroIGFBP5b-ΔSP using the primer pairs TroIGFBP5b-F1/TroIGFBP5b-R3 or TroIGFBP5b-F2/TroIGFBP5b-R3 and TroIGFBP5b-F3/TroIGFBP5b-R1 by overlap PCR assay and named TroIGFBP5b-ΔHBM and TroIGFBP5b-Δ(HBM+SP), respectively.
To construct a eukaryotic expression vector for overexpressing IGFBP5 in vivo, TroIGFBP5b and TroIGFBP5b-ΔHBM were inserted into the pCN3 vector, which expresses the human cytomegalovirus immediate-early promoter, at the EcoR V site, resulting in pTroIGFBP5b and pTroIGFBP5b-ΔHBM [34]. pEGFPX-N3 was used for subcellular localization and was reformed from the pEGFP-N3 vector [35]. Recombinant GFP plasmids were constructed by connecting TroIGFBP5b, TroIGFBP5b-ΔSP, TroIGFBP5b-ΔHBM, and TroIGFBP5b-Δ(HBM+SP) to pEGFPX-N3 at the SmaI site, resulting in pTroIGFBP5b-WT-N3, pTroIGFBP5b-ΔHBM-N3, pTroIGFBP5b-ΔSP-N3, and pTroIGFBP5b-Δ(HBM+SP)-N3, respectively. To obtain biologically active recombinant TroIGFBP5b-ΔSP and TroIGFBP5b-Δ(HBM+SP) proteins, pET-32a, which could express a His-tag and a thioredoxin protein (Trx), was used and linearized at the EcoRV site. All of the above-mentioned positive constructs were confirmed by colony PCR and sequencing. The plasmids used in the cell-related experiments, as well as those injected into the fish body, were endotoxin-free plasmids harvested using Plasmid Extraction Kit (TransGen, China) according to the supplier’s instructions.
The siRNA synthesis followed the instructions of RiboMAX™ Express RNAi System (Promega, USA) as described [36]. Briefly, two pairs of primers, siTroIGFBP5b-P1/siTroIGFBP5b-P2 and siTroIGFBP5b-P3/siTroIGFBP5b-P4, were designed to obtain two DNA oligonucleotides after incubation at 95°C for 5 min. The templates were allowed to cool slowly to room temperature (RT). Next, these two DNA oligonucleotides were used to separately synthesize the sense strand RNA or the antisense strand RNA templates at 37°C for 2 h. Afterwards, the DNA template was removed from the separate short RNA strands by digestion with DNase, and then the two RNA strands were mixed to synthesize the siRNA. Finally, the synthesized siRNA was purified following the manufacturer’s instructions. The control siRNA (siTroIGFBP5b-C) was synthesized with siTroIGFBP5b- C-P1/P2 and siTroCCL4-C-P3/P4 as described above. The primers used in this study are listed in Supplementary Table S1.
## In vivo overexpression and knockdown of TroIGFBP5b
To evaluate the in vivo role of TroIGFBP5b, the fish were intramuscularly injected with 15 μg (0.1 ml) overexpression plasmids (pTroIGFBP5b and pCN3). The knockdown of TroIGFBP5b was achieved by an intramuscular injection of 15 μg siTroIGFBP5b or siTroIGFBP5b-C into the fish. For these experiments, 0.1 ml PBS was injected as a control. To further study the function of HBM on bacterial infection in vivo, the pTroIGFBP5b-ΔHBM plasmid was also injected into the fish as described above, and the pTroIGFBP5b, pCN3, and PBS groups were repeated to compare differences.
## In vivo antibacterial ability assay
After the post-injection of overexpressing plasmids for 5 days and the post-injection of siRNA for 12 h, 0.1 ml of V. harveyi (2 × 107 CFU/ml) suspension was injected into all groups intraperitoneally. The appropriate size of the liver, spleen, and head kidney tissue blocks was determined aseptically at 6, 9, and 12 hpi. The weighed tissue blocks were ground in 300 µl PBS, and 700 µl PBS was added. In total, 100 µl homogenate was spread evenly onto LB plates (containing 100 μg/ml Amp) to measure the bacterial loads of the different tissues. Finally, the number of colonies per gram was calculated.
## Subcellular localization
GPS cells were cultivated to analyze the subcellular localization of TroIGFBP5b according to the method of Chen et al. [ 33]. Briefly, before transfection, GPS cells were grown in six-well plates overnight until reaching $60\%$ confluence. pTroIGFBP5b-WT-N3, pTroIGFBP5b-ΔHBM-N3, pTroIGFBP5b-ΔSP-N3, pTroIGFBP5b-Δ(HBM+SP)-N3, and pEGFPX-N3 (4 μg) were transfected into GPS cells using LipoFiter3.0 (Hanbio, Shanghai, China). At 48 h post-transfection, the cells were fixed for 15 min with $4\%$ (v/v) paraformaldehyde at RT. 4′,6-Diamidino-2-phenylindole (1 μg/ml) was used to stain the nuclei for 20 min, and rhodamine B was used to highlight the whole cell. Finally, the cells were washed with PBS until the cleaning solution became colorless, and the cells were observed using an inverted microscope. To monitor the localization of TroIGFBP5b after stimulation, V. harveyi (1 × 105 CFU/ml) or lipopolysaccharide (LPS; 100 ng/ml) was added to the transfected cells (TroIGFBP5b-WT and TroIGFBP5b-ΔHBM) for 4 h after transfection for 48 h. The ensuing steps were the same as those described above.
## Protein expression and purification
Recombinant TroIGFBP5b (rTroIGFBP5b), HBM mutant (rTroIGFBP5b-ΔHBM), and rTrx were purified as described in a previous report [33]. After exploring the induction conditions, the optimum induction condition was determined by incubating at 20°C for 8 h after adding isopropyl β-d-1-thiogalactopyranoside (0.5 mM). After purification by a Ni Sepharose column, PBS was used to dialyze the recombinant proteins which were concentrated with PEG8000. The concentration of the purified recombinant protein was measured by the Bradford method.
## Phagocytic activity detection of head kidney macrophages through flow cytometry
T. ovatus head kidneys were collected and rinsed with PBS three times aseptically. Density gradient centrifugation was used to obtain head kidney macrophages (HKMs) from T. ovatus as previously described with Percoll (GE Healthcare, USA) [37]. The isolated cells were added to a six-well plate (1 × 107 cells/well) containing L-15 medium for 2 h, and then 100 μl each of 200 μg/ml rTroIGFBP5b, rTroIGFBP5b-ΔHBM, and rTrx was infused into each well; the mixture was incubated at 26°C overnight. Green fluorescent microspheres (Aladdin, China) were diluted to $1\%$ (w/v) L-15 (containing $10\%$ FBS) medium and then added to each well at 26°C in the dark for 2 h. Before being subjected to Guava easyCyte™ Flow Cytometer (Millipore, USA), the cells were washed and resuspended in L-15 to 1 × 109 cells/ml. The experiment was performed in triplicate.
## Proliferation detection of head kidney lymphocytes by CCK-8 assay
T. ovatus head kidneys were collected and rinsed aseptically with PBS three times. Density gradient centrifugation was used to obtain head kidney lymphocytes (HKLs) from T. ovatus as previously described by Percoll (GE Healthcare, USA) [36]. The prepared HKLs resuspended in L-15 medium with $10\%$ FBS were added (90 μl) to a 96-well culture plate (1 × 105 cells/well). Subsequently, 10 μl of rTroIGFBP5b and rTroIGFBP5b-ΔHBM (final concentrations of 10, 50, 100, 200, and 300 μg/ml) or rTrx (300 μg/ml) was incubated for 12 h at 26°C, and PBS was added as a control. As per the instructions, Cell Counting Kit-8 (CCK8) (Hanbio, Shanghai, China) was applied to measure the proliferation of HKLs. The results were calculated as follows: (A 450 of protein-treated cells − A 450 of the empty well)/(A 450 of control cells − A 450 of the empty well). The empty well contained only medium and CCK-8 solution. The experiment was repeated in triplicate.
## Immunofluorescence microscopy assay
293T cells were transfected with pTroIGFBP5b, pTroIGFBP5b-ΔHBM, and pCN3 as described above. The immunofluorescence microscopy assay was performed as previously described [38]. The primary antibody used was an anti-p65 polyclonal antibody (Bioss, Beijing, China) at $\frac{1}{200}$ dilution.
## Western blot assay
GPS cells were transfected with pTroIGFBP5b-WT-N3, pTroIGFBP5b-ΔHBM-N3, pTroIGFBP5b-ΔSP-N3, pTroIGFBP5b-Δ(HBM+SP)-N3, or pEGFPX-N3 in 10-cm-diameter culture dishes. Nuclear and Cytoplasmic Extraction Reagent Kit (Beyotime, Beijing, China) was used to separately extract nuclear and cytoplasmic proteins. After protein separation by $15\%$ SDS-PAGE and transfer to a PVDF membrane (Millipore, Germany), the membrane was blocked with $5\%$ BSA for 1 h. Then, the membrane was incubated with anti-EGFP ($\frac{1}{2}$,000 dilution, Bioss, Beijing, China) at 4°C overnight. After 10 min of washing with TBST three times, the secondary antibody (HRP-conjugated goat anti-mouse IgG and $\frac{1}{2000}$ dilution) was added and incubated for 1 h at RT. Anti-β tubulin and anti-Histone H3 (Bioss, Beijing, China) were used as the nuclear and cytoplasmic internal references, respectively. The experiment was performed in triplicate.
## Dual-luciferase reporter assay
A DLR assay was performed to examine the activation of NF-κB. GPS and 293T cells (1 × 106 cells/well) were seeded in a 24-well plate. A total of 0.2 mg of NF-κB-specific firefly luciferase reporter vector, the pGL4.32 vector (luc2P/NF-κB, Promega, USA), 0.05 mg of pRL-CMV (a control vector), and 0.2 mg of pTroIGFBP5b or pTroIGFBP5b-ΔHBM were co-transfected into cells using LipoFiter3.0 (Hanbio, Shanghai, China). After transfection for 48 h, the firefly and Renilla luciferase activities in the cell lysates were measured using Dual-Luciferase Reporter Assay Kit (Promega, USA). All experiments were conducted three times independently.
## Statistical analysis
All data in this study were statistically processed using GraphPad Prism (version 8.0.2). Statistically significant differences were evaluated by the t-test, with the P-value indicated (*$P \leq 0.05$, **$P \leq 0.01$).
## Sequence characterization of TroIGFBP5b
TroIGFBP5b is 801 bp in length and encodes 266 amino acids (a.a.) ( NCBI GenBank accession number OP712620). According to SMART prediction analysis, TroIGFBP5b is composed of a signal peptide (SP) (1–20 a.a.), an insulin growth factor-binding (IB) domain (23–100 a.a.), and a thyroglobulin type I repeat (TY) domain (210–261 a.a.) ( Figure 1A). In addition, TroIGFBP5b contains a highly conserved HBM (220–226 a.a.) in the NLS sequence (215–232 a.a.) of the C-terminal domain (Figure 1B). The sequence alignment analysis showed that the IB and TY domains of TroIGFBP5b were very highly conserved among vertebrates, including mammals (Homo sapiens and Mus musculus), amphibians (Xenopus tropicalis), reptiles (Chelonia mydas), avians (Gallus gallus), and teleosts (D. rerio), suggesting its conservation during species evolution. The N-domain contains 12 conserved cysteine residues, and the C-domain contains six, which contributed to the structural stability by intradomain disulfide bonds between cysteine residues (Figure 1C). From the 3D predicted structure, HBM is located in the cavity formed by the C- and N- termini (Figure 1D). Identities with mammals, avians, reptiles, and amphibians are relatively lower, ranging from $51.66\%$ to $58.74\%$. According to the multiple sequence alignment analysis, identities vary in teleosts, ranging from $60.15\%$ to $97.38\%$. TroIGFBP5b shows a high identity with IGFBP5b in Seriola dumerili ($97.38\%$), Lates calcarifer ($94.91\%$), and *Larimichthys crocea* ($92.54\%$) (Table 1). A phylogenetic tree was constructed using the NJ algorithm, showing that TroIGFBP5b clusters with IGFBP5b of other teleosts and resembles Lates calcarifer IGFBP5b, the closest phylogenetically, with a bootstrap value of 92 (Figure 1E).
**Figure 1:** *Predicted domains, multiple alignments, and phylogenetic tree for TroIGFBP5b amino acid sequence. (A) The domains were predicted by SMART. The red box was a signal peptide (SP) as detected by the SignalP v4.0 program. (B) The TroIGFBP5b sequence in the background shown in gray was the SP, and the purple color indicated the heparin-binding motif (HBM) sequence. (C) Multiple sequence alignment analyses of TroIGFBP5b. The consensus and ≥$75\%$ identical residues were in black and pink among the aligned sequences. The putative SP, insulin growth factor-binding, and thyroglobulin type I repeat regions were marked by the yellow, green, and red boxes, respectively. The red triangles indicated the 18 conserved cysteine residues. (D) Surface view of the TroIGFBP5b 3D structure. The N-terminal and C-terminal were marked with green and blue colors, respectively, and the purple region represented the HBM. (E) Phylogenetic tree. The selected protein sequences are listed in
Table 1
. The newly characterized TroIGFBP5b was marked with an arrow.* TABLE_PLACEHOLDER:Table 1
## The expression profiles of TroIGFBP5b were regulated by V. harveyi infection
The expression pattern of TroIGFBP5b in T. ovatus was detected in 11 tissues using qRT-PCR: heart, stomach, brain, liver, skin, head kidney, intestine, spleen, gills, blood, and muscle. The results showed that it was the lowest in the muscle, set as 1. In contrast to the set criterion of muscle, the expression levels of the other tissues ranking from high to low were as follows: liver (134.44-fold), spleen (33.19-fold), brain (30.43-fold), gill (27.51-fold), head kidney (17.69-fold), intestine (17.18-fold), stomach (12.67-fold), skin (10.12-fold), blood (5.51-fold), and heart (5.11-fold) (Figure 2A). The results for the expression of TroIGFBP5b during V. harveyi infection in the three main immune organs all displayed a significant enhancement with a similar expression pattern, which tended to decrease after an initial increase. The time points at which the peaks appeared were different. In the liver, the expression peak was at 9 hpi with a 6.86-fold change, and it was also at 9 hpi with a 4.64-fold change in the head kidney. For the spleen, the highest expression (10.93-fold) was observed at 12 hpi (Figure 2B).
**Figure 2:** *Relative mRNA expression levels of TroIGFBP5b. (A) The mRNA expressions of TroIGFBP5b were detected in 11 tissues using qRT-PCR, and the muscle was set as 1. Groups with the same letters are not significantly different from each other (P < 0.05). (B) The IGFBP5 expressions of different times infected with V. harveyi in the liver, spleen, and head kidney were determined by qRT-PCR, and phosphate-buffered-saline-injected group—as the control—was set as 1. Beta-2-microglobulin was used as a reference gene in normalizing. Data were presented as means ± SD (N, number of fish used; N = 3). *P < 0.05, **P < 0.01.*
## In vivo antibacterial ability after TroIGFBP5b overexpression and knockdown
To explore the function of TroIGFBP5b in response to bacterial infection, TroIGFBP5b was overexpressed in vivo by injection with pTroIGFBP5b, pCN3, or PBS (as a control). On the 5th day after the plasmid injection, the expression levels of TroIGFBP5b in the liver, spleen, and head kidney of fish treated with pTroIGFBP5b were significantly higher than in the control using qRT-PCR analysis, indicating that the overexpression of TroIGFBP5b was successful (Supplementary Figure S2). In the liver of the pTroIGFBP5b overexpression group, the bacterial load decreased by 1.7- and 1.85-fold compared with that of the control at 9 and 12 hpi, respectively. In the spleen, it was decreased by 1.05-fold and 2.23-fold in the pTroIGFBP5b group compared with the control group at the two time points. Furthermore, the pTroIGFBP5b group had an approximately 1.46- and 1.67-fold reduction in head kidney bacterial load at 9 and 12 hpi, respectively (Figure 3A).
**Figure 3:** *Antibacterial ability after TroIGFBP5b overexpression and knockdown. The bacteria colony counts in the liver, spleen, and head kidney were detected after the overexpression plasmid was injected for 5 days (A) and siRNA was injected for 12 h (B). Data were shown as means ± SD (N = 3), and the statistical significance was indicated. *P < 0.05, **P < 0.01.*
In vivo siRNA technology was used to further analyze the effect of TroIGFBP5b against pathogen infection. The qRT-PCR analysis showed that the expression levels of TroIGFBP5b in the liver, spleen, and head kidney of fish treated with pTroIGFBP5b were significantly decreased than in the control group using qRT-PCR analysis, indicating that the knockdown of TroIGFBP5b was successful after siRNA injection for 12 h (Supplementary Figure S3). After being challenged by V. harveyi, the liver bacterial counts were approximately 1.90-, 1.50-, and 1.55-fold higher in the siTroIGFBP5b-injected group at 6, 9, and 12 hpi than in the control group, respectively. On the other hand, at 6, 9, and 12 hpi, the splenic bacterial load after siTroIGFBP5b injection was approximately 1.97-, 1.87-, and 1.69-fold higher than that in the control group. In the head kidney, the bacterial loads increased by 1.63-, 1.54-, and 1.94-fold compared with the control at 6, 9, and 12 hpi, respectively (Figure 3B).
## Subcellular localization of TroIGFBP5b WT and HBM- and SP-deficient mutants
As mentioned above, TroIGFBP5b contains an SP sequence and an HBM in its NLS domain. To explore the role of these two motifs in the subcellular localization characteristics of TroIGFBP5b, recombinant plasmids containing TroIGFBP5b wild type, HBM-deleted TroIGFBP5b, SP-deleted TroIGFBP5b, and both HBM- and SP-deleted TroIGFBP5b were constructed based on pEGFPX-N3, which were named TroIGFBP5b-WT, TroIGFBP5b-ΔHBM, TroIGFBP5b-ΔSP, and TroIGFBP5b-Δ(HBM+SP), respectively (Figure 4A). GPS cells were transfected using LipoFiter3.0. The inverted fluorescence microscopy results showed IGFBP5-WT and IGFBP5-ΔHBM to be mainly observed in the cytoplasm, indicating that TroIGFBP5b might be localized in the cytoplasm of GPS cells. While in the absence of the SP sequence its localization changed, the main localization was transferred from the cytoplasm to the nucleus (Figure 4B). The protein level displayed by the green fluorescence was consistent with those observed by fluorescence microscopy (Figure 4C).
**Figure 4:** *Subcellular localization of TroIGFBP5b and its mutants in golden pompano snout (GPS) cells. (A) Schematic diagrams of the structural domain comparison between the TroIGFBP5b wild type (TroIGFBP5b-WT), heparin-binding motif (HBM)-deleted TroIGFBP5b (IGFBP5-ΔHBM), signal peptide (SP)-deleted TroIGFBP5b (IGFBP5-ΔSP), and both HBM- and SP-deleted TroIGFBP5b [IGFBP5-Δ(HBM+SP)]. Different colors represented different structural domains. (B) GPS cells transfected with pTroIGFBP5b-N3 and mutants were observed under fluorescence microscopy. Bar = 20 μm. (C) Western blot analysis of the nuclear and cytoplasmic protein extracted from the cells mentioned above.*
## IGFBP5-ΔHBM altered the intracellular actions of TroIGFBP5b in vitro
Although the subcellular localization of IGFBP5-WT and IGFBP5-ΔHBM did not seem different through observation, we wondered if the HBM deficiency would affect the subcellular localization under pathogen stimulation. To explore this postulate, GPS cells transfected with TroIGFBP5b-WT and TroIGFBP5b-ΔHBM were treated with LPS or V. harveyi. According to the results, green fluorescence could be observed in the nucleus after stimulation in the TroIGFBP5b-WT transfected cells. However, the cells transfected with TroIGFBP5b-ΔHBM did not undergo such transfer, suggesting that the deletion of HBM would result in the loss of nuclear transfer reaction in response to stimulation (Figure 5A). We also evaluated the green fluorescence at the protein level, and the results were in agreement with the results mentioned above (Figure 5B).
**Figure 5:** *IGFBP5-ΔHBM subcellular localization after stimulations. (A) After being transfected with pTroIGFBP5b-WT-N3 and pTroIGFBP5b-ΔHBM-N3 for 48 h, golden pompano snout cells were stimulated with lipopolysaccharide and V. haveyi. Bar = 20 μm. (B) The Western blot analysis of the nuclear and cytoplasmic protein extracted from the cells mentioned above was stimulated by V. haveyi for 4 h.*
## In vitro effects on the lymphocytes and macrophages of rTroIGFBP5b and rTroIGFBP5b-ΔHBM
Since TroIGFBP5b was involved in defense against the pathogen in vivo, we wonder whether TroIGFBP5b had any effect on immune cell activities and if HBM contributed to the processes. To prove this opinion, rTroIGFBP5b, rTroIGFBP5b-ΔHBM, and rTrx (control) were purified, and the lymphocytes and macrophage cells were extracted from the head kidney. According to the CCK-8 assay results, rTroIGFBP5b enhanced HKL proliferation in a dose-dependent manner, and it was shown that 200 mg/ml exhibited the best effect, compared with the cells incubated with PBS and rTrx, while rTroIGFBP5b-ΔHBM had no effects on HKL proliferation (Figure 6A). From the results derived by checking using a flow cytometer, it was shown that the phagocytosis rate of the rTroIGFBP5b group was extremely higher than that in the rTrx and rTroIGFBP5b-ΔHBM cells, while the phagocytic activity of those cells that were treated with rTroIGFBP5b-ΔHBM was comparable with that of the rTrx-treated group (Figure 6B).
**Figure 6:** *Effect of TroIGFBP5b and heparin-binding motif mutant recombinant proteins on the proliferation of head kidney lymphocytes (HKLs) and phagocytosis of head kidney macrophages (HKMs). (A) The proliferation of HKLs was examined by CCK8 assay after incubation with different concentrations of rTroIGFBP5b or rTroIGFBP5b-ΔHBM. (B) The phagocytic activity of HKMs treated with recombinant proteins was examined with a flow cytometer after incubating with fluorescent microsphere for 2 h Values are shown as means ± SD (N = 3). N, number of times the experiment was performed. The statistical significance was indicated (*P < 0.05, **P < 0.01).*
## TroIGFBP5b overexpression could activate the NF-κB pathway, while TroIGFBP5b-ΔHBM could not
A dual-luciferase reporter assay was performed to determine the role of TroIGFBP5b in the NF-κB pathway. It was found that the overexpression of TroIGFBP5b significantly activated the NF-κB luciferase reporter activity in a dose-dependent manner, while the activating effects on the NF-κB signaling did not occur in the TroIGFBP5b-ΔHBM overexpression cells in both 293T and GPS cells (Figures 7A, B). This indicated that HBM was important in TroIGFBP5b activation to the NF-κB pathway. The immunofluorescence staining results proved that TroIGFBP5b overexpression promoted the nuclear translocation of p65 in 293T cells. However, this p65 translocation was blocked by overexpressing TroIGFBP5b-ΔHBM in 293T cells (Figure 7C). However, due to the poor specificity of the p65 antibody in GPS cells, we did not achieve satisfactory results (data not shown). These results demonstrated that the HBM of TroIGFBP5b seemed to function as a key for activating the NF-κB pathway.
**Figure 7:** *Effects of TroIGFBP5b overexpression on the activity of NF-κB pathways in vitro. (A) pTroIGFBP5b (50, 150, 200, and 250 ng/well) was co-transfected with pGL4.32 (Luc2P/NF-κB) and pRL-CMV into 293T cells incubated in a 24-well cell plate. (B) pTroIGFBP5b (200 ng/well) and pTroIGFBP5b-ΔHBM (200 ng/well) were co-transfected with 200 ng/well pGL4.32 (Luc2P/NF-kB) and 50 ng/well pRL-CMV into 293T and golden pompano snout cells incubated in a 24-well cell plate, respectively. After 48 h post-transfection, firefly and renilla luciferase activities were detected in the cell lysates. The data were shown as means ± SD (N = 3). N, number of times the experiment was performed. (C) 293T cells transfected with pCN3, pTroIGFBP5b, and pTroIGFBP5b-ΔHBM were stained with anti-p65 antibody and AlexaFluor-488. Bar = 30 μm. The experiment was done in triplicate, and one of them was displayed. The statistical significance was indicated (*P < 0.05, **P < 0.01).*
## IGFBP5-ΔHBM lost the antibacterial and activated immune-related gene abilities in vivo
Since the HBM sequence could affect the nuclear transfer of TroIGFBP5b protein in response to pathogenic stimulation, we further investigated whether it could affect its antimicrobial activity in vivo. Taking the same approach as before, the fish were injected with overexpression plasmids pTroIGFBP5b-WT, pTroIGFBP5b-ΔHBM, pCN3, or PBS (control), respectively. After having been infected with V. harveyi for 9 and 12 h, the bacterial loads in the liver, spleen, and head kidney of the pTroIGFBP5b-ΔHBM group were all significantly higher than that in the pTroIGFBP5b group. In the head kidney, the bacterial load in the mutant group was not any different from that of the control group, while in the liver the bacterial load was lower than the control at 9 and 12 hpi, and in the spleen, it was shown to be lower at 12 hpi (Figure 8A).
**Figure 8:** *In vivo antibacterial ability of IGFBP5-ΔHBM. (A)
T. ovatus, which was injected with pTroIGFBP5b, pTroIGFBP5b-ΔHBM, pCN3, and phosphate-buffered saline (control) for 5 days, was infected with V. harveyi; then, the bacterial load in the tissues was determined. Effect of TroIGFBP5b and TroIGFBP5b-ΔHBM on the expressions of proinflammatory cytokines in the liver (B), spleen (C), and head kidney (D) at 5 days after the injection of pTroIGFBP5b and pTroIGFBP5b-ΔHBM. The data were shown as means ± SD (N = 3), and the statistical significance was indicated (*P < 0.05, **P < 0.01).*
Furthermore, the in vivo function mechanism after the overexpression of TroIGFBP5b and TroIGFBP5b-ΔHBM was figured out by examining the expression of immune-related genes using qRT-PCR. According to the results, TroIGFBP5b overexpression could significantly raise the expression level of the selected immune-related genes (p65, IKBα, IL-8, IL-10, IL-1β, and TNF-α). TroIGFBP5b-ΔHBM could also affect some genes compared with pCN3, such as TNF-α, IKBα, IL-8, and IL-1β in the liver and IL-8 and IL-1β in the spleen. However, compared with the TroIGFBP5b group, the upregulated effect on those genes was significantly decreased in TroIGFBP5b-ΔHBM. To sum up, the upregulated expression of immune-related genes induced by TroIGFBP5b was practically shut down in the absence of HBM (Figures 8B–D).
## Discussion
IGF is instrumental in growth regulation, development, and immune response [1, 4, 39]. Meanwhile, IGFBPs have crucial roles due to their high binding affinity to IGFs [3, 40]. In this study, a teleost IGFBP5 of golden pompano, TroIGFBP5b, was cloned, and its expression and biological properties were analyzed accordingly.
Similar to other members of the IGFBP family, TroIGFBP5b was found to have a highly conserved structure containing N- and C-terminal domains, suggesting that IGFBP5 might present as highly conservative during structural and functional evolution [8, 16, 29, 41]. Similar to other reported IGFBP5, the NLS sequence of TroIGFBP5b also included a putative classical HBM (206KRKQCK211), which might be critical in the diverse functions of IGFBP5 (26–28, 42). Due to the teleost-specific whole-genome duplication, some fish were reported to retain two copies of IGFBP5 (IGFBP5a and IGFBP5b), such as zebrafish, grass carp, and Atlantic salmon [8, 16, 17]. In this study, the phylogenetic tree results suggested that TroIGFBP5b clustered with IGFBP5b of other teleosts and had the closest phylogenetic relationship with Lates calcarifer IGFBP5b. This indicated that IGFBP5 was an evolutionarily conserved protein, and the duplication of the IGFBP5a/b subfamily probably occurred during fish evolution from a genome duplication event. There might be TroIGFBP5ba in the golden pompano genome waiting for us to discover.
A number of studies have reported the tissue-specific expression of IGFBP5. According to these reports, IGFBP5 has been identified in multiple tissues and different types of cells not only in humans and mice (13, 22, 43–46) but also in teleost and invertebrates [8, 16, 19, 47, 48]. In mammals, the transcription levels of other IGFBPs were usually more abundant in the liver, while IGFBP5 was different and was more abundant in the kidney [49, 50]. In teleosts, the duplicated IGFBP5 showed different expression patterns in the tested tissues. The IGFBP5b mRNA of zebrafish was detected in the brain, gill, eye, heart, gut, kidney, and gonad, while IGFBP5a was detected with a high level in the brain and gill but could not be detected in the liver and muscle [8]. Compared with the expression in grass carp, GcIGFBP5b was markedly present in the liver and brain as well as in the heart, skin, and muscles at low levels [16]. In the current study, TroIGFBP5b expression was most abundant in immune organs. The top five tissues with the highest expression level were the liver, spleen, brain, gill, and head kidney. In humans, IGFBP5 was closely related to many diseases, such as colorectal cancer, chronic rhinosinusitis, sarcopenia, and so on (51–55), and pathogen infection induced a significantly higher expression of IGFBP5 in mammals—for example, IGFBP5 expression was significantly upregulated after *Salmonella enterica* stimulation in pigs [56]. In our study, V. harveyi caused a significant induction of TroIGFBP5b in the liver, spleen, and head kidney. All of these suggested that TroIGFBP5b was involved in antimicrobial immunity.
Studies proved that IGFBP5 was a secreted protein, while it was also found in the nucleus to interact with nuclear proteins [23, 44, 57]. The nucleus IGFBP5 was detected in the breast cancer cell line (T47D), lung fibroblasts from idiopathic pulmonary fibrosis patients, and vascular smooth muscle cells [25, 41, 58]. Various studies also reported that the localization of IGFBP determines its roles, and the NLS domain was crucial for its subcellular location [24, 28, 58, 59]. In MDA-MB-435 cells (a kind of breast cancer cell), wild-type IGFBP5 could translocate to the nucleus and inhibit cell proliferation and migration; on the contrary, NLS-mutant was mainly detected in the cytoplasm and enhanced the proliferation and migration of cells [24]. In MCF-7 (breast) and LnCaP (prostate) cells, IGFBP2 possessed a functional NLS sequence, and the activation of VEGF expression and subsequent angiogenesis required the nuclear IGFBP2 [60]. HBMs are found in many secreted proteins and responsible for binding to heparan sulfate proteoglycans which take part in a variety of biological processes, including signal transduction, cell adhesion, and blood coagulation [61, 62]. Furthermore, the HBM in the NLS domain of IGFBP5 C-terminal domain was functional heparin binding motif [42]. In teleosts, studies on the relationship between localization and functions of IGFBP are very limited. In zebrafish, the subcellular localizations of IGFBP5a and IGFBP5b were different. IGFBP5a was only found in the nucleus, and IGFBP5b was found in both the nucleus and the cytoplasm [8]. Our results showed that TroIGFBP5b with SP was mostly in the cytoplasm, and mature TroIGFBP5b without SP was found in both the nucleus and the cytoplasm. In mammary epithelium, the overexpression of mature IGFBP5 resulted in nuclear localization, whereas upon expression of the secreted form, no nuclear localization was observed under physiological conditions [63]. Previous studies proved that the location of protein always affected its role. In the present study, TroIGFBP5b was transferred from the cytoplasm to the nucleus after being stimulated by LPS or V. harveyi, while the phenomenon of TroIGFBP5b-ΔHBM that was to be found in the cytoplasm did not occur. Similar findings were found in T47D breast cancer cells, which provided the necessary NLS residues for nuclear accumulation [23]. It was interesting that, in mammary epithelium cells, it was found that intracellular trafficking of IGFBP5 would be restricted to vesicular structures in the cytoplasm and not be uptaken into the nucleus unless the integrity of the plasma membrane was compromised [63]. In our results, TroIGFBP5b nuclear uptake occurred probably due to the cell membrane damage after LPS or bacteria stimulation, leading to the loss of membrane integrity [64]. Our results indicated that HBM plays a key role in the trigger of the TroIGFBP5b translocation to the nucleus upon stimulation.
In mammals, many studies revolved around the function of IGFBP5 in modulating cell migration and proliferation and in regulating immune processes [65]. The recombinant IGFBP5 could increase human RPE cell proliferation, promote periodontal tissue regeneration, and reduce local inflammation [43, 66, 67]. Multiple studies showed that IGFBP5 could directly affect inflammation mediated by immune cells and suggested that IGFBP5 exerts anti-inflammatory effects by maintaining immune homeostasis [14, 43]. In the current study, the biological properties of TroIGFBP5b were analyzed. The in vitro assays showed that rTroIGFBP5b enhanced the cell proliferation of PBLs in a dose-dependent manner and significantly enhanced HKM phagocytic activity, suggesting that rTroIGFBP5b could induce the activation of some immune cells. In contrast, rTroIGFBP5b-ΔHBM was significantly reduced in the activation of PBLs and HKMs. Furthermore, in vivo overexpression and knockdown experiments confirmed the role of TroIGFBP5b in fish disease resistance. However, after the HBM of TroIGFBP5b was deleted, its antibacterial ability after overexpression was inhibited. These results further confirmed the role of TroIGFBP5b in antibacterial immune response, and HBM played an important role in antibacterial immunity. However, the underlying mechanisms by which IGFBP and HBM participate in immune response remained an area that has not been thoroughly studied and thus needed more research. Relative reports suggested that the IGF system regulated the immune function and represented an important switch governing immune responses [68].
The NF-κB signaling pathway was the main regulator of inflammatory responses to pathogens, and p65, which belonged to the five NF-κB monomers, would translocate from the cytoplasm to the nucleus after activation [69, 70]. Nowadays, many studies found that the IGFBP family got its job done via the NF-κB signaling pathway, such as IGFBP2, which promotes PDAC cell invasion and metastasis through the NF-κB pathway [69]. In prostate cancer cells, rIGFBP-3 significantly suppressed the NF-κB activity [71]. IGFBP5 was proven to inhibit the phorbol myristate acetate-induced NF-κB activity and IL-6 expression in U-937 cells [72]. The novel findings in this study showed that, in both 293T and GPS cells, TroIGFBP5b showed a significant upregulation effect in NF-κB activity, while overexpression of TroIGFBP5b-ΔHBM did not. Similar results were also discovered in stimulating the transfer of p65 to the nucleus. On the other hand, activating the NF-κB pathway can regulate the expression of related genes, especially inflammatory cytokines (73–75). In our study, the in vivo analysis showed that, after the injection with pTroIGFBP5b, the mRNA transcriptions of NF-κB-related genes (p65 and ikBα) and several cytokines were significantly induced, including IL-8, IL-10, IL-1β, and TNF-α. However, after the HBM of TroIGFBP5b was deleted, its function of upregulating the expression of inflammatory cytokines in immune tissues was almost lost. Therefore, these results demonstrated that TroIGFBP5b could not only activate the NF-κB activity and p65 nuclear translocation but also increase the proinflammatory cytokine level, and this indicated that the HBM domain of TroIGFBP5b seemed to function a key role in these processes.
To sum up, the TroIGFBP5b was cloned and identified in this study. TroIGFBP5b was expressed higher in vivo in some immune-related tissues and showed a significant upregulated response after the bacterial infection. Overexpressing TroIGFBP5b could improve the body’s antibacterial immunity significantly. In contrast, this ability was decreased after its knockdown. An HBM-deficient mutation of TroIGFBP5b was constructed to better understand the mechanism of its antibacterial immunity. The in vitro studies demonstrated that TroIGFBP5b could promote PBL proliferation, stimulated macrophage activation, induced the NF-κB promoter activity, and promoted the nuclear translocation of p65, while the HBM mutant, compared with the wild type, failed to function with those abilities. Overall, the results showed that TroIGFBP5b was essential in the antimicrobial immunity of golden pompano and that HBM was also of great importance in the NF-κB pathway activation.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material.
## Ethics statement
The animal study was reviewed and approved by Animal Care and Use Committee of the Hainan University. Written informed consent was obtained from the owners for the participation of their animals in this study.
## Author contributions
HD and YZ conceived and designed the experiments and wrote the manuscript draft. XD, PZ, and ZC performed the experiments and analyzed the data. YS was responsible for forming the hypothesis, project development, data coordination, and writing, finalizing, and submitting the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1126843/full#supplementary-material
## References
1. Song F, Zhou XX, Hu Y, Li G, Wang Y. **The roles of insulin-like growth factor binding protein family in development and diseases**. *Adv Ther* (2021) **38** 885-903. DOI: 10.1007/s12325-020-01581-x
2. Kiepe D, Ulinski T, Powell DR, Durham SK, Mehls O, Tonshoff B. **Differential effects of insulin-like growth factor binding proteins-1, -2, -3, and -6 on cultured growth plate chondrocytes**. *Kidney Int* (2002) **62**. DOI: 10.1046/j.1523-1755.2002.00603.x
3. Nguyen DV, Li Calzi S, Shaw LC, Kielczewski JL, Korah HE, Grant MB. **An ocular view of the IGF-IGFBP system**. *Growth Horm IGF Res* (2013) **23** 45-52. DOI: 10.1016/j.ghir.2013.03.001
4. Firth SM, Baxter RC. **Cellular actions of the insulin-like growth factor binding proteins**. *Endocrine Rev* (2002) **23**. DOI: 10.1210/er.2001-0033
5. Baxter Robert C. **Insulin-like growth factor (IGF)-binding proteins: interactions with IGFs and intrinsic bioactivities**. *Am J Physiol* (2000) **278**. DOI: 10.1152/ajpendo.2000.278.6.E967
6. Duan C, Allard JB. **Insulin-like growth factor binding protein-5 in physiology and disease**. *Front Endocrinol (Lausanne)* (2020) **11**. DOI: 10.3389/fendo.2020.00100
7. Andress DL, Birnbaum RS. **A novel human insulin-like growth factor binding protein secreted by osteoblast-like cells**. *Biochem Biophys Res Commun* (1991) **176**. DOI: 10.1016/0006-291x(91)90911-p
8. Wei D, Hiroyasu K, Yang Z, Jun D, Zhou D, Cunming D. **Duplicated zebrafish insulin-like growth factor binding protein-5 genes with split functional domains: evidence for evolutionarily conserved IGF binding, nuclear localization, and transactivation activity**. *FASEB J* (2010) **24**. DOI: 10.1096/fj.09-149435
9. Allander SV, Coleman M, Luthman H, Powell DR. **Chicken insulin-like growth factor binding protein (IGFBP)-5: conservation of IGFBP-5 structure and expression during evolution**. *Comp Biochem Physiol Part B Biochem Mol Biol* (1997) **116** 477. DOI: 10.1016/s0305-0491(96)00289-1
10. Rehman MS, Mushtaq M, Hassan FU, Zia-Ur R, Mushahid M, Shokrollahi B. **Comparative genomic characterization of insulin-like growth factor binding proteins in cattle and buffalo**. *BioMed Res Int* (2022) **2022**. DOI: 10.1155/2022/5893825
11. Hiscott P, Sheridan C, Magee RM, Grierson I. **Matrix and the retinal pigment epithelium in proliferative retinal disease**. *Prog Retinal Eye Res* (1999) **18**. DOI: 10.1016/s1350-9462(98)00024-x
12. Simon CM RS, Gunnersen JM, Holtmann B, Drepper C, Dombert B, Braga M. **Dysregulated IGFBP5 expression causes axon degeneration and motoneuron loss in diabetic neuropathy**. *Acta Neuropathol* (2015) **130**. DOI: 10.1007/s00401-015-1446-8
13. Wang S, Hong Q, Geng X, Chi K, Cai G, Wu D. **Insulin-like growth factor binding protein 5–a probable target of kidney renal papillary renal cell carcinoma**. *BioMed Res Int* (2019) **2019** 1-10. DOI: 10.1155/2019/3210324
14. Zhu M, Han H, Hu L, Cao Y, Fan Z. **Insulin-binding protein-5 down-regulates the balance of Th17/Treg**. *Front Immunol* (2022) **13**. DOI: 10.3389/fimmu.2022.1019248
15. Liu D, Wang Y, Jia Z, Wang L, Wang J, Yang D. **Demethylation of IGFBP5 by histone demethylase KDM6B promotes mesenchymal stem cell-mediated periodontal tissue regeneration by enhancing osteogenic differentiation and anti-inflammation potentials**. *Stem Cells (Dayton Ohio)* (2015) **33**. DOI: 10.1002/stem.2018
16. Zheng GD, Zhou CX, Lin ST, Chen J, Jiang XY, Zou SM. **Two grass carp (**. *Comp Biochem Physiol B Biochem Mol Biol* (2017) **204** 69-76. DOI: 10.1016/j.cbpb.2016.11.008
17. Macqueen DJ, Garcia de la serrana D, Johnston IA. **Evolution of ancient functions in the vertebrate insulin-like growth factor system uncovered by study of duplicated salmonid fish genomes**. *Mol Biol Evol* (2013) **30**. DOI: 10.1093/molbev/mst017
18. Kamangar BB, Gabillard JC, Bobe J. **Insulin-like growth factor-binding protein (IGFBP)-1, -2, -3, -4, -5, and -6 and IGFBP-related protein 1 during rainbow trout postvitellogenesis and oocyte maturation: molecular characterization, expression profiles, and hormonal regulation**. *Endocrinology* (2006) **147**. DOI: 10.1210/en.2005-1570
19. Safian D, Fuentes EN, Valdés JA, Molina A. **Dynamic transcriptional regulation of autocrine/paracrine igfbp1, 2, 3, 4, 5, and 6 in the skeletal muscle of the fine flounder during different nutritional statuses**. *J Endocrinol* (2012) **214** 95-108. DOI: 10.1530/joe-12-0057
20. Garcia de la Serrana D, Macqueen DJ. **Insulin-like growth factor-binding proteins of teleost fishes**. *Front Endocrinol* (2018) **9**. DOI: 10.3389/fendo.2018.00080
21. Wood AW, Duan C, Bern HA. **Insulin-like growth factor signaling in fish**. *Int Rev Cytol* (2005) **243**. DOI: 10.1016/s0074-7696(05)43004-1
22. Assassi S, Su Y, Nishimoto T, Feghali-Bostwick C. **IGFBP-5 promotes fibrosis independently of its translocation to the nucleus and its interaction with nucleolin and IGF**. *PloS One* (2015) **10**. DOI: 10.1371/journal.pone.0130546
23. Schedlich LJ, Le Page SL, Firth SM, Briggs LJ, Jans DA, Baxter RC. **Nuclear import of insulin-like growth factor-binding protein-3 and -5 is mediated by the importin beta subunit**. *J Biol Chem* (2000) **275**. DOI: 10.1074/jbc.M002208200
24. Akkiprik M, Hu L, Sahin A, Hao X, Zhang W. **The subcellular localization of IGFBP5 affects its cell growth and migration functions in breast cancer**. *BMC Cancer* (2009) **9**. DOI: 10.1186/1471-2407-9-103
25. Yasuoka H, Hsu E, Ruiz XD, Steinman RA, Choi AM, Feghali-Bostwick CA. **The fibrotic phenotype induced by IGFBP-5 is regulated by MAPK activation and egr-1-dependent and -independent mechanisms**. *Am J Pathol* (2009) **175**. DOI: 10.2353/ajpath.2009.080991
26. Abrass CK, Berfield AK, Andress DL. **Heparin binding domain of insulin-like growth factor binding protein-5 stimulates mesangial cell migration**. *Am J Physiol* (1997) **273**. DOI: 10.1152/ajprenal.1997.273.6.F899
27. Shand JH, Beattie J, Song H, Phillips K, Kelly SM, Flint DJ. **Specific amino acid substitutions determine the differential contribution of the n- and c-terminal domains of insulin-like growth factor (IGF)-binding protein-5 in binding IGF-I**. *J Biol Chem* (2003) **278**. DOI: 10.1074/jbc.M300526200
28. Allan GJ, Tonner E, Szymanowska M, Shand JH, Kelly SM, Phillips K. **Cumulative mutagenesis of the basic residues in the 201-218 region of insulin-like growth factor (IGF)-binding protein-5 results in progressive loss of both IGF-I binding and inhibition of IGF-I biological action**. *Endocrinology* (2006) **147**. DOI: 10.1210/en.2005-0582
29. Beattie J, Allan GJ, Lochrie JD, Flint DJ. **Insulin-like growth factor-binding protein-5 (IGFBP-5): a critical member of the IGF axis**. *Biochem J* (2006) **395** 1-19. DOI: 10.1042/BJ20060086
30. Ni X, Li X, Ran G, Chen J, Jiang X, Sun J. **Determination of the geographical origin of**. *Food Chem* (2022) **394**. DOI: 10.1016/j.foodchem.2022.133457
31. Tu Z, Li H, Zhang X, Sun Y, Zhou Y. **Complete genome sequence and comparative genomics of the golden pompano (**. *PeerJ* (2017) **5**. DOI: 10.7717/peerj.4127
32. Yu Y, Wei S, Wang Z, Huang X, Huang Y, Cai J. **Establishment of a new cell line from the snout tissue of golden pompano trachinotus ovatus, and its application in virus susceptibility**. *J Fish Biol* (2016) **88**. DOI: 10.1111/jfb.12986
33. Chen Y, Zhou Y, Yang X, Cao Z, Chen X, Qin Q. **Insulin-like growth factor binding protein 3 gene of golden pompano (TroIGFBP3) promotes antimicrobial immune defense**. *Fish Shellfish Immunol* (2020) **103** 47-57. DOI: 10.1016/j.fsi.2020.04.002
34. Jiao XD, Zhang M, Hu YH, Sun L. **Construction and evaluation of DNA vaccines encoding**. *Vaccine* (2009) **27**. DOI: 10.1016/j.vaccine.2009.06.071
35. Diao Q, Du H, Zhao N, Wu Y, Du X, Sun Y. **Cathepsin C (CTSC) contributes to the antibacterial immunity in golden pompano**. *Fish Shellfish Immunol* (2022) **128**. DOI: 10.1016/j.fsi.2022.07.078
36. Sun B, Lei Y, Cao Z, Zhou Y, Sun Y, Wu Y. **TroCCL4, a CC chemokine of**. *Fish Shellfish Immunol* (2018) **86**. DOI: 10.1016/j.fsi.2018.11.080
37. Yun S, Sun L, Yolande R. **CsBAFF, a teleost b cell activating factor, promotes pathogen-induced innate immunity and vaccine-induced adaptive immunity**. *PloS One* (2015) **10**. DOI: 10.1371/journal.pone.0136015
38. Du H, Cao Z, Liu Z, Wang G, Wu Y, Du X. *Viruses* (2022) **14** 2184. DOI: 10.3390/v14102184
39. LeRoith D, Holly JMP, Forbes BE. **Insulin-like growth factors: Ligands, binding proteins, and receptors**. *Mol Metab* (2021) **52**. DOI: 10.1016/j.molmet.2021.101245
40. Waters JA, Urbano I, Robinson M, House CD. **Insulin-like growth factor binding protein 5: Diverse roles in cancer**. *Front Oncol* (2022) **12**. DOI: 10.3389/fonc.2022.1052457
41. Xu Q, Li S, Zhao Y, Maures TJ, Yin P, Duan C. **Evidence that IGF binding protein-5 functions as a ligand-independent transcriptional regulator in vascular smooth muscle cells**. *Circ Res* (2004) **94**. DOI: 10.1161/01.Res.0000124761.62846.Df
42. Song H, Shand JH, Beattie J, Flint DJ, Allan GJ. **The carboxy-terminal domain of IGF-binding protein-5 inhibits heparin binding to a site in the central domain**. *J Mol Endocrinol* (2001) **26**. DOI: 10.1677/jme.0.0260229
43. Yasuoka H, Yamaguchi Y, Feghali-Bostwick CA. **The pro-fibrotic factor IGFBP-5 induces lung fibroblast and mononuclear cell migration**. *Am J Respir Cell Mol Biol* (2009) **41**. DOI: 10.1165/rcmb.2008-0211OC
44. Schedlich LJ, Muthukaruppan A, O'Han MK, Baxter RC. **Insulin-like growth factor binding protein-5 interacts with the vitamin D receptor and modulates the vitamin D response in osteoblasts**. *Mol Endocrinol* (2007) **21**. DOI: 10.1210/me.2006-0558
45. Moralez AM, Maile LA, Clarke J, Busby WH, Clemmons DR. **Insulin-like growth factor binding protein-5 (IGFBP-5) interacts with thrombospondin-1 to induce negative regulatory effects on IGF-I actions**. *J Cell Physiol* (2005) **203**. DOI: 10.1002/jcp.20343
46. Schneider MR, Wolf E, Hoeflich A, Lahm H. **IGF-binding protein-5: flexible player in the IGF system and effector on its own**. *J Endocrinol* (2002) **172**. DOI: 10.1677/joe.0.1720423
47. Sharker MR, Kim SC, Hossen S, Kho KH. **Characterization of insulin-like growth factor binding protein-5 (IGFBP-5) gene and its potential roles in ontogenesis in the pacific abalone,**. *Biol (Basel)* (2020) **9** 216. DOI: 10.3390/biology9080216
48. Fan S, Wang Z, Yu D, Xu Y. **Molecular cloning and expression profiles of an insulin-like growth factor binding protein IGFBP5 in the pearl oyster,**. *J Appl Anim Res* (2018) **46**. DOI: 10.1080/09712119.2018.1517647
49. Schuller AG, Groffen C, van Neck JW, Zwarthoff EC, Drop SL. **cDNA cloning and mRNA expression of the six mouse insulin-like growth factor binding proteins**. *Mol Cell Endocrinol* (1994) **104** 57-66. DOI: 10.1016/0303-7207(94)90051-5
50. Shimasaki S, Shimonaka M, Zhang HP, Ling N. **Identification of five different insulin-like growth factor binding proteins (IGFBPs) from adult rat serum and molecular cloning of a novel IGFBP-5 in rat and human**. *J Biol Chem* (1991) **266**. DOI: 10.1016/S0021-9258(18)99272-0
51. Deng Y, Yang X, Hua H, Zhang C. **IGFBP5 is upregulated and associated with poor prognosis in colorectal cancer**. *Int J Gen Med* (2022) **15**. DOI: 10.2147/ijgm.S370576
52. Sanada F, Taniyama Y, Muratsu J, Otsu R, Shimizu H, Rakugi H. **IGF binding protein-5 induces cell senescence**. *Front Endocrinol (Lausanne)* (2018) **9**. DOI: 10.3389/fendo.2018.00053
53. Mueller SK, Nocera AL, Workman A, Libermann T, Dillon ST, Stegmann A. **Significant polyomic and functional upregulation of the PAPP-A/IGFBP-4/5/IGF-1 axis in chronic rhinosinusitis with nasal polyps**. *Int Forum Allergy Rhinol* (2020) **10**. DOI: 10.1002/alr.22512
54. Bayol S, Loughna PT, Brownson C. **Phenotypic expression of IGF binding protein transcripts in muscle,**. *Biochem Biophys Res Commun* (2000) **273**. DOI: 10.1006/bbrc.2000.2667
55. Spangenburg EE, Abraha T, Childs TE, Pattison JS, Booth FW. **Skeletal muscle IGF-binding protein-3 and -5 expressions are age, muscle, and load dependent**. *Am J Physiol Endocrinol Metab* (2003) **284**. DOI: 10.1152/ajpendo.00253.2002
56. Davis BL, Fraser JN, Burkey TE, Skjolaas KA, Dritz SS, Johnson BJ. **Oral inoculation with salmonella enterica serovar typhimurium or choleraesuis promotes divergent responses in the somatotropic growth axis of swine**. *J Anim Sci* (2010) **88**. DOI: 10.2527/jas.2009-2259
57. Amaar YG, Thompson GR, Linkhart TA, Chen ST, Baylink DJ, Mohan S. **Insulin-like growth factor-binding protein 5 (IGFBP-5) interacts with a four and a half LIM protein 2 (FHL2)**. *J Biol Chem* (2002) **277**. DOI: 10.1074/jbc.M110872200
58. Schedlich LJ, Young TF, Firth SM, Baxter RC. **Insulin-like growth factor-binding protein (IGFBP)-3 and IGFBP-5 share a common nuclear transport pathway in T47D human breast carcinoma cells**. *J Biol Chem* (1998) **273**. DOI: 10.1074/jbc.273.29.18347
59. Poreba E, Durzynska J. **Nuclear localization and actions of the insulin-like growth factor 1 (IGF-1) system components: Transcriptional regulation and DNA damage response**. *Mutat Res* (2020) **784**. DOI: 10.1016/j.mrrev.2020.108307
60. Azar WJ, Zivkovic S, Werther GA, Russo VC. **IGFBP-2 nuclear translocation is mediated by a functional NLS sequence and is essential for its pro-tumorigenic actions in cancer cells**. *Oncogene* (2014) **33**. DOI: 10.1038/onc.2012.630
61. Nesterenko AM, Orlov EE, Ermakova GV, Ivanov IA, Semenyuk PI, Orlov VN. **Affinity of the heparin binding motif of Noggin1 to heparan sulfate and its visualization in the embryonic tissues**. *Biochem Biophys Res Commun* (2015) **468**. DOI: 10.1016/j.bbrc.2015.10.100
62. Li JP, Kusche-Gullberg M. **Heparan sulfate: Biosynthesis, structure, and function**. *Int Rev Cell Mol Biol* (2016) **325**. DOI: 10.1016/bs.ircmb.2016.02.009
63. Jurgeit A, Berlato C, Obrist P, Ploner C, Massoner P, Schmolzer J. **Insulin-like growth factor-binding protein-5 enters vesicular structures but not the nucleus**. *Traffic* (2007) **8**. DOI: 10.1111/j.1600-0854.2007.00655.x
64. Nova Z, Skovierova H, Calkovska A. **Alveolar-capillary membrane-related pulmonary cells as a target in endotoxin-induced acute lung injury**. *Int J Mol Sci* (2019) **20** 831. DOI: 10.3390/ijms20040831
65. Ha YM, Nam JO, Kang YJ. **Pitavastatin regulates ang II induced proliferation and migration**. *Korean J Physiol Pharmacol* (2015) **19** 499-506. DOI: 10.4196/kjpp.2015.19.6.499
66. Samuel W, Kutty RK, Vijayasarathy C, Pascual I, Duncan T, Redmond TM. **Decreased expression of insulin-like growth factor binding protein-5 during n-(4-hydroxyphenyl)retinamide-induced neuronal differentiation of ARPE-19 human retinal pigment epithelial cells: regulation by CCAAT/enhancer-binding protein**. *J Cell Physiol* (2010) **224**. DOI: 10.1002/jcp.22191
67. Han N, Zhang F, Li G, Zhang X, Lin X, Yang H. **Local application of IGFBP5 protein enhanced periodontal tissue regeneration**. *Stem Cell Res Ther* (2017) **8** 210. DOI: 10.1186/s13287-017-0663-6
68. Smith TJ. **Insulin-like growth factor-I regulation of immune function: a potential therapeutic target in autoimmune diseases**. *Pharmacol Rev* (2010) **62** 199-236. DOI: 10.1124/pr.109.002469
69. Gao S, Sun Y, Zhang X, Hu L, Liu Y, Chua CY. **IGFBP2 activates the NF-kappaB pathway to drive epithelial-mesenchymal transition and invasive character in pancreatic ductal adenocarcinoma**. *Cancer Res* (2016) **76**. DOI: 10.1158/0008-5472.CAN-16-0438
70. Dorrington MG, Fraser IDC. **NF-kappaB signaling in macrophages: Dynamics, crosstalk, and signal integration**. *Front Immunol* (2019) **10**. DOI: 10.3389/fimmu.2019.00705
71. Han J, Jogie-Brahim S, Harada A, Oh Y. **Insulin-like growth factor-binding protein-3 suppresses tumor growth**. *Cancer Lett* (2011) **307**. DOI: 10.1016/j.canlet.2011.04.004
72. Hwang JR, Huh JH, Lee Y, Lee SI, Rho SB, Lee JH. **Insulin-like growth factor-binding protein-5 (IGFBP-5) inhibits TNF-alpha-induced NF-kappaB activity by binding to TNFR1**. *Biochem Biophys Res Commun* (2011) **405**. DOI: 10.1016/j.bbrc.2011.01.064
73. Singh S, Singh TG. **Role of nuclear factor kappa b (NF-κB) signalling in neurodegenerative diseases: An mechanistic approach**. *Curr Neuropharmacol* (2020) **18**. DOI: 10.2174/1570159x18666200207120949
74. Nilsson BO. **Mechanisms involved in regulation of periodontal ligament cell production of pro-inflammatory cytokines: Implications in periodontitis**. *J Periodontal Res* (2021) **56**. DOI: 10.1111/jre.12823
75. Gallucci GM, Alsuwayt B, Auclair AM, Boyer JL, Assis DN, Ghonem NS. **Fenofibrate downregulates NF-κB signaling to inhibit pro-inflammatory cytokine secretion in human THP-1 macrophages and during primary biliary cholangitis**. *Inflammation* (2022) **45**. DOI: 10.1007/s10753-022-01713-1
|
---
title: Sports foods are not all they shake up to be. An audit of formulated supplementary
sports food products and packaging in Australian retail environments
authors:
- Celeste I. Chapple
- Catherine G. Russell
- Alissa J. Burnett
- Julie L. Woods
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9972582
doi: 10.3389/fnut.2023.1042049
license: CC BY 4.0
---
# Sports foods are not all they shake up to be. An audit of formulated supplementary sports food products and packaging in Australian retail environments
## Abstract
### Objective
To determine store availability, total number of products, and types of Formulated Supplementary Sports Foods in Australia, along with their stated nutrition content, sweeteners added, total number, and type of claims displayed on the packaging.
### Design
A cross-sectional, visual product audit of mainstream retailers.
### Setting
Supermarkets, pharmacies, health food stores, and gym/fitness centres.
### Results
A total of 558 products were captured in the audit, 275 of which displayed the correct mandatory packaging attributes. Three categories of products were identified, based on the dominant nutrient. Only 184 products appeared to display the correct energy value based on the listed macronutrient content (protein, fat, carbohydrate, dietary fibre). The stated nutrient content was highly variable across all product subcategories. Nineteen different sweeteners were identified, with most foods containing only one ($38.2\%$) or two ($34.9\%$) types. The predominant sweetener was stevia glycosides. Packages displayed multiple claims, with a maximum of 67 and minimum of 2 claims. Nutrition content claims were most frequently displayed (on $98.5\%$ of products). Claims included regulated, minimally regulated and marketing statements.
### Conclusion
Sports food consumers should be provided with accurate and detailed on pack nutrition information, to ensure informed choices are made. However, this audit showed multiple products which did not conform to current standards, appeared to provide inaccurate nutrition information, contained multiple sweeteners, and displayed an overwhelming number of on-pack claims. The increase in sales, availability, and products available in mainstream retail environments, could be impacting both intended consumers (athletes), and general non-athlete population. The results indicate underperformance in manufacturing practices which preference marketing over quality and stronger regulatory approaches are needed to protect consumer health and safety, and to prevent misleading consumers.
## Introduction
For athletes, specialised nutrition is key to enabling optimum performance [1, 2]. When nutritional needs for protein or carbohydrate are not met via diet, formulated food products can be used [3]. The Australian Institute of Sport (AIS) states that sports foods can play a small but important role for high performance athletes and developed a classification system based on the level of scientific evidence to support their use in this group [4]. Sports foods dominant in protein [powders/Ready-To-Drink (RTD) beverages/bars/snacks], carbohydrate (gels/powders) and some additional products such as beta alanine and creatine are permitted for use by identified athletes and considered as group A foods/supplements, with strong scientific evidence for use in specific situations [4]. While most of the additional products available such as branched chain amino acids, glutamine, and pre workout are considered to have either not enough scientific evidence to support use or are banned due to contamination risk [4]. Sports Integrity Australia recommends that no supplement is safe for athletes to consume and if they are to consume supplements that only batch tested products with the trusted by sport or Human And Supplement Testing Australia (HASTA) are consumed [4]. These other sports foods generally contain one or two ingredients with specific physiological purposes such as stimulating muscle protein synthesis, or providing an ergogenic aid, such as caffeine, to enhance physical performance [2, 3].
In some countries these are regulated by different agencies for example, food supplements by the Food Standards Agency (United Kingdom) [5], foodstuffs, the European Commission (European Union) [6], dietary supplements, National Institutes of Health, Office of Dietary Supplements (United States) [7], and supplemented foods, the Government of Canada (Canada) [8]. The regulations cover labelling for health or performance claims, marketing aspects in relation to the contents of vitamins, minerals, and any other substances, directions for use and safe consumption (5–9). In Australia, the location of the present study, these products are known as “Formulated Supplementary Sports Foods” (herein referred to as sports foods) and are regulated by Food Standards Australia New Zealand (FSANZ), under Standard 2.9.4 of the Food Standards Code (FSC) [10]. Generally, products are not evaluated by the relevant agencies, prior to being made commercially available and it is the manufacturers’ responsibility to ensure that the benefits, safety, and compositional specifications are met [5, 7, 8, 11]. Standard 2.9.4 also sets the permitted ranges of macronutrients, added vitamins and minerals for all sports food products and the contribution to energy of certain nutrients for a set of specific sports food categories [10]. These include high carbohydrate supplements (contains between 15 and $90\%$ average energy content from carbohydrate), protein energy supplements (contains between 15 and $30\%$ average energy content from protein and not more than $25\%$ from fat), and energy supplements (contains not more than $20\%$ average energy content from protein) [10]. Furthermore, in the Australian context, sports foods are required to display a warning statement to indicate they are not suitable for children under 15 years or pregnant women and that they must be consumed under medical or dietetic supervision. They must also provide consumption recommendations and restriction guides for safe intake [10]. Sports foods are intended for athletes and may be recommended in some instances. They are not meant for consumption by the general non-athlete population, however, their sale in mainstream retail outlets make them more available to this population and may normalise their consumption for those who do not need the additional nutrients.
Previous Australian population surveys suggest that some non-athlete consumers could be consuming sports foods unnecessarily, incorrectly or in a harmful manner [12, 13]. From an Australian public health perspective this is concerning as these foods include nutrients not deficient in the population [14], a large array of added sweeteners, and could lead to over consumption of energy, added sugars or sodium, thereby exacerbating existing diet related chronic disease risk [14]. Despite their speciality nature, these products are available for sale in mainstream retail outlets such as supermarkets and pharmacies, making them easily available to the general public [15]. The retail sales value of these products in Australia has increased by $195\%$ since 2011 and at the same time availability across mainstream retail has also increased [15]. It is likely, therefore, that a proportion of these sales are to non-athlete consumers who shop in mainstream retail environments. Similar trends in sales and availability have been observed in other countries, such as the United Kingdom, Eastern and Western Europe, United States, Canada, and throughout the world (16–21).
An Australian study conducted in 2013, which examined consumer use of sports foods, found that product labels such as claims and marketing statements on-pack were the most frequently used consumer sources of information to determine the risks and benefits of consumption [12]. Sports food packaging in Australia can display a range of claims and marketing statements, many of which are not covered by the FSC and hence are not as strongly regulated as those that are covered by the code. With many different claims able to be displayed, the general population may find it difficult to ascertain the appropriateness of the product for their needs, or whether the product is safe to consume. To date, there have been no product audits conducted to our knowledge which examine the number and types of sports foods being sold in mainstream retail environments. Limited research which examines packaging attributes and how these foods are being marketed via on pack attributes [22], even though they are important in influencing consumption (23–26) and the nutrients/ingredient composition [22, 27], however, this research examines either only protein dominant products, or is based on a chemical analysis of the composition of these foods. Other existing research focuses on patterns of consumption by non-athletes, however, were conducted over 9 years ago and are therefore now of limited relevance due to the rapid expansion in the sports food market [12, 13, 27].
Detailed understanding of the current availability, product ranges, stated composition, ingredients such as sweeteners and the display of packaging attributes on these products, is important for determining if current food regulatory measures are fit for purpose. The objectives of this study were to conduct a visual analysis of the sports food products available in mainstream retail environments and to investigate the total number and types of products available in stores, the stated nutrient composition, the addition of sweeteners and the nature, number and type of written claims and marketing statements displayed on the packaging. We hypothesise that there will be multiple products with differing nutritional attributes, sweeteners will be used widely and there will be numerous claims on labels.
## Study design
This study was a retail product audit of sports foods located in store based retailers such as supermarkets, pharmacies, health food stores and gyms, these were chosen over online retailers as the majority of sports food sales ($61.5\%$) are made in these locations in Australia [15]. This study involved no human participants, and therefore ethical approval was not required.
## Data collection
Data were collected from 18 stores throughout Melbourne, Victoria, Australia, between March 2021 and May 2021. The two largest or flagship store-based retailers (communication direct from companies—3rd–5th February 2021), were chosen to increase the likelihood that the most comprehensive product variety would be available. These were: Coles, Woolworths, SUPA IGA, ALDI, COSTCO which make up $85\%$ total grocery market share in Australia [28]; The My Chemist Retail Group (Chemist Warehouse), which has the largest ($20.6\%$) pharmacy market share [29]; The health food retailer GoVita which consist of $22.9\%$ of store based retail locations for sports foods in Australia [15]; and Goodlife health club, and Fitness First gym facilities, both owned by the Fitness and Lifestyle Group, constituting $28.2\%$ of the gym and fitness centre market share in Australia [30].
In-store data were collected using a smart phone device to capture all sides of the product packaging which were subsequently compiled into a database by researcher CC. Images of items unavailable at the time of the audit or those with illegible labels were collected from company websites or the Mintel Global New Products Database. All data were manually entered into a Microsoft Excel spreadsheet for further analysis. As per previous research [31], a random sample of $10\%$ of products was extracted and examined by a second researcher (JW) to ensure accuracy of data entry and where discrepancies arose, agreement was reached via discussion and products were excluded when they did not meet the criteria.
Extracted data included: product brand, product name, flavour/variety, sports food product category (namely protein, carbohydrate dominant, other sports food), store name, aisle location (namely sports and diet, sports nutrition, health food, healthy living aisles), presence/absence of prescribed name/warning statement, nutrition information for energy, protein, fat, saturated fat, carbohydrate, sugars, dietary fibre (when displayed as it is not mandatory to display this unless a claim about fibre is made) and sodium per 100 g, whether the ingredients list was displayed, presence of sweeteners by name or code from ingredients list, serving size, pack size, serves per pack, price in AUD (unit price), and numbers of the following types of claims: nutrition content claims (e.g., high protein, 30 g protein); general level health claims (nutrient and physiological function relationship, e.g., protein for increased muscle mass); high level health claims (nutrient and disease relationship); sports effect claims (effect of nutrient on sports participation or sports outcome, e.g., bulk, shred, recover); product quality (premium, high quality); no/free from (no additives, preservatives, colours, flavours, and/or free from dairy, hormones and/or additives); taste (tastes great, delicious); natural (all natural, natural energy); sporting/organisational (trusted by sport, HACCP tested, guaranteed); vegan/vegetarian/plant-based (vegan, vegetarian, made from plants, plant-based); dieting/weight loss (slim, lose weight, diet); organic (certified organic, organic ingredients); diet style keto/paleo ($100\%$ keto, paleo friendly); health star rating and; daily intake guide. The FSC Standard 1.2.7 was used to classify regulated nutrition content and health claims [32] and additional claim classifications were determined after extensive inspection of the product packaging and a nomenclature was developed based on the type of message that each claim was conveying. Where products were available in multiple flavours, each flavour was identified and counted as a separate product. Products were only counted once even if the same product was available in multiple stores. However, each store the products were available in was recorded separately, to capture the true availability.
## Product categorisation
All products that resembled sports foods (sports food like products) located in the designated aisles of the audited stores were initially collected. Products included in the audit were products displaying either the prescribed name, “Formulated Supplementary Sports Food,” or the warning and advisory statements as required by FSC Standard 2.9.4. There were 39 products removed due to displaying the prescribed names “Supplemented food”; 19 “Formulated Supplementary food”; 38 “Dietary supplement” and 2 displaying “Formulated Caffeinated Beverage.” The remaining excluded products either did not display any prescribed name or warning/advisory statements or displayed statements that were not part of any standard such as “flavoured protein bar.” After completion of further data cleaning [determining % energy from nutrients to their energy factors, using the Nutrient Panel Calculator energy equation (protein 17 kJ/g, carbohydrate 17 kJ/g, fat 37 kJ/g, dietary fibre 8 kJ/g) [33] and comparing to stated energy in kJ per 100 g on the Nutrition Information Panel (NIP)], it became clear that some of these products did not appear to have an accurate NIP and so a further criterion of nutrients within $5\%$ of calculated energy content based on stated nutrient content was applied. These differences may be due to the use of different energy factors for all nutrients including available carbohydrate and the inclusion or exclusion of other energy yielding substances [33]. The products containing sugar alcohols only had these listed in the ingredients list and as such, their contribution to energy content could not be calculated. All products that were correctly identified as sports foods via the prescribed name or warning were included in the frequency and labelling results, but only those meeting the % energy criteria were included in the nutritional analysis and comparison.
Products were further identified as meeting the nutrient criteria for three additional specific categories (high carbohydrate, protein energy and energy supplement), identified in Standard 2.9.4 of the FSC. However, as very few ($$n = 9$$) products met these criteria, it was determined that the best way of categorising the products was as follows: protein dominant powder, protein dominant RTD shake, protein dominant bar/snack, carbohydrate dominant powder/gel, and other sports food product.
## Data analysis
Data were analysed using the Statistical Package for the Social Sciences (SPSS for Macintosh) version 28.0.1.0 (SPSS Inc., Chicago, IL, USA). Tests for normality were conducted on all data, which were not normally distributed. Descriptive statistics were used to examine the median, interquartile range, and minimum/maximum ranges of nutrients per serving suggestion within each sports food category; the number of sweeteners added to each product and the most frequently used sweeteners. A Kruskal Wallis test for medians was used to determine statistical differences in nutrient composition between sports food categories. The claim frequency data were not normally distributed, however, the mean, standard deviation, and minimum/maximum claim frequency for all products and for each of the sports food categories was used. This was due to the mean and standard deviation providing a more meaningful interpretation of the data. Additionally, significance testing could not be conducted as differences in package size between the categories was a contributing factor in how many claims could be displayed.
## General characteristics
There were 558 products captured during the audit, with 283 being excluded for not displaying the prescribed name “Formulated Supplementary Sports Food (FSSF)” and/or warning and advisory statements. There were 275 products in the final data set for the packaging attribute and sweetener analyses, with $83.3\%$ being protein dominant ($49.5\%$ powders, $8.7\%$ RTD beverages, and $25.1\%$ bars/snacks). Only $4.7\%$ of products were carbohydrate dominant (powders, gels) and $12.0\%$ were other sports food products. Only 184 products ($66.9\%$) appeared to have a sufficiently accurate NIP values as to fall within $5\%$ of calculated energy content from protein/carbohydrate/fat/fibre, with $60.3\%$ protein dominant powders, $9.8\%$ protein dominant RTD beverages, $16.8\%$ protein dominant bars/snacks, $5.4\%$ carbohydrate dominant (powders, gels), and $7.6\%$ other sports food products (Figure 1). Of the products which appeared to have an accurate NIP detail that met the specific compositional categories outlined by the Standard 2.9.4, only 6 ($4.1\%$) met the protein energy criteria, all carbohydrate products met the high carbohydrate criteria and 3 ($20\%$) met the energy criteria.
**FIGURE 1:** *Product categorisation and products per sports food category.*
Chemist Warehouse had the largest selection of sports food products available, with $47.6\%$ available in these locations, followed by GoVita and Coles which had 25.8 and $24.7\%$ available, respectively. SUPA IGA and Woolworths had 19.6 and $18.2\%$ of products available in these locations. The remaining stores ALDI, COSTCO, and Goodlife/Fitness First had the least products available in these locations.
## Nutritional characteristics
The following data represents only those products whose NIP calculations appeared to be accurate ($$n = 184$$). Table 1 outlines the nutrient content of the major sports foods categories. For all categories there were large variations in nutrient content, particularly in the energy, fat, saturated fat, dietary fibre, and sodium content. In relation to comparisons between protein categories, protein dominant bars/snacks had significantly higher ($p \leq 0.05$) median energy (2261 kJ), significantly higher ($p \leq 0.01$) total sugars (3.6 g) and significantly higher (all $p \leq 0.001$) median fat (19.8 g), saturated fat (7.2 g), carbohydrate (14.4 g), dietary fibre (12.0 g), and sodium (504.0 mg) per serving suggestion, compared to protein powders and RTD protein shakes. Carbohydrate dominant and other sports food products were similarly highly variable in the nutrients they contained. Other Sports foods were less variable, generally containing only one or two ingredients and very few nutrients apart from small amounts of energy (Table 1).
**TABLE 1**
| Nutrient per recommended servings per day | Protein powder(n = 111) | Protein powder(n = 111).1 | Protein powder(n = 111).2 | Protein RTD(n = 18) | Protein RTD(n = 18).1 | Protein RTD(n = 18).2 | Protein bar/snack(n = 31) | Protein bar/snack(n = 31).1 | Protein bar/snack(n = 31).2 | Carbohydrate powder/gel(n = 10) | Carbohydrate powder/gel(n = 10).1 | Carbohydrate powder/gel(n = 10).2 | Other sports food(n = 14) | Other sports food(n = 14).1 | Other sports food(n = 14).2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max | Median IQR Min-Max |
| Energy (kJ) | 1008 | 971 | 265–4779 | 741 | 811 | 554–2070 | 2261* | 1424 | 680–3293 | 589 | 3489 | 495–13440 | 33 | 72 | 12–318 |
| Protein (g) | 44.3 | 41.1 | 15.0–102.1 | 29.3 | 44.1 | 17.3–70.3 | 44.3 | 46.4 | 8.8–63.0 | – | – | – | 1.5 | 1.4 | 0.0–14.6 |
| Fat (g) | 2.6 | 4.2 | 0.0–30.2 | 3.1 | 2.1 | 2.1–5.0 | 19.8*** | 11.3 | 6.1–60.5 | – | 0.1 | 0.0–0.1 | – | – | 0.0–0.7 |
| Saturated fat (g) | 1.5 | 2.1 | 0.0–23.5 | 1.9 | 1.5 | 0.0–2.3 | 7.2*** | 8.3 | 1.0–38.6 | – | – | – | – | – | 0.0–0.7 |
| Carbohydrate (g) | 5.5 | 6.7 | 0.0–131.6 | 5.7 | 12.0 | 2.3–50.3 | 14.4*** | 10.3 | 5.0–78.5 | 34.7 | 201.1 | 29.8–768.0 | – | 2.5 | 0.0–9.8 |
| Total sugars (g) | 2.2 | 4.9 | 0.0–56.4 | 2.1 | 7.0 | 0.1–43.5 | 3.6** | 14.1 | 1.5–46.3 | 13.7 | 113.2 | 2.9–384.0 | – | 0.4 | 0.0–2.9 |
| Dietary fibre (g) | 0.3 | 2.0 | 0.0–7.3 | 0.3 | 2.4 | 0.0–6.8 | 12.0*** | 37.3 | 0.0–50.4 | – | – | – | – | – | 0.0–4.1 |
| Sodium (mg) | 162.4 | 250.4 | 0.2–1262.3 | 421.2 | 149.7 | 230.8–750.0 | 504.0*** | 511.8 | 92.0–811.8 | 36.0 | 601.7 | 17.5–1603.2 | – | 3.8 | 0.0–789.1 |
## Sweeteners
Across all products ($$n = 275$$), there were 19 different sweetener types identified in the ingredients list and these ranged from basic mono- and disaccharides (e.g., glucose, fructose, sucrose) to more novel sweeteners such as steviol glycosides (stevia), erythritol, and monk fruit, and non-nutritive sweeteners such as acesulphame potassium. There were $38.2\%$ of products that contained only 1 sweetener, $34.9\%$ contained 2 sweeteners, $12.0\%$ contained 3 and $8.7\%$ contained 4 or more sweeteners. The most prolific sweetener was steviol glycosides found in $44.4\%$ of the sports foods, followed by sucralose ($39.3\%$) and maltodextrin found in $22.9\%$ of the sports foods identified. Only 17 ($6.2\%$) products did not contain any sweeteners, the majority of these (11 products) were other sports foods. Products with 2 or more sweeteners were likely to be protein powders, RTDs, and bars.
For the products which had accurate calculated energy content, only 1 product contained both saccharin and cyclamate. The most prolific sweeteners were sucralose ($46.1\%$), stevia ($31.1\%$), and maltodextrin ($28.7\%$). In the sports foods with calculated energy content below what was stated on the NIP, stevia was the most prolific ($65.8\%$), followed by maltitol ($35.4\%$), and erythritol ($31.6\%$). The sports foods with calculated energy content above also contained stevia as the most prolific sweetener ($62.1\%$), followed by maltodextrin ($34.5\%$) and sucralose ($24.1\%$).
There were minimal differences in the number of sweeteners used between the products with accurate and those that appeared to have inaccurately calculated energy content. Sports foods with below the calculated energy content were more likely to contain 2 sweeteners per product ($35.4\%$) and had the most products which contained 5 sweeteners ($21.5\%$), compared to the accurate products which were more likely to contain one sweetener ($46.7\%$) and only one sports food contained 5 sweetener types in the one product.
## Percentage of items displaying a claim, claims per item, and claims per product category
All the audited sports foods displayed multiple claims or marketing statements on the packaging. The most prevalent claims were nutrition content (on $98.5\%$ of products), general level health ($65.1\%$ of products), sports effect ($62.2\%$ of products), and product quality claims ($52.7\%$ of products). The highest number of claims on any pack was 67 which was on an “other sports food” product containing electrolytes, magnesium, and branched chain amino acids. Protein dominant powders displayed the highest mean number of claims per pack ($M = 25.3$ ± 13.1, range 1–57), closely followed by other sports foods ($M = 24.7$ ± 15.2, range 2–67). There were no high-level health claims displayed on any of the products. For protein dominant powders ($M = 9.4$ ± 5.0, range 1–24), RTD shakes ($M = 10.6$ ± 3.4, range 4–17), and other sports foods ($M = 7.0$ ± 4.2, range 0–20) nutrition content claims were the most prolific. Carbohydrate dominant gels had a higher ($M = 2.9$ ± 3.0, range 0–8) mean number of general level health claims. Carbohydrate dominant products displayed the smallest mix of claims with only 6 different types of claims compared to most other categories with 9–13 different types of claims (Table 2).
**TABLE 2**
| Claim category | All productsn (%) | All productsn (%).1 | Protein dominant powder (n = 136) | Protein dominant powder (n = 136).1 | Protein dominant powder (n = 136).2 | Protein dominant RTD(n = 24) | Protein dominant RTD(n = 24).1 | Protein dominant RTD(n = 24).2 | Protein dominant bar/snack(n = 69) | Protein dominant bar/snack(n = 69).1 | Protein dominant bar/snack(n = 69).2 | Carbohydrate dominant powder/gel (n = 13) | Carbohydrate dominant powder/gel (n = 13).1 | Carbohydrate dominant powder/gel (n = 13).2 | Other sports food(n = 33) | Other sports food(n = 33).1 | Other sports food(n = 33).2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | n | % | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max | Mean SD Min-Max |
| All claim categories | 275 | 100 | 25.3 | 13.1 | 1–57 | 19.5 | 5.8 | 9–26 | 11.3 | 5.5 | 3–23 | 8.0 | 3.7 | 5–14 | 24.7 | 15.2 | 2–67 |
| Nutrition content | 271 | 98.5 | 9.4 | 5.0 | 1–24 | 10.6 | 3.4 | 4–17 | 6.2 | 2.7 | 3–14 | 1.9 | 0.7 | 0–3 | 7.0 | 4.2 | 0–20 |
| Health—General | 179 | 65.1 | 4.1 | 4.3 | 0–18 | 0.8 | 1.2 | 0–4 | 1.7 | 2.8 | 0–8 | 2.9 | 3.0 | 0–8 | 6.3 | 5.5 | 0–18 |
| Health—High | – | – | – | – | | – | – | – | – | – | – | – | – | – | – | – | – |
| Sports effect | 171 | 62.2 | 2.6 | 3.1 | 0–13 | 1.9 | 2.0 | 0–7 | 0.6 | 1.2 | 0–6 | 2.2 | 1.5 | 1–5 | 2.6 | 2.5 | 0–9 |
| Dieting/Weight loss | 35 | 12.7 | 0.5 | 1.2 | 0–9 | – | – | – | 0.1 | 0.2 | 0–1 | – | – | – | 0.1 | 0.2 | 0–1 |
| Product quality | 145 | 52.7 | 2.2 | 2.6 | 0–11 | 1.6 | 1.6 | 0–5 | 0.3 | 0.5 | 0–1 | – | – | – | 0.9 | 1.3 | 0–6 |
| Taste | 109 | 39.6 | 0.8 | 0.9 | 0-3 | 0.8 | 0.9 | 0–2 | 0.3 | 0.7 | 0–3 | 0.2 | 0.4 | 0–1 | 0.2 | 0.4 | 0–1 |
| No/Free from | 120 | 43.6 | 1.5 | 2.3 | 0-11 | 1.0 | 1.6 | 0–4 | 1.0 | 1.5 | 0–8 | – | – | – | 2.4 | 3.4 | 0–13 |
| Natural | 96 | 34.9 | 0.9 | 1.3 | 0-9 | 0.3 | 0.4 | 0–1 | 0.2 | 0.5 | 0–2 | 0.3 | 0.5 | 0–1 | 1.6 | 2.7 | 0–10 |
| Organic | 25 | 9.1 | 0.5 | 2.1 | 0–13 | – | – | – | 0.1 | 0.5 | 0–2 | 0.5 | 1.0 | 0–3 | 0.1 | 0.2 | 0–1 |
| Sporting/Organisation | 90 | 32.7 | 1.3 | 2.0 | 0-8 | 0.3 | 0.5 | 0–1 | 0.2 | 0.6 | 0–2 | – | – | – | 1.0 | 1.1 | 0–3 |
| Vegan/Plant based | 87 | 31.6 | 1.3 | 2.2 | 0-7 | 2.0 | 3.7 | 0–9 | 0.5 | 1.1 | 0–3 | – | – | – | 1.9 | 2.5 | 0–8 |
| Diet style—keto/paleo | 25 | 9.1 | 0.4 | 1.6 | 0-9 | – | – | – | 0.0 | 0.2 | 0–1 | – | – | – | 0.8 | 2.5 | 0–11 |
## Discussion
To our knowledge, this is the first product audit conducted on all sports food types in these retail locations. This study aimed to determine the availability of sports foods in Australian mainstream retail, including the types of products available, the nutrient and sweetener content and the number, type and frequency of claims displayed on the packaging. The key findings suggest that numerous sports food like products are available in mainstream retailers, however, just under half ($49.3\%$) of these were actual FSSF. Other key findings were the appearance of inaccuracy in the calculated energy content of fat, carbohydrate, protein, and dietary fibre, stated on the NIP, the variation in nutrient content within sports food categories, the prolific use of multiple sweeteners, and the vast number of claims displayed on the packaging.
This study demonstrated that there are a large number of sports food like products ($$n = 558$$) located in the designated aisles, currently sold in Australian mainstream retail environments. The sports foods ($$n = 283$$) that were technically not FSSF (i.e., they did not meet the criteria set by FSANZ) were visually comparable, contained similar ingredients, displayed many of the same claims and could therefore be confused by consumers to be genuine sports foods. Given their availability in local retail outlets, it is likely that these products are not only being purchased by the target market (athletes) but are being purchased by the general (non-athlete) population. The sheer number and variety of products in mainstream retail environments (whether they are true FSSF or not) suggests that non-athlete consumers may be being misled and deceived, purchasing products that may or may not meet their needs. This suggests that a stronger approach to regulation may be needed, to either clearly differentiate products or potentially restrict where these foods can be purchased from. It also suggests that with such a large number of sports food like foods on the market, the time is ripe to apply further regulatory oversight.
Just under half ($49.3\%$, $$n = 275$$) of the sports foods could be classified as FSSF by virtue of being labelled with the prescribed name and warning advisory statements. The presence of warnings or advisory statements, which could guide selection and use, were generally located on the rear of the packaging in small font and previous research has found that consumers are generally unaware of their presence, or meaning [13]. This important information is usually overlooked during the selection process [13] and consumers are therefore less likely to appreciate the associated consumption risks and to know whether these products are beneficial for them. This could lead to consumption of products containing nutrients or substances that consumers do not need and/or in harmful quantities [34], potentially exacerbating diet related disease [35].
A surprising finding of this study, with potentially serious regulatory consequences, was the discrepancy between the energy (kJ) from the nutrients listed on the NIP and the stated energy content. Many of the products appeared to display inaccurate calculated average energy content ($33.1\%$) which was either above ($14.2\%$) or below ($18.9\%$) the stated figure, although not all products ($$n = 111$$) displayed dietary fibre on the NIP, so these figures may change if energy from fibre could have been calculated. The NIP is an important on-pack attribute which provides the consumer with details about the nutrients present in food and what is contained in a serving of the product. Standard 1.2.8 of the FSC states that all packaged food (besides a specified list of items), must display a NIP that provides consistent and accurate information on serving size, servings per package, quantity of macro and micronutrients per 100 g and per serving size [36]. There is also Australian Consumer Law that states “A person must not, in trade or commerce, engage in conduct that is misleading or deceptive or is likely to mislead or deceive” [[37], p. 104] with potential criminal charges for non-compliance.
Protein bars were the category that appeared to display inaccurate NIPs most frequently ($69.2\%$), with lower calculated energy content ranging from 73.5 to $93.4\%$ of stated energy content. Protein products such as bars have been identified throughout the world as the most frequently sold and consumed sports food products [12, 21], with protein bars the most frequently selected [38]. It is unclear why there were so many products with false and potentially inaccurate information within the NIP, in particular those that underestimated the energy content [39]. Some products lacked dietary fibre information on the NIP and the energy from sweeteners besides sugar could not be calculated, therefore it was difficult to replicate the calculation used by the manufacturer, but this is unlikely to explain all cases of inaccurate NIPs. The detection of both potentially inaccurately calculated average energy content and substantially lower energy contents of protein bars, is concerning from a consumer and public health standpoint, as consumers could be ingesting different nutrient amounts compared to what they intended. Sports foods must display an NIP which is a true and accurate reflection of the actual nutritional quality of the product [36] and the extent of this level of inaccuracy is a clear indication that some manufacturers are not practicing sufficient quality assurance processes and that enforcement agencies need to step in now and act to safeguard public health and safety. The findings also create doubt regarding the accuracy of all nutrition levels provided, the ingredients included and of the veracity of claims displayed on-pack. Analytical studies of claims relating to single ingredients in selected sports foods, concluded similarly that most of the claims made should be either modified or eliminated and could be misleading consumers [40].
This study identified a high level of variation in the dominant nutrients between each sports food category, such as protein, fat, saturated fat, carbohydrate, and dietary fibre, as has been found by other analytical studies [22, 40]. This variation may be due to manufacturer determined serving size and recommended serves per day, as well as to the form of the product (e.g., powder, liquid, or bar). To account for some of these factors we only reported on the recommended servings per day for each product within each category but still identified a high level of variance despite products claiming to be sources of certain nutrients and the concomitant health or sports effects. This high level of variation in nutrients requires the consumer to be vigilant in reading all the nutrition information and serving suggestions, which consumers do not tend to do when selecting foods [41, 42]. With so many different sports food products to compare, it is understandable consumers may become confused and unable to make informed choices. Consumers may reasonably expect that a sports food claiming to provide protein for instance would not provide an increased amount of fat to other protein containing products. The current Standard 2.9.4 has nutrient specifications for certain subclasses of FSSF, however, we have found here, that the majority of these foods do not fit within these sub-classes and hence there are no expected nutritional specifications for most of the sports foods on the market [10]. Future consideration of these findings would assist in creating a clearer classification within the Standard, providing clear required specifications of nutrient ranges for sports food categories, or a minimum nutrient content for classification.
Nineteen different sweeteners were found in the sports foods examined in the audit ranging from mono- and di-saccharides, non-nutritive, and novel sweeteners. Interestingly, the most prolific of these were sucralose (a synthetic non-nutritive sweetener) and stevia a natural non-nutritive sweetener, derived from the leaves of the Stevia Rebaudiana, a shrub native to South America [43, 44]. Both of these have a kilojoule content which is far lower than sucrose [39]. Sweeteners are added to foods for a variety of reasons, to provide a sweet taste, ensure the product is palatable, and replace sensory qualities, without increasing the energy or carbohydrate content associated with regular sweeteners such as sucrose [45]. Different sweeteners will have different levels of sweetness and there may be more than 1–2 different sweeteners included in the one product for palatability. Certain sweeteners are also added to provide carbohydrate content to those sports foods described as carbohydrate dominant to fuel performance. There is some controversy around non-nutritive sweeteners and health outcomes, particularly with the more novel sweeteners such as sucralose and stevia. There are observational research findings showing associations between consumption and changes to the gut microbiome [43], weight gain [46], and an increased risk of type-two diabetes [47]. In this study, there was found to be minimal difference between the inclusion of sweeteners and the accuracy of calculated energy content as per the NIP and it was observed that sweeteners were contained in most sports foods and in some cases multiple in one product. Yet, packaged foods are not required to display the nutrient content in grams on the NIP, they are only required to state if they are contained in the product via the ingredients list. The type and amount of sweeteners used, is most likely dependant on the type of product such as a powder vs. a bar. Whether these are exceeding the upper limits is unknown, as are the health effects of these sweeteners when consumed in combination.
The audited sports foods typically displayed a range of on pack claims and marketing statements. The most frequently displayed on-pack attributes were nutrition content claims ($98.5\%$) and general level health claims ($65.1\%$), which is expected given the nature of these foods is to enhance some aspect of physical performance or health. However, $62.2\%$ of products also displayed “sports effects claims” (i.e., effect of nutrient on sports participation or sports outcome, e.g., bulk, shred, recover) which do not have the same regulatory oversight as nutrition and health claims. Nutrition content claims and general level health claims, are regulated by the FSC and can only be made where certain nutrient criteria are met [32]. Given the level of potential inaccuracies found in relation the energy and nutrients on the NIP of many of these foods, there are implications for the veracity of these claims. A very recent sports food audit conducted in Spain, found that most of the products complied with the relevant labelling standards [22]. However, the sample was smaller than in the current audit, included only protein isolate products and examined protein quality, which was not a focus of the current study. Additionally, the European Union legislative framework has no specific regulations with reference to claims for sports foods and these are categorised as foodstuffs, therefore it is difficult to establish whether these foods do in fact meet the labelling requirements [22]. Claims are important to consumers and influence choice, preference, and consumption (25, 48–52), and are sources of information that are easily processed by consumers via heuristics [53]. This process involves making decisions using fast and automatic processes which are emotional or intuitive [54]. It is likely therefore that consumers may be making product choices based on false and misleading claims, or on claims and marketing statements that are minimally regulated and are hence being deceived in the process.
Another interesting finding was the vast cacophony of claims displayed on many of these foods, with a mean of around 11–25 claims per pack on most types of foods (except for carbohydrate foods which had a mean of eight claims). The sheer quantity of claims could make it difficult for consumers to process all of the information and then to make an informed choice. Studies have indicated that multiple, competing pieces of information on food packs can increase consumer confusion (23–25, 49). With the expansion of the market and so many claims per pack, which are not regulated in the same way as nutrition and health claims, it is timely to consider further regulatory changes that would reduce the messaging load on consumers and enhance their decision-making processes.
## Strengths and limitations
This study had several strengths. Due to the comprehensive number of stores that were audited and the collection of information from the two largest stores of each retailer, it is likely that the majority of products available in mainstream retailers within Australia were captured. To our knowledge, this is the first study of this kind undertaken in Australia and can add considerably to regulatory decisions currently under investigation. Furthermore, this study provides a novel, comprehensive classification system for the on-pack attributes displayed on sports foods, which has not previously existed and can be used as a baseline for future research in this area.
The limitations of this study include the use of a cross-sectional methodology, which depicts the products available at only one point in time. It also relies on the nutrition information printed on the packaging, which did not include all nutrients in some cases, which makes it difficult to fully replicate the manufacturers calculation for all products. Therefore, a large number of products appeared to be inaccurate in this study. Additionally, this study only examined products sold at mainstream physical stores in Australia and not products sold through digital retailers located online (comprising $38.5\%$ of the retail market in Australia). As no online stores were included in this audit, it is possible that a sector of the sports food market was missed. This could be mitigated, in future by conducting a comprehensive audit which also includes digital retailers.
## Conclusion
It is vitally important that consumers are provided accurate and detailed on-pack information regarding nutrition content, ingredients, additives, claims, and potential warnings about the foods they select. The present study showed that there were a large number of sports food like products being sold in mainstream retail markets. Just over half ($50.7\%$) did not conform with the current display of required statements for formulated supplementary sports foods, contained multiple non-nutritive sweeteners, displayed an overwhelming number of claims and approximately $33\%$ of sports food products appeared to have inaccurate nutrition information. The current Standard was published (gazetted) in 2001 and is clearly outdated. Considering the expansion in the sales, the large number of products and their availability in mainstream retail, this is of concern and could impact not only the intended market (athletes) but also the general non-athlete consumer. The results indicate potentially underperforming manufacturing processes that preference marketing over product quality and call for a strengthening of regulatory approaches to protect consumer health and safety and prevent consumer deception.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
CC and JW conceptualised the study and analysed the data. CC conducted the data collection. JW, CR, and AB provided input into the study design and methods and contributed to writing and editing. JW provided input into the data analysis and interpretation and conducted the data checks. All authors read and approved the final manuscript.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. 1.Department of Health & Human Services.
Sporting Performance and Food State of Victoria.
Melbourne, VIC: State Government of Victoria (2020).. (2020)
2. 2.Australian Institute of Sport.
Nutrition. (2022). Available online at: https://www.ais.gov.au/nutrition
(accessed June 22, 2022).. (2022)
3. 3.Australian Institute of Sport.
Supplements: The Benefits and Risks of Using Supplements and Sports Foods. (2022). Available online at: https://www.ais.gov.au/nutrition/supplements
(accessed June 22, 2022).. (2022)
4. 4.Australian institute of sport.
The Ais Sports Supplement Framework. (2021). Available online at: https://www.ais.gov.au/nutrition/supplements
(accessed August 13, 2022).. (2021)
5. 5.Food Standards Agency.
Food Supplements. (2021). Available online at: https://www.food.gov.uk/business-guidance/food-supplements#what-a-food-supplement-is
(accessed August 4, 2022).. (2021)
6. 6.European Union.
Directive 2009:39:Ec of the European Parliment Ond of the Council. (2009). Available online at: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32009L0039&qid=1659576097417
(accessed August 4, 2022).. (2009)
7. 7.National Institutes of Health Office of Dietary Supplements.
Dietary Supplements for Exercise and Athletic Performance.
Bethesda, MD: Office of Dietary Supplements (2021).. (2021)
8. 8.Government of Canada.
About Supplemented Foods and Thier Labels. (2022). Available online at: https://www.canada.ca/en/health-canada/services/food-nutrition/supplemented-foods/about.html
(accessed August 4, 2022). (2022)
9. 9.European Union.
Corrigendum to Regulation (Ec) No 1924:2006 of the European Parliament and of the Council of 20 December 2006 on Nutrition and Health Claims Made on Foods. (2006). Available online at: https://eur-lex.europa.eu/eli/reg/2006/1924/oj
(accessed August 4, 2022).. (2006)
10. 10.FSANZ.
Food Standards Code, Formulated Supplementary Sports Food. (2020). Available online at: https://www.legislation.gov.au/Series/F2015L00421
(accessed July 1, 2020).. (2020)
11. Martinez-Sanz J, Sospedra I, Baladia E, Arranz L, Ortiz-Moncada R, Gil-Izquierdo A. **Current status of legislation on dietary products for sportspeople in a European framework.**. (2017) **9**. DOI: 10.3390/nu9111225
12. 12.Food Standards Australia New Zealand.
Sports Foods Consumption in Australia and New Zealand.
Food Standards Australia New Zealand (2013).. (2013)
13. 13.Food Standards Australia New Zealand.
Consumer Research Investigating the Use of Formulated Supplementary Sports Foods (2010).. (2010)
14. 14.ABS.
National Health Survey: First Results, 2017-18. (2017-2018). Available online at: https://www.abs.gov.au/ausstats/[email protected]/Lookup/by%20Subject/4364.0.55.001~2017-18~Main%20Features~Overweight%20and%20obesity~90
(accessed March 20, 2019).. (2017-2018)
15. 15.Euromonitor International.
Sports Nutrition in Australia.
London: Euromonitor International (2021).. (2021)
16. 16.Euromonitor International.
Sports Nutrition in the United Kingdom.
London: Euromonitor International (2022).. (2022)
17. 17.Euromonitor International.
Sports Nutrition in Eastern Europe Datagraphic.
London: Euromonitor International (2022).. (2022)
18. 18.Euromonitor International.
Sports Nutrition in Western Europe.
London: Euromonitor International (2022).. (2022)
19. 19.Euromonitor International.
Sports Nutrition in the United States of America.
London: Euromonitor International (2022).. (2022)
20. 20.Euromonitor International.
Sports Nutrition in Canada.
London: Euromonitor International (2022).. (2022)
21. 21.Euromonitor International.
Sports Nutrition in World.
London: Euromonitor International (2021).. (2021)
22. Rodriguez-Lopez P, Rueda-Robles A, Sanchez-Rodriguez L, Blanca-Herrera R, Quirantes-Pine R, Borras-Linares I. **Analysis and screening of commercialized protein supplements for sports practice.**. (2022) **11**. DOI: 10.3390/foods11213500
23. Talati Z, Pettigrew S, Hughes C, Dixon H, Kelly B, Ball K. **The combined effect of front-of-pack nutrition labels and health claims on consumers’ evaluation of food products.**. (2016) **53** 57-65. DOI: 10.1016/j.foodqual.2016.05.016
24. Gorski Findling M, Werth P, Musicus A, Bragg M, Graham D, Elbel B. **Comparing five front-of-pack nutrition labels’ influence on consumers’ perceptions and purchase intentions.**. (2018) **106** 114-21. DOI: 10.1016/j.ypmed.2017.10.022
25. Acton R, Hammond D. **Do Manufacturer ‘nutrient claims’ influence the efficacy of mandated front-of-package labels?**. (2018) **21** 3354-9. DOI: 10.1017/s1368980018002550
26. Ni Mhurchu C, Eyles H, Jiang Y, Blakely T. **Do nutrition labels influence healthier food choices? Analysis of label viewing behaviour and subsequent food purchases in a labelling intervention trial.**. (2018) **121** 360-5. DOI: 10.1016/j.appet.2017.11.105
27. 27.LGC.
Australian Supplement Survey.
Teddington: LGC (2016).. (2016)
28. 28.IBIS world.
Supermarkets and Grocery Stores in Australia.
Los Angeles, CA: IBIS world (2022). p. G4111.. (2022) G4111
29. 29.IBIS world.
Pharmacies in Australia.
Los Angeles, CA: IBIS world (2020).. (2020)
30. 30.IBIS world.
Gyms and Fitness Centres in Australia.
Los Angeles, CA: IBIS world (2022). p. R9111.. (2022) R9111
31. McCann J, Russell C, Campbell K, Woods J. **Nutrition and packaging characteristics of toddler food and milks in Australia.**. (2020). DOI: 10.1017/S1368980020004590
32. 32.FSANZ.
Food Standards Code, Nutrition, Health and Related Claims. (2020). Available from: https://www.legislation.gov.au/Series/F2015L00394
(accessed July 1, 2020).. (2020)
33. 33.Food Standards Australia New Zealand.
Nutrients in the Npc. (2020). Available online at: https://www.foodstandards.gov.au/industry/npc/Pages/Nutrients-in-the-NPC.aspx#energy
(accessed September 10, 2020).. (2020)
34. Duiven E, van Loon L, Spruijt L, Koert W, de Hon O. **Undeclared doping substances are highly prevalent in commercial sports nutrition supplements.**. (2021) **20** 328-38. DOI: 10.52082/jssm.2021.328
35. Housman J, Dorman S. **Dietary and sports supplements the need for a systematic tracking system.**. (2008) **23**
36. 36.FSANZ.
Food Standards Code, Nutrition Information Requirements. (2020). Available online at: https://www.foodstandards.gov.au/code/Pages/default.aspx
(accessed July 10, 2020).. (2020)
37. 37.Australian Competition and Consumer Commission.
Competition and Consumer Act 2010.
Canberra, ACT: Office of Parlimentary Counsel (2010).
38. Harwood W, Drake M. **Understanding implicit and explicit consumer desires for protein bars, powders, and beverages.**. (2019) **34**. DOI: 10.1111/joss.12493
39. 39.Food Standards Australia New Zealand. Intense Sweeteners. (2021). Available online at: https://www.foodstandards.gov.au/consumer/additives/Pages/Sweeteners.aspx
(accessed July 10, 2022).. (2021)
40. Molina Juan L, Sospedra I, Perales A, Gonzalez-Diaz C, Gil-Izquierdo A, Martinez-Sanz J. **Analysis of health claims regarding creatine monohydrate present in commercial communications for a sample of european sports foods supplements.**. (2021) 24:632-40. DOI: 10.1017/S1368980020005121
41. Hodgkins C, Barnett J, Wasowicz-Kirylo G, Stysko-Kunkowska M, Gulcan Y, Kustepeli Y. **Understanding how consumers categorise nutritional labels: a consumer derived typology for front-of-pack nutrition labelling.**. (2012) **59** 806-17. DOI: 10.1016/j.appet.2012.08.014
42. Bialkova S, Grunert K, van Trijp H. **Standing out in the crowd: the effect of information clutter on consumer attention for front-of-pack nutrition labels.**. (2013) **41** 65-74. DOI: 10.1016/j.foodpol.2013.04.010
43. Ruiz-Ojeda F, Plaza-Diaz J, Saez-Lara M, Gil A. **Effects of sweeteners on the gut microbiota: a review of experimental studies and clinical trials.**. (2019) **10** S31-48. DOI: 10.1093/advances/nmy037
44. Chattopadhyay S, Raychaudhuri U, Chakraborty R. **Artificial sweeteners – a review.**. (2014) **51** 611-21. DOI: 10.1007/s13197-011-0571-1
45. Russell C, Dickie S, Baker P, Lawrence M. **Does the Australian health star rating system encourage added sugar reformulation? Trends in sweetener use in Australia.**. (2021) **13**. DOI: 10.3390/nu13030898
46. Fowler S, Williams K, Resendez R, Hunt K, Hazuda H, Stern M. **Fueling the obesity epidemic? Artificially sweetened beverage use and long-term weight gain.**. (2008) **16** 1894-900. DOI: 10.1038/oby.2008.284
47. O’Connor L, Imamura F, Lentjes M, Khaw K, Wareham N, Forouhi N. **Prospective associations and population impact of sweet beverage intake and type 2 diabetes, and effects of substitutions with alternative beverages.**. (2015) **58** 1474-83. DOI: 10.1007/s00125-015-3572-1
48. Aschemann-Witzel J, Hamm U. **Do consumers prefer foods with nutrition and health claims? Results of a purchase simulation.**. (2010) **16** 47-58. DOI: 10.1080/13527260903342746
49. Bialkova S, Sasse L, Fenko A. **The role of nutrition labels and advertising claims in altering consumers’ evaluation and choice.**. (2016) **96** 38-46. DOI: 10.1016/j.appet.2015.08.030
50. Franco-Arellano B, Vanderlee L, Ahmed M, Oh A, L’Abbe M. **Influence of front-of-pack labelling and regulated nutrition claims on consumers’ perceptions of product healthfulness and purchase intentions: a randomized controlled trial.**. (2020) **149**. DOI: 10.1016/j.appet.2020.104629
51. Nobrega L, Ares G, Deliza R. **Are nutritional warnings more efficient than claims in shaping consumers’ healthfulness perception?**. (2020) **79**. DOI: 10.1016/j.foodqual.2019.103749
52. Talati Z, Pettigrew S, Neal B, Dixon H, Hughes C, Kelly B. **Consumers’ responses to health claims in the context of other on-pack nutrition information: a systematic review.**. (2017) **75** 260-73. DOI: 10.1093/nutrit/nuw070
53. Kahneman D.. (2013)
54. Chaiken S. **Heuristic versus systematic information processing and the use of source versus message cues in persuasion.**. (1980) **39** 752-66
|
---
title: SIRT2-PFKP interaction dysregulates phagocytosis in macrophages with acute
ethanol-exposure
authors:
- Anugraha Gandhirajan
- Sanjoy Roychowdhury
- Christopher Kibler
- Emily Cross
- Susamma Abraham
- Annett Bellar
- Laura E. Nagy
- Rachel Greenberg Scheraga
- Vidula Vachharajani
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9972587
doi: 10.3389/fimmu.2022.1079962
license: CC BY 4.0
---
# SIRT2-PFKP interaction dysregulates phagocytosis in macrophages with acute ethanol-exposure
## Abstract
Alcohol abuse, reported by $\frac{1}{8}$th critically ill patients, is an independent risk factor for death in sepsis. Sepsis kills over 270,000 patients/year in the US. We reported that the ethanol-exposure suppresses innate-immune response, pathogen clearance, and decreases survival in sepsis-mice via sirtuin 2 (SIRT2). SIRT2 is an NAD+-dependent histone-deacetylase with anti-inflammatory properties. We hypothesized that in ethanol-exposed macrophages, SIRT2 suppresses phagocytosis and pathogen clearance by regulating glycolysis. Immune cells use glycolysis to fuel increased metabolic and energy demand of phagocytosis. Using ethanol-exposed mouse bone marrow- and human blood monocyte-derived macrophages, we found that SIRT2 mutes glycolysis via deacetylating key glycolysis regulating enzyme phosphofructokinase-platelet isoform (PFKP), at mouse lysine 394 (mK394, human: hK395). Acetylation of PFKP at mK394 (hK395) is crucial for PFKP function as a glycolysis regulating enzyme. The PFKP also facilitates phosphorylation and activation of autophagy related protein 4B (Atg4B). Atg4B activates microtubule associated protein 1 light chain-3B (LC3). LC3 is a driver of a subset of phagocytosis, the LC3-associated phagocytosis (LAP), which is crucial for segregation and enhanced clearance of pathogens, in sepsis. We found that in ethanol-exposed cells, the SIRT2-PFKP interaction leads to decreased Atg4B-phosphorylation, decreased LC3 activation, repressed phagocytosis and LAP. Genetic deficiency or pharmacological inhibition of SIRT2 reverse PFKP-deacetylation, suppressed LC3-activation and phagocytosis including LAP, in ethanol-exposed macrophages to improve bacterial clearance and survival in ethanol with sepsis mice.
## Graphical Abstract
## Introduction
Sepsis, the leading cause of death in hospitalized patients in the US, kills over 270,000 patients/year (1–3). Immune response in sepsis transitions from early/hyper-inflammation to a late/hypo-inflammatory and immunosuppressive phase [4, 5]. Majority of sepsis-mortality occurs during immunosuppression [6]. Alcohol use disorder, reported by $\frac{1}{8}$th critically ill patients, is an independent risk factor for sepsis-mortality [7]. Immunosuppression and inability to clear infection with acute ethanol-exposure in sepsis are well described but the molecular targets to reverse immunosuppression have remained elusive [8]. We have reported previously, that decreased pathogen clearance and increased mortality in ethanol-drinking mice with sepsis occur via increased expression of sirtuin 2 (SIRT2) [9].
Sirtuins (SIRTs) are a highly conserved family of NAD+-dependent histone deacetylases (class III HDAC) that regulate immuno-metabolic responses during acute and chronic inflammatory conditions (10–14). The seven members of SIRT family (SIRTs1-7) disperse throughout cell compartments (15–18). SIRTs 1, 6 and 7 are nuclear, SIRT3, 4 and 5 mitochondrial and SIRT2 is cytoplasmic. While predominantly cytoplasmic, SIRT2 translocates to the nucleus during cellular stress [19]. Previously, we have implicated SIRTs in sepsis-induced hypo-inflammation and immunosuppression (20–26). We and others have shown that SIRT2 deacetylates and deactivates NFκB p65 and prolongs the immunosuppressive hypo-inflammatory phase of sepsis with obesity, a common co-morbidity in sepsis patients [22, 27, 28].
Acute ethanol-exposure impairs phagocytosis, which is crucial for pathogen clearance by innate-immune cells including macrophages, during sepsis (29–33). Immune cells use glycolysis to support the high-energy demand during phagocytosis (34–37). The effect of acute ethanol-exposure on glycolysis and glycolysis-regulating enzymes in immunosuppressed macrophages is not fully understood [9, 33, 38]. Glycolytic enzymes are known to regulate basic cellular functions and immune response, in addition to the regulation of glycolysis (39–41). Specifically, the platelet isoform of phosphofructokinase (PFKP), a key glycolysis-regulating enzyme, serves as a protein kinase that regulates autophagy. Autophagy is a cytoprotective mechanism by which misfolded proteins, apoptotic cells, cellular debris and pathogens are degraded [42].
PFKP facilitates phosphorylation of autophagy related protein 4B (Atg4B), which activates microtubule associated protein 1 light chain-3B (LC3) to regulate autophagy [43]. LC3-associated phagocytosis (LAP) is a subset of phagocytosis that uses parts of autophagy-machinery to enhance segregation and degradation of pathogens and dead-cell debris [44, 45]. In LAP, the LC3-II is conjugated to the phagosome membrane to form LAPosome (Graphical abstract) [46]. Dysregulated LAP is implicated in immunosuppression during sepsis, however the effect of acute ethanol exposure on LAP or LAPosome formation is not well studied [47].
We have reported that ethanol-induced increase in SIRT2 represses immune response and bacterial clearance, while SIRT2 deficiency is associated with increased bacterial clearance and decreased mortality in ethanol with sepsis-mice [25]. In the current project, we aimed to elucidate the mechanism by which ethanol-induced SIRT2 regulates the innate immune function of bacterial clearance, phagocytosis, LAP, and cellular metabolism, using mouse-bone marrow- and human blood monocyte- derived macrophages.
## Antibodies and reagents used for western blot and immunocytochemistry
PFKP (Cell signaling Technology, Danvers, MA, CAT# 8164S), LC3 (Cell signaling Technology, Danvers, MA, CAT# E5Q2K: western blot), LC3 (Novus Biologicals, Centennial, Colorado, CAT# NC100-2220: immunocytochemistry), SIRT2 (Cell signaling Technology, Danvers, MA, CAT# D4050), Atg4B (Cell signaling Technology, Danvers, MA, CAT# 13507S), Rubicon (Cell signaling Technology, Danvers, MA, CAT# 8465S), Beclin-1 (Cell signaling Technology, CAT# 3495S), PFKM (Abcam, Waltham, Boston, USA,CAT#ab154804), VPS34 (Abcam, Waltham, Boston, CAT# ab124905), PFKL (Santa Cruz Biotechnology, Dallas, Texas, USA, CAT# sc-393713), F$\frac{4}{80}$ (Invitrogen, Rockford, Illinois, USA, CAT# MA1-91124), Anti-rabbit Alexa Fluor 488 (Invitrogen, Rockford, Illinois, USA, CAT# A21206), Anti-rat Alexa Fluor 594 (Invitrogen, Rockford, Illinois, USA, CAT# A21209), Anti-rabbit Alexa Fluor 647 (Invitrogen, Rockford, Illinois, USA, CAT# A21245), Anti rabbit IgG control (Cell signaling Technology, Danvers, MA, CAT# 2729S), anti-mouse IgG, HRP-linked antibody (Cell signaling Technology, Danvers, MA, CAT# 7076), Anti-rabbit IgG, HRP-linked antibody (Cell signaling Technology, CAT# 7074), Acetyl lysine (Novus Biologicals, Centennial, CO, USA, CAT# NB 100-74339) pSerine (Origene, Rockville, MD, USA, CAT# AM00114PU-N), Anti- Turbo GFP (tGFP) antibody (Origene, Rockville, MD, CAT# TA150041), DDK antibody (Origene, Rockville, MD, CAT# TA50011-100), Anti-Ubiquitin antibody (EMD Millipore, Burlington, Massachusetts, USA, CAT#5-944), Biotinylated anti-SIRT2 antibody (R&D system, Minneapolis, MN, USA, CAT# BAF4358), CPA (Novus Biologicals, CAT# NBP1-30993), Beta-actin(Abcam, Waltham, Boston, CAT# ab8226), ECL (Bio-Rad, Hercules, CA, USA, CAT# 1705061). ChromoTek TurboGFP-Trap Magnetic Agarose (Proteintech Group, Inc, Rosemont, IL, CAT# tbtma-10), Magnetic-TUBEs (Life Sensors, Malvern, PA, USA, CAT# UM501M), Zymosan A pHrodo red bio-particles (Invitrogen, Waltham, MA, USA, CAT# P35364), *Vybrant phagocytosis* assay (Thermo Fisher Scientific, Waltham, MA, USA, CAT# V-6694).
Mouse studies: Cremophor EL (EMD Millipore Corp, Madison, WI, USA, CAT# 238470), AK-7 (Tocris, Minneapolis, MN, USA, CAT# 4754), Lipopolysaccharide (LPS) (Sigma Aldrich, St. Louis, MO, USA, E. Coli. O111:B4, CAT# L2630-100 mg), Ethanol (Pharmco by Greenfield global, 200 proof, Brookfield, Connecticut, USA, CAT# 111000200),
## Glycolysis assay reagents
Glycolysis stress test kit (Agilent technologies, Santa Clara, CA, USA, CAT# 103020-100), glucose-free XF DMEM media (Agilent technologies, Santa Clara, CA, CAT#103575-100) containing 2 mM glutamine (Agilent technologies, Santa Clara, CA, CAT# 103579-100), Hoechst 33342 (Thermo Fisher Scientific, Waltham, MA, USA, CAT# 62249).
## Immunoprecipitation experiments
Streptavidin magnetic beads (Thermo Fisher Scientific, Waltham, MA, USA, CAT# 88816), Dynabeads protein G magnetic beads (Thermo Fisher Scientific, Waltham, MA, USA, CAT# 10003D).
## Surface plasmon resonance assay reagents
Recombinant human SIRT2 protein (Sigma-Aldrich, St. Louis, MO, CAT# SRP0116), Human recombinant PFKP protein (His and GST tag), Sino Biological, Wayne, PA, USA, CAT# 15003-H20B), Biacore series S Sensor Chip CM5 (Cytiva, Marlborough, Massachusetts, USA CAT# 29104992), NAD+ (nicotinamide adenine dinucleotide, Sigma Aldrich, St. Louis, MO, CAT# 10127973001).
Biologicals for PFKP overexpression in HEK293T cells: mouse wtPFKP (turbo-GFP-tagged) shRNA (Origene, Rockville, MD, CAT# MG210641), KAT5 mouse plasmid (Origene, Rockville, MD, CAT# TR512714), SIRT2 (Myc-DDK-tagged) ShRNA (Origene, Rockville, MD, CAT# MR225715), transfection reagent (Thermo scientific, CAT# R0531), control plasmids (control turbo GFP plasmid pCMV6-AC-GFP (Origene, Rockville, MD, CAT# PS100010), control pCMV6-Entry plasmid (Myc-DDK-tagged; Origene, Rockville, MD, CAT# PS100001), Opti-MEM reduced serum medium (Thermo Fisher Scientific, Waltham, MA, USA, CAT# 31985-062).
## PFKP plasmid construct and mutagenesis
Mutagenesis was performed on PFKP (turbo-GFP-tagged; Origene, CAT# MG210641: wtPFKP) plasmid at nucleotide residue 1181 from “a” to “g” (a1181g) which change the amino acid from lysine (K) to arginine (R) at 394 residue on PFKP protein sequence (mtPFKP: Origene, Rockville, MD, USA, Ref# CW308061)} by Origene.
A single site-directed mutagenesis was performed on PFKP (turbo-GFP-tagged wtPFKP) plasmid at nucleotide residue 1181 from adenine to guanine (a1181g) which changes the amino acid from lysine (K) to arginine (R) at 394 residue on PFKP protein sequence mtPFKP (Origene, Rockville, MD, Ref# CW308061) by Origene.
## Tandem Ubiquitin Binding Entities assay reagents
PR-619 (UB/UBI protease inhibitor; Life Sensors CAT# SI9619), 1, 10-phenanthroline (o-PA), (Life Sensors CAT# SI9649).
## Nucleofection of PFKP siRNA in SIRT2KO-BMDM
PFKP (B) siRNA [(Mouse PFKP siRNA, Origene, Rockville, MD, CAT# SR420114/CAT# SR427511: updated version), nucleofection solution (Lonza Bioscience, Morrisville, NC, USA, Amaxa cell line Nucleofector Kit V, CAT# PS100001, VCA-1003).
For silencing PFKP, nucleofection was performed using mouse PFKP siRNA in SIRT2KO-BMDM. Cells were removed from petri plates by scraping into 50ml tube and were spun at 335xg for 7 minutes. The supernatant was aspirated completely without any residual media from the cell pellet. Approximately 20uM (0.5 µl) of mouse PFKP (B) siRNA was added to cell pellet (each nucleofection required 15 x106cells/tube) and resuspended the cells in 100 µl Nucleofection solution. The cell suspension was transferred to cuvette and inserted the cuvette in nucleofector cuvette holder to run the nucleofector program in D-032 instrument. The cells were transferred to pre-warmed media after completing the program, and plated in to 100mm tissue culture plate for 24 hours.
## Human blood isolation
Rapidspheres (STEMCELL technologies, Cambridge, MA, USA, Easy Sep, CAT # 19669), used with magnetic stand (STEMCELL technologies, Easy 50, CAT# 18002). ( Macrophage colony-stimulating factor; concentration: 100ug/ml; Murine macrophage colony stimulating factor (M-CSF, Pepro tech, CAT# 315-02-100UG). Gluta MaxTM-1 (Life technology, CAT# 61870-036), Human Serum (Sigma Aldrich, St. Louis, MO, CAT# H4522) penicillin and streptomycin (Life technology, CAT# 15070-063), Fetal bovine serum (FBS, Life technology, Grand Island, NY, USA, CAT # 10082-147) and 1mM EDTA (Ambion, Inc, Austin, TX, USA, CAT # AM9260G), Human M-CSF (Pepro tech, Cranbury, NJ, USA, Cat # 300-25-50Ug).
## Monocyte isolation and macrophage differentiation from healthy volunteer blood samples and ex vivo ethanol exposure
All studies were approved by local IRB. Informed consent, as approved by IRB (IRB #19-132), was obtained. Healthy volunteers without active infection and/or immunosuppression/active cancer diagnosis were enrolled and consented for blood draw. Approximately 25 ml blood was collected via peripheral vein venipuncture, by a certified phlebotomist and using K2EDTA (BD vacutainer, Franklin Lakes, NJ, USA, CAT#366643, as an anticoagulant) tubes. PBS with $1\%$ FBS and 1mM EDTA was added to the blood to make up a total volume of 50ml. The enriched cell suspension (~25ml) was mixed with Rapidspheres (50µL/ml blood volume). After mixing well, this cell suspension was incubated at room temperature (RT) for 5 minutes. The tube (without lid) was carefully placed into the magnetic stand and incubated at RT for 5 minutes. The enriched cell suspension (~25ml) was transferred into a new tube and again placed into the magnetic stand and incubated at RT for 5 minutes. Final enriched cell suspension containing monocytes was transferred into a new tube and centrifuged at 300 x g for 8 mins at RT. The isolated monocytes were resuspended with human macrophage media RPMI + Gluta Max™-1 along with $10\%$ human serum, penicillin and streptomycin (Life technology, CAT# 15070-063) and the cells were counted. Approximately 2 million monocytes were cultured into a 75cm2 -T flask along with Human M-CSF at a ratio of 1:1000 to differentiate into macrophages. The flask was placed at 37 °C for 7 days in the CO2 incubator. On the 7th day, the cells were scraped aseptically and exposed to phosphate buffered saline (PBS, vehicle) or ethanol (final concentration, 25 mM) 20h, followed by further stimulation with LPS (final concentration, 100ng/mL) for 4h. Supernatant was collected and cells were lysed subsequently for western blot analysis.
## Mouse studies
The study was approved by the Institutional Animal Care and Use Committee (IACUC) at the Cleveland Clinic Lerner Research Institute (LRI) and experiments were performed according to the NIH guidelines (ACUC approval#: 00002194). The C57BL/6 (wild type: WT) and B6.129-Sirt2tm1.1Fwa/J (global SIRT2 knockout: SIRT2KO) breeding pairs were purchased from the Jackson laboratories (Bar Harbor, ME, USA) and mice were bred in the AAALAC approved animal facility of LRI.
## Bone marrow isolation and ex vivo ethanol exposure
Bone marrow derived macrophages were isolated as reported previously [48]. Five to six week old male and female mice were used for this study. The bone marrow cells were collected from the femur and tibia of mice and cultured in Roswell Park Memorial Institute (RPMI) (Lonza, Walkersville, MD, USA, CAT#12-702F) containing $10\%$ fetal bovine serum (FBS), $1\%$ penicillin and streptomycin at 37° C and $5\%$ CO2 for 6 days along with mouse M-CSF to differentiate into bone marrow-derived macrophages (BMDM). On day 6, the cells were scraped aseptically and exposed to PBS (vehicle) or ethanol (final concentration 25 mM) 20h [49], followed by further stimulation with LPS (final concentration, 100ng/mL) for 4hours. Supernatant was collected and cells were lysed subsequently for western blot analysis. For immunohistochemistry, following ethanol/vehicle exposure and LPS stimulation for 4hours, cells were fixed using $4\%$ paraformaldehyde, washed with PBS and permeabilized with $0.1\%$ Triton-X-100 for 10 minutes. Cells were then washed with PBS, blocked with $2\%$ bovine serum album in PBS for 1 hour, and incubated overnight with respective primary antibody (1:250 dilution in blocking buffer) at 4 °C. Cells were then washed with PBS and incubated in the dark with secondary antibodies for 1 hour at RT. Cells were again washed with PBS and mounted with a DAPI-containing mounting media. Images were acquired using a Leica-confocal microscope using 63X objectives.
## RAW 264.7 cell culture
RAW 264.7 cell macrophages (RAW cells, 1X 105 cells/ml) were cultured in Dulbecco’s modified Eagle’s media (DMEM, Life technologies, CAT# 11995-065) containing $10\%$ FBS, $1\%$ penicillin and streptomycin at 37° C and $5\%$ CO2. Cells were stimulated with and without LPS (final concentration, 100ng/mL) for 4hours. Supernatant was collected and cells were lysed subsequently for SIRT2 immunoprecipitation and Western blot analysis.
## Human embryonic kidney (HEK293T) cell culture
HEK293T cells were grown in Minimal *Essential media* (MEM) supplemented with $10\%$ fetal bovine serum and $1\%$ penicillin and streptomycin at 37° C and $5\%$ CO2. Cells were transfected as detailed below in the Transfection method section.
## Immunocytochemistry
Human monocyte- and mouse bone marrow-derived macrophages were exposed to either phosphate buffered saline (PBS: Vehicle) or ethanol (final concentration 25 mM: Ethanol) for 20 hours, followed by further stimulation with LPS (final 100ng/mL) for 4 hours. Cells were fixed using $4\%$ paraformaldehyde and washed with phosphate-buffered saline (PBS). Cells were then again washed with PBS, blocked for 1 hour with $2\%$ BSA containing $0.1\%$ Triton-X-100 and incubated overnight with rabbit anti-SIRT2 or rat anti-F$\frac{4}{80}$ primary antibody (1:250 dilution in blocking buffer) at 4 °C. Next day, cells were washed three times (duration: 5 minutes) with PBS and incubated in the dark with anti-rabbit or anti-rat secondary antibody for 2 hours at RT. Cells were again washed with PBS and mounted with a DAPI-containing mounting media. Images were acquired using a Leica-confocal microscope using 63X objectives. Images were analyzed using ImagePro Plus software.
## Phagocytosis using Zymosan A pHrodo bio-particles
Cells were exposed to PBS (vehicle) or ethanol (final concentration 25 mM) for 20 hours, followed by further stimulation with LPS (final concentration, 100ng/mL) for 4 hours. Zymosan A pHrodo bioparticles (50 µg/ml/) were added 20 minutes prior to end of the incubation period. At the end of the incubation, cells were fixed with $4\%$ paraformaldehyde for 20 minutes at RT in dark. Cells were then washed with PBS three times and intracellular bio-particles were visualized using a confocal microscope. Zymosan A pHrodo bioparticles stimulate phagocytosis, specifically LAP. Zymosan A bioparticle assay is commonly used to study phagocytosis, including LAP (50–52).
## LC3-associated phagocytosis
LAP was monitored using protocol published in literature [53]. Cells were plated on chamber slides exposed to phosphate buffered saline (PBS, vehicle) or ethanol (final concentration 25 mM) for 20 hours, followed by further stimulation with LPS (final 100ng/mL) for 4h. PHrodo bioparticles were added 20 minutes prior to end of the incubation period. At the end of the incubation, fixed with $4\%$ paraformaldehyde for 20 minutes at RT in dark. Cells were then washed with PBS 3 times, blocked for 1 hour, and incubated overnight with LC3 primary antibody at 4°C in dark. The following day, the cells were washed with PBS 3 times and incubated with respective secondary antibody for 2 hours. Cells were again washed with PBS and mounted with a DAPI-containing mounting media. Images were acquired using a Leica-confocal microscope using 63X objectives. Images were analyzed using ImagePro Plus software.
## Phagocytosis using Vybrant phagocytosis assay
The *Vybrant phagocytosis* assay was carried out per manufacturer’s instruction. Briefly, WT and SIRT2KO BMDM cells were treated with vehicle/ethanol and induced with LPS, as mentioned previously in 96-well microplate. Hereafter, the assay was performed in dark. The fluorescent particles were resuspended completely with concentrated HBSS and sonicated for 2 minutes using water bath sonicator. The suspension was transferred into a clean glass tube containing 4.5 ml of deionized water and sonicated for 2 minutes until all the fluorescent particles are homogeneously dispersed. The media were removed by vacuum aspiration. Fluorescent bio-particle suspension (100 µl/well) was added to all the wells except the unstained control wells. The plate was incubated at 37°C for 2 hours. In the meantime, concentrated trypan blue was resuspended with 4 ml of deionized water in a glass tube and sonicated for 2 minutes. After 2 hours of incubation, the bioparticles were removed by vacuum aspiration, 100 µL of trypan blue suspension was added and incubated for 1 minute. Subsequently, the excess trypan blue was removed by vacuum aspiration. The plate was read on the flex station at RT with the specific wavelength [480, 520, 495] and settings (reading 6 and PMT-auto).
## Glycolysis stress test using Seahorse XF24 analyzer
Extracellular acidification rate (ECAR) was monitored per the manufacturer’s instruction using the glycolysis stress test kit and Seahorse XFe24 Analyzer (Agilent technologies, Santa Clara, CA, USA). BMDM or RAW 264.7 (80000 cells/well) were plated on the XFe24 Seahorse plate were exposed to PBS (vehicle) or ethanol (final concentration 25 mM) for 20h, followed by further stimulation with LPS (final 100ng/mL). At the end of the incubation, cells were washed with the glucose-free XF DMEM media containing 2 mM glutamine and incubated in that media at a non-CO2 incubator at 37°C for 1 hr. The glycolysis stress test was performed and analyzed according to the manufacturer’s protocol. At the end of the experiment, the cells were stained with Hoechst 33342 and counted using Cytation 5 analyzer. The extracellular acidification rate was presented as ECAR (mpH/minute/10000 cells).
## Lactate assay
Lactate assay kit (Cell Biolabs, Cat. No. MET 5013, San Diego, CA) was used to assess intracellular lactate levels. In brief, WT, SIRT2KO or AK-7-treated WT-BMDM cells were exposed to vehicle/ethanol and induced with LPS, as mentioned previously in 6-well microplates. At the end of the incubation, cells were counted using an automated cell counter and lysed by sonication in assay buffer (100 µl per 105 cells). Cells were then centrifuged at 500g for 5 minutes and supernatants were used for lactate assay as manufacturer’s instruction. Values were normalized to 105 cells.
## Western blot analysis
Whole-cell lysates and immunoprecipitated samples were subjected to SDS–PAGE (4–$15\%$ gel) followed by transfer into 0.2 µm pore size polyvinylidene fluoride (PVDF) membrane. Each lane was loaded with 40 µg of protein, in all the gels. The membrane was blocked with $5\%$ skimmed milk in Tris-buffered saline TBS with tween ($0.05\%$) (TBST) for 90 minutes at RT. Blots were incubated with primary antibodies to analyze the expression of proteins overnight at 4°C. The blots were washed three times with TBST and incubated with anti-rabbit/mouse IgG, HRP-linked secondary antibody for 1hour at RT. ECL was used for detection, and images were captured using the ChemiDoc imaging system (Bio-Rad, Hercules, CA, USA) and were quantified using Image J software (NIH, Bethesda, MD, USA).
## Immunoprecipitation
For SIRT2 or Atg4B immunoprecipitation, BMDMs were treated with vehicle/ethanol and stimulated with LPS, as mentioned previously. Post-treatment, the cells were washed with PBS and resuspended in NP-40 lysis buffer (containing $1\%$ Nonidet P-40, 50 mM Tris (pH 8), 150 mM NaCl) along with phosphatase and protease inhibitor mixture. Cells were then lysed using a sonicator for 5 × 10 seconds with a 20 second interval between each sonication, at a frequency of 10 kHz. Cell debris was removed by centrifugation at 10,000× g for 15 minutes, and the supernatant was treated with biotinylated anti-SIRT2 antibody or with Atg4B antibody as indicated. Respective IgG control antibodies served as negative controls. SIRT2 and biotinylated antibody complexes were immunoprecipitated using streptavidin magnetic beads. Similarly, Atg4B and the antibody complexes were immunoprecipitated using dynabeads protein G magnetic beads. The beads were extensively washed 3 times with washing buffer. After the final wash, the supernatant was discarded, and the pellet was resuspended in 100 µL of 2x sample buffer with BME and denatured at 95°C for 10 minutes. Denatured precipitates were subjected to SDS–PAGE ($12\%$ gel) followed by transfer to 0.2 μM PVDF membrane.
## Surface plasma resonance study
The surface plasmon resonance (SPR) experiments were performed in a Biacore model S200 equipment (Cytiva) to analyze the interaction between SIRT2 and PFKP. Recombinant human SIRT2 protein was immobilized on a sensor chip and different concentrations of human recombinant PFKP protein were flowed. Briefly, for the immobilization of human SIRT2, Biacore series S Sensor Chip CM5, which has carboxylic groups available for covalent coupling was used. The carboxylic groups present on the carboxyl methylated 5 (CM5) sensor chip surface were activated with a mixer of EDC (n-ethyl-n-[dimethylaminopropyl] carbodiimide) and NHS (N-hydroxysuccinimide) to form the N-Hydroxysuccinimide (NHS) ester reactive intermediate, after washing with HBS-P+ buffer (0.1 M HEPES, 1.5 M NaCl, $0.5\%$ v/v surfactant P20, pH 7.4), will react with amines dissolved in the immobilization solution. The human SIRT2 ligand (1 μg) was dissolved in 10 mM sodium acetate, pH 4.0 with 20 μM NAD+, which was manually immobilized onto the Biacore sensor chip as manufacturer´s instructions. To detect interactions between SIRT2 and PFKP, the human PFKP protein at different concentrations were injected onto the chip where SIRT2 was immobilized. PFKP protein samples were injected serially from lowest to highest concentration (15.62, 31.25, 62.5, 125, 250, 500 and 1000 nM) and a contact time of 120 seconds, a 15 μL/minute flow rate. The surface was regenerated between samples by injecting running buffer (HBS-P+) (600 seconds, 30 μL/minute). The data was quantified using BIAevaluation software S200.
## PFKP over expression in HEK293T cells by transient transfection
Human embryonic kidney (HEK293T) cells were transfected with either mouse wtPFKP or mtPFKP (turbo-GFP-tagged) shRNA to over express PFKP and KAT5 mouse plasmid in the presence or absence of mouse SIRT2 (Myc-DDK-tagged) ShRNA plasmid, using Turbofect as transfection reagent. The control plasmids (control turbo GFP plasmid pCMV6-AC-GFP, control pCMV6-Entry plasmid (Myc-DDK-tagged) were transfected as transfection control. Briefly, on the day before transfection, the cells (density: 1-3 x 105) were plated on 6-well plates in complete medium in order to obtain 50-$70\%$ confluence for the day of the experiment. The next day, before transfection, 3 μg of DNA was diluted in 250 μL of Opti-MEM in reduced serum medium. TurboFectin reagent (9 μL; 1: 3 ratio of DNA: transfecting agent) was added to the diluted DNA and gently mixed with the pipet then incubated for 20 minutes at RT. The mixture was added dropwise to the cells while gently shaking the plate to distribute the complex evenly then incubated at 37°C for 24 hours.
## PFKP over expression and immunoprecipitation using TurboGFP-Trap in HEK293T cells
HEK293T cells were transfected with non-mutated mouse wtPFKP and mtPFKP plasmids in the presence or absence of mouse SIRT2 plasmid along with KAT5 mouse plasmid as described previously. After transfection, cells were resuspended in NP-40 lysis buffer and lysed using a sonicator. Cell debris was removed by centrifugation at 10,000× g for 15 minutes, and the supernatant was used for TurboGFP immunoprecipitation using TurboGFP-Trap magnetic agarose beads. The immunoprecipitation protocol was followed per manufacturer’s instructions. Briefly, 25 μL of bead slurry was transferred into a 1.5 ml tube and equilibrated the beads 3 times with 500 μL of dilution buffer. Approximately, 500 μg/ml lysate was added into the equilibrated beads. After overnight incubation at 4°C, beads were washed 3 times with washing buffer. After the final wash, the supernatant was discarded, and the pellet was resuspended in 100 µL of 2x SDS sample buffer with BME and denatured at 95°C for 10 minutes. Denatured precipitates were subjected to SDS–PAGE ($12\%$ gel) followed by transfer to 0.2 μM PVDF membrane.
## PFKP over expression and immunoprecipitation of poly-ubiquitinated proteins using magnetic tandem ubiquitin binding entities in HEK293T cells
HEK293T cells were transfected with mouse wtPFKP and mtPFKP plasmids in the presence or absence of mouse SIRT2 plasmid along with KAT5 mouse plasmid as described previously. During 24 hours of transfection 10 μM MG-132 was added to the cells for 4 hours before harvest. After transfection, the cells were resuspended in TUBE lysis buffer (50mM Tris-HCl, pH 7.5, 0.15M NaCl, 1mM EDTA, $1\%$ NP-40, $10\%$ glycerol) with the protease, phosphatase, PR-619 (50µM final concentration, UB/UBI protease inhibitor) and lysed using a sonicator. Also 1, 10-phenanthroline (o-PA), a metal chelator which is a potent inhibitor of metalloproteases was added in the lysis buffer to prevent poly-ubiquitinated protein degradation during cell lysis. Cell debris was removed by centrifugation at 10000X g for 15 minutes, and the supernatant was used for immunoprecipitation using Magnetic-TUBEs. The immunoprecipitation was performed per manufacturer’s instructions. Briefly, 100 μL of bead slurry was transferred into a 1.5 ml tube and equilibrated the beads 3 times with 1 ml of TBST wash buffer (20mM Tris-HCl, pH 8.0, 0.15M NaCl, $0.1\%$ Tween-20 with inhibitors). Approximately, 1 mg of total protein containing lysate was added into the equilibrated beads. After 2 hours incubation at 4°C, beads were extensively washed 3 times with 1 ml of washing buffer. After the final wash, the supernatant was discarded, and the pellet was resuspended in 50 µL of 2x SDS sample buffer with BME and denatured at 95°C for 10 minutes. Denatured precipitates were subjected to SDS–PAGE ($12\%$ gel) followed by transfer to 0.2 μM PVDF membrane.
## PFKP over expression in RAW 264.7 cell macrophages
RAW 264.7 cell macrophages were transfected with mouse wtPFKP and mtPFKP plasmids in the presence or absence of mouse SIRT2 plasmid along with KAT5 mouse plasmid as described previously. Cells were exposed with ethanol and stimulated with and without LPS (final concentration 100ng/mL) for 4hours simultaneously. Supernatant were collected and cells were lysed subsequently for western blot analysis.
## Ethanol exposure in mice
Male and Female C57BL/6 mice (6 weeks old) were housed in standard animal care facilities (5 mice per cage). Age- and gender-matched mice ($50\%$ male and $50\%$ female) were randomized and allowed free access to ethanol or water-containing bottles. Mice were exposed to increasing dose of ethanol via drinking water; $5\%$ ethanol vol/vol 2 days, followed by $10\%$ ethanol vol/vol for 2 days followed by $30\%$ ethanol vol/vol for 7 days, as described in our previous study[9]. On day 11, mice were subjected to experimental conditions as indicated.
## Cecal slurry injection model of sepsis and survival study with AK-7
We studied 7-day survival in ethanol-fed wild type mice with SIRT2 inhibitor AK-7 using cecal slurry (CS) model of sepsis as described previously[9]. Mice were given either a single intraperitoneal dose of AK-7 (40mg/Kg body weight; prepared in $25\%$ Cremophor in PBS) [54] or vehicle (Dimethyl sulfoxide, DMSO in $25\%$ Cremophor in PBS), up to 15 minutes prior to CS injection. All mice were injected with CS and received subcutaneous Meropenem (25mg/Kg body weight) twice daily for five doses, starting 18 hours post-CS. Mice were monitored at least twice a day. Pain and distress monitoring, and timely euthanasia as a humane end point to decrease pain and distress, were followed using scoring system as published [22].
## Peritoneal lavage colony forming unit
Bacterial colony forming unit (CFU) was measured in the peritoneal lavage fluid of ethanol-fed AK-7/DMSO-treated mice. Briefly, the ethanol-fed wild-type mice received intraperitoneal injection of either AK-7 or DMSO (vehicle), followed by induction of sepsis using cecal slurry injection. After completion of 7-day survival study (above), the mice were euthanized using cervical dislocation under anesthesia (1–$3\%$ Isoflurane- O2 mixture via nose cone) on day 8. The anterior abdominal wall was cleaned using $70\%$ ethanol solution. Using aseptic precautions, sterile PBS (3 ml) was injected intraperitoneally, allowed to circulate in the peritoneal cavity and cautiously aspirated using glass pipette with bulb, to perform peritoneal lavage. This fluid, transferred to sterile tubes was used to quantify peritoneal bacterial CFU. Serially diluted peritoneal lavage fluid was plated on LB agar plates using aseptic precautions and incubated overnight at 37°C. The number of aerobic bacterial colonies were counted and expressed as CFU. CFU was calculated using the following formula. CFU/ml = (Number of colonies*dilution factor)/volume of culture plated.
## Image analysis
Signal intensities of Western blot images were quantified by densitometry using Image J software (NIH, Bethesda, MD) and values were normalized with respective loading controls as indicated in the figure. All immunofluorescent images were semi-quantified using ImagePro Plus software (Media Cybernetics, Bethesda, MD) and values were normalized per cell in the field.
## Statistical analysis
All data was expressed as the mean ± standard error of the mean (SEM) with $$n = 3$$-4 data point per experimental group, as indicated in figure legends. Statistical analyses was performed using GraphPad Prism software Version 5.02 (GraphPad Software, San Diego, CA, USA). For comparing multiple groups, analysis of variance was used with a NewMann-Koyle post hoc test. Mouse survival with AK-7 vs. vehicle (DMSO) treatment was analyzed using Log-Rank test (GraphPad Prism software, as above). Statistical significance was defined as $p \leq 0.05.$
## Results
We examined the effect of acute ethanol-exposure on phagocytosis, glycolysis, glycolytic enzymes and LAP in macrophages in a stepwise manner, as depicted in Figure 1.
**Figure 1:** *Steps 1-7 describe the order in which results are described.*
## Acute ethanol-exposure increases SIRT2 expression and decreases phagocytosis in macrophages
Phagocytosis is critical for bacterial clearance in sepsis. We used BMDM from C57BL/6 (WT) mice with LPS stimulation model of sepsis. Differentiation into macrophages was confirmed by F$\frac{4}{80}$ expression in over $95\%$ of cells (Supplementary Figure S1) [55, 56]. To examine the effect of acute ethanol-exposure (duration: 24h) [38, 57] on WT-BMDM-phagocytosis, we used Zymosan A pHrodo bioparticles (Step 1: Figure 1). Zymosan A bioparticles are known to stimulate phagocytosis (50–52). As expected, the Vehicle-exposed WT-BMDM showed a robust increase in phagocytosis with LPS (Vehicle: + LPS/-LPS= 2.5 fold-increase). However, the Ethanol-exposed macrophages showed a profoundly muted phagocytosis (Ethanol: +LPS/-LPS=0.38 fold-change) (representative image: Figure 2A, image quantification: Figure 2B). The red color in pHrodo assay is pH sensitive and indicates internalization of bioparticle in a phagosome within the cell. To further investigate whether the ethanol-exposure affects the ability of the macrophages to engulf/internalize the bio particles, we used Vybrant assay with fluorescein-labeled E. coli bio particles. We observed a significantly decreased capacity to engulf E. coli particles in Ethanol-exposed WT-BMDM with LPS stimulation compared to vehicle-exposure (Supplementary Figure S2A). Lactate can suppress pro-inflammatory immune response in macrophages [58]. To assess whether increased lactate was responsible for repressed phagocytosis here, we studied lactate concentrations in vehicle and Ethanol-exposed macrophages± LPS. We observed, that the lactate levels were significantly lower in Ethanol-exposed macrophages with LPS (Supplementary Figure S3A).
**Figure 2:** *The effect of acute ethanol-exposure on mouse bone marrow derived macrophages (BMDM). Phagocytosis in BMDM exposed to vehicle or ethanol ± LPS to study phagocytosis using pHrodo bioparticles and SIRT2 expression. (A) Representative images of intracellular pHrodo bioparticles in vehicle or Ethanol-exposed WT-BMDM ± LPS. (B) Fluorescence quantification of pHrodo bioparticles in WT-BMDM (n=4 repetitions/group; * p<0.05). (C) SIRT2 protein expression detected by western blot in Vehicle- or Ethanol-exposed BMDM cells ± LPS. (D) Western blot image quantification of SIRT2 protein blot in vehicle or ethanol-exposed BMDM cells ± LPS (n = 4 blots/group; * p < 0.05). (E) Representative images of pHrodo bioparticles in Ethanol-exposed WT-BMDM and SIRT2KO-BMDM ± LPS. (F) Fluorescence quantification of pHrodo bioparticles in Ethanol-exposed WT-BMDM, and SIRT2KO-BMDM ± LPS. * p < 0.05. (G) Representative images of pHrodo bioparticles in Ethanol-exposed WT-BMDM, co-treated with SIRT2 inhibitor AK-7 or DMSO ± LPS. (H) Fluorescence quantification of pHrodo bioparticles in Ethanol-exposed WT-BMDM with AK-7/DMSO ± LPS stimulation. * p < 0.05.*
We reported increased SIRT2 expression in acute Ethanol-exposed RAW 264.7 cell macrophages previously [9]. Here, using western blot assay, we observed increased SIRT2 expression in ethanol- vs. Vehicle-exposed WT-BMDM without LPS ($60\%$ increase in Ethanol-LPS, $p \leq 0.05$) and with LPS ($50\%$ increase in Ethanol+ LPS, $p \leq 0.05$) vs. control (Vehicle-LPS) (representative image: Figure 2C and WB quantification: Figure 2D) (Step 2: Figure 1), consistent with our previous report [9].
## SIRT2 deficiency preserves phagocytosis and glycolysis in macrophages with acute ethanol-exposure
To further investigate the role of SIRT2 with acute ethanol-exposure, we studied the effect of SIRT2 deficiency on phagocytosis, using genetic and pharmacological approaches. Ethanol-exposed BMDM from genetically deficient SIRT2KO mice (SIRT2KO-BMDM) showed increased phagocytosis vs. WT-BMDM without LPS (SIRT2KO/WT = 9 fold-increase, $p \leq 0.05$) and with LPS (SIRT2KO/WT = 5 fold-increase, $p \leq 0.05$) (representative image: Figure 2E, image quantification: Figure 2F). The engulfment capacity using E. coli bioparticles (Vybrant assay) also significantly increased in ethanol exposed SIRT2KO-BMDM vs. WT-BMDM without LPS, and remained high with LPS stimulation (Supplementary Figure S2B).
SIRT2 inhibitor AK-7 binds to the NAD+ binding site on SIRT2 leading to a competitive inhibition of SIRT2 [59]. Using pharmacological approach, we observed as expected, that acute ethanol-exposure repressed phagocytosis in WT-BMDM with DMSO (vehicle for AK-7)-treatment. In contrast, the AK-7-treated-Ethanol-exposed WT-BMDM exhibited a dramatic increase in phagocytosis with LPS stimulation (representative image: Figure 2G, image quantification: Figure 2H) (DMSO: +LPS/-LPS= 1.3 fold-increase, $p \leq 0.05$ vs. AK-7: +LPS/-LPS=4 fold-increase, $p \leq 0.05$). There was no significant increase in phagocytosis in the AK-7 vs. DMSO-treated cells without LPS. Together, these data demonstrate that increased SIRT2 is associated with repression while SIRT2 deficiency with preserved phagocytic response, in Ethanol-exposed macrophages. Furthermore, we found higher lactate concentrations in Ethanol-exposed SIRT2 deficient BMDM (SIRT2KO-BMDM: Supplementary Figure S3B and AK-7 treated WT-BMDM: Supplementary Figure S3C) vs. WT-BMDM counterparts.
Glycolysis is essential to fuel increased energy-demand for phagocytosis (34–37). Hence, we studied the effect of ethanol-exposure on glycolysis (glycolysis stress test: Seahorse XF24) in WT-BMDM (Step 3: Figure 1). We observed a muted basal glycolysis in response to LPS in ethanol- vs. Vehicle-exposed WT-BMDM (Vehicle: +LPS/-LPS=2.65 fold-increase vs. Ethanol: +LPS/-LPS= 1.93 fold-increase). However, Ethanol-exposed SIRT2KO-BMDM showed a robust increase in basal glycolysis with LPS (+LPS/-LPS=2.35 fold-increase) (Figure 3A).
**Figure 3:** *The effect of acute ethanol-exposure on glycolysis in macrophages. Glycolysis assay in BMDM exposed to vehicle or ethanol ± LPS. Y-axis represents response to LPS (+ LPS) as a fold of respective group without LPS (-LPS) represented as “Fold of own control”. (A) Basal glycolysis (mean ECAR after glucose addition) in Vehicle-exposed WT-BMDM, Ethanol-exposed WT-BMDM and Ethanol-exposed SIRT2KO-BMDM ± LPS stimulation. (B) Glycolytic capacity (mean ECAR upon addition of ATP synthase inhibitor oligomycin) in Vehicle-exposed WT-BMDM, Ethanol-exposed WT-BMDM and Ethanol-exposed SIRT2KO-BMDM ± LPS stimulation (*p<0.05).*
Similarly, we observed muted glycolytic capacity in response to LPS in ethanol- versus Vehicle-exposed-WT-BMDM (Vehicle: +LPS/-LPS=2.25 vs. Ethanol: +LPS/-LPS=1.11 fold-increase). However, Ethanol-exposed SIRT2KO-BMDM exhibited robust increase in glycolytic capacity in response to LPS (+LPS/-LPS=2.37-fold-increase) (Figure 3B).
## Acute ethanol-exposure impairs PFKP expression in macrophages
To investigate the mechanisms by which ethanol mutes glycolysis, we examined the expressions of key glycolysis-regulating enzyme phosphofructokinase (PFK) in Ethanol-exposed WT-BMDM (Step 4: Figure 1). We examined three isoforms of PFK, the platelet isoform (PFKP), liver isoform (PFKL) and muscle isoform (PFKM). We observed that the PFKP expression decreased in ethanol- versus Vehicle-exposed WT-BMDM ± LPS. Specifically, without LPS, we found, that PFKP expression in Ethanol group was $79\%$ of Vehicle, a decrease by $21\%$. In cells with LPS, the PFKP expression was found to be $62\%$ of the Vehicle, a decrease by $38\%$ (representative WB image Figure 4A; WB quantification: Figure 4B). The other two isoforms of PFK, PFKL (representative blot: Supplementary Figure S4A, WB quantification: Figure S4B) or PFKM (representative blot: supplementary Figure S4C, WB quantification: Supplementary Figure S4D) remained unchanged in ethanol vs. Vehicle-exposed WT-BMDM ± LPS. Similarly, we did not find differential expressions of other important glycolysis regulating enzymes hexokinase I, hexokinase II, pyruvate kinase M1 or pyruvate kinase M2 in ethanol- vs. Vehicle-exposed WT-BMDM ± LPS stimulation (data not shown).
**Figure 4:** *The effect of acute ethanol-exposure-induced SIRT2 on PFKP expression in macrophages. (A) PFKP expression in WT-BMDM exposed to vehicle or ethanol ± LPS by western blot analysis. (B) PFKP western blot image quantification of PFKP protein in vehicle vs. ethanol exposed WT-BMDM, Y axis represents fold of vehicle-LPS (fold of vehicle control) (n = 4 blots; * p < 0.05). (C) PFKP expression in Ethanol-exposed WT-BMDM and SIRT2KO-BMDM ± LPS. (D) Western blot image quantification of PFKP protein in ethanol exposed WT-BMDM and SIRT2KO-BMDM. Y axis represents fold of Ethanol-exposed WT-LPS (fold of WT- ethanol control) (n = 4 blots; * p < 0.05). (E) PFKP expression in Ethanol-exposed WT-BMDM treated with AK-7 or DMSO ± LPS. (F) Western blot image quantification of PFKP in AK-7 vs. DMSO treated-Ethanol-exposed WT-BMDM ± LPS, Y axis represents fold of Ethanol-exposed WT-LPS (fold of WT- ethanol control) (n = 4 blots; * p < 0.05).*
To investigate the role of SIRT2 further, we examined PFKP expression in Ethanol-exposed macrophages with SIRT2 deficiency. Ethanol-exposed SIRT2KO-BMDM exhibited significantly higher PFKP expression vs. WT-BMDM ± LPS. Specifically, in Ethanol-exposed cells without LPS, the PFKP expression increased 1.9-fold in SIRT2KO vs. WT-BMDM-LPS baseline. With LPS stimulation, we found that PFKP expression increased by 2.4 fold in SIRT2KO+LPS vs. WT-BMDM baseline (representative image Figure 4C; WB quantification: Figure 4D).
Using a pharmacological approach, we found that in Ethanol-exposed WT-BMDM without LPS stimulation, the PFKP expression increased significantly to 2.5 fold in AK-7 vs. DMSO-LPS baseline ($p \leq 0.05$). Similarly, in ethanol-exposure with LPS, the PFKP expression increased to 2-fold in AK-7 treated cells vs. DMSO-LPS baseline; although a strong trend, this increase was not statistically significant (representative image Figure 4E; WB image quantification: Figure 4F). We then proceeded to investigate the interaction between SIRT2 and PFKP.
## SIRT2 directly interacts with and deacetylates PFKP
Multiple lysine acetylation and ubiquitination sites for PFKP protein are described in literature [60]. SIRTs1 and 2 are known to promote poly-ubiquitination and degradation of proteins through deacetylation [61]. Acetylation of PFKP at human lysine 395 (hK395) is critical for its glycolytic function [62]. Therefore we tested whether a direct interaction with SIRT2 leads to PFKP-deacetylation at mouse mK394 (hK395), followed by ubiquitination and subsequent proteasomal degradation, in Ethanol-exposed WT-BMDM. Post-translational modification analysis of PFKP protein by PhosphoSitePlus (https://www.phosphosite.org) revealed that mK394 (hK395) residue of PFKP is also a target of ubiquitination, which supports our hypothesis.
We first studied SIRT2-PFKP interaction (Step 4: Figure 1) using immunoprecipitation of Vehicle-exposed RAW 264.7 cells. With SIRT2-immunoprecipitation (IP), we observed PFKP co-expression by immunoblot (IB) (representative IP images: Figure 5A and respective input blots: Figure 5B), indicating SIRT2-PFKP interaction. To further confirm the SIRT2-PFKP interaction in vitro, we performed surface plasmon resonance (SPR) assay. SPR results represent steady state equilibrium binding model and demonstrated a direct association between SIRT2 and PFKP proteins. Concentration of PFKP is indicated on X-axis and the Y axis represents response units (RU) as a quantitative assessment of protein-protein (SIRT2-PFKP) interaction [63]. The interaction kinetics revealed, that increasing concentrations of PFKP (15.62, 31.25, 62.5, 125, 250, 500 and 1000nM), had increasing binding to affinity for SIRT2 (increasing RU) (Figure 5C) with KD (affinity constant) value of 100nM.
**Figure 5:** *SIRT2-PFKP in vivo and in vitro interaction. (A) RAW264.7 cell macrophages (RAW) ± LPS. IP of whole-cell lysates using an anti-SIRT2 antibody followed by IB analysis of PFKP and SIRT2. IP with isotype IgG control antibody was used as a negative control. (B) Western blot analysis of PFKP and SIRT2 in the whole cell lysate used as input for the SIRT2 IP. (C)
In-vitro interaction between SIRT2 and PFKP using SPR. SIRT2 protein immobilized onto sensor chip and the PFKP was flowed at various concentration (15.62, 31.25, 62.5, 125, 250, 500 and 1000nM). The response units on Y axis (RU) represent quantitative assessment of protein-protein interaction. (D) wtPFKP and control plasmid transfection and IP, using turbo-GFP-trap in HEK293T cells, in presence or absence of SIRT2 followed by IB analysis of acetyl lysine, turbo-GFP-wtPFKP and control for PFKP (turbo-GFP). HEK293T cell lysate without transfection used as a negative control. (E) Western blot analysis of control-PFKP (Turbo-GFP), wtPFKP (Turbo-GFP-wtPFKP), DDK-SIRT2 and CPA in whole cell lysate used as input for the turbo-GFP IP. (F) wtPFKP transfection and IP using magnetic-TUBEs in HEK293T cells, in presence or absence of SIRT2 followed by IB analysis of ubiquitination. (G) Western blot analysis of turbo-GFP wtPFKP, DDK-SIRT2, turbo-GFP and CPA in whole cell lysate used as an input for the TUBE IP.*
SIRT2 is a deacetylating enzyme. To further investigate whether the direct interaction between SIRT2 and PFKP leads to PFKP-deacetylation (Step 5: Figure 1), we studied acetylated PFKP expression using co-transfection of PFKP and SIRT2 in HEK293T cells. We used HEK293T cells to allow for stable transfection of both the plasmids. With immunoprecipitation (IP) for PFKP (Figures 5D, E), we observed acetylated-PFKP (IB) in cells transfected with PFKP plasmid alone (wild type: wtPFKP). However, in SIRT2+wtPFKP-co-transfected cells, we found significantly less acetylation (deacetylation) of PFKP.
Thus, we found that the SIRT2-PFKP interaction decreased PFKP expression, either in Ethanol-exposed BMDM (Figures 4A, B) or SIRT2+wtPFKP co-transfected (SIRT2+wtPFKP) RAW 264.7 cells (Input: Figure 5E). Hence, we further investigated whether SIRT2 affects ubiquitination and degradation of PFKP, by co-transfecting wtPFKP and SIRT2 in HEK293T cells. Using Tandem Ubiquitin binding entities (TUBE) assay [64], we observed higher ubiquitination in cells co-transfected with wtPFKP + SIRT2 plasmids vs. PFKP-plasmid alone (ubiquitination: Figures 5F and input: Figure 5G). Together, these data demonstrate that SIRT2 directly deacetylates PFKP which then promotes its ubiquitination and subsequent degradation.
To elucidate the mechanisms by which the SIRT2 interaction affects the stability of PFKP, we performed a site specific mutation of mouse PFKP at K394 amino acid residue from lysine to arginine (K394R), a lysine-acetylation site known to be crucial for glycolysis [62]. First, we studied the effect of SIRT2 co-transfection on stability of wtPFKP versus K394R mutant (mtPFKP) in HEK293T cells. We observed decreased wtPFKP expression in cells with wtPFKP+SIRT2 vs. wtPFKP alone (Figure 6A). However, the mtPFKP expression was well preserved in cells with SIRT2+mtPFKP vs. mtPFKP alone (Figure 6B).
**Figure 6:** *Effect of K394R mutation on PFKP. (A, B) HEK293T cells transfected with wtPFKP/mtPFKP in the presence or absence of SIRT2. Western blot analysis of Turbo-GFP-wtPFKP, DDK-SIRT2, turbo-GFP (control for wtPFKP plasmid transfected) and CPA. (C) mtPFKP transfection and IP using turbo-GFP-trap in HEK293T cells, in presence or absence of SIRT2 followed by IB analysis of acetyl lysine, turbo-GFP-mtPFKP and turbo-GFP (control for wtPFKP plasmid transfected). Pulldown with HEK293T cell lysate without transfection was used as a negative control. (D) Western blot analysis of turbo-GFP mtPFKP, DDK-SIRT2, turbo-GFP and CPA in whole cell lysate, used as an input for the turbo-GFP IP. (E) mtPFKP transfection and IP using magnetic-TUBEs in HEK293T cells, in presence or absence of SIRT2 followed by IB analysis of ubiquitination. (F) Western blot analysis of turbo-GFP mtPFKP, DDK-SIRT2, turbo-GFP and CPA in whole cell lysate used as input for the TUBE IP.*
To further investigate whether relatively better preservation of mtPFKP expression is due to post-translational modifications, we studied the effect of SIRT2+mtPFKP co-transfection on mtPFKP acetylation and ubiquitination. We found that the SIRT2+mtPFKP-co-transfection also led to PFKP deacetylation (immunoprecipitation: Figure 6C and input: Figure 6D), however, this deacetylation was to a lesser degree compared to wtPFKP (Figures 5D, E). Furthermore, the ubiquitination in SIRT2+ mtPFKP co-transfected cells was also to a lesser degree (ubiquitination: Figure 6E and input: Figure 6F) compared to SIRT2+wtPFKP co-transfected cells (Figures 5F, G).
Next, we sought to answer the question of whether mtPFKP is biochemically active, by transfecting RAW 264.7 cells with mtPFKP and wtPFKP. With ethanol exposure, we observed higher basal glycolysis (Supplementary Figure S5A) and glycolytic capacity (supplementary figure: Supplementary Figure S5B) in mtPFKP- vs. wtPFKP-transfected WT-BMDM, suggesting the mutant to be enzymatically active. The green fluorescence protein (GFP) expression confirms successful transfection of plasmids (Supplementary Figure S5C).
These results demonstrate that mtPFKP: 1). Undergoes SIRT2 mediated deacetylation followed by ubiquitination to a lesser degree vs. wtPFKP and 2). Is enzymatically active, evidenced by its ability to participate in glycolysis. Together, we conclude that mK394 is a crucial deacetylation target of SIRT2.
## Acute ethanol-exposure decreases Light Chain3B activation
PFKP acetylation is critically important for Atg4B phosphorylation, so we further investigated the effect of SIRT2-PFKP interaction on Atg4B phosphorylation and LC3 activation [43, 65, 66]. Evidence suggests that LPS stimulation activates Atg4B via serine phosphorylation in macrophages [67]. We first studied whether ethanol-exposure affects Atg4B-phosphorylation. We immunoprecipitated Atg4B in ethanol vs. Vehicle-exposed WT-BMDM ± LPS stimulation to study serine phosphorylation. As expected, we observed that Atg4B-phosphorylation increased in Vehicle-exposed [67] but not in Ethanol-exposed cells with LPS stimulation (immunoprecipitation: Figures 7A, B). However, Ethanol-exposed SIRT2KO-BMDM exhibited increased Atg4B-phosphorylation in response to LPS (immunoprecipitation: Figures 7C, D). We found total Atg4B expression to be unchanged.
**Figure 7:** *The effect of acute ethanol-exposure on pAtg4B and microtubule associated protein 1 light chain-3B (LC3) I and II expression in macrophages. (A) WT-BMDM were exposed to vehicle or ethanol ± LPS. Atg4B was immunoprecipitated (IP) from whole-cell lysates of WT-BMDM using an anti-Atg4B antibody followed by IB analysis of pSerine and total Atg4B. IP with IgG control antibody was used as a negative control. (B) Western blot analysis of total Atg4B in the whole cell lysate used as input for the Atg4B IP. (C) SIRT2KO-BMDM were exposed to ethanol ± LPS. IP for Atg4B from whole-cell lysates of SIRT2KO-BMDM using an anti-Atg4B antibody followed by IB analysis of pSerine and total Atg4B. IP with IgG control antibody was used as a negative control. (D) Western blot analysis of total Atg4B in the whole cell lysate used as input for the Atg4B IP. (E) LC3-I and LC3-II expression were analyzed by western blot in WT-BMDM exposed to vehicle or ethanol ± LPS. Western blot image quantification of ethanol vs. vehicle-expose WT-BMDM± LPS, showing LC3-I in (F) and LC3-II in (G), normalized to vehicle control (Vehicle-LPS) (n = 4 blots; * p < 0.05). (H) Western blot analysis of LC3-I and II expression in Ethanol-exposed WT-BMDM and SIRT2KO-BMDM ± LPS. Western blot image quantification in Ethanol-exposed WT-BMDM and SIRT2KO-BMDM ± LPS for LC3-I in (I) and LC3-II in (J), normalized to ethanol control (Ethanol-LPS) (n = 4 blots; * p < 0.05). (K). Western blot analysis of LC3-I and II expression of Ethanol-exposed WT-BMDM treated with AK-7 or DMSO ± LPS. Western blot image quantification of Ethanol-exposed WT-BMDM treated with AK-7 or DMSO ± LPS for LC3-I in (L) and LC3-II in (M) normalized to ethanol control (Ethanol-LPS) (n = 3 blots; * p < 0.05).*
Phosphorylated-Atg4B activates LC3 by priming pro-LC3 for lipidation to LC3-I and subsequently to its active form, LC3-II [65, 66]. Here, we studied the effect of SIRT2-PFKP interaction and decreased Atg4B phosphorylation on LC3-activation (Step 6: Figure 1), in ethanol exposed cells. We found (representative image Figure 7E; WB image quantification: Figures 7F, G), that LC3-I expression decreased in ethanol vs. Vehicle-exposed WT-BMDM without LPS ($54\%$ of vehicle-LPS, a decrease by $46\%$, $p \leq 0.05$) and with LPS (Ethanol $85\%$ of Vehicle-LPS, decrease by $15\%$, $p \leq 0.05$). We also found that LC3-II expression with LPS stimulation decreased in Ethanol vs. Vehicle- exposure (Ethanol $63\%$ of Vehicle; a decrease by $37\%$, $p \leq 0.05$). There was a strong trend towards decreased expression of LC3-II in Ethanol vs. Vehicle exposed cells without LPS (Ethanol $88\%$ of Vehicle, a decrease by $12\%$, $p \leq 0.05$). Ethanol-exposed cells with LPS showed a strong trend of decreased LC3-II expression vs. Vehicle-exposed cells without LPS (ethanol $76\%$ of vehicle-exposure, a $24\%$ decrease, $p \leq 0.05$).
In contrast, we observed that both LC3-I and LC3-II expressions showed a trend towards higher expressions in Ethanol-exposed SIRT2KO-BMDM (representative image Figure 7H; WB image quantification: Figures 7I, J). Specifically, without LPS, LC3-I expression was higher in SIRT2KO vs. WT-BMDM by $40\%$ ($p \leq 0.05$). With LPS, the LC3-I expression in SIRT2KO was $100\%$ higher than WT-BMDM without LPS ($p \leq 0.05$). We observed significantly higher LC3-II expression with LPS in Ethanol-exposed in SIRT2KO vs. WT-BMDM group by $30\%$ ($p \leq 0.05$). Without LPS, we found a trend towards increase in SIRT2KO vs. WT-BMDM by $30\%$ ($p \leq 0.05$).
Similarly, both LC3-I and LC3-II expressions were higher in Ethanol-exposed and AK-7-treated WT-BMDM with LPS stimulation vs. DMSO treatment (representative image Figure 7K; WB image quantification: Figures 7L, M). Specifically, with LPS, LC3-II expression was significantly higher in AK-7 vs. DMSO treatment by $47\%$. Together, these data demonstrate that ethanol-exposure decreases Atg4B-phosphorylation and LC3 activation via SIRT2. Given the strong trends in decreased LC3 activation, we studied the effect of ethanol-exposure on LAP.
## Ethanol-exposure represses LAP via SIRT2-PFKP interaction
LC3-associated phagocytosis (LAP) involves incorporation of LC3-II in to LAPosome to enhance pathogen clearance [68]. Since ethanol-exposure decreased LC3 activation via SIRT2-PFKP interaction (Figure 7), we wondered if that would affect LAP. First, we studied LAP in vehicle vs. Ethanol-exposed WT-BMDM (Step 7: Figure 1). We observed that LAP increased with LPS stimulation in Vehicle-exposed WT-BMDM by 15 fold (vehicle: +LPS/-LPS=15), but the response was profoundly muted in Ethanol-exposed BMDM to 4.3-fold increase (ethanol: +LPS/-LPS=4.3) (representative image Figure 8A; Image quantification: Figure 8B). Next, we studied Beclin-1, RUN domain-containing protein (Rubicon) and vacuolar protein sorting 34 (VPS34), proteins other than LC3 that are crucial for LAPosome formation [46]. We observed a lack of differential expression between ethanol vs. Vehicle-exposed WT-BMDM ± LPS of Rubicon (representative Western Blot: Supplementary Figure S6A; WB quantification: Supplementary Figure S6B), Beclin-1 (representative Western Blot: Supplementary Figure S6C; WB quantification: Supplementary Figure S6D) and VPS34 ((representative Western Blot: Supplementary Figure S6E; WB quantification: Supplementary Figure S6F). These data demonstrate that the repression of LAP in Ethanol-exposed WT-BMDM is mainly driven by decreased LC3 activation with acute ethanol-exposure.
**Figure 8:** *The effect of acute ethanol-exposure on LC3-associted phagocytosis in macrophages. LC3-associated phagocytosis (LAP) in WT-BMDM and SIRT2KO-BMDM, exposed to vehicle or ethanol ± LPS. (A) Representative images of intracellular pHrodo bioparticles during phagocytosis, co-stained for LC3. (B) For each image, the co-localization of intracellular pHrodo (red) and LC3 (green) were determined as LAP and divided by the total numbers of cells (nuclei). Graph represents fluorescence quantification of LAP in WT-BMDM (n=4; * p<0.05). (C) Representative images of LAP in WT-BMDM and SIRT2KO-BMDM exposed to ethanol ± LPS. (D) Fluorescence quantification of LAP in WT-BMDM and SIRT2KO-BMDM (n=5; * p<0.05). (E) Representative images of LAP in either control siRNA or PFKP siRNA nucleo-transfected SIRT2KO-BMDM. (F) Fluorescence quantification of LAP in siRNA nucleo-transfected SIRT2KO-BMDM (n=4; * p<0.05). (G) PFKP expression was analyzed in either control siRNA or PFKP siRNA nucleo-transfected SIRT2KO-BMDM ± LPS. PFKP protein expression was detected by western blot in whole cell lysate.*
To investigate the role of SIRT2, we studied LAP in Ethanol-exposed SIRT2KO vs. WT-BMDM (representative image Figure 8C; Image quantification: Figure 8D). In Ethanol-exposed cells without LPS, LAP increased by 7.58-fold in SIRT2KO vs. WT-BMDM. With LPS, the LAP increased in SIRT2KO vs. WT-BMDM by 8.7 fold.
Thus, we observed that SIRT2 mutes phagocytosis, LAP and PFKP expression in WT-BMDM, while SIRT2 deficiency preserves all three with ethanol-exposure. So to further confirm the direct role of PFKP in preservation of LAP with SIRT2-deficiency, we silenced PFKP using nucleofection of PFKP siRNA in SIRT2KO-BMDM, and studied LAP (representative image Figure 8E; Image quantification: Figure 8F). We observed that the PFKP siRNA in SIRT2KO-BMDM abrogated LAP dramatically by $55\%$ without LPS and $88\%$ with LPS vs. control siRNA without LPS. Decreased PFKP expression with nucleofection was confirmed by western blot analysis (representative blot Figure 8G). These data show that the preservation of LAP in SIRT2KO-BMDM is PFKP-dependent. We also found the Atg4B-phosphorylation and LC3-I and II expressions to be decreased in PFKP siRNA vs. control siRNA in SIRT2KO-BMDM (supplementary figures: Figures S7A, B).
We then examined whether the mtPFKP transfection affects LC3 expression and LAP, in Ethanol-exposed WT-BMDM (representative Western Blot: Figure 9A and WB quantification: Figures 9B, C). We observed that Ethanol-exposed and mtPFKP transfected RAW 264.7 cells showed increased LC3-I without LPS (mtPFKP-LPS/wtPFKP-LPS= 1.9 fold-increase $p \leq 0.05$) and with LPS (mtPFKP+LPS/wtPFKP-LPS=2.4 fold-increase $p \leq 0.05$). LC3-II expressions also increased in mtPFKP vs. wtPFKP transfection without LPS (mtPFKP-LPS/wtPFKP-LPS=1.4 fold-increase, $p \leq 0.05$) and showed a trend towards increase with LPS (mtPFKP+LPS/wtPFKP-LPS=1.2 fold-increase, $p \leq 0.05$). We then studied LAP in mtPFKP transfected cells (representative image: Figure 9D, image quantification: Figure 9E). Here, the LC3 was stained using Alexa Fluor 647, and artificially colored green (and not an indicator of turbo-GFP-tag) for congruency (with Figures 8, 10, 11). We observed that the Ethanol-exposed and mtPFKP-transfected RAW 264.7 cells showed a robust LAP vs. wtPFKP transfection in response to LPS (+LPS: mtPFKP/wtPFKP=8.5 fold-increase, $p \leq 0.05$). The GFP expression in cells (Supplementary Figure 8) confirmed the wtPFKP and mtPFKP transfections. Thus we show that the mtPFKP is biologically active, in addition to enzymatic activity (Supplementary Figures S5A, B).
**Figure 9:** *Effect of LC3-I/II and LC3-associted phagocytosis in Ethanol-exposed macrophages with wtPFKP and mtPFKP. (A) Western blot analysis of LC3-I and LC3-II expression in Ethanol-exposed RAW 264.7 cells with wtPFKP or mtPFKP transfection± LPS. Western blot image quantification of Ethanol-exposed RAW 264.7 cells with wtPFKP or mtPFKP transfection± LPS for LC3-I in (B) and LC3-II in C (n = 4 blots; * p < 0.05). (C, D) Representative images of intracellular pHrodo bioparticles (Red) and LC3 (green) showing LAP in Ethanol-exposed RAW264.7 cell macrophages transfected with wtPFKP or mtPFKP± LPS. (E) LAP quantification: For each image in “E”, the co-localization of intracellular pHrodo (red) and LC3 (green) were determined as LC3-associated phagocytosed particles and divided by the total numbers of cells (nuclei). Graph represents fluorescence quantification of LAP in WT-BMDM (n=4; * p<0.05).* **Figure 10:** *The effect of AK-7 survival in ethanol with sepsis mice and LC3-associated phagocytosis in Ethanol-exposed macrophages. (A) Representative images of Ethanol-exposed WT-BMDM with AK-7 or DMSO ± LPS. Staining of intracellular pHrodo bioparticles (red) and LC3 (green). (B) Fluorescence quantification of LAP in Ethanol-exposed WT-BMDM with AK-7 or DMSO ± LPS quantified as the co-localization of intracellular pHrodo (red) and LC3 (green) per cell (n=4; * p<0.05). (C) Effect of AK-7 vs. DMSO treatment on 7-day survival in ethanol-drinking WT mice with cecal slurry-induced sepsis. Kaplan-Meier survival curve shows significantly higher survival in AK-7 vs. DMSO treatment (AK-7: 60% vs. Vehicle: 25%) (n=10 each group; *p<0.05 vs ethanol sepsis + DMSO using Log-Rank test). (D) Peritoneal lavage from Ethanol-exposed and AK-7 or DMSO treated WT sepsis mice at 8-days post-CS injection. Bacterial colony forming units (CFU) are presented.* **Figure 11:** *The effect of acute ethanol-exposure on SIRT2 expression and the effect of AK-7 on LC3-associated phagocytosis in Ethanol-exposed Human macrophages. Human macrophages were exposed to vehicle or ethanol ± LPS to study SIRT2 expression and LAP. SIRT2 expression was analyzed by immunostaining (A) Representative images of SIRT2 immunostaining in vehicle or Ethanol-exposed human macrophages ± LPS. (B) Fluorescence quantification of SIRT2 immunostaining in human macrophages (n=4; *p<0.05). (C) LAP in human macrophages with vehicle or ethanol-exposure ± LPS. Representative images of intracellular pHrodo bioparticles during phagocytosis and co-stained for LC3. (D) For each image, the co-localization of intracellular pHrodo (red) and LC3 (green) were determined as LAP and divided by the total numbers of nuclei. Graph represents fluorescence quantification of LAP in human macrophages (n=4; * p<0.05). (E) Ethanol-exposed human macrophages were co-treated with SIRT2 inhibitor AK-7 or DMSO ± LPS. Representative images of intracellular pHrodo bioparticles during phagocytosis and stained for LC3 to study LAP. (F) Graph represents fluorescence quantification of LAP in Ethanol-exposed human macrophages ± AK-7 ± LPS stimulation (n=4; * p<0.05).*
Together, these data demonstrate, that in Ethanol-exposed cells, increased SIRT2 represses LC3 activation and LAP via PFKP deacetylation at mK394.
## SIRT2 inhibitor AK-7 reverses repression of LAP with acute ethanol-exposure in macrophages, and improves survival in ethanol with sepsis mice
We then studied the effect of AK-7 on LAP in Ethanol-exposed WT-BMDM± LPS (representative image Figure 10A; Image quantification: Figure 10B). We observed that similar to SIRT2KO-BMDM (above), while DMSO-treated WT-BMDM showed repressed LAP, AK-7 treated cells showed a robust increase in LAP with LPS vs. DMSO control (AK-7+LPS/DMSO-LPS= 5.3 fold increase, $p \leq 0.05$) and DMSO with LPS (AK-7+LPS/DMSO+LPS=5.24 fold-increase, $p \leq 0.05$) Previously, we reported that ethanol with sepsis decreases 7-day survival with inability to clear pathogen in mice via increased SIRT2 expression [9]. Here, we treated Ethanol-exposed WT mice with SIRT2-specific inhibitor AK-7 (40mg/kg intraperitoneally, once) [22] and induced polymicrobial sepsis using intraperitoneal injection of cecal slurry [9]. We observed significantly improved 7-day survival in AK-7- vs. DMSO-treated ethanol with sepsis mice (AK-7: $60\%$ vs. Vehicle: $25\%$; $p \leq 0.005$ using Log-Rank test: Figure 10C). In the surviving mice (day 8), we observed a strong trend towards higher bacterial growth in the peritoneal cavity (peritoneal lavage) in DMSO-treated vs. AK-7-treated mice, although this difference was not statistically significant (median CFU Sepsis+ Ethanol= 10,100 vs. Sepsis+ AK-7 = 200, $$p \leq 0.057$$) (Figure 10D).
## SIRT2 represses LAP in human monocyte-derived macrophages with acute ethanol-exposure
Lastly, we confirmed the WT-BMDM findings of increased SIRT2 expression (Graphical Abstract: Step 2) and repression of LAP (Graphical Abstract: Step 7), in Ethanol-exposed human monocyte-derived macrophages ± LPS. Using immunocytochemistry, we observed a trend towards increased SIRT2 expression in Ethanol-exposed macrophages without LPS (Ethanol/Vehicle-LPS= 1.6-fold increase $p \leq 0.05$) and with LPS (Ethanol+LPS/Vehicle-LPS= 4.6 fold-increase $p \leq 0.05$) vs. vehicle control (representative image: Figure 11A and fluorescence quantification: Figure 11B). We confirmed these findings with Western blot (WB) analysis, which also revealed a strong trend towards increased SIRT2 (isoforms 43 kDa and 39 kDa) ± LPS (representative blot: Supplementary Figure S9A and WB quantification: Supplementary Figure S9B). We observed decreased PFKP (Supplementary Figure S10A) and LC3-I and II expressions (Supplementary Figure S10B) in Ethanol-exposed macrophages ± LPS. Similar to WT-BMDM, we observed increased LAP in Vehicle-exposed, but profoundly repressed LAP in Ethanol-exposed human macrophages with LPS (Vehicle: +LPS/-LPS= 12.6 vs. Ethanol: +LPS/-LPS= 2.3 fold-increase, $p \leq 0.05$) (representative image Figure 11C; Image quantification: Figure 11D). AK-7 treated human-macrophages exhibited preserved PFKP (Supplementary Figure S10C), LC3-I and II (Supplementary Figure S10D) expressions. Lastly, we observed reversal of LAP-repression in ethanol- exposed and AK-7 vs. DMSO-treated human macrophages without LPS (-LPS: AK-7/DMSO= 10.27 fold-increase, $p \leq 0.05$) and with LPS (+LPS: AK-7/DMSO=6.11 fold-increase $p \leq 0.05$) (representative image Figure 11E; Image quantification: Figure 11F).
## Discussion
We report here, for the first time, that in ethanol-exposed macrophages, increased SIRT2 expression leads to PFKP-deacetylation, and that SIRT2-PFKP interaction impairs phagocytosis, mutes glycolysis, decreases Atg4B-phosphorylation and LC3 activation to repress LAP, which is crucial for bacterial clearance. Specifically, we found that SIRT2 directly interacts and deacetylates PFKP at mK394; acetylation of PFKP at mK394 is essential for its function [43]. We also demonstrate that deacetylated PFKP at mK394 (hK395) is more prone to ubiquitination and degradation. Furthermore, we show that the SIRT2 deficiency (genetic deficiency or pharmacological inhibition) stabilizes PFKP expression, preserves acetylation of PFKP and glycolysis in response to LPS, Atg4B phosphorylation, LC3 activation and LAP in ethanol-exposed macrophages.
The effect of SIRT2 on phagocytosis is reported in literature, but here we demonstrate for the first time, that the SIRT2-PFKP interaction plays a critical mechanistic role in dysregulating phagocytosis, including LAP, a subset of phagocytosis crucial for pathogen degradation, in ethanol-exposure induced innate immune function [69]. We have implicated SIRT2 in prolonged immunosuppression in vivo in obesity with sepsis mice, via NFκB p65 deacetylation and deactivation [22]. We have also shown the effect of SIRT2 deficiency on sepsis response in vivo in lean mice previously [70]. Here, we show the mechanism by which SIRT2 regulates immuno-metabolic response in macrophages.
Repressed LAP is known to cause sepsis-induced immunosuppression [47]. LAP is a bridge between phagocytosis and autophagy [71]. We observed that the ethanol-exposure dysregulates phagocytosis in macrophages, as shown in literature [32, 72, 73]. A number of other proteins such as Beclin-1, Rubicon and VPS34 are crucial for LAP. We did not find the effect of ethanol exposure on the expression profiles of either of these proteins with or without LPS, suggesting that the repressed LAP observed in our studies was driven by decreased LC3 activation. Evidence suggests that Rubicon selectively enhances LAPosome formation and negatively regulates autophagy at multiple steps [44, 74, 75]. While we didn’t observe differential expression, we found Rubicon to be induced in all groups of cells. Although LAP uses some of the machinery used in induction of autophagy, LAP and autophagy are two distinctly separate entities, regulated by different mechanisms [51]. The distinguishing features include single membrane structure of LAP vs. double membranes of autophagosome, and the requirement of the autophagosome to be induced by pre-initiation complex, while LAP is independent of the pre-initiation complex and induced by cell surface structure [44, 53, 71].
We find that decreased expression of PFKP represses LAP via dysregulation of Atg4B-LC3 activation. Consistent with the published literature, we found that PFKP serves as a kinase that phosphorylates Atg4B which subsequently lipidates and activates LC3 [43]. Stimulation with LPS in macrophages is known to activate Atg4B via phosphorylation [67]. Recent evidence suggests that SIRT2 directly deacetylates and activates Atg4B to induce starvation-induced autophagy [76]. In contrast however, we find that with acute ethanol-exposure induced increase in SIRT2 expression decreases Atg4B phosphorylation in WT-BMDM, which is preserved in SIRT2-deficient cells. This difference may be partly explained by biological contexts differentially regulating LC3 activation and autophagy in starvation vs. inflammatory stimuli. The biological-context-driven regulation of LC3 and autophagy need further evaluation. Also, while we have implicated SIRT2 in dysregulated autophagy with high-fat exposure, the effect of ethanol-exposure and SIRT2-PFKP interaction on autophagy in ethanol-exposed macrophages needs further evaluation [77].
Lysine acetylation of PFKP at mK394 in mice (hK395) is critical for its glycolytic function [62]. We found that when co-transfected with SIRT2 plasmid, PFKP underwent degradation in HEK293T cells. Furthermore, PFKP expression was preserved in SIRT2 deficient (genetic deletion and pharmacological inhibition) macrophages (Figures 4C-F), suggesting SIRT2-PFKP interaction to be critical for PFKP degradation. Lysine 394 (mK394) is a crucial ubiquitination site for PFKP, in addition to acetylation. Site directed mutation of PFKP at mK394 from lysine to arginine (K394R), decreased ubiquitination and degradation of PFKP, even in the presence of SIRT2. However, the mutation did not completely abolish deacetylation of PFKP. This is somewhat expected, since we mutated one of the many acetylation sites, albeit a crucial one for its function. We contribute the partial deacetylation of mtPFKP at one or many other acetylation sites by SIRT2 and other deacetylating enzymes. Importantly however, we found, for the first time, that the mK394R mutant (mtPFKP) is enzymatically and biologically active, suggesting K394 site to be crucial for its function in regards to glycolysis and LC3 activation consistent with literature [62]. We extended this finding further, to study LAP and bacterial clearance, important for sepsis survival with ethanol-exposure.
We show that the SIRT2 inhibitor AK-7 reverses repressed LAP, improves peritoneal bacterial clearance and improves survival in ethanol with sepsis mice. Thus, we speculate, that the reversal of phagocyte dysregulation with AK-7 is largely due to the LAPosome formation. Lastly, using ethanol-exposure in human monocyte-derived macrophages, we confirmed the key findings of increased SIRT2 expression, repressed LAP and reversal of LAP-repression by SIRT2 inhibition.
Acute ethanol-exposure leads to immunosuppression [38, 49, 57, 78, 79]. However, chronic ethanol-exposure is shown to induce both, pro-inflammatory and anti-inflammatory phenotypes in macrophages. For example, chronic ethanol exposure shows pro-inflammatory and pro-glycolytic phenotypes in peritoneal macrophages [80]. Recent evidence indicates that chronic ethanol-exposure induces pro-glycolytic phenotype in alveolar macrophages via HIF-1α induction [81]. However, in human and experimental models, chronic ethanol-exposure induces oxidative stress in the alveolar macrophages along with suppression of phagocytosis, via several mechanisms such as modulation of zinc metabolism, Peroxisome Proliferator-Activated *Receptor gamma* (PPARγ)-regulation, AMP-activated protein kinase (AMPK) signaling amongst others (31, 82–84). Thus, the effects of ethanol on macrophages, in vivo and in vitro, are more nuanced, and several factors such as biological context, site of organ injury etc. affect macrophage behavior. While we elucidated glycolysis function here, oxidative stress and PPARγ- related modulation of macrophage function by SIRT2 in ethanol-exposed macrophages, needs further evaluation.
Our study focused on acute ethanol-exposure with immunosuppression and showed effects on glycolysis, as expected. Brain and Muscle Arnt-Like Protein-1 (Bmal1) is shown to negatively regulate macrophage polarization to M1 phenotype via modulation of glycolysis in experimental model of alcoholic liver disease [85]. Interestingly, SIRT1-Bmal1 interactions were found to be protective of acute ethanol-exposure-induced liver injury in an experimental model [86]. We have shown differential roles of SIRT1 and SIRT2 in sepsis in a biological context-dependent manner previously [22]. Circadian rhythm disruption and dysregulation of CLOCK genes such as Bmal1, are reported in sepsis patients [87]. The role of SIRT2-Bmal1 interactions and their effect on immune and metabolic functions of macrophages during sepsis remain to be elucidated. Similarly, a cross talk between SIRT1 and SIRT2 during acute ethanol-induced immunosuppression in ethanol with sepsis, needs evaluation.
There are several limitations to our study. Ethanol-exposure is known to impair autophagy in immune cells (88–90). We showed that the PFKP-SIRT2 interaction impairs LC3-activation, the effect of this interaction on autophagy, a critical cytoprotective pathway, needs detailed investigation with and without ethanol exposure. We pursued repressed LAP with ethanol-exposure via metabolic regulation. However, other molecular mechanisms responsible for repression of LAP, including transcriptional-regulation and post-translational modification of proteins responsible for phagosome formation, need further investigation (91–94). Similarly, the effect of SIRT2-PFKP interaction on cell death pathways, including necroptosis, and how they modulate autophagy, need further evaluation [95].
In conclusion, we report, for the first time, that acute ethanol-exposure induced SIRT2 interacts with PFKP, specifically targeting mouse K394 (human K395) for deacetylation leading to PFKP ubiquitination and degradation to repress Atg4B phosphorylation, LC3 activation and LAP, in macrophages. Dysregulated LAP is implicated in immunosuppression during sepsis [47, 96]. We also show that SIRT2 inhibitor AK-7 reverses dysregulated phagocytosis and LAP and improves sepsis survival in mice. Thus, SIRT2 inhibition maybe a potential therapeutic option in alcohol with sepsis.
Study approvals: The animal experiments were was approved by the Institutional Animal Care and Use Committee (IACUC) at the Cleveland Clinic Lerner Research Institute (LRI). All the experiments were performed according to the NIH guidelines (ACUC approval #: 00002194, PI: Vachharajani). The human healthy volunteers without active infection and/or immunosuppression/active cancer diagnosis were consented by the Respiratory Institute Research Coordinators using an informed consent approved by Cleveland Clinic IRB (IRB # 19-132, PI: Vachharajani).
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
The studies involving human participants were reviewed and approved by Institutional Review Board, Cleveland Clinic, Cleveland OH 44195. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Animal Care and Use Committee, Lerner Research Institute Cleveland Clinic, Cleveland OH 44195.
## Author contributions
Designing research studies: VV, SR, AG. Conducting experiments and acquisition of data: AG, SR, EC, CK, SA, RS, AB. Data analysis: VV, AG, SR, CK, RS. Providing reagents: LN, RS, AB. Writing and editing the manuscript: VV, AG, SR, LN, RS, AB. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.1079962/full#supplementary-material
## References
1. Rhee C, Jones TM, Hamad Y, Pande A, Varon J, O’Brien C. **Prevalence, underlying causes, and preventability of sepsis-associated mortality in US acute care hospitals**. *JAMA Netw Open* (2019) **2**. DOI: 10.1001/jamanetworkopen.2018.7571
2. Buchman TG, Simpson SQ, Sciarretta KL, Finne KP, Sowers N, Collier M. **Sepsis among Medicare beneficiaries: 1. the burdens of sepsis, 2012-2018**. *Crit Care Med* (2020) **48**. DOI: 10.1097/CCM.0000000000004224
3. Torio CM, Moore BJ. *National inpatient hospital costs: The most expensive conditions by payer, 2013: Statistical brief 204* (2006)
4. Vachharajani VT, Fu Liu T, Brown CM, Wang X, Buechler NL, Wells JD. **Inhibition during the hypoinflammatory phenotype of sepsis enhances immunity and improves outcome**. *J Leukoc Biol* (2014). DOI: 10.1189/jlb.3MA0114-034RR
5. Boomer JS, To K, Chang KC, Takasu O, Osborne DF, Walton AH. **Immunosuppression in patients who die of sepsis and multiple organ failure**. *JAMA: J Am Med Assoc* (2011) **306**. DOI: 10.1001/jama.2011.1829
6. Otto GP, Sossdorf M, Claus RA, Rodel J, Menge K, Reinhart K. **The late phase of sepsis is characterized by an increased microbiological burden and death rate**. *Crit Care* (2011) **15** R183. DOI: 10.1186/cc10332
7. O’Brien JM, Lu B, Ali NA, Martin GS, Aberegg SK, Marsh CB. **Alcohol dependence is independently associated with sepsis, septic shock, and hospital mortality among adult intensive care unit patients**. *Crit Care Med* (2007) **35**. DOI: 10.1097/01.CCM.0000254340.91644.B2
8. Jin L, Batra S, Jeyaseelan S. **Diminished neutrophil extracellular trap (NET) formation is a novel innate immune deficiency induced by acute ethanol exposure in polymicrobial sepsis, which can be rescued by CXCL1**. *PloS Pathog* (2017) **13**. DOI: 10.1371/journal.ppat.1006637
9. Gandhirajan A, Roychowdhury S, Kibler C, Bauer SR, Nagy LE, Vachharajani V. **Ethanol exposure attenuates immune response in sepsis**. *Alcoholism Clin Exp Res* (2021) **45**. DOI: 10.1111/acer.14542
10. Gomes P, Outeiro TF, Cavadas C. **Emerging role of sirtuin 2 in the regulation of mammalian metabolism**. *Trends Pharmacol Sci* (2015) **36**. DOI: 10.1016/j.tips.2015.08.001
11. Schug TT, Li X. **Sirtuin 1 in lipid metabolism and obesity**. *Ann Med* (2011) **43** 198-211. DOI: 10.3109/07853890.2010.547211
12. Michan S, Sinclair D. **Sirtuins in mammals: Insights into their biological function**. *Biochem J* (2007) **404** 1-13. DOI: 10.1042/BJ20070140
13. Vachharajani VT, Liu T, Wang X, Hoth JJ, Yoza BK, McCall CE. **Sirtuins link inflammation and metabolism**. *J Immunol Res* (2016) **2016** 8167273. DOI: 10.1155/2016/8167273
14. Suffredini AF. **Targeting sirtuin to modulate human inflammation**. *Crit Care Med* (2015) **43**. DOI: 10.1097/CCM.0000000000001030
15. Guarente L. **Calorie restriction and SIR2 genes–towards a mechanism**. *Mech Ageing Dev* (2005) **126**. DOI: 10.1016/j.mad.2005.03.013
16. Imai S, Guarente L. **NAD+ and sirtuins in aging and disease**. *Trends Cell Biol* (2014) **24**. DOI: 10.1016/j.tcb.2014.04.002
17. Guarente L. **Sirtuins in aging and disease**. *Cold Spring Harbor Symp quantitative Biol* (2007) **72**. DOI: 10.1101/sqb.2007.72.024
18. Guarente L. **Sirtuins, aging, and metabolism**. *Cold Spring Harbor Symp quantitative Biol* (2011) **76** 81-90. DOI: 10.1101/sqb.2011.76.010629
19. North BJ, Verdin E. **Interphase nucleo-cytoplasmic shuttling and localization of SIRT2 during mitosis**. *PloS One* (2007) **2**. DOI: 10.1371/journal.pone.0000784
20. Liu TF, Vachharajani VT, Yoza BK, McCall CE. **NAD+-dependent sirtuin 1 and 6 proteins coordinate a switch from glucose to fatty acid oxidation during the acute inflammatory response**. *J Biol Chem* (2012) **287**. DOI: 10.1074/jbc.M112.362343
21. Liu TF, Yoza BK, El Gazzar M, Vachharajani VT, McCall CE. **NAD+-dependent SIRT1 deacetylase participates in epigenetic reprogramming during endotoxin tolerance**. *J Biol Chem* (2011) **286**. DOI: 10.1074/jbc.M110.196790
22. Wang X, Buechler NL, Martin A, Wells J, Yoza B, McCall CE. **Sirtuin-2 regulates sepsis inflammation in ob/ob mice**. *PloS One* (2016) **11**. DOI: 10.1371/journal.pone.0160431
23. Wang X, Buechler NL, Yoza BK, McCall CE, Vachharajani VT. **Resveratrol attenuates microvascular inflammation in sepsis**. *Obesity.* (2015) **23**. DOI: 10.1002/oby.21086
24. Smith LM, Yoza BK, Hoth JJ, McCall CE, Vachharajani V. **SIRT1 mediates septic cardiomyopathy in a murine model of polymicrobial sepsis**. *Shock.* (2020) **54** 96-101. DOI: 10.1097/SHK.0000000000001429
25. Gandhirajan A, Roychowdhury S, Kibler C, Bauer SR, Nagy LE, Vachharajani V. **Ethanol exposure attenuates immune response in sepsis via sirtuin 2 expression**. *Alcoholism Clin Exp Res* (2020). DOI: 10.1111/acer.14542
26. Liu TF, Vachharajani V, Millet P, Bharadwaj MS, Molina AJ, McCall CE. **Sequential actions of SIRT1-RELB-SIRT3 coordinate nuclear-mitochondrial communication during immunometabolic adaptation to acute inflammation and sepsis**. *J Biol Chem* (2015) **290** 396-408. DOI: 10.1074/jbc.M114.566349
27. Wang X, Buechler NL, Long DL, Furdui CM, Yoza BK, McCall CE. **Cysteine thiol oxidation on SIRT2 regulates inflammation in obese mice with sepsis**. *Inflammation.* (2019) **42**. DOI: 10.1007/s10753-018-0881-9
28. Lin J, Sun B, Jiang C, Hong H, Zheng Y. **Sirt2 suppresses inflammatory responses in collagen-induced arthritis**. *Biochem Biophys Res Commun* (2013) **441** 897-903. DOI: 10.1016/j.bbrc.2013.10.153
29. Martins PS, Kallas EG, Neto MC, Dalboni MA, Blecher S, Salomao R. **Upregulation of reactive oxygen species generation and phagocytosis, and increased apoptosis in human neutrophils during severe sepsis and septic shock**. *Shock.* (2003) **20**. DOI: 10.1097/01.shk.0000079425.52617.db
30. Scher MB, Vaquero A, Reinberg D. **SirT3 is a nuclear NAD+-dependent histone deacetylase that translocates to the mitochondria upon cellular stress**. *Genes Dev* (2007) **21**. DOI: 10.1101/gad.1527307
31. Kaphalia L, Srinivasan MP, Kakumanu RD, Kaphalia BS, Calhoun WJ. **Ethanol exposure impairs AMPK signaling and phagocytosis in human alveolar macrophages: Role of ethanol metabolism**. *Alcoholism Clin Exp Res* (2019) **43**. DOI: 10.1111/acer.14131
32. Karavitis J, Murdoch EL, Deburghgraeve C, Ramirez L, Kovacs EJ. **Ethanol suppresses phagosomal adhesion maturation, rac activation, and subsequent actin polymerization during FcgammaR-mediated phagocytosis**. *Cell Immunol* (2012) **274** 61-71. DOI: 10.1016/j.cellimm.2012.02.002
33. Karavitis J, Murdoch EL, Gomez CR, Ramirez L, Kovacs EJ. **Acute ethanol exposure attenuates pattern recognition receptor activated macrophage functions**. *J Interferon Cytokine Res* (2008) **28**. DOI: 10.1089/jir.2007.0111
34. Pavlou S, Wang L, Xu H, Chen M. **Higher phagocytic activity of thioglycollate-elicited peritoneal macrophages is related to metabolic status of the cells**. *J Inflammation* (2017) **14** 4. DOI: 10.1186/s12950-017-0151-x
35. Kelly B, O’Neill LA. **Metabolic reprogramming in macrophages and dendritic cells in innate immunity**. *Cell Res* (2015) **25**. DOI: 10.1038/cr.2015.68
36. Arts RJ, Gresnigt MS, Joosten LA, Netea MG. **Cellular metabolism of myeloid cells in sepsis**. *J Leukoc Biol* (2017) **101**. DOI: 10.1189/jlb.4MR0216-066R
37. Freemerman AJ, Johnson AR, Sacks GN, Milner JJ, Kirk EL, Troester MA. **Metabolic reprogramming of macrophages: Glucose transporter 1 (GLUT1)-mediated glucose metabolism drives a proinflammatory phenotype**. *J Biol Chem* (2014) **289**. DOI: 10.1074/jbc.M113.522037
38. Goral J, Choudhry MA, Kovacs EJ. **Acute ethanol exposure inhibits macrophage IL-6 production: Role of p38 and ERK1/2 MAPK**. *J Leukoc Biol* (2004) **75**. DOI: 10.1189/jlb.0703350
39. Wang J, Zhang P, Zhong J, Tan M, Ge J, Tao L. **The platelet isoform of phosphofructokinase contributes to metabolic reprogramming and maintains cell proliferation in clear cell renal cell carcinoma**. *Oncotarget.* (2016) **7**. DOI: 10.18632/oncotarget.8382
40. Keating ST, Groh L, Thiem K, Bekkering S, Li Y, Matzaraki V. **Rewiring of glucose metabolism defines trained immunity induced by oxidized low-density lipoprotein**. *J Mol Med (Berl)* (2020) **98**. DOI: 10.1007/s00109-020-01915-w
41. Xu Y, Chen Y, Zhang X, Ma J, Liu Y, Cui L. **Glycolysis in innate immune cells contributes to autoimmunity**. *Front Immunol* (2022) **13**. DOI: 10.3389/fimmu.2022.920029
42. Vernon PJ, Tang D. **Eat-me: autophagy, phagocytosis, and reactive oxygen species signaling**. *Antioxid Redox Signaling* (2013) **18**. DOI: 10.1089/ars.2012.4810
43. Li X, Sun L, Yan G, Yan X. **PFKP facilitates ATG4B phosphorylation during amino acid deprivation-induced autophagy**. *Cell signalling* (2021) **82** 109956. DOI: 10.1016/j.cellsig.2021.109956
44. Herb M, Gluschko A, Schramm M. **LC3-associated phagocytosis - the highway to hell for phagocytosed microbes**. *Semin Cell Dev Biol* (2020) **101** 68-76. DOI: 10.1016/j.semcdb.2019.04.016
45. Vazquez BN, Thackray JK, Simonet NG, Kane-Goldsmith N, Martinez-Redondo P, Nguyen T. **SIRT7 promotes genome integrity and modulates non-homologous end joining DNA repair**. *EMBO J* (2016) **35**. DOI: 10.15252/embj.201593499
46. Martinez J, Malireddi RK, Lu Q, Cunha LD, Pelletier S, Gingras S. **Molecular characterization of LC3-associated phagocytosis reveals distinct roles for Rubicon, NOX2 and autophagy proteins**. *Nat Cell Biol* (2015) **17** 893-906. DOI: 10.1038/ncb3192
47. Akoumianaki T, Vaporidi K, Diamantaki E, Pene F, Beau R, Gresnigt MS. **Uncoupling of IL-6 signaling and LC3-associated phagocytosis drives immunoparalysis during sepsis**. *Cell Host Microbe* (2021) **29** 1277-93.e6. DOI: 10.1016/j.chom.2021.06.002
48. Wang X, Buechler NL, Yoza BK, McCall CE, Vachharajani V. **Adiponectin treatment attenuates inflammatory response during early sepsis in obese mice**. *J Inflammation Res* (2016) **9**. DOI: 10.2147/JIR.S119021
49. Szabo G, Mandrekar P, Girouard L, Catalano D. **Regulation of human monocyte functions by acute ethanol treatment: decreased tumor necrosis factor-alpha, interleukin-1 beta and elevated interleukin-10, and transforming growth factor-beta production**. *Alcoholism Clin Exp Res* (1996) **20**. DOI: 10.1111/j.1530-0277.1996.tb05269.x
50. Marchi LF, Sesti-Costa R, Ignacchiti MD, Chedraoui-Silva S, Mantovani B. *Int immunopharmacol* (2014) **18**. DOI: 10.1016/j.intimp.2013.12.010
51. Stempels FC, Janssens MH, Ter Beest M, Mesman RJ, Revelo NH, Ioannidis M. **Novel and conventional inhibitors of canonical autophagy differently affect LC3-associated phagocytosis**. *FEBS letters.* (2022) **596** 491-509. DOI: 10.1002/1873-3468.14280
52. Kim EY, Ner-Gaon H, Varon J, Cullen AM, Guo J, Choi J. **Post-sepsis immunosuppression depends on NKT cell regulation of mTOR/IFN-gamma in NK cells**. *J Clin Invest* (2020) **130**. DOI: 10.1172/JCI128075
53. Martinez J. **Detection of LC3-associated phagocytosis (LAP)**. *Curr Protoc Cell Biol* (2020) **87**. DOI: 10.1002/cpcb.104
54. Taylor DM, Balabadra U, Xiang Z, Woodman B, Meade S, Amore A. **A brain-permeable small molecule reduces neuronal cholesterol by inhibiting activity of sirtuin 2 deacetylase**. *ACS Chem Biol* (2011) **6**. DOI: 10.1021/cb100376q
55. Lee YS, Kim MH, Yi HS, Kim SY, Kim HH, Kim JH. **CX(3)CR1 differentiates F4/80(low) monocytes into pro-inflammatory F4/80(high) macrophages in the liver**. *Sci Rep* (2018) **8** 15076. DOI: 10.1038/s41598-018-33440-9
56. Lin HH, Faunce DE, Stacey M, Terajewicz A, Nakamura T, Zhang-Hoover J. **The macrophage F4/80 receptor is required for the induction of antigen-specific efferent regulatory T cells in peripheral tolerance**. *J Exp Med* (2005) **201**. DOI: 10.1084/jem.20042307
57. Goral J, Kovacs EJ. *J Immunol* (2005) **174**. DOI: 10.4049/jimmunol.174.1.456
58. Yang K, Xu J, Fan M, Tu F, Wang X, Ha T. **Lactate suppresses macrophage pro-inflammatory response to LPS stimulation by inhibition of YAP and NF-kappaB activation**. *Front Immunol* (2020) **11**. DOI: 10.3389/fimmu.2020.587913
59. Chen X, Wales P, Quinti L, Zuo F, Moniot S, Herisson F. **The sirtuin-2 inhibitor AK7 is neuroprotective in models of parkinson’s disease but not amyotrophic lateral sclerosis and cerebral ischemia**. *PloS One* (2015) **10**. DOI: 10.1371/journal.pone.0116919
60. Lang L, Chemmalakuzhy R, Shay C, Teng Y. **PFKP signaling at a glance: An emerging mediator of cancer cell metabolism**. *Adv Exp Med Biol* (2019) **1134**. DOI: 10.1007/978-3-030-12668-1_13
61. Wang F, Chan CH, Chen K, Guan X, Lin HK, Tong Q. **Deacetylation of FOXO3 by SIRT1 or SIRT2 leads to Skp2-mediated FOXO3 ubiquitination and degradation**. *Oncogene.* (2012) **31**. DOI: 10.1038/onc.2011.347
62. Lee JH, Liu R, Li J, Wang Y, Tan L, Li XJ. **EGFR-phosphorylated platelet isoform of phosphofructokinase 1 promotes PI3K activation**. *Mol Cell* (2018) **70** 197-210 e7. DOI: 10.1016/j.molcel.2018.03.018
63. Nikolovska-Coleska Z. **Studying protein-protein interactions using surface plasmon resonance**. *Methods Mol Biol* (2015) **1278**. DOI: 10.1007/978-1-4939-2425-7_7
64. Dominguez-Acosta O, Vega L, Estrada-Muniz E, Rodriguez MS, Gonzalez FJ, Elizondo G. **Activation of aryl hydrocarbon receptor regulates the LPS/IFNgamma-induced inflammatory response by inducing ubiquitin-proteosomal and lysosomal degradation of RelA/p65**. *Biochem Pharmacol* (2018) **155**. DOI: 10.1016/j.bcp.2018.06.016
65. Kabeya Y, Mizushima N, Yamamoto A, Oshitani-Okamoto S, Ohsumi Y, Yoshimori T. **LC3, GABARAP and GATE16 localize to autophagosomal membrane depending on form-II formation**. *J Cell Sci* (2004) **117**. DOI: 10.1242/jcs.01131
66. Agrotis A, Pengo N, Burden JJ, Ketteler R. **Redundancy of human ATG4 protease isoforms in autophagy and LC3/GABARAP processing revealed in cells**. *Autophagy.* (2019) **15**. DOI: 10.1080/15548627.2019.1569925
67. Lee JW, Nam H, Kim LE, Jeon Y, Min H, Ha S. **TLR4 (toll-like receptor 4) activation suppresses autophagy through inhibition of FOXO3 and impairs phagocytic capacity of microglia**. *Autophagy.* (2019) **15**. DOI: 10.1080/15548627.2018.1556946
68. Sil P, Muse G, Martinez J. **A ravenous defense: Canonical and non-canonical autophagy in immunity**. *Curr Opin Immunol* (2018) **50** 21-31. DOI: 10.1016/j.coi.2017.10.004
69. Ciarlo E, Heinonen T, Theroude C, Herderschee J, Mombelli M, Lugrin J. **Sirtuin 2 deficiency increases bacterial phagocytosis by macrophages and protects from chronic staphylococcal infection**. *Front Immunol* (2017) **8**. DOI: 10.3389/fimmu.2017.01037
70. Buechler N, Wang X, Yoza BK, McCall CE, Vachharajani V. **Sirtuin 2 regulates microvascular inflammation during sepsis**. *J Immunol Res* (2017) **2017** 2648946. DOI: 10.1155/2017/2648946
71. Lai SC, Devenish RJ. **LC3-associated phagocytosis (LAP): Connections with host autophagy**. *Cells.* (2012) **1** 396-408. DOI: 10.3390/cells1030396
72. Liang Y, Harris FL, Brown LA. **Alcohol induced mitochondrial oxidative stress and alveolar macrophage dysfunction**. *BioMed Res Int* (2014) **2014** 371593. DOI: 10.1155/2014/371593
73. Pruett SB, Fan R, Cheng B, Glover M, Tan W, Deng X. **Innate immunity and inflammation in sepsis: Mechanisms of suppressed host resistance in mice treated with ethanol in a binge-drinking model**. *Toxicol Sci* (2010) **117**. DOI: 10.1093/toxsci/kfq215
74. Zhong Y, Wang QJ, Li X, Yan Y, Backer JM, Chait BT. **Distinct regulation of autophagic activity by Atg14L and Rubicon associated with beclin 1-phosphatidylinositol-3-kinase complex**. *Nat Cell Biol* (2009) **11**. DOI: 10.1038/ncb1854
75. Matsunaga K, Saitoh T, Tabata K, Omori H, Satoh T, Kurotori N. **Two beclin 1-binding proteins, Atg14L and Rubicon, reciprocally regulate autophagy at different stages**. *Nat Cell Biol* (2009) **11**. DOI: 10.1038/ncb1846
76. Sun L, Xiong H, Chen L, Dai X, Yan X, Wu Y. **Deacetylation of ATG4B promotes autophagy initiation under starvation**. *Sci Adv* (2022) **8**. DOI: 10.1126/sciadv.abo0412
77. Roychowdhury S, Gandhirajan A, Kibler C, Wang X, Vachharajani V. **Sirtuin 2 dysregulates autophagy in high-Fat-Exposed immune-tolerant macrophages**. *Cells.* (2021) **10**. DOI: 10.3390/cells10040731
78. Norkina O, Dolganiuc A, Catalano D, Kodys K, Mandrekar P, Syed A. **Acute alcohol intake induces SOCS1 and SOCS3 and inhibits cytokine-induced STAT1 and STAT3 signaling in human monocytes**. *Alcoholism Clin Exp Res* (2008) **32**. DOI: 10.1111/j.1530-0277.2008.00726.x
79. Mandrekar P, Catalano D, White B, Szabo G. **Moderate alcohol intake in humans attenuates monocyte inflammatory responses: Inhibition of nuclear regulatory factor kappa b and induction of interleukin 10**. *Alcoholism Clin Exp Res* (2006) **30**. DOI: 10.1111/j.1530-0277.2006.00012.x
80. Dai Q, Pruett SB. **Different effects of acute and chronic ethanol on LPS-induced cytokine production and TLR4 receptor behavior in mouse peritoneal macrophages**. *J Immunotoxicol* (2006) **3**. DOI: 10.1080/15476910601080156
81. Morris NL, Michael DN, Crotty KM, Chang SS, Yeligar SM. **Alcohol-induced glycolytic shift in alveolar macrophages is mediated by hypoxia-inducible factor-1 alpha**. *Front Immunol* (2022) **13**. DOI: 10.3389/fimmu.2022.865492
82. Mehta AJ, Yeligar SM, Elon L, Brown LA, Guidot DM. **Alcoholism causes alveolar macrophage zinc deficiency and immune dysfunction**. *Am J Respir Crit Care Med* (2013) **188**. DOI: 10.1164/rccm.201301-0061OC
83. Yeligar SM, Harris FL, Hart CM, Brown LA. **Glutathione attenuates ethanol-induced alveolar macrophage oxidative stress and dysfunction by downregulating NADPH oxidases**. *Am J Physiol Lung Cell Mol Physiol* (2014) **306**. DOI: 10.1152/ajplung.00159.2013
84. Yeligar SM, Mehta AJ, Harris FL, Brown LA, Hart CM. **Peroxisome proliferator-activated receptor gamma regulates chronic alcohol-induced alveolar macrophage dysfunction**. *Am J Respir Cell Mol Biol* (2016) **55** 35-46. DOI: 10.1165/rcmb.2015-0077OC
85. Zhou Y, Wu M, Xu L, Cheng J, Shen J, Yang T. **Bmal1 regulates macrophage polarize through glycolytic pathway in alcoholic liver disease**. *Front Pharmacol* (2021) **12**. DOI: 10.3389/fphar.2021.640521
86. Yao Y, Zhang W, Ming R, Deng Q, Zuo A, Zhang S. **Noninvasive 40-Hz light flicker rescues circadian behavior and abnormal lipid metabolism induced by acute ethanol exposure**. *Front Pharmacol* (2020) **11**. DOI: 10.3389/fphar.2020.00355
87. Acuna-Fernandez C, Marin JS, Diaz-Casado ME, Rusanova I, Darias-Delbey B, Perez-Guillama L. **Daily changes in the expression of clock genes in sepsis and their relation with sepsis outcome and urinary excretion of 6-sulfatoximelatonin**. *Shock.* (2020) **53**. DOI: 10.1097/SHK.0000000000001433
88. Kong X, Yang Y, Ren L, Shao T, Li F, Zhao C. **Activation of autophagy attenuates EtOH-LPS-induced hepatic steatosis and injury through MD2 associated TLR4 signaling**. *Sci Rep* (2017) **7** 9292. DOI: 10.1038/s41598-017-09045-z
89. Ilyas G, Cingolani F, Zhao E, Tanaka K, Czaja MJ. **Decreased macrophage autophagy promotes liver injury and inflammation from alcohol**. *Alcoholism Clin Exp Res* (2019) **43**. DOI: 10.1111/acer.14041
90. Williams JA, Ding WX. **Role of autophagy in alcohol and drug-induced liver injury**. *Food Chem Toxicol* (2020) **136** 111075. DOI: 10.1016/j.fct.2019.111075
91. Jiang C, Liu Z, Hu R, Bo L, Minshall RD, Malik AB. **Inactivation of Rab11a GTPase in macrophages facilitates phagocytosis of apoptotic neutrophils**. *J Immunol* (2017) **198**. DOI: 10.4049/jimmunol.1601495
92. Koike S, Yamasaki K, Yamauchi T, Shimada-Omori R, Tsuchiyama K, Ando H. **TLR3 stimulation induces melanosome endo/phagocytosis through RHOA and CDC42 in human epidermal keratinocyte**. *J Dermatol Sci* (2019) **96**. DOI: 10.1016/j.jdermsci.2019.11.005
93. Seixas E, Escrevente C, Seabra MC, Barral DC. **Rab GTPase regulation of bacteria and protozoa phagocytosis occurs through the modulation of phagocytic receptor surface expression**. *Sci Rep* (2018) **8** 12998. DOI: 10.1038/s41598-018-31171-5
94. Wang Q, Li H, Zhou K, Qin X, Wang Q, Li W. **Rab7 controls innate immunity by regulating phagocytosis and antimicrobial peptide expression in Chinese mitten crab**. *Fish Shellf Immunol* (2019) **95**. DOI: 10.1016/j.fsi.2019.10.037
95. Wu X, Poulsen KL, Sanz-Garcia C, Huang E, McMullen MR, Roychowdhury S. **MLKL-dependent signaling regulates autophagic flux in a murine model of non-alcohol-associated fatty liver and steatohepatitis**. *J hepatol* (2020) **73**. DOI: 10.1016/j.jhep.2020.03.023
96. Akoumianaki T, Kyrmizi I, Valsecchi I, Gresnigt MS, Samonis G, Drakos E. **Aspergillus cell wall melanin blocks LC3-associated phagocytosis to promote pathogenicity**. *Cell Host Microbe* (2016) **19** 79-90. DOI: 10.1016/j.chom.2015.12.002
|
---
title: 'You gotta walk the walk to talk the talk: protocol for a feasibility study
of the Happy Older Latino Adults (HOLA) health promotion intervention for older
HIV-positive Latino men'
authors:
- Daniel E. Jimenez
- Elliott R. Weinstein
- John Batsis
journal: Pilot and Feasibility Studies
year: 2023
pmcid: PMC9972624
doi: 10.1186/s40814-023-01262-w
license: CC BY 4.0
---
# You gotta walk the walk to talk the talk: protocol for a feasibility study of the Happy Older Latino Adults (HOLA) health promotion intervention for older HIV-positive Latino men
## Abstract
### Background
Older Latinos living with the human immunodeficiency virus (HIV) have been disproportionately affected by the epidemic and experience compounded health disparities that have deepened over time. These health disparities are largely related to lifestyle and are either preventable or amenable to early detection or intervention. Despite existing resources to deliver an intervention to reduce this compounded health disparity, there is little information on the effects of health promotion interventions on indices of cardiometabolic risk in midlife and older Latinos living with HIV. The Happy Older Latinos are Active (HOLA) intervention is an innovative health promotion program that is uniquely tailored to meet the diverse needs and circumstances of older Latinos with HIV. The goal of this manuscript is to describe the protocol of a feasibility study of the HOLA health promotion intervention for older HIV-positive Latino men.
### Methods/design
HOLA, which is informed by Behavioral Activation and Social Learning theory is a community health worker (CHW)-led, multicomponent, health promotion intervention consisting of: [1] a social and physical activation session; [2] a moderately intense group walk led by a CHW for 45 min, 3×/week for 16 weeks; [3] pleasant events (e.g., going to brunch with friends) scheduling. Eighteen community dwelling Latinos living with HIV aged 50+ will be recruited for this feasibility study adapting the HOLA intervention. Participants will be assessed at three time points (baseline, post-intervention, and 3 months post-intervention) on measures of cardiometabolic risk factors (waist circumference, dyslipidemia, hypertension, and glucose), psychosocial functioning, and health-related quality of life.
### Conclusions
If HOLA can be delivered successfully by CHWs, then the scalability, accessibility, and potential for dissemination is increased. Additionally, this study will inform feasibility and identify modifications needed in the design of a larger hypothesis testing study.
### Trial registration
Clinicaltrials.gov Identifier: NCT 03839212. Date of Registration: 8 February, 2019.
## Background
Due to the changing landscape of the human immunodeficiency virus (HIV) epidemic, older adults are living with HIV at rates higher than ever before. In 2018, the prevalence of diagnosed HIV infection in the USA was 374.6 per 100,000 population with an increased number of estimated cases affecting older Americans over the age of 50 [1]. Even though HIV is no longer a death sentence due to the development of effective antiretroviral therapy (ART) and other HIV focused therapeutics, significant disparities in treatment outcomes persist among certain demographics. *In* general, HIV continues to not only affect racial/ethnic minorities at higher rates, but also gay, bisexual, and men who have sex with men (MSM) compared to their cis-gender heterosexual white counterparts [2]. Therefore, research must look to the intersections of age, race, ethnicity, and sexual identity in developing interventions that may best support those most at risk for poor HIV-related treatment outcomes.
Significantly more people living with HIV (PLWH) are living into older adulthood demonstrating a growing need for HIV-focused research and treatment examined through an aging lens. According to 2018 CDC data, more than half of PLWH in the USA are over the age of 50 years [1] with predictions that by the start of 2021 almost $70\%$ of PLWH in the USA will be at least 50 years old [3]. Older adults are often diagnosed with HIV much later (~ 4.5 years after infection) indicating more advanced disease progression associated with the epidemic among this age group [1]. Additionally, for PLWH in the USA aged 55 and above, while $90\%$ knew their HIV status, only $64\%$ were virally suppressed and barely more than half ($57\%$) were actively engaged in care, suggesting that the UNAIDS “90-90-90” goals toward ending the HIV epidemic by 2050 [4] are currently out of reach among older adults living with HIV (OALWH) [1].
Latinos have been disproportionately affected by HIV since the origins of the epidemic, and that increased vulnerability has deepened over time. Despite representing less than $20\%$ of the entire U.S. population, Latinos make up $27\%$ of new HIV diagnoses and account for over $20\%$ of the national prevalence [5]. More specifically, Latino sexual minority men (LSMM) are responsible for almost $25\%$ of new HIV infections among all gay, bisexual, and MSM identified individuals [2] despite Latino men making up less than $9\%$ of the entire U.S. population [6]. These disparities persist beyond just HIV incidence with Latino identified PLWH facing additional challenges with their HIV treatment care due to suboptimal rates of viral suppression [7, 8] and ART adherence [9] compared to their non-Latino SMM peers.
Unsurprisingly, these racial/ethnic disparities become significantly more observable among older adults. Older Latinos in the USA are slightly more than 5 times more likely to acquire HIV [10] and are more likely to have an AIDS diagnosis, detectable viral load, and poorer treatment adherence compared to their age-matched white non-Latino counterparts [11, 12]. Additionally, older Latinos seem to exhibit mild to moderate cognitive impairment in learning, memory, and processing speed compared to their non-Latino white peers [12, 13]. It is likely that these intersecting social determinants (i.e., age, race/ethnicity, sexual identity) will continue as the HIV population ages and that many of these poor health outcomes will be felt most intensely by older Latinos.
Older Latinos living with HIV experience various physical health disparities due to their intersecting minority status that must be considered. *In* general, OALWH face increased rates of age-related comorbidities due to a phenomenon called accelerated aging [14, 15]. Data from a large observational cohort study of OALWH reported that over $77\%$ of participants reported suffering from two or more health comorbidities in addition to HIV [16] with the average number of physical comorbidities per participants landing at three [17]. In particular, older HIV-positive Latino individuals are disproportionately affected with cardiometabolic diseases including metabolic syndrome (MetS), a precursor to diabetes, as well as cardiometabolic risk factors such as obesity and hypertension compared to their non-Latino older white peers [18–20]. Moreover, older Latinos living with HIV are more likely to be sedentary and not as actively engaged in pursuing changes in their physical activity compared to their non-Latino white counterparts [21]. Despite evidence of the “Hispanic Paradox1” [22], this lack of physical activity in combination with possible issues connected to ART medication and Hepatitis-C co-infection make older Latino adults living with HIV more likely to suffer from other complicating physical conditions like nonalcoholic fatty liver disease [23, 24] and cardiovascular issues compared to their non-Latino white counterparts.
Older Latinos living with HIV face elevated rates of mental health concerns in addition to these physical comorbidities. *More* generally, OALWH have documented rates of major depressive episodes anywhere between 18 and $52\%$ [17, 25, 26], significantly higher than the 1–$8\%$ documented rates of depression among older adults in the general population [27]. Rates of social isolation in the general aging public (60 years or older) are estimated to be anywhere between $33\%$ and $50\%$ [28] with evidence suggesting that OALWH face social isolation more often than their age matched peers in the general population [29, 30]. Similarly, there is strong evidence to suggest that loneliness exponentially increases with age and one could predict OALWH bear even a greater amount of loneliness due to reduced social networks and ostracism [31, 32]. Additionally, loneliness and social isolation have been correlated with levels of morbidity and mortality comparable to more established biopsychosocial risk factors like obesity, sedentary behavior, smoking, and hypertension [28, 33, 34].
For OALWH, one of the biggest contributing factors to loneliness and social isolation is that of co-occurring stigma. While exact estimates of HIV stigma and age-related stigma in the USA are hard to calculate, OALWH must often navigate the dueling stigmas of HIV stigma, ageism, and stigma resulting from other possible marginalized identities [35–37]. It is possible that rates of stigma and loneliness seem to skyrocket in OALWH due to the increased likelihood that OALWH live alone and have limited and often inconsistent social networks [25, 37]. OALWH can face ostracism from the larger LGBTQ+ community and stigma due to the intersection of their age and HIV status compared to their non-infected peers which may in turn contribute to the increased levels of depression among this already vulnerable population [38]. Additionally, since familism and social cohesion are strong hallmarks of Latino culture [39, 40], Latino OALWH may experience the harmful effects of co-morbid stigma and social isolation more intensely than their non-Latino HIV-positive peers due to societal expectation that they be more connected to family as they age; however, more research must be conducted to determine the veracity of such a hypothesis.
As outlined above, compounded health disparities place older Latinos living with HIV at particularly high risk for diminished quality of life due to physical and mental health morbidity. These data underscore the public health importance of increased efforts to address the multiplicative and unequal burden of HIV, MetS, and diabetes shouldered by older Latinos. Therefore, based on this gap in the literature, there is a compelling need to develop and disseminate interventions that promote healthy living, combat social isolation, and improve HIV-related health outcomes among older Latinos living with HIV. The goal of this manuscript is to describe the protocol of a feasibility study of the Happy Older Latinos are Active (HOLA) health promotion intervention for older HIV-positive Latino men. Since this is a feasibility study, researchers will be focused on evaluating the feasibility of recruitment, retention, assessment procedures, and acceptability of an innovative application of an already established health promotion intervention, HOLA, to a new population, older Latino men living with HIV [41]. Also, in accordance with recommendations from biostatistical workgroups funded by NIH [42], this study will not powered to test a hypothesis. Rather, this study will serve as an initial step in establishing feasibility and acceptability of an approach that is intended to ultimately be used in a larger scale study. The specific aims of this study will be to:Evaluate the feasibility of recruitment, assessment procedures, retention, acceptability, and implementation of HOLA in a sample of midlife and older Latinos with HIV.Identify modifications needed in the design of a larger, confirmatory randomized controlled trial. Explore changes in cardiometabolic risk factors (waist circumference, dyslipidemia, hypertension, and glucose), psychosocial functioning (depression and anxiety severity, social support), and health-related quality of life in a sample of midlife and older Latinos with HIV enrolled in the HOLA health promotion intervention.
## Methods
All study methods, protocols, and participant incentive structures were approved by the university’s Internal Review Board (IRB ID: 20181032). In addition, a SPIRIT checklist has been completed to serve as a brief, structured summary of the trial. See Table 1.Table 1SPIRIT ChecklistData categoryInformationPrimary registry and trial identifying numberClinicaltrials.gov Identifier: NCT 03839212Date of registration in primary registry8 February, 2019Secondary identifying numbersU54MD002266Source(s) of monetary or material supportNational Institute on Minority Health and Health Disparities (NIMHD)Primary sponsorUniversity of MiamiContact for public queriesDaniel Jimenez, Ph.D. ([email protected])Contact for scientific queriesDaniel Jimenez, Ph.D. ([email protected])Public titleThe Happy Older Latinos Are Active (HOLA) Health Promotion Study in HIV-Infected Latino Men (HOLAHIV)Scientific titlePreventing Cardiometabolic Disease in HIV-Infected Latino Men *Through a* Culturally Tailored Health Promotion InterventionCountries of recruitmentUSAHealth condition(s) or problem(s) studiedHuman immunodeficiency virus (HIV),cardiometabolic riskIntervention(s)Happy Older Latinos are Active (HOLA):A 16-week multi-component, health promotion interventionKey inclusion and exclusion criteriaAges eligible for study: ≥ 50 yearsSexes eligible for study: menAccepts healthy volunteers: noInclusion criteria: older Latino (≥ 50 years); HIV infected but are virologically suppressed have documented risk of cardiometabolic disease. Exclusion criteria: diagnosis of diabetes, any neurodegenerative disorder, or dementia, or significant cognitive impairment; contraindications to physical activity; terminal physical illness; acute or severe medical illness that precludes safe participation. Study typeInterventionalInterventional study model: single group assignmentNumber of arms: 1Masking: none (open label)Allocation: N/ATarget sample size18Recruitment statusNot recruitingRecruitment Rate6 participants per monthPrimary outcome(s)Number of eligible participants refusing to participate: $20\%$ or less of eligible participants refusing to participateRetention rate: $85\%$ or more of participants completing the post-intervention assessmentAcceptability of intervention: $80\%$ or more of sessions attended by participantsKey secondary outcomesChange in cardiometabolic riskChange in psychosocial functioningChange in health-related quality of lifeStatistical methodsDescriptive statistics with $95\%$ confidence intervals;*For continuous* variables that are normally distributed: means and standard deviations. For continuous variables that are skewed: median and range;Categorical variables: summarized using counts and percentages.
## Participants
This feasibility study will enroll 18 Latino older (aged 50+) men living with HIV who will then be assigned to three intervention groups composed of 6 participants each. Although small, this sample size is consistent with similar feasibility studies focused on physical activity among older adults [43, 44] and will be large enough to establish feasibility of the intervention in a population of older Latino men living with HIV. Additionally, informed consent will be obtained from each participant prior to enrollment and all enrollees will be provided with an initial verbal summary of the study. Payments will be graduated so participants received $15 on the first visit, $25 on the second visit, and $35 on the third visit (total of honoraria = $75). Financial incentives have the potential to serve as undue inducements by diminishing peoples’ sensitivity to research risks or unjust inducements by preferentially increasing enrollment among underserved individuals. However, results from two low-risk randomized clinical trials indicate that there is no evidence from studies of participation in hypothetical or real randomized clinical trials that incentivizing enrollment is undue or unjust, suggesting that studies that offer participation incentives are not unethical [45]. For a detailed description of the inclusion/exclusion criteria, see Table 2.Table 2Inclusion/exclusion criteriaInclusion criteriaExclusion criteriaLatino (self-identified)*Have a* diabetes diagnosisAge 50+*Have a* diagnosis of any neurodegenerative disorder or dementia (Parkinson’s disease, Alzheimer’s, vascular, frontotemporal dementia, etc.) or significant cognitive impairment as indicated by a Mini-Mental Status Exam score < 24MaleHave contraindications to physical activity outlined in the American College of Sports Medicine standardsHIV infected but are virologically suppressed (viral load < 200 copies/mL)Are unable to complete 10-m walk testVolunteer informed consentCurrently residing in a nursing or group homeExpect to stay in Miami for the next 6 monthsHave a terminal physical illness expected to result in the death within 1 yearHave documented risk of cardiometabolic disease. Have an acute or severe medical illness that precludes them from safely participating in a health promotion intervention (e.g., progressive, degenerative neurologic disease, such as Parkinson’s Disease, multiple sclerosis, ALS; severe arthritis or orthopedic condition that would prevent participation in a physical activity program; lung disease requiring either oral or injected steroids, or the use of supplemental oxygen; New York Heart Association Class III or IV congestive heart failure, clinically significant aortic stenosis, history of cardiac arrest, use of a cardiac defibrillator, or uncontrolled angina; renal disease requiring the use of dialysis; cancer being actively treated with radiation or chemotherapy; myocardial infarction, CABG, or valve replacement within the past 6 months; serious conduction disorder, such as 3rd degree heart block; uncontrolled arrhythmia; pulmonary embolism or deep venous thrombosis within past 6 months; uncontrolled diabetes with recent weight loss, diabetic coma or frequent insulin reactions; stroke, hip fracture, hip or knee replacement, or spinal surgery in the past 6 months; receiving physical therapy for gait, balance, or other lower extremity training; severe, uncontrolled hypertension-systolic blood pressure > 200 mmHg and/or diastolic blood pressure > 110 mmHg)Are currently taking antidepressant medications in doses indicated for weight reduction.
## Recruitment
Participants will be recruited through two consent-to-contact databases with over 1200 participants each—one of people with HIV (recruited from the university-affiliated adult HIV clinic) and another focused on a community needs assessment (composed of HIV-negative and HIV-positive community dwelling adults). These databases include contact information, demographic information and data associated with HIV-related risk factors such as homelessness and psychological distress. Only participants who had indicated that they were HIV+ will be recruited from these databases. More information on how these consent to contact databases work can be found here [46].
## Conceptual framework
The conceptual model which serves as the foundation for the intervention (Fig. 1) was crafted to address comorbid depression and anxiety symptoms as well as both physical and psychosocial functioning of older Latinos. HOLA, is informed by Behavioral Activation (BA) [47] and Social Learning Theory (SLT) [48]. A major component of BA is scheduling activities into an individual’s day-to-day routine as a way of activating them out of a depressive episode. In HOLA, we incorporated components of BA in two ways. First, we encouraged participants to engage in a physical activity routine (i.e., an activity) and to schedule in pleasant events to their day-to-day (i.e., activity scheduling) to combat incident and recurrent episodes of depression and anxiety disorders as well as subdue symptom intensity [47]. As a complement, SLT’s tenets of reinforcement, observational learning, and enhanced self-efficacy are utilized to bolster participant engagement and success in the intervention [48]. The relationship between the participants and the community health worker (CHW) capitalizes on the personal relationship to motivate, model, and maintain health behavior change. Fig. 1Conceptual framework CHWs are an effective and culturally acceptable means of reaching the population with health information and motivating health behaviors [49, 50]. CHWs are lay community members who work almost exclusively in community settings and connect consumers to providers in order to promote health and prevent diseases among groups that have traditionally lacked access to adequate care [49]. CHWs are assumed to be effective because they possess an intimate understanding of community social networks and health needs; communicate in a similar language; and recognize and incorporate culture to promote health [49, 50]. The use of CHWs has emerged as a strategy to reduce or eliminate health disparities and is an important means of task shifting to enable efficient utilization of scarce mental health resources (see footnote below)2. Additionally, since engaging in health behavior change via physical activity is challenging, the HOLA intervention offers several opportunities for participants to be held accountable to their goals in the intervention. The CHW holds the individuals accountable, and individuals hold themselves accountable to the group, providing extra motivation to engage in the intervention. Accountability is an ideal way to help participants maintain their commitment, keep their energy and enthusiasm high and feel like they are not alone [51]. CHWs in this study will be trained in the HOLA intervention protocol and supervised by the senior author.
## Intervention
Happy Older Latino Adults (HOLA) is a multi-component health promotion intervention for midlife and older Latinos [42]. The first component consists of two manualized social and physical activation sessions. Prior to beginning the group walk phase, each participant will meet individually with a CHW for a 30-min physical and social activation session to (a) educate potential participants about the goals of the intervention; (b) provide information surrounding HIV/AIDS, cardiometabolic disorders such as diabetes and metabolic syndrome, how these physical conditions impact mental health, and ways they can improve their cardiometabolic health; (c) motivate participants to engage in physical activity; (d) increase participants’ social activities (e) identify potential obstacles that may interfere with meeting the demands of the intervention; and (f) brainstorm ways to overcome these obstacles. After week 8, participants will again meet one-on-one with the CHW for the second session so that they could discuss their own individual progress in relation to their physical and social activity goals.
The second component is centered around a group walk meant to facilitate both physical activity and social interaction between participants. This group walk will meet for 45 min, three times a week, for a total of 16 weeks. The group walk component was designed with interval training in mind and gradually increases in workload (defined by intensity, volume, and work/recovery cycle) over the course of the intervention. Each group walk will begin with 10 min of stretching and warm up, followed by 30 min of walking, and will end with 5 min of stretching/cool down. Each group walk will be led by the CHW and will be composed of six bilingual and monolingual Spanish-speaking participants.
The third component consists of scheduling pleasant events. During the cool down phase of each walking session, the CHW will ask each participant to identify a pleasant event that they intend to do with another person before the next meeting (e.g., going to brunch with friends). Individuals may choose to do this activity with another member of the group, with family, or with friends outside the group. Subsequent sessions will start with participants reporting on how effectively they implemented their pleasant event plan while the CHW and the group provide positive reinforcement and feedback. This component provides a means to generalize the intervention into the participants’ everyday lives and relationships. Participants will walk at a centrally located public park, which is owned and operated by the Miami-Dade County Parks and Recreation Department.
The fourth and final component of HOLA in the context of this feasibility study focuses on maintaining behavior change gleaned during intervention. Participants will engage in “booster” walking sessions, twice a month for 3 months post-intervention (starting the week after the 16-week program concluded) to capitalize on beneficial physical and mental health effects gained during the 16-week program. Encouraged by the prior literature, this maintenance phase was added to this feasibility study with the hopes of cultivating more sustained treatment effects over time [52]. A more in-depth overview of the HOLA intervention can be reviewed in the main protocol paper published by Jimenez and colleagues [41]. Adaptations made to the original HOLA intervention to make it specific to a sample of OALWH can be found in Table 3.Table 3Summary of adaptations made to original HOLAOriginal HOLAAdaptations madeTwo social and physical activation sessions focused on education on depression and anxiety. The focus of the social and physical activation sessions is to provide information surrounding HIV/AIDS, cardiometabolic disorders such as diabetes and metabolic syndrome, how these physical conditions impact mental health, and ways they can improve their cardiometabolic health. Moderately intense group walkNo changesPleasant events schedulingNo changesNo maintenance phase3-month maintenance phase
## Measures and analysis
This quasi-experimental feasibility study of an adapted health promotion intervention will examine feasibility of recruitment, assessment procedures, retention, acceptability, and implementation of HOLA in a sample of older Latinos living with HIV. In keeping with guidance from NIH funded biostatistical workgroups, this feasibility study was not designed to be powered to test a hypothesis [42]. Additionally, the proposed analysis of feasibility data parallels a similar structure employed in the first HOLA trial and comparable feasibility trials [41, 53]. For this study, successful recruitment will be defined as $100\%$ of the targeted sample ($$n = 18$$) be enrolled and less than $20\%$ of eligible subjects refusing to participate. Additionally, adequate retention will be characterized as $85\%$ or more of enrolled participants completing all post-intervention assessments while acceptability will be defined as participants attending at least $80\%$ of sessions. Finally, with goals of scaling up the intervention in the future, an established project evaluation questionnaire developed by investigators at the University of Miami will be used to pinpoint any modifications needed for the design of a larger, confirmatory randomized control trial. The questionnaire is made up of a series of closed ended yes/no questions, rating scales, and open-ended questions that allow for more qualitative data regarding participants’ opinions of the specific components of the overall intervention. Study measures will be administered at baseline, end of intervention, and 3 months post-intervention. Trained research assistants (RA) will administer all of the assessments.
## Feasibility outcomes
Study feasibility will be evaluated via participant recruitment, retention, and acceptability of the overall intervention. First, authors will measure study feasibility via recruitment and retention of eligible participants. For this study, successful recruitment will be defined as $100\%$ of the targeted sample ($$n = 18$$) be enrolled and less than $20\%$ of eligible subjects refusing to participate. Additionally, adequate retention will be characterized as $85\%$ or more of enrolled participants completing all post-intervention assessments. Second, authors will assess participant acceptability of the intervention as another component of study feasibility. For this study, acceptability will be defined as participants attending at least $80\%$ of sessions. Finally, to identify any specific challenges to scaling up the intervention in the future, an established project evaluation questionnaire developed by investigators at the University of Miami will be used to pinpoint any modifications needed for the design of a larger, confirmatory randomized control trial. The questionnaire is made up of a series of closed ended yes/no questions, rating scales, and open-ended questions allowing for more qualitative data on participants’ opinions of the specific components of the intervention to improve the overall feasibility of the study for future iterations of implementation.
## Study measures
Based on our exclusion criteria, participants will complete the Mini-Mental Status Exam (MMSE) [54] to recognize potential subjects with dementia or severe cognitive abnormalities. To establish baseline walking ability, eligible participants will be required to complete the 10-m walk test which has been shown to have not only excellent reliability when used in older adult populations, but also be comparable in validity with other longer measures [55]. Participants’ viral load will be ascertained to determine viral suppression which is defined as < 200 copies/mL [56]. Potential participants will self-confirm safe participation by not having an acute or severe medical illness.
## Cardiometabolic measurements
Just prior to baseline, blood draws will be completed at the university affiliated adult HIV outpatient clinic from which participant recruitment will take place. These blood draws will yield baseline data on participants’ levels of HDL-C, LDL-C, triglycerides, insulin, and HbA1c. Fasting blood samples will be drawn via venipuncture and stored at 4 °C until analysis could be completed. Homeostatic model assessment (HOMA) will be calculated, providing a measure of insulin resistance. All the samples will be evaluated by commercial laboratory services using commercially available enzyme-linked immunosorbent assays. Simultaneously, participant physical characteristics such as blood pressure and hip-to-waist circumference will be collected to complement the measures of glucose, insulin resistance, and blood lipid profile mentioned previously. All of these measurements of cardiometabolic risk will be collected immediately post-intervention, and 3 months post-intervention as well.
## Psychosocial functioning
Measures of psychosocial functioning will be collected at all three timepoints as well -baseline, post-intervention, and 3 months post-intervention. To measure depression symptom severity, participants will complete the Center for Epidemiologic Scale of Depression [57], 9-item Patient Health Questionnaire [58], the Perceived Stress Scale [59], and the 7-item Generalized Anxiety Disorder scale to ascertain anxiety symptom intensity [60]. Finally, participants’ perceived social support will be measured by the frequently used and validated Multidimensional Scale of Perceived Social Support [61].
## Additional measures
Additional measures will be administered to participants to gain more insight on factors of acculturation, physical activity, stigma, quality of life, and overall patient satisfaction. At baseline, participants will complete a demographics form, provide a list of current medications, and respond to the Bidimensional Acculturation Scale [62]. Additionally, at all three time points-baseline, post-intervention, and 3 months post-intervention—subjects will be asked to complete the Global Physical Activity Questionnaire [63], the 12-item Short Form health survey [64], and the HIV Stigma Scale [65] to measure their overall physical activity levels, general health-related quality of life, and HIV-related stigma participants face on a day-to-day basis. Finally, participants will complete a project evaluation questionnaire developed by the investigators to indicate individual satisfaction in the intervention.
## Sample size
The total sample size for this pilot study will be 18 older Latino men living with HIV. A sample size of 18 will allow researchers to conduct three separate intervention groups of 6 people each. Prior work conducted by the authors indicated that 6 participants per intervention group was the optimum size to generate social interaction between participants, guard against attrition, while still being manageable for the CHW [41]. Although small, this sample size is consistent with similar pilot studies focused on physical activity among older adults [43, 44] and will be large enough to establish feasibility of the intervention in a population of older Latino men living with HIV.
## Anticipated statistical analysis plan
In keeping with guidance from NIH funded biostatistical workgroups, this pilot study was not designed to be powered to test a hypothesis [42]. Therefore, since this study is designed to be a small pilot feasibility trial, best practices caution against using hypothesis testing in that they are likely to produce non-significant p values due to the study being underpowered. The CONSORT extension considers the use of descriptive statistics with $95\%$ confidence intervals as more meaningful, an approach that parallels a similar structure employed in the first HOLA trial and comparable pilot trials [41, 53]. For variables continuous variables that are normally distributed, data will be analyzed and summarized using means and standard deviations. Alternatively, for continuous variables that are skewed, the median and range will be presented. Furthermore, data generated from categorical variables will be analyzed and summarized using counts and percentages (Fig. 2).Fig. 2CONSORT diagram
## Discussion
As the population of PLWH increasingly ages, high comorbidity between HIV and cardiometabolic diseases make older *Latinos a* high-risk population for whom innovative, scalable health promotion intervention could make a significant and lasting public health impact [19]. Despite the fact that older Latinos with HIV experience high levels of cardiometabolic disease due to issues associated with accelerated aging or potential adverse effects of ART medication, there is still a considerable dearth of relevant information on the effects of positive health promotion interventions in older Latinos living with HIV and comorbid cardiometabolic illness. Currently in preparation for a n adequately powered randomized control trial (e.g., securing grant funding), this study will generate evidence as to whether an already established health promotion intervention, HOLA, can be adapted for older Latino men with HIV to improve physical health, psychosocial functioning, and health-related quality of life in the midst of a rapid demographic transition. Authors are optimistic that this pilot project will yield significant results because this study builds upon prior work by authors delivering a health promotion intervention to prevent anxiety and depression in Latino older adults, a population living with high exposure to risk factors (comorbid physical and mental health conditions) and disparities in access to and engagement in mental health services [53].
The HOLA intervention is a multifaceted, innovative health promotion program that is uniquely tailored to meet the diverse needs and circumstances of older Latinos with HIV. Since many older adults who identify as racial/ethnic minorities hold stigmatizing views of mental health services [66, 67], HOLA builds on prior research and incorporates culturally relevant strategies to health promotion/behavior change as way of reducing mental and physical health risk factors among older Latinos with HIV [68]. Specifically, the use of a CHW to deliver a dual mental/physical health promotion intervention is an innovative approach to minimize disease burden in a population with high exposure to risk factors (in addition to HIV) and established disparities in both access to and engagement in beneficial mental and physical health services [69–71]. We believe that such an approach will appeal to older Latinos with HIV at risk for cardiometabolic disorders and psychological distress because of its non-stigmatizing presentation and the incorporation of cultural values/beliefs that promote the varying sociocultural influences contributing to the health of Latinos.
## Conclusions
High prevalence of HIV [2] combined with comorbid cardiometabolic diseases [7] makes older *Latinos a* high-risk population for whom scalable health promotion interventions could have great public health impact. Despite the high prevalence of cardiometabolic diseases in this population, there is a dearth of information on the effects of health promotion interventions on indices of cardiometabolic risk in midlife and older Latinos living with HIV. Thus, the study will provide valuable insight to evaluate the feasibility and acceptability of HOLA among HIV-infected Latinos aged 50 years and older and identify modifications needed in the design of a larger, ensuing hypothesis testing study. For example, there may be some challenges with attributing changes in cardiometabolic risk (blood pressure, fasting glucose, waist circumference) to the HOLA intervention using only three measurements in total over the intervention and post-intervention/maintenance phase. Some of these outcome measures may require more frequent measurement and others may change a very small amount over the short time period of the feasibility study. In addition, potential sustainability might be best measured by the drop-off after the last payment to the participants. With the evidence collected as part of the feasibility and acceptability trial, the authors plan to generate hypotheses concerning the intervention’s effects on cardiometabolic risk factors, psychosocial functioning, and health-related quality of life in a sample of midlife and older Latinos living with HIV.
## References
1. 1.CDC. HIV and older Americans. Centers for disease control and prevention. https://www.cdc.gov/hiv/group/age/olderamericans/index.html. Accessed 28 Jan 2021. Published September 14, 2020.
2. 2.CDC. Hiv and gay and bisexual men. Centers for disease control and prevention. https://www.cdc.gov/hiv/group/msm/index.html. Accessed 7 Oct 2020. Published September 16, 2020.
3. Karpiak SE, Lunievicz JL. **Age is not a condom: HIV and sexual health for older adults**. *Curr Sex Health Rep* (2017.0) **9** 109-115. DOI: 10.1007/s11930-017-0119-0
4. 4.90-90-90: treatment for all | UNAIDS. https://www.unaids.org/en/resources/909090. Accessed 27 Jan 2021.
5. 5.Centers for Disease Control and Prevention. COVID-19 in racial and ethnic minority groups. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/racial-ethnic-minorities.html. Accessed 18 June 2020. Published February 11, 2020.
6. 6.U.S. Census BureauHispanic or Latino Origin by Specific Origin2018. *Hispanic or Latino Origin by Specific Origin* (2018.0)
7. Crepaz N, Tang T, Marks G, Hall HI. **Viral suppression patterns among persons in the United States with diagnosed HIV infection in 2014**. *Ann Intern Med* (2017.0) **167** 446-447. DOI: 10.7326/L17-0278
8. Hood JE, Golden MR, Hughes JP. **Projected demographic composition of the United States population of people living with diagnosed HIV**. *AIDS Care* (2017.0) **29** 1543-1550. DOI: 10.1080/09540121.2017.130846669
9. Hoots BE, Finlayson TJ, Wejnert C, Paz-Bailey G. **Updated data on linkage to human immunodeficiency virus care and antiretroviral treatment among men who have sex with men—20 cities, United States**. *J Infect Dis* (2017.0) **216** 808-812. DOI: 10.1093/infdis/jix007
10. Linley L, Prejean J, An Q, Chen M, Hall HI. **Racial/ethnic disparities in HIV diagnoses among persons aged 50 years and older in 37 US States, 2005–2008**. *Am J Public Health* (2012.0) **102** 1527-1534. DOI: 10.2105/AJPH.2011.300431
11. Cruz JJD, Karpiak SE, Brennan-Ing M. **Health outcomes for older hispanics with HIV in New York City using the oaxaca decomposition approach**. *Global J Health Sci* (2015.0) **7** 133-143. DOI: 10.5539/gjhs.v7n1p133
12. Mindt MR, Miranda C, Arentoft A. **Aging and HIV/AIDS: neurocognitive implications for older HIV-positive Latina/o Adults**. *Behav Med* (2014.0) **40** 116-123. DOI: 10.1080/08964289.2014.914464
13. Marquine MJ, Heaton A, Johnson N. **Differences in neurocognitive impairment among HIV-infected Latinos in the United States**. *J Int Neuropsychol Soc* (2018.0) **24** 163-175. DOI: 10.1017/S1355617717000832
14. Guaraldi G, Zona S, Alexopoulos N. **Coronary aging in HIV-infected patients**. *Clin Infect Dis* (2009.0) **49** 1756-1762. DOI: 10.1086/648080
15. 15.Kalayjian RC, Landay A, Pollard RB, et al. Age-related immune dysfunction in health and in human immunodeficiency virus (HIV) disease: association of age and HIV infection with naive CD8+ cell depletion, reduced expression of CD28 on CD8+ cells, and reduced thymic volumes. 2003;187(12):1924-33.
16. Brennan M, Karpiak SE, Shippy RA, Cantor MH. *Older adults with HIV: an in-depth examination of an emerging population* (2009.0)
17. Havlik RJ, Brennan M, Karpiak SE. **Comorbidities and depression in older adults with HIV**. *Sex Health* (2011.0) **8** 551-559. DOI: 10.1071/SH11017
18. Frontini M, Chotalia J, Spizale L, Onya W, Ruiz M, Clark RA. **Sex and race effects on risk for selected outcomes among elderly HIV-infected patients**. *J Int Assoc Phys AIDS Care* (2012.0) **11** 12-15. DOI: 10.1177/1545109711404947
19. Lombo B, Alkhalil I, Golden MP. **Prevalence of metabolic syndrome in patients with HIV in the era of highly active antiretroviral therapy**. *Conn Med* (2015.0) **79** 277-281. PMID: 26245015
20. Ramírez-Marrero FA, De Jesús E, Santana-Bagur J, Hunter R, Frontera W, Joyner MJ. **Prevalence of cardio-metabolic risk factors in Hispanics living with HIV**. *Ethn Dis* (2010.0) **20** 423-428. PMID: 21305832
21. Ramírez-Marrero FA, Rivera-Brown AM, Nazario CM, Rodríguez-Orengo JF, Smit E, Smith BA. **Self-reported physical activity in hispanic adults living with HIV: comparison with accelerometer and pedometer**. *J Assoc Nurs AIDS Care* (2008.0) **19** 283-294. DOI: 10.1016/j.jana.2008.04.003
22. Markides KS, Coreil J. **The health of Hispanics in the southwestern United States: an epidemiologic paradox**. *Public Health Rep* (1986.0) **101** 253-265. PMID: 3086917
23. Guaraldi G, Squillace N, Stentarelli C. **Nonalcoholic fatty liver disease in HIV-infected patients referred to a metabolic clinic: prevalence, characteristics, and predictors**. *Clin Infect Dis* (2008.0) **47** 250-257. DOI: 10.1086/589294
24. Merriman RB. **Nonalcoholic fatty liver disease and HIV infection**. *Curr HIV/AIDS Rep* (2006.0) **3** 113-117. DOI: 10.1007/BF02696654
25. Grov C, Golub SA, Parsons JT, Brennan M, Karpiak SE. **Loneliness and HIV-related stigma explain depression among older HIV-positive adults**. *AIDS Care* (2010.0) **22** 630-639. DOI: 10.1080/09540120903280901
26. Horberg MA, Silverberg MJ, Hurley LB. **Effects of depression and selective serotonin reuptake inhibitor use on adherence to highly active antiretroviral therapy and on clinical outcomes in HIV-infected patients**. *J Acquir Immune Defic Syndr* (2008.0) **47** 384-390. DOI: 10.1097/QAI.0b013e318160d53e
27. Hasin DS, Goodwin RD, Stinson FS, Grant BF. **Epidemiology of major depressive disorder: results from the National epidemiologic survey on alcoholism and related conditions**. *Arch Gen Psychiatry* (2005.0) **62** 1097-1106. DOI: 10.1001/archpsyc.62.10.1097
28. 28.Landeiro F, Leal J, Gray AM. The impact of social isolation on delayed hospital discharges of older hip fracture patients and associated costs. Osteoporos Int. 2016;27(2) https://ora.ox.ac.uk/objects/uuid:77d7ac37-b6a1-498d-8368-47d9ea3c5fe0. Accessed 28 Jan 2021.
29. Brennan-Ing M, Seidel L, Karpiak SE. **Social support systems and social network characteristics of older adults with HIV**. *HIV Aging* (2017.0) **42** 159-172. DOI: 10.1159/000448561
30. Webel AR, Longenecker CT, Gripshover B, Hanson JE, Schmotzer BJ, Salata RA. **Age, stress, and isolation in older adults living with HIV**. *AIDS Care* (2014.0) **26** 523-531. DOI: 10.1080/09540121.2013.845288
31. Dykstra PA, van Tilburg TG, Gierveld JD. **Changes in older adult loneliness: results from a seven-year longitudinal study**. *Res Aging* (2005.0) **27** 725-747. DOI: 10.1177/0164027505279712
32. Cornwell EY, Waite LJ. **Measuring social isolation among older adults using multiple indicators from the NSHAP study**. *J Gerontol B Psychol Sci Soc Sci* (2009.0) **64B** i38-i46. DOI: 10.1093/geronb/gbp037
33. Luanaigh CO, Lawlor BA. **Loneliness and the health of older people**. *Int J Geriatr Psychiatry* (2008.0) **23** 1213-1221. DOI: 10.1002/gps.2054
34. Holt-Lunstad J, Smith TB, Layton JB. **Social relationships and mortality risk: a meta-analytic review**. *PLoS Med* (2010.0) **7** e1000316. DOI: 10.1371/journal.pmed.1000316
35. Vanable PA, Carey MP, Blair DC, Littlewood RA. **Impact of HIV-related stigma on health behaviors and psychological adjustment among HIV-positive men and women**. *AIDS Behav* (2006.0) **10** 473-482. DOI: 10.1007/s10461-006-9099-1
36. Wilson DM, Errasti-Ibarrondo B, Low G. **Where are we now in relation to determining the prevalence of ageism in this era of escalating population ageing?**. *Ageing Res Rev* (2019.0) **51** 78-84. DOI: 10.1016/j.arr.2019.03.001
37. Emlet CA. **“You’re awfully old to have this disease”: experiences of stigma and ageism in adults 50 years and older living with HIV/AIDS**. *Gerontologist* (2006.0) **46** 781-790. DOI: 10.1093/geront/46.6.781
38. Yoo-Jeong M, Hepburn K, Holstad M, Haardörfer R, Waldrop-Valverde D. **Correlates of loneliness in older persons living with HIV**. *AIDS Care* (2019.0) **0** 1-8. DOI: 10.1080/09540121.2019.1659919
39. Miyawaki CE. **Association of social isolation and health across different racial and ethnic groups of older Americans**. *Ageing Soc* (2015.0) **35** 2201-2228. DOI: 10.1017/S0144686X14000890
40. Tomaka J, Thompson S, Palacios R. **The relation of social isolation, loneliness, and social support to disease outcomes among the elderly**. *J Aging Health* (2006.0) **18** 359-384. DOI: 10.1177/0898264305280993
41. Jimenez DE, Reynolds CF, Alegría M, Harvey P, Bartels SJ. **The Happy Older Latinos are Active (HOLA) health promotion and prevention study: study protocol for a pilot randomized controlled trial**. *Trials* (2015.0) **16** 579. DOI: 10.1186/s13063-015-1113-3
42. Leon AC, Davis LL, Kraemer HC. **The role and interpretation of pilot studies in clinical research**. *J Psychiatr Res* (2011.0) **45** 626-629. DOI: 10.1016/j.jpsychires.2010.10.008
43. Heckman TG, Sikkema KJ, Hansen N. **A randomized clinical trial of a coping improvement group intervention for HIV-infected older adults**. *J Behav Med* (2011.0) **2** 102-111. DOI: 10.1007/s10865-010-9292-6
44. 44.Rissel C, Passmore E, Mason C, Merom D. Two pilot studies of the effect of bicycling on balance and leg strength among older adults. J Environ Public Health. 2013. 10.1155/2013/686412.
45. Halpern SD, Chowdhury M, Bayes B. **Effectiveness and ethics of incentives for research participation: 2 randomized clinical trials**. *JAMA Intern Med* (2021.0) **181** 1479-1488. DOI: 10.1001/jamainternmed.2021.5450
46. Weinstein ER, Jimenez DE. **Prioritizing recruitment: the benefits to using a disease registry to recruit older adults with HIV and intersecting identities**. *AIDS Care* (2022.0) **8** 1-5. DOI: 10.1080/09540121.2022.2085867
47. Jacobson NS, Martell CR, Dimidjian S. **Behavioral activation treatment for depression: returning to contextual roots**. *Clin Psychol Sci Pract* (2001.0) **8** 255-270. DOI: 10.1093/clipsy.8.3.255
48. Bandura A. *Social learning theory* (1977.0)
49. Perez M, Findley SE, Mejia M, Martinez J. **The impact of community health worker training and programs in NYC**. *J Health Care Poor Underserved* (2006.0) **17** 26-43. DOI: 10.1353/hpu.2006.0049
50. Kenya S, Jones J, Arheart K. **Using community health workers to improve clinical outcomes among people living with HIV: a randomized controlled trial**. *AIDS Behav* (2013.0) **17** 2927-2934. DOI: 10.1007/s10461-013-0440-1
51. Barrera M, Toobert DJ, Angell KL, Glasgow RE, Mackinnon DP. **Social support and social-ecological resources as mediators of lifestyle intervention effects for type 2 diabetes**. *J Health Psychol* (2006.0) **11** 483-495. DOI: 10.1177/1359105306063321
52. Martinson BC, Crain AL, Sherwood NE, Hayes M, Pronk NP, O’Connor PJ. **Maintaining physical activity among older adults: six-month outcomes of the Keep Active Minnesota randomized controlled trial**. *Prev Med* (2008.0) **46** 111-119. DOI: 10.1016/j.ypmed.2007.08.007
53. Jimenez D, Reynolds CF, Alegria M, Harvey PS, Bartels SJ. **Preventing anxiety in depression in older latinos: the Happy Older Latinos are Active (HOLA) health promotion study**. *Am J Geriatr Psychiatry* (2017.0) **25** S124. DOI: 10.1016/j.jagp.2017.01.156
54. Folstein MF, Folstein SE, McHugh PR. **Mini-mental state: a practical method for grading the cognitive state of patients for the clinician**. *J Psychiatr Res* (1975.0) **12** 189-198. DOI: 10.1016/0022-3956(75)90026-6
55. Peters DM, Fritz SL, Krotish DE. **Assessing the reliability and validity of a shorter walk test compared with the 10-Meter Walk Test for measurements of gait speed in healthy, older adults**. *J Geriatr Phys Ther* (2013.0) **36** 24-30. DOI: 10.1519/JPT.0b013e318248e20d
56. 56.About #UequalsU | United States | Prevention Access Campaign. prevention. https://www.preventionaccess.org/about. Accessed 28 Jan 2021.
57. Radloff LS. **The CES-D scale: a self-report depression scale for research in the general population**. *Appl Psychol Meas* (1977.0) **1** 385-401. DOI: 10.1177/014662167700100306
58. Kroenke K, Spitzer RL, Williams JB. **The patient health questionnaire-2: validity of a two-item depression screener**. *Med Care* (2003.0) **41** 1284-1292. DOI: 10.1097/01.MLR.0000093487.78664.3C
59. Cohen S, Kamarck T, Mermelstein R. **A global measure of perceived stress**. *J Health Soc Behav* (1983.0) **24** 385-396. DOI: 10.2307/2136404
60. Spitzer RL, Kroenke K, Williams JBW, Löwe B. **A brief measure for assessing generalized anxiety disorder: the GAD-7**. *Arch Intern Med* (2006.0) **166** 1092-1097. DOI: 10.1001/archinte.166.10.1092
61. Zimet GD, Powell SS, Farley GK, Werkman S, Berkoff KA. **Psychometric characteristics of the multidimensional scale of perceived social support**. *J Pers Assess* (1990.0) **55** 610-617. DOI: 10.1080/00223891.1990.9674095
62. Marin G, Gamba RJ. **A new measurement of acculturation for hispanics: the Bidimensional Acculturation Scale for Hispanics (BAS)**. *Hisp J Behav Sci* (1996.0) **18** 297-316. DOI: 10.1177/07399863960183002
63. Bull FC, Maslin TS, Armstrong T. **Global Physical Activity Questionnaire (GPAQ): nine Country reliability and validity study**. *J Phys Act Health* (2009.0) **6** 790-804. DOI: 10.1123/jpah.6.6.790
64. Ware J, Kosinski M, Keller SD. **A 12-item short-form health survey: construction of scales and preliminary tests of reliability and validity**. *Med Care* (1996.0) **34** 220-233. DOI: 10.1097/00005650-199603000-00003
65. Berger BE, Ferrans CE, Lashley FR. **Measuring stigma in people with HIV: psychometric assessment of the HIV stigma scale**. *Res Nurs Health* (2001.0) **24** 518-529. DOI: 10.1002/nur.10011
66. Conner KO, Copeland VC, Grote NK. **Mental health treatment seeking among older adults with depression: the impact of stigma and race**. *Am J Geriatr Psychiatry* (2010.0) **18** 531-543. DOI: 10.1097/JGP.0b013e3181cc0366
67. Jimenez DE, Bartels SJ, Cardenas V, Alegría M. **Stigmatizing attitudes toward mental illness among racial/ethnic older adults in primary care**. *Int J Geriatr Psychiatry* (2013.0) **28** 1061-1068. DOI: 10.1002/gps.3928
68. Jimenez DE, Begley A, Bartels SJ. **Improving health-related quality of life in older African American and Non-Latino White patients**. *Am J Geriatr Psychiatry* (2015.0) **23** 548-558. DOI: 10.1016/j.jagp.2014.08.001
69. Tobias CR, Cunningham W, Cabral HD. **Living with HIV but without medical care: barriers to engagement**. *AIDS Patient Care STDs* (2007.0) **21** 426-434. DOI: 10.1089/apc.2006.0138
70. Warren-Jeanpiere L, Dillaway H, Hamilton P, Young M, Goparaju L. **Taking it one day at a time: African American women aging with HIV and co-morbidities**. *AIDS Patient Care STDs* (2014.0) **28** 372-380. DOI: 10.1089/apc.2014.0024
71. Sangaramoorthy T, Jamison AM, Dyer TV. **HIV stigma, retention in care, and adherence among older Black women living with HIV**. *J Assoc Nurs AIDS Care* (2017.0) **28** 518-531. DOI: 10.1016/j.jana.2017.03.003
|
---
title: 'Improving diagnosis and treatment of knee osteoarthritis in persons with type
2 diabetes: development of a complex intervention'
authors:
- Lauren K. King
- Noah M. Ivers
- Esther J. Waugh
- Crystal MacKay
- Ian Stanaitis
- Owen Krystia
- Jane Stretton
- Sim Wong
- Alanna Weisman
- Zahra Bardai
- Susan Ross
- Shawn Brady
- Marlee Shloush
- Tara Stier
- Natasha Gakhal
- Payal Agarwal
- Janet Parsons
- Lorraine Lipscombe
- Gillian A. Hawker
journal: Implementation Science Communications
year: 2023
pmcid: PMC9972628
doi: 10.1186/s43058-023-00398-3
license: CC BY 4.0
---
# Improving diagnosis and treatment of knee osteoarthritis in persons with type 2 diabetes: development of a complex intervention
## Abstract
### Background
Symptomatic knee osteoarthritis (OA) commonly co-occurs in people with type 2 diabetes (T2DM) and increases the risk for diabetes complications, yet uptake of evidence-based treatment is low. We combined theory, stakeholder involvement and existing evidence to develop a multifaceted intervention to improve OA care in persons with T2DM. This was done in partnership with Arthritis Society Canada to leverage the existing infrastructure and provincial funding for community arthritis care.
### Methods
Each step was informed by a User Advisory Panel of stakeholder representatives, including persons with lived experience. First, we identified the target groups and behaviours through consulting stakeholders and current literature. Second, we interviewed persons living with T2DM and knee OA ($$n = 18$$), health professionals (HPs) who treat people with T2DM ($$n = 18$$) and arthritis therapists (ATs, $$n = 18$$) to identify the determinants of seeking and engaging in OA care (patients), assessing and treating OA (HPs) and considering T2DM in OA treatment (ATs), using the Theoretical Domains Framework (TDF). We mapped the content to behavioural change techniques (BCTs) to identify the potential intervention components. Third, we conducted stakeholder meetings to ascertain the acceptability and feasibility of intervention components, including content and modes of delivery. Fourth, we selected intervention components informed by prior steps and constructed a programme theory to inform the implementation of the intervention and its evaluation.
### Results
We identified the barriers and enablers to target behaviours across a number of TDF domains. All stakeholders identified insufficient access to resources to support OA care in people with T2DM. Core intervention components, incorporating a range of BCTs at the patient, HP and AT level, sought to identify persons with knee OA within T2DM care and refer to Arthritis Society Canada for delivery of evidence-based longitudinal OA management. Diverse stakeholder input throughout development allowed the co-creation of an intervention that appears feasible and acceptable to target users.
### Conclusions
We integrated theory, evidence and stakeholder involvement to develop a multifaceted intervention to increase the identification of knee OA in persons with T2DM within diabetes care and improve the uptake and engagement in evidence-based OA management. Our partnership with Arthritis Society Canada supports future spread, scalability and sustainability. We will formally assess the intervention feasibility in a randomized pilot trial.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s43058-023-00398-3.
## Background
Driven by the ageing of the population and the epidemic of overweight and obesity, the prevalence of osteoarthritis (OA), the most common form of arthritis, is rapidly rising [1]. This has resulted in an increasing number of people living with OA-related functional limitations and has situated OA as a leading cause of disability worldwide [1]. Knee OA accounts for nearly $80\%$ of the burden of OA [1]. Knee OA-related disability has many potential consequences, including impacts on individuals’ other complex chronic conditions [2–8].
In people with type 2 diabetes (T2DM), OA frequently co-occurs [9] and has detrimental effects [10]. At least one in six individuals with T2DM also has knee OA [11], due to shared risk factors and potentially metabolic pathways [12]. In those with T2DM and knee OA, OA-related walking difficulty increases the risk for diabetes-specific complications and cardiovascular events [4], which may be a result of more sedentary time and/or less engagement in the physical activity [13] that is a cornerstone of T2DM management [14]. Symptomatic OA may also challenge T2DM self-management through poor sleep, low mood and fatigue [15] limiting reserves for the “extra work” of T2DM management [16]. It is therefore incumbent upon the medical community to improve recognition of OA and implementation of evidence-based OA treatment in people with T2DM.
Despite the consequences of knee OA-related functional limitations, safe, effective and guideline-recommended [17] knee OA treatments, such as education, physical activity and weight management, are underused [18]. One problem is the under-diagnosis of OA in the community [19], precluding patient provision of and engagement in care [20, 21]. Those with other chronic conditions, such as T2DM, are even less likely to have their OA addressed [22]. A further challenge is care delivery, with a need for services and programmes to support the necessary behavioural changes that are inherent in OA first-line treatments [23, 24]. Physical activity is also a key treatment for OA, resulting in long-term improvements in pain and function [25]. However, without adequate guidance from health professionals, people often are unclear about what they should do and may avoid participating in physical activity for fear of causing harm [26]. Finally, the current single-condition paradigm for chronic disease management [27] inefficiently slices up care, placing added burden and responsibility on patients for harmonizing chronic disease management strategies. Services that situate OA within the context of multimorbidity may be most successful and best optimize whole-person health.
Multiple complex interventions have been developed in an attempt to put evidence-based OA care into practice, including providing support for the behaviour change required [28]. Most interventions target persons with an established diagnosis of OA and have used strategies such as leveraging non-physician clinicians and/or digital technologies in the provision of care [29–41]. The Goodlife with osteoArthritis in Denmark (GLA:D) programme is an example of a successful education and exercise intervention delivered by trained physical therapists (and other clinicians) to improve pain and function in people with knee and/or hip OA [42]. However, few strategies have been developed to identify, assess and diagnose the many people with joint symptoms consistent with OA who lack a formal OA diagnosis. Marra et al. showed that a complex intervention involving screening persons with knee pain presenting to pharmacies improved the utilization of OA treatments and patient outcomes [43]. To our knowledge, no intervention has been developed specifically to improve OA care in individuals with other complex chronic conditions, such as T2DM, where competing demands may make OA care particularly challenging and necessitate a personalized approach [44]. Overcoming these challenges to improving uptake of and engagement in evidence-based knee OA care in persons with other chronic conditions, such as T2DM, with a view to increasing physical activity, holds the potential to improve both OA outcomes and outcomes related to the other chronic conditions.
Our aim, guided by the UK Medical Research Council (MRC) framework [45], was to use a systematic process combining theory, stakeholder involvement and existing evidence to develop a multifaceted implementation intervention to improve the uptake of evidence-based OA care including physical activity in persons with T2DM and knee OA. A broader aim was to outline this process of systematically developing a complex intervention that seeks to change the behaviours of health professionals (HPs) and patients to provide a template for researchers tackling similar implementation problems.
## Setting
In Ontario, Canada, individuals with chronic conditions, such as T2DM, present to primary care providers (family physicians or nurse practitioners) as the first point of contact in the health care system. A referral from a primary care provider or other physician is needed for an individual to access medical specialist services. The health care system in *Ontario is* publicly funded and privately administered. The Ontario Health Insurance Plan provides coverage for most medical and emergency services provided in Ontario. However, it does not provide universal coverage. Relevant to persons with T2DM and OA, prescription drugs and physiotherapy for those who are not on social assistance and/or under age 65 are paid for out-of-pocket by patients.
## Overarching framework
We developed our complex intervention within the first phase of the Treatment of Knee Osteoarthritis in Persons with Diabetes Mellitus (TOP-DM) study, combining relevant theory, current evidence and stakeholder input. The intervention development work took place from 2020 to 2021. We followed the 2008 and 2021 UK MRC updated guidance for the development and evaluation of complex interventions [45, 46] that divide the research process into four phases. As recommended within the intervention development phase, we used theory to comprehensively identify the determinants of behaviour and linked them to the mechanisms of change [47], while meaningfully engaging stakeholders [45], to maximize the potential for developing an intervention that will have positive impacts on health-related outcomes. We also placed strong importance on understanding context throughout the research process, including theorizing how the intervention generates its effects and ensuring it would be implementable among the target population and setting.
## Arthritis Society Canada
At the conception of this study, we partnered with Arthritis Society Canada, a not-for-profit non-governmental organization in Canada that seeks to elevate arthritis awareness, education and research. Within the province of Ontario, Arthritis Society *Canada is* directly involved in the provision of arthritis care through the Arthritis Rehabilitation and Education Program (AREP) [48], which provides provincially funded arthritis services, including group and one-on-one education and self-management sessions delivered by a team of trained physical therapists and occupational therapists, at no cost to patients. Thinking ahead to the eventual spread, scalability and sustainability of our intervention, the partnership allowed us to benefit from the existing infrastructure and provincial funding for arthritis care.
## User advisory panel
We constructed a user advisory panel (UAP) comprising diverse stakeholder membership to facilitate intervention co-design [49–51]. The UAP comprised three patient research partners living with T2DM and OA, the director of AREP and HPs from physical therapy, family medicine, endocrinology and rheumatology. Members of the UAP were consulted throughout the research process.
## Approach to intervention development
As the MRC framework lacks detailed operational guidance on the intervention development process, we followed the systematic step-wise approach to intervention development described by French et al. [ 47]. We outline these four steps below. We also present a summary of our intervention development process in Fig. 1.Fig. 1Overview of the step-wise intervention development process. Figure adapted from Riordan et al. [ 52]. BCT, behavioural change technique; TDF; theoretical domains framework Ethics approval was obtained from Women’s College Hospital and University of Toronto Research Ethics Boards.
## Step 1: Who needs to do what, differently?
The process of identifying individuals with T2DM who also have knee OA and providing evidence-based OA care involves multiple separate behaviours being performed by different individuals. Our research team, comprising individuals with expertise in both OA and T2DM, began by brainstorming potential behaviours to target and by whom. We first envisioned the care pathway of a person with T2DM and the steps that are needed for them to have joint symptoms assessed and diagnosed and for evidence-based OA care to be provided. Behaviours were reviewed with and refined through consultation with the UAP.
## Step 2: Using a theoretical framework, which barriers and enablers need to be addressed?
We undertook qualitative studies in three stakeholder groups (patients; diabetes HPs, including family physicians, endocrinologists and diabetes educators; and AREP arthritis therapists [ATs]), to identify the barriers and enablers to the target behaviours [53–55]. Semi-structured telephone interviews, conducted between September 2020 and January 2021, comprehensively explored the behaviours of interest for each group, guided by the Theoretical Domains Framework (TDF) [56]. The interview guides are provided in Additional file 2: Tables B, C and D.
We recruited individuals who had a physician diagnosis of T2DM and knee OA (“patients”), from a hospital-based family medicine clinic and through an email invitation to past clients of AREP. Interviews focused on individuals’ prior experiences living with T2DM and knee OA and behavioural determinants of seeking and engaging in OA care. We purposefully sampled diabetes HPs according to role and practice location to achieve a mix of family physician, endocrinologist and diabetes educator participants and practice locations in Ontario, Canada. Interviews explored the HP experiences with individuals with T2DM who also had knee OA and behavioural determinants of addressing and managing OA. We recruited practising AREP ATs through email invitations. These interviews explored the ATs’ experiences caring for persons with knee OA and other complex chronic conditions and the behavioural determinants of considering T2DM when formulating an OA treatment plan, including prescribing and monitoring physical activity. These interviews also allowed us to better understand the structure and practices within the current AREP care model to enrich our contextual understanding.
All interviews were digitally recorded and transcribed verbatim, and data were organized in NVivo 10. We deductively analysed the data informed by the TDF; within each TDF domain, data were inductively analysed to develop themes/belief statements [57].
## Mapping TDF domains to appropriate behaviour change techniques
We mapped the barriers and enablers, organized by TDF domains, to behaviour change techniques (BCTs), using the Theory and Technique Tool (https://theoryandtechniquetool.humanbehaviourchange.org/) developed by Michie et al. [ 58]. A BCT is defined as “a replicable component of an intervention designed to alter or redirect causals processes that regular behaviour” [59]. The tool shows where there are links between BCTs and mechanisms of action (including each TDF domain) based on a literature synthesis and expert consensus. Using this tool, we generated a list of potential BCTs for each identified TDF domain including those with confirmed or inconclusive evidence to support a link.
The list of BCTs was refined by members of the research team as those considered feasible, locally relevant and that could be operationalized within the scope of the current study. Multiple BCTs spanned more than one TDF domain.
## Developing intervention components and combining them into an acceptable deliverable intervention
To develop intervention components that were likely to be feasible, relevant in the local context and acceptable to stakeholders, we conducted two meetings (2 h each) with our UAP. Meetings were conducted by videoconference and facilitated by two of the authors (LK and GH). At the first meeting, we reviewed the existing literature and our qualitative interview findings and brainstormed potential intervention components. This information was then used by the research team to develop a preliminary sketch of intervention components, considering the APEASE (affordability, practicability, effectiveness, acceptability, side effects, equity) criteria [60]. We drew on practice guidelines for persons with knee OA [17], T2DM [61] and results of prior OA interventions and considered many different potential intervention components and modes of delivery.
At the second meeting, we discussed the sketch of the intervention. We presented unrefined potential components to invite input from our UAP. The UAP deliberated on the modes of delivery of intervention components and how to select and tailor specific strategies to address contextual needs. The research team made revisions to the draft intervention and presented the updates to our three patient partners, separately in 30–60-min meetings, to confirm acceptability and feasibility and whether other alternatives should be considered. Based on these discussions, we made further modifications. We then discussed the proposed intervention with two family physicians from our UAP, one rural and one urban, separately, to review the feasibility of the intervention components in their clinical practices. We reviewed the intervention with a rheumatologist, to confirm the acceptability of the identified ways to address OA. We conducted a meeting with a group of four endocrinologists who practised in different clinical settings to get diverse perspectives on how the intervention could be applied. We then presented and discussed the intervention with stakeholders at Arthritis Society Canada, including three ATs, the director of AREP and the vice president of AREP for Arthritis Society Canada. Some components that were not considered feasible were removed.
## Step 4: How can behaviour change be measured and understood?
We conducted evaluability assessments [45] through engaging experts in quality and innovation (NG) and implementation science (NI) to decide on proximal and feasibility outcomes of the intervention, the data to be collected and assessed and the options for evaluation. This resulted in a plan for feasibility evaluation that will be fully reported separately.
To describe our programme theory, we developed logic models of the final intervention, presenting the inputs, processes and the causal mechanisms by which we expect intervention components to have positive effects.
## Results
The final intervention has been reported according to TiDierR [62].
## Step 1: Identify who needs to do what, differently
We confirmed the following behaviours of interest: [1] for HPs, to identify and treat knee OA; [2] for persons with T2DM and knee OA, to seek and engage in knee OA care; and [3] for Arthritis Society Canada ATs, to consider T2DM when formulating an OA treatment plan, including a focus on prescribing and monitoring physical activity. Using the Action, Actor, Context, Target, Time (AACTT) framework [63], we further specify these behaviours in Table 1.Table 1Behaviours of interest specified using the Action, Actor, Context, Target, Time (AACTT) framework. Behaviour 1 (persons with type 2 diabetes (T2DM) and knee osteoarthritis (OA)): to seek and engage in knee OA care. Behaviour 2 (health professionals who treat T2DM): to identify and treat knee OA. Behaviour 3 (Arthritis Society *Canada arthritis* therapists): to consider T2DM when formulating an OA treatment plan, including a focus on prescribing and monitoring physical activityBehaviour 1Behaviour 2Behaviour 3ActionPresent to health professional for OA evaluation and care and engage with OA managementAssess for joint symptoms and treat/refer for treatment if presentEmphasize physical activity for the treatment of OA in the context of T2DMActorPeople with T2DMDiabetes health professionals (primary care providers, endocrinologists providing diabetes care, diabetes educators)Arthritis therapistsContextDaily lifePrimary care or diabetes clinicArthritis care visitTargetPeople with T2DM and OAPeople with T2DM and OAPeople with T2DM and OATimeWhen symptoms presentWhen patients attendWhen patients attend
## Step 2: Identify the barriers and enablers that need to be addressed using a theoretical framework
We conducted qualitative interviews with 18 persons with T2DM and knee OA, 18 HPs who treat persons with T2DM (8 endocrinologists, 7 family physicians, 3 diabetes educators) and 18 ATs. These studies, reported elsewhere [53–55], are summarized below, and we list the TDF domains that we identified as relevant in parentheses.
## Interviews with persons living with knee OA and T2DM
Of the 14 TDF domains, seven prominently influenced the behaviour of patients to seek and engage in OA care. Important barriers included the insufficient provision of OA knowledge to fully engage in care (knowledge), feeling incapable of participating in physical activity/exercise due to joint pain (beliefs about capabilities), lack of guidance from HPs and insufficient access to community programmes/supports (environmental context and resources) and being uncertain that OA therapies would help them (optimism). Key enablers were strong social support (social influences), sources of accountability (behavioural regulation) and experiencing benefit from prior use of treatment (reinforcement).
## Interviews with T2DM health professionals
We identified six TDF domains that prominently influenced the behaviours of HPs to assess and treat knee OA. For all HPs, important barriers included not seeing joint pain as a priority (intention), perceived lack of programmes to which they could refer their patients (environmental context and resources), insufficient knowledge and skills to assess OA, particularly for endocrinologists and diabetes educators (knowledge, skills), belief that it was not within their professional role to address OA (professional role and identity) and that other physicians would not want to receive a referral for OA care (social influences).
## Interviews with AREP arthritis therapists
We identified five TDF domains that were relevant to the ATs’ behaviour to consider T2DM when formulating a knee OA management plan. ATs’ perceived lack of specific knowledge around comorbidities including T2DM (knowledge); there was a lack of breadth in skills in behavioural change techniques to help patients set and reach their goals, particularly when it came to physical activity (skills); therapists generally had no intention for a patient’s comorbidity profile to influence their treatment recommendations (intention); they saw their role as joint focused (professional role and identity); and lack of a formalized follow-up structure of the current Arthritis Society Canada AREP programme limited sufficient patient monitoring and follow-up (environmental context and resources).
## Identify potential behavioural change techniques and modes of delivery to overcome barriers and enhance the enablers
Our initial list of BCTs, at each of the patient; HP; and AT levels, is shown in Additional file 1: Table A.
## Identify what is likely to be feasible, locally relevant and acceptable and combine identified components into an acceptable intervention that can be delivered
At our first UAP meeting, there was a broad agreement with qualitative findings and support for leveraging the Arthritis Society Canada AREP programme infrastructure as a vehicle to provide OA care. UAP members suggested the following ideas to operationalize BCTs and optimize modes of delivery: development of simple ways T2DM clinicians could screen for OA, improving diabetes HPs awareness around the impact of OA, different ways to provide T2DM patients with guidance about exercise for OA and use of diabetes flow sheets to prompt discussion about reasons for physical inactivity, including inquiring about OA. Based on this discussion, we refined our list of BCTs and excluded those deemed outside the scope of the study or not feasible. Our selected operationalizable BCTs, within each domain and mode of delivery, targeting patient, diabetes HP and AT level, are summarized in Additional file 1: Table A.
At the second UAP meeting, all members supported our intervention sketch. In particular, stakeholders from Arthritis Society Canada supported adapting the existing AREP model to deliver longitudinal OA care. There was a widespread interest in ensuring that access to the intervention would be equitable for all and not rely on the need for advanced technology, and therefore, we removed some elements of the proposed intervention that centred around digital technologies. There was however interest in ensuring flexibility in how care was delivered to take into account patient preferences and so designed the intervention to be delivered in-person or virtually (telephone and/or video visits).
There were two main steps of the draft intervention. The first step involved screening for and identification of symptomatic knee OA within diabetes care, with referral to Arthritis Society Canada AREP in those identified as having suspected or confirmed OA for further evaluation and care. The second step involved a longitudinal treatment programme over 4 months delivered by AREP ATs, comprising one-on-one individualized OA management within the context of T2DM and including a focus on supporting the behaviour change requirement to increasing aerobic physical activity. We named this the Arthritis Society Diabetes & Osteoarthritis Program.
During small group meetings, reviewing detailed intervention components, patient partners described that an early check-in would help to support engagement with OA care through promoting accountability and allowing early troubleshooting to take place if any barriers arose. Several physicians emphasized the need to provide communication from the OA programme back to primary care and endocrinology so that care plans could be recognized and reinforced at those clinical encounters. We refined the intervention to incorporate these suggestions. We heard from AREP ATs about specific elements that would be required to support their delivery of OA care as part of the intervention, to prepare them for assessing and treating persons with T2DM and knee OA. We confirmed topics to be delivered in a 1-day workshop for ATs, drawn from results of the AT qualitative interviews, which included an overview of T2DM, behavioural change techniques and health coaching, wearables and technology that can be offered to support patients to meet physical activity goals and an update on the management of knee OA.
In Table 2, we show the final intervention components, including content and modes of delivery, mapped to the selected BCT and TDF domain being targeted. We organize this by group (patient, HP and AT); however, the order is not meant to convey the temporality or importance of a single group or behaviour. Intervention steps and major components are shown in Fig. 2.Table 2Final intervention components mapped to the behavioural change technique (BCT) and theoretical domains framework (TDF) barrier or enabler being targeted, by participant group: (A) patients, (B) health professionals and (C) arthritis therapistsStep 2: *Using a* theoretical framework, which barriers and enablers need to be addressed?Step 3: Which intervention components could overcome the modifiable barriers and enhance the enablers?Barrier or enablerTDF domainBehavioural change techniqueModes of delivery and contentA. Patient Understanding about OA and its managementKnowledgeInstructions on how to perform behaviourPersonalized OA treatment planInformation about health consequencesWritten information and education through one-on-one AT visits about the interaction between OA and T2DM and the consequences of untreated symptomatic OA Capability to engage in exercise with joint painBeliefs about capabilitiesProblem solvingATs help deconstruct physical activity barriers and co-develop goalsInstructions on how to perform behaviourWritten and verbal advice on engaging in OA careGraded tasksIndividualized goal setting and titration of physical activity Expecting OA treatment will helpOptimismReview outcome goal(s)Personalized OA care plan, including physical activity goals Feedback (internal or external) that OA treatment is helpingReinforcementFeedback on behaviourAT to develop a monitoring plan with patient for OA care, including PAPrompts/cuesAT to develop individualized reminder plan (emails, use of wearable device) Support from health professionalsEnvironmental context and resourcesPrompts/cues–Social support (practical)ATs to help the patient determine community supports to meet goals, in addition to communicating care plan to the health care teamRestructuring of physical environmentLongitudinal relationship with AT to support necessary behavioural change Access to facilities, programmes and resourcesEnvironmental context and resourcesPrompts/cues–Social support (practical)–Restructuring of the physical environmentDiabetes & Osteoarthritis Program provides comprehensive OA care at no out-of-pocket costs Social support to encourage engagement in OA treatmentSocial influencesSocial support (practical)ATs to help connect patients with potential sources of support at home and in their community, including access to Arthritis Society Canada social workers as needed Peer influence on OA therapiesSocial influencesSocial support (practical)Welcome package for Diabetes & Osteoarthritis Program to include peer stories and experiences Sources of accountabilityBehavioural regulationGoal settingATs to deploy a wide range of BCTs to help patients set, titrate, troubleshoot their goals and progress and provide sources of accountabilityGraded tasksProblem solvingPrompts/cuesB. Health professional Knowledge about OA diagnosis and treatmentKnowledgeInstructions on how to perform behaviourElectronic educational materials providing information on how to screen for knee OA, including patient screening questionsInformation about health consequencesElectronic educational materials describing health consequences of untreated symptomatic knee OA Skills in joint examinationSkillsInstructions on how to perform behaviourElectronic education materials providing screening questions for knee OA that remove the need for physical examInformation about health consequences– Role in OA managementSocial and professional role and identityCredible sourceEducational materials from the study team that include input from endocrinologists and family physicians Priority of OA careIntentionsInformation about health consequencesElectronic educational materials describing health consequences of untreated symptomatic knee OAInformation about others’ approvalEach referral to Diabetes & Osteoarthritis Program accompanied by a confirmation note to referring provider with approval and appreciation for referral Resources for OA care, including access to physical therapyEnvironmental context and resourcesPrompts/cuesUse of various methods to remind clinicians of Diabetes & Osteoarthritis Program (modification of diabetes flow sheets, clinic posters)Conserving mental resourcesEfficient referral process with little time of referring provider requiredRestructuring the physical environmentCreation of Diabetes & Osteoarthritis Program as a resource to refer to for timely OA care at no cost to the patient Perception rheumatologists do not want to manage OASocial influencesInformation about others’ approvalEach referral to Diabetes & Osteoarthritis Program accompanied by a confirmation note to referring provider with approval and appreciation for referralC. Arthritis therapist Paucity of specific diabetes knowledgeKnowledgeInstructions on how to perform behaviourWorkshop for ATs to increase T2DM-specific knowledgeInformation about health consequences Variability in skills in behaviour change techniquesSkillsInstructions on how to perform behaviourWorkshop for ATs to increase confidence in the use of BCTsInformation about health consequences Variability in perceived role in optimizing overall healthSocial and professional role and identityCredible sourceArthritis Society Canada leadership supporting a focus on whole-person healthSocial support Variable intention to consider comorbidity in OA management planIntentionInformation about health consequencesWorkshop for ATs to increase knowledge about the impact of OA on other chronic conditions, including T2DM and importance of physical activityInformation about others’ approvalArthritis Society Canada leadership supporting a focus on whole-person healthGoal setting Existing AREP programme limits the provision of longitudinal OA careEnvironmental context and resourcesPrompts/cuesPhysical goals and plan sheet for ATs to complete with patientsRestructuring of the physical environmentThrough the creation of Diabetes & Osteoarthritis Program, follow-up visits scheduled over 6 months“–” indicates not applicableFig. 2Organization of final intervention, with major components summarized. AT, arthritis therapist; BCT, behavioural change technique; HP, health professional
## Programme theory
We expect our intervention to work by enabling change in the behaviours of patients, diabetes HPs and ATs, as shown in our logic models (Fig. 3). For patients, our intervention will increase intention and motivation to engage in OA care, through both facilitating receipt of a diagnosis of OA and providing support for management. For HPs, it will increase the intention to screen for knee OA and refer for assessment and treatment when OA is suspected or confirmed. For ATs, through adapting the existing AREP programme and creating the Arthritis Society Diabetes & Osteoarthritis Program, we have created an environmental change to support the provision of individualized longitudinal care. This programme also shifts the focus in care from joint-specific therapeutic exercise to increase overall physical activity. We intend for there to be flexibility in the delivery of intervention components to allow for variation in practices of different diabetes HPs, including clinic resources, yet maintain the integrity of the core intervention components [45].Fig. 3Logic models of the multi-level intervention for A patients, B health professionals and C arthritis therapists. Barriers and enablers according to the Theoretical Domains Framework (TDF) are mapped to behavioural change techniques and then proximal, feasibility and clinical outcomes. AT, Arthritis Therapist; BCT, behavioural change technique; HP, health professional; PA, physical activity; TDF; Theoretical Domains Framework
## Discussion
In this paper, we describe the development phase of a multifaceted intervention to overcome barriers to the assessment and diagnosis of OA in persons with T2DM. The TOP-DM intervention promotes evidence-based OA treatment with a focus on physical activity, including the mechanisms to support the behaviour change this requires, given its importance in both T2DM and OA care. We expect our intervention to work by enabling change in behaviours of patients, diabetes HPs and ATs and have targeted multiple groups given the complexity of this health challenge. In keeping with MRC guidance [45], our intervention development process has incorporated theory, in this case, of behaviour change [64], existing evidence and stakeholder involvement, while considering local context, to maximize the likelihood of success. The final intervention brings together a range of components that were specifically developed in the context of concomitant T2DM. Some of the components are similar to those incorporated in prior knee OA interventions, including strategies to screen for knee OA [43], increase health professional knowledge [65, 66], provide patient education [67, 68] and improve uptake of physical activity through health professional support [42].
We involved multiple stakeholders in a co-design process, to develop our intervention alongside those for whom it is designed [49–51]. Our UAP brought together patient partners, Arthritis Society Canada and diverse HPs, in a focus group-like setting where concepts could be tackled from many important perspectives. Given the focus on implementation in the context of multiple chronic conditions, this involved a large number of individuals. One lesson learned was that when bringing a large group together, any one individual could get relatively little “air time”. To address this, we also engaged stakeholders (patients, Arthritis Society Canada and HPs) individually or in small stakeholder groups to allow sufficient time to garner their inputs and to mitigate any possible hierarchal dynamics that might prevent individuals from expressing their views. Teams undertaking implementation research should carefully consider the modes in which they plan to engage stakeholders [69].
Strengths of this work include the use of a systematic step-wise approach [47]. Through the use of theory, and linking identified barriers to health behaviour to relevant and effective BCTs, we have explicitly outlined how we expect our intervention to work, and we will be able to evaluate these proposed mechanisms of change in future work. With our transdisciplinary approach, including collaborating with end-users and community stakeholders throughout the research process, we have sought to enhance the potential feasibility and effectiveness of our intervention [70, 71]. This work fills an important gap. To our knowledge, our intervention is one of only a few seeking to increase the identification and diagnosis of individuals with symptomatic knee OA to facilitate care, and none to our knowledge has done so within the context of another complex chronic condition. Our work to integrate OA care within T2DM complex chronic disease management is in an effort to break down the current, mostly siloed, models of chronic disease care. Our intervention leverages the existing Arthritis Society Canada AREP infrastructure and provincial funding, supporting potential intervention spread, scalability and sustainability.
Our work has some limitations. First, this approach to intervention development requires significant time and resources. While explicit use of theory has several advantages, including helping to inform important intervention elements [72], the evidence base to support that theory-informed interventions are superior to those not based on theory is sparse, largely due to the challenges of empirically addressing this question [73]. Multiple theories and frameworks of individual and organizational behaviour change exist, with little consensus on how to optimally select one [74]. We selected the TDF as it is recognized as the most comprehensive framework for designing implementation interventions [47]; however, other frameworks or theories can be used. While we sought to bridge the distance between OA and T2DM care, we expect many patients to have additional chronic conditions that may present additional barriers to OA care that were not explicitly addressed through this intervention. Our intervention may have limited generalizability given the use of AREP, as other jurisdictions may not have a similar infrastructure.
## Conclusions
In conclusion, using a systematic process combining theory, stakeholder involvement and existing evidence, we have developed a complex implementation intervention to improve OA care in persons with T2DM with the goal to improve both OA and T2DM outcomes and optimize overall health and well-being. While we have used robust methods in development, our next steps include assessment of proximal and feasibility outcomes using rapid-cycle change quality improvement methods and engaging potential intervention users to inform refinements to our intervention before evaluation of both feasibility and effectiveness outcomes in a pilot cluster randomized clinical trial.
## Supplementary Information
Additional file 1: Table A. Our initial list of behavioural change techniques (BCTs), mapped to each relevant theoretical domains framework (TDF) domain for A) Patients, B) Health professionals, and C) Arthritis therapists with either a confirmed link or inconclusive evidence for a link according to the Theory and Technique Tool (https://theoryandtechniquetool.humanbehaviourchange.org/) [70]. Those in bold font indicates the BCTs selected in the research process as potentially operationalizable and feasible. Additional file 2: Table B. Interview Guide: Patients with Diabetes and Osteoarthritis. Table C. Interview Guide: Physicians and Diabetes Educators. Table D. Interview Guide: Arthritis Therapists.
## References
1. Vos T, Allen C, Arora M, Barber RM, Bhutta ZA, Brown A. **Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015**. *The Lancet* (2016.0) **388** 1545-1602. DOI: 10.1016/S0140-6736(16)31678-6
2. Hawker GA, Croxford R, Bierman AS, Harvey PJ, Ravi B, Stanaitis I. **All-cause mortality and serious cardiovascular events in people with hip and knee osteoarthritis: a population based cohort study**. *PLoS ONE* (2014.0) **9** e91286. DOI: 10.1371/journal.pone.0091286
3. Kendzerska T, King LK, Lipscombe L, Croxford R, Stanaitis I, Hawker G. **The impact of hip and knee osteoarthritis on the subsequent risk of incident diabetes: a population-based cohort study**. *Diabetologia* (2018.0) **61** 2290-2299. DOI: 10.1007/s00125-018-4703-2
4. Hawker GA, Croxford R, Bierman AS, Harvey P, Ravi B, Kendzerska T. **Osteoarthritis-related difficulty walking and risk for diabetes complications**. *Osteoarthritis Cartilage* (2017.0) **25** 67-75. DOI: 10.1016/j.joca.2016.08.003
5. Zheng S, Tu L, Cicuttini F, Zhu Z, Han W, Antony B. **Depression in patients with knee osteoarthritis: risk factors and associations with joint symptoms**. *BMC Musculoskelet Disord* (2021.0) **22** 40. DOI: 10.1186/s12891-020-03875-1
6. Kendzerska T, Juni P, King LK, Croxford R, Stanaitis I, Hawker GA. **The longitudinal relationship between hand, hip and knee osteoarthritis and cardiovascular events: a population-based cohort study**. *Osteoarthritis Cartilage* (2017.0) **25** 1771-1780. DOI: 10.1016/j.joca.2017.07.024
7. Innes KE, Sambamoorthi U. **The association of osteoarthritis and related pain burden to incident Alzheimer’s disease and related dementias: a retrospective cohort study of U.S. Medicare beneficiaries**. *J Alzheimers Dis* (2020.0) **75** 789-805. DOI: 10.3233/JAD-191311
8. Kiadaliri A, Englund M. **Osteoarthritis and risk of hospitalization for ambulatory care-sensitive conditions: a general population-based cohort study**. *Rheumatology (Oxford)* (2021.0) **60** 4340-4347. DOI: 10.1093/rheumatology/keab161
9. Quiñones AR, Markwardt S, Botoseneanu A. **Diabetes-multimorbidity combinations and disability among middle-aged and older adults**. *J Gen Intern Med* (2019.0) **34** 944-951. DOI: 10.1007/s11606-019-04896-w
10. Swain S, Sarmanova A, Coupland C, Doherty M, Zhang W. **Comorbidities in osteoarthritis: a systematic review and meta-analysis of observational studies**. *Arthritis Care Res* (2020.0) **72** 991-1000. DOI: 10.1002/acr.24008
11. Louati K, Vidal C, Berenbaum F, Sellam J. **Association between diabetes mellitus and osteoarthritis: systematic literature review and meta-analysis**. *RMD Open* (2015.0) **1** e000077. DOI: 10.1136/rmdopen-2015-000077
12. Veronese N, Cooper C, Reginster JY, Hochberg M, Branco J, Bruyère O. **Type 2 diabetes mellitus and osteoarthritis**. *Semin Arthritis Rheum* (2019.0) **49** 9-19. DOI: 10.1016/j.semarthrit.2019.01.005
13. 13.Centers for Disease Control and Prevention (CDC). Arthritis as a potential barrier to physical activity among adults with diabetes--United States, 2005 and 2007. MMWR Morb Mort Wkly Rep. 2008;57:486–9.
14. Sigal RJ, Armstrong MJ, Bacon SL, Boule NG, Dasgupta K, Kenny GP. **Physical activity and diabetes**. *Can J Diabetes* (2018.0) **42** S54-s63. DOI: 10.1016/j.jcjd.2017.10.008
15. 15.King LK, Waugh E, McKay C, Stanaitis I, Stretton J, Weisman A, et al. 'It’s a dance between managing both': a qualitative study exploring perspectives of persons with knee osteoarthritis and type 2 diabetes mellitus on the impact of osteoarthritis on diabetes management and daily life. BMJ Open. 2022;2(11):061472.
16. Stuckey H, Peyrot M. **Living with diabetes: literature review and secondary analysis of qualitative data**. *Diabet Med* (2020.0) **37** 493-503. PMID: 31984543
17. Bannuru RR, Osani MC, Vaysbrot EE, Arden N, Bennell K, Bierma-Zeinstra SMA. **OARSI guidelines for the non-surgical management of knee, hip, and polyarticular osteoarthritis**. *Osteoarthritis Cartilage* (2019.0) **27** 1578-1589. DOI: 10.1016/j.joca.2019.06.011
18. King LK, Marshall DA, Faris P, Woodhouse LJ, Jones CA, Noseworthy T. **Use of recommended non-surgical knee osteoarthritis management in patients prior to total knee arthroplasty: a cross-sectional study**. *J Rheumatol* (2020.0) **47** 1253-1260. DOI: 10.3899/jrheum.190467
19. Yu D, Jordan KP, Peat G. **Underrecording of osteoarthritis in United Kingdom primary care electronic health record data**. *Clin Epidemiol* (2018.0) **10** 1195-1201. DOI: 10.2147/CLEP.S160059
20. Jackson H, Barnett LA, Jordan KP, Dziedzic KS, Cottrell E, Finney AG. **Patterns of routine primary care for osteoarthritis in the UK: a cross-sectional electronic health records study**. *BMJ Open* (2017.0) **7** e019694. DOI: 10.1136/bmjopen-2017-019694
21. Edwards JJ, Jordan KP, Peat G, Bedson J, Croft PR, Hay EM. **Quality of care for OA: the effect of a point-of-care consultation recording template**. *Rheumatology* (2014.0) **54** 844-853. DOI: 10.1093/rheumatology/keu411
22. Christiansen MB, White DK, Christian J, Waugh E, Gakhal N, King L. **“It … doesn’t always make it [to] the top of the list”: primary care physicians’ experiences with prescribing exercise for knee osteoarthritis**. *Can Fam Physic* (2020.0) **66** e14-e20
23. Egerton T, Diamond LE, Buchbinder R, Bennell KL, Slade SC. **A systematic review and evidence synthesis of qualitative studies to identify primary care clinicians’ barriers and enablers to the management of osteoarthritis**. *Osteoarthritis Cartilage* (2017.0) **25** 625-638. DOI: 10.1016/j.joca.2016.12.002
24. Rosemann T, Wensing M, Joest K, Backenstrass M, Mahler C, Szecsenyi J. **Problems and needs for improving primary care of osteoarthritis patients: the views of patients, general practitioners and practice nurses**. *BMC Musculoskelet Disord* (2006.0) **7** 48. DOI: 10.1186/1471-2474-7-48
25. Kraus VB, Sprow K, Powell KE, Buchner D, Bloodgood B, Piercy K. **Effects of physical activity in knee and hip osteoarthritis: a systematic umbrella review**. *Med Sci Sports Exerc* (2019.0) **51** 1324-1339. DOI: 10.1249/MSS.0000000000001944
26. Hurley M, Dickson K, Hallett R, Grant R, Hauari H, Walsh N. **Exercise interventions and patient beliefs for people with hip, knee or hip and knee osteoarthritis: a mixed methods review**. *Cochrane Database Syst Rev* (2018.0) **4** Cd010842. PMID: 29664187
27. Grembowski D, Schaefer J, Johnson KE, Fischer H, Moore SL, Tai-Seale M. **A conceptual model of the role of complexity in the care of patients with multiple chronic conditions**. *Med Care* (2014.0) **52** S7-S14. DOI: 10.1097/MLR.0000000000000045
28. Tan BY, Thach T, Munro YL, Skou ST, Thumboo J, Car J. **Complex lifestyle and psychological intervention in knee osteoarthritis: scoping review of randomized controlled trials**. *Int J Environ Res Public Health* (2021.0) **18** 12757. DOI: 10.3390/ijerph182312757
29. Allen KD, Oddone EZ, Coffman CJ, Datta SK, Juntilla KA, Lindquist JH. **Telephone-based self-management of osteoarthritis: a randomized trial**. *Ann Intern Med* (2010.0) **153** 570-579. DOI: 10.7326/0003-4819-153-9-201011020-00006
30. Bennell KL, Lawford BJ, Keating C, Brown C, Kasza J, Mackenzie D. **Comparing video-based, telehealth-delivered exercise and weight loss programs with online education on outcomes of knee osteoarthritis**. *Ann Intern Med* (2021.0) **175** 198-209. DOI: 10.7326/M21-2388
31. Baker K, LaValley MP, Brown C, Felson DT, Ledingham A, Keysor JJ. **Efficacy of computer-based telephone counseling on long-term adherence to strength training in elderly patients with knee osteoarthritis: a randomized trial**. *Arthritis Care Res* (2020.0) **72** 982-990. DOI: 10.1002/acr.23921
32. Wang L, Chen H, Lu H, Wang Y, Liu C, Dong X. **The effect of transtheoretical model-lead intervention for knee osteoarthritis in older adults: a cluster randomized trial**. *Arthritis Res Ther* (2020.0) **22** 134. DOI: 10.1186/s13075-020-02222-y
33. Nelligan RK, Hinman RS, Kasza J, Crofts SJC, Bennell KL. **Effects of a self-directed web-based strengthening exercise and physical activity program supported by automated text messages for people with knee osteoarthritis: a randomized clinical trial**. *JAMA Intern Med* (2021.0) **181** 776-785. DOI: 10.1001/jamainternmed.2021.0991
34. Kloek CJJ, Bossen D, Spreeuwenberg PM, Dekker J, de Bakker DH, Veenhof C. **Effectiveness of a blended physical therapist intervention in people with hip osteoarthritis, knee osteoarthritis, or both: a cluster-randomized controlled trial**. *Phys Ther* (2018.0) **98** 560-570. DOI: 10.1093/ptj/pzy045
35. Bennell KL, Nelligan R, Dobson F, Rini C, Keefe F, Kasza J. **Effectiveness of an Internet-delivered exercise and pain-coping skills training intervention for persons with chronic knee pain**. *Ann Intern Med* (2017.0) **166** 453-462. DOI: 10.7326/M16-1714
36. Bennell KL, Ahamed Y, Jull G, Bryant C, Hunt MA, Forbes AB. **Physical therapist-delivered pain coping skills training and exercise for knee osteoarthritis: randomized controlled trial**. *Arthritis Care Res* (2016.0) **68** 590-602. DOI: 10.1002/acr.22744
37. Preece SJ, Brookes N, Williams AE, Jones RK, Starbuck C, Jones A. **A new integrated behavioural intervention for knee osteoarthritis: development and pilot study**. *BMC Musculoskelet Disord* (2021.0) **22** 526. DOI: 10.1186/s12891-021-04389-0
38. Hinman RS, Campbell PK, Lawford BJ, Briggs AM, Gale J, Bills C. **Does telephone-delivered exercise advice and support by physiotherapists improve pain and/or function in people with knee osteoarthritis? Telecare randomised controlled trial**. *Br J Sports Med* (2020.0) **54** 790-797. DOI: 10.1136/bjsports-2019-101183
39. Bennell K, Nelligan RK, Schwartz S, Kasza J, Kimp A, Crofts SJ. **Behavior change text messages for home exercise adherence in knee osteoarthritis: randomized trial**. *J Med Internet Res* (2020.0) **22** e21749e. DOI: 10.2196/21749
40. Thorstensson CA, Garellick G, Rystedt H, Dahlberg LE. **Better management of patients with osteoarthritis: development and nationwide implementation of an evidence-based supported osteoarthritis self-management programme**. *Musculoskeletal Care* (2015.0) **13** 67-75. DOI: 10.1002/msc.1085
41. Allen KD, Woolson S, Hoenig HM, Bongiorni D, Byrd J, Caves K. **Stepped exercise program for patients with knee osteoarthritis: a randomized controlled trial**. *Ann Intern Med* (2021.0) **174** 298-307. DOI: 10.7326/M20-4447
42. Skou ST, Roos EM. **Good Life with osteoArthritis in Denmark (GLA:D™): evidence-based education and supervised neuromuscular exercise delivered by certified physiotherapists nationwide**. *BMC Musculoskelet Disord* (2017.0) **18** 72. DOI: 10.1186/s12891-017-1439-y
43. Marra CA, Cibere J, Grubisic M, Grindrod KA, Gastonguay L, Thomas JM. **Pharmacist-initiated intervention trial in osteoarthritis: a multidisciplinary intervention for knee osteoarthritis**. *Arthritis Care Res* (2012.0) **64** 1837-1845. DOI: 10.1002/acr.21763
44. Bossen D, Buskermolen M, Veenhof C, de Bakker D, Dekker J. **Adherence to a web-based physical activity intervention for patients with knee and/or hip osteoarthritis: a mixed method study**. *J Med Internet Res* (2013.0) **15** e223. DOI: 10.2196/jmir.2742
45. Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM. **A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance**. *BMJ (Clinical research ed)* (2021.0) **374** n2061. PMID: 34593508
46. Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. **Developing and evaluating complex interventions: the new Medical Research Council guidance**. *BMJ (Clinical research ed)* (2008.0) **337** a1655. PMID: 18824488
47. French SD, Green SE, O’Connor DA, McKenzie JE, Francis JJ, Michie S. **Developing theory-informed behaviour change interventions to implement evidence into practice: a systematic approach using the Theoretical Domains Framework**. *Implement Sci* (2012.0) **7** 38. DOI: 10.1186/1748-5908-7-38
48. 48.Arthritis Society Canada. Arthritis Rehabilitation and Education Program. https://arthritis.ca/support-education/support-in-your-community/arthritis-rehabilitation-and-education-program-(ar. Accessed 30 Aug 2022.
49. Harte R, Quinlan LR, Glynn L, Rodríguez-Molinero A, Baker PM, Scharf T. **Human-centered design study: enhancing the usability of a mobile phone app in an integrated falls risk detection system for use by older adult users**. *JMIR Mhealth Uhealth* (2017.0) **5** e71. DOI: 10.2196/mhealth.7046
50. McCurdie T, Taneva S, Casselman M, Yeung M, McDaniel C, Ho W. **mHealth consumer apps: the case for user-centered design**. *Biomed Instrum Technol* (2012.0) **46** 49-56. DOI: 10.2345/0899-8205-46.s2.49
51. Portalupi LB, Lewis CL, Miller CD, Whiteman-Jones KL, Sather KA, Nease DE. **Developing a patient and family research advisory panel to include people with significant disease, multimorbidity and advanced age**. *Fam Pract* (2017.0) **34** 364-369. PMID: 28122848
52. Riordan F, Racine E, Phillip ET, Bradley C, Lorencatto F, Murphy M. **Development of an intervention to facilitate implementation and uptake of diabetic retinopathy screening**. *Implement Sci* (2020.0) **15** 34. DOI: 10.1186/s13012-020-00982-4
53. King LK, Krystia O, Waugh EJ, MacKay C, Stanaitis I, Stretton J. **Barriers and enablers to health care providers assessment and treatment of knee osteoarthritis in persons with type 2 diabetes mellitus: a qualitative study using the Theoretical Domains Framework**. *Osteoarthritis Cartilage Open* (2022.0) **4** 100299. DOI: 10.1016/j.ocarto.2022.100299
54. King LK, Krystia O, Waugh EJ, MacKay C, Stanaitis I, Stretton J. **Understanding the behavioural determinants of seeking and engaging in care for knee osteoarthritis in persons with type 2 diabetes mellitus: a qualitative study using the theoretical domains framework**. *Osteoarthritis Cartilage Open* (2022.0) **4** 100305. DOI: 10.1016/j.ocarto.2022.100305
55. 55.King LK, Waugh EJ, MacKay C, Stanaitis I, Krystia O, Stretton J, et al. Formulating knee osteoarthritis management plans taking type 2 diabetes into account: qualitative study of arthritis therapists using theoretical domains framework. J Rheumatol. 2022;9(12):1365–1.
56. Cane J, O’Connor D, Michie S. **Validation of the theoretical domains framework for use in behaviour change and implementation research**. *Implement Sci* (2012.0) **7** 37. DOI: 10.1186/1748-5908-7-37
57. Atkins L, Francis J, Islam R, O’Connor D, Patey A, Ivers N. **A guide to using the Theoretical Domains Framework of behaviour change to investigate implementation problems**. *Implement Sci* (2017.0) **12** 77. DOI: 10.1186/s13012-017-0605-9
58. Michie S, Johnston M, Rothman AJ, de Bruin M, Kelly MP, Carey RN. *Health services and delivery research. Developing an evidence-based online method of linking behaviour change techniques and theoretical mechanisms of action: a multiple methods study* (2021.0)
59. Carey RN, Connell LE, Johnston M, Rothman AJ, de Bruin M, Kelly MP. **Behavior change techniques and their mechanisms of action: a synthesis of links described in published intervention literature**. *Ann Behav Med* (2019.0) **53** 693-707. PMID: 30304386
60. Michie S, Atkins L, West R. *The behaviour change wheel: a guide to designing interventions* (2014.0)
61. 61.International Diabetes Federation. Recommendations for managing type 2 diabetes in primary care, 2017. www.idf.org/managing-type2-diabetes. Accessed 30 Aug 2022.
62. Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D. **Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide**. *BMJ (Clinical research ed)* (2014.0) **348** g1687. PMID: 24609605
63. Presseau J, McCleary N, Lorencatto F, Patey AM, Grimshaw JM, Francis JJ. **Action, Actor, Context, Target, Time (AACTT): a framework for specifying behaviour**. *Implement Sci* (2019.0) **14** 102. DOI: 10.1186/s13012-019-0951-x
64. Davis R, Campbell R, Hildon Z, Hobbs L, Michie S. **Theories of behaviour and behaviour change across the social and behavioural sciences: a scoping review**. *Health Psychol Rev* (2015.0) **9** 323-344. DOI: 10.1080/17437199.2014.941722
65. Dziedzic KS, Healey EL, Porcheret M, Afolabi EK, Lewis M, Morden A. **Implementing core NICE guidelines for osteoarthritis in primary care with a model consultation (MOSAICS): a cluster randomised controlled trial**. *Osteoarthritis Cartilage* (2018.0) **26** 43-53. DOI: 10.1016/j.joca.2017.09.010
66. Østerås N, Blaker IB, Hjortland T, Cottrell E, Quicke JG, Dziedzic KS. **Improving osteoarthritis management in primary healthcare: results from a quasi-experimental study**. *BMC Musculoskelet Disord* (2021.0) **22** 79. DOI: 10.1186/s12891-021-03959-6
67. Smink AJ, Bierma-Zeinstra SM, Schers HJ, Swierstra BA, Kortland JH, Bijlsma JW. **Non-surgical care in patients with hip or knee osteoarthritis is modestly consistent with a stepped care strategy after its implementation**. *Int J Qual Health Care* (2014.0) **26** 490-498. DOI: 10.1093/intqhc/mzu058
68. Allen KD, Oddone EZ, Coffman CJ, Jeffreys AS, Bosworth HB, Chatterjee R. **Patient, provider, and combined interventions for managing osteoarthritis in primary care: a cluster randomized trial**. *Ann Intern Med* (2017.0) **166** 401-411. DOI: 10.7326/M16-1245
69. Concannon TW, Grant S, Welch V, Petkovic J, Selby J, Crowe S. **Practical guidance for involving stakeholders in health research**. *J Gen Intern Med* (2019.0) **34** 458-463. DOI: 10.1007/s11606-018-4738-6
70. van Meijel B, Gamel C, van Swieten-Duijfjes B, Grypdonck MH. **The development of evidence-based nursing interventions: methodological considerations**. *J Adv Nurs* (2004.0) **48** 84-92. DOI: 10.1111/j.1365-2648.2004.03171.x
71. Leask CF, Sandlund M, Skelton DA, Altenburg TM, Cardon G, Chinapaw MJM. **Framework, principles and recommendations for utilising participatory methodologies in the co-creation and evaluation of public health interventions**. *Res Involv Engagem* (2019.0) **5** 2. DOI: 10.1186/s40900-018-0136-9
72. **Designing theoretically-informed implementation interventions**. *Implement Sci* (2006.0) **1** 4. DOI: 10.1186/1748-5908-1-4
73. O’Cathain A, Croot L, Duncan E, Rousseau N, Sworn K, Turner KM. **Guidance on how to develop complex interventions to improve health and healthcare**. *BMJ Open* (2019.0) **9** e029954. DOI: 10.1136/bmjopen-2019-029954
74. Birken SA, Powell BJ, Shea CM, Haines ER, Alexis Kirk M, Leeman J. **Criteria for selecting implementation science theories and frameworks: results from an international survey**. *Implement Sci* (2017.0) **12** 124. DOI: 10.1186/s13012-017-0656-y
|
---
title: The safety and prognosis of radical surgery in colorectal cancer patients over
80 years old
authors:
- Fu-Qiang Zhao
- Yu-Juan Jiang
- Wei Xing
- Wei Pei
- Jian-Wei Liang
journal: BMC Surgery
year: 2023
pmcid: PMC9972629
doi: 10.1186/s12893-023-01938-3
license: CC BY 4.0
---
# The safety and prognosis of radical surgery in colorectal cancer patients over 80 years old
## Abstract
### Objective
The purpose of this study was to assess the safety and feasibility of radical surgery and to investigate prognostic factors influencing in colorectal cancer (CRC) patients over the age of 80.
### Methods
Between January 2010 and December 2020, 372 elderly CRC patients who underwent curative resection at the National Cancer Center were enrolled in the study. Preoperative clinical characteristics, perioperative outcomes, and postoperative pathological features were all collected.
### Results
A total of 372 elderly patients with colorectal cancer were included in the study, including 226 ($60.8\%$) men and 146 ($39.2\%$) women. A total of 219 ($58.9\%$) patients had a BMI < 24 kg/m2, and 153 ($41.1\%$) patients had a BMI ≥ 24 kg/m2. The mean operation time and intraoperative blood loss were 152.3 ± 58.1 min and 67.6 ± 35.4 ml, respectively. The incidence of overall postoperative complications was $28.2\%$ ($\frac{105}{372}$), and the incidence of grade 3–4 complications was $14.7\%$ ($\frac{55}{372}$). In the multivariable Cox regression analysis, BMI ≥ 24 kg/m2 (HR, 2.30, $95\%$ CI, 1.27–4.17; $$P \leq 0.006$$) and N1-N2 stage (HR: 2.97; $95\%$ CI, 1.48–5.97; $$P \leq 0.002$$) correlated with worse CSS.
### Conclusion
The findings of this study showed that radical resection for CRC is safe and feasible for patients over the age of 80. After radical resection, BMI and N stage were independent prognostic factors for elderly CRC patients.
## Introduction
Colorectal cancer (CRC) is one of the most common causes of cancer death worldwide, and its morbidity and mortality are on the rise [1, 2]. With the expansion of the population and the improvement of living standards, the ageing of the population continues to increase [3]. Therefore, in clinical practice, the proportion of older patients receiving surgical treatment for colorectal cancer is increasing. Elderly patients with CRC have unusual clinicopathological features and genetic backgrounds [4, 5]. In addition, these individuals often have comorbidities such as cardiovascular and cerebrovascular diseases and diabetes and often need more rigorous and prudent standardized management during the perioperative period [6]. According to the clinical consensus and guidelines, adjuvant treatment such as chemotherapy and radiotherapy is not recommended for CRC patients older than 80 years of age regardless of TNM stage, but traditional prognostic indicators may not be suitable for elderly patients with CRC over 80 years old [8–15]. Therefore, the main purpose of the present study was to demonstrate the safety and feasibility of radical surgery for CRC in elderly patients over 80 years of age, to evaluate the prognosis of elderly CRC patients without adjuvant therapy using the tumour-specific survival rate, and to comprehensively explore relevant prognostic factors.
## Patients
From January 2010 to December 2020, all consecutive CRC patients older than 80 years of age who underwent curative resection at the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, were retrospectively collected and analysed. The inclusion criteria were as follows: [1] age 80 or above; [2] pathologically confirmed colorectal adenocarcinoma; [3] no evidence of distant metastasis; and [4] no adjuvant therapy, such as radiotherapy or chemotherapy, after the operation. Patients who underwent emergency surgery or had other malignant tumours were excluded from the analysis. The study was approved by the Ethics Committee of the Cancer Hospital, Chinese Academy of Medical Sciences and was conducted in accordance with the Declaration of Helsinki and Ethical Guidelines for Clinical Research. All patients provided written informed consent.
Clinical characteristics, perioperative variables, pathological results and survival outcomes for all patients were obtained from the medical records, operation records, and pathology records in our hospital database. Postoperative complications were assessed using the Clavien-Dindo classification (CD) categories and were defined as any condition that occured within 30 days after surgery that affected the normal recovery process and required conservative or surgical intervention [16]. All procedures were performed by surgeons with more than 20 years of experience in colorectal surgery. The American Joint Committee on Cancer (AJCC, eighth edition) staging system was used for tumour staging.
## Surgical procedures
Curative-intent surgery was performed for all patients diagnosed with CRC. All patients were placed in the modified lithotomy position, and patients underwent laparoscopic surgery or open surgery. In principle, laparoscopic surgery was performed by the five-port method under general anaesthesia. The TME and CME techniques were standardized as described previously. Briefly, the concept of TME or CME was based upon continuous sharp separation of the visceral fascial layer from the parietal layer. Then, the entire mesentery, completely covered by the visceral fascial layer, was obtained, ensuring safe exposure and ligation of the beginning of the supplying artery. The extent of surgery was determined by the location of the tumour and the pattern of underlying lymphatic metastases.
## Follow-up
The long-term outcome of the present study was the 3-year cancer-specific survival (CSS) rate. All patients received a follow-up survey every 2 months for the first 2 years and every 6 months for the next 3 years. The postoperative review examinations included physical examination, biomarkers (CEA and CA-199); CT scans of the chest, abdomen, and pelvis, and colonoscopy if necessary. CSS was defined as the time between the date of surgery and the date of death from cancer. Disease free survival (DFS) is defined as the time from surgery to disease recurrence or last follow-up. Overall survival (OS) is defined as the time from surgery to the time of death of the patient for any cause or the time to the last follow-up. The deadline for follow-up in this study was December 2022.
## Statistical analysis
The mean ± standard deviation was used to represent quantitative data, while frequencies and percentages were used to represent categorical variables. The factors predicting CSS were identified using univariate and multivariate Cox regression models. To analyse the 3-year CSS of the patients in different groups, the Kaplan–Meier survival method was used, and significant differences in CSS were compared using the log-rank test. The variables that were statistically significant ($P \leq 0.20$) in univariate analysis were then tested in multivariate analysis using a Cox regression model, and the effect of each variable was assessed using the hazard ratio (HR) and $95\%$ confidence interval ($95\%$ CI). P values less than 0.05 were considered statistically significant. IBM SPSS Statistics software version 24.0 was used for statistical analyses (IBM Corporation, Armonk, NY, USA).
## Short-term outcomes
Table 1 summarizes the baseline characteristics of the patients. Among the 372 elderly CRC patients included in this study, 226 ($60.8\%$) were male, and 146 ($39.2\%$) were female. Among all patients, 36 ($9.7\%$) patients were older than 85 years. In addition, 104 ($27.9\%$) patients had tumours in the right colon, 132 ($35.5\%$) patients had tumours in the left colon, and 136 ($36.6\%$) patients had tumours in rectum. The perioperative outcomes and pathological results are listed in Table 2. The mean operation time and intraoperative blood loss were 152.3 ± 58.1 min and 67.6 ± 35.4 ml, respectively. Regarding postoperative recovery, the mean postoperative hospital stay was 11.0 ± 5.6 days, and only one ($0.2\%$) patient died in the perioperative period. Table 1Baseline characteristicsVariablesN = 372Gender Male226 (60.8) Female146 (39.2)Age at operation (years old) < 85336 (90.3) ≥ 8536 (9.7)Body mass index (kg/m2) < 24219 (58.9) ≥ 24153 (41.1)ASA score II218 (58.6) III154 (41.4)Preoperative albumin (g/L) < 3574 (19.9) ≥ 35298 (80.1)Preoperative HGB (g/L) < 11077 (20.7) ≥ 110295 (79.3)Habits Drinking72 (19.4) Smoking90 (24.2)Comorbidity Hypertension170 (45.7) Diabetes mellitus48 (12.9) Coronary artery disease36 (9.7) Arrhythmia70 (18.8) Respiratory diseases46 (12.4) Other22 (5.9) Previous abdominal surgery76 (20.4)Tumour location Right colon104 (30.0) Left colon132 (35.5) Rectum136 (36.6)Table 2Pathological data and perioperative outcomeVariablesN = 372T stage T1-T276 (20.4) T3-T4296 (79.6)N stage N0204 (54.8) N1-N2168 (45.2)Tumour grade I86 (23.1) II195 (52.4) III91 (24.5)Tumour size (cm, mean ± SD)4.7 ± 2.2Perineural invasion106 (28.5)Lymphatic invasion110 (29.6)LN harvest (days, mean ± SD)17.7 ± 8.4Surgical procedure Open164 (44.1) Laparoscope208 (55.9) Operative time (min, mean ± SD)152.3 ± 58.1 Estimated intraoperative blood loss (ml, mean ± SD)67.6 ± 35.4 Postoperative complications105 (28.2) Grade 3–4 postoperative complications55 (14.7) Time to first flatus (days, mean ± SD)5.1 ± 2.2 Postoperative hospital stay (days, mean ± SD)11.0 ± 5.6 Re-operation15 (4.0) Mortality1 (0.2) Table 3 lists the postoperative complications of the 372 elderly CRC patients. The incidence rates of overall complications, grade 1–2 complications, and grade 3–4 complications were $28.2\%$, $13.5\%$, and $14.7\%$, respectively. Among the overall complications, abdominal abscess ($5.4\%$), anastomotic leakage ($4.6\%$), and ileus ($4.6\%$) were the most common. The most common grade 3–4 complication was urinary retention ($2.4\%$), followed by pleural effusion ($2.2\%$) and abdominal abscess ($1.9\%$).Table 3Overall and grade 3–4 postoperative complications of 372 elderly patientsGrade 1–2 complicationsGrade 3–4 complicationsAll complicationsn%n%n%Complications Total5013.55514.710528.2Cardiac disorders Arrhythmia51.361.6112.9 Cardiac failure30.810.241.0 Acute coronary sy ndrome10.320.530.8 Hypertensive emergencies10.30010.3Respiratory disorder Pneumonia41.161.6102.7 Pleural effusion20.582.2102.7 Atelectasis30.851.382.1Gastrointestinal haemorrhage Anastomotic leakage113.061.6174.6 Ileus113.061.6174.6 GastrointestinaI hemorrhage41.120.561.6Renal and urinary disorders Urinary infection71.910.282.1 Renal failure0010.210.2 Urinary retention30.892.4123.2Other disorders Abdominal abscess133.571.9205.4 Rectovaginal fistula0010.210.2 Intra-abdominal haemorrhage30.841.171.9 Wound infection123.220.5143.7 Pulmonary embolism0020.520.5
## Survival analysis
The mean follow-up period for the whole group was 60 months (range, 29–150 months). During this period, 130 of the 372 patients died ($34.9\%$). Among them, 102 died from tumour recurrence or metastasis ($27.4\%$). In the univariate analysis, sex, age, BMI, preoperative HGB, lifestyle habits, surgical procedure, T stage, N stage, perineural invasion, lymphatic invasion, and reoperation significantly affected CSS ($P \leq 0.2$). These variables were thus incorporated into the multivariate analysis, and the results revealed that the CSS was significantly affected by BMI (HR, 2.30, $95\%$ CI, 1.27–4.17; $$P \leq 0.006$$) and N stage (HR: 2.97; $95\%$ CI, 1.48–5.97; $$P \leq 0.002$$) (Table 4). The Kaplan curves showed that the CSS rate of patients was affected by the BMI ($$P \leq 0.046$$, Fig. 1) and N stage ($P \leq 0.001$, Fig. 2). Figure 3 shows the forest plots for CSS of elderly CRC patients based on the multivariable Cox proportional hazard model. Next, we performed prognostic analysis on DFS and OS, and found that BMI and N stage were independent prognostic factors (Tables 5 and 6).Table 4Univariate and multivariate Cox regression analyses of cancer specific survival in 372 elderly patients after curative resectionVariablesCancer specific survivalUnivariate analysisMultivariate analysisHR($95\%$CI)PHR($95\%$CI)PGender: male/female1.63 (0.88–3.02)0.1181.18 (0.60–2.29)0.636Age at operation: ≥ 85/ < 851.84 (0.83–4.08)0.1371.57 (0.65–3.83)0.317ASA score: III/II1.08 (0.62–1.90)0.779Body mass index < 24Reference-Reference- ≥ 241.76 (1.01–3.05)0.0462.30 (1.27–4.17)0.006Preoperative albumin: ≥ 35/ < 350.74 (0.40–1.35)0.322Preoperative HGB: ≥ 110/ < 1100.63 (0.34–1.19)0.1580.70 (0.35–1.42)0.326Habits: drinking1.46 (0.79–2.70)0.233Habits: smoking1.49 (0.83–2.69)0.1841.55 (0.80–3.00)0.193Comorbity: yes/no0.78 (0.45–1.36)0.382Previous abdominal surgery:yes/no0.69 (0.33–1.48)0.342Tumor location: left colon and rectum/ right colon1.09 (0.58–2.04)0.797Surgical procedure: open/laparoscope1.46 (0.83–2.57)0.1851.20 (0.65–2.22)0.564Operative time: ≥ 135/ < 1351.12 (0.65–1.94)0.688T stage: T3-T4/T1-T22.28 (0.97–5.35)0.0581.34 (0.54–3.35)0.528N stage: N1-N2/N02.90 (1.63–5.18) < 0.0012.97 (1.48–5.97)0.002Tumor grade IReference- II0.71 (0.28–1.83)0.483 III0.94 (0.33–2.67)0.904Perineural invasion: yes/no1.74 (0.95–3.19)0.0741.19 (0.59–2.42)0.627Lymphatic invasion1.99 (1.14–3.47)0.0161.03 (0.53–1.98)0.939Grade 3–4 complications: yes/no0.82 (0.35–1.92)0.641Re-operation: yes/no3.21 (0.77–13.37)0.1091.67 (0.35–8.03)0.523Fig. 1Cancer-specific survival curve of overweight group and control groupFig. 2Cancer-specific survival curve of N0 group and N1-2 groupFig. 3Forest plots for Cancer-specific survival of elderly CRC patients after curative resection based on multivariable COX proportional hazard modelTable 5Univariate and multivariate Cox regression analyses of overall survival in 372 elderly patients after curative resectionVariablesOverall survivalUnivariate analysisMultivariate analysisHR($95\%$CI)PHR($95\%$CI)PGender: male/female1.51 (1.06–2.17)0.0241.31 (0.91–1.90)0.151Age at operation: ≥ 85/ < 851.65 (0.77–3.54)0.1990.56 (0.37–0.85)0.107ASA score: III/II0.37 (0.25–0.54)0.225Body mass index: ≥ 24/ < 241.99 (1.22–3.25)0.0061.45 (0.87–2.40)0.005Preoperative albumin: ≥ 35/ < 351.15 (0.80–1.66)0.442Preoperative HGB: ≥ 110/ < 1101.34 (0.83–2.16)0.236Habits: drinking0.93 (0.58–1.48)0.754Habits: smoking0.93 (0.58–1.48)0.753Comorbity: yes/no1.51 (1.03–2.20)0.0351.34 (0.90–1.99)0.144Previous abdominal surgery:yes/no1.16 (0.76–1.79)0.488Tumor location Left colonReference- Right colon0.94 (0.58–1.53)0.805 Rectum1.00 (0.66–1.52)0.997Surgical procedure: open/laparoscope0.24 (0.16–0.35)0.2230.34 (0.23–0.53)0.121Operative time: ≥ 135/ < 1351.07 (0.72–1.59)0.734T stage: T3-T4/T1-T20.87 (0.57–1.32)0.505N stage: N1-N2/N02.56 (1.72–3.80) < 0.0012.14 (1.40–3.26) < 0.001Tumor grade IReference- II2.48 (1.15–5.36)0.0211.66 (0.75–3.70)0.215 III3.34 (1.45–7.69)0.0051.71 (0.71–4.10)0.230Perineural invasion: yes/no0.97 (0.67–1.39)0.850Lymphatic invasion0.95 (0.63–1.43)0.812Grade 3–4 complications: yes/no0.58 (0.36–0.92)0.0210.78 (0.48–1.27)0.345Re-operation: yes/no1.59 (0.51–5.02)0.427Table 6Univariate and multivariate Cox regression analyses of disease free survival in 372 elderly patients after curative resectionVariablesDisease free survivalUnivariate analysisMultivariate analysisHR($95\%$CI)PHR($95\%$CI)PGender: male/female1.42 (1.01–1.99)0.0461.28 (0.90–1.82)0.171Age at operation: ≥ 85/ < 851.69 (0.82–3.45)0.1521.31 (0.62–2.75)0.480ASA score: III/II1.01 (0.72–1.43)0.939Body mass index: ≥ 24/ < 240.46 (0.32–0.66) < 0.0010.56 (0.37–0.85)0.046Preoperative albumin: ≥ 35/ < 351.85 (1.17–2.93)0.0081.38 (0.85–2.23)0.190Preoperative HGB: ≥ 110/ < 1101.37 (0.87–2.14)0.1751.30 (0.81–2.10)0.281Habits: drinking0.95 (0.61–1.47)0.811Habits: smoking1.11 (0.72–1.69)0.640Comorbity: yes/no1.27 (0.89–1.81)0.1881.17 (0.81–1.69)0.397Previous abdominal surgery: yes/no1.24 (0.83–1.87)0.293Tumor location Left colonReference– Right colon0.99 (0.63–1.57)0.968 Rectum0.95 (0.64–1.42)0.802Surgical procedure: open/laparoscope0.96 (0.68–1.36)0.813Operative time: ≥ 135/ < 1351.20 (0.83–1.73)0.338T stage: T3-T4/T1-T21.05 (0.69–1.60)0.816N stage: N1-N2/N00.35 (0.24–0.50) < 0.0010.51 (0.34–0.76) < 0.001Tumor grade IReference– II2.36 (1.14–4.86)0.0201.85 (0.87–3.92)0.109 III3.38 (1.54–7.41)0.0022.26 (0.99–5.15)0.053Perineural invasion: yes/no2.07 (1.41–3.03)0.211Lymphatic invasion1.07 (0.73–1.56)0.729Grade 3–4 complications: yes/no0.63 (0.41–0.98)0.0400.85 (0.53–1.35)0.483Re-operation: yes/no1.24 (0.39–3.88)0.718
## Discussion
One of the biggest challenges in healthcare is the ageing population; in 2015, the life expectancy at birth was 82.9 years, with males expected to live to 80.5 years old and females expected to live to 85.1 years old. Elderly CRC patients are regarded as a special population with unique clinicopathological characteristics, and the increase in comorbidities typically observed in this population tends to increase the potential risks during the perioperative period. In the present study, we aimed to investigate the short-term safety and long-term prognosis of radical surgery for CRC in older adults over 80 years of age.
The safety of radical surgery for elderly patients with colorectal cancer is a concern for surgeons. Prior works have reported that the incidence of overall complications in elderly patients with CRC after radical surgery is 9.9–$25.4\%$, and the incidence of grade 3–5 complications is 6.5–$20.1\%$ [12–15]. Our study showed that the incidence rates of overall complications, grade 1–2 complications, and grade 3–4 complications were $28.2\%$, $13.5\%$, and $14.7\%$, respectively, which were consistent with previous reports in the literature. In addition, this study revealed that the most common overall complication after radical resection of elderly patients with CRC is an abdominal abscess ($5.4\%$), and the most common grade 3–4 postoperative complication is urinary retention ($2.4\%$). Before surgery, we should pay attention to and try to improve the patient's general condition, perform transfusion, supplement albumin, carry out enteral nutrition to improve the patient's nutritional status, and actively treat basic diseases such as hypertension, heart disease, and diabetes. According to the blood supply and tension of the patient's intestinal tube, the operation was performed gently, and the principle of being sterile and tumour-free was strictly followed. Postoperative nutritional support should also be actively carried out to provide sufficient raw materials for the growth of the anastomotic mouth.
Along with the increase in material wealth, the incidence of obesity has increased and become a medical and social problem. Obesity is clearly associated with the incidence of CRC [17–22], and the relationship between obesity and colorectal cancer has been previously reported but remains controversial. Several studies have reported that a high BMI is associated with a poor prognosis in patients with CRC [20, 23], while other studies have reported that a high BMI is not related to prognosis [24, 25] or is even related to a better prognosis [26, 27]. This study explores the prognostic factors related to elderly patients with CRC after curative resection, and the results show that BMI ≥ 24 kg/m2 (HR, 2.30, $95\%$ CI, 1.27–4.17; $$P \leq 0.006$$) and N1-N2 stage (HR: 2.97; $95\%$ CI, 1.48–5.97; $$P \leq 0.002$$) were independent prognostic factors affecting CSS. Scarpa et al. grouped 595 CRC patients based on BMI and conducted postoperative follow-ups. Multivariate analysis showed that BMI > 30 kg/m2 was an independent risk factor for prognosis and recurrence after surgery (HR: 2.2; $95\%$ CI, 1.3–3.9; $$P \leq 0.003$$) [28]. Doria-Rose et al. obtained similar results: patients with a high BMI, especially a BMI > 35 kg/m2, had a higher recurrence rate and poorer overall survival than those with a normal BMI [29]. The results of the above studies were basically consistent with our findings.
Over the past two decades, laparoscopic colorectal resection has grown in popularity. Laparoscopic colectomy is linked to better immunological and inflammatory responses, shorter hospitalization, and similar long-term oncologic outcomes compared to open surgery, according to a number of randomized, prospective clinical trials [30]. Nevertheless, the complexity of the pelvis' anatomical structure, the need for higher technical expertise during total mesorectal excision (TME), and the fact that colectomy preserves the autonomic nerves make minimally invasive surgery for rectal cancer contentious. Laparoscopic rectal cancer surgery has been shown to be safe and to result in better functional recovery and oncological outcomes than open surgery in a number of randomized controlled trials (RCTs) and meta-analyses [31]. Several recent studies have shown that laparoscopic rectal cancer resection might safely be performed irrespective of age [30, 32]. However, there is a lack of data about the long-term results of laparoscopic versus open resection in senior rectal cancer patients.
Particular attention is required when planning chemotherapy for elderly cancer patients because of reductions in organ function and pre-existing comorbidities. Most of the current randomized trials did not include many elderly patients. In 2012, Sanoff et al. [ 33] reported a cohort study combining four large databases of patients diagnosed with stage III CRC between 2004 and 2007. A total of 5489 patients with stage III CRC aged ≥ 75 years were analysed using covariate-adjusted and propensity score-matched proportional hazards models. Compared with surgery alone, 5-FU-based adjuvant chemotherapy had a significant survival benefit, whereas the addition of oxaliplatin to 5-FU-based chemotherapy provided no significant benefit over 5-FU alone, although it tended to improve prognosis. In future studies, we will use our data to further explore the efficacy of adjuvant therapy in older adults.
The most significant limitation of the present study is its retrospective nature, and only 372 patients were included, which may have caused some inherent selection bias. In addition, compared to rectal cancer, colon cancer is more likely to cause systemic consumption and lower BMI, and we did not calculate colon and rectal cancer separately. Therefore, multicentre, large-scale, prospective studies are warranted to verify our results.
In conclusion, our findings show that radical resection for CRC is safe and feasible for patients over the age of 80. After radical resection, BMI and N stage were independent prognostic factors for elderly CRC patients.
## References
1. Jemal A, Ward EM, Johnson CJ. **Annual Report to the Nation on the Status of Cancer, 1975–2014, Featuring Survival**. *J Natl Cancer Inst* (2017.0) **109** 030. DOI: 10.1093/jnci/djx030
2. Malvezzi M, Bertuccio P, Levi F. **European cancer mortality predictions for the year 2014**. *Ann Oncol* (2014.0) **25** 1650-1656. DOI: 10.1093/annonc/mdu138
3. 3.(UNFPA), U.N.P.F., http://www.unfpa.org/ageing.
4. Kotake K, Asano M, Ozawa H. **Tumour characteristics, treatment patterns and survival of patients aged 80 years or older with colorectal cancer**. *Colorectal Dis* (2015.0) **17** 205-215. DOI: 10.1111/codi.12826
5. Siegel RL, Miller KD, Goding Sauer A. **Colorectal cancer statistics, 2020**. *CA Cancer J Clin* (2020.0) **70** 145-164. DOI: 10.3322/caac.21601
6. Itatani Y, Kawada K, Sakai Y. **Treatment of elderly patients with colorectal cancer**. *Biomed Res Int* (2018.0) **2018** 2176056. DOI: 10.1155/2018/2176056
7. 7.NCCN. Clinical practice guidelines in oncology. Colon cancer, version 2. 2017. [EB/OL]. Fort Washington: NCCN, 2017. [2017–3–13].
8. Zhou S, Wang X, Zhao C. **Laparoscopic vs open colorectal cancer surgery in elderly patients: short- and long-term outcomes and predictors for overall and disease-free survival**. *BMC Surg* (2019.0) **19** 137. DOI: 10.1186/s12893-019-0596-3
9. Miguchi M, Yoshimitsu M, Shimomura M. **Long-term outcomes of laparoscopic surgery in elderly patients with colorectal cancer: A single institutional matched case-control study**. *Asian J Endosc Surg* (2021.0) **14** 200-206. DOI: 10.1111/ases.12848
10. Joachim C, Godaert L, Dramé M. **Overall survival in elderly patients with colorectal cancer: A population-based study in the Caribbean**. *Cancer Epidemiol* (2017.0) **48** 85-91. DOI: 10.1016/j.canep.2017.03.005
11. Yamano T, Yamauchi S, Kimura K. **Influence of age and comorbidity on prognosis and application of adjuvant chemotherapy in elderly Japanese patients with colorectal cancer: A retrospective multicentre study**. *Eur J Cancer* (2017.0) **81** 90-101. DOI: 10.1016/j.ejca.2017.05.024
12. Sueda T, Tei M, Nishida K. **Evaluation of short- and long-term outcomes following laparoscopic surgery for colorectal cancer in elderly patients aged over 80 years old: a propensity score-matched analysis**. *Int J Colorectal Dis* (2021.0) **36** 365-375. DOI: 10.1007/s00384-020-03770-0
13. Sakamoto Y, Miyamoto Y, Tokunaga R. **Long-term outcomes of colorectal cancer surgery for elderly patients: a propensity score-matched analysis**. *Surg Today* (2020.0) **50** 597-603. DOI: 10.1007/s00595-019-01934-2
14. Ueda Y, Shiraishi N, Kawasaki T. **Short- and long-term outcomes of laparoscopic surgery for colorectal cancer in the elderly aged over 80 years old versus non-elderly: a retrospective cohort study**. *BMC Geriatr* (2020.0) **20** 445. DOI: 10.1186/s12877-020-01779-2
15. Seow-En I, Tan WJ, Dorajoo SR. **Prediction of overall survival following colorectal cancer surgery in elderly patients**. *World J Gastrointest Surg* (2019.0) **11** 247-260. DOI: 10.4240/wjgs.v11.i5.247
16. Clavien PA, Barkun J, de Oliveira ML. **The Clavien-Dindo classification of surgical complications: five-year experience**. *Ann Surg* (2010.0) **250** 187-196. DOI: 10.1097/SLA.0b013e3181b13ca2
17. Sweigert PJ, Chen C, Fahmy JN. **Association of obesity with postoperative outcomes after proctectomy**. *Am J Surg* (2020.0) **220** 1004-1009. DOI: 10.1016/j.amjsurg.2020.03.028
18. Liu H, Wei R, Li C. **BMI may be a prognostic factor for local advanced rectal cancer patients treated with long-term neoadjuvant chemoradiotherapy**. *Cancer Manag Res* (2020.0) **12** 10321-10332. DOI: 10.2147/CMAR.S268928
19. Shahjehan F, Merchea A, Cochuyt JJ. **Body Mass Index and Long-Term Outcomes in Patients with Colorectal Cancer**. *Front Oncol* (2018.0) **8** 620. DOI: 10.3389/fonc.2018.00620
20. Lino-Silva LS, Aguilar-Cruz E, Salcedo-Hernández RA. **Overweight but not obesity is associated with decreased survival in rectal cancer**. *Contemp Oncol (Pozn)* (2018.0) **22** 158-164. PMID: 30455587
21. Zhang X, Wu Q, Gu C. **The effect of increased body mass index values on surgical outcomes after radical resection for low rectal cancer**. *Surg Today* (2019.0) **49** 401-409. DOI: 10.1007/s00595-019-01778-w
22. Campbell PT, Jacobs ET, Ulrich CM. **Case-control study of overweight, obesity, and colorectal cancer risk, overall and by tumor microsatellite instability status**. *J Natl Cancer Inst* (2010.0) **102** 391-400. DOI: 10.1093/jnci/djq011
23. Ashktorab H, Mokarram P, Azimi H. **Targeted exome sequencing reveals distinct pathogenic variants in Iranians with colorectal cancer**. *Oncotarget* (2017.0) **8** 7852-7866. DOI: 10.18632/oncotarget.13977
24. Ballian N, Yamane B, Leverson G. **Body mass index does not affect postoperative morbidity and oncologic outcomes of total mesorectal excision for rectal adenocarcinoma**. *Ann Surg Oncol* (2010.0) **17** 1606-1613. DOI: 10.1245/s10434-010-0908-4
25. Garcia-Oria Serrano MJ, Armengol Carrasco M. **Is body mass index a prognostic factor of survival in colonic cancer? A multivariate analysis**. *Cir Esp* (2011.0) **89** 152-158. DOI: 10.1016/j.ciresp.2010.12.005
26. Asghari-Jafarabadi M, Hajizadeh E, Kazemnejad A. **Site-specific evaluation of prognostic factors on survival in Iranian colorectal cancer patients: a competing risks survival analysis**. *Asian Pac J Cancer Prev* (2009.0) **10** 815-821. PMID: 20104971
27. Hines RB, Shanmugam C, Waterbor JW. **Effect of comorbidity and body mass index on the survival of African-American and Caucasian patients with colon cancer**. *Cancer* (2009.0) **115** 5798-5806. DOI: 10.1002/cncr.24598
28. Scarpa M, Ruffolo C, Erroi F. **Obesity is a risk factor for multifocal disease and recurrence after colorectal cancer surgery: a case-control study**. *Anticancer Res* (2014.0) **34** 5735-5741. PMID: 25275082
29. Doria-Rose VP, Newcomb PA, Morimoto LM. **Body mass index and the risk of death following the diagnosis of colorectal cancer in postmenopausal women (United States)**. *Cancer Causes Control* (2006.0) **17** 63-70. DOI: 10.1007/s10552-005-0360-0
30. Zeng WG, Zhou ZX, Hou HR. **Outcome of laparoscopic versus open resection for rectal cancer in elderly patients**. *J Surg Res* (2015.0) **193** 613-618. DOI: 10.1016/j.jss.2014.08.012
31. Peltrini R, Imperatore N, Carannante F. **Age and comorbidities do not affect short-term outcomes after laparoscopic rectal cancer resection in elderly patients A multi-institutional cohort study in 287 patients**. *Updates Surg.* (2021.0) **73** 527-537. DOI: 10.1007/s13304-021-00990-z
32. Manceau G, Hain E, Maggiori L, Mongin C, Prost À, Denise J, Panis Y. **Is the benefit of laparoscopy maintained in elderly patients undergoing rectal cancer resection? An analysis of 446 consecutive patients**. *Surg Endosc* (2017.0) **31** 632-642. DOI: 10.1007/s00464-016-5009-4
33. Sanoff HK, Carpenter WR, Stürmer T. **Effect of adjuvant chemotherapy on survival of patients with stage III colon cancer diagnosed after age 75 years**. *J Clin Oncol* (2012.0) **30** 2624-2634. DOI: 10.1200/JCO.2011.41.1140
|
---
title: The application of new complex indicators in the detection of urine
authors:
- Ying-xiang Li
- Yang Li
- Si-yu Bao
- Ning Xue
- Xiao-qiang Ding
- Yi Fang
journal: BMC Nephrology
year: 2023
pmcid: PMC9972632
doi: 10.1186/s12882-023-03087-4
license: CC BY 4.0
---
# The application of new complex indicators in the detection of urine
## Abstract
### Background
Accurate diagnosis and assessment of hematuria is crucial for the early detection of chronic kidney disease(CKD). As instability of urinary RBC count (URBC) often results with clinical uncertainty, therefore new urinary indexes are demanded to improve the accuracy of diagnosis of hematuria. In this study, we aimed to investigate the benefit of applying new complex indicators based on random urine red blood cell counts confirmed in hematuric kidney diseases.
### Methods
All patients enrolled underwent renal biopsy, and their clinical information was collected. Urinary and blood biomedical indexes were implemented with red blood cell counts to derive complex indicators. Patients were divided into two groups (hematuria-dominant renal histologic lesions and non-hematuria-dominant renal histologic lesions) based on their renal pathological manifestations. The target index was determined by comparing the predictive capabilities of the candidate parameters for hematuric kidney diseases. Hematuria stratification was divided into four categories based on the scale of complex indicators and distributional features. The practicality of the new complex indicators was demonstrated by fitting candidate parameters to models comprising demographic information.
### Results
A total of 1,066 cases (678 hematuria-dominant renal histologic lesions) were included in this study, with a mean age of 44.9 ± 15 years. In differentiating hematuria-dominant renal histologic lesion from the non-hematuria-dominant renal histologic lesion, the AUC value of “The ratio of the random URBC to 24-h albumin excretion” was 0.76, higher than the standard approach of Lg (URBC) [AUC = 0.744] ($95\%$ Confidence interval (CI) 0.712 ~ 0.776). The odds ratio of hematuria-dominant renal histologic lesion (Type I) increased from Q2 (3.81, $95\%$ CI 2.66 ~ 5.50) to Q4 (14.17, $95\%$ CI 9.09 ~ 22.72). The predictive model, composed of stratification of new composite indexes, basic demographic characteristics, and biochemical parameters, performed best with AUC value of 0.869 ($95\%$ CI 0.856–0.905).
### Conclusion
The new urinary complex indicators improved the diagnostic accuracy of hematuria and may serve as a useful parameter for screening hematuric kidney diseases.
## Introduction
Hematuria is a common urinary abnormality indicative of chronic kidney disease (CKD). In the long asymptomatic early phase of CKD, urinalysis was considered a high priority for evaluating patients with suspected kidney diseases that manifest as hematuria [1]. Urine red blood cells (RBCs) count, a strong indicator of hematuria, has true variability in individual patients ranging from asymptomatic to rapidly progressive stages[2]. Fluctuations in urinary component concentration are affected by water intake, exercise, and improper urine sample collection and are associated with the instability of urinary RBC count (URBC) [3]. Although limited data are available regarding the test performance of RBCs count [4], efforts are being made to clarify urine test competence and identify candidates for enhancing the accuracy of hematuria diagnosis. In this study, we aimed to optimize the URBC based routine urinalysis, explore new urinary indexes, and subsequently improve accuracy and stability in hematuria diagnosis and early detection of CKD.
## Study participants
A total of 1,066 patients were hospitalized at the Department of Nephrology, Zhongshan Hospital, Fudan University, between August 2018 and July 2021. All patients underwent renal biopsy, and their medical records (including blood chemistry and urinalysis) were retrospectively retrieved. Inclusion criteria were age > 18 years and had not yet received renal replacement therapy. The exclusion criteria were as follows: transplant recipients, pregnant women, female patients who were menstruating, patients with active tuberculosis, malignancy, acute hemorrhage, an indwelling urinary catheter, infection, or urolithiasis. The study was approved by the Clinical Research Ethical Committee of the Zhongshan Hospital, Fudan University. Informed consent was obtained from all patients. All methods were carried out in accordance with relevant guidelines and regulations.
## Clinical data and definitions
Demographic data, including age, sex, height, weight, body mass index (BMI = weight/height2[kg/m2]), history of diabetes, and hypertension, were retrieved from electronic medical records. Laboratory data from spot urine samples included urinary specific gravity (SG), urinary RBCs, dipstick protein measurements, urinary microalbumin (µmol/L), urinary creatinine level (mg/L), and the percentage of dysmorphic RBCs (%). Quantitative measurements of urine chemistry and protein levels were also collected. Blood chemistry included serum urea nitrogen (mmol/L), estimated glomerular filtration rate (eGFR[ml/min/1.73 m2]), serum creatinine (µmol/L), uric acid (µmol/L), and albumin (g/L]).
Laboratory data were collected from the latest pre-renal biopsy tests. Urinary blood cell count was performed simultaneously within 2 h after collection and was recorded as RBCs/µL using an automated method with a urine sediment analyzer (UF-1000, Sysmex). Microscopic urine examination was performed using a bright-field microscope according to the recommendations of the Clinical & Laboratory Standards Institute (CLSI) guidelines as RBC/high-power field (HPF) [5]. Microscopic hematuria was defined as three or more RBCs/high-power field [6] or > 25 RBC/µL [7] using the automated method. The percentage of dysmorphic RBCs was calculated using phase-contrast microscopy. Urine dipstick protein was categorized as negative, +, ++, or +++.
The urinary albumin-to-creatinine ratio (ACR, µg/mgCr) was calculated as the urinary albumin concentration divided by the urinary creatinine concentration, and the protein-to-creatinine ratio (PCR) was calculated as the urinary protein concentration divided by the urinary creatinine concentration. Estimated GFR (eGFR) was calculated according to the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Equation. [ 8]. Hypertension was defined as a systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg [9]. Diabetes was defined according to the new criteria of the American Diabetes Association and provisional criteria of the World Health Organization [10].
## Renal biopsy-based pathological classification and criteria
Ultrasound-guided percutaneous renal biopsy was routinely performed at the core laboratory of Zhongshan Hospital, Fudan University. Renal pathologists collaborated with a nephrologist to make a definitive clinicopathological diagnosis. Renal pathological assessment was performed using light microscopy, immunofluorescence microscopy, and electron microscopy.
Based on the pathological lesions detected in renal biopsy combined with clinical manifestations, the spectra of kidney diseases were classified into two types to identify the correlation between RBC measurement in urinalysis and pathological diagnosis. Type I, hematuria-dominant renal histologic lesion; Type II, non-hematuria-dominant renal histologic lesion. Type I features proliferative lesions associated with hematuria and is usually characterized by diffuse mesangial cell proliferation and/or capillary proliferation with or without extensive crescent formation, often with renal interstitial inflammation [11]. Segmental fibrinoid necrosis of the glomerular tuft has been observed in severe cases. Mesangial cell proliferative lesions coexisting with segmental sclerosis and/or capsular adhesion can also be observed [12]. Mesangial cell proliferative glomerulonephritis such as IgA nephropathy [13], endocapillary proliferative glomerulonephritis, membranoproliferative glomerulonephritis, and crescentic glomerulonephritis exhibit hematuria-dominant renal histologic lesions [14]. In our study, pathological patterns presenting with proliferative lesions confirmed by biopsy were classified as Type I. Type II involves pathological patterns with nonproliferative glomerular lesions [15]. Membranous nephropathy, minimal change disease, focal segmental glomerulosclerosis (FSGS), hypertensive nephropathy, diabetic nephropathy, renal amyloidosis, and tubulointerstitial nephropathy manifest as non-proliferative lesions [16].
## Identification of new urinary complex indicators
Complex indicators that adjust the parameters for surrogates of routine urinalysis RBC counts were developed to yield hematuria measurements. The relationship between urinalysis results, biochemical indices, and renal pathological classification was analyzed. Urinary parameters included URBC, creatine level, albumin, albumin-creatinine ratio, 24-h urine protein excretion, and 24-h creatinine excretion. The serum biochemical parameters included creatinine, albumin, and urea nitrogen levels. We determined a series of new complex indicators based on the URBC, which included the ratio of random URBC to urinary specific gravity (URBC/SG), the ratio of random URBC to urinary microalbumin (URBC/UAlb), the ratio of random URBC to urinary creatinine (URBC/UCr), the ratio of random URBC to 24-h urinary protein quantification (URBC/24 hUP), and the ratio of random URBC to 24-h urinary creatinine quantification (URBC/24 hUCr).
## Hematuria stratification and model establishment
The entire range of parameters was stratified into four equal parts based on the degree of hematuria. Hematuria stratification was defined accordingly, and their correlations with hematuria-dominant renal histologic lesions (Type I) were analyzed. Three models of the new urinary complex indicator (Lg(URBC/24 hUP)), combining diverse factors, were constructed. Models adjusted for demographic characteristics, past medical history, and biochemical data were utilized separately based on hematuria stratification. The relationship between the three models and Type I was analyzed. Model 1 was a univariate model with hematuria stratification alone, Model 2 = Model 1 + demographics (age, sex, and BMI) and comorbidities (diabetes and hypertension), and Model 3 = Model 2 + biochemical data (eGFR and albumin level).
## Statistical analysis
Statistical analysis was performed using Statistical Package for Social Sciences (SPSS), version 25.0 (SPSS Inc., Chicago, IL, USA). Qualitative data were presented as numbers and percentages (%). Quantitative data were presented as means with standard deviations (SD) for continuous variables with approximately normal distributions and interquartile ranges (IQR) for non-normally distributed data. All indexes with skewed distributions were log-transformed and dimension-transformed to obtain normal distributions. An independent sample t-test was used to compare the group differences for normally distributed continuous variables. The non-parametric rank-sum test was used to compare group differences for non-normally distributed data. All categorical data were analyzed using the χ2 test. The differences in the distribution of each urinary index between patients with Type I and Type II were compared. The logarithm of the composite index was analyzed, and a receiver operating characteristic (ROC) curve was drawn. The predictive performance of each new urinary complex indicator for hematuria-dominant renal histological lesions was assessed by calculating the area under the ROC curve (AUC). We classified the composite index into four categories for hematuria stratification. The Cochran-Mantel-Haenszel test was used to evaluate hematuria stratification. Multivariate logistic regression models were fitted to the data to test the relationship between the new urinary complex indicators, and Type I and AUC was used to evaluate predictive capacity. Statistical significance was set at $p \leq 0.05$ for a 2-sided test.
## Clinical and demographic characteristics
A total of 1,066 hospitalized patients were included in this study. The mean age was 44.9 ± 15.0 years; men accounted for $59.1\%$ ($$n = 630$$). Of these patients, 139 ($13.0\%$) had a history of diabetes, and 552 ($51.8\%$) had hypertension.
In the data obtained from the Shanghai Laboratory of Kidney Disease and Dialysis, 872 patients were diagnosed with nephritic glomerular disease (674 with IgA nephropathy, 83 with membranous nephropathy, 72 with FSGS, 34 with minimal change disease, and 9 with crescentic glomerulonephritis), 15 with lupus nephritis, 11 with Henoch-Schönlein purpura, and 3 with hepatitis B virus-related glomerulonephritis. Additionally, 25, 30, 53, and 57 patients were diagnosed with tubulointerstitial nephropathy, hypertensive nephropathy, diabetic nephropathy, and other types, respectively. Of these, 736 ($69.0\%$) patients were classified as hematuria-dominant renal histologic lesions (Type I), and the other 330 ($30.9\%$) patients were classified as non-hematuria-dominant renal histologic lesions (Type II). The clinical and pathological characteristics were described in Table 1.
Table 1Characteristics by pathologic classification and distributionCharacteristicsPathologic classification and distributionType I($$n = 724$$)Type II($$n = 342$$)Total ($$n = 1066$$)StatisticsP-value Demographics age, years41.77 ± 13.7151.38 ± 15.5344.85 ± 15.00-8.545< 0.001*male/female$\frac{399}{325231}$/$\frac{111630}{43614.855}$< 0.001†history of hypertension350($48.3\%$)202($59.1\%$)552($51.8\%$)10.6940.001†history of diabetes40($5.5\%$)99($28.9\%$)139($13.0\%$)112.381< 0.001†BMI24.24 ± 3.8825.05 ± 3.9524.50 ± 3.92-3.1010.002* Urinalysis urinary RBC(analyzer)64.0[20.8,207.3]13.0[3.0,42.0]39.5[10.0,133.0]——< 0.001#urinary RBC/HP(microscopy)5.0[0,30.0]0.0[0.0,4.0]4.0[0.0,20.3]——< 0.001#dysmorphic percentage(%)$5\%$[$0\%$,$50\%$]$0\%$[$0\%$,$5\%$]$5\%$[$0\%$,$14\%$]——< 0.001#specific gravity1.02 ± 0.011.02 ± 0.011.02 ± 0.01-0.7150.475*urinary creatine(µmol/L)9128[6292,13406]6744[4876,10008]8413[5748,12511]——< 0.001#microalbumin(mg/L)587[275,1300]746[172,2227]617[248,1473]——0.061#ACR(µg/mgCr)602[231, 1317]888[190,2792]634[221,1713]——< 0.001#24-h urinary protein excretion(g/24 h)1.14[0.65,2.12]1.55[0.58,4.53]1.24[0.64,2.75]——< 0.001#24-h urinary creatinine excretion(µmol/24 h)10,259[7971,13156]10,000[7844,13246]10,201[7907,13170]——< 0.001# Biochemical index creatinine(µmol/L)104[77,146]110[77,174]105[77,155]——0.221#eGFR(EPI)(ml/min/1.73m2)68.30 ± 31.8764.18 ± 33.6566.97 ± 32.50-1.9360.053*albumin(g/L)38.21 ± 5.4834.81 ± 8.6437.10 ± 6.86-7.656< 0.001*urea nitrogen(mmol/L)6.3[5.0,8.5]7.5[5.4,10.7]6.6[5.0,9.6]——< 0.001** Student’s t-test; # Mann–Whitney test; †Pearson testBMI, body mass index; HP, high power field; GFR, glomerular filtration rate; ACR, albumin to creatinine ratio
## Distributions of indexes in pathological classification
Among urinary parameters, 24-h urinary protein excretion significantly differed between Types I and II (1.14 g/24 h vs. 1.55 g/24 h, $p \leq 0.001$). The 24-h urinary creatinine excretion was higher in Type I (10,259 µmol/24 h vs. 10,000 µmol/24 h, $p \leq 0.001$) than in Type II. As for patients’ biochemical data, no significant differences in serum creatinine, eGFR, and uric acid were observed between Types I and II kidney diseases ($p \leq 0.05$). Serum urea nitrogen (6.3 mmol/Lvs.7.5 mmol/L, $p \leq 0.001$) level was higher in Type II kidney diseases than in Type I kidney diseases. In contrast, serum albumin (38.21 ± 5.48 g/L vs. 34.81 ± 8.64 g/L, $p \leq 0.001$) was lower in Type II kidney diseases than in Type I kidney diseases (Table 1).
## Predictive performance of new urinary complex indicators
*We* generated five URBC-based new urinary complex indicators referring to each candidate’s correlation with Type I according to Sect. 2.2. As shown in Fig. 1, the AUC value of Lg (URBC) for predicting Type I was 0.744 ($95\%$ CI 0.712 ~ 0.776) at baseline, whereas the prediction ability of the complex indicators was ranked from high to low as follows: Lg (URBC/24 hUP) [AUC = 0.766] > Lg (URBC/SG) [AUC = 0.73] > Lg (URBC/24 hUCr) [AUC = 0.714] > Lg (URBC/UAlb) [AUC = 0.708] > Lg (URBC/UCr) [AUC = 0.692]. The Delong test confirmed that URBC/24 hUP was superior to URBC alone in predicting hematuria-dominant renal histologic lesions (Type I), with p-values of 0.002 and 0.012, respectively.
Fig. 1Predictive capability of new urinary complex indicators. ( The predictive capability was measured by ROC-curve with AUC)
## Correlation between Lg (URBC/24 hUP) stratification and hematuria-dominant renal histologic lesions
Lg (URBC/24 hUP) was divided into quartiles characterized as Q1 (< 0.85 µL/(g/L)), Q2 (0.85 ~ 1.50 µL/(g/L)), Q3 (1.50 ~ 2.06 (µL/(g/L)) and Q4 (≥ 2.06 µL/(g/L)). The corresponding URBC/24 hUP ratios were < 7 µL/(g/L), 8 ~ 32 µL/(g/L), 33–115 µL/(g/L), and ≥ 116 µL/(g/L) (Table 2).
Table 2Correlation between hematuria stratification and pathological classificationLg(URBC/24hUP)URBC/24hUP(µL/(g/L))Type IN = 716(%)Type IIN = 330(%)Odds ratio($95\%$CI)P-valueQ1(< 0.85)< 793(35.5)169(64.5)Ref(1.0)Q2(0.85 ~ 1.50)8 ~ 32174(67.7)83(32.3)3.81(2.66 ~ 5.50)< 0.001Q3(1.50 ~ 2.06)33 ~ 115215(81.7)48(18.3)8.14(5.48 ~ 12.27)< 0.001Q4(≥ 2.06)≥ 116234(88.6)30(11.4)14.17(9.09 ~ 22.72)< 0.001*20 participants were not available due to lacking 24-h urinary protein excretion; Compared with the minimized Lg (URBC/24 hUP) level, the odds ratio of Type 1 increased from Q2 (3.81, $95\%$ CI 2.66–5.50) to Q4 (14.17, $95\%$ CI 9.09–22.72). There was a 3.72-fold increase in the odds ratio for doubling of Lg (URBC/24 hUP) in the range of 0.85 ~ 1.50. This indicated that a higher quartile of Lg (URBC/24 hUP) facilitated the detection of hematuria-dominant renal histological lesions with higher discrimination (Table 2).
## Performance of predictive models based on new urinary composite indexes
As shown in Fig. 2, Lg (URBC/24 hUP) was applied to the three models to predict hematuria-dominant kidney disease (Type I). In Model 1, the AUC of hematuria stratification measured by complex indices for identifying hematuria-dominant renal histologic lesions (Type I) was 0.756 ($95\%$ CI 0.725–0.787). In Model 2, the AUC of the combination of hematuria stratification and demographic characteristics for identifying hematuria-dominant renal histologic lesions (Type I) was 0.869 ($95\%$ CI, 0.845–0.892). Model 3 achieved an AUC of 0.880 ($95\%$ CI, 0.856–0.905) with biochemical information.
Fig. 2Performance of predictive models based on new urinary complex indicators. ( model 1 only enrolled the Lg (URBC/24 hUP) stratification, model2 was model 1 plus demographic factors and medical history(diabetes and hypertension), model 3 was model 2 plus biochemical data)
## Discussion
Hematuria, defined as the presence of RBCs in urine, was identified by urinalysis of a concentrated urine sediment specimen [17]. Prompt referral to a nephrologist is indicated when hematuria does not resolve within weeks of onset. Effective and accurate URBC-based hematuria measurements provide explainable insights into hematuria resolution and chronic disease management. Our study investigated new complex indicators to improve URBC confirmed by a particular renal biopsy. Urine RBC counts can be measured using automated urine sediment analyzers [4]. A previous study suggested that the determination of urinary RBC distribution with an automated analyzer and analysis of the distribution curves may be a more reliable measure of erythrocyte morphology [18]. Renal biopsy, the gold standard for the diagnosis of kidney diseases, can be used to verify the sources of urinary RBCs, and the count and morphology of urinary RBCs can, in turn, provide important information on patients’ renal pathological changes [19]. In our study, we focused on hematuria originated from kidney diseases.
Furthermore, because the concentration of urine components varies greatly under the influence of drinking water, exercise, and other factors, random urine sample tests lack stability. Our findings suggest that patients with biopsy-proven non-hematuria-dominant renal histologic lesions may have high URBCs on automated urine sediment analyzers. In addition, we observed that some patients with hematuria-dominant renal histologic lesions had negative hematuria on routine urinalysis with less than 25 RBCs/µL. According to Yang et al. [ 20], the urine SG affects RBCs’ morphology, thus altering the identification of RBCs via automated urine sediment analyzers [21]. Considering the mechanism by which the urine sediment analyzer operates, the counting and classification of urinary RBCs are based on signals of forwarding scattered light, side scattered light, and side-fluorescent light patterns. The pattern of individual light signals is transformed by specific algorithms into individual “fingerprints,” allowing the counting, identification, and classification of the particles [22]. This inconsistency might mainly be attributed to a variable mixture of components in the urine, such as cells, casts, crystals, and bacteria. Furthermore, when the automated urine sediment analyzer interferes with various particle sizes, it may mistake other urinary components for RBCs [23]. In addition, the urine sediment analyzer itself cannot calibrate the urinary concentration or reduced kidney function. Morphological changes in urinary RBCs may be related to counting errors. Urine RBCs in a hypotonic environment exhibit a certain degree of swelling due to deteriorating kidney function [24]. We agree that URBC-based diagnosis of hematuria has certain limitations because RBC morphology and identification are unstable, with marked variability in size and shape affected by hydration status and hemodynamic status [3]. Reliable quality of URBC is critical for prognosis judgment and treatment, indicating a need for differential diagnosis [25].
We developed a parameter (URBC/24 hUP) that adjusts 24-h urinary protein quantification for surrogates of random urine RBC count to better reflect hematuria. From our study results, 24-h urinary protein quantification displayed significant distribution discrepancies in the two pathological types and was more related to Type I. Clinical practice guidelines recommend screening for and monitoring albuminuria and incorporating increased albuminuria into the definition and staging of CKD [26]. The 24-h urinary protein quantification known as the “gold standard”[27] method for evaluating proteinuria is an ideal target to be adjusted, as it avoids protein fluctuations during the day and can indirectly reflect patients’ kidney functions. The findings of “patients in the hematuria group often had overt proteinuria [11]” support the application of the 24-h urinary protein quantification.
In capturing trends in upgrading hematuria stratification-derived AUC values, we observed that patients with a higher URBC/24 hUP showed more specific interactions with Type I changes. Stratification of Lg (URBC/24 hUP) achieved an AUC value of 0.751 ($95\%$ CI, 0.719–0.782). When fitted into the model incorporating demographic characteristics and past medical history, the AUC value increased to 0.866 ($95\%$ CI 0.842–0.889). After the integration of biochemical data, the AUC further increased to 0.880 ($95\%$ CI, 0.855–0.905). These findings indicate that new urinary complex indicators corresponded better with Type I in the population at high risk of CKD, such as patients with hypertension and diabetes. For patients in the high-risk group (Q4), 24-h urine collection is a typical method of disease assessment and management. Compared to routine RBCs count based on a single collection, URBC/24 hUP corrects the urine RBC count by “24-h urinary protein quantification,” reflecting a time-accumlation effect.
Patients presenting with proteinuria and /or hematuria are usually recommended to have renal biopsy, especially other less invasive procedures are not conclusive enough[28]. Since renal biopsy is not routinely performed in remote areas, let alone inaccessibility of microscopic examinations. These non-invasive urinary complex indicators which based solely on urinalysis, will be helpful for patients in those remote areas. On the other hand, patients with contraindications of renal biopsy may also benefit from our new complex index for non-invasive diagnosis of hematuria. However, not all positive URBC results reflect factual bleeding in the urinary system due to physical and/or chemical interference and limitations of urinalysis. The correspondence of hematuria severity with values of our urinary indicator contributes to brief judgement of illness, and accelerates patients’ orientation to nephrology.
This study had some limitations. First, due to the diversity of renal pathological changes, glomeruli, renal tubules, or interstitium lesions may exist alone or together. Therefore, the corresponding urine abnormalities may interfere with one another. Second, the limited sample size and the unbalanced constituent ratio between Type I and Type II barriers further generalization. Third, a discrepancy between the urinalysis and clinical pathology manifestation existed in some cases, which might be attributed to intermittent onset of hematuria, while other patients who presented with recurrent hematuria and no urinary abnormalities between the hematuric bouts may be recorded in this study as well. As a result, patients with proliferative glomerulonephritis may have negative hematuria, especially in well-rested conditions. Failure of patients to comply with the standards of urine collection or any mistakes in urine sample transport and storage would lead to pre-analytical errors and inaccurate results [29]. However, the accuracy of urine tests can be improved by repeated urine tests. Fourth, random urine data collected at a single time point cannot fully and accurately reflect the state of kidney diseases. Last but not the least, the complex indicators were analyzed by urine RBCs intead of RBCs/HPF. The reason was that microscopy of urine RBCs is performed only when the occult (dry chemical analysis) is inconsistent with urine RBCs count (flow cytometry) in our center. Further study can be conducted to explore other complex indicators based on RBCs/HPF and extend the practical utility to centers without automated analyzers.
## Conclusion
In combination with clinical information, the accuracy of urine RBC counting could be improved, and the integrity of hematuria diagnosis could also be promoted simultaneously to achieve or approach the non-invasive diagnosis of CKD [2]. This small sample size study might provide crucial support for the diagnosis and treatment of patients who are contraindicated for renal biopsy and likely guide and support clinical activities for primary health care institutes that do not qualify to perform renal biopsy.
## References
1. Xue N, Zhang X, Teng J, Fang Y, Ding XA. **Cross-sectional study on the Use of Urinalysis for Screening Early-Stage Renal Insufficiency**. *Nephron* (2016.0) **132** 335-41. DOI: 10.1159/000444650
2. Higashihara E, Nishiyama T, Horie S, Marumo K, Mitarai T, Koyama T, Matsuyama T, Ito K, Yuno T. **Working Group for the creation of Hematuria Guidelines. Hematuria: definition and screening test methods**. *Int J Urol* (2008.0) **15** 281-4. DOI: 10.1111/j.1442-2042.2008.02009.x
3. Kavuru V, Vu T, Karageorge L, Choudhury D, Senger R, Robertson J. **Dipstick analysis of urine chemistry: benefits and limitations of dry chemistry-based assays**. *Postgrad Med* (2020.0) **132** 225-33. DOI: 10.1080/00325481.2019.1679540
4. Tsai JJ, Yeun JY, Kumar VA, Don BR. **Comparison and interpretation of urinalysis performed by a nephrologist versus a hospital-based clinical laboratory**. *Am J Kidney Dis* (2005.0) **46** 820-9. DOI: 10.1053/j.ajkd.2005.07.039
5. Humphries RM, Abbott AN, Hindler JA. **Understanding and addressing CLSI breakpoint revisions: a primer for Clinical Laboratories**. *J Clin Microbiol* (2019.0) **57** e00203-19. DOI: 10.1128/JCM.00203-19
6. Barocas DA, Boorjian SA, Alvarez RD, Downs TM, Gross CP, Hamilton BD, Kobashi KC, Lipman RR, Lotan Y, Ng CK, Nielsen ME, Peterson AC, Raman JD, Smith-Bindman R, Souter LH. **Microhematuria: AUA/SUFU Guideline**. *J Urol* (2020.0) **204** 778-86. DOI: 10.1097/JU.0000000000001297
7. Yu GZ, Guo L, Dong JF, Shi SF, Liu LJ, Wang JW, Sui GL, Zhou XJ, Xing Y, Li HX, Lv JC, Zhang H. **Persistent hematuria and kidney Disease Progression in IgA Nephropathy: a Cohort Study**. *Am J Kidney Dis* (2020.0) **76** 90-9. DOI: 10.1053/j.ajkd.2019.11.008
8. 8.Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F, Zhang YL, Coresh J, Levey AS. ; CKD-EPI Investigators. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20 – 9. 10.1056/NEJMoa1114248. Erratum in: N Engl J Med. 2012 Aug 16;367(7):681. Erratum in: N Engl J Med. 2012;367(21):2060.
9. Petersmann A, Müller-Wieland D, Müller UA, Landgraf R, Nauck M, Freckmann G, Heinemann L, Schleicher E. **Definition, classification and diagnosis of diabetes Mellitus**. *Exp Clin Endocrinol Diabetes* (2019.0) **127** 1-S7
10. Jones NR, McCormack T, Constanti M, McManus RJ. **Diagnosis and management of hypertension in adults: NICE guideline update 2019**. *Br J Gen Pract* (2020.0) **70** 90-1. DOI: 10.3399/bjgp20X708053
11. Wu Y, Zhang J, Wang Y, Wang T, Han Q, Guo R, Zhang R, Ren H, Zhu Y, Xu H, Li L, Tong N, Liu F. **The association of hematuria on kidney clinicopathologic features and renal outcome in patients with diabetic nephropathy: a biopsy-based study**. *J Endocrinol Invest* (2020.0) **43** 1213-20. DOI: 10.1007/s40618-020-01207-7
12. Fogazzi GB, Bosan IB. **Garigali G.A theoretical and practical course on urine microscopy in Nigeria: a model for nephrological skill transfer**. *Nephron Clin pract* (2011.0) **117** C398-402. DOI: 10.1159/000319881
13. Vedula R, Iyengar AA. **Approach to diagnosis and management of Hematuria**. *Indian J Pediatr* (2020.0) **87** 618-24. DOI: 10.1007/s12098-020-03184-4
14. Hicks J, Mierau G, Wartchow E, Eldin K. **Renal diseases associated with hematuria in children and adolescents: a brief tutorial**. *Ultrastruct Pathol* (2012.0) **36** 1-18. DOI: 10.3109/01913123.2011.620731
15. Pesce F, Stea ED, Rossini M, Fiorentino M, Piancone F, Infante B, Stallone G, Castellano G, Gesualdo L. **Glomerulonephritis in AKI: from pathogenesis to therapeutic intervention**. *Front Med (Lausanne)* (2021.0) **7** 582272. DOI: 10.3389/fmed.2020.582272
16. 16.Palsson R, Srivastava A, Waikar SS. Performance of the Automated Urinalysis in Diagnosis of Proliferative Glomerulonephritis. Kidney Int Rep. 2019 Feb 12;4(5):723–727.
17. 17.Mariani AJ, Mariani MC, Macchioni C, Stams UK, Hariharan A, Moriera A. The significance of adult hematuria: 1,000 hematuria evaluations including a risk-benefit and cost-effectiveness analysis. J Urol. 1989 Feb;141(2):350–5.
18. Cho H, Yoo J, Kim H, Jang H, Kim Y, Chae H. **Diagnostic characteristics of urinary red blood cell distribution Incorporated in UF-5000 for differentiation of glomerular and non-glomerular hematuria**. *Ann Lab Med* (2022.0) **42** 160-8. DOI: 10.3343/alm.2022.42.2.160
19. Ercan M, Akbulut ED, Abuşoğlu S, Yilmaz FM, Oğuz EF, Topçuoğlu C. **Öztekin V and Boğdaycioğlu N: Stability of urine specimens stored with and without preservatives at room temperature and on ice prior to Urinalysis**. *Clin Biochem* (2015.0) **48** 919-22. DOI: 10.1016/j.clinbiochem.2015.05.016
20. Yang WS. **Automated urine sediment analyzers underestimate the severity of hematuria in glomerular diseases**. *Sci Rep* (2021.0) **11** 20981. DOI: 10.1038/s41598-021-00457-6
21. Nakayama K, Ohsawa I, Maeda-Ohtani A, Murakoshi M, Horikoshi S, Tomino Y. **Prediction of diagnosis of immunoglobulin A nephropathy prior to renal biopsy and correlation with urinary sediment findings and prognostic grading**. *J Clin Lab Anal* (2008.0) **22** 114-8. DOI: 10.1002/jcla.20227
22. 22.You-Hsien Lin H, Yen CY, Lim LM, Hwang DY, Tsai JC, Hwang SJ, Hung CC, Chen HC. Microscopic haematuria and clinical outcomes in patients with stage 3–5 nondiabetic chronic kidney disease. Sci Rep. 2015 Oct;16:5:15242.
23. 23.Fritzenwanker M, Grabitz MO, Arneth B, Renz H, Imirzalioglu C, Chakraborty T, Wagenlehner F. Comparison of Urine Flow Cytometry on the UF-1000i System and Urine Culture of Urine Samples from Urological Patients.Urol Int. 2021 Dec29:1–10.
24. 24.Babcock JG, Lippert LE, Derse-Anthony CP, Mechling M, Hess JR. A hypotonic storage solution did not prolong the viability of RBCs. Transfusion. 2000ug;40(8):994-9.
25. Köhler H, Wandel E, Brunck B. **Acanthocyturia–a characteristic marker for glomerular bleeding**. *Kidney Int* (1991.0) **40** 115-20. DOI: 10.1038/ki.1991.188
26. Bauer L, Emrich IE, Pickering JW, Untersteller K, Sandermann F, Rogacev KS, Seiler-Mussler S, Fliser D, Heine GH. **Best Albuminuria measurement to Predict Cardiovascular and renal events**. *Am J Nephrol* (2016.0) **43** 383-8. DOI: 10.1159/000446483
27. Andrassy KM. **Comments on ‘KDIGO 2012 Clinical Practice Guideline for the evaluation and management of chronic kidney disease’**. *Kidney Int* (2013.0) **84** 622-3. DOI: 10.1038/ki.2013.243
28. **Improving global outcomes (KDIGO) glomerular Diseases Work Group. KDIGO 2021 Clinical Practice Guideline for the management of glomerular Diseases**. *Kidney Int* (2021.0) **100** 1-S276
29. Bacârea A, Fekete GL, Grigorescu BL, Bacârea VC. **Discrepancy in results between dipstick urinalysis and urine sediment microscopy**. *Exp Ther Med* (2021.0) **21** 538. DOI: 10.3892/etm.2021.9971
|
---
title: 'Escore SAGE em Normotensos e Pré-Hipertensos: Uma Prova de Conceito'
authors:
- Rayne Ramos Fagundes Rigonatto
- Priscila Valverde Oliveira Vitorino
- Adriana Camargo Oliveira
- Ana Luiza Lima Sousa
- Paulo César Brandão Veiga Jardim
- Pedro Miguel Guimarães Marques Cunha
- Eduardo Costa Duarte Barbosa
- Panagiotis Xaplanteris
- Charalambos Vlachopoulos
- Weimar Kunz Sebba Barroso
journal: Arquivos Brasileiros de Cardiologia
year: 2023
pmcid: PMC9972660
doi: 10.36660/abc.20220291
license: CC BY 4.0
---
# Escore SAGE em Normotensos e Pré-Hipertensos: Uma Prova de Conceito
## Resumo
### Fundamento
O SAGE foi desenvolvido para identificar hipertensos com chance de velocidade de onda de pulso (VOP) aumentada. Até o momento, as publicações do escore foram em hipertensos.
### Objetivo
Verificar a capacidade do SAGE de identificar os normotensos ou pré-hipertensos com chance de aumento da VOP.
### Métodos
Transversal retrospectivo, incluiu exames de normotensos e pré-hipertensos que realizaram a medida central da pressão arterial e apresentavam os parâmetros para o cálculo do escore. Para cada pontuação do escore, foi analisada a sensibilidade, especificidade, valor preditivo positivo e negativo utilizando como ponto de corte para o diagnóstico positivo VOP ≥ 10m/s, ≥9,08 m/s (percentil 75) e ≥7,30 m/s (percentil 50). Um valor de $p \leq 0$,05 foi adotado como estatisticamente significante.
### Resultados
A amostra foi de 100 participantes normotensos ou pré-hipertensos, com média (DP) de 52,64 [14,94] anos e VOP mediana de 7,30 m/s (6,03 – 9,08). O SAGE apresentou correlação com idade ($r = 0$,938, $p \leq 0$,001), glicemia ($r = 0$,366, $p \leq 0$,001) e taxa de filtração de glomerular (r=-0,658, $p \leq 0$,001). A área sob a curva ROC foi de 0,968 ($p \leq 0$,001) para VOP≥10 m/s, 0,977 ($p \leq 0$,001) para VOP≥9,08 m/s e 0,967 ($p \leq 0$,001) para VOP≥7,30 m/s. O escore 7 apresentou especificidade de 95,$40\%$ e sensibilidade de $100\%$ para VOP≥10 m/s. O ponto de corte seria cinco para VOP≥9,08 m/s ($s = 96$,$00\%$, $e = 94$,$70\%$), e dois para VOP≥7,30 m/s.
### Conclusão
O SAGE foi capaz de identificar indivíduos com maior chance de apresentar rigidez arterial, utilizando diferentes pontos de corte de VOP. Entretanto, o desenvolvimento de um escore específico para normontensos e pré-hipertensos faz-se necessário.
## Introdução
A velocidade de onda de pulso (VOP) é um biomarcador consolidado na estratificação do risco cardiovascular e identificação de lesões subclínicas. Quando maior que 10 m/s, a VOP também significa lesão em órgão alvo. 1 - 4 Entretanto, ainda é subutilizada na prática clínica pelo alto-custo e pela baixa disponibilidade de equipamentos de avaliação. 5 O escore SAGE foi desenvolvido com o intuito de difundir o conhecimento e o conceito da avaliação de dano e envelhecimento vascular, utilizando quatro parâmetros simples – idade, pressão arterial sistólica (PAS), glicemia de jejum e taxa de filtração glomerular (TFG) – para calcular a possibilidade de o indivíduo apresentar aumento da rigidez arterial. De acordo com a pontuação obtida, pode-se encaminhar com maior assertividade os pacientes para a realização da medida central da pressão arterial (MCPA) e análise da VOP. 5 No estudo de desenvolvimento do escore, o ponto de corte do SAGE foi calculado utilizando a VOP carotídeo-femoral na população hipertensa. 5 Em seguida, o escore foi calculado usando a VOP tornozelo braquial em japoneses hipertensos 6 e, recentemente, o escore foi calculado utilizando o método oscilométrico, mais difundido no Brasil, com brasileiros hipertensos. 7 Em 2021, um estudo 8 com 760 chineses desenvolveu um novo escore clínico utilizando idade, PAS periférica (PASp) e pressão arterial diastólica periférica (PADp), peso e altura, também visando identificar os indivíduos com aumento da rigidez arterial. Porém, esse estudo foi feito especificamente com diabéticos, e utilizou a análise da VOP tornozelo-braquial.
Há ainda uma lacuna na literatura em relação ao uso desses escores para identificar o aumento da rigidez arterial nos indivíduos não hipertensos, mas que já podem apresentar VOP elevada e risco de desfechos cardiovasculares, e, de estudos utilizando o método oscilométrico, que apresenta menor custo e é de fácil aplicação.
Diante do exposto, o objetivo do estudo foi verificar a capacidade do escore SAGE em identificar a chance de aumento da VOP em uma amostra de brasileiros normotensos ou pré-hipertensos como uma prova de conceito.
## Tipo de estudo e local da pesquisa
Estudo transversal que avaliou prontuários de pacientes de dois centros de referência no diagnóstico e acompanhamento de hipertensão arterial no Brasil.
## População e amostra
No período de setembro de 2012 a novembro de 2019, foram realizadas 1594 avaliações da MCPA pelo método oscilométrico. Desses exames foram excluídos: Portanto, a amostra foi constituída por 100 indivíduos normotensos ou pré-hipertensos, que apresentavam todos os dados necessários para o cálculo do escore SAGE (Figura 1).
Figura 1– Fluxograma de seleção dos participantes. Fonte: Autor, 2022.
## Procedimentos do estudo
Foram identificados, nos dois centros de referência, todos os exames em arquivo eletrônico de MCPA realizados entre setembro de 2012 a novembro de 2019. Em seguida, foram analisados os prontuários dos pacientes para verificar os critérios de elegibilidade do estudo.
Dentre os elegíveis, os seguintes dados foram coletados do exame da MCPA: data de nascimento, data de realização do exame, peso, altura, PAS e PAD periféricas e centrais, pressões de pulso periférica e central, Augmentation Index (AIx) e VOP. Para os parâmetros centrais e periféricos, a média de três medidas foi considerada para a análise.
Além disso, foram coletados os seguintes dados: sexo, tabagismo, sedentarismo, estado civil, dados sobre os medicamentos em uso, diagnósticos clínicos, e os resultados dos exames de glicemia jejum e creatinina realizados três meses antes ou após o exame da MCPA. Nos casos em que o mesmo exame foi realizado mais de uma vez nesse período, foi considerado o mais próximo da data de realização da MCPA.
O índice de massa corporal foi calculado utilizando a fórmula: peso(Kg)/[altura(m)]2. 11
## Avalição da medida central da pressão arterial
A avaliação da MCPA foi realizada com o Mobil-O-Graph® (IEM, Stolber, Alemanha) e com o Dyna MAPA AOP® (Cardios, São Paulo, Brasil). Essa avaliação é feita de forma não invasiva, os parâmetros periféricos (pressão sistólica e diastólica) são aferidos com esfigmomanômetro e o algoritmo ARCSolver é utilizado para estimar o valor da pressão arterial a nível central. 12 A avaliação da VOP com uso da braçadeira, pelo método oscilométrico, apresenta valores semelhantes aos obtidos de forma invasiva com cateter intra-aórtico, 12 e é mais reprodutível que os equipamentos de avaliação da VOP carotídeo-femoral. 13 Ela também é validada para a avaliação da PAS central em comparação com a avaliação pelo método invasivo e pelo método tonométrico. 14 *Aumento da* rigidez arterial compatível com lesão em órgão-alvo foi identificada pela VOP maior ou igual a 10 m/s. 9, 15
## Cálculo do escore SAGE
O SAGE escore é definido a partir de quatro variáveis: glicemia de jejum, PASp, idade e taxa estimada de filtrado glomerular (Figura 2).
Figura 2– Identificação do escore SAGE de acordo com as quatro variáveis que o constituem; traduzido de Xaplanteris et al.5 Por exemplo, um indivíduo com pressão sistólica de 145 mmHg, glicemia de 110 mg/dl, 65 anos e TFG de 69 mL/min/1,73m2 receberá o escore SAGE 8 e, portanto, conforme definido pelo estudo de desenvolvimento do escore, será indicado para realizar a avaliação da rigidez arterial, devido à maior chance de seu aumento. 5 O SAGE foi calculado para cada um dos participantes. A PASp foi obtida no exame de MCPA, a idade foi estimada pela diferença obtida a partir da diferença entre a data de realização desse exame e a data de nascimento dos participantes. A glicemia foi coletada do prontuário e a TFG foi calculada utilizando a fórmula CKD-EPI 2021, a partir dos valores de creatinina coletados do prontuário.
## Análise estatística
Os dados foram coletados por duas pesquisadoras, utilizando um formulário elaborado no programa Epidata, versão 3.1. 16 Também por meio do programa, a dupla digitação dos dados foi validada para a verificação de possíveis inconsistências e correção.
Os dados foram analisados com o Statistical Package for Social Science versão 23.0 (SPSS). Foi aplicado teste de Kolmogorov-*Smirnov para* verificar a normalidade de distribuição dos dados e foi realizada a análise descritiva dos dados, utilizando média e desvio-padrão para os dados paramétricos e mediana e intervalo interquartil para os não paramétricos. Os dados qualitativos foram apresentados como frequências absoluta e relativa.
A correlação entre o SAGE e cada uma das quatro variáveis que o compõe foi realizada com o teste de correlação de Spearman.
A análise de sensibilidade, especificidade, valor preditivo positivo e negativo foi realizada para cada pontuação SAGE, utilizando como diagnóstico positivo três valores de VOP ≥ 10m/s e VOP ≥ percentis 50 e 75, que corresponderam a 7,3 e 9,8 m/s, respectivamente. Para cada um desses valores foi construída a curva ROC e definido o melhor ponto de corte do escore SAGE, isto é, aquele com maior sensibilidade e especificidade para a identificação dos pacientes com maior chance de VOP elevada. Considerou-se como significativo $p \leq 0$,05.
## Aspectos éticos
A pesquisa seguiu as normas da resolução nº $\frac{466}{12}$ e foi aprovada pelo Comitê de Ética do Hospital das Clínicas da Universidade Federal de Goiás sob os pareceres nº 1.500.463 e 3.792.750 (emenda), com dispensa do Termo de Consentimento Livre e Esclarecido (TCLE).
## Resultados
Foram analisados dados de 100 participantes, com média de idade 52,64 ± 14,94 anos. A maioria dos participantes eram do sexo masculino, com dislipidemia, com PA ótima e valores de VOP inferiores a 8 m/s (Tabela 1).
Tabela 1– Características sociodemográficas e clínicas dos participantes ($$n = 100$$)Variáveln/%SexoFeminino45Masculino55Estado civilSem companheiro29Com companheiro57Não informado14Idade< 50 anos4250 a 59 anos2460 a 69 anos18≥70 anos16Fator de riscoFumante5 / 5,$3\%$*IMC > 30Kg/m224Diabetes Mellitus11 / 11,$7\%$*Dislipidemia53 / 65,$4\%$*Colesterol total< 150 mg/dl29150 - 199 mg/dl41200 - 249 mg/dl18250 - 299 mg/dl4≥ 300 mg/dl1Não informado7LDL≤50 mg/dl851–69 mg/dl1070–99 mg/dl26100–129 mg/dl31≥130 mg/dl16Não informado9Triglicérides<150 mg/dl63≥150 mg/dl26Não informado11Glicemia< 126 mg/dl94≥ 126 mg/ dl6Taxa de Filtração Glomerular30 a 59560 a 8948≥ 9047Classificação da Pressão ArterialPA ótima46PA normal34Pré-hipertensão20Rigidez arterialVOP < 8 m/s59VOP 8 - 10 m/s29VOP > 10 m/s12Parâmetros de Pressão CentralMédia (DP) / Mediana (25 – 75)PASp (mmHg)119,43 [9,59]PADp (mmHg)75,50 (67,00 – 79,75)PPp (mmHg)45,00 (39,00 – 52,00)PASc (mmHg)109,15 [9,38]PADc (mmHg)77,00 (67,25 – 81,00)PPc (mmHg)32,00 (29,00 – 39,00)AI (%)18,87 [11,30]VOP (m/s)7,30 (6,03 – 9,08) *Alguns pacientes não continham esses dados, logo a frequência foi diferente do n. AI(%): augmentation index; HDL: lipoproteína de alta densidade; IMC: índice de massa corporal; LDL: lipoproteína de baixa densidade; PADc: pressão arterial diastólica central; PADp: pressão arterial diastólica periférica; PASc: pressão arterial sistólica central; PASp: pressão arterial sistólica periférica; PPc: pressão de pulso central; PPp: pressão de pulso periférica; VOP: velocidade da onda de pulso O escore SAGE mais frequente na amostra foi 0, seguido de 1 e 2 (Figura 03). Ao verificar as características desses pacientes que justifiquem o escore, verificou-se que dentre os 13, 12 participantes estavam na faixa etária de 70 anos ou mais e apresentavam glicemia < 126mg/dL e TFG de 60 a 89 mL/min/1,73m2. O outro paciente apresentou idade entre 60 e 69 anos, glicemia ≥ 126mg/dL e a mesma TFG.
Figura 3– Frequência relativa de distribuição do escore SAGE ($$n = 100$$).
Entre os pacientes com rigidez arterial (VOP ≥ 10 m/s), o escore mais frequente foi sete (Figura 4). Todos os pacientes com VOP ≥ 10 m/s ($$n = 13$$, $100\%$) apresentavam 70 anos ou mais e glicemia < 126 mg/dL; 10 (76,$9\%$) apresentagvam TFG entre 60 e 89 ml/min/1,73m2, e três ($23\%$) entre 30 e 59 ml/min/1,73m2. Onze (84,$6\%$) apresentavam dislipidemia.
Figura 4– Frequência absoluta e relativa de distribuição do escore SAGE, dentre os participantes; (A) pacientes com velocidade de onda de pulso (VOP) ≥ 10 m/s, (B) pacientes com VOP ≥ 9,08 m/s e (C) pacientes com VOP ≥ 7,30 m/s.
Analisando-se o percentil 75 (9,08 m/s) e o percentil 50 da VOP (7,30 m/s), o escore SAGE mais frequente também foi sete. Dentre os pacientes com VOP ≥ 9,08 m/s, $88\%$ tinham glicemia < 126 mg/dL, $64\%$ tinha 70 anos ou mais (e os demais estavam na faixa etária entre 60 e 69 anos), e $80\%$ tinham TFG entre 60 e 89 ml/min/1,73m2.
A distribuição dos parâmetros de SAGE de acordo com a idade, a PASp, a glicemia de jejum e a TFG (segundo grupo CKD-EPI) (Figura 5), demonstrou correlação positiva com a idade e com a taxa de glicemia, e correlação negativa com a TFG. Não foi encontrada correlação entre o SAGE e a PASp.
Figura 5– Distribuição do SAGE (A- de acordo com a idade, B- de acordo com a PASp, C- de acordo com a taxa de glicemia, D- de acordo com o CKD-EPI).
Na análise da curva ROC, a área sob a curva para VOP ≥ 10 m/s, foi de 0,968 ($p \leq 0$,001), para VOP ≥ 9,08 m/s foi 0,977 ($p \leq 0$,001) e para a VOP ≥ 7,30 m/s foi 0,967 ($p \leq 0$,001) (Figura 6).
Figura 6– Curva ROC do escore SAGE (A - para VOP ≥ 10 m/s, B – para VOP ≥ 9,08 m/s e C – para VOP ≥ 7,30 m/s.
De acordo com a análise de sensibilidade e especificidade (Tabela 2), para os indivíduos com rigidez arterial (VOP ≥ 10 m/s), o escore sete apresentou alta especificidade (95,$40\%$) associado à uma sensibilidade de $100\%$ e um valor preditivo negativo de $100\%$. Considerando o percentil 75 (VOP ≥ 9,08 m/s), o ponto de corte seria o SAGE ≥ 5, com sensibilidade de 96,$00\%$ e especificidade de 94,$70\%$. Já para a mediana da VOP (≥ 7,30 m/s), o ponto de corte reduziria para dois.
Tabela 2– Sensibilidade e especificidade do escore SAGE por ponto de corte e considerando diferentes valores de velocidade da onda de pulso SAGESensibilidadeEspecificidadeVPP/ Corretamente classificadoVPNVOP ≥ 10 m/s0100,$00\%$0,$00\%$13,$00\%$-1100,$00\%$27,$59\%$17,$11\%$100,$00\%$2100,$00\%$47,$13\%$22,$03\%$100,$00\%$3100,$00\%$63,$22\%$28,$89\%$100,$00\%$4100,$00\%$73,$56\%$36,$11\%$100,$00\%$5100,$00\%$82,$76\%$46,$43\%$100,$00\%$6100,$00\%$91,$95\%$65,$00\%$100,$00\%$7100,$00\%$95,$40\%$76,$47\%$100,$00\%$823,$08\%$98,$85\%$75,$00\%$89,$60\%$90,$00\%$98,$85\%$0,$00\%$86,$90\%$VOP ≥ 9,08 m/s0100,$00\%$0,$00\%$25,$00\%$-1100,$00\%$32,$00\%$32,$89\%$100,$00\%$2100,$00\%$54,$70\%$42,$40\%$100,$00\%$3100,$00\%$73,$30\%$55,$60\%$100,$00\%$4100,$00\%$85,$30\%$69,$40\%$100,$00\%$596,$00\%$94,$70\%$85,$70\%$98,$60\%$672,$00\%$97,$30\%$90,$00\%$91,$30\%$768,$00\%$100,$00\%$100,$00\%$90,$40\%$816,$00\%$100,$00\%$100,$00\%$78,$10\%$94,$00\%$100,$00\%$100,$00\%$75,$80\%$VOP ≥ 7,30 m/s0100,$00\%$0,$00\%$51,$00\%$-1100,$00\%$48,$98\%$67,$11\%$100,$00\%$298,$00\%$81,$60\%$84,$70\%$97,$60\%$382,$40\%$93,$90\%$93,$30\%$83,$60\%$468,$60\%$98,$00\%$97,$20\%$75,$00\%$554,$90\%$100,$00\%$100,$00\%$68,$10\%$639,$20\%$100,$00\%$100,$00\%$61,$30\%$733,$30\%$100,$00\%$100,$00\%$59,$00\%$87,$80\%$100,$00\%$100,$00\%$51,$00\%$92,$00\%$100,$00\%$100,$00\%$49,$50\%$ VOP: velocidade de onda de pulso; VPN: valor preditivo negativo, VPP: valor preditivo positivo.
## Discussão
No presente estudo identificamos que o escore 0 foi o mais frequente no total da amostra de não hipertensos. Por outro lado, naqueles com VOP ≥ 7,3 m/s, 9,08 m/s ou 10 m/s o escore sete foi o mais frequente. O escore SAGE apresentou correlação positiva e moderada com a glicemia, positiva e muito forte com a idade, e, negativa e forte com a TFG. Não foi identificada correlação do SAGE com a PASp. Com base nas análises de sensibilidade e especificidade, o escore sete foi definido como o ponto de corte recomendado para indicar a realização da análise da rigidez arterial considerando como diagnóstico positivo a VOP ≥ 10 m/s. Já para VOP ≥ 9,08 m/s e ≥ 7,30 m/s, os pontos de corte foram, respectivamente, 5 e 2.
No presente estudo, o fato de os pacientes serem não hipertensos, o qual é um dos parâmetros que compõem o escore SAGE, não resultou em um ponto de corte menor na análise incluindo a VOP ≥ 10 m/s, assim como no estudo de desenvolvimento do escore. 5 O ponto de corte do SAGE foi semelhante ao definido pelo estudo original realizado com pacientes hipertensos caucasianos de origem europeia, 5 e ao estudo feito com 837 brasileiros hipertensos, 7 que obtiveram o ponto de corte de oito. Ainda, o ponto de corte foi igual ao do estudo feito com 1816 japoneses hipertensos, 6 em que o ponto de corte foi sete. Isso pode ser justificado pelo fato de que, mesmo não apresentando hipertensão arterial, que é um dos fatores que contribuem com a pontuação do SAGE, todos os participantes com VOP ≥ 10 m/s apresentavam idade elevada (≥ 70 anos), o que já atribui seis pontos ao escore. A relação entre o aumento da idade cronológica e a rigidez arterial já está bem estabelecida na literatura, 17, 18 uma vez que, concomitantemente ao envelhecimento cronológico do organismo, ocorre também o envelhecimento vascular, culminando no aumento da rigidez arterial. 18 - 24 Por outro lado, é possível que o estabelecimento de pontos de corte para estratos de PAS menores que 140 mmHg, e a inclusão de outros parâmetros no escore, como o colesterol, possa otimizar ainda mais a aplicabilidade desse escore para a população de normotensos e pré-hipertensos.
Além do fator idade, verificou-se que a maioria dos indivíduos com VOP ≥ 10 m/s apresentou dislipidemia. Esse fator de risco, apesar de não ser contemplado pelo escore SAGE, também contribui para o desenvolvimento e a progressão da rigidez arterial. As taxas basais triglicérides (TG) e a razão entre os níveis TG e de colesterol de alta densidade (TG/HDL) apresentaram associação independente com o aumento persistente da VOP e com a incidência de VOP elevada em homens saudáveis acompanhados por 4,1 anos. 25 Analisamos também os percentis 75 e 50 da VOP considerando que a VOP ≥ 10 m/s já identifica lesão de órgão-alvo, 9, 15 e nessa população não hipertensa, valores da VOP menores que 10 m/s já representam um aumento da rigidez arterial e por consequência, do risco cardiovascular. 26 Foram identificados 25 e 51 participantes com VOP acima dos percentis 75 e 50, respectivamente.
Outro aspecto a se considerar é a aplicação do escore SAGE com um ponto de corte mais baixo, por exemplo cinco, como estratégia para identificar valores de VOP maiores que o percentil 75, o que resultaria na identificação de um a cada quatro não hipertensos. Já o uso do escore dois, definido para o percentil 50, não seria tão viável, pois recomendaria quase todos os pacientes para a realização do exame de análise da rigidez arterial.
Ao nosso ver, a avaliação do risco de aumento na rigidez arterial mesmo em indivíduos não hipertensos, representa uma enorme janela de oportunidade para identificar precocemente lesões subclínicas e possibilitar o estabelecimento de estratégias não farmacológicas e/ou farmacológicas com o objetivo de otimizar a prevenção e proteção cardiovascular.
Na investigação do papel dos biomarcadores na prevenção primária, a avaliação da rigidez arterial foi recomendada também para pacientes com diabetes, dislipidemias e com doença renal crônica, reforçando a influência desses outros fatores de risco na VOP. 1 A taxa de glicemia e a TFG foram identificadas como preditores independente da VOP, 5 e no presente estudo elas apresentaram correlação com o SAGE. Ainda, a redução da complacência e/ou da distensibilidade dos vasos ocorre independentemente da pressão arterial na presença de outros fatores de risco, dentre eles, a diabetes mellitus, o envelhecimento cronológico, a síndrome metabólica, a obesidade, a doença arterial periférica e a doença renal em estágio final. 27 Além disso, apesar de a maioria dos estudos apresentarem a hipertensão como um dos principais fatores de risco para o aumento da rigidez arterial, o próprio aumento da rigidez prediz a ocorrência de hipertensão arterial e contribui para sua patogênese, reforçando a importância de analisar a VOP mesmo em indivíduos não hipertensos. 17, 18, 28 - 34 *Em um* estudo de seguimento de uma coorte de Framingham com 1048 participantes, acompanhados por quatro a 10 anos, a VOP, avaliada pelo método tonométrico carotídeo-femoral, foi identificada como preditora de hipertensão arterial, enquanto o aumento da pressão arterial não foi preditor da rigidez elevada. E cada aumento de um desvio-padrão na VOP carotídeo-femoral, aumenta em $30\%$ o risco de desenvolver a hipertensão arterial. 27 Uma das limitações do estudo é que o escore SAGE foi desenvolvido para indivíduos hipertensos e, por isso, não há pontuação diferenciada para normotensos e pré-hipertensos. Por outro lado, cria-se aqui a oportunidade do desenvolvimento de escores específicos para essa população, por exemplo com atribuição de uma pontuação também para os indivíduos pré-hipertensos, visto que eles já apresentam um risco aumentado para as doenças cardiovasculares. 35 - 37 Outra limitação refere-se à idade dos participantes em nossa amostra, principal fator correlacionado à chance de VOP aumentada nesse modelo. Portanto, seria importante a realização de novos estudos incluindo amostras maiores e com mais indivíduos em cada faixa etária para verificar se, em indivíduos não hipertensos, seria mais recomendado medir a VOP apenas pelo critério da idade (≥ 70 anos) em vez do cálculo do escore.
## Conclusões
Na amostra estudada, a aplicação do escore SAGE foi capaz de identificar indivíduos com maior chance de apresentar rigidez arterial para diferentes pontos de corte de VOP. Entretanto, o desenvolvimento de um escore específico para a população de normotensos ou pré-hipertensos se faz necessário, e pode contribuir de maneira significativa na incorporação da análise de risco de envelhecimento vascular nessa população.
## Abstract
### Background
The SAGE score was developed to detect individuals at risk for increased pulse wave velocity (PWV). So far, studies have been focused on hypertensive patients.
### Objective
To assess the ability of the score to detect non-hypertensive and pre-hypertensive patients at risk for increased PWV.
### Methods
Retrospective cross-sectional study of analysis of central blood pressure data and calculation of the SAGE score of non-hypertensive and pre-hypertensive patients. Each score point was analyzed for sensitivity, specificity, positive and negative predictive values, using the cut-off point for positive diagnosis a PVW ≥ 10m/s, ≥9.08 m/s (75thpercentile) and ≥7.30 m/s (50thpercentile). A $p \leq 0.05$ was considered statistically significant.
### Results
The sample was composed of 100 normotensive and pre-hypertensive individuals, with mean age of 52.64 ± 14.94 years and median PWV of 7.30 m/s (6.03 – 9.08). The SAGE score was correlated with age ($r = 0.938$, $p \leq 0.001$), glycemia ($r = 0.366$, $p \leq 0.001$) and glomerular filtration rate (r=-0.658, $p \leq 0.001$). The area under the ROC curve was 0.968 ($p \leq 0.001$) for PWV ≥ 10 m/s, 0.977 ($p \leq 0.001$) for PWV ≥ 9.08 m/s and 0.967 ($p \leq 0.001$) for PWV ≥ 7.30 m/s. The score 7 showed a specificity of $95.40\%$ and sensitivity of $100\%$ for PWV≥10 m/s. The cut-off point would be of five for a PWV≥9.08 m/s (sensitivity =$96.00\%$, specificity = $94.70\%$), and two for a PWV ≥ 7.30 m/s.
### Conclusion
The SAGE score could identify individuals at higher risk of arterial stiffness, using different PWV cutoff points. However, the development of a specific score for normotensive and pre-hypertensive subjects is needed.
## Introduction
Pulse wave velocity (PWV) is a well-established biomarker in cardiovascular risk stratification and identification of subclinical lesions, and it can also be an indicator of target-organ lesion when higher than 10m/s. 1 - 4 However, PWV is still underutilized in clinical practice due to its high cost and low availability of the equipment. 5 The SAGE score was developed to spread knowledge and the concept about the assessment of vascular aging and damage, based on four simple parameters – age, systolic blood pressure (SBP), fasting glycemia and glomerular filtration rate (GFR) – to calculate the probability of an individual developing increased arterial stiffness. Based on the values obtained, patients can be more assertively referred to central blood pressure (CBP) measurements and analysis of PWV. 5 In the study in which the score was developed, the SAGE cut-off was calculated using the carotid-femoral PWV obtained from a hypertensive population. 5 Subsequently, the score was calculated using the brachial-ankle PWV in Japanese subjects with hypertension, 6 and more recently, it was calculated in Brazilian hypertensive individuals using the oscillometric method, which is a more commonly used method in Brazil. 7 In 2021, a study 8 with 760 Chinese individuals developed a new clinical score using age, peripheral systolic blood pressure (pSBP), peripheral diastolic blood pressure (pDBP), weight and height, also aiming at identifying individuals with increased arterial stiffness. However, the study was conducted specifically on diabetic patients, and using brachial-ankle PWV measurements only.
There is still a gap in the literature regarding the use of these scores to identify increased arterial stiffness in non-hypertensive individuals that may already have increased PWV and increased risk of cardiovascular outcomes, and studies involving the oscillometric method, which is a low-cost, easy-to-use method.
Therefore, the aim of this study was to assess the ability of the SAGE score to identify individuals at high risk for increased PWV in a sample of normotensive and pre-hypertensive Brazilian individuals, as a proof of concept.
## Study design and place
This was a cross sectional study in which medical records of patients attending two referral centers for diagnosis and treatment of hypertension in Brazil were analyzed.
## Population and sample
From September 2012 to November 2019, a total of 1594 measurements of CBP were made by the oscillometric method. Of these, we excluded: Then, the sample was composed of 100 normotensive or pre-hypertensive individuals, who had all data required for calculation of the SAGE score available (Figure 1).
Figure 1– Flowchart of patient selection. Source: Author, 2022.
## Study procedure
Electronic files of CBP measurements performed between September 2012 and November 2019 were identified. Then, medical files of these patients were analyzed for eligibility for the study.
Among the eligible patients, the following data were collected from the electronic files – date of birth, date of the CBP measurement, weight, height, peripheral and central SBP and DBP, peripheral and central pulse pressure, Augmentation Index (AIx) and PWV. For central and peripheral parameters, the mean of three measurements was considered for analysis.
In addition, the following data were collected: sex, smoking status, sedentary lifestyle, marital status, medications used, clinical diagnoses, and fasting glucose and creatinine levels obtained within three months before or after the CBP measurement. When glucose and creatinine levels were measured more than once within this period, those obtained on the closest date to the CBP measured were used for analysis.
Body mass index was calculated using the formula: weight(Kg)/[height(m)]2. 11
## Assessment of central blood pressure
Measurements of CBP were performed using the Mobil-O-Graph®(IEM, Stolber, Germany) and the Dyna MAPA AOP®(Cardios, São Paulo, Brazil). This evaluation is performed non-invasively; pSBP and DBP are measured using a sphygmomanometer and the ARCSolver algorithm is used to derive central pressure. 12 The assessment of PWV using the sphygmomanometer and the oscillometric method yields comparable values to those obtained invasively using the intra-aortic catheter, 12 in addition to being more reproducible than the devices used for assessment of carotid-femoral PWV. 13 The method has also been validated for assessment of central SBP compared with the assessment by the invasive method and the tonometric method. 14 An increase in arterial stiffness compatible with target organ damage was detected by PWV ≥ 10m/s. 9, 15
## Calculation of the SAGE score
The SAGE score is defined based on four variables – fasting glucose, pSBP, age, and estimated GFR (Figure 2).
Figure 2– SAGE score classification and its four variables; translated from Xaplanteris et al.5 For example, an individual with a SBP of 145 mmHg, glycemia of 110 mmHg, 65 years old and GFR of 69 mL/min/1.73m2will be assigned a SAGE score of eight and, therefore, as defined by the study in which the score was developed, will be referred for assessment of increased arterial stiffness due to higher risk of its occurrence. 5 The SAGE score was calculated for each participant. Measurements of pSBP were obtained from the CBP measurement; age was calculated by the difference between the date of birth and the date that of the CBP measurement. Glycemia was obtained from patient medical record and GFR was calculated using the CKD-EPI creatinine equation [2021], using serum creatinine levels obtained from the medical record.
## Statistical analysis
Data were collected by two investigators using a form developed with the Epidata software version 3.1. 16 The program was also used for validation of the form in terms of potential inconsistencies and errors.
Data analysis was made using the Statistical Package for Social Science (SPSS) version 23.0. Normality of data distribution was tested using the Kolmogorov-Smirnov test; descriptive analysis was performed using mean and standard deviation and using median and interquartile range for parametric and non-parametric data, respectively. Qualitative data were described as absolute and relative frequencies.
The correlation between SAGE score and the four variables of the score was assessed by the Spearman correlation.
Analysis of sensitivity, specificity, positive predictive value and negative predictive value was made for each SAGE rating. A positive diagnosis was defined as the presence of three PWV measurements ≥ 10m/s and ≥ 50thand 75thpercentiles, which correspond to 7.3 and 9.8 m/s, respectively. For each of these values, a ROC curve was constructed to define the best cut-off point for the SAGE score, i.e., the one with the highest sensitivity and specificity to detect patients at higher risk for increased PWV. A $p \leq 0.05$ was considered statistically significant.
## Ethical aspects
The study was conducted according to the resolution number $\frac{466}{12}$ and approved by the ethics committee of the General Hospital of the Federal University of Goias (approval and amendment number 1.500.463 and 3.792.750, including approval of the waiver of the consent form).
## Results
Data of 100 participants were analyzed, with mean age of 52.64 ± 14.94 years. Most patients were male, and had dyslipidemia, with optmial blood pressure and PWV lower than 8m/s (Table 1).
Table 1– Clinical and sociodemographic characteristics of participants ($$n = 100$$)Variablen/%SexFemale45Male55Marital statusLiving without a partner29Living with a partner57Not reported14Age< 50 years4250 - 59 years2460 - 69 years18≥70 years16Risk factorsCurrent smoking5 / $5.3\%$*BMI > 30Kg/m224Diabetes mellitus11 / $11.7\%$*Dyslipidemia53 / $65.4\%$*Total cholesterol< 150 mg/dl29150 - 199 mg/dl41200 - 249 mg/dl18250 - 299 mg/dl4≥ 300 mg/dl1Not reported7LDL≤50 mg/dl851–69 mg/dl1070–99 mg/dl26100–129 mg/dl31≥130 mg/dl16Not reported9Triglycerides<150 mg/dl63≥150 mg/dl26Not reported11Glycemia< 126 mg/dl94≥ 126 mg/ dl6Glomerular filtration rate30 - 59560 - 8948≥ 9047Classification of blood pressureOptimal BP46Normal BP34Pre-hypertension20Arterial stiffnessPWV < 8 m/s59PWV 8 - 10 m/s29PWV > 10 m/s12Central pressure parametersMédia (DP) / Mediana (25 – 75)pSBP (mmHg)119.43 (9.59)pDBP (mmHg)75.50 (67.00 – 79.75)pPP (mmHg)45.00 (39.00 – 52.00)cSBP (mmHg)109.15 (9.38)cDBP (mmHg)77.00 (67.25 – 81.00)cPP (mmHg)32.00 (29.00 – 39.00)AI (%)18.87 (11.30)PWV (m/s)7.30 (6.03 – 9.08) **These data* were not available from some of the patients and the frequency was then different from the number; AI (%): augmentation index; HDL: high-density lipoprotein; BMI: body mass index; LDL: low-density lipoprotein; cDBP: central diastolic blood pressure; pDBP: peripheral diastolic blood pressure; cSBP: central systolic blood pressure; pSBP: peripheral systolic blood pressure; cPP: central pulse pressure; pPP: peripheral pulse pressure; PWV: pulse wave velocity The most frequent SAGE score in the sample was 0, followed by 1 and 2 (Figure 3). Patients’ characteristics that could justify the scores were analyzed; of 13 participants, 12 were aged 70 years or older and had fasting glucose < 126 mg/dL and GFR of 60 - 89 mL/min/1.73m2. The other patient was aged between 60 and 69, had fasting glucose ≥ 126mg/dL and the same GFR.
Figure 3– Relative frequency of the SAGE scores of the study patients ($$n = 100$$).
Among patients with arterial stiffness (PWV ≥ 10 m/s), the most frequent score was seven (Figure 4). All patients with PWV ≥ 10 m/s ($$n = 13$$, $100\%$) were aged 70 years or older, and had fasting glucose < 126 mg/dL; 10 ($76.9\%$) had GFR between 60 and 89 mL/min/1.73m2, and three ($23\%$) between 30 e 59 ml/min/1,73m2. Eleven (84,$6\%$) had dyslipidemia.
Figure 4– Absolute and relative frequency of SAGE score ratings of the study patients; (A) patients with pulse wave velocity (PWV) ≥ 10 m/s, (B) patients with PWV ≥ 9.08 m/s and (C) patients with PWV ≥ 7.30 m/s.
Analysis of the 75thpercentile (9.08 m/s) and the 50thpercentile of the PWV (7.3 m/s), the most frequent SAGE score was also seven. Among patients with PWV ≥ 9.08 m/s, $88\%$ had fasting glucose < 126 mg/dL, $64\%$ were aged 70 years or older (and the others between 60 and 69 years), and $80\%$ had GFR between 60 and 89 mL/min/1.73m2.
Distribution of SAGE parameters by age, pSBP, fasting glucose, and GFR (according to the CKD-EPI group) (Figure 5) showed a positive correlation with age and glucose levels, and a negative correlation with GFR. No correlation was found between SAGE and pSBP.
Figure 5– Distribution of SAGE score by age (A) peripheral systolic blood pressure (B), glycemia (C) and glomerular filtration rate (D).
In the analysis of the ROC curve, the area under the curve for PWV ≥ 10 m/s was 0.968 ($p \leq 0.001$), for PWV ≥ 9.08 m/s was 0.977 ($p \leq 0.001$) and for PWV ≥ 7.30 m/s was 0.967 ($p \leq 0.001$) (Figure 6).
Figure 6– ROC curve of the SAGE score for pulse wave velocity (PWV) ≥ 10 m/s (A), PWV ≥ 9.08 m/s (B) and PWV ≥ 7.30 m/s.
According to the sensitivity and specificity analysis (Table 2), for individuals with arterial stiffness (PWV ≥ 10 m/s), a SAGE score of seven showed high specificity ($95.40\%$) associated with a sensitivity of $100\%$ and a negative predictive value of $100\%$. Considering the percentile 75th(PWV ≥ 9.08 m/s), the cut-off point for SAGE would be ≥ 5, with a sensitivity of $96\%$ and specificity of $94.7\%$. For the median PWV (≥ 7.30 m/s), the cut-off point would be lower (SAGE score of two).
Table 2– Sensitivity and specificity of the SAGE score by cutoff point and pulse wave velocity value SAGESensitivitySpecificityPPV/ Correctly classifiedNPVPWV ≥ 10 m/s$0100.00\%$$0.00\%$$13.00\%$-$1100.00\%$$27.59\%$$17.11\%$$100.00\%$$2100.00\%$$47.13\%$$22.03\%$$100.00\%$$3100.00\%$$63.22\%$$28.89\%$$100.00\%$$4100.00\%$$73.56\%$$36.11\%$$100.00\%$$5100.00\%$$82.76\%$$46.43\%$$100.00\%$$6100.00\%$$91.95\%$$65.00\%$$100.00\%$$7100.00\%$$95.40\%$$76.47\%$$100.00\%$$823.08\%$$98.85\%$$75.00\%$$89.60\%$$90.00\%$$98.85\%$$0.00\%$$86.90\%$PWV ≥ 9.08 m/s$0100.00\%$$0.00\%$$25.00\%$-$1100.00\%$$32.00\%$$32.89\%$$100.00\%$$2100.00\%$$54.70\%$$42.40\%$$100.00\%$$3100.00\%$$73.30\%$$55.60\%$$100.00\%$$4100.00\%$$85.30\%$$69.40\%$$100.00\%$$596.00\%$$94.70\%$$85.70\%$$98.60\%$$672.00\%$$97.30\%$$90.00\%$$91.30\%$$768.00\%$$100.00\%$$100.00\%$$90.40\%$$816.00\%$$100.00\%$$100.00\%$$78.10\%$$94.00\%$$100.00\%$$100.00\%$$75.80\%$PWV ≥ 7.30 m/s$0100.00\%$$0.00\%$$51.00\%$-$1100.00\%$$48.98\%$$67.11\%$$100.00\%$$298.00\%$$81.60\%$$84.70\%$$97.60\%$$382.40\%$$93.90\%$$93.30\%$$83.60\%$$468.60\%$$98.00\%$$97.20\%$$75.00\%$$554.90\%$$100.00\%$$100.00\%$$68.10\%$$639.20\%$$100.00\%$$100.00\%$$61.30\%$$733.30\%$$100.00\%$$100.00\%$$59.00\%$$87.80\%$$100.00\%$$100.00\%$$51.00\%$$92.00\%$$100.00\%$$100.00\%$$49.50\%$ NPV: negative predictive value, PPV: positive predictive value; PWV: pulse wave velocity.
## Discussion
In the present study, we found that a SAGE score of zero was the most frequent in this sample of non-hypertensive patients. On the other hand, a SAGE score of seven was the most common among patients with PWV ≥ of 7,3 m/s, 9,08 m/s or 10 m/s. The SAGE score showed a moderate positive correlation with glycemia, a very strong positive correlation with age, and a strong negative correlation with GFR. No correlation was observed between SAGE and pSBP. Based on the analysis of sensitivity and specificity, the score seven was defined as arterial stiffness considering a PWV ≥ 10 m/s as the positive diagnosis. For PWV ≥ 9.08 m/s and ≥ 7.30 m/s, the cut-off points were 5 and 2, respectively.
In the present study, the fact that the patients were not hypertensive, which is one of the parameters of the SAGE score, did not lead to a lower cut-off point in the analysis including PWV values ≥ 10 m/s, like the study in which the score was developed. 5 The cut-off point in our study was similar to that established in the original study with European Caucasian hypertensive patients 5 and to the Brazilian study with 837 hypertensive patients 7 which established a cut-off of eight. In addition, it was equal to that reported in a Japanese study with 1,816 hypertensive individuals, 6 in which the cut-off was seven. This may be justified by the fact that, even though these patients do not have hypertension, which is a condition that already contributes to the SAGE score, all patients with PWV ≥ 10m/s were aged 70 years or older, which already contributes to six points to the score. The relationship between aging arterial stiffness is already well established in the literature 17, 18 since concomitantly with chronological aging, vascular aging occurs, culminating in increased arterial stiffness. 18 - 24 On the other hand, one may consider that the establishment of cutoffs for blood pressure levels lower than 140 mmHg and the inclusion of other parameters, like cholesterol, may optimize the applicability of this score in normotensive and pre-hypertensive populations.
In addition to the age factor, most individuals with PWV ≥ 10 m/s had dyslipidemia. Although this risk factor was included in the SAGE score, it also contributes to the development of arterial stiffness. Baseline triglyceride (TG) levels and the TG/high-density lipoprotein (HDL) ratio are independently associated with the persistent increase in PWV and the incidence of increased PWV in healthy men followed-up for 4.1 years. 25 We also analyzed the 75thand the 50thpercentiles, since PWV ≥ 10m/s can already be an indicator of target-organ damage. 9, 15 Also, in this population of non-hypertensive individuals, PWV values lower than 10m/s already represents an increase in arterial stiffness and consequently in cardiovascular risk. 26 Twenty-five and 51 participants were found to have PWV above the 75thand 50thpercentiles, respectively.
Another aspect to be considered is the application of the SAGE score with a lower cut-off point (e.g. five) as a strategy to detect PWV values above the 75thpercentile, which would result in the identification of one in every four non-hypertensive individuals. The use of a score of two, defined for the 50thpercentile, would not be feasible, as almost all patients would have to be referred to assessment of arterial stiffness.
In our opinion, the risk assessment of increased arterial stiffness even in non-hypertensive individuals represents a great window of opportunity to identify early subclinical lesions and to establish non-pharmacological and pharmacological strategies aiming at optimizing cardiovascular protection and prevention.
In the investigation of the role of biomarkers in primary prevention, the assessment of arterial stiffness was also recommended for patients with diabetes, dyslipidemias and chronic renal disease, reinforcing the influence of these risk factors on PWV. 1 *Both glycemia* and GFR were identified as independent predictors of PWV and, 5 in this present study, they were correlated with SAGE. Also, the reduction in arterial compliance and/or distensibility occurs independently of blood pressure in the presence of other risk factors, including diabetes mellitus, chronological aging, metabolic syndrome, obesity, peripheral artery disease, and end-stage renal disease. 27 Besides, although most studies have reported that hypertension is one of the main risk factors for increased arterial stiffness, this increase, in turn, is a predictor of hypertension and contributes to its pathogenesis, reinforcing the importance of assessing the PWV even in non-hypertensive individuals. 17, 18, 28 - 34 *In a* follow-up study of the Framingham cohort with 1048 participants, followed-up for four to 10 years, carotid-femoral PWV, obtained by tonometry, was identified as a predictor of arterial hypertension, while the increase in blood pressure was not a predictor of increased arterial stiffness. A one standard deviation in carotid-femoral PWV increased by $30\%$ the risk of arterial hypertension. 27 One limitation of this study is that the SAGE score was developed for hypertensive subjects, and, for this reason, there are no differential ratings between normotensive and pre-hypertensive patients. On the other hand, an opportunity for the development of specific scores for this population is warranted, including the assignment of ratings to pre-hypertensive patients also, considering their increased risk for cardiovascular diseases. 35 - 37 Another limitation was the age of the study population, which is the main factor correlated to increased PWV in this model. Thus, further studies are needed including larger samples of individuals in different age ranges to determine whether it would be more appropriate to measure PWV of non-hypertensive patients using the criterion of age (≥70 years) rather than the score calculation.
## Conclusions
In the study sample, the SAGE score could identify patients at higher risk of arterial stiffness by different PWV cutoffs. However, the development of a specific score for non-hypertensive and pre-hypertensive patients is needed and could contribute significantly to the implementation of the analysis of the risk of vascular aging in this population.
## References
1. Vlachopoulos C, Xaplanteris P, Aboyans V, Brodmann M, Cífková R, Cosentino F. **The Role of Vascular Biomarkers For Primary And Secondary Prevention. A Position Paper From the European Society of Cardiology Working Group on Peripheral Circulation: Endorsed By the Association For Research Into Arterial Structure and Physiology (ARTERY) Society**. *Atherosclerosis* (2015) **241** 507-532. DOI: 10.1016/j.atherosclerosis.2015.05.007
2. Ben-Shlomo Y, Spears M, Boustred C, May M, Anderson SG, Benjamin EJ. **Aortic Pulse Wave Velocity Improves Cardiovascular Event Prediction: An Individual Participant Meta-Analysis Of Prospective Observational Data from 17,635 Subjects**. *J Am Coll Cardiol* (2014) **63** 636-646. DOI: 10.1016/j.jacc.2013.09.063
3. Díaz A, Galli C, Tringler M, Ramírez A, Cabrera Fischer EI. **Reference values of Pulse Wave Velocity in Healthy People From an Urban and Rural Argentinean Population**. *Int J Hypertens* (2014) **2014**. DOI: 10.1155/2014/653239
4. Mitchell GF. **Does Measurement of Central Blood Pressure Have Treatment Consequences in the Clinical Praxis?**. *Curr Hypertens Rep* (2015) **17**. DOI: 10.1007/s11906-015-0573-x
5. Xaplanteris P, Vlachopoulos C, Protogerou AD, Aznaouridis K, Terentes-Printzios D, Argyris AA. **A Clinical Score for Prediction of Elevated Aortic Stiffness: Derivation and Validation in 3943 Hypertensive Patients**. *J Hypertens* (2019) **37** 339-346. DOI: 10.1097/HJH.0000000000001904
6. Tomiyama H, Vlachopoulos C, Xaplanteris P, Nakano H, Shiina K, Ishizu T. **Usefulness of the SAGE Score To Predict Elevated Values of Brachial-Ankle Pulse Wave Velocity in Japanese Subjects with Hypertension**. *Hypertens Res* (2020) **43** 1284-1292. DOI: 10.1038/s41440-020-0472-7
7. Oliveira AC, Barroso WKS, Vitorino PVO, Sousa ALL, Fagundes RR, Deus GD. **A SAGE Score Cutoff That Predicts High-Pulse Wave Velocity as Measured by Oscillometric Devices in Brazilian Hypertensive Patients**. *Hypertens Res* (2022) **45** 315-323. DOI: 10.1038/s41440-021-00793-0
8. Li Q, Xie W, Li L, Wang L, You Q, Chen L. **Development and Validation of a Prediction Model for Elevated Arterial Stiffness in Chinese Patients with Diabetes Using Machine Learning**. *Front Physiol* (2021) **12**. DOI: 10.3389/fphys.2021.714195
9. Barroso WKS, Rodrigues CIS, Bortolotto LA, Mota-Gomes MA, Brandão AA, Feitosa ADM. **Brazilian Guidelines of Hypertension - 2020**. *Arq Bras Cardiol* (2021) **116** 516-558. DOI: 10.36660/abc.20201238.
10. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M. **2018 ESC/ESH Guidelines for the Management Of Arterial Hypertension**. *Eur Heart J* (2018) **39** 3021-3104. DOI: 10.1093/eurheartj/ehy339
11. Quetelet A. *Antropométrie ou Mesure des Différentes Facultés de L’homme* (1870)
12. Hametner B, Wassertheurer S, Kropf J, Mayer C, Eber B, Weber T. **Oscillometric Estimation of Aortic Pulse Wave Velocity: Comparison with Intra-Aortic Catheter Measurements**. *Blood Press Monit* (2013) **18** 173-176. DOI: 10.1097/MBP.0b013e3283614168
13. Grillo A, Parati G, Rovina M, Moretti F, Salvi L, Gao L. **Short-Term Repeatability of Noninvasive Aortic Pulse Wave Velocity Assessment: Comparison between Methods and Devices**. *Am J Hypertens* (2017) **31** 80-88. DOI: 10.1093/ajh/hpx140
14. Weber T, Wassertheurer S, Rammer M, Maurer E, Hametner B, Mayer CC. **Validation of A Brachial Cuff-Based Method for Estimating Central Systolic Blood Pressure**. *Hypertension* (2011) **58** 825-832. DOI: 10.1161/HYPERTENSIONAHA.111.176313
15. Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Böhm M. **2013 ESH/ESC Guidelines For The Management of Arterial Hypertension: The Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC)**. *Eur Heart J* (2013) **34** 2159-2219. DOI: 10.1093/eurheartj/eht151
16. Lauritsen JM, Bruus M. *EpiData Help File v3.1. EpiData Assoc Odense Denmark* (2004)
17. Zhang Y, Lacolley P, Protogerou AD, Safar ME. **Arterial Stiffness in Hypertension and Function of Large Arteries**. *Am J Hypertens* (2020) **33** 291-296. DOI: 10.1093/ajh/hpz193
18. Barroso WKS, Barbosa ECD, Mota-Gomes MA. *Rigidez Arterial e Hemodinâmica Central: do Endotélio à Camada Média* (2020)
19. D’Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM. **General Cardiovascular Risk Profile for use in Primary Care: The Framingham Heart Study**. *Circulation* (2008) **117** 743-753. DOI: 10.1161/CIRCULATIONAHA.107.699579
20. Terentes-Printzios D, Vlachopoulos C, Xaplanteris P, Loakeimidis N, Aznaouridis K, Baou K. **Cardiovascular Risk Factors Accelerate Progression of Vascular Aging in the General Population: Results from the CRAVE Study (Cardiovascular Risk Factors Affecting Vascular Age)**. *Hypertension* (2017) **70** 1057-1064. DOI: 10.1161/HYPERTENSIONAHA.117.09633
21. Costantino S, Paneni F, Cosentino F. **Ageing, Metabolism and Cardiovascular Disease**. *J Physiol* (2016) **594** 2061-2073. DOI: 10.1113/JP270538
22. Nilsson PM, Lurbe E, Laurent S. **The Early Life Origins of Vascular Ageing and Cardiovascular Risk: The EVA Syndrome**. *J Hypertens* (2008) **26** 1049-1057. DOI: 10.1097/HJH.0b013e3282f82c3e
23. Nilsson PM, Boutouyrie P, Cunha P, Kotsis V, Narkiewicz K, Parati G. **Early Vascular Ageing in Translation: From Laboratory Investigations to Clinical Applications in Cardiovascular Prevention**. *J Hypertens* (2013) **31** 1517-1526. DOI: 10.1097/HJH.0b013e328361e4bd
24. Cunha PG, Boutouyrie P, Nilsson PM, Laurent S. **Early Vascular Ageing (EVA): Definitions and Clinical Applicability**. *Curr Hypertens Rev* (2017) **13** 8-15. DOI: 10.2174/1573402113666170413094319
25. Sang Y, Cao M, Wu X, Ruan L, Zhang C. **Use of Lipid Parameters to Identify Apparently Healthy Men At High Risk Of Arterial Stiffness Progression**. *BMC Cardiovasc Disord* (2021) **21**. DOI: 10.1186/s12872-020-01846-x
26. Vlachopoulos C, Aznaouridis K, Stefanadis C. **Prediction of Cardiovascular Events And All-Cause Mortality with Arterial Stiffness: A Systematic Review and Meta-Analysis**. *J Am Coll Cardiol* (2010) **55** 1318-1327. DOI: 10.1016/j.jacc.2009.10.061
27. Safar ME, Levy BI. **Studies On Arterial Stiffness and Wave Reflections in Hypertension**. *Am J Hypertens* (2015) **28** 1-6. DOI: 10.1093/ajh/hpu155
28. Dernellis J, Panaretou M. **Aortic Stiffness is an Independent Predictor of Progression to Hypertension in Nonhypertensive Subjects**. *Hypertension* (2005) **45** 426-431. DOI: 10.1161/01.HYP.0000157818.58878.93
29. Kaess BM, Rong J, Larson MG, Hamburg NM, Vita JA, Levy D. **Aortic Stiffness, Blood Pressure Progression, and Incident Hypertension**. *JAMA* (2012) **308** 875-881. DOI: 10.1001/2012
30. Koivistoinen T, Lyytikäinen LP, Aatola H, Luukkaala T, Juonala M, Viikari J. **Pulse Wave Velocity Predicts the Progression of Blood Pressure and Development of Hypertension in Young Adults**. *Hypertension* (2018) **71** 451-456. DOI: 10.1161/HYPERTENSIONAHA.117.10368
31. Mitchell GF. **Arterial Stiffness and Hypertension**. *Hypertension* (2014) **64** 13-18. DOI: 10.1161/HYPERTENSIONAHA.114.00921
32. Oh YS. **Arterial Stiffness and Hypertension**. *Clin Hypertens* (2018) **24**. DOI: 10.1186/s40885-018-0102-8
33. Sun Z. **Aging, Arterial Stiffness, and Hypertension**. *Hypertension* (2015) **65** 252-256. DOI: 10.1161/HYPERTENSIONAHA.114.03617
34. Weisbrod RM, Shiang T, Al Sayah L, Fry JL, Bajpai S, Reinhart-King CA. **Arterial Stiffening Precedes Systolic Hypertension in Diet-Induced Obesity**. *Hypertension* (2013) **62** 1105-1110. DOI: 10.1161/HYPERTENSIONAHA.113.01744
35. x guo, x zhang, l guo, z li, l zheng, s yu. **Association between Pre-Hypertension and Cardiovascular Outcomes: A Systematic Review and Meta-Analysis of Prospective Studies**. *Curr Hypertens Rep* (2013) **15** 703-716. DOI: 10.1007/s11906-013-0403-y
36. Huang Y, Wang S, Cai X, Mai W, Hu Y, Tang H. **Prehypertension and Incidence of Cardiovascular Disease: A Meta-Analysis**. *BMC Med* (2013) **11**. DOI: 10.1186/1741-7015-11-177
37. Huang Y, Su L, Cai X, Mai W, Wang S, Hu Y. **Association of All-Cause and Cardiovascular Mortality with Prehypertension: A Meta-Analysis**. *Am Heart J* (2014) **167** 160-8.e1. DOI: 10.1016/j.ahj.2013.10.023
|
---
title: Eficácia da Hidratação Oral na Prevenção da Nefropatia Induzida por Contraste
em Indivíduos Submetidos a Intervenções Coronárias Eletivas
authors:
- Mariana Rodrigues Pioli
- Renata Muller Couto
- José de Arimatéia Francisco
- Diego Quilles Antoniassi
- Célia Regina de Souza
- Matheus Ynada de Olivio
- Gabriel Forato Anhê
- Silvio Giopatto
- Andrei C. Sposito
- Wilson Nadruz
- Otavio Rizzi Coelho-Filho
- Rodrigo Modolo
journal: Arquivos Brasileiros de Cardiologia
year: 2023
pmcid: PMC9972663
doi: 10.36660/abc.20220529
license: CC BY 4.0
---
# Eficácia da Hidratação Oral na Prevenção da Nefropatia Induzida por Contraste em Indivíduos Submetidos a Intervenções Coronárias Eletivas
## Resumo
### Fundamento
A nefropatia induzida por contraste (NIC) é definida como deterioração da função renal, representada por um aumento da creatinina sérica ≥$25\%$ ou ≥0,5 mg/dL até 72 horas após a exposição ao meio de contraste iodado (MCI). A medida preventiva mais eficaz até o momento é a hidratação venosa (HV). Pouco se sabe sobre a eficácia da hidratação oral (HO) ambulatorial.
### Objetivo
Investigar se a HO ambulatorial com água é tão eficaz quanto a HV com solução salina a 0,$9\%$ na prevenção de NIC em procedimentos coronarianos eletivos.
### Métodos
Neste estudo observacional retrospectivo, foram analisados prontuários médicos e dados laboratoriais para coletar dados de indivíduos submetidos a procedimentos coronarianos percutâneos com MCI. Os dados coletados entre 2012 e 2015 avaliaram indivíduos que foram submetidos à HV e entre 2016 e 2020 (após a implementação de um protocolo de HO), os indivíduos que foram submetidos à HO em casa antes e depois de procedimentos coronarianos, conforme orientação da equipe de enfermagem. A significância estatística adotada foi de α=0,05.
### Resultados
No total, 116 pacientes foram incluídos neste estudo, 58 no grupo HV e 58 no grupo HO. Observou-se incidência de NIC de $15\%$ ($\frac{9}{58}$) no grupo que recebeu HV e $12\%$ ($\frac{7}{58}$) no grupo que recebeu HO ($$p \leq 0$$,68).
### Conclusão
O protocolo de HO realizado pelo paciente parece ser tão eficaz quanto o protocolo de HV hospitalar na proteção renal de indivíduos suscetíveis a desenvolver NIC em intervenções coronarianas eletivas. Essas descobertas devem ser testadas em ensaios mais abrangentes.
## Introdução
A nefropatia induzida por contraste (NIC) foi descrita em 1954 por Bartels et al. 1 e definida por Mehran et al. 2 como um aumento na creatinina sérica ≥$25\%$ ou ≥0,5mg/dL até 72 horas após a exposição a meio de contraste iodado (MCI). É considerada uma iatrogênese altamente incidente em intervenções coronarianas, atingindo até $2\%$ da população exposta ao MCI 3, 4 ou até $50\%$ em populações de alto risco. 5, 6 Além disso, está fortemente associada a desfechos clínicos desfavoráveis, como morbidade e mortalidade a longo prazo. 7, 8 O desenvolvimento da NIC está associado tanto a características do MCI 9, 10 como à condição clínica do paciente, já que indivíduos com insuficiência renal preexistente, diabetes e idosos são mais propensos a esse desfecho. 11, 12 Medidas profiláticas foram relatadas como eficazes para reduzir a incidência de NIC, a saber: a identificação de fatores de risco nos pacientes, o uso do menor volume possível de MCI e a proteção renal antes e depois do procedimento por meio de hidratação venosa (HV) com solução salina a 0,$9\%$. 13, 14 Apesar de ser seguro e recomendado por várias diretrizes, 15 - 18 a HV tem alguns aspectos que às vezes limitam sua aplicação, como o aumento do tempo de internação, gerando altos custos para o hospital e desconforto para os pacientes. Portanto, uma estratégia alternativa como a hidratação oral (HO) poderia ser uma opção importante já que, além de causar a expansão adequada do volume, é fácil de ser realizada antes e depois do procedimento, econômica e confortável para o paciente.
Estudos anteriores mostraram que a HO pode reduzir a incidência de NIC, 19 mas outros resultados mostraram que a HV é superior. 20 *Em vista* desses resultados conflitantes, pretendemos investigar se a HO com água, antes e depois da administração de MCI, é tão eficaz quanto a HV com solução salina a 0,$9\%$, na proteção renal de indivíduos suscetíveis a desenvolver NIC em procedimentos coronarianos eletivos para cateterismo cardíaco e intervenções coronarianas.
## Métodos
Este estudo foi aprovado pelo Comitê de Ética em *Pesquisa da* Faculdade de Ciências Médicas da UNICAMP (#4.124.863 e CAAE: 33427720.2.0000.5404). Por tratar-se de um estudo observacional retrospectivo, os participantes da pesquisa foram dispensados do consentimento informado, aprovado pelo comitê de ética.
## População de pacientes e definição de NIC
Selecionaram-se 116 pacientes consecutivos que foram submetidos a procedimentos eletivos de cateterização cardíaca e/ou intervenção coronariana percutânea (ICP), entre janeiro de 2012 e janeiro de 2020, com alto risco de desenvolver NIC (critérios descritos abaixo). Este é um estudo de centro único do Laboratório de Cateterização Cardíaca do Hospital das Clínicas da Universidade Estadual de Campinas - UNICAMP.
Todos os pacientes foram submetidos à avaliação do histórico médico com a equipe de enfermagem para avaliar o risco de desenvolver NIC, que foi definida como um aumento da creatinina sérica ≥$25\%$ ou ≥0,5mg/dL até 72 horas após a exposição ao MCI. 2 Os pacientes com creatinina sérica >1,5mg/dL ou taxa de filtração glomerular estimada (TFGe) entre 40-60mL/min, foram automaticamente incluídos no “Protocolo de Prevenção de Nefropatia Induzida por Contraste”.
No caso de pacientes com níveis normais de creatinina sérica, outras características clínicas, que estão relacionadas à deterioração da função renal, também foram avaliadas, a fim de classificar o paciente como grupo de risco para desenvolver NIC. Os fatores considerados para avaliação de risco foram: idosos (>75 anos), comorbidades preexistentes como diabetes mellitus (DM), hipertensão e doença renal crônica (DRC), instabilidade hemodinâmica e também uso de drogas nefrotóxicas. 12, 21 Os critérios de exclusão foram os seguintes: pacientes em diálise; casos de urgência e emergência; indivíduos com insuficiência cardíaca congestiva em classe funcional III e IV; e sem informações nos prontuários médicos.
## Protocolo de estudo
Até 2015, o paciente que foi incluído no “Protocolo de Prevenção de NIC” deveria ser admitido para receber HV com solução salina a 0,$9\%$, a 1mL/kg/h, 24 horas antes, durante e 12 horas após o procedimento. Entre 48-72 horas após a exposição ao MCI, colheu-se amostra de sangue para análise dos níveis de creatinina sérica (método baseado na reação Jaffe), a fim de avaliar a função renal. Para superar as dificuldades do sistema de saúde pública (má disponibilidade de leitos hospitalares) para internar estes pacientes para um procedimento diagnóstico simples devido ao alto risco de NIC, os diretores do laboratório de cateterização desenvolveram, em 2016, um novo protocolo com HO ambulatorial, evitando assim a hospitalização no dia anterior.
Conforme esse novo protocolo, os pacientes eram aconselhados a beber 2 litros de água oralmente, em casa, 24 horas antes e 24 horas após a exposição ao MCI. Durante o tempo de espera e durante o procedimento, realizou-se HV com solução salina a 0,$9\%$, 1mL/kg/h, permanecendo na sala de recuperação após o procedimento por 6 horas, após as quais o paciente recebia alta. Entre 48-72 horas após o procedimento, o paciente retornava ao hospital para recolher amostra de sangue para avaliar o nível de creatinina sérica (método baseado na reação Jaffe). Se não fosse detectado qualquer NIC, o paciente recebia alta definitiva, caso contrário, o paciente era convocado a uma avaliação por cardiologistas e nefrologistas (Figura 1).
Figura 1– Desenho do estudo com protocolos para a prevenção de nefropatia induzida por contraste, por via venosa e oral. NIC: *Nefropatia induzida* por contraste; MCI: Meio de contraste iodado; HV: hidratação venosa; SS: Solução salina; HO: hidratação oral.
Em todos os pacientes, foi administrada uma MCI não iônico e de baixa osmolaridade ou isosmolar, utilizando o menor volume possível.
## Coleta de dados
Realizou-se a coleta de dados retrospectivamente através dos prontuários médicos físicos e eletrônicos do Serviço de Arquivo Médico e também através do Portal de Sistemas do Hospital das Clínicas da Universidade Estadual de Campinas - UNICAMP. Analisaram-se 5393 procedimentos de indivíduos submetidos a HV entre janeiro de 2012 e dezembro de 2015 e 6073 procedimentos de indivíduos submetidos a HO entre janeiro de 2016 e janeiro de 2020 (Figura 2).
Figura 2– Linha do tempo com a seleção dos participantes do estudo.
Coletaram-se os seguintes dados: idade; sexo; raça; peso; tabagismo; níveis de creatinina sérica antes e depois do procedimento; data dos procedimentos; histórico de doenças cardiovasculares como hipertensão, insuficiência cardíaca, infarto do miocárdio e acidente vascular cerebral anterior; histórico de doença renal; histórico de doenças metabólicas como DM e dislipidemia; tipo e volume (mL) de MCI aplicado; procedimento realizado (cateterismo cardíaco e/ou angioplastia coronária) e medicações em uso pelos pacientes. A depuração de creatinina foi calculada usando a equação de Cockcroft-Gault.
Todos os dados foram coletados e verificados por apenas 2 membros da equipe de pesquisa.
## Desfecho
O desfecho primário foi o desenvolvimento de NIC em pacientes submetidos a procedimentos eletivos de cateterismo cardíaco e/ou angioplastia coronária.
## Análise estatística
Para a análise do desfecho primário, os pacientes foram dicotomizados de acordo com a presença ou ausência de NIC e aplicou-se o teste de Fisher. Utilizou-se o teste de Mann-Whitney ou o teste t de Student não pareado para comparar dados clínicos e laboratoriais, como idade, creatinina sérica, TFGe e volume de MCI, de acordo com a distribuição dos dados, que foram verificados pelo teste de Shapiro-Wilk. Para todos os outros dados, realizou-se uma dicotomização e aplicou-se o teste de Fisher. Variáveis categóricas foram expressas como porcentagem (%) e número absoluto e variáveis contínuas como média e desvio padrão para dados normalmente distribuídos, ou mediana e intervalo interquartil para dados não distribuídos normalmente. Procedeu-se à análise de regressão logística multivariada para analisar os parâmetros de creatinina sérica basal, volume de MCI, terapia antiplaquetária dupla e heparinização, com o desenvolvimento da NIC como fator dependente. A significância estatística adotada foi α=0,05. Todas as análises estatísticas foram realizadas usando os programas GraphPad Prism, versão 6 para Windows (GraphPad Software, San Diego, CA, USA) e SigmaPlot, versão 12 (Systat Software Inc).
## Resultados
Neste estudo retrospectivo, analisaram-se 11 466 registros e foram coletados dados do período de janeiro de 2012 e janeiro de 2020. Foram selecionados 116 indivíduos que participaram do “Protocolo de Prevenção de NIC” que preencheram os critérios de inclusão e exclusão, onde 58 pacientes receberam HV e 58 pacientes receberam HO (Figura 2).
## Dados demográficos, clínicos e de medicamentos dos sujeitos do estudo
As características gerais basais dos 116 pacientes encontram-se na Tabela 1. Comparando os grupos, não houve diferenças de idade, sexo, hipertensão, raça, DM, dislipidemia, DRC, insuficiência cardíaca, acidente vascular cerebral e TFGe. Entretanto, constatou-se que aqueles que receberam HV apresentavam percentual maior de infarto do miocárdio prévio, níveis mais altos de creatinina sérica basal, administraram o maior volume de MCI e realizaram mais procedimentos para cateterismo cardíaco mais ICP. A tabela 2 mostra a terapia medicamentosa.
Tabela 1– Dados demográficos e clínicos da população estudadaCaracterísticasHidratação Venosa ($$n = 58$$)Hidratação oral ($$n = 58$$)Valor de pIdade (anos)67 ± 1069 ± 90,09Homens n (%)42 [72]34 [59]0,17Cor branca n (%)47 [81]51 [88]0,44Fumante n (%)27 [47]33 [57]0,35Hipertensão arterial n (%)55 [95]56 [97]1,00Diabetes mellitus n (%)34 [59]24 [41]0,09Dislipidemia n (%)45 [78]45 [78]1,00Doença renal crônica n (%)42 [72]31 [53]0,05Insuficiência cardíaca n (%)12 [21]18 [31]0,29Infarto do miocárdio anterior n (%)33 [57]20 [34]0,02Acidente vascular cerebral n (%)7 [12]8 [14]1,00Creatinina sérica basal (mg/dL)1,77 (1,29 – 2,16)1,44 (1,18 – 1,87)0,03TFGe basal (mL/min)39,89 (32,11 – 57,57)41,88 (35,40 – 49,43)0,57Volume de MCI (mL)100 (50 – 100)60 (50 – 100)<0,001AC n (%)41 [71]56 [97]<0,001ICP n (%)2 [3]0 [0]0,50AC + ICP n (%)15 [26]2 [3]0,001 A idade está representada como média ± DP. Os outros dados são expressos em n (%) ou mediana e intervalo interquartil. TFGe: taxa de filtração glomerular estimada; MCI: meio de contraste iodado AC: angiografia coronariana; ICP: intervenção coronariana percutânea.
Tabela 2– Medicamentos utilizados pela população estudadaMedicamentosHidratação Venosa ($$n = 58$$)Hidratação oral ($$n = 58$$)Valor de pDrogas anti-hipertensivasIECAs n (%)18 [31]22 [38]0,56BRAs n (%)19 [33]19 [33]1,00Diuréticos n (%)30 [53]37 [64]0,26BCCs n (%)19 [33]21 [36]0,84β-bloqueadores n (%)37 [64]45 [77]0,15Vasodilatadores n (%)13 [22]12 [21]1,00Simpaticomiméticos n (%)3 [5]5 [9]0,72HipoglicemiantesBiguanidas n (%)10 [17]7 [12]0,60Sulfonilureias n (%)3 [5]2 [3]1,00IDPP-4 n (%)0 [0]2 [3]0,49Insulinas n (%)22 [38]12 [21]0,06HipolipemiantesEstatinas n (%)40 [69]42 [72]0,84Fibratos n (%)3 [5]5 [9]0,72Ezetimiba n (%)0 [0]3 [5]0,24Outras classes farmacológicasAAS n (%)42 [72]41 [71]1,00Inibidores dos receptores P2Y12 n (%)30 [52]14 [24]0,004TAPD n (%)29 [50]11 [19]<0,001Anticoagulantes orais n (%)1 [2]0 [0]1,00Heparinização n (%)21 [36]0 [0]<0,001Anti-inflamatórios n (%)5 [9]3 [5]0,72 Os valores são expressos em número e percentual. IECAs: inibidores de enzimas conversoras de angiotensina; BRAs: bloqueadores de receptores de angiotensina; BCCs: bloqueadores de canais de cálcio; IDPP-4: inibidores da dipeptidil peptidase-4; AAS: ácido acetilsalicílico; TAPD: terapia antiplaquetária dupla (inibidores P2Y12 + AAS).
A análise multivariada mostrou que a creatinina sérica basal [RC 1,457; IC$95\%$ 0,75 - 2,82; $$p \leq 0$$,46], volume de contraste [RC 0,998; IC$95\%$ 0,99 - 1,01; $$p \leq 0$$,80], terapia antiplaquetária dupla [RC 1,678; IC$95\%$ 0,46 - 6,12; $$p \leq 0$$,43] e uso de heparina [RC 0,979; IC$95\%$ 0,19 - 5,10; $$p \leq 0$$,98] não interferiu no desenvolvimento de NIC na população estudada.
Apenas 2 pacientes do grupo HV receberam MCI não iônico isosmolar (iodixanol) e todos os outros pacientes do estudo receberam MCI não iônico de baixa osmolaridade (iobitridol, iopamidol, ioexol ou ioversol) (dados não revelados).
## Incidência de NIC
Observou-se NIC em $\frac{9}{58}$ pacientes ($15\%$) no grupo HV e em $\frac{7}{58}$ pacientes ($12\%$) no grupo HO ($$p \leq 0$$,68; Figura central). Observou-se aumento ≥0,5mg/dL da creatinina sérica em 6 pacientes ($66\%$) do grupo HV e 3 pacientes ($43\%$) do grupo HO e 4 pacientes em cada grupo ($44\%$ e $57\%$, respectivamente) apresentaram aumento ≥$25\%$ após a administração de MCI (dados não revelados).
Figura Central: Eficácia da Hidratação *Oral na* Prevenção da Nefropatia Induzida por Contraste em Indivíduos *Submetidos a* Intervenções Coronárias EletivasIncidência de nefropatia induzida por contraste em ambos os protocolos de estudo. Os dados são expressos em porcentagem. Teste de Fisher; $$p \leq 0$$,68.
## Discussão
Nosso principal resultado é que a HO pode auxiliar no processo de prevenção de NIC antes e depois de procedimentos percutâneos eletivos e pode ser tão eficaz quanto a infusão de solução salina intravenosa a 0,$9\%$. Deve-se enfatizar que todos os pacientes estavam em alto risco de desenvolver NIC, e o grupo selecionado é compatível com outros pacientes submetidos a procedimentos cardíacos eletivos na prática clínica atual.
Os laboratórios de cateterização seguem recomendações específicas a fim de reduzir a incidência de NIC, como o uso de MCI não iônico de baixa osmolaridade e no menor volume possível, 15 mas ainda assim os casos de NIC ainda ocorrem. Por isso, é essencial associar outras alternativas, como a hidratação. Atualmente, a HV é a mais indicada para a prevenção de NIC, 15 - 18 porém estudos mais recentes mostram resultados favoráveis à administração de fluidos oralmente em ICP. 22 - 28 Zhang et al. 19 conduziram uma metanálise e observaram que a HO foi tão eficaz quanto a HV em pacientes submetidos a angiografia coronariana ou intervenção para prevenção de NIC (5,$88\%$ vs. 8,$43\%$; RC: 0,73; IC$95\%$: 0,36-1,47; $p \leq 0$,05). Esses resultados tiveram um impacto tal que uma diretriz recente do Instituto Nacional de Excelência em Saúde e Cuidados (NICE) do Reino *Unido recomenda* a HO antes e depois de procedimentos com MCI. 29 É importante notar que todos esses estudos têm variação metodológica e diferenças nas populações estudadas, resultando em uma variação considerável de 1 a $50\%$ na incidência de NIC com a administração de HO em ICPs invasivos. 20, 23 - 27, 30 Além disso, nenhum estudo comparativo indicou o volume ideal de HO. Em nosso protocolo, o volume de ingestão de água foi padronizado em 2 litros antes e depois dos procedimentos, sem ajuste para peso ou condições clínicas dos pacientes, sendo a maior ingesta de líquidos entre todos os estudos anteriores. 23 - 26 Neste estudo, os pacientes que receberam HV apresentavam condição clínica mais grave, devido a maior incidência de infarto agudo do miocárdio prévio e níveis mais elevados de creatinina sérica basal quando comparados com os pacientes que receberam HO. Dependendo da complexidade do procedimento, é necessário utilizar diferentes volumes de MCI; assim pacientes graves, como possivelmente os casos do grupo de HV, necessitaram tanto de procedimentos de angiograma quanto de ICP, exigindo volume maior de MCI, causando assim mais NIC. Mesmo nesses pacientes, o volume de MCI administrado foi igual ou menor ao encontrado em outros estudos randomizados que comparavam os protocolos de HO e HV. 20, 22 - 25, 27 O perfil dos pacientes do grupo HV também refletiu o uso de medicamentos, com maior uso de inibidores P2Y12 (clopidogrel), terapia antiplaquetária dupla (ácido acetilsalicílico mais clopidogrel) e heparinização (enoxaparina ou heparina), porém isso não parece ter afetado a incidência de NIC.
Nosso trabalho apresenta várias limitações, inerentes a estudos observacionais. Primeiro, é um estudo observacional retrospectivo, o que impossibilitou a randomização dos pacientes, resultando na heterogeneidade dos grupos. Em segundo lugar, foi realizado em um único centro e com um tamanho de amostra relativamente pequeno, o que confere baixo poder estatístico. Também sugerimos que a extrapolação dos resultados não é recomendada para procedimentos radiológicos utilizando MCI intravenoso. Finalmente, procedimentos coronarianos invasivos podem levar a um processo de embolia de colesterol das artérias renais e, portanto, insuficiência renal aguda após alguns dias, tornando-se um fator de confusão para o MCI. 31 Essa complicação é pouco relatada e pode ocorrer tanto em pacientes que recebem HV como naqueles que recebem HO. Além disso, nosso estudo foi projetado para comparar a incidência de NIC entre estratégias de HO e HV. Portanto, não foi projetado para avaliar resultados a longo prazo, como mortalidade ou internação hospitalar prolongada.
Nossos resultados corroboram resultados anteriores que sugerem que a HO poderia ser usado na prática clínica, para potencialmente reduzir os custos hospitalares, melhorando o rodízio de leitos hospitalares e propiciar menos internação hospitalar para o paciente. Entretanto, são necessários ensaios clínicos aleatórios e multicêntricos mais cautelosos para confirmar essas descobertas.
## Conclusão
De acordo com os dados analisados, podemos sugerir que um protocolo de HO, em casa, pelo paciente, é tão eficaz quanto o de HV realizado em hospital, visando a proteção renal de indivíduos suscetíveis a desenvolver NIC em procedimentos eletivos de cateterismo cardíaco e angioplastia coronária.
## Abstract
### Background
Contrast-induced nephropathy (CIN) is defined as worsening renal function, represented by an increase in serum creatinine of ≥ $25\%$ or ≥ 0.5 mg/dL up to 72 h after exposure to iodinated contrast medium (ICM). The most effective preventive measure to date is intravenous hydration (IVH). Little is known about the effectiveness of outpatient oral hydration (OH).
### Objetive
To investigate whether outpatient OH with water is as effective as IVH with $0.9\%$ saline solution in preventing CIN in elective coronary procedures.
### Methods
In this retrospective observational study, we analyzed the medical records and laboratory data of individuals undergoing percutaneous coronary procedures with ICM. Data collected between 2012 and 2015 refer to individuals who underwent IVH and those collected between 2016 and 2020 (after implementation of an OH protocol) correspond to individuals who underwent OH at home before and after coronary procedures as instructed by the nursing team. Statistical significance was established at α = 0.05.
### Results
In total, 116 patients were included in this study: 58 in the IVH group and 58 in the OH group. An incidence of CIN of $15\%$ ($\frac{9}{58}$) was observed in the group that received IVH and an incidence of $12\%$ ($\frac{7}{58}$) was seen in the group that received OH ($$p \leq 0.68$$).
### Conclusion
The OH protocol, performed by the patient, appears to be as effective as the in-hospital IVH protocol for the renal protection of individuals susceptible to CIN in elective coronary interventions. These findings should be put to test in larger trials.
## Introduction
Contrast-induced nephropathy (CIN) was described in 1954 by Bartels et al. 1 and defined by Mehran et al. 2 as an increase in serum creatinine of ≥ $25\%$ or ≥ 0.5 mg/dL up to 72 h after exposure to iodinated contrast medium (ICM). It is considered a highly incident iatrogenesis in coronary interventions, reaching up to $2\%$ of the population exposed to ICM 3, 4 or up to $50\%$ of high-risk populations. 5, 6 Moreover, it is strongly associated with unfavorable clinical outcomes such as long-term morbidity and mortality. 7, 8 The development in CIN is associated with both ICM characteristics 9, 10 and the patient’s clinical condition, since individuals with preexisting renal failure, diabetes, and older adults are more prone to this outcome. 11, 12 Prophylactic measures have been reported to be effective in reducing the incidence of CIN, namely: identification of risk factors, use of the lowest possible volume of ICM, and renal protection before and after the procedure by means of intravenous hydration (IVH) with $0.9\%$ saline solution. 13, 14 Despite being safe and recommended by several guidelines, 15 - 18 IVH has characteristics that sometimes limit its application, such as increased hospitalization time, which generates high costs for the hospital and discomfort for the patients. An alternative strategy such as oral hydration (OH) could be a relevant option, since in addition to causing an adequate volume expansion, it is easily performed before and after the procedure, low-cost, and comfortable for the patient.
Previous studies have shown that OH may reduce the incidence of CIN, 19 but others showed superiority of IVH. 20 In view of these conflicting results, we aimed to investigate whether OH with water, pre- and post-ICM administration, is as effective as IVH with $0.9\%$ saline solution in the renal protection of individuals susceptible to CIN in elective procedures for cardiac catheterization and coronary interventions.
## Methods
This study was approved by the Research Ethics Committee of the School of Medical Sciences of UNICAMP (#4.124.863 and CAAE: 33427720 2 0000 5404). Since this is a retrospective observational study, there was no need for an informed consent form by the research participants, as approved by the ethics committee.
## Patient population and definition of CIN
This study selected 116 consecutive patients who underwent elective procedures of cardiac catheterization and/or percutaneous coronary intervention (PCI) between January 2012 and January 2020 and were at high risk for developing CIN (criteria described below). This is a single-center study from the Cardiac Catheterization Laboratory of Hospital de Clínicas da Universidade Estadual de Campinas – UNICAMP.
All patients underwent medical history evaluation with the nursing team for assessing the risk of developing CIN, which was defined as an increase in serum creatinine of ≥ $25\%$ or ≥ 0.5 mg/dL up to 72 h after exposure to ICM. 2 Patients with serum creatinine > 1.5 mg/dL or an estimated glomerular filtration rate (eGFR) between 40 and 60 mL/min were automatically included in the “CIN Prevention Protocol.” For those with normal serum creatinine levels, other clinical features (related to worsening renal function) were assessed to classify them as at risk for developing CIN. The factors considered for this risk assessment were: older adults (> 75 years old); preexisting comorbidities such as diabetes mellitus (DM), hypertension, and chronic kidney disease (CKD); hemodynamic instability; and use of nephrotoxic drugs. 12, 21 *Exclusion criteria* were as follows: dialysis patients, urgency and emergency cases, individuals with congestive heart failure (functional class III and IV), and those without information in their medical records.
## Study protocol
Until 2015, patients included in the “CIN Prevention Protocol” should be admitted to receive IVH with $0.9\%$ saline solution at 1 mL/kg/h 24 h before, during, and 12 h after the procedure. Between 48 and 72 h after exposure to ICM, a blood sample was collected for analyzing serum creatinine levels (method based on the Jaffe reaction) to assess renal function. In view of the difficulties of the public health system (poor availability of hospital beds) to hospitalize these patients for a simple diagnostic procedure due to the high risk of CIN, the directors of the catheterization laboratory developed a new protocol with outpatient OH in 2016, thus precluding hospitalization a day before the procedure.
In this new protocol, patients were advised to drink 2 liters of water at home, 24 h before and 24 h after exposure to ICM. During the waiting time and throughout the procedure, IVH with $0.9\%$ saline solution at 1 mL/kg/h was performed, continuing after the procedure (in the recovery room) for 6 h, after which the patient was discharged. Between 48 and 72 h after the procedure, the patient returned to the hospital for blood sample collection to assess serum creatinine levels (method based on the Jaffe reaction). If no CIN was detected, the patient was discharged definitively; otherwise, the patient was referred to an evaluation by cardiologists and nephrologists (Figure 1).
Figure 1– Study design with protocols for the prevention of contrast-induced nephropathy through intravenous and oral hydration. CIN: contrast-induced nephropathy; ICM: iodinated contrast medium; IV: intravenous; SS: saline solution.
The lowest possible volume of a non-ionic and low-osmolar or iso-osmolar ICM was administered to all patients.
## Data collection
Data collection was performed retrospectively from the physical and electronic medical records of the Medical Archive Service and also through the Systems Portal of Hospital de Clínicas da Universidade Estadual de Campinas – UNICAMP. Between January 2012 and December 2015, we analyzed 5,393 procedures in individuals who underwent IVH and, between January 2016 and January 2020, 6,073 procedures in individuals who underwent OH (Figure 2).
Figure 2– Timeline of study participant selection.
The following data were collected: age; sex; race; weight; smoking status; serum creatinine levels before and after the procedure; date of procedures; history of cardiovascular diseases such as hypertension, heart failure, previous myocardial infarction, and stroke; history of kidney disease; history of metabolic diseases such as DM and dyslipidemia; type and volume (mL) of ICM used; procedure performed (cardiac catheterization and/or coronary angioplasty); and medications used by the patients. Creatinine clearance was calculated using the Cockcroft-Gault equation.
All data were collected and checked by 2 members of the research team.
## Outcome
The primary outcome was the development of CIN in patients undergoing elective procedures of cardiac catheterization and/or coronary angioplasty.
## Statistical analysis
For analyzing the primary outcome, patients were dichotomized according to the presence or absence of CIN, and the Fisher’s test was applied. For comparing clinical and laboratory data such as age, serum creatinine, eGFR, and ICM volume, the Mann-Whitney test or the unpaired Student’s t-test was applied according to the data distribution, which was verified by the Shapiro-Wilk test. All other data were dichotomized and analyzed by the Fisher’s test. Categorical variables were expressed as percentages (%) and absolute numbers, whereas continuous variables were expressed as means and standard deviations for normally distributed data or median values and interquartile ranges for non-normally distributed data. A multivariate logistic regression analysis was performed to analyze baseline serum creatinine, ICM volume, dual antiplatelet therapy, and heparinization, with the development of CIN as a dependent factor. Statistical significance was established at α = 0.05. All statistical analyzes were performed using GraphPad Prism version 6 for Windows (GraphPad Software, San Diego, CA, USA) and SigmaPlot version 12 (Systat Software Inc., Chicago, IL, USA).
## Results
In this retrospective study, we analyzed 11,466 records and collected data from January 2012 to January 2020. One hundred and sixteen individuals who participated in the “CIN Prevention Protocol” were selected after applying the inclusion and exclusion criteria; 58 patients received IVH and 58 patients received OH (Figure 2).
## Demographic, clinical, and medication use data of the study participants
General baseline characteristics of the 116 patients are shown in Table 1. When comparing the IVH and OH groups, there were no differences in age, sex, race, hypertension, DM, dyslipidemia, CKD, heart failure, stroke, and eGFR. However, those who received IVH had a higher percentage of previous myocardial infarction, higher baseline levels of serum creatinine, were administered the largest volumes of ICM, and performed a greater number of procedures for cardiac catheterization plus PCI. Table 2 shows the medication therapies taken by the participants.
Table 1– Demographic and clinic data of the study populationCharacteristicsIV hydration ($$n = 58$$)Oral hydration ($$n = 58$$)p valueAge (years)67 ± 1069 ± 90.09Male n (%)42 [72]34 [59]0.17White race n (%)47 [81]51 [88]0.44Smoker n (%)27 [47]33 [57]0.35Arterial hypertension n (%)55 [95]56 [97]1.00Diabetes mellitus n (%)34 [59]24 [41]0.09Dyslipidemia n (%)45 [78]45 [78]1.00Chronic kidney disease n (%)42 [72]31 [53]0.05Heart failure n (%)12 [21]18 [31]0.29Previous myocardial infarction n (%)33 [57]20 [34]0.02Stroke n (%)7 [12]8 [14]1.00Serum creatinine, baseline (mg/dL)1.77 (1.29 – 2.16)1.44 (1.18 – 1.87)0.03eGFR baseline (mL/min)39.89 (32.11 – 57.57)41.88 (35.40 – 49.43)0.57ICM volume (mL)100 (50 – 100)60 (50 – 100)<0.001CA n (%)41 [71]56 [97]<0.001PCI n (%)2 [3]0 [0]0.50CA + PCI n (%)15 [26]2 [3]0.001 *Age is* presented as mean ± standard deviation (SD). All other data are expressed in n (%) or median values and interquartile ranges. IV: intravenous; eGFR: estimated glomerular filtration rate; ICM: iodinated contrast medium; CA: coronary angiogram; PCI: percutaneous coronary intervention.
Table 2– Medications used by the study populationCharacteristicsIV hydration ($$n = 58$$)Oral hydration ($$n = 58$$)p valueAntihypertensive drugsACEIs n (%)18 [31]22 [38]0.56ARBs n (%)19 [33]19 [33]1.00Diuretics n (%)30 [53]37 [64]0.26CCBs n (%)19 [33]21 [36]0.84β-blockers n (%)37 [64]45 [77]0.15Vasodilators n (%)13 [22]12 [21]1.00Sympathomimetics n (%)3 [5]5 [9]0.72Antidiabetic drugsBiguanide n (%)10 [17]7 [12]0.60Sulphonylureas n (%)3 [5]2 [3]1.00DPP-4 inhibitors n (%)0 [0]2 [3]0.49Insulin n (%)22 [38]12 [21]0.06Hypolipidemic drugsStatins n (%)40 [69]42 [72]0.84Fibrates n (%)3 [5]5 [9]0.72Ezetimibe n (%)0 [0]3 [5]0.24Other pharmacological classesASA n (%)42 [72]41 [71]1.00P2Y12 inhibitors n (%)30 [52]14 [24]0.004DAPT n (%)29 [50]11 [19]<0.001Oral anticoagulation n (%)1 [2]0 [0]1.00Heparinization n (%)21 [36]0 [0]<0.001bn Anti-inflammatory drugs n (%)5 [9]3 [5]0.72 Values are expressed in numbers and percentages. IV: intravenous; ACEIs: angiotensin-converting enzyme inhibitors; ARBs: angiotensin receptor blockers; CCBs: calcium channel blockers; DPP-4 inhibitors: dipeptidyl peptidase-4 inhibitors; ASA: acetylsalicylic acid; DAPT: dual antiplatelet therapy (P2Y12 inhibitors + ASA).
A multivariate analysis showed that baseline serum creatinine (odds ratio [OR] 1.457; $95\%$ confidence interval [CI] 0.75-2.82; $$p \leq 0.46$$), contrast volume (OR 0.998; $95\%$ CI 0.99-1.01; $$p \leq 0.80$$), dual antiplatelet therapy (OR 1.678; $95\%$ CI 0.46-6.12; $$p \leq 0.43$$), and use of heparin (OR 0.979; $95\%$ CI 0.19-5.10; $$p \leq 0.98$$) did not interfere with the development of CIN in the studied population.
Only two patients in the IVH group received non-ionic iso-osmolar ICM (iodixanol); all the other patients in this study received nonionic low-osmolar ICM (iobitridol, iopamidol, iohexol, or ioversol) (data not shown).
## Incidence of CIN
CIN was seen in $\frac{9}{58}$ patients ($15\%$) in the IVH group and in $\frac{7}{58}$ patients ($12\%$) in the OH group ($$p \leq 0.68$$; Central Figure). Six patients ($66\%$) in the IVH group and 3 patients ($43\%$) in the OH group had increases in serum creatinine levels ≥ 0.5mg/dL, and 4 patients from each group ($44\%$ and $57\%$, respectively) had increases in serum creatinine levels ≥ $25\%$ after ICM administration (data not shown).
## Discussion
Our main result is that OH before and after elective percutaneous procedures may assist in the process of preventing CIN and may be as effective as IV infusion with $0.9\%$ saline solution. It should be emphasized that all patients were at high risk for developing CIN, and the selected group is compatible with other patients undergoing elective cardiac procedures in current clinical practice.
Catheterization laboratories follow specific recommendations in order to reduce the incidence of CIN, such as the use of non-ionic, low-osmolar ICM and the lowest possible contrast volume, 15 but CIN events still manage to occur; it is thus essential to associate these measures with other alternatives such as hydration. Currently, IVH is the most indicated measure for preventing CIN, 15 - 18 although more recent studies show favorable results of the oral administration of fluids in patients undergoing PCI. 22 - 28 A meta-analysis by Zhang et al. 19 analyzed such studies and noticed that OH was as effective as IVH in patients undergoing coronary angiography or intervention for preventing CIN (5.88 vs $8.43\%$; OR 0.73; $95\%$ CI 0.36-1.47; $p \leq 0.05$). These results had such an impact that a recent guideline by the National Institute for Health and Care Excellence (NICE) encourages OH before and after procedures using ICM. 29 Of note, all these studies have methodological variations and differences between studied populations, resulting in considerable variation (ranging from 1 to $50\%$ in the incidence of CIN with OH in percutaneous invasive coronary procedures). 20, 23 - 27, 30 In addition, no comparative studies stated the ideal volume for OH. In our protocol, the volume of water ingestion was standardized at 2 liters before and after the procedures, without adjusting for the weight or clinical conditions of patients; this is the largest fluid intake among all studies to date. 23 - 26 *In this* study, patients who received IVH had more severe clinical conditions due to a higher incidence of previous acute myocardial infarction and higher baseline serum creatinine levels when compared to patients who received OH. Depending on the complexity of the procedure, different volumes of ICM are required; severely ill patients (as were potentially the cases in the IVH group) thus needed both angiogram and PCI procedures, requiring larger volumes of ICM that could lead to more CIN. Even in these patients, the volume of ICM administered was equal to or less than that found in other randomized studies comparing OH and IVH protocols. 20, 22 - 25, 27 The profile of patients in the IVH group also reflected their medication therapy—with greater use of P2Y12 inhibitors (clopidogrel), dual antiplatelet therapy (acetylsalicylic acid plus clopidogrel), and heparinization (enoxaparin or heparin)—; however, this does not seem to have impacted the incidence of CIN.
Our work has several limitations that are inherent to observational studies. First, it is a retrospective observational study, which made it impossible to randomize patients and resulted in heterogeneous groups. Second, it was carried out in a single center with a relatively small sample size, which gives it low statistical power. We also do not recommend extrapolating the results for radiological procedures using intravenous ICM. Finally, invasive coronary procedures can lead to a process of atheroembolism of the renal arteries and consequent acute renal failure after a few days, becoming a confounding factor for CIN. 31 This complication is underreported and can occur both in patients who receive IVH and those who receive OH. In addition, our study was designed to compare the incidence of CIN between oral and intravenous hydration strategies; therefore, it was not designed to evaluate long-term results such as mortality or prolonged hospital stay.
Our results corroborate previous findings that suggest that OH could be used in clinical practice to potentially reduce hospital costs (improving hospital bed rotation) and provide shorter in-hospital stays for the patients. However, more thorough randomized and multi-center clinical trials are needed to confirm these findings.
## Conclusion
According to the analyzed data, we can suggest that an OH protocol performed at home, by the patient, is as effective as hospital IVH for the renal protection of individuals susceptible to the development of CIN in elective procedures of cardiac catheterization and coronary angioplasty.
## References
1. Bartels ED, Brun GC, Gammeltoft A, Gjørup PA. **Acute anuria Following Intravenous Pyelography in a Patient with Myelomatosis**. *Acta Med Scand* (1954) **150** 297-302. DOI: 10.1111/j.0954-6820.1954.tb18632.x
2. Mehran R, Nikolsky E. **Contrast-Induced Nephropathy: Definition, Epidemiology, and Patients at Risk**. *Kidney Int Suppl* (2006) S11-S15. DOI: 10.1038/sj.ki.5000368
3. Rihal CS, Textor SC, Grill DE, Berger PB, Ting HH, Best PJ. **Incidence and Prognostic Importance of acute Renal Failure after Percutaneous Coronary Intervention**. *Circulation* (2002) **105** 2259-2264. DOI: 10.1161/01.cir.0000016043.87291.33
4. Iakovou I, Dangas G, Mehran R, Lansky AJ, Ashby DT, Fahy M. **Impact of Gender on the Incidence and Outcome of Contrast-Induced Nephropathy after Percutaneous Coronary Intervention**. *J Invasive Cardiol* (2003) **15** 18-22. PMID: 12499523
5. Aspelin P, Aubry P, Fransson SG, Strasser R, Willenbrock R, Berg KJ. **Nephrotoxic Effects in High-Risk Patients Undergoing Angiography**. *N Engl J Med* (2003) **348** 491-499. DOI: 10.1056/NEJMoa021833
6. Waybill MM, Waybill PN. **Contrast Media-Induced Nephrotoxicity: Identification of Patients at Risk and Algorithms for Prevention**. *J Vasc Interv Radiol* (2001) **12** 3-9. DOI: 10.1016/s1051-0443(07)61394-3
7. James MT, Samuel SM, Manning MA, Tonelli M, Ghali WA, Faris P. **Contrast-Induced Acute Kidney Injury and Risk of Adverse Clinical Outcomes after Coronary Angiography: A Systematic Review and Meta-Analysis**. *Circ Cardiovasc Interv* (2013) **6** 37-43. DOI: 10.1161/CIRCINTERVENTIONS.112.974493
8. Nash K, Hafeez A, Hou S. **Hospital-Acquired Renal Insufficiency**. *Am J Kidney Dis* (2002) **39** 930-936. DOI: 10.1053/ajkd.2002.32766
9. Rudnick MR, Goldfarb S, Wexler L, Ludbrook PA, Murphy MJ, Halpern EF. **Nephrotoxicity of Ionic and Nonionic Contrast Media in 1196 Patients: A Randomized Trial**. *Kidney Int* (1995) **47** 254-261. DOI: 10.1038/ki.1995.32
10. Li LP, Franklin T, Du H, Papadopoulou-Rosenzweig M, Carbray J, Solomon R. **Intrarenal Oxygenation by Blood Oxygenation Level-Dependent MRI in Contrast Nephropathy Model: Effect of the Viscosity and Dose**. *J Magn Reson Imaging* (2012) **36** 1162-1167. DOI: 10.1002/jmri.23747
11. McCullough PA, Adam A, Becker CR, Davidson C, Lameire N, Stacul F. **Risk Prediction of Contrast-Induced Nephropathy**. *Am J Cardiol* (2006) **98** 27K-36K. DOI: 10.1016/j.amjcard.2006.01.022
12. Kroll RG, Yelavarthy P, Menees DS, Sutton NR. **Predicting Contrast-Induced Renal Complications**. *Interv Cardiol Clin* (2020) **9** 321-333. DOI: 10.1016/j.iccl.2020.02.003
13. Zhang F, Lu Z, Wang F. **Advances in the Pathogenesis and Prevention of Contrast-Induced Nephropathy**. *Life Sci* (2020) **259**. DOI: 10.1016/j.lfs.2020.118379
14. Jurado-Román A, Hernández-Hernández F, García-Tejada J, Granda-Nistal C, Molina J, Velázquez M. **Role of Hydration in Contrast-Induced Nephropathy in Patients who Underwent Primary Percutaneous Coronary Intervention**. *Am J Cardiol* (2015) **115** 1174-1178. DOI: 10.1016/j.amjcard.2015.02.004
15. Khwaja A. **KDIGO Clinical Practice Guidelines for Acute Kidney Injury**. *Nephron Clin Pract* (2012) **120** c179-c184. DOI: 10.1159/000339789
16. Windecker S, Kolh P, Alfonso F, Collet JP, Cremer J, Falk V. **2014 ESC/EACTS Guidelines on Myocardial Revascularization: The Task Force on Myocardial Revascularization of the European Society of Cardiology (ESC) and the European Association for Cardio-Thoracic Surgery (EACTS)Developed with the Special Contribution of the European Association of Percutaneous Cardiovascular Interventions (EAPCI)**. *Eur Heart J* (2014) **35** 2541-2619. DOI: 10.1093/eurheartj/ehu278
17. Levine GN, Bates ER, Blankenship JC, Bailey SR, Bittl JA, Cercek B. **2011 ACCF/AHA/SCAI Guideline for Percutaneous Coronary Intervention: Executive Summary: A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines and the Society for Cardiovascular Angiography and Interventions**. *Catheter Cardiovasc Interv* (2012) **79** 453-495. DOI: 10.1002/ccd.2343
18. van der Molen AJ, Reimer P, Dekkers IA, Bongartz G, Bellin MF, Bertolotto M. **Post-Contrast Acute Kidney Injury. Part 2: Risk Stratification, Role of Hydration and Other Prophylactic Measures, Patients Taking Metformin and Chronic Dialysis Patients : Recommendations for updated ESUR Contrast Medium Safety Committee guidelines**. *Eur Radiol* (2018) **28** 2856-2869. DOI: 10.1007/s00330-017-5247-4
19. Zhang W, Zhang J, Yang B, Wu K, Lin H, Wang Y. **Effectiveness of Oral Hydration in Preventing Contrast-Induced Acute Kidney Injury in Patients Undergoing Coronary Angiography or Intervention: A Pairwise and Network Meta-Analysis**. *Coron Artery Dis* (2018) **29** 286-293. DOI: 10.1097/MCA.0000000000000607
20. Trivedi HS, Moore H, Nasr S, Aggarwal K, Agrawal A, Goel P. **A Randomized Prospective Trial to Assess the Role of Saline Hydration on the Development of Contrast Nephrotoxicity**. *Nephron Clin Pract* (2003) **93** C29-C34. DOI: 10.1159/000066641
21. Mehran R, Aymong ED, Nikolsky E, Lasic Z, Iakovou I, Fahy M. **A Simple Risk Score for Prediction of Contrast-Induced Nephropathy after Percutaneous Coronary Intervention: Development and Initial Validation**. *J Am Coll Cardiol* (2004) **44** 1393-1399. DOI: 10.1016/j.jacc.2004.06.068
22. Taylor AJ, Hotchkiss D, Morse RW, McCabe J. **PREPARED: Preparation for Angiography in Renal Dysfunction: A Randomized Trial of Inpatient vs Outpatient Hydration Protocols for Cardiac Catheterization in Mild-To-Moderate Renal Dysfunction**. *Chest* (1998) **114** 1570-1574. DOI: 10.1378/chest.114.6.1570
23. Angoulvant D, Cucherat M, Rioufol G, Finet G, Beaune J, Revel D. **Preventing Acute Decrease in Renal Function Induced by Coronary Angiography (PRECORD): A Prospective Randomized Trial**. *Arch Cardiovasc Dis* (2009) **102** 761-767. DOI: 10.1016/j.acvd.2009.07.001
24. Cho R, Javed N, Traub D, Kodali S, Atem F, Srinivasan V. **Oral Hydration and Alkalinization is Noninferior to Intravenous Therapy for Prevention of Contrast-Induced Nephropathy in Patients with Chronic Kidney Disease**. *J Interv Cardiol* (2010) **23** 460-466. DOI: 10.1111/j.1540-8183.2010.00585.x
25. Wróbel W, Sinkiewicz W, Gordon M, Woźniak-Wiśniewska A. **Oral versus Intravenous Hydration and Renal Function in Diabetic Patients Undergoing Percutaneous Coronary Interventions**. *Kardiol Pol* (2010) **68** 1015-1020. PMID: 20859892
26. Kong DG, Hou YF, Ma LL, Yao DK, Wang LX. **Comparison of Oral and Intravenous Hydration Strategies for the Prevention of Contrast-Induced Nephropathy in Patients Undergoing Coronary Angiography or Angioplasty: A Randomized Clinical Trial**. *Acta Cardiol* (2012) **67** 565-569. DOI: 10.1080/ac.67.5.2174131
27. Akyuz S, Karaca M, Kemaloglu Oz T, Altay S, Gungor B, Yaylak B. **Efficacy of Oral Hydration in the Prevention of Contrast-Induced Acute Kidney Injury in Patients Undergoing Coronary Angiography or Intervention**. *Nephron Clin Pract* (2014) **128** 95-100. DOI: 10.1159/000365090
28. Song F, Sun G, Liu J, Chen JY, He Y, Liu L. **Efficacy of Post-Procedural Oral Hydration Volume on Risk of Contrast-Induced Acute Kidney Injury Following Primary Percutaneous Coronary Intervention: Study Protocol for a Randomized Controlled Trial**. *Trials* (2019) **20**. DOI: 10.1186/s13063-019-3413-5
29. Barrett T, Khwaja A, Carmona C, Martinez Y, Nicholas H, Rogers G. **Acute Kidney Injury: Prevention, Detection, and Management. Summary of Updated NICE Guidance for Adults Receiving Iodine-Based Contrast Media**. *Clin Radiol* (2021) **76** 193-199. DOI: 10.1016/j.crad.2020.08.039
30. Song F, Sun G, Liu J, Chen JY, He Y, Chen S. **The Association between Post-Procedural Oral Hydration and Risk of Contrast-Induced Acute Kidney Injury Among ST-Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention**. *Ann Transl Med* (2019) **7**. DOI: 10.21037/atm.2019.06.05
31. Wichmann JL, Katzberg RW, Litwin SE, Zwerner PL, De Cecco CN, Vogl TJ. **Contrast-Induced Nephropathy**. *Circulation* (2015) **132** 1931-1936. DOI: 10.1161/CIRCULATIONAHA.115.014672
|
---
title: Do intravitreal anti-vascular endothelial growth factor agents lead to renal
adverse events? A pharmacovigilance real-world study
authors:
- Lin Jiang
- Liying Peng
- Yangzhong Zhou
- Gang Chen
- Bin Zhao
- Mingxi Li
- Xuemei Li
journal: Frontiers in Medicine
year: 2023
pmcid: PMC9972674
doi: 10.3389/fmed.2023.1100397
license: CC BY 4.0
---
# Do intravitreal anti-vascular endothelial growth factor agents lead to renal adverse events? A pharmacovigilance real-world study
## Abstract
### Purpose
Intravitreal vascular endothelial growth factor (VEGF) blockade is essential in many macular edema diseases treatment. However, intravitreal VEGF treatment has been reported to lead to deteriorated proteinuria and renal function. This study aimed to explore the relationship between renal adverse events (AEs) and the intravitreal use of VEGF inhibitors.
### Method
In the FDA’s Adverse Event Reporting System (FAERS) database, we searched for renal AEs of patients receiving various anti-VEGF drugs. We performed statistics on renal AEs in patients treated with Aflibercept, Bevacizumab, Ranibizumab, and Brolucizumab (from January 2004 to September 2022) using disproportionate and Bayesian analysis. We also investigated the time to onset, fatality, and hospitalization rates of renal AEs.
### Results
We identified 80 reports. Renal AEs were most frequently associated with Ranibizumab ($46.25\%$) and Aflibercept ($42.50\%$). However, the association between intravitreal anti-VEGFs and renal AEs was insignificant since the reporting odds ratio of Aflibercept, Bevacizumab, Ranibizumab, and Brolucizumab were 0.23 (0.16, 0.32), 0.24 (0.11, 0.49), 0.37 (0.27, 0.51) and 0.15 (0.04, 0.61), respectively. The median time to renal AEs onsets was 37.5 (interquartile range 11.0–107.3) days. The hospitalization and fatality rates in patients who developed renal AEs were 40.24 and $9.76\%$, respectively.
### Conclusion
There are no clear signals for the risk of renal AEs following various intravitreal anti-VEGF drugs based on FARES data.
## Background
The systemic administration of vascular endothelial growth factor (VEGF) inhibiting monoclonal antibodies has been applied in oncology to inhibit angiogenesis in varied neoplasms since the 1990s [1]. The United States Food and Drug Administration (FDA) has approved several types of anti-VEGF agents, including Bevacizumab (Avastin®, 2004), Pegaptanib (Macugen®, 2004), Ranibizumab (Lucentis®,2006), Aflibercept (Zaltrap®; Eylea®, 2011), and Brolucizumab (Beovu®, 2019). Nowadays, the clinical use of anti-VEGF agents has expanded to intravitreal treatment since angiogenesis is essential for the progression of ophthalmic diseases [2]. Evidence shows that VEGF injections are effective in clinical trials involving several types of retinal vascular pathology and ocular neovascularization [3, 4]. After 2000, Aflibercept, Ranibizumab, and Pegaptanib received approvals for indications like proliferative diabetic retinopathy (DR), diabetic macular edema (DME), age-related macular degeneration (AMD), and retinal vein occlusion (RVO) [5]. Bevacizumab and Brolucizumab have also been used off-label for vascular-related ophthalmic diseases [6].
It has been widely recognized that systemically administrated anti-VEGF agents are linked to increased risks of cardiovascular events [7] and renal adverse events (AEs), including proteinuria, acute kidney injury (AKI), glomerular disease, and thrombotic microangiopathy (TMA) (8–10). For intravitreal anti-VEGF administrations, the dose usually ranged from $\frac{1}{150}$ to $\frac{1}{400}$ of that in systemic use (11–13). However, systemic absorption is noted after intravitreal use [14]. One post-marketing study informed that the anti-VEGF-related severe AEs were beyond expectations when applied in an ophthalmology setting [15]. Renal AEs after intravitreal anti-VEGFs have emerged in clinical cases [12, 16, 17] and have been listed in meta-analysis [18, 19].
As a complement to clinical trials, post-marketing AE monitoring is essential to expand our understanding of the potential renal AEs of intravitreal anti-VEGFs. Unfortunately, except for intravitreal anti-VEGFs [15], there were limited pharmacovigilance studies on other aspects of systemic safety. Therefore, renal AEs have been neglected. Knowledge of the detailed safety profile of renal AEs following intravitreal anti-VEGF regimens in real-world clinical practice is lacking. Therefore, we aimed to evaluate the links between various intravitreal anti-VEGF agents and renal AEs in a real-world setting based on the FDA’s Adverse Event Reporting System (FAERS) until September 2022. The FAERS database contains adverse event reports, medication error reports, and product quality complaints resulting in adverse events that were submitted to FDA. Furthermore, we examined the time to onset, fatality rate, and hospitalization rate for renal AEs following intravitreal anti-VEGF regimens.
## Data source
We performed a retrospective pharmacovigilance study using data from the FAERS database between January 2004 and September 2022. The FAERS is a public spontaneous reporting system (SRS) that contains information about adverse drug events provided by global health professionals, patients, and manufacturers. FAERS data files describe demographic and administrative information (DEMO), drug information (DRUG), preferred terms (PTs) coded for the adverse events (REAC), patient outcomes (OUTC), report sources (RPSR), therapy start dates, and end dates for reported drugs (THER), and indications for drug administration (INDI).
We screened 18,611,009 reports from the FAERS database. We removed duplicated records by selecting the latest FDA_DT (Date FDA received Case) when the CASEID (Number for identifying a FAERS case) and FDA_DT were the same. Finally, we included 15,598,683 reports for further analysis (Figure 1).
**Figure 1:** *Process of the selection of cases of intravitreal anti-VEGF-associated renal adverse effects from the FAERS database. VEGF, vascular endothelial growth factor; FAERS, Food and Drug Administration’s Adverse Event Reporting System.*
## Data mapping
We investigated the REAC files for the comprehensive Medical Dictionary for Regulatory Activities (MedDRA v23.1). MedDRA defined terms related to renal AEs as follows: “acute kidney injury,” “subacute kidney injury,” “kidney failure,” “oliguria,” “anuria,” “dialysis,” “proteinuria,” “hematuria,” “blood creatinine increased,” “blood urea increased,” “nephritis,” “nephritis toxic,” “tubulointerstitial nephritis,” “renal tubular injury,” “glomerulonephritis acute,” “glomerulonephritis rapid progressive,” “autoimmune nephritis,” “glomerulonephritis membranous,” “glomerulonephritis minimal lesion,” “glomerulonephritis membranoproliferative,” “glomerulonephritis proliferative,” “nephritic syndrome,” “thrombotic microangiopathy.” We chose generic and brand names of anti-VEGF regimes by utilizing the MICROMEDEX (Index Nominum) as a dictionary in the data mining process. The assessment considered drugs recorded as “Primary Suspect” or “Secondary Suspect” (PS an SS in role code field) and routed as “INTRAVITREAL.”
## Data mining
Based on the rationale of disproportionality analysis and Bayesian analysis, we employed the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the multi-item gamma Poisson shrinker (MGPS) algorithms to investigate the associations between the drug and the given AEs. The equations and criteria for the four algorithms are listed in Table 1.
**Table 1**
| Algorithms | Equation* | Criteria |
| --- | --- | --- |
| ROR | ROR = (a/b)/(c/d) | 95% CI > 1, N ≥ 2 |
| ROR | 95%CI = eln(ROR) ± 1.96(1/a + 1/b + 1/c + 1/d)^0.5 | 95% CI > 1, N ≥ 2 |
| PRR | PRR = (a/(a + c))/(b/(b + d)) | PRR ≥ 2, χ2 ≥ 4, N ≥ 3 |
| PRR | χ2 = Σ((O-E)2/E); (O = a, E = (a + b)(a + c)/(a + b + c + d)) | PRR ≥ 2, χ2 ≥ 4, N ≥ 3 |
| BCPNN | IC = log2a(a + b + c + d)/((a + c)(a + b)) | IC025 > 0 |
| BCPNN | IC025 = eln(IC)-1.96(1/a + 1/b + 1/c + 1/d)^0.5 | IC025 > 0 |
| MGPS | EBGM = a(a + b + c + d)/((a + c)(a + b)) | EBGM05 > 2, N > 0 |
| MGPS | EBGM05 = eln(EBGM)-1.64(1/a + 1/b + 1/c + 1/d)^0.5 | EBGM05 > 2, N > 0 |
We compared the associations between renal AEs and different anti-VEGF agents. We also evaluated the time to onset of renal AEs for different intravitreal anti-VEGF agents, defined as the interval between the EVENT_DT (adverse event onset date) and the START_DT (start date of the intravitreal anti-VEGF administration). The records with incorrect entries or erred input (EVETN_DT earlier than START_DT) and duplicate reports were excluded. Additionally, we analyzed reports of fatal events due to adverse drug reactions and calculated the fatality rate by dividing the catastrophic events by the total number of occurrences of intravitreal anti-VEGF-induced renal AEs.
## Statistical analysis
We used descriptive analysis to summarize the clinical features of the patients with renal AEs resulting from intravitreal administration of anti-VEGFs in the FAERS database. The time to onset of renal AEs among different anti-VEGFs was compared using non-parametric tests (the Mann–Whitney test for dichotomous variables and the Kruskal–Wallis test when there were more than two subgroups of respondents). Pearson’s Chi-square or Fisher’s exact test was used to compare the fatality rates between different anti-VEGFs. We set statistical significance at $p \leq 0.05$ with $95\%$ confidence intervals. Data mining and statistical analysis were performed by SAS, version 9.4 (SAS Institute Inc., Cary, NC, United States).
## Descriptive analysis
A total number of 30,776 AEs related to intravitreal administration of anti-VEGFs and 278,759 renal AEs were documented in the FAERS database dated from January 2004 to September 2022 (Figure 1). We merged the signals above and finally screened 80 renal AE reports suspected of intravitreal administration of anti-VEGFs and summarized the clinical features of these patients in Table 2. The case numbers were comparable in North America ($36.25\%$) and Europe ($40.00\%$). Healthcare professionals reported $67.50\%$ of the cases. The morbidity seemed to be equal between males ($\frac{29}{50}$, $58.0\%$) and females ($\frac{21}{50}$, $42.0\%$). The average age for all patients was 70.86 (±11.78) years, and we found no age difference between affected males and females ($$p \leq 0.671$$). Most of the affected patients were elderly (>65-year-old, $73.81\%$) and middle-aged (45–64 years old, $21.43\%$). The renal AEs related to intravitreal anti-VEGFs were most frequently associated with Ranibizumab ($46.25\%$) and Aflibercept ($42.50\%$). Intravitreal pegaptanib has not been reported with renal AEs in the current FAERS database. Among the renal AEs, anti-VEGFs were dominantly administrated in AMD ($50.00\%$), DRE ($20.00\%$), and DR ($10.00\%$).
**Table 2**
| Characteristics | Reports, no. (%) |
| --- | --- |
| Reporting region | Reporting region |
| North America | 29 (36.25) |
| Europe | 32 (40.00) |
| Asia | 12 (15.00) |
| Oceania | 4 (5.00) |
| South America | 3 (3.75) |
| Africa | 0 (0.00) |
| Reporters | Reporters |
| Health-care professionals | 54 (67.50) |
| Non-health-care professionals | 15 (18.75) |
| Unspecified | 11 (13.75) |
| Reporting year | Reporting year |
| 2022 (Until September) | 1 (1.25) |
| 2021 | 5 (6.25) |
| 2020 | 9 (11.25) |
| 2019 | 6 (7.50) |
| 2018 | 4 (5.00) |
| 2017 | 17 (21.25) |
| 2016 | 8 (10.00) |
| 2015 | 7 (8.75) |
| 2014 | 7 (8.75) |
| 2013 | 0 (0.00) |
| 2012 | 2 (2.50) |
| 2011 | 6 (7.50) |
| 2010 | 3 (3.75) |
| 2009 | 5 (6.25) |
| Sex | Sex |
| Male | 29/50 (58.0) |
| Female | 21/50 (42.0) |
| Age (years) | Age (years) |
| <18 | 0 (0.00) |
| 18–44 | 2/42 (4.76) |
| 45–64 | 9/42 (21.43) |
| >65 | 31/42 (73.81) |
| Unknown or missing | 38/80 (47.50) |
| Intraocular anti-VEGFs as suspected drugs | Intraocular anti-VEGFs as suspected drugs |
| Aflibercetp | 34 (42.50) |
| Bevacizumab | 7 (8.75) |
| Ranibizumab | 37 (46.25) |
| Brolucizumab | 2 (2.50) |
| Indications | Indications |
| Age-related macular degeneration | 40 (50.00) |
| Diabetic retinal edema | 16 (20.00) |
| Diabetic retinopathy | 8 (10.00) |
| Retinal vein occlusion | 2 (2.50) |
| Retinal neovascularization | 1 (1.25) |
| Neovascular glaucoma | 1 (1.25) |
| Choroidal neovascularization | 1 (1.25) |
| Retinal detachment | 1 (1.25) |
| Unknown or missing indications | 10 (12.50) |
## Disproportionality analysis and Bayesian analysis
Based on the four algorithms’ criteria, we detected renal AEs signals for different anti-VEGFs for intravitreal administration and listed the results in Table 3. All anti-VEGFs showed insignificant associations with renal AEs due to their weak ROR, PRR, IC025, and empirical Bayes geometric mean (EBGM) values. The ROR of Aflibercept, Bevacizumab, Ranibizumab, and Brolucizumab were 0.23 (0.16, 0.32), 0.24 (0.11, 0.49), 0.37 (0.27, 0.51) and 0.15 (0.04, 0.61), respectively.
**Table 3**
| Drug | N | ROR (95% two-sided CI) | PRR (χ2) | IC (IC025) | EBGM (EBGM05) |
| --- | --- | --- | --- | --- | --- |
| Aflibercept | 34 | 0.23 (0.16, 0.32) | 0.23 (86.9) | −2.1 (0.00) | 0.23 (0.18) |
| Bevacizumab | 7 | 0.24 (0.11, 0.49) | 0.24 (17.31) | −2.07 (0.00) | 0.24 (0.13) |
| Ranibizumab | 37 | 0.37 (0.27, 0.51) | 0.37 (39.28) | −1.42 (0.00) | 0.37 (0.29) |
| Brolucizumab | 2 | 0.15 (0.04, 0.61) | 0.15 (9.37) | −2.69 (0.00) | 0.15 (0.05) |
## Time to onset of renal AEs associated with intravitreal anti-VEGFs
Overall, the median time to onset renal AEs associated with intravitreal anti-VEGFs was 37.5 days (interquartile range [IQR] 11.0–107.3 days) after administering drugs. The times to onset of renal AEs for each intravitreal anti-VEGF regimen was described in Figure 2. Near $40\%$ of the renal AEs occurred in the first month ($37.5\%$), and more than half ($57.81\%$) occurred in the first 2 months. Noteworthily, we found that $12.5\%$ of the renal AEs could happen as soon as the first dose of intravitreal anti-VEGF administration. Kruskal–Wallis test detected no significant difference in time to onset of renal AEs among different anti-VEGFs ($$p \leq 0.492$$).
**Figure 2:** *Time to event onset of renal adverse effects following intravitreal administration of anti-VEGF agents. VEGF, vascular endothelial growth factor.*
## Prognosis due to intravitreal anti-VEGF-associated renal AEs
To analyze the prognosis of renal AEs associated with intravitreal anti-VEGFs, we assessed the fatality and hospitalization rate (initial or prolonged) due to renal AEs following Aflibercept, Bevacizumab, Ranibizumab, and Brolucizumab in the FAERS database (Table 4). Generally, the hospitalization rate of intravitreal anti-VEGF-associated renal AEs was $40.24\%$, the life-threatening events rate was $8.54\%$, and the fatality rate of $9.76\%$. There was no significant difference in hospitalization rates ($$p \leq 0.693$$), life-threatening rates ($$p \leq 0.758$$), and fatality rates ($$p \leq 0.630$$) across different intravitreal anti-VEGFs (Pearson’s Chi-square test for overall comparison).
**Table 4**
| Unnamed: 0 | Aflibercept | Bevacizumab | Ranibizumab | Brolucizumab | Total |
| --- | --- | --- | --- | --- | --- |
| Death | 3 (8.57) | 0 (0) | 5 (13.51) | 0 (0) | 8 (9.76) |
| Disabled | 1 (2.86) | 0 (0) | 0 (0) | 1 (33.33) | 2 (2.44) |
| Hospitalization | 15 (42.86) | 2 (28.57) | 14 (37.84) | 2 (66.67) | 33 (40.24) |
| Life-threatening | 2 (5.71) | 1 (14.29) | 4 (10.81) | 0 (0) | 7 (8.54) |
| Other serious | 14 (40.00) | 4 (57.14) | 14 (37.84) | 0 (0) | 32 (39.02) |
## Discussion
In this study, we completed the first collection until recently to seek confirmation of renal AEs after intravitreal anti-VEGF agents based on the FAERS pharmacovigilance real-world practice. Interestingly, although we found 80 renal AE reports in the database, all four members of intravitreal anti-VEGFs demonstrated little association with renal AEs by signal detection algorithms.
Our findings were consistent with some studies and reports that focused on endothelial toxicity and renal damage after intravitreal anti-VEGF. A retrospective cohort in Japan, which included 69 diabetic patients with DR, showed no significant increase in creatinine 7–30 days after applying intravitreal Bevacizumab, Aflibercept, or Ranibizumab [20]. Another retrospective review of 85 patients with DME suggested that regular intravitreal VEGF inhibition did not induce increased proteinuria or affect kidney function over a mean duration of 2.6 years [21]. A recent randomized control trial that enrolled 660 DME patients revealed no significant change in proteinuria after intravitreal VEGFs for up to 52 weeks [22].
Our pharmacovigilance analysis echoed such clinical studies and detected no association between intravitreal anti-VEGFs and renal AEs. In the scenario of a rare and potential AE issue, clinical cohorts and trials are far from convincing to draw a definitive conclusion due to their strict inclusion criteria, limited sample sizes, and relatively short observation periods. The SRS could be a fitted source for new evidence.
However, the relationship between systemic damages and local anti-VEGF injections is still controversial (12, 16–19). Some mechanisms of anti-VEGF drugs predispose the potential to develop renal AEs. VEGF performs specific effects on vascular endothelial cells. It is believed that the VEGF-driven results on neovascularization are essential for the reperfusion of ischemic tissues. The anti-VEGF drugs may increase the risk of cardiovascular and renal AEs (7–10, 23–26). The anti-VEGF agents interrupt the podocyte-endothelial VEGF signaling axis, resulting in decreased glomerular capillary endothelial cell permeability, reduced endothelial cell proliferation, and podocyte detachment [27]. Meanwhile, anti-VEGF effects decrease nephrin expression in glomeruli, leading to the detachment and atrophy of endothelial cells [28]. Though far lower dose than systemic administration, the intravitreal injection of anti-VEGFs still results in detectable serum levels [14, 29] and glomeruli bindings [30], then consequently leads to systemic VEGF inhibition [14, 29]. Proteinuria [16] and TMA [12] cases after intravitreal VEGF inhibitors hinted at the possible side effects on podocytes and endothelial cells.
Analyzing from another perspective, we cannot completely exclude the possibility of the systemic damages induced by local anti-VEGF injections. Interestingly, we noticed that Pegaptanib resulted in zero renal AEs among four members of intravitreal anti-VEGFs. The possibly fewer clinical application would be a convenient explanation, but Pegaptanib possesses properties that distinguish it from other intravitreal anti-VEGFs. Other than antibodies, *Pegaptanib is* a ribonucleic acid aptamer that binds to VEGF isoform [1]. It is pharmacokinetically short-acting, and its systemic absorption is limited when used intravitreally [1]; such characteristics may contribute to the rarity of renal AEs related to intravitreal pegaptanib. On the other hand, we found that Aflibercept resulted in more intravitreal anti-VEGF-associated renal AE reports than Bevacizumab in the current FAERS database ($\frac{34}{80}$ cases vs. $\frac{7}{80}$ cases). This was consistent with a previous finding that Aflibercept was more potent than Bevacizumab in systemic VEGF inhibition after intravitreal injection [29].
Although we spotted some renal AE reports after intravitreal anti-VEGFs in the FARES database, we trusted the accuracy of the renal AEs because healthcare professionals contributed most of the reports. Still, the causality could not be set up due to the negative signals in ROR, PRR, IC, and EBGM for all four anti-VEGFs. No evidence has indicated that renal involvements like proteinuria or decreased glomerular filtration rate occurred more frequently in patients after intravitreal anti-VEGF injections. Additionally, the discrepancies in clinical observations (12, 16–22) also raised the possibility that genetic background might contribute to patients’ susceptibility to renal toxicity.
Based on the above analysis, we should admit that there has been no concrete evidence to prove intravitreal anti-VEGF-associated renal AEs. However, we should keep in mind that this side effect could be possible in elderly diabetic patients.
Once they happened, drug-associated renal AEs in diabetic groups would be harmful. Our data indicated that the hospitalization rate was as high as $31.25\%$ in patients who developed renal AEs after intravitreal anti-VEGFs, and the related fatality rate reached an unneglectable $6.25\%$. The median time to renal AEs onsets after overall intravitreal anti-VEGFs was 37.5 days, and more than half of the insulted cases were reported to occur within 2 months. Therefore, we should carefully monitor the potential renal AEs in elderly patients during the early administration of intravitreal anti-VEGFs. It was noted that immediate renal AEs could occur in around $10\%$ of all affected patients. Based on current findings, it is not trivial to consider tracing the changes in kidney function in patients who tend to develop AKI.
We acknowledge that our study has some limitations. First, unlike researchers who use standardized data collection methods to report AEs in clinical trials, the FAERS database has inherent limitations in reporting form, such as under-reporting, false, incomplete, inaccurate, and arbitrary reporting. Second, because we lack the total number of patients receiving treatment, we cannot calculate some statistics, such as the incidence of each suspect drug, so signals from the spontaneous reporting system can only be used for qualitative studies. It is also difficult to control for confounding factors such as baseline renal insufficiency, pre-existing kidney disease and comorbidities, and renal complications due to diabetes itself that may influence renal AEs due to the lack of sufficient information. Therefore, a definitive causal relationship between anti-VEGF agents and renal AEs cannot be accurately inferred. Third, accurate dosages for patients are not accurately available from the FARES database, making it impossible to analyze the timing or total dosages of different types of antivascular drugs.
## Conclusion
In this study, we utilized the FAERS database and identified no clear signals for renal AEs following various intravitreal VEGFs in real-world practice. Based on FARES data, it is not possible to infer that local anti-VEGF drug injection causes renal AEs, contrary to some previous case reports. Our findings pave the way for the following pharmacovigilance investigation. We recommend accessing renal AEs and other systemic damages as the primary outcomes in high-quality clinical trials and real-world studies to explore the relationship between intravitreal anti-VEGFs and renal AEs.
## Data availability statement
All necessary data have been presented as tables and figures in the manuscript. Related information is accessible under request to the corresponding author.
## Author contributions
GC and LJ designed the study, analyzed and interpreted data, generated figures and tables, and drafted the manuscript. LP and YZ contributed to manuscript drafting. BZ designed the study and directed the data mining in the FAERS database. ML and XL reviewed and corrected the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by the Sansheng Yeehong TCP Research Foundation (GC), Bethune Charitable Foundation (J202103E006) (GC), and National High-Level Hospital Clinical Research Funding [2022-PUMCH-B-021].
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## References
1. Hanna RM, Barsoum M, Arman F, Selamet U, Hasnain H, Kurtz I. **Nephrotoxicity induced by intravitreal vascular endothelial growth factor inhibitors: emerging evidence**. *Kidney Int* (2019) **96** 572-80. DOI: 10.1016/j.kint.2019.02.042
2. Capitao M, Soares R. **Angiogenesis and inflammation crosstalk in diabetic retinopathy**. *J Cell Biochem* (2016) **117** 2443-53. DOI: 10.1002/jcb.25575
3. Gragoudas ES, Adamis AP, Cunningham ET, Feinsod M, Guyer DR. **Pegaptanib for neovascular age-related macular degeneration**. *N Engl J Med* (2004) **351** 2805-16. DOI: 10.1056/NEJMoa042760
4. Nguyen QD, Brown DM, Marcus DM, Boyer DS, Patel S, Feiner L. **Ranibizumab for diabetic macular edema: results from 2 phase III randomized trials: RISE and RIDE**. *Ophthalmology* (2012) **119** 789-801. DOI: 10.1016/j.ophtha.2011.12.039
5. Ferrara N, Adamis AP. **Ten years of anti-vascular endothelial growth factor therapy**. *Nat Rev Drug Discov* (2016) **15** 385-403. DOI: 10.1038/nrd.2015.17
6. Stewart MW. **Treatment of diabetic retinopathy: recent advances and unresolved challenges**. *World J Diabetes* (2016) **7** 333-41. DOI: 10.4239/wjd.v7.i16.333
7. Ranpura V, Hapani S, Chuang J, Wu S. **Risk of cardiac ischemia and arterial thromboembolic events with the angiogenesis inhibitor bevacizumab in cancer patients: a meta-analysis of randomized controlled trials**. *Acta Oncol* (2010) **49** 287-97. DOI: 10.3109/02841860903524396
8. Zhu X, Wu S, Dahut WL, Parikh CR. **Risks of proteinuria and hypertension with bevacizumab, an antibody against vascular endothelial growth factor: systematic review and meta-analysis**. *Am J Kidney Dis* (2007) **49** 186-93. DOI: 10.1053/j.ajkd.2006.11.039
9. Eremina V, Jefferson JA, Kowalewska J, Hochster H, Haas M, Weisstuch J. **VEGF inhibition and renal thrombotic microangiopathy**. *N Engl J Med* (2008) **358** 1129-36. DOI: 10.1056/NEJMoa0707330
10. Izzedine H, Escudier B, Lhomme C, Pautier P, Rouvier P, Gueutin V. **Kidney diseases associated with anti-vascular endothelial growth factor (VEGF): an 8-year observational study at a single center**. *Medicine (Baltimore)* (2014) **93** 333-9. DOI: 10.1097/MD.0000000000000207
11. Georgalas I, Papaconstantinou D, Papadopoulos K, Pagoulatos D, Karagiannis D, Koutsandrea C. **Renal injury following intravitreal anti-VEGF administration in diabetic patients with proliferative diabetic retinopathy and chronic kidney disease – a possible side effect?**. *Curr Drug Saf* (2014) **9** 156-8. DOI: 10.2174/1574886309666140211113635
12. Pellé G, Shweke N, Duong Van Huyen JP, Tricot L, Hessaïne S, Frémeaux-Bacchi V. **Systemic and kidney toxicity of intraocular administration of vascular endothelial growth factor inhibitors**. *Am J Kidney Dis* (2011) **57** 756-9. DOI: 10.1053/j.ajkd.2010.11.030
13. Baillif S, Levy B, Girmens JF, Dumas S, Tadayoni R. **Systemic safety following intravitreal injections of anti-VEGF**. *J Fr Ophtalmol* (2018) **41** 271-6. DOI: 10.1016/j.jfo.2017.11.006
14. Avery RL, Castellarin AA, Steinle NC, Dhoot DS, Pieramici DJ, See R. **Systemic pharmacokinetics and pharmacodynamics of intravitreal AFLIBERCEPT, bevacizumab, and ranibizumab**. *Retina* (2017) **37** 1847-58. DOI: 10.1097/IAE.0000000000001493
15. Cutroneo PM, Giardina C, Ientile V, Potenza S, Sottosanti L, Ferrajolo C. **Overview of the safety of anti-VEGF drugs: analysis of the Italian spontaneous reporting system**. *Drug Saf* (2017) **40** 1131-40. DOI: 10.1007/s40264-017-0553-y
16. Pérez-Valdivia MA, López-Mendoza M, Toro-Prieto FJ, Cabello-Chaves V, Toro-Ramos M, Martín-Herrera MC. **Relapse of minimal change disease nephrotic syndrome after administering intravitreal bevacizumab**. *Nefrologia* (2014) **34** 421-2. DOI: 10.3265/Nefrologia.pre2014.Mar.12388
17. Hanna RM, Lopez EA, Hasnain H, Selamet U, Wilson J, Youssef PN. **Three patients with injection of intravitreal vascular endothelial growth factor inhibitors and subsequent exacerbation of chronic proteinuria and hypertension**. *Clin Kidney J* (2019) **12** 92-100. DOI: 10.1093/ckj/sfy060
18. van der Reis MI, La Heij EC, De Jong-Hesse Y, Ringens PJ, Hendrikse F, Schouten JS. **A systematic review of the adverse events of intravitreal anti-vascular endothelial growth factor injections**. *Retina* (2011) **31** 1449-69. DOI: 10.1097/IAE.0b013e3182278ab4
19. Micieli JA, Micieli A, Smith AF. **Identifying systemic safety signals following intravitreal bevacizumab: systematic review of the literature and the Canadian adverse drug reaction database**. *Can J Ophthalmol* (2010) **45** 231-8. DOI: 10.3129/i10-027
20. Kameda Y, Babazono T, Uchigata Y, Kitano S. **Renal function after intravitreal administration of vascular endothelial growth factor inhibitors in patients with diabetes and chronic kidney disease**. *J Diabetes Investig* (2018) **9** 937-9. DOI: 10.1111/jdi.12771
21. O’Neill RA, Gallagher P, Douglas T, Little JA, Maxwell AP, Silvestri G. **Evaluation of long-term intravitreal anti-vascular endothelial growth factor injections on renal function in patients with and without diabetic kidney disease**. *BMC Nephrol* (2019) **20** 478. DOI: 10.1186/s12882-019-1650-1
22. Glassman AR, Liu D, Jampol LM, Sun JK. **Changes in blood pressure and urine albumin-creatinine ratio in a randomized clinical trial comparing Aflibercept, bevacizumab, and Ranibizumab for diabetic macular edema**. *Invest Ophthalmol Vis Sci* (2018) **59** 1199-205. DOI: 10.1167/iovs.17-22853
23. Estrada CC, Maldonado A, Mallipattu SK. **Therapeutic inhibition of VEGF signaling and associated Nephrotoxicities**. *J Am Soc Nephrol* (2019) **30** 187-200. DOI: 10.1681/ASN.2018080853
24. den Deurwaarder ES, Desar IM, Steenbergen EJ, Mulders PF, Wetzels JF, van Herpen CM. **Kidney injury during VEGF inhibitor therapy**. *Neth J Med* (2012) **70** 267-71. DOI: 10.1016/j.mayocp.2012.02.027
25. Pou M, Saval N, Vera M, Saurina A, Sole M, Cervantes F. **Acute renal failure secondary to imatinib mesylate treatment in chronic myeloid leukemia**. *Leuk Lymphoma* (2003) **44** 1239-41. DOI: 10.1080/1042819031000079140
26. Hanna RM, Lopez E, Wilson J, Barathan S, Cohen AH. **Minimal change disease onset observed after bevacizumab administration**. *Clin Kidney J* (2016) **9** 239-44. DOI: 10.1093/ckj/sfv139
27. Schrijvers BF, Flyvbjerg A, De Vriese AS. **The role of vascular endothelial growth factor (VEGF) in renal pathophysiology**. *Kidney Int* (2004) **65** 2003-17. DOI: 10.1111/j.1523-1755.2004.00621.x
28. Hara A, Wada T, Furuichi K, Sakai N, Kawachi H, Shimizu F. **Blockade of VEGF accelerates proteinuria, via decrease in nephrin expression in rat crescentic glomerulonephritis**. *Kidney Int* (2006) **69** 1986-95. DOI: 10.1038/sj.ki.5000439
29. Avery RL, Castellarin AA, Steinle NC, Dhoot DS, Pieramici DJ, See R. **Systemic pharmacokinetics following intravitreal injections of ranibizumab, bevacizumab or aflibercept in patients with neovascular AMD**. *Br J Ophthalmol* (2014) **98** 1636-41. DOI: 10.1136/bjophthalmol-2014-305252
30. Tschulakow A, Christner S, Julien S, Ludinsky M, van der Giet M, Schraermeyer U. **Effects of a single intravitreal injection of aflibercept and ranibizumab on glomeruli of monkeys**. *PLoS One* (2014) **9** e113701. DOI: 10.1371/journal.pone.0113701
|
---
title: Identification and verification of feature biomarkers associated with immune
cells in neonatal sepsis
authors:
- Weiqiang Liao
- Huimin Xiao
- Jinning He
- Lili Huang
- Yanxia Liao
- Jiaohong Qin
- Qiuping Yang
- Liuhong Qu
- Fei Ma
- Sitao Li
journal: European Journal of Medical Research
year: 2023
pmcid: PMC9972688
doi: 10.1186/s40001-023-01061-2
license: CC BY 4.0
---
# Identification and verification of feature biomarkers associated with immune cells in neonatal sepsis
## Abstract
### Background
Neonatal sepsis (NS), a life-threatening condition, is characterized by organ dysfunction and is the most common cause of neonatal death. However, the pathogenesis of NS is unclear and the clinical inflammatory markers currently used are not ideal for diagnosis of NS. Thus, exploring the link between immune responses in NS pathogenesis, elucidating the molecular mechanisms involved, and identifying potential therapeutic targets is of great significance in clinical practice. Herein, our study aimed to explore immune-related genes in NS and identify potential diagnostic biomarkers. Datasets for patients with NS and healthy controls were downloaded from the GEO database; GSE69686 and GSE25504 were used as the analysis and validation datasets, respectively. Differentially expressed genes (DEGs) were identified and Gene Set Enrichment Analysis (GSEA) was performed to determine their biological functions. Composition of immune cells was determined and immune-related genes (IRGs) between the two clusters were identified and their metabolic pathways were determined. *Key* genes with correlation coefficient > 0.5 and $p \leq 0.05$ were selected as screening biomarkers. Logistic regression models were constructed based on the selected biomarkers, and the diagnostic models were validated.
### Results
Fifty-two DEGs were identified, and GSEA indicated involvement in acute inflammatory response, bacterial detection, and regulation of macrophage activation. Most infiltrating immune cells, including activated CD8 + T cells, were significantly different in patients with NS compared to the healthy controls. Fifty-four IRGs were identified, and GSEA indicated involvement in immune response and macrophage activation and regulation of T cell activation. Diagnostic models of DEGs containing five genes (PROS1, TDRD9, RETN, LOC728401, and METTL7B) and IRG with one gene (NSUN7) constructed using LASSO algorithm were validated using the GPL6947 and GPL13667 subset datasets, respectively. The IRG model outperformed the DEG model. Additionally, statistical analysis suggested that risk scores may be related to gestational age and birth weight, regardless of sex.
### Conclusions
We identified six IRGs as potential diagnostic biomarkers for NS and developed diagnostic models for NS. Our findings provide a new perspective for future research on NS pathogenesis.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40001-023-01061-2.
## Background
Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, mainly manifested as an inflammatory response and immunosuppression, and is currently the main cause of death in critically ill patients worldwide [1]. In the US, the present incidence of sepsis is approximately three per thousand, and severe sepsis kills at least 200,000 people annually [2]. Severe sepsis and septic shock account for 30–$50\%$ of hospital-reported deaths around the world [3]. Neonatal sepsis (NS) refers to bacteraemia with systemic infection occurring within the first month of life [4]. It is the most common cause of neonatal death, and its associated mortality is currently a major health concern worldwide [5]. NS can be divided into early- and late-onset, with 72 h after birth as the demarcation between the two. Neonatal infections account for an estimated $26\%$ of under-five deaths [6]. In low- and middle-income countries, the reported incidence of NS in 2022 was $17.7\%$ ($\frac{5425}{30577}$) and the mortality rate was $16.2\%$ ($\frac{877}{5425}$) [7]. Development of primary and secondary prevention strategies based on different types of infections has become a hot area of NS-related research in recent decades [8].
Immune and inflammatory responses play important roles in the pathogenesis of NS. Currently, the commonly used clinical inflammatory markers are interleukin-6 (IL-6), C-reactive protein (CRP), and procalcitonin (PCT). IL-6 is a cytokine produced by mononuclear phagocytes, endothelial cells, fibroblasts, and decidual, chorionic, amniotic, and trophoblast cells upon stimulation with microbial products [9]. CRP, a protein synthesized in the liver, is currently used as an important biomarker to assess the severity and prognosis of NS [10]. PCT is produced by the parathyroid and neuroendocrine cells and acts as a precursor of calcitonin, which was formally proposed as a diagnostic marker for NS [11–13] in 2008 and can increase more than 1000-fold during active infection. However, these are not ideal for the diagnosis and prognosis of NS [14]. In the early stages of NS, various immune cells (such as monocytes and macrophages) and released inflammatory mediators and cytokines can induce an excessive inflammatory response, whereas in the late stage, immunosuppression is predominant [15, 16]. Exploring the link between immune responses in the pathogenesis of NS, elucidating the molecular mechanisms involved, and identifying potential therapeutic targets will be of great significance in clinical practice.
Bioinformatic analysis helps to understand the underlying mechanisms of NS by screening gene expression datasets. In the present study, differentially expressed genes (DEGs) between NS and healthy controls were identified through bioinformatic analysis, and the underlying pathology of NS was explored through detection of the immune microenvironment, clustering, and protein–protein network analysis. In addition, we constructed a diagnostic model of six identified DEGs using least absolute shrinkage and selection operator (LASSO) regression analysis. Finally, we confirmed the effectiveness of the diagnostic model of immune-related genes (IRGs) using the GSE25504 dataset. In the present study, we explored the pathogenesis of NS from the perspectives of immunity and inflammation, which can identify potential targets for treating NS.
## Gene expression features of NS samples
The DEGs between NS and normal samples are shown in Fig. 1. t-Distributed stochastic neighbour embedding (t-SNE) was conducted to evaluate the differences in gene expression between NS and normal samples (Fig. 1a, see Additional file 1: Table S1). Compared with normal samples, 52 DEGs were found in NS samples, most of which were up-regulated (Fig. 1b). Heatmaps were conducted to visualize the 52 DEGs (Fig. 1c). Gene Set Enrichment Analysis (GSEA) analysis was also carried out to explore the functional pathways between NS and normal samples, and the results indicated that the DEGs were considerably enriched in acute inflammatory response, detection of bacterium, and regulation of macrophage activation (Fig. 1d).Fig. 1Gene expression characteristics in neonatal sepsis (NS) samples. a Dimension reduction algorithm was used to evaluate the differences between patients with NS and normal samples. b The differentially expressed genes (DEGs) in total RNA expression profiles between NS and normal samples were visualized by Vioplot. c Heatmaps presented the expression of all DEGs. d Gene Set Enrichment Analysis (GSEA) analysis was performed to evaluate the differences of the biological states between NS and normal samples
## Immunological characteristics of NS samples
To explore the immune microenvironment in patients with NS, the concentration of immune cells was quantified. As shown in Fig. 2a, most of the infiltrating immune cells showed significant differences in patients with NS, which was also demonstrated by hierarchical clustering analysis (Fig. 2b) and t-SNE plot (Fig. 2d). In addition, the concentration of most of the immune cells were considerably correlated (Fig. 2c). The all detected immune cells see Additional file 2: Table S2.Fig. 2Characteristics of the immune cell microenvironment in NS. a Differences in immune cell compositions between NS and normal samples. b The differences of immune cell compositions between NS and normal samples were visualized by heatmap; grouped by age. c The correlation of the immune cells was visualized by corrplot. d Dimension reduction algorithm was conducted to evaluate the differences in immune cell compositions between NS and normal samples. t-SNE, t-distributed stochastic neighbour embedding The IRGs in patients with NS were further explored. UMAP plot showed that there were significant differences in gene signatures between the two clusters divided by immune cell composition (Fig. 3a). Compared with normal samples, 54 IRGs were found, with 30 down-regulated and 24 up-regulated genes (Fig. 3b, see Additional file 3: Table S3). Heatmaps were generated to visualize IRG expression in the two clusters (Fig. 3c). GSEA was carried out, and the results indicated that the functions of IRGs were considerably enriched in activation of immune response, macrophage activation, and regulation of T cell activation (Fig. 3d).Fig. 3Gene expression characteristics of the two immune-related clusters. a Dimension reduction algorithm was used to evaluate the differences between cluster 1 and cluster 2. b Immune-related DEGs (IRGs) of total RNA expression profile between cluster 1 and cluster 2 were visualized by Vioplot. c Heatmaps presented the expression of all IRGs. d GSEA was performed to evaluate the differences of the biological states between cluster 1 and cluster 2
## Significance of gene expression signatures in NS diagnosis
After filtering gene signatures with random forest method, 20 DEGs and 15 IRGs were used to build the diagnostic models, as shown in Fig. 4a and b. LASSO algorithm was used to construct a diagnostic model to classify the training dataset into NS and control groups. Two diagnostic models were built, respectively, with DEG and IRG signatures (see Additional file 4: Table S4, Additional file 5: Table S5). For the DEG model, 5 regulators (PROS1, TDRD9, RETN, LOC728401, and METTL7B) and corresponding coefficients were identified with minimum fivefold cross-validated mean square error in GSE69686. For the IRG model, there was only one regulator NSUN7. The risk score for each patient was calculated as the product of coefficient and the sum of gene expression. As shown in Fig. 4c, the risk scores of gene signatures could robustly predict diagnosis for patients in both models. Additionally, bootstrap method was adopted to confirm the robustness of the two diagnostic models. The results of 1000 repeated tests are shown in Fig. 4d. Fig. 4Construction of NS diagnostic models based on DEGs and IRGs. a Top 20 DEGs sorted by mean decrease accuracy based on random forest method. b Top 15 IRGs sorted by mean decrease accuracy based on random forest method. c Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic efficiency of the DEG and IRG gene signatures with the training dataset. d AUC values of both models obtained by 1000 repeated tests based on bootstrap methods were shown in the density plot to validate the conclusions. AUC, area under the curve; CI, confidence interval; DEG, differentially expressed gene; IRG, immune-related gene In addition, we evaluated the effectiveness of the two diagnostic models in the validation dataset GSE25504 (platform GPL6947 as validation dataset 1 and platform GPL13667 as validation dataset 2). It should be noted that the DEG model’s regulator LOC728401 is missing in both validation datasets; however, the coefficient is much smaller than other regulators (about $\frac{1}{5}$) and could be ignored. Receiver operating characteristic (ROC) curve and bootstrap methods were used again (Fig. 5). The results showed that both models were applicable to validation dataset 1 (Fig. 5a, b), and the IRG model was more robust than the DEG model in validation dataset 2, because it had only one gene signature (Fig. 5c, d).Fig. 5Evaluation of NS diagnostic models based on DEGs and IRGs. a ROC curves were calculated to evaluate the diagnostic efficiency of the DEG and IRG gene signatures with the validation dataset 1. b AUC values of both models obtained by 1000 repeated tests based on bootstrap methods were shown in the density plot to validate the conclusions. c ROC curves were calculated to evaluate the diagnostic efficiency of the DEG and IRG signatures with the validation dataset 2. d AUC values of both models obtained by 1000 repeated tests based on bootstrap methods were shown in the density plot to validate the conclusions. AUC area under the curve, CI confidence interval, DEG differentially expressed gene, IRG immune-related gene Finally, the relationship between risk scores of the diagnostic models and phenotype in the validation datasets was analysed (Table 1). The results showed that the risk scores were probably related to gestational age and birthweight and not to sex. Table 1Relationship between risk scores of both diagnostic models and phenotype in the two validation datasetsValidation dataset 1 (DEG model)LevelHigh riskLow riskP valuen = 31n = 32Group (%) Control13 (41.9)24 (75.0)0.011 Infected18 (58.1)8 (25.0)Sex (%) Female13 (41.9)13 (40.6)1 Male18 (58.1)19 (59.4)Corrected gestational age (mean (SD))236.90 (36.03)262.69 (35.67)0.006Birthweight (mean (SD))1863.29 (1233.33)2593.25 (1389.79)0.031Validation dataset 1 (IRG model)LevelHigh riskLow riskP valuen = 31n = 32Group (%) Control9 (29.0)28 (87.5) < 0.001 Infected22 (71.0)4 (12.5)Sex (%) Female14 (45.2)12 (37.5)0.613 Male17 (54.8)20 (62.5)Corrected gestational age (mean (SD))223.94 (28.09)275.25 (27.65) < 0.001Birthweight (mean (SD))1394.23 (929.43)3047.66 (1204.01) < 0.001Validation dataset 2 (DEG model)LevelHigh riskLow riskP valuen = 10n = 10Group (%) Control1 (10.0)5 (50.0)0.141 Infected9 (90.0)5 (50.0) Sex (%)Female2 (20.0)2 (20.0)1 Male8 (80.0)8 (80.0)Corrected gestational age (mean (SD))242.50 (18.74)234.40 (25.98)0.434Birthweight (mean (SD))1344.50 (309.98)1029.50 (385.61)0.059Validation dataset 2 (IRG model)LevelHigh riskLow riskP valuen = 10n = 10Group (%) Control0 (0.0)6 (60.0)0.011 Infected10 (00.0)4 (40.0)Sex (%) Female4 (40.0)0 (0.0)0.087 Male6 (60.0)10 (100.0)Corrected gestational age (mean (SD))244.20 (24.56)232.70 (19.64)0.263Birthweight (mean (SD))1156.00 (348.22)1218.00 (420.14)0.724DEG differentially expressed gene, SD standard deviation, IRG immune-related gene
## Discussion
NS, a life-threatening condition, can lead to microcirculatory disturbances, immune dysfunction, and tissue and organ dysfunction, and is becoming the most common cause of neonatal death worldwide [4]. Hence, NS and its related mortality and complications represent a major global health concern [2–6].
Impaired inflammatory immune responses during the onset and recovery phases are considered a hallmark of severe NS. Abnormal activation of macrophages and neutrophils occurs in the early stage of NS [17], and the recovery period is mainly characterized by immunosuppression. Sepsis is characterized by upregulation of CD4 + and CD8 + T cells, T helper 17 cells, and regulatory T cells [16], lymphopenia, and loss of immune function. Microarray analysis has indicated abnormalities in the expression of immune-related genes in children with sepsis, including FYN, FBL, ATM, WDR75, FOXO1, and ITK [18]. Alterations in gene expression related to innate immunity have also been reported in NS [19, 20]. The innate immune response in NS is driven by genes involved in innate immunity, such as IL1R2, ILRN, and SOCS3 [21]. The risk of developing NS is also associated with polymorphisms in exon 1 of mannose-binding lectin and Toll-like receptor 4 [22]. Based on the immunomodulatory effects of rhIL-7 in sepsis [23], targeting T cell immunometabolism in early or late sepsis has great therapeutic potential [16]. However, the pathogenesis of NS has not yet been fully established and needs further understanding.
In the present study, bioinformatic analysis and GSEA of DEGs in the merged dataset showed significant enrichment of immune and inflammatory responses, including acute inflammatory response, bacterial detection (including coagulase-negative Staphylococcus, Enterococcus species, et al. [ 19, 24]), and regulation of macrophage activation, which play important roles in the pathogenesis of NS. Most infiltrating immune cells were significantly different in patients with NS compared to the control group; activated CD8 + T and B cells, CD56 natural killer cells, naïve dendritic cells, and T helper cells were significantly enriched in the sepsis group, whereas activated dendritic cells, memory CD8 + T cells, macrophages, plasmacytoid dendritic cells, and neutrophils were significantly enriched in the control group. GSEA of IRGs showed that their functions were significantly enriched in the activation of immune response, macrophage, and the regulation of T cells. The diagnostic model of DEG containing five genes (PROS1, TDRD9, RETN, LOC728401, and METTL7B) and that of IRG with one gene (NSUN7) were constructed using LASSO algorithm, and their diagnostic performance verified by correlation and logical analyses showed good area under the curve (AUC) scores. Additionally, the DEG and IRG models were verified in the GPL6947 and GPL13667 sub-datasets, respectively. The IRG model performed better than the DEG model. The IRG model contained only NSUN7 suggesting that this gene may be important for the diagnosis and treatment of NS. Finally, statistical analysis of the validation datasets suggested that the risk scores may be related to gestational age and birth weight, regardless of sex.
Current knowledge of human B and T cells in sepsis is sparse, discordant, and at variance with findings reported from animal models. Our research find the activated B cell and activated CD8 T cells showed lesser expression in sepsis cases compared to control. These data are in agreement with those published in previous studies. Hotchkiss et al. [ 25] demonstrated that patients with sepsis show a severe B-cell deficiency. Monserrat et al. [ 17] pointed that B-cell lymphopenia affects the B-cell subsets heterogeneously, with marked reduction of CD19 + CD23 + B cells (activated regulatory B cells) and CD19 + CD5 + B cells (natural responder B-1a cells), but with normal numbers of CD19 + CD69 + early activated B cells. Similar findings were reported by other groups [26]. Meanwhile it is established that septic shock is associated with a severe exhaustion and depletion of T lymphocytes [27]. So the present results establish an association between decreased lymphocytes and sepsis but do not establish causality between lymphocyte apoptosis and outcome in patients with sepsis, which required further investigation.
Sun RNA methyltransferase 7 (NSUN7) belonging to the methyltransferase superfamily is located on chromosome 4p14 and consists of 12 exons and 718 amino acids. It reduces protein activity and motility of sperms and is associated with male infertility [28]. High expression of NSUN7 is associated with shortened survival in low-grade gliomas [29]. The overall survival in *Ewing sarcoma* is significantly associated with NSUN7 immunoreactivity, an independent favourable prognostic marker [30]. NSUN7 may also serve as a pivotal biomarker for predicting biochemical recurrence in patients with prostate cancer [31]. An increase in the mean precursor strength of plasma protein polypeptides, such as NSUN7, is associated with sepsis [32]. NSUN7 may also be associated with psychiatric disorders, including schizophrenia, bipolar disorder [33], and major depressive disorders. In eukaryotes, the NSUN family is the major RNA m5C modifying enzyme and includes seven family members (NSUN1–7). The biological function and significance of RNA m5C modification in maintaining mRNA stability is essential during early embryonic development and in the post-embryonic immune system. NSUN7 has been systematically studied in male sperm motility, but its mechanism of action in tumours and sepsis has not been elucidated. In the present study, NSUN7 expression was up-regulated in the NS group. Combined with bioinformatic analyses, NSUN7 may be used as a biomarker for the pathogenesis of NS.
Resistin (RETN), located on chromosome 19p13.2, encodes an anti-retro-transcriptional protein and belongs to the resistance protein-like gene family. Its encoded protein, a 114 amino acid polypeptide (12.5 kDa) hormone, is secreted by adipocytes and is a member of the cysteine-rich small secreted protein gene family [34, 35]. RETN activates monocytes and macrophages and induces the release of proinflammatory cytokines including lipopolysaccharides, IL-1, IL-6, and tumour necrosis factor (TNF)-α [36–38]. RETN promotes endothelial cell activation and smooth muscle cell proliferation [39]. Elevated RETN levels have been reported in sepsis samples [40–43]. Clinical observations have indicated that plasma RETN levels are highly correlated with the levels of inflammatory markers, such as CRP and IL-6 [44]. Additionally, RETN increases endothelial cell permeability, thereby promoting the adhesion and infiltration of endothelial cells and monocytes. RETN also mediates immunosuppression, directly suppresses neutrophil function, and is associated with poor outcomes in sepsis [45]. These findings suggest a link between RETN, immunity, and inflammation. In the present study, RETN expression was up-regulated in the NS group, indicating that RETN may be involved in the occurrence and development of NS.
Protein S1 (PROS1), located on chromosome 3q11.1, is a vitamin K-dependent plasma protein that activates coagulation factors V and VIII by activating protein C while promoting the clearance of early apoptotic cells [46]. Tyrosine kinase receptor (TAM receptor) regulates the basic mediator of inflammatory response; PROS1 acts as a ligand of TAM receptor; and the expression of proinflammatory factors, such as TNF-α and CCL3, is increased during PROS1 deficiency [45]. PROS1 expression is positively correlated with neutrophil count and activity and oxidative burst, and is a potential therapeutic target for decompensated cirrhosis and sepsis [46]. PROS1 can be used as a targeted drug for the treatment of inflammatory diseases, such as spinal cord injury and ankylosing spondylitis [47]. In the present study, PROS1 expression was up-regulated in the NS group. The role of PROS1 in the coagulation mechanism has been systematically studied; however, its role in NS has not been elucidated.
Methyltransferase 7B (METTL7B) belongs to the methyltransferase-like protein family, and is located on chromosome 12. To date, the function of METTL7B is unclear, although several studies have linked it to specific disease states, subcellular localization, and cellular processes [48, 49]. A recent study found that METTL7B has methylase activity, which can methylate intracellular alkanethiol molecules and reduce associated cellular toxicity [49, 50]. METTL7B expression is associated with immune cells, such as B cells, CD4 + T cells, CD8 + T cells, monocytes, neutrophils, macrophages, and activated mast cells. Clinical studies have shown that METTL7B responds to inflammatory signals via Janus Kinase 1 [51]. In the present study, METTL7B expression was up-regulated in the NS group, indicating that METTL7B may be involved in the occurrence and development of NS.
Tudor domain-containing protein 9 (TDRD9) is a DEXH-box RNA helicase, which is involved in PIWI-interacting RNA formation [52]. TDRD9 is a DNA damage and repair-associated gene and is mainly expressed in sperms [53]. It can be used to predict disease-free survival in cancers, such as clear cell renal cell carcinoma and thyroid cancer [54, 55]. In addition to the male reproductive system, it is mainly expressed in the blood cells, including monocytes and dendritic cells, which play important roles in the innate immune response [56].
The novelty of our study is as follows. First, we used bioinformatic analysis to investigate the molecular mechanisms of NS from the perspectives of immunity and inflammation. Second, we found that NSUN7, PROS1, TDRD9, RETN, LOC728401, and METTL7B may be potential diagnostic biomarkers for NS, particularly NSUN7. However, this study has some limitations. First, we could not determine whether a causal relationship exists between the differences in gene expression and pathophysiological mechanisms of NS or if it is simply a compensatory change. Second, the study was a retrospective data analysis; therefore, we lacked detailed clinical and prognostic data, which limited further exploration of the genes for their clinical characteristics and outcomes. Finally, our study was based on bioinformatic analysis of transcriptome data from public datasets, which may be inconsistent with the actual situation. Further clinical trials are needed to validate our findings.
## Conclusions
Through bioinformatic analysis of published transcriptional data, NSUN7, PROS1, TDRD9, RETN, LOC728401, and METTL7B were identified as potential biomarkers of NS from the perspective of immune cell infiltration combined with logistic regression. More importantly, the developed diagnostic models provide a new perspective for future research on the pathogenesis of NS.
## NS datasets and data process
RNA sequencing data that investigated gene expression in peripheral blood samples from patients with NS were downloaded from the Gene Expression Omnibus (GEO) database, which included GSE69686 (including 64 NS and 85 control samples), and GSE25504 (including 170 samples, which were divided into four platforms, involving GPL570, GPL6947, GPL13667, and GPL15158). In consideration of sample size and sequencing platforms, GSE69686 was used as analysis dataset and GSE25504 (GPL13667 and GPL6947 platform) was used as validation datasets. Next, the corresponding expression matrix and clinical information were download and matched. The expression matrix were pre-processed via quantile normalization with R package limma [57].
## Identifying DEGs between NS and normal samples
In order to identify DEGs, the R package limma [1] which implements an empirical Bayesian approach to estimate gene-expression changes using moderated t-tests, was applied to determine DEGs among different groups; DEGs were screened by criteria (adjusted P value < 0.05) as implemented in limma. Volcano plots were generated to visualize the expression of DEGs. Hierarchal clustering was also conducted to measure the correlation of DEGs and identify potential gene modules by using R package pheatmap. In addition, to identify the potential function and involved pathways, we performed GSEA based on the differential expression profiles using the clusterProfiler R package [58].
## Depicting immunological characteristics of immune cell microenvironment in neonatal samples
The immunological characteristics of immune cell microenvironment in neonatal samples were depicted with the GSE69686 dataset. The Single-Sample Gene-Set Enrichment Analysis (ssGSEA) algorithm was used to quantify the relative abundance of tumour-infiltrating immune cells based on specific immune cell gene sets obtained from Charoentong et al. [ 59]. The differences between NS and normal samples were visualized with boxplots by using R package ggpubr, and the correlations among immune cells were shown in correlation heatmap.
## Unsupervised clustering by immune cell composition
To explore differences related with immune cell microenvironment between patients with NS and normal samples, we applied consensus clustering analysis to GSE69686 dataset based on the immune cell composition calculated by ssGSEA algorithm. This was performed using the Consensus Cluster Plus R package [60], and two subgroups were identified.
## Identifying IRGs between NS and normal samples
The R package limma was used to calculate IRGs between two clusters. Heatmap and volcano plots were generated to visualize the IRGs in two clusters. Furthermore, GSEA was performed based on IRGs to estimate related pathways.
## Gene expression signature identification and diagnostic model construction
DEGs and IRGs were used to build diagnostic models. Firstly, the random forest algorithm was used to filter genes used in model construction. According to the cross-validation results, the top 20 DEGs and top 15 IRGs sorted by mean decrease accuracy were selected (see Additional file 6: Fig. S6). Then, the LASSO algorithm was used to build classification models based on the actual diagnosis. At last, risk score of all samples was calculated according to the coefficients in the diagnostic models.
## Evaluating the effectiveness of diagnostic models
The effectiveness of the two diagnostic models was evaluated in the training dataset GSE69686 and validation datasets GSE25504 (GPL13667 and GPL6947 platform). ROC curve was used to evaluate the accuracy of the signatures in predicting the diagnostic results. In addition, bootstrap method was adopted to validate the reliability of ROC curve. The density plots showed the results of AUC calculated 1000 times for both datasets and models.
## Statistical analysis
Data were analysed with R (version 4.1.0) and R Bioconductor packages. Fisher’s exact test was used to analyse differences between high-risk and low-risk samples. P-values less than 0.05 were considered statistically significant.
## Supplementary Information
Additional file 1: Table S1. The DEGs between neonatal sepsis patients and normal samples. Additional file 2: Table S2. The immune cell compositions of neonatal sepsis patients and normal samples. Additional file 3: Table S3. The IRGs between neonatal sepsis patients and normal samples. Additional file 4: Table S4. The coefficients of regulators in the DEG diagnostic model. Additional file 5: Table S5. The coefficients of regulators in the IRG diagnostic model. Additional file 6: Figure S6. Cross-validation error of classification with DEGs (a) and IRGs (b) based on random forest method.
## References
1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M. **The third international consensus definitions for sepsis and septic shock (Sepsis-3)**. *JAMA* (2016) **315** 801-810. DOI: 10.1001/jama.2016.0287
2. Soong J, Soni N. **Sepsis: recognition and treatment**. *Clin Med (Lond)* (2012) **12** 276-280. DOI: 10.7861/clinmedicine.12-3-276
3. Levy MM, Dellinger RP, Townsend SR, Linde-Zwirble WT, Marshall JC, Bion J. **The Surviving Sepsis Campaign: results of an international guideline-based performance improvement program targeting severe sepsis**. *Crit Care Med* (2010) **38** 367-374. DOI: 10.1097/CCM.0b013e3181cb0cdc
4. Vergnano S, Sharland M, Kazembe P, Mwansambo C, Heath PT. **Neonatal sepsis: an international perspective**. *Arch Dis Child Fetal Neonatal Ed* (2005) **90** F220-F224. DOI: 10.1136/adc.2002.022863
5. Yadav P, Yadav SK. **Progress in diagnosis and treatment of neonatal sepsis: a review article**. *JNMA J Nepal Med Assoc* (2022) **60** 318-324. DOI: 10.31729/jnma.7324
6. Liu L, Oza S, Hogan D, Perin J, Rudan I, Lawn JE. **Global, regional, and national causes of child mortality in 2000–13, with projections to inform post-2015 priorities: an updated systematic analysis**. *Lancet* (2015) **385** 430-440. DOI: 10.1016/S0140-6736(14)61698-6
7. Milton R, Gillespie D, Dyer C, Taiyari K, Carvalho MJ, Thomson K. **Neonatal sepsis and mortality in low-income and middle-income countries from a facility-based birth cohort: an international multisite prospective observational study**. *Lancet Glob Health* (2022) **10** e661-e672. DOI: 10.1016/S2214-109X(22)00043-2
8. van Herk W, el Helou S, Janota J, Hagmann C, Klingenberg C, Staub E. **Variation in current management of term and late-preterm neonates at risk for early-onset sepsis: an international survey and review of guidelines**. *Pediatr Infect Dis J* (2016) **35** 494-500. DOI: 10.1097/INF.0000000000001063
9. Fan Y, Yu JL. **Umbilical blood biomarkers for predicting early-onset neonatal sepsis**. *World J Pediatr* (2012) **8** 101-108. DOI: 10.1007/s12519-012-0347-3
10. Póvoa P, Coelho L, Almeida E, Fernandes A, Mealha R, Moreira P. **C-reactive protein as a marker of infection in critically ill patients**. *Clin Microbiol Infect* (2005) **11** 101-108. DOI: 10.1111/j.1469-0691.2004.01044.x
11. O'Grady NP, Barie PS, Bartlett JG, Bleck T, Carroll K, Kalil AC. **Guidelines for evaluation of new fever in critically ill adult patients: 2008 update from the American College of Critical Care Medicine and the Infectious Diseases Society of America**. *Crit Care Med* (2008) **36** 1330-1349. DOI: 10.1097/CCM.0b013e318169eda9
12. Tang BM, Eslick GD, Craig JC, McLean AS. **Accuracy of procalcitonin for sepsis diagnosis in critically ill patients: systematic review and meta-analysis**. *Lancet Infect Dis* (2007) **7** 210-217. DOI: 10.1016/S1473-3099(07)70052-X
13. Wacker C, Prkno A, Brunkhorst FM, Schlattmann P. **Procalcitonin as a diagnostic marker for sepsis: a systematic review and meta-analysis**. *Lancet Infect Dis* (2013) **13** 426-435. DOI: 10.1016/S1473-3099(12)70323-7
14. Henriquez-Camacho C, Losa J. **Biomarkers for sepsis**. *BioMed Res Int* (2014) **2014** 547818. DOI: 10.1155/2014/547818
15. Boomer JS, Green JM, Hotchkiss RS. **The changing immune system in sepsis: is individualized immuno-modulatory therapy the answer?**. *Virulence* (2014) **5** 45-56. DOI: 10.4161/viru.26516
16. Kumar V. **T cells and their immunometabolism: a novel way to understanding sepsis immunopathogenesis and future therapeutics**. *Eur J Cell Biol* (2018) **97** 379-392. DOI: 10.1016/j.ejcb.2018.05.001
17. Monserrat J, de Pablo R, Diaz-Martín D, Rodríguez-Zapata M, de la Hera A, Prieto A. **Early alterations of B cells in patients with septic shock**. *Crit Care* (2013) **17** R105. DOI: 10.1186/cc12750
18. Wang H, Huang J, Yi W, Li J, He N, Kang L. **Identification of immune-related key genes as potential diagnostic biomarkers of sepsis in children**. *J Inflam Res* (2022) **15** 2441-2459. DOI: 10.2147/JIR.S359908
19. Wynn JL, Guthrie SO, Wong HR, Lahni P, Ungaro R, Lopez MC. **Postnatal age is a critical determinant of the neonatal host response to sepsis**. *Mol Med* (2015) **21** 496-504. DOI: 10.2119/molmed.2015.00064
20. Cernada M, Serna E, Bauerl C, Collado MC, Pérez-Martínez G, Vento M. **Genome-wide expression profiles in very low birth weight infants with neonatal sepsis**. *Pediatrics* (2014) **133** e1203-e1211. DOI: 10.2119/molmed.2015.00064
21. Smith CL, Dickinson P, Forster T, Craigon M, Ross A, Khondoker MR. **Identification of a human neonatal immune-metabolic network associated with bacterial infection**. *Nat Commun* (2014) **5** 4649. DOI: 10.1038/ncomms5649
22. Sljivancanin Jakovljevic T, Martic J, Jacimovic J, Nikolic N, Milasin J, Mitrović TL. **Association between innate immunity gene polymorphisms and neonatal sepsis development: a systematic review and meta-analysis**. *World J Pediatr* (2022). DOI: 10.1007/s12519-022-00569-7
23. Shindo Y, Fuchs AG, Davis CG, Eitas T, Unsinger J, Burnham CD. **Interleukin 7 immunotherapy improves host immunity and survival in a two-hit model of**. *J Leukoc Biol* (2017) **101** 543-554. DOI: 10.1189/jlb.4A1215-581R
24. Dickinson P, Smith CL, Forster T, Craigon M, Ross AJ, Khondoker MR. **Whole blood gene expression profiling of neonates with confirmed bacterial sepsis**. *Genom Data* (2014) **15** 41-48. DOI: 10.1016/j.gdata.2014.11.003
25. Hotchkiss RS, Tinsley KW, Swanson PE, Schmieg RE, Hui JJ, Chang KC. **Sepsis-induced apoptosis causes progressive profound depletion of B and CD4+ T lymphocytes in humans**. *J Immunol* (2001) **166** 6952-6963. DOI: 10.4049/jimmunol.166.11.6952
26. Gustave CA, Gossez M, Demaret J, Rimmelé T, Lepape A, Malcus C. **Septic shock shapes B cell response toward an exhausted-like/ immunoregulatory profile in patients**. *J Immunol* (2018) **200** 2418-2425. DOI: 10.4049/jimmunol.1700929
27. Boomer JS, Shuherk-Shaffer J, Hotchkiss RS, Green JM. **A prospective analysis of lymphocyte phenotype and function over the course of acute sepsis**. *Crit Care* (2012) **17** R112. DOI: 10.1186/cc11404
28. Khosronezhad N, Hosseinzadeh Colagar A, Mortazavi SM. **The Nsun7 (A11337)-deletion mutation, causes reduction of its protein rate and associated with sperm motility defect in infertile men**. *J Assist Reprod Genet* (2015) **32** 807-815. DOI: 10.1007/s10815-015-0443-0
29. Sato K, Tahata K, Akimoto K. **Five genes associated with survival in patients with lower-grade gliomas were identified by information-theoretical analysis**. *Anticancer Res* (2020) **40** 2777-2785. DOI: 10.21873/anticanres.14250
30. Chen Y, Su H, Su Y, Zhang Y, Lin Y, Haglund F. **Identification of an RNA-binding-protein-based prognostic model for Ewing sarcoma**. *Cancers (Basel)* (2021) **13** 3736. DOI: 10.3390/cancers13153736
31. Xing Q, Liu S, Luan J, Wang Y, Ma L. **A novel 13 RNA binding proteins (RBPs) signature could predict prostate cancer biochemical recurrence**. *Pathol Res Pract* (2021) **225** 153587. DOI: 10.1016/j.prp.2021.153587
32. Thavarajah T, Dos Santos CC, Slutsky AS, Marshall JC, Bowden P, Romaschin A. **The plasma peptides of sepsis**. *Clin Proteom* (2020) **17** 26. DOI: 10.1186/s12014-020-09288-5
33. Tang J, Chen X, Cai B, Chen G. **A logical relationship for schizophrenia, bipolar, and major depressive disorder. Part 4: evidence from chromosome 4 high-density association screen**. *J Comp Neurol* (2019) **527** 392-405. DOI: 10.1002/cne.24543
34. Steppan CM, Brown EJ, Wright CM, Bhat S, Banerjee RR, Dai CY. **A family of tissue-specific resistin-like molecules**. *Proc Natl Acad Sci U S A* (2001) **98** 502-506. DOI: 10.1073/pnas.98.2.502
35. Liu CW, Yang SY, Lin CK, Liu HS, Ho LT, Wu LY. **The forkhead transcription factor FOXO1 stimulates the expression of the adipocyte resistin gene**. *Gen Comp Endocrinol* (2014) **196** 41-51. DOI: 10.1016/j.ygcen.2013.11.018
36. Jung HS, Park KH, Cho YM, Chung SS, Cho HJ, Cho SY. **Resistin is secreted from macrophages in atheromas and promotes atherosclerosis**. *Cardiovasc Res* (2006) **69** 76-85. DOI: 10.1016/j.cardiores.2005.09.015
37. Tilg H, Moschen AR. **Adipocytokines: mediators linking adipose tissue, inflammation and immunity**. *Nat Rev Immunol* (2006) **6** 772-783. DOI: 10.1038/nri1937
38. Tsiotra PC, Boutati E, Dimitriadis G, Raptis SA. **High insulin and leptin increase resistin and inflammatory cytokine production from human mononuclear cells**. *BioMed Res Int* (2013) **2013** 487081. DOI: 10.1155/2013/487081
39. Calabro P, Samudio I, Willerson JT, Yeh ET. **Resistin promotes smooth muscle cell proliferation through activation of extracellular signal-regulated kinase 1/2 and phosphatidylinositol 3-kinase pathways**. *Circulation* (2004) **110** 3335-3340. DOI: 10.1161/01.CIR.0000147825.97879.E7
40. Gong F, Li R, Zheng X, Chen W, Zheng Y, Yang Z. **OLFM4 regulates lung epithelial cell function in sepsis-associated ARDS/ALI via LDHA-mediated NF-κB signaling**. *J Inflam Res* (2021) **14** 7035-7051. DOI: 10.2147/JIR.S335915
41. Lee KO, Lee KY, Lee CY, Kim JH, Kang J, Lee HY. **High serum levels of resistin is associated With acute cerebral infarction**. *Neurologist* (2021) **27** 41-45. DOI: 10.1097/NRL.0000000000000362
42. Xu Z, Jiang M, Bai X, Ding L, Dong P, Jiang M. **Identification and verification of potential core genes in pediatric septic shock**. *Comb Chem High Throughput Screen* (2022). DOI: 10.2174/1386207325666220310110902
43. Lu J, Chen R, Ou Y, Jiang Q, Wang L, Liu G. **Characterization of immune-related genes and immune infiltration features for early diagnosis, prognosis and recognition of immunosuppression in sepsis**. *Int Immunopharmacol* (2022) **107** 108650. DOI: 10.1016/j.intimp.2022.108650
44. Stejskal D, Adamovská S, Bartek J, Juráková R, Prosková J. **Resistin—concentrations in persons with type 2 diabetes mellitus and in individuals with acute inflammatory disease**. *Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub* (2003) **147** 63-69. DOI: 10.5507/bp.2003.009
45. Miller L, Singbartl K, Chroneos ZC, Ruiz-Velasco V, Lang CH, Bonavia A. **Resistin directly inhibits bacterial killing in neutrophils**. *Intensive Care Med Exp* (2019) **7** 30. DOI: 10.1186/s40635-019-0257-y
46. Anderson HA, Maylock CA, Williams JA, Paweletz CP, Shu H, Shacter E. **Serum-derived protein S binds to phosphatidylserine and stimulates the phagocytosis of apoptotic cells**. *Nat Immunol* (2003) **4** 87-91. DOI: 10.1038/ni871
47. Wang C, Ma H, Wu W, Lu X. **Drug discovery in spinal cord injury with ankylosing spondylitis identified by text mining and biomedical databases**. *Front Genet* (2022) **13** 799970. DOI: 10.3389/fgene.2022.799970
48. McKinnon CM, Mellor H. **The tumor suppressor RhoBTB1 controls Golgi integrity and breast cancer cell invasion through METTL7B**. *BMC Cancer* (2017) **17** 145. DOI: 10.1186/s12885-017-3138-3
49. Maldonato BJ, Russell DA, Totah RA. **Human METTL7B is an alkyl thiol methyltransferase that metabolizes hydrogen sulfide and captopril**. *Sci Rep* (2021) **11** 4857. DOI: 10.1038/s41598-021-84218-5
50. Sehgal R, Kaur N, Maiwall R, Ramakrishna G, Maras JS, Trehanpati N. **Plasma proteomic analysis identified proteins associated with faulty neutrophils functionality in decompensated cirrhosis patients with sepsis**. *Cells* (2022) **11** 1745. DOI: 10.3390/cells11111745
51. Dowty ME, Lin TH, Jesson MI, Hegen M, Martin DA, Katkade V. **Janus kinase inhibitors for the treatment of rheumatoid arthritis demonstrate similar profiles of in vitro cytokine receptor inhibition**. *Pharmacol Res Perspect* (2019) **7** e00537. DOI: 10.1002/prp2.537
52. Shoji M, Tanaka T, Hosokawa M, Reuter M, Stark A, Kato Y. **The TDRD9-MIWI2 complex is essential for piRNA-mediated retrotransposon silencing in the mouse male germline**. *Dev Cell* (2009) **17** 775-787. DOI: 10.1016/j.devcel.2009.10.012
53. Kherraf ZE, Cazin C, Bouker A, Fourati Ben Mustapha S, Hennebicq S, Septier A. **Whole-exome sequencing improves the diagnosis and care of men with non-obstructive azoospermia**. *Am J Hum Genet* (2022) **109** 508-517. DOI: 10.1016/j.ajhg.2022.01.011
54. Wu Y, Wei X, Feng H, Hu B, Liu B, Luan Y. **Transcriptome analyses identify an RNA binding protein related prognostic model for clear cell renal cell carcinoma**. *Front Genet* (2020) **11** 617872. DOI: 10.3389/fgene.2020.617872
55. Zhen J, Song Z, Su W, Zeng QC, Li J, Sun Q. **Integrated analysis of RNA-binding proteins in thyroid cancer**. *PLoS ONE* (2021) **16** e0247836. DOI: 10.1371/journal.pone.0247836
56. Hosseinipour M, Shahbazi S, Roudbar-Mohammadi S, Khorasani M, Marjani M. **Differential genes expression analysis of invasive aspergillosis: a bioinformatics study based on mRNA/microRNA**. *Mol Biol Res Commun* (2020) **9** 173-180. DOI: 10.22099/mbrc.2020.37432.1509
57. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W. **Limma powers differential expression analyses for RNA-sequencing and microarray studies**. *Nucleic Acids Res* (2015) **43** e47. DOI: 10.1093/nar/gkv007
58. Yu G, Wang LG, Han Y, He QY. **clusterProfiler: an R package for comparing biological themes among gene clusters**. *OMICS* (2012) **16** 284-287. DOI: 10.1089/omi.2011.0118
59. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D. **Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade**. *Cell Rep* (2017) **18** 248-262. DOI: 10.1016/j.celrep.2016.12.019
60. Wilkerson MD, Hayes DN. **ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking**. *Bioinformatics* (2010) **26** 1572-1573. DOI: 10.1093/bioinformatics/btq170
|
---
title: 'Prediction of outcomes in subjects with type 2 diabetes and diabetic foot
ulcers in Catalonian primary care centers: a multicenter observational study'
authors:
- Magdalena Bundó
- Bogdan Vlacho
- Judit Llussà
- Isabel Bobé
- Meritxell Aivar
- Carmen Ciria
- Ana Martínez-Sánchez
- Jordi Real
- Manel Mata-Cases
- Xavier Cos
- Montserrat Dòria
- Jordi Viade
- Josep Franch-Nadal
- Dídac Mauricio
journal: Journal of Foot and Ankle Research
year: 2023
pmcid: PMC9972716
doi: 10.1186/s13047-023-00602-6
license: CC BY 4.0
---
# Prediction of outcomes in subjects with type 2 diabetes and diabetic foot ulcers in Catalonian primary care centers: a multicenter observational study
## Abstract
### Background
Diabetic foot and lower limb complications are an important cause of morbidity and mortality among persons with diabetes mellitus. Very few studies have been carried out in the primary care settings. The main objective was to assess the prognosis of diabetic foot ulcer (DFU) in patients from primary care centers in Catalonia, Spain, during a 12-month follow-up period.
### Methods
We included participants with type 2 diabetes and a new DFU between February 2018 and July 2019. We estimated the incidence of mortality, amputations, recurrence and healing of DFU during the follow-up period. A multivariable analysis was performed to assess the association of these outcomes and risk factors.
### Results
During the follow-up period, $9.7\%$ of participants died, $12.1\%$ required amputation, $29.2\%$ had a DFU recurrence, and $73.8\%$ healed. Having a caregiver, ischemia or infection were associated with higher mortality risk (hazard ratio [HR]:3.63, $95\%$ confidence interval [CI]:1.05; 12.61, HR: 6.41, $95\%$CI: 2.25; 18.30, HR: 3.06, $95\%$CI: 1.05; 8.94, respectively). Diabetic retinopathy was an independent risk factor for amputation events (HR: 3.39, $95\%$CI: 1.37; 8.39). Increasing age decreased the risk for a DFU recurrence, while having a caregiver increased the risk for this event (HR: 0.97, $95\%$CI: 0.94; 0.99). The need for a caregiver and infection decreased the probability of DFU healing (HR: 0.57, $95\%$CI: 0.39; 0.83, HR: 0.64, $95\%$CI: 0.42; 0.98, respectively). High scores for PEDIS (≥7) or SINBAD (≥3) were associated with an increased risk for DFU recurrence and a lower probability of DFU healing, respectively.
### Conclusions
We observed high morbidity among subjects with a new DFU in our primary healthcare facilities. Peripheral arterial disease, infection, and microvascular complications increased the risk of poor clinical outcomes among subjects with DFU.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13047-023-00602-6.
## Background
Diabetic foot and lower limb complications are an important cause of morbidity and mortality among people with diabetes mellitus (DM) [1, 2]. People with diabetic foot ulcers (DFU) require more hospital visits and admissions than those without this complication [3]. Disease-related complications such as DFU can negatively impact the patient's quality of life, as well as increase healthcare costs [1, 2].
Primary healthcare centers are the patient's first contact with the health system in many countries, and its role in the prevention and treatment of chronic conditions such as DM and its complications is fundamental. Therefore, the task of primary health professionals is crucial for the prevention, early detection, and treatment of diabetic foot complications. Increasing the knowledge and awareness of the risk factors that worsen the prognosis of people with DFU at this level of the healthcare system (i.e. primary care) is necessary to act in a more focused, resourceful and decisive way. So far, several studies on the prognosis of the diabetic foot and its associated contributing factors have been carried out in hospital settings, in specialized diabetes clinics and multidisciplinary foot centers [4–11]. However, very few studies have been carried out in the primary care setting [12, 13], and therefore the existing data at this level of the healthcare system are scarce.
The International Working Group of the Diabetic Foot (IWGDF) has published an evidence-based guideline concerning the classification of DFU and the use of classification systems in routine clinical practice [14]. Three types of classifications have been defined: patient-related (morbidity of the patient, e.g., presence of chronic kidney disease), limb-related (peripheral artery disease and loss of protective sensation), and ulcer-related (area, depth, localization, number, and infection). The IWGDF [14] recommends these classifications to facilitate communication among health professionals, for treatment guidance, and for audits of clinical outcomes in healthcare units and populations, but does not recommend their use for prognostic purposes in patients with DM foot ulcers. Indeed, there is a lack of information on the applicability of the different DFU classifications and their prognostic value in primary care.
We carried out a multicenter study in Catalonia (Spain), where the annual incidence of the occurrence of a new DFU during the recruitment period was $0.42\%$ [15].
## Aim, design and setting of the study
The aim of the current study was to assess the clinical progression of DFU during a 12-month follow-up period in people with type 2 diabetes (T2DM) with a diagnosis of a new DFU. We conducted a prospective single-cohort observational study in 36 primary care healthcare centers in Catalonia. The health care system in *Catalonia is* public and universal to all residents. In each primary care center, the health care user (patient) is allocated a medical doctor and a nurse. The primary healthcare centers act as gatekeepers to access other healthcare levels (secondary and tertiary care). In our study, people with T2DM with a new DFU who attended one of the participating centers were included between February 1st, 2018 and July 31st, 2019. The follow-up period was up to 12 months or until premature discontinuation (death or loss to follow-up). The detailed methodology has been previously published [15].
## Study procedures
During the first month after the inclusion, weekly follow-up visits for each participant were performed. After the first month, in case of active foot ulcers, follow-up visits were scheduled monthly or more frequently when deemed necessary by the treating team. Upon foot ulcer healing, follow-up visits were scheduled every three months or until the end of the follow-up period. All of the study procedures, data collection check-ups and fulfilment of good clinical practice were externally monitored to ensure correct study practices.
## Definition of diabetic foot ulcer, study variables and outcomes
We defined a foot ulcer as a full-thickness lesion below the ankle, regardless of the presence of neuropathy and/or peripheral artery disease. In individuals with more than one ulcer at baseline, the most clinically relevant lesion was selected as the index DFU.
At the inclusion, for each participant in the study, we collected the following demographic and clinical information (variables): socio-demographic characteristics (age, gender, and self-reported ethnicity), toxic habits (smoking, alcohol intake), cardiovascular risk factors and concomitant disease (hypertension, hyperlipidemia, previous history of stroke, ischemic heart disease, peripheral artery disease, and heart failure), and data on diabetes (disease duration, antidiabetic treatment, and previous diagnosis of diabetic microvascular complications such as retinopathy, nephropathy, and peripheral neuropathy), and previous history of amputation or foot ulcers. In addition, for all participants, laboratory parameters were requested at inclusion for HbA1c, lipid profile, kidney function, and clinical parameters such as body mass index (BMI) and blood pressure were measured. We also collected information related to visual acuity, degree of mobility, caregiver access, and any podiatrist visits.
At all study visits, the site researchers also collected information related to the DFU, such as duration, location, the extension of ulcer after surgical debridement (longest diameter multiplied by the second longest diameter of the ulcer), ulcer´s depth (superficial ulcer: loss of superficial substance which does not penetrate beyond the dermis; deep ulcer: loss of substance below the dermis to subcutaneous structures or joint or bone exposure), presence or absence of infection, and infection severity.
The primary study outcomes were: mortality, amputation, recurrence and healing of DFU. Mortality was considered as death for any reason. The amputation event included both minor and major amputations. We defined a minor amputation as any surgical procedure resulting in an amputation of any part of the limb below the foot ankle, and major amputation was defined as any amputation above the foot ankle. DFU recurrence was considered if a new ulcer appeared during the follow-up period once the index ulcer had healed entirely. The healed DFU was defined as a fully epithelialized lesion (with or without amputation).
We used two DFU classification systems: the PEDIS classification [16], which evaluates variables such as Perfusion, Extent, Depth, Infection and Sensation, and the SINBAD classification [17] which includes variables related to the ulcer Site, Ischemia, Neuropathy, Bacterial Infection and Depth based on a scale of 0 to 6. For the PEDIS we used a scoring system (0 to 12) developed by Chuan et al. [ 18] to facilitate the use of the PEDIS in clinical practice. A PEDIS score of at least seven was considered clinically important, based on the study by Chuan et al. that found that patients with a PEDIS score of at least seven had an increased risk for the composite endpoint of non-healing amputation and death [18]. For the SINBAD classification, we used a scoring system (between 0 and 6) that was created by Ince et al. [ 17]; we considered a SINBAD score of at least three to be clinically important based on the study by Ince et al. [ 17] reporting that patients with a score of at least three had a higher risk of non-healing of the ulcer (including amputation and death).
## Statistical methods
Initially we carried out a descriptive analysis of the participants. The qualitative variables were described for number and frequency, and the quantitative variables were summarized by measures of central tendency and dispersion (mean, median, standard deviation, interquartile range). Subsequently we estimated the incidence (cumulative and event rates) for different study outcomes (mortality, amputations, recurrence and healing of DFU) during the follow-up to determine the evolution of the ulcers and the prognosis. Each event rate was estimated as the number of new cases of the event divided by the total person-time at risk during the follow-up period, stratified by type of the DFU (ischemic, neuro-ischemic or neuropathic). After this, we performed a univariate and multivariable proportional hazards analysis to assess the association of the main study outcomes and risk factors, considering the follow-up time. The variables included in the multivariable models were selected based on the clinical criteria. A complete case-analysis was performed. Estimated measures of association were expressed as crude and adjusted hazards ratios (HRs) and their $95\%$ confidence intervals ($95\%$CI). HRs are a measure of how often a particular event happens in one group compared to how often it happens in another group over time. A HR of 1 indicates a lack of an association between the variable (e.g. age) and the event happening (e.g. mortality), a HR greater than 1 indicates an increased risk of the event happening, and a HR below 1 suggests a lower risk of the event happening. To prevent variable collinearity, two different multivariable models were performed for the PEDIS and SIMBAD variables. We used the cox.zph function from the survival package in R (R statistical software) to check the proportional hazards assumption of the Cox models [19]. Furthermore, we included R2 Nagelkerke as an appropriate measure of goodness of fit for each model. Additionally, assumptions of PH Cox model were checked for each parameter [20, 21]. Thereafter, additional reduced models were done removing statistical non-significant variables. For this analysis we used the function cox.zph from {survival} R package (Version 3.3–1). The statistical analyses were performed using R3.6.1 (https://www.r-project.org).
## Results
A total of 256 participants were included. The baseline characteristics are presented in Table 1. Their mean age was 72.2 (12.7) years. Mean diabetes duration was 13.5 (8.1) years, $69.5\%$ were male, $51.6\%$ were treated with insulin, $27.3\%$ had a previous history of DFU, and $8.9\%$ had a previous amputation. Regarding comorbidities, $64.5\%$ of participants had peripheral neuropathy, $65.5\%$ had peripheral artery disease, $32\%$ had diabetic retinopathy, and $57.8\%$ had chronic kidney disease. Table 1Baseline characteristics of the study participantsVariableAll participants ($$n = 256$$)Age, mean (SD), years72.2 (12.7)Gender, n (%) Male178 (69.5)Toxic habits, n (%) Smokers50 (19.5) Former smokers88 (34.4) Non-smokers118 (46.1) High-risk alcohol intake14 (5.4)Comorbidities, n (%) Hypertension207 (80.9) Hyperlipidemia175 (68.4) Stroke37 (14.5) Ischemic heart disease55 (21.5) Hearth failure50 (19.5) Peripheral artery disease165 (64.5) Macrovascular complications101 (39.5) Retinopathy82 (32.0) Kidney disease148 (57.8) Peripheral neuropathy165 (64.5)Clinical variables Diabetes duration, mean (SD), years13.5 (8.1) BMI, mean (SD),29.6 (5.35)) HbA1c, mean (SD), %HbA1c, mean (SD), mmol/mol7.9 (1.9) 61.3 (14.6)Foot characteristics,n(%) Previous history of DFU70 (27.3) *Any previous* amputation23 (8.9) *Any previous* major amputation2 (0.8) Foot deformities104 (40.6) Inadequate footwear167 (65.2) Decreased visual acuity112 (43.8) Problems with mobility106 (41.4) Need of a caregiver89 (34.8) At least one podiatrist visit in the previous year125 (48.8)Ulcer site, n (%) Toes, plantar31 (12.1) Toes, dorsal or interdigital aspect112 (43.8) Dorsal or lateral aspect of the foot53 (20.7) Plantar forefoot or midfoot23 (8.9) Heel37 (14.5)Depth of the ulcer* Superficial ulcer197 (77.0) Deep ulcer59 (23.0)Extension of the ulcer** ≤ 1 cm2144 (56.2) > 1cm2112 (43.8)Ulcer type,n (%) Neuropathic ulcers52 (20.3) Neuro-ischemic ulcers113 (44.1) Ischemic ulcers52 (20.3) Without peripheral neuropathy or ischemic disease39 (15.3)Infection status,n (%) No infection150 (58.6) Infection106 (41.4) Neuropathy and infection67 (26.1) PAD and infection65 (25.3)SINBAD classification SINBAD, mean (SD)2.48 (1.17) SINBAD < 3139 (54.3) SINBAD ≥ 3117 (45.7)PEDIS classification PEDIS, mean (SD)5.21 (1.87) PEDIS < 7191 ($74.6\%$) PEDIS ≥ 765 ($25.4\%$)BMI Body mass index, DFU Diabetic foot ulcer, HbA1c Glycated hemoglobin, PAD Peripheral arterial disease, SD Standard deviation; * superficial ulcer: loss of superficial substance which does not penetrate beyond the dermis; deep ulcer: loss of substance below the dermis to subcutaneous structures or joint or bone exposure The highest mortality, amputation and DFU recurrence rates were observed among the 113 ($44.1\%$) participants with peripheral neuropathy and peripheral artery disease, while the highest healing rate and shortest time to healing were observed among those without peripheral neuropathy or peripheral artery disease. Supplementary Table 1 shows the events rates and cumulative incidence for the different types of DFU.
In the un-adjusted HR analysis, age, being female, the presence of macrovascular complications, problems with mobility and the need for a caregiver was associated with an increased risk of mortality. Diabetic retinopathy, chronic kidney disease, previous amputation history, or a baseline SINBAD score of 3 points or higher was associated with an increased risk of amputation. Diabetic retinopathy, a personal history of ulcers or amputations, or a baseline PEDIS score of 7 points or higher increased the risk for DFU recurrence, while age and being female decreased the risk for this event. Regarding non-healing, a higher risk was associated with diabetes duration, diabetic retinopathy, chronic kidney disease, a personal history of amputation, frailty variables (mobility or having a caregiver), ischemia, infection and DFU depth. Table 2 shows the results of the unadjusted HR for the different study outcomes and variables. Table 2Un-adjusted and adjusted hazards ratios for the main study outcomesEventsMortality($$n = 25$$)Amputations($$n = 31$$)DFU recurrence($$n = 75$$)DFU healing($$n = 189$$)Risk factors at baselineUn AdjustedHR[$95\%$CI]AdjustedHR[$95\%$CI]Un AdjustedHR[$95\%$CI]AdjustedHR[$95\%$CI]Un AdjustedHR[$95\%$CI]AdjustedHR[$95\%$CI]Un AdjustedHR[$95\%$CI]AdjustedHR[$95\%$CI]Age (SD)*1.06[1.02;1.10]1.00[0.94; 1.05]1.01[0.98;1.04]1.02[0.97; 1.06]*0.98[0.97;1.00]*0.97[0.95; 0.99]0.99[0.98;1.00]0.98[0.97; 1.00]Sex (female), ref.: male*2.58[1.17;5.65]2.01[0.71; 5.72]0.82[0.37;1.83]0.71[0.24; 2.05]0.58[0.33;1.01]NA1.01[0.74;1.38]1.17[0.83; 1.64]Current smoker, ref.: no0.42[0.12;1.44]0.38[0.07; 2.13]1.47[0.63;3.44]1.77[0.65; 4.82]1.48[0.84;2.60]1.22[0.67; 2.22]1.06[0.73;1.55]0.75[0.50; 1.13]Any alcohol risk, ref.: no0.91[0.40;2.07]1.41[0.47; 4.20]1.97[0.97;4.01]2.04[0.82; 5.05]1.48[0.84;2.60]1.35[0.98; 1.04]0.83[0.61;1.11]NADiabetes duration1.02[0.98;1.06]1.00[0.95; 1.06]1.02[0.98;1.06]0.98[0.94; 1.03]*1.02[1.00;1.05]1.01[0.98; 1.04]*0.98[0.96;0.99]0.98[0.97; 1.01]Hypertension, ref.: no1.61[0.48;5.39]0.78[0.18; 3.40]1.12[0.43;2.93]0.78[0.22; 2.86]0.75[0.43;1.31]0.85[0.42; 1.72]0.81[0.57;1.16]0.98[0.66; 1.45]Dislipidemia, ref.: no0.82[0.36;1.86]0.57[0.22; 1.52]0.82[0.39;1.71]0.48[0.20; 1.15]0.74[0.46;1.18]0.64[0.38; 1.07]0.95[0.70;1.29]1.06[0.77; 1.47]Macrovascular complications, ref.: no*3.67[1.58;8.52]1.08[0.12; 10.12]1.19[0.58;2.43]1.37[0.25; 7.63]1.38[0.87;2.18]1.31[0.56; 3.07]0.81[0.60;1.09]1.00[0.62 1.60]Retinopathy, ref.: no1.17[0.50;2.72]1.03[0.37; 2.86]*2.97[1.46;6.04]*3.39[1.37; 8.39]*1.64[1.03;2.61]1.17[0.69; 1.98]*0.64[0.47;0.88]0.67[0.47; 0.96]Chronic kidney disease (CKD), ref.: no2.11[0.96;4.66]0.79[0.28; 2.22]*2.15[1.06;4.36]1.60[0.66; 3.86]1.48[0.92;2.37]1.68[0.96; 2.94]*0.55[0.40;0.77]0.73[0.51; 1.06History of previous ulcers, ref.: no0.73[0.27;1.94]0.80[0.23; 2.80]1.76[0.85;3.64]1.28[0.42; 3.91]*1.85[1.14;2.99]NA0.73[0.52;1.00]NAHistory of previous amputation, ref.: no0.46[0.06;3.44]0.34[0.03; 3.47]*3.08[1.32;7.18]1.30[0.36; 4.70]*2.78[1.48;5.19]2.62[1.26; 5.45]*0.55[0.33;0.94]0.64[0.37; 1.11]HbA1c (%)0.99[0.79;1.24]1.04[0.79; 1.36]1.09[0.90;1.30]0.96[0.77; 1.20]1.03[0.91;1.16]0.98[0.86; 1.12]0.98[0.90;1.06]1.00[0.91; 1.08]BMI1.00[0.93;1.08]1.00[0.91; 1.10]0.97[0.90;1.04]1.01[0.92; 1.10]0.98[0.93;1.02]0.97[0.92; 1.02]1.02[0.99;1.05]NADecreased visual acuity, ref.: no1.03[0.47;2.26]0.57[0.22; 1.51]1.20[0.59;2.43]0.63[0.27; 1.46]1.16[0.74;1.83]0.93[0.55; 1.57]0.80[0.60;1.07]1.20[0.87; 1.66]Problems with mobility, ref.: no*3.90[1.63;9.35]2.87[0.84; 9.84]1.56[0.77;3.15]1.23[0.47; 3.24]1.16[0.74;1.83]1.16[0.65; 2.07]*0.64[0.48;0.86NANeed of a caregiver, ref.: no*5.63[2.35;13.5]*3.63[1.05; 12.61]2.00[0.99;4.06]2.26[0.92; 5.55]1.51[0.95;2.40]*1.81[1.03; 3.19]*0.52[0.38;0.72]*0.55[0.39; 0.79]Ischemia, ref.: no*4.30[1.90;9.74]*6.41[2.25; 18.30]*2.20[1.08;4.45]1.52[0.59; 3.87]1.56[0.98;2.50]1.47[0.83; 2.62]*0.62[0.45;0.85]0.84[0.59; 1.18]Infection, ref.: no1.07[0.48;2.38]*3.06[1.05; 8.94]*3.33[1.57;7.08]2.26[0.69; 7.36]1.38[0.88;2.17]1.50[0.76; 93]*0.57[0.42;0.77]*0.63[0.42; 0.96]Deep or very deep (Ref: superficial)0.50[0.15;1.69]*0.09[0.02; 0.54]*2.94[1.45;5.97]1.95[0.61; 6.24]1.49[0.90;2.45]0.68[0.30; 1.54]*0.55[0.38;0.80]0.64[0.38; 1.07]DFU Extension: > 1 cm, ref. ≤11.09[0.88;1.34]0.72[0.26; 1.94]0.85[0.69;1.05]1.84[0.82; 4.15]1.07[0.94;1.21]NA1.00[0.92;1.08]NAR2 Nagelkerke0.2170.2180.1260.234Global p-value**0.5720.7950.1460.837*In bold: statistically significant HR; BMI Body mass index, CI Confidence interval, DFU Diabetic foot ulcer, HbA1c Glycated hemoglobin, HR Hazard ratio, SD Standard deviation** Proportional Hazards Assumption of a Cox Regression testNA: Estimation not evaluable because it cannot be assumed that it meets the Proportional Hazards Assumption of the COX models Regarding the multivariable analysis, the need for a caregiver, ischemia or infection were associated with a higher mortality risk. Diabetic retinopathy was an independent risk factor for amputation events. Increasing age decreased the risk for a DFU recurrence, while having a caregiver increased the risk for this event. The need for a caregiver and the presence of infection decreased the probability of healing in the main model. For the additional multivariable models, a PEDIS score of 7 points or higher was only associated with an increased risk of developing a new ulcer, while a SINBAD score of 3 points or higher was only associated with a lower probability of healing. Table 2 and Fig. 1 show the multivariable analysis of risk factors for the main model, while Supplementary Table 2 and 3 and Fig. 2 shows the multivariable analysis for the PEDIS and SINBAD models. Checking the assumptions of PH Cox model, we observed that in the additional reduced models the assumption of PH was not rejected. In these reduced models, similar results were observed for the probability of occurrence of the study events (recurrence and healing of DFU). These additional models are presented in Supplementary Table 4.1–4.12.Fig. 1Associations of the main study outcomes with different risk factors, A) Mortality B) Amputations C) DFU recurrence D) DFU healing, BMI: body mass index; CKD: chronic kidney disease; CI: confidence intervals; DFU: diabetic foot ulcer; HbA1c: glycated hemoglobin; HR: hazard ratioFig. 2Associations of the main study outcomes with different risk factors in PEDIS and SINBAD models, A) Mortality B) Amputations C) DFU recurrence D) DFU healing, BMI: body mass index; CKD: chronic kidney disease; CI: confidence intervals; DFU: diabetic foot ulcer; HbA1c: glycated hemoglobin; HR: hazard ratio
## Discussion
Among the 256 participants with T2DM and a new DFU in this multicenter prospective cohort study from different primary care centers in Catalonia, we found a high risk for mortality, amputations and recurrence of a new DFU. So far, similar studies to ours have been carried out in a hospital setting or in multidisciplinary foot-care centers [4–11]. These studies differ from ours, especially in the level of the healthcare system where participants were recruited, and also for the inclusion criteria, the definition of the foot ulcer, and the follow-up time, which make comparisons with our findings difficult. From the studies conducted in primary care settings, similar to ours, Boyko et al. [ 12] performed a study in US veterans with a follow-up period of 22 years, where all of the participants were males. The study carried out by Muller et al. [ 13], which assessed the annual incidence of DFU and amputations among T2DM people registered in a database of 4,500 people with different chronic conditions, used a different methodology to ours; around 677 people with diabetes per year were studied between 1993 and 1998, with a reported annual incidence of DFU and amputation of $2.1\%$ and $0.6\%$, respectively, however, the authors provided very few clinical data, precluding a comparison with participants from our study.
In our cohort, we found a mortality rate of $9.7\%$. Three hospital-based studies have reported mortality rates in people with DFU with similar follow-up periods to our study [6,8.9]. In the study by Prompers et al. [ 6] with patients from 14 European hospitals, the authors reported lower mortality rates ($6\%$). However, these people were much younger (mean age 65 years) compared with our participants (mean age 72.2 years). The other two hospital-based studies with similar follow-up periods reported mortality rates much higher than in our study. The study carried out in Germany [8] with type 1 and type 2 diabetic patients showed a mortality rate of $15.4\%$, while a study carried out in China by Jiang et al. [ 9] reported mortality rates of $14.4\%$. The higher prevalence of comorbidities among the people included in the study from Germany and the large number of smokers ($43\%$) in the study from China, could partially explain these high mortality rates. As early as 1990, Apelqvist [22] warned that diabetic patients with a foot ulcer are at high risk of death. A meta-analysis performed by Saluja et al. [ 23] and by Brownrigg et al. [ 24] showed that DFU is associated with an increased risk of all-cause mortality compared to those without foot ulceration. In our study, in the adjusted model we did not find that macrovascular events (stroke, ischemic heart disease, heart failure) were associated with an increased risk of mortality in people with DFU as has been reported previously by other authors [5, 11]. In the meta-analysis conducted by Brownrigg et al. [ 24] the authors observed similar findings to ours regarding cardiovascular events and mortality between people with DFU and without DFU. In our study, we found an increase in mortality among women compared to men, which some authors have previously described and attributed to a greater frailty in women with DFU [25]. We also observed that ischemia and infection increased the risk of death in our multivariable analysis. These variables are well-known risk factors for a poor prognosis in subjects with DFU [26, 27]. The inverse relationship between ulcer depth and mortality may be explained by the difficulty in measuring ulcer depth on many occasions [17, 28].
Regarding amputations, we observed 31 ($9.7\%$) events during the follow-up period, much higher than the annual incidence of this event ($0.6\%$) reported in the cohort study by Muller et al. [ 13]. In similar hospital-based studies, the incidences of amputation events ranged from 0.05 to $19\%$ [6, 11, 29]. Significant variation exists in the incidence of lower limb amputation even within the same country [30, 31]. We found that the association between amputation and retinopathy was consistent throughout all the models performed. This association was previously described in other studies [32] and indicates the importance of performing ophthalmic examinations in patients with DFU and increasing foot care at the moment of diagnosis of diabetic retinopathy.
Overall, $29.2\%$ of participants in our study experienced a recurrence of a new foot ulcer during the follow-up. There is great variability between the studies regarding this outcome, ranging, for example, from $25\%$ in a study by Muller et al. [ 13], $32\%$ in a study by Jiang et al. [ 9] and $43\%$ in a study by Winkley et al. [ 5]. Age was negatively associated with DFU recurrence, however it is possible that some of the older adults in our study died before the DFU recurrence, and therefore this result should be interpreted with caution. The relationship between the history of ulcers or amputation and recurrence of ulcers found in the univariate analysis disappeared in the multivariable analysis, however this is in contrast to previous studies where both variables (history of ulcers or amputation) have been reported to be poor prognostic factors for DFU recurrence [33].
Regarding DFU healing, we found that in $73.8\%$ of participants the index ulcer healed with or without amputation. Similar results were reported previously by other authors [4, 6, 7, 34]. No relationship was observed between the ulcer's depth, extension and healing, adjusting for different variables, in contrast to what has been reported by other authors [26, 34, 35]. Our analysis highlights that infection is the main variable that interferes with healing. In a study by Prompers et al. [ 26] no differences were observed for major amputation or healing rate between neuropathic ulcers with and without infection, although infection was a risk factor for minor amputation. In contrast, infection was an independent predictor of poor outcome in patients with peripheral arterial disease, but the prevalence of infection varied markedly between the centers (28–$74\%$)[26].
Needing a caregiver was associated with an increased risk of mortality and with DFU recurrence, while it was negatively associated with ulcer healing. The need for a caregiver may be regarded as a surrogate of frailty. It is well known that frailty is a clinical syndrome associated with dependence and mortality in the older adults, including those with diabetes. Moreover, frailty may be a more powerful prognostic marker than the burden of comorbidity itself [36–38]. This was also the case in the study by Gershater et al. [ 7], where the authors analyzed a cohort of 2,480 diabetics with a first ulcer and observed that patients with an informal caregiver patient were more likely to have a major amputation or to have died before healing compared to those who did not have a caregiver (odds ratio (OR): 2.16, $95\%$ CI 0.43 – 3.28. $p \leq 0.005$). The role of informal caregivers remains largely unexplored, and its importance is fundamental in the care of a patient with an ulcer [39].
There are many classifications of people with diabetes mellitus and DFU [14]. The PEDIS classification was designed as an aid for prospective research [14]. Using this classification, Chuan et al. [ 18] created a scoring system with 364 diabetic foot patients treated in a hospital with a mean follow up of 25 months. The study outcomes were healed DFU and a combination of unhealed DFU, amputation and death. They observed that a PEDIS classification score with a value of at least seven was associated with the worst clinical prognosis of the patients. In our study, a PEDIS score of ≥ 7 was associated with an increased risk of ulcer recurrence during the follow-up period.
Ince et al. [ 17] conducted a study with diabetic patients with foot ulcers from the UK, Germany, Tanzania and Pakistan to determine the prognostic value of the SINBAD classification score for healing vs no healing, including amputation or death. The authors observed that despite all the differences between countries, ulcers with a SINBAD score of at least 3 had a worse clinical prognosis. In the annual report of the UK National Diabetes Foot Care Audit [40] from 2018, with 19,453 patients with DFU, the SINBAD classification was also used for the same purpose. It was observed that patients with a SINBAD score equal to or greater than 3 were less likely to be alive and ulcer-free at 12 and 24 months. Our study observed that a SINBAD score ≥ 3 was associated with a risk of non-healing during the 12 months follow up period.
This study has some limitations. We have no information on follow-up in 34 patients who discontinued the study. Some limitations, such as possible underreporting, selection bias, and the absence of socioeconomic data, as well as the absence of the prevalence of mental health disorders (depression and anxiety) were previously acknowledged in a prior study [15].
## Conclusions
In conclusion, we observed high morbidity among subjects with a new DFU seen in primary healthcare. As described in previous studies, peripheral arterial disease, infection, and microvascular complications increased the risk of poor clinical outcomes. Further large population-based primary healthcare studies are needed to evaluate the association between different risk factors, especially frailty and severity outcomes of DFUs. Additionally, primary healthcare professionals play a fundamental role in educating people with DM and preventing complications, such as the diabetic foot. Likewise, these professionals must be aware of the importance of ruling out the presence of ischemia and infection in the evaluation and follow-up of DFUs, and to make a prompt referral to secondary/tertiary levels of care when necessary. Coordination between levels of healthcare must be fluid and coordinated. The IWGDF [14] advises using the SINBAD classification in communication between professionals in its latest recommendations. Based on our experience, we believe it can also be a helpful tool for DFU disease course prognosis.
## Supplementary Information
Additional file 1: Supplementary Table 1. Event rates for different types of DFU. Supplementary Table 2. Adjusted HR for different study outcomes in the PEDIS models. Supplementary Table 3. Adjusted HR for different study outcomes in the SINBAD model. Supplementary Table 4.1 DFU recurrence as outcome. Supplementary Table 4.2 Supplementary Table 4.2 DFU recurrence as outcome. Supplementary Table 4.3 DFU healing as outcome. Supplementary Table 4.4 DFU healing as outcome. Supplementary Table 4.5 PEDIS models for DFU recurrence as outcome. Supplementary Table 4.6 PEDIS models for DFU recurrence as outcome. Supplementary Table 4.7 PEDIS models for DFU healing as outcome. Supplementary Table 4.8 PEDIS models for DFU healing as outcome. Supplementary Table 4.9 SINBAD models for DFU recurrence as outcome. Supplementary Table 4.10 SINBAD models for DFU recurrence as outcome. Supplementary Table 4.11 SINBAD models for DFU healing as outcome. Supplementary Table 4.12 SINBAD models for DFU healing as outcome. Supplementary table 5. Study site investigators. Supplementary table 6. Scientific, clinical and administrative support.
## References
1. 1.IDF atlas. https://diabetesatlas.org/idfawp/resource-files/2021/07/IDF_Atlas_10th_Edition_2021.pdf
2. 2.NICE. Diabetic Foot Problems: Inpatient Management of Diabetic Foot Problems. Nice Cg 119. 2011; (March):1–30. http://www.nice.org.uk/guidance/cg119/chapter/guidance.
3. Frykberg RG, Zgonis T, Armstrong DG. **Diabetic foot disorders: a clinical practice guideline**. *J Foot Ankle Surg.* (2006.0) **45** 1-66. DOI: 10.1016/S1067-2516(07)60001-5
4. Pound N, Chipchase S, Treece K, Game F, Jeffcoate W. **Ulcer-free survival following management of foot ulcers in diabetes**. *Diabet Med* (2005.0) **22** 1306-1309. DOI: 10.1111/j.1464-5491.2005.01640.x
5. Winkley K, Stahl D, Chalder T, Edmonds ME, Ismail K. **Risk factors associated with adverse outcomes in a population-based prospective cohort study of people with their first diabetic foot ulcer**. *J Diabetes Complications* (2007.0) **21** 341-349. DOI: 10.1016/j.jdiacomp.2007.09.004
6. Prompers L, Schaper N, Apelqvist J. **Prediction of outcome in individuals with diabetic foot ulcers: Focus on the differences between individuals with and without peripheral arterial disease**. *The EURODIALE Study. Diabetologia* (2008.0) **51** 747-755. DOI: 10.1007/s00125-008-0940-0
7. Gershater MA, Löndahl M, Nyberg P. **Complexity of factors related to outcome of neuropathic and neuroischaemic/ischaemic diabetic foot ulcers: A cohort study**. *Diabetologia* (2009.0) **52** 398-407. DOI: 10.1007/s00125-008-1226-2
8. Morbach S, Furchert H, Gröblinghoff U. **Long-term prognosis of diabetic foot patients and their limbs: Amputation and death over the course of a decade**. *Diabetes Care* (2012.0) **35** 2021-2027. DOI: 10.2337/dc12-0200
9. Jiang Y, Wang X, Xia L. **A cohort study of diabetic patients and diabetic foot ulceration patients in China**. *Wound Repair Regen* (2015.0) **23** 222-230. DOI: 10.1111/wrr.12263
10. Monteiro-Soares M, Martins-Mendes D, Vaz-Carneiro A, Dinis-Ribeiro M. **Lower-limb amputation following foot ulcers in patients with diabetes: classification systems, external validation and comparative analysis**. *Diabetes Metab Res Rev* (2015.0) **31** 515-529. DOI: 10.1002/dmrr.2634
11. Iwase M, Fujii H, Nakamura U. **Incidence of diabetic foot ulcer in Japanese patients with type 2 diabetes mellitus: The Fukuoka diabetes registry**. *Diabetes Res Clin Pract* (2018.0) **137** 183-189. DOI: 10.1016/j.diabres.2018.01.020
12. Boyko EJ, Seelig AD, Ahroni JH. **Limb- A nd person-level risk factors for lower-limb amputation in the prospective seattle diabetic foot study**. *Diabetes Care* (2018.0) **41** 891-898. DOI: 10.2337/dc17-2210
13. 13.Muller IS, De Grauw WJC, Van Gerwen WHEM, et al (2002). Foot Ulceration and Lower Limb Amputation in Type 2 Diabetic Patients in Dutch Primary Health Care 25: :570-574 10.2337/diacare.25.3.570.
14. Monteiro-Soares M, Russell D, Boyko EJ. **Guidelines on the classification of diabetic foot ulcers (IWGDF 2019)**. *Diabetes Metab Res Rev* (2020.0) **36** e3273. DOI: 10.1002/dmrr.3273
15. Bundó M, Llussà J, Serra M. **Incidence and characteristics of diabetic foot ulcers in subjects with type 2 diabetes in Catalonian primary care centers: An observational multicentre study**. *Prim Care Diabetes* (2021.0) **15** 1033-1039. DOI: 10.1016/j.pcd.2021.08.002
16. Schaper NC. **Diabetic foot ulcer classification system for research purposes: A progress report on criteria for including patients in research studies**. *Diabetes Metab Res Rev* (2004.0) **20** S90-95. DOI: 10.1002/dmrr.464
17. Ince P, Abbas ZG, Lutale JK. **Use of the SINBAD classification system and score in comparing outcome of foot ulcer management on three continents**. *Diabetes Care* (2008.0) **31** 964-967. DOI: 10.2337/dc07-2367
18. 18.Chuan F, Tang K, Jiang P, Zhou B, He X (2015). Reliability and validity of the perfusion, extent, depth, infection and sensation (PEDIS) classification system and score in patients with diabetic foot ulcer. PLoS One10. e0124739. 10.1371/journal.pone.0124739.
19. 19.Therneau, T.M., Grambsch, P.M. (2000). The Cox Model. In: Modeling Survival Data: Extending the Cox Model. Statistics for Biology and Health. Springer, New York, NY. 10.1007/978-1-4757-3294-8_3
20. 20.Therneau T (2022). _A Package for Survival Analysis in R_. R package version 3.3–1, <https://CRAN.R-project.org/package=survival>.
21. Therneau TM, Grambsch PM. *_Modeling Survival Data: Extending the Cox Model_* (2000.0)
22. Apelqvist J, Larsson J, Agardh CD. **The influence of external precipitating factors and peripheral neuropathy on the development and outcome of diabetic foot ulcers**. *J Diabet Complications* (1990.0) **4** 21-25. DOI: 10.1016/0891-6632(90)90060-i
23. Saluja S, Anderson SG, Hambleton I. **Foot ulceration and its association with mortality in diabetes mellitus: a meta-analysis**. *Diabet Med* (2020.0) **37** 211-218. DOI: 10.1111/dme.14151
24. Brownrigg JRW, Davey J, Holt PJ. **The association of ulceration of the foot with cardiovascular and all-cause mortality in patients with diabetes: a meta-analysis**. *Diabetologia* (2012.0) **55** 2906-2912. DOI: 10.1007/s00125-012-2673-3
25. Seghieri G, De Bellis A, Seghieri M. **Gender Difference in the Risk of Adverse Outcomes After Diabetic Foot Disease: A Mini-Review**. *Curr Diabetes Rev* (2021.0) **17** 207-213. DOI: 10.2174/1573399816666200716195600
26. Prompers L, Huijberts M, Apelqvist J. **High prevalence of ischemia, infection and serious comorbidity in patients with diabetic foot disease in Europe. Baseline results from the Eurodiale study**. *Diabetologia* (2007.0) **50** 18-25. DOI: 10.1007/s00125-006-0491-1
27. Hinchliffe RJ, Forsythe RO, Apelqvist J. **Guidelines on diagnosis, prognosis, and management of peripheral artery disease in patients with foot ulcers and diabetes (IWGDF 2019 update)**. *Diabetes Metab Res Rev* (2020.0) **36** e3276. DOI: 10.1002/dmrr.3276
28. Oyibo SO, Jude EB, Tarawneh I. **The effects of ulcer size and site, patient's age, sex and type and duration of diabetes on the outcome of diabetic foot ulcers**. *Diabet Med.* (2001.0) **18** 133-8. DOI: 10.1046/j.1464-5491.2001.00422.x
29. Jiang Y, Ran X, Jia L. **Epidemiology of type 2 diabetic foot problems and predictive factors for amputation in china**. *Int J Low Extrem Wounds* (2015.0) **14** 19-27. DOI: 10.1177/1534734614564867
30. Jeffcoate W, Young B. **Reducing amputation in diabetes: work from the West Country provides both evidence and a tool-kit**. *Diabet Med* (2018.0) **35** 8-9. DOI: 10.1111/dme.13538
31. Narres M, Kvitkina T, Claessen H. **Incidence of lower extremity amputations in the diabetic compared with the non-diabetic population: A systematic review**. *PLoS One* (2017.0) **12** e0182081. DOI: 10.1371/journal.pone.0182081
32. Beckman JA, Duncan MS, Damrauer SM. **Microvascular Disease, Peripheral Artery Disease, and Amputation**. *Circulation* (2019.0) **140** 449-458. DOI: 10.1161/CIRCULATIONAHA.119.040672
33. Armstrong DG, Boulton AJM, Bus SA. **Diabetic foot ulcers and their recurrence**. *N Engl J Med* (2017.0) **376** 2367-2375. DOI: 10.1056/NEJMra1615439
34. Zhang Y, Cramb S, McPhail SM. **Diabetic Foot Working Group, Queensland Statewide Diabetes Clinical Network, Australia Factors Associated With Healing of Diabetes-Related Foot Ulcers: Observations From a Large Prospective Real-World Cohort**. *Diabetes Care* (2021.0) **44** e143-e145. DOI: 10.2337/dc20-3120
35. Ince P, Kendrick D, Game F, Jeffcoate W. **The association between baseline characteristics and the outcome of foot lesions in a UK population with diabetes**. *Diabet Med.* (2007.0) **24** 977-81. DOI: 10.1111/j.1464-5491.2007.02189.x
36. Sinclair A, Dunning T. **Rodriguez-Ma˜nas L Diabetes in older people: New insights and remaining challenges**. *Lancet DiabetesEndocrinol* (2015.0) **3** 275-85
37. Hubbard RE, Andrew MK, Fallah N, Rockwood K. **Comparison of the prognostic importance of diagnosed diabetes, co-morbidityand frailty in older people**. *Diabet Med* (2010.0) **27** 603-606. DOI: 10.1111/j.1464-5491.2010.02977.x
38. Garcia-Esquinas E, Graciani A, Guallar-Castillon P, Lopez-Garcia E, Rodriguez-Ma˜nas L, Rodriguez-Artalejo F. **Diabetesand risk of frailty and its potential mechanisms: A prospectivecohort study of older adults**. *J Am Med Dir Assoc* (2015.0) **16** 54
39. Messenger G, Taha N, Sabau S. **Is There a Role for Informal Caregivers in the Management of Diabetic Foot Ulcers? A Narrative Review**. *Diabetes Ther* (2019.0) **10** 2025-2033. DOI: 10.1007/s13300-019-00694-z
40. 40.NHS, National Diabetes Foot Care Audit third annual report, H.Q.I. Partnership, Editor. 2018. https://www.hqip.org.uk/wp-content/ uploads/2018/03/National-Diabetes-Foot-Care-Audit-2014–2017.pdf.
|
---
title: Associations between socioeconomic status and risk of obesity and overweight
among Chinese children and adolescents
authors:
- Youzhi Ke
- Shikun Zhang
- Yueran Hao
- Yang Liu
journal: BMC Public Health
year: 2023
pmcid: PMC9972743
doi: 10.1186/s12889-023-15290-x
license: CC BY 4.0
---
# Associations between socioeconomic status and risk of obesity and overweight among Chinese children and adolescents
## Abstract
### Background
In China, the threat of obesity and overweight in children and adolescents is developing quickly. It may be possible to lower the risk of obesity and overweight in children and adolescents by understanding the factors that drive these conditions. Therefore, this study aimed to investigate the association between SES and risk of obesity and overweight among children and adolesecnts in China’s provinces of Jiangsu, Anhui, Zhejiang, and Shanghai.
### Methods
Chinese children and adolescents ($$n = 2$$,746; $46.3\%$ boys) were recruited using multistage sampling. SES was measured using self-reported questionnaires, the specific indicators were parental education, perceived family wealth, and Family Affluence Scale II. Height and weight were measured and used to calculate body mass index (BMI, categorized into obesity or overweight). The definition of obesity or overweight was based on the Chinese standard "Screening for obesity and overweight among school-age children and adolescents". Descriptive statistics, independent sample t-tests, and a Chi-square test were used to report the sample characteristics and analyse BMI differences across different sociodemographic groups. A binary logistic regression was then applied to analyse the association of SES indicators with BMI in children and adolescents.
### Results
Overall, $22.5\%$ of children and adolescents were obese or overweight. Participants with medium and high maternal education levels were 1.48 [$95\%$ CI 1.15–1.91] and 1.47 [$95\%$ CI 1.03–2.11] times more likely to be obese/overweight. Girls with medium maternal education levels were 1.70[$95\%$ CI 1.21–2.40] times more likely to be obese/overweight. For boys, no association was observed. Junior middle school students with medium maternal education levels were 1.51[$95\%$ CI 1.10–2.07] times more likely to be obese/overweight. Participants with medium or high FAS, perceived family wealth, or paternal education levels were not associated with obesity/overweight.
### Conclusions
The findings of this study indicated a positive association between SES and risk of overweight/obesity in girls, suggesting that maternal education level may have a substantial impact on future prevention efforts for these conditions in girls. To increase the effectiveness of interventions, longitudinal studies are necessary to better understand the causal association between SES and obesity/overweight.
## Background
Obesity (OB) or being overweight (OW) in children and adolescents has become a major global public health issue [1]. Body mass index (BMI) is commonly used to assess the prevalence of overweight and obesity. BMI is one of the most important indicators of nutrition and obesity among children and adolescents, both in theory and in practice. According to relevant research findings, BMI is not only significantly related to hyperglycemia, hypertension, and other diseases, but it also directly reflects in body fat level [2]. At the same time, high BMI in children and adolescents can affect their health status in adulthood [3]. Despite the numerous dangers of being obese or overweight, the prevalence of obesity or overweight in children and adolescents remains high. A study collected 2416 population-based studies from the world and measured the height and weight of 31 million participants aged 5–19. The results showed that from 1975 to 2016, the obesity rate of girls increased from $0.7\%$ to $5.6\%$, and that of boys increased from $0.9\%$ to $7.8\%$ [4]. According to the China Childhood Obesity Report, just in major and medium-sized cities, the obesity rate among children has reached $4.3\%$, and in 2014, China had the most obese population in the world [5]. If appropriate intervention strategies are not implemented, the childhood obesity rate will increase to $6\%$ by 2030, significantly endangering the health of children [6]. The Chinese government has implemented health education initiatives and issued dietary recommendations in an effort to combat the threat of obesity. These initiatives are intended to increase people's dietary knowledge, foster the development of healthier eating patterns, and maintain a healthy lifestyle [7]. Despite progress in preventing childhood and adolescent obesity, there is still uncertainty about the key factors that must be addressed [8].
Evidence suggests that socioeconomic status (SES) is associated with overweight and obesity in children and adolescents [9]. In adolescence and early adulthood, those who experience early disadvantage are more likely to have a higher body mass index and to be overweight or obese [10], and these correlations will influence them in midlife and into old age [11, 12]. It should be noted that these negative consequences are more pronounced and long-lasting in women, as well as in early adulthood [13–15] and midlife [11, 12]. About $70\%$ of teenagers with obesity will become obese adults when they grow up, which emphasizes the urgency and necessity of solving this problem as soon as possible [16]. Studies on overweight or obesity in most developed countries have shown a negative association between SES and overweight or obesity in children and adolescents [17–21]. In contrast, studies in developing countries have shown that the relationship between SES and overweight or obese children and adolescents is controversial. Data from the poorest countries show a positive association between SES and obesity or overweight [18, 22–26]. However, some middle-income countries have a negative association between SES and obesity or overweight [27]. In comparison to most Western countries, China has experienced the fastest economic growth and urbanization process. In addition to affecting nutrition and lifestyle, it leads to growing inequality in SES [28]. Many researchers have investigated the association between SES and overweight or obesity in the Chinese population as a potentially significant cause of illness, but the results have been inconsistent [29–33]. According to several studies, those with lower education or higher household incomes are more likely to be overweight or obesity [29–32]. Other studies have revealed that throughout the same time period, obesity rates trended upward in all SES groups, but the upward trend was more prominent in the low SES group and somewhat downward in the high SES group [33]. According to a study, there is a negative correlation between parents' SES and their children's BMI [34]. Different SES groups and stages of industrialization may exist in different parts of China, which could account for regional variations in obesity rates.
However, this issue has received little attention in the existing research [28]. With a total area of over 358,000 square kilometers and a population of around 227 million, the Yangtze River Delta region in eastern China includes Jiangsu, Anhui, Zhejiang province, and Shanghai, which are the Yangtze River Delta regional economic integration areas. These four regions' social and economic levels differ somewhat from one another from the perspective of GDP, and to some extent, these discrepancies can be interpreted as social and economic level differences. This study investigates the association between SES and children and adolescents obesity/overweight using these four locations as examples.
## Study design and participants
This study undertook a cross-sectional school survey, which was conducted in China’s provinces of Jiangsu, Anhui, Zhejiang, and Shanghai. The participants were 3,368 children drawn from the selected primary school (Grades 4 to 6, aged 9 to 11 years old, $$n = 399$$), junior middle school (Grades 7 to 9, 12 to 14 years old, $$n = 1765$$), and junior high school (Grades 10 to 12, 15 to 17 years old, $$n = 582$$), with participants thus ranging in age from 9 to17 years old. In terms of responses, 2,746 students (response rate = $81.53\%$) completed the self-reported questionnaire, with an average age of 13.57 ± 2.26 years.
## Procedures
The study protocol and procedure were approved by the Institutional Review Board (IRB) of Shanghai University of Sport (SUS), while further permission to conduct the study was obtained from the teachers and principals of the participating schools. The IRB of SUS agreed that verbal consent was sufficient for the conduction of this study because none of the survey items related to any personal or ethical issues. All children and adolescents involved in the study were asked to answer the self-reported questionnaire. And all participants, along with their parents or guardians, were advised that participation was completely voluntary, verbal informed consent was obtained from all parents or guardians, and positive assent was obtained from all children and adolescents before data collection. Trained research assistants implemented the survey in a prearranged manner according to a standardized administration protocol during regular school hours, and the survey was thus completed on paper in the classroom setting. Students were instructed on how to complete the survey and were provided with ample time for questions. Data from the survey were then collected and analyzed anonymously.
## Dependent variables
Standing height barefoot was measured using a stable stadiometer (GMCS-SGZG3, Jian-Min, Beijing) to the nearest 0.001 m. Bodyweight with light clothes was measured using a portable scale (GMCS-YERCS3, Jian-Min, Beijing) to the nearest 0.1 kg (kg). BMI was calculated by height and body weight:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{BMI }(\mathrm{kg}/{m}^{2})=\frac{\mathrm{Weight }(\mathrm{kg})}{{\left(\mathrm{Height}\right)}^{2} ({m}^{2})}$$\end{document}BMI(kg/m2)=Weight(kg)Height2(m2) Due to the differences in body fat percentage between East Asians and Europeans, the definition of overweight or obesity was based on the Chinese standard "Screening for overweight and obesity among school-age children and adolescents", standard number WS/T 586–2018 [35, 36]. The process for identifying overweight or obesity in school-aged children and adolescents between the ages of 6 and 18 was described in the standard, together with the technical prerequisites for this procedure. This recommendation for screening school-age children and adolescents between the ages of 6 and 18 for overweight and obesity shall apply to all regions of China. The specific specifications were detailed in Table 1.Table 1BMI screening for overweight and obesity cut-off values for school-age children and adolescents aged 6–18 years by sex and ageAgeBoyGirlOverweightObesityOverweightObesity6.0 ~ 16.417.716.217.56.5 ~ 16.718.116.518.07.0 ~ 17.018.716.818.57.5 ~ 17.419.217.219.08.0 ~ 17.819.717.619.48.5 ~ 18.120.318.119.99.0 ~ 18.520.818.520.49.5 ~ 18.921.419.021.010.0 ~ 19.221.919.521.510.5 ~ 19.622.520.022.111.0 ~ 19.923.020.522.711.5 ~ 20.323.621.123.312.0 ~ 20.724.121.523.912.5 ~ 21.024.721.924.513.0 ~ 21.425.222.225.013.5 ~ 21.925.722.625.614.0 ~ 22.326.122.825.914.5 ~ 22.626.423.026.315.0 ~ 22.926.623.226.615.5 ~ 23.126.923.426.916.0 ~ 23.327.123.627.116.5 ~ 23.527.423.727.417.0 ~ 23.727.623.827.617.5 ~ 23.827.823.927.818.0 ~ 24.028.024.028.0Note: unit: kg / m2
## Independent variables
The individual measures of SES were examined based on Family Affluence Scale II (FAS II), parental education, and perceived family wealth. The FAS II has been used extensively in the Health Behaviour in School-aged Children study in the past decade to examine and describe socioeconomic inequalities in adolescent health outcomes [37]. The 3-point FAS II (low, medium, or high) was developed based on four measures of material family wealth, as reported by the students (car ownership, bedroom sharing, holiday travel, and computer ownership) [38]. FAS II was assessed by using the question: "Do you have a car at home?" ( No = 0; one = 1; yes, more than two = 2); "How many computers do you have at home?" ( No = 0; one set = 1; two sets = 2; more than two sets = 3); "In the past year, how many times did you and your family travel during the holidays?" ( No = 0; once = 1; twice = 2; more than twice = 3); "Do you have your own room?" ( No = 0; Yes = 1). In the analysis process, FAS II was divided into three categories ("low", "medium" and "high"). The FAS II category corresponds to the tertile of the total score ("low" = 0–2; "medium" = 3–5, "High" = 6–9) [39].
Parental education was determined based on reported data, categorizing parental educational experience into seven groups: 1) Below elementary school; 2) Elementary school; 3) Junior middle school; 4) High school or occupational school; 5) College; 6) Undergraduate; and 7) Postgraduate and above. In the process of analysis, the parental educational backgrounds were divided into three categories: a low education level (below elementary school, elementary school, and junior middle school), a medium education level (high school or occupational school and college), and a high education level (undergraduate or postgraduate and above) [39].
The perceived family wealth measure was designed to assess the students’ perceptions of their family’s SES. This variable was developed from the question “How well off do you think your family is?” with the available response categories being “very well off”, “quite well off”, “average”, “not very well off”, and “not at all well off”. In the process of analysis, perceived family wealth was again divided into three categories: a low economic level (not very well off and not at all well off), a medium economic level (average), and a high economic level (very well off and quite well off) [39].
## Control variables
The following socio-demographic control variables were used: demographic information, including sex (1 = boy, 2 = girl), age (9–17 years old) and grade (Grades 4 to 12), and ethnicity (Han or other).
## Statistical analyses
The collected questionnaires were sorted in Excel and analyzed further using IBM SPSS 24. All missing cases and abnormal values were first removed, to address the aims of this study, the variables of grade, sex, and obesity/overweight were made the focus of full statistical analysis. Descriptive statistics were used to analyze the basic situation of the survey objects (demographic information, obesity/overweight, and SES), with continuous variables expressed in the form of mean ± standard deviation, and categorical variables expressed as numbers (n) or percentages (%). Independent sample t-tests and chi-square tests were used to analyze the differences between the characteristics of the different samples. Binary logistic regression was used to analyze the association of SES and BMI, adjusted for sociodemographic factors. All logistic regression analysis results were presented as odds ratios (OR) with a $95\%$ confidence interval (CI). All p ≤ 0.05 were deemed statistically significant.
## Results
The descriptive characteristics of the samples in this study were shown in Table 2. A total of 2,746 subjects were finally included in the survey, including 1,272 boys ($46.3\%$) and 1,474 girls ($53.7\%$), with an average age of 13.57 ± 2.26 years (13.36 ± 2.18 of boys, 13.74 ± 2.30 of girls, $p \leq 0.001$). The proportion of primary school was $14.5\%$ (boys $15.0\%$, girls $14.1\%$); junior middle school accounted for $64.3\%$ (boys $71.7\%$, girls $57.9\%$); The proportion of high school was $21.2\%$ (boys $13.3\%$, girls $28.0\%$). There was a statistically significant sex difference among grade groups ($p \leq 0.001$). The BMI of the participants was 19.83 kg/m2 (20.15 kg/m2 for boys, 19.55 kg/m2 for girls, $p \leq 0.001$). The majority of participants were Han Chinese ($97.2\%$), and no significant difference was found between ethnic groups ($p \leq 0.05$). $42.3\%$ of the participants reported that their fathers had low levels of education ($41.0\%$ for boys,$43.5\%$ for girls, $p \leq 0.05$). And $49.4\%$ of the participants reported that their mothers had low levels of education ($47.5\%$ for boys,$51.1\%$ for girls, $p \leq 0.05$). About $58.6\%$ of the participants had perceived family wealth to be medium ($56.2\%$ for boys and $60.6\%$ for girls), and $13.3\%$ of participants with low FAS ($13.7\%$ for boys, $12.9\%$ for girls, $p \leq 0.05$).Table 2The Characteristics of the SampleOverall [2746]Boys [1272]Girls [1474]PHeight(cm)159.51161.63157.68< 0.001Weight(kg)50.9253.2548.97< 0.001BMI(kg/m2)19.8320.1519.55< 0.001Age (years), M ± SD13.57 ± 2.2613.36 ± 2.1813.74 ± 2.30< 0.001Grade groups, n (%) Primary school399(14.5)191(15.0)208(14.1)< 0.001 Junior middle school1765(64.3)912(71.7)853(57.9) High school582(21.2)169(13.3)413(28.0)Ethnicity, n (%) Han2663(97.0)1236(97.2)1427(96.8)0.920 Others83(3.0)36(2.8)47(3.2)SES, n (%)Paternal education level Low1163(42.3)521(41.0)642(43.5)0.390 Medium 955(34.8)453(35.6)502(34.1) High628(22.9)298(23.4)330(22.4)Maternal education level Low1357(49.4)604(47.5)753(51.1)0.169 Medium832(30.3)401(31.5)431(29.2) High557(20.3)267(21.0)290(19.7)Perceived family wealth Low319(11.6)139(10.9)180(12.2)0.005 Medium1608(58.6)715(56.2)893(60.6) High819(29.8)418(32.9)401(27.2)FAS Low364(13.3)174(13.7)190(12.9)0.598 Medium1143(41.6)517(40.6)626(42.5) High1239(45.1)581(45.7)658(44.6)M ± SD mean ± standard deviation; BMI mean body mass indexSES means socioeconomic status; FAS means Family Affluence ScaleP values: sex differencesPrimary school: 9–11 years oldJunior middle school: 12–14 years oldHigh school: 15–17 years old Table 3 shows the prevalence of obesity or overweight among participants. The percentage of OW or OB was $22.5\%$, and boys were higher than girls ($29.8\%$ vs $16.3\%$, $p \leq 0.001$). The percentages of OW or OB in the three grade groups were different (primary school: $36.1\%$; junior school: $19.7\%$; high school: $22.0\%$, $p \leq 0.001$).Table 3The Prevalence of OW/OBnon-OW/OBOW/OBPn%n%Total212777.561922.5< 0.001Sex Boy89370.237929.8< 0.001 Girl123483.724016.3Grade groups Primary school25563.914436.1< 0.001 Junior middle school141880.334719.7 High school45478.012822.0OW means Overweight, OB means ObesityP values: Differences between non-OW/OB and OW/OBPrimary school: 9–11 years oldJunior middle school: 12–14 years oldHigh school: 15–17 years old The associations between SES and BMI in adolescents are presented in Fig. 1. Participants with medium and high maternal education levels were 1.48 [$95\%$ CI 1.15–1.91] and 1.47 [$95\%$ CI 1.03–2.11] times more likely to be obese/overweight than participants with low maternal education levels. Participants with medium or high FAS, perceived family wealth, or paternal education levels were not associated with overweight or obesity. Fig. 1Regression analysis of the socioeconomic status and body mass index The summarized results for the OR for participants whose BMI was overweight or obese by sex are shown in Fig. 2. Boys with medium or high FAS, perceived family wealth, and parental education levels were not associated with overweight or obesity. The findings only found that girls with medium maternal education levels were 1.70[$95\%$ CI:1.21–2.40] times more likely to be obesity/overweight than participants with low maternal education levels. Girls with medium or high FAS, perceived family wealth, or paternal education levels were not associated with the obesity/overweight. Fig. 2Regression analysis of sex differences in socioeconomic status and body mass index
The summarized results for the OR for participants whose BMI was overweight or obese by grade group are shown in Fig. 3. Participants from junior middle school students with medium maternal education levels were 1.51[$95\%$ CI: 1.10–2.07] times more likely to be obese/overweight than participants with low maternal education levels. Participants from primary and high school students with medium or high FAS, family wealth perceived ability, or parental education levels were not significantly associated with obesity/overweight. Fig. 3Regression analysis of grade differences in socioeconomic status and body mass index
## Discussion
Maternal education level is significantly correlated with obesity/overweight among children and adolescents, particularly for girls in junior middle school, according to an analysis of the association between SES and obesity or overweight in Chinese children and adolescents. There was no association between other SES indicators and obesity or overweight in children and adolescents. This demonstrates that in China, we should pay more attention to the influence of maternal education levels on overweight or obesity in children and adolescents, particularly among highly educated mothers.
The economic growth that *China is* experiencing so quickly makes it possible to guarantee children's and teenagers' nutrition. Compared to families from poorer socioeconomic backgrounds, mothers with higher education are more likely to provide their kids with the high-fat, high-sugar meals they prefer, which may contribute to children and adolescents being overweight and obesity [8]. However, women with less education may be busier at work, their kids may need help with chores in their free time, and they may have fewer chances of picking up undesirable behaviours (such as being sedentary and eating fast food) [40]. Other SES indicators, however, do not significantly associate with obesity or overweight in children and adolescents, suggesting that there is only a tenuous association between SES and obesity or adolescent overweight in children and adolescents. Sedentary behaviour or physical activity may have a significant impact on obesity or overweight in children and adolescents as a result of the changing social economy and way of life [6, 41].
Some developing countries, like China, have seen a growth in obesity or overweight rates more quickly in recent years than other developed countries, like the USA [42]. According to numerous studies, low-SES groups in developing countries and high-SES groups in developing countries (including China) with greater access to calorie-dense foods are more likely to acquire obesity than their counterparts [42, 43]. And there are also differences between rural and urban areas in China, the association between SES and overweight and obesity is higher in urban areas than in rural areas [44]. In terms of specific indicators, there are also studies from Chinese regions that differ from our results, for example, one study showed that the prevalence of overweight and obesity among children in Chongqing was positively associated with the paternal education level but not with the mother's [45]. However, a longitudinal survey of the China Family Panel Studies showed that the prevalence of overweight and obesity was only negatively associated with the parental education level up to the age of 10 years but not in children 11–15 years old [46]. Our findings conflict with earlier research, which demonstrates a significant inverse relationship between SES and BMI [47, 48]. There are two main reasons for the conflicting results. First of all, there is no universal method for classifying SES, and various SES indicators correspond to various SES latitudes. Several indicators of SES can be used to study the association between SES and BMI, and each indicator has its advantages and disadvantages [49]. For instance, it has been demonstrated that the number of family members and BMI is positively correlated, whereas the educational attainment of parents and BMI are negatively correlated [50]. The results were masked by the use of various SES indicator combinations. Studies indicate that there are no discernible differences between SES and BMI when the occupation is used to represent SES [51]. Children and adolescents find it challenging to appropriately describe the jobs of their parents in self-reported questionnaires [52]. This is another justification for not using the occupation indicator in this analysis. The household income index is a measure of social and economic standing, however, there is significant debate around it. Its sensitivity results in a poor response rate [53]. FAS is now extensively utilized as an accepted measure of children's and adolescents' SES [37]. And indicators of parental education level are frequently used to assess the legitimacy and respect of SES [54, 55]. It is very important to note that perceived family wealth is children's and adolescents' subjective assessment of their family's SES. Thus, the FAS, parental education level, and perceived family wealth are mostly used in this study as the main indicators to evaluate the SES. The evaluation standard of obesity/overweight is another potential factor. Different countries define being overweight or obese in different ways. For instance, some studies utilize the International Obesity Task Force standard and some studies use the WHO classification criteria for BMI [28, 56] The proportions of overweight or obese people vary according to different standards, which will have an effect on the findings of the study. The BMI classification released in China was chosen based on the real circumstances of the study participants.
Boys and girls are equally unaffected by parental education levels, FAS, and perceived family wealth. However, girls with medium maternal education levels were more likely to be obese/overweight. The possible causes of this could be that mothers with higher education are stricter about controlling the physical size of girls, while boys are generally more tolerant [57–59]. On the other hand, girls are more concerned about body image and more interested in weight control than boys [60]. Maybe it's because boys consume more physical strength and eat more food, and they tend to eat more calories without being aware of weight control.
This study found a significant association between higher maternal education and obesity or overweight in junior middle school students across a range of grade levels. Mothers with higher education levels are more likely to meet or satisfy their children's nutritional demands since junior middle school pupils are in puberty, which is also a crucial time for their growth. And students in junior middle school are also at a point where they can form their own opinions about the outside world and are more likely to be open to trying new things [61]. Additionally, there is more pressure on kids to continue their education during their high school years. Teenagers are more likely to enroll in various extracurricular tutoring classes if their mothers have advanced degrees [62]. As a result, more time is spent sitting down, less time is spent exercising, and more stress is felt. All of these could raise the likelihood of obesity or overweight people. Teenagers whose mothers have low levels of education, however, experience significantly fewer limitations and have more free time for extracurricular activities. As a result, the likelihood of being overweight or obese during this time is relatively low.
Future research should focus on SES-affected girls, as they are more likely to experience the risk of being overweight or obese. A cross-sectional study has revealed that teenagers with high BMI had poor physical fitness, supporting earlier findings that obesity causes speed, strength, flexibility, and other physical fitness to decline noticeably [63]. Furthermore, obesity and being overweight can lead to noncommunicable diseases such as diabetes, cancer, and musculoskeletal disorders [64, 65]. It is important to note that children and adolescents who are obese or overweight are more likely to have sedentary habits (e.g., spend more time watching TV) [66], and spend less time participating in physical activities [67], resulting in poorer physical health. Overweight or obese children and teenagers need the attention of the government, school departments, and parents.
Our study has a number of limitations. First, cross-sectional data rather than longitudinal data was used. Prospective longitudinal studies may assist identify risk factors causing obesity in Chinese children and adolescents because BMI might fluctuate over time as a result of a range of causes. Second, the respondents' involvement in this study was voluntary and convenience sampling was utilized, which to some extent impacts the sample's accuracy and makes it less representative. Thirdly, it has been shown in recent literature that genes and lifestyle factors, such as nutrition and physical activity, may have an impact on BMI. The absence of information prevented this work from studying it. Fourthly, it's important to discuss how body mass index is used. Although it is useful as a demographic indicator for identifying overweight and obesity, the fact that it is not the gold standard for measuring body composition may have some limits. Despite its limitations, this study has enriched the literature in two aspects. Firstly, the SES indicators used in this study were widely recognized. Secondly, the results of this study show that among the numerous SES indicators, only maternal education levels are related to overweight or obesity among children and adolescents. This finding is inconsistent with previous research results in developed countries [2]. According to the findings of our study, children and adolescents have a socioeconomic situation that is more problematic due to obesity or overweight when compared to developed nations. More specialized programs and regulations must be created in order to significantly enhance the health behaviours of children and adolescents.
## Conclusions
The results of this study show that girls in junior middle school students with medium maternal education levels are more likely to be overweight or obese, while paternal education levels, FAS, and perceived family wealth were not associated with the overweight or obesity of children and adolescents. These discoveries might be important for public health. When examining the health behaviours of children and adolescents with high SES, it is crucial to take into account how maternal education levels may affect their children's tendency to be overweight or obese. Additionally, longitudinal research is required to enhance the efficacy of interventions and better understand the causal association between SES and BMI. To fully and successfully enhance the health of children and adolescents, people must also be aware of the risks associated with obesity and being overweight.
## References
1. 1.Narayan, et al., Obesity in Low- and Middle-Income Countries: Burden, Drivers, and Emerging Challenges. Annu Rev Public Health. 2017;38:145–64.
2. Crofts SJC. **Association of Adult Socioeconomic Status with Body Mass Index: A Within- and Between-Twin Study**. *Twin Res Hum Genet* (2021.0) **24** 123-129. DOI: 10.1017/thg.2021.14
3. Carson V. **Health associations with meeting new 24-h movement guidelines for Canadian children and youth**. *Prev Med* (2017.0) **95** 7. DOI: 10.1016/j.ypmed.2016.12.005
4. Abarca-Gómez L. **Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults - ScienceDirect**. *Lancet* (2017.0) **390** 2627-2642. DOI: 10.1016/S0140-6736(17)32129-3
5. Di Cesare M. **Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants**. *Lancet* (2016.0) **387** 1377-1396. DOI: 10.1016/S0140-6736(16)30054-X
6. Yin L. **The Influence of Family Sports Attitude on Children's Sports Participation, Screen Time, and Body Mass Index**. *Front Psychol* (2021.0) **12** 697358. DOI: 10.3389/fpsyg.2021.697358
7. Yu J. **Better Dietary Knowledge and Socioeconomic Status (SES), Better Body Mass Index? Evidence from China-An Unconditional Quantile Regression Approach**. *Nutrients* (2020.0) **12** 1197. DOI: 10.3390/nu12041197
8. Cardenas-Fuentes G. **Prospective association of maternal educational level with child's physical activity, screen time, and diet quality**. *Nutrients* (2022.0) **14** 160. DOI: 10.3390/nu14010160
9. 9.Vieira LS, et al. Socioeconomic status and body mass index life course models: the 1993 Pelotas (Brazil) birth cohort. Cadernos de Saúde Pública. 2021;37(10:e00260820.
10. Lee H, Harris KM, Gordon-Larsen P. **life course perspectives on the links between poverty and obesity during the transition to young adulthood**. *Popul Res Policy Rev* (2009.0) **28** 505-532. DOI: 10.1007/s11113-008-9115-4
11. Giskes K. **Socioeconomic position at different stages of the life course and its influence on body weight and weight gain in adulthood: a longitudinal study with 13-year follow-up**. *Obesity* (2012.0) **16** 1377-1381. DOI: 10.1038/oby.2008.54
12. Pudrovska T, Logan ES, Richman A. **Early-life social origins of later-life body weight: The role of socioeconomic status and health behaviors over the life course**. *Soc Sci Res* (2014.0) **46** 59-71. DOI: 10.1016/j.ssresearch.2014.02.007
13. Gustafsson PE, Mats P, Anne H. **Socio-economic disadvantage and body mass over the life course in women and men: results from the Northern Swedish Cohort**. *Eur J Pub Health* (2012.0) **3** 322. DOI: 10.1093/eurpub/ckr061
14. Khlat M, Jusot F, Ville I. **Social origins, early hardship and obesity: A strong association in women, but not in men?**. *Soc Sci Med* (2009.0) **68** 1692-1699. DOI: 10.1016/j.socscimed.2009.02.024
15. Vieira LS. **Socioeconomic status throughout life and body mass index: a systematic review and meta-analysis**. *Cad Saude Publica* (2019.0) **35** e00125518. DOI: 10.1590/0102-311x00125518
16. Dehghan M, Akhtar-Danesh N, Merchant AT. **Childhood Obesity, Prevalence and Prevention**. *Nutr J* (2005.0) **4** 24. DOI: 10.1186/1475-2891-4-24
17. Caballero B. **The global epidemic of obesity: an overview**. *Epidemiol Rev* (2007.0) **29** 1-5. DOI: 10.1093/epirev/mxm012
18. Dinsa GD. **Obesity and socioeconomic status in developing countries: a systematic review obesity reviews an official**. *J Int Assoc Study Obesity* (2012.0) **13** 1067-1079. DOI: 10.1111/j.1467-789X.2012.01017.x
19. 19.Godley J, Mclaren L, Socioeconomic Status and Body Mass Index in Canada: Exploring Measures and Mechanisms. Canadian Review of Sociology/Revue canadienne de sociologie. 2010;47(4);381–403.
20. Yang TC, South SJ. **Neighborhood effects on body mass: Temporal and spatial dimensions**. *Soc Sci Med* (2018.0) **217** 45-54. DOI: 10.1016/j.socscimed.2018.09.055
21. Jang HJ, Oh H. **Trends and inequalities in overall and abdominal obesity by sociodemographic factors in Korean Adults, 1998–2018**. *Int J Environ Res Public Health* (2021.0) **18** 4162. DOI: 10.3390/ijerph18084162
22. Jaacks LM. **The obesity transition: stages of the global epidemic**. *Lancet Diabetes Endocrinol* (2019.0) **7** 231-40. DOI: 10.1016/S2213-8587(19)30026-9
23. Yiengprugsawan V. **Body size dynamics in young adults: 8-year follow up of cohorts in Brazil and Thailand**. *Nutr Diabetes* (2016.0) **6** e219. DOI: 10.1038/nutd.2016.24
24. Newton S, Braithwaite D, Akinyemiju TF. **Socio-economic status over the life course and obesity: Systematic review and meta-analysis**. *PLoS ONE* (2017.0) **12** e0177151. DOI: 10.1371/journal.pone.0177151
25. Monteiro CA. **Socioeconomic status and obesity in adult populations of developing countries: a review**. *Bull World Health Organ* (2004.0) **82** 940. PMID: 15654409
26. Fernald L. **Socio-Economic Status and Body Mass Index in Low-Income Mexican Adults**. *Soc Sci Med* (2007.0) **64** 2030-2042. DOI: 10.1016/j.socscimed.2007.02.002
27. Barquera S. **Energy and nutrient consumption in Mexican women 12-49 years of age: analysis of the National Nutrition Survey 1999**. *Salud Publica Mex* (2009.0) **51** 562-573
28. Li W, Li S, Feldman MW. **Socioeconomic Status, Institutional Power, and Body Mass Index among Chinese Adults**. *Int J Environ Res Public Health* (2021.0) **18** 10620. DOI: 10.3390/ijerph182010620
29. Zhao P. **Socioeconomic disparities in abdominal obesity over the life course in China**. *Int J Equity Health* (2018.0) **17** 96. DOI: 10.1186/s12939-018-0809-x
30. Hou X. **Risk factors for overweight and obesity, and changes in body mass index of Chinese adults in Shanghai**. *BMC Public Health* (2008.0) **8** 1-9. DOI: 10.1186/1471-2458-8-389
31. Wang Y. **Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China**. *Nat Pub Group* (2007.0) **1** 177-88
32. Cai L. **Association of obesity with socio-economic factors and obesity-related chronic diseases in rural southwest China**. *Public Health* (2013.0) **127** 247-251. DOI: 10.1016/j.puhe.2012.12.027
33. Wang H. **Epidemiology of general obesity, abdominal obesity and related risk factors in urban adults from 33 communities of northeast china: the CHPSNE study**. *BMC Public Health* (2012.0) **12** 967. DOI: 10.1186/1471-2458-12-967
34. Fu Q, George LK. **Socioeconomic determinants of childhood overweight and obesity in China: the long arm of institutional power**. *Sociol Health Illn* (2015.0) **37** 805-822. DOI: 10.1111/1467-9566.12234
35. Xie Y. **Gender Differences in the Effects of BMI on school bullying and victimization in china: comparing normal weight underweight and overweight secondary school students**. *Children-Basel* (2022.0) **9** 1388. DOI: 10.3390/children9091388
36. Zhou Y. **Association of cesarean birth with body mass index trajectories in adolescence**. *Int J Environ Res Pub Health* (2020.0) **17** 2003. DOI: 10.3390/ijerph17062003
37. 37.Liu Y, et al. Reliability and Validity of Family Affluence Scale (FAS II) among Adolescents in Beijing, China. Child Indicators Res. 2012;5(2):235–51.
38. Currie C. **Researching health inequalities in adolescents: The development of the Health Behaviour in School-Aged Children (HBSC) Family Affluence Scale**. *Soc Sci Med* (2008.0) **66** 1429-1436. DOI: 10.1016/j.socscimed.2007.11.024
39. 39.Ke Y, et al. Associations between socioeconomic status and physical activity: A cross-sectional analysis of Chinese children and adolescents. Front in Psychol, 2022;13:904506.
40. Sherar LB. **Association between maternal education and objectively measured physical activity and sedentary time in adolescents**. *J Epidemiol Community Health* (2016.0) **70** 541-548. DOI: 10.1136/jech-2015-205763
41. Dong XS. **Physical activity screen-based sedentary behavior and physical fitness in chinese adolescents a cross-sectional study**. *Front Pediatrics* (2021.0) **9** 722079. DOI: 10.3389/fped.2021.722079
42. Wang Y, Lim H. **The global childhood obesity epidemic and the association between socio-economic status and childhood obesity**. *Int Rev Psychiatry* (2012.0) **24** 176-188. DOI: 10.3109/09540261.2012.688195
43. 43.Lim H, Wang Y. Socioeconomic disparities in obesity among children and future actions to fight obesity in China. Ann Transl Med. 2019;7(8):S377.
44. Sobal J, Stunkard AJ. **Socioeconomic Status and Obesity: A Review of the Literature**. *Psychol Bull* (1989.0) **105** 260-275. DOI: 10.1037/0033-2909.105.2.260
45. 45.Chen J, et al. The relationship between socioeconomic status and childhood overweight/obesity is linked through paternal obesity and dietary intake: a cross-sectional study in Chongqing, China. Environ Health Prev Med. 2021;26(1):1–14.
46. Ding S. **Association between parental socioeconomic status and offspring overweight/obesity from the China Family Panel Studies: a longitudinal survey**. *Bmj Open* (2021.0) **11** e045433. DOI: 10.1136/bmjopen-2020-045433
47. Morgenstern M, Sargent JD, Hanewinkel R. **Relation between socioeconomic status and body mass index: evidence of an indirect path via television use**. *Arch Pediatr Adolesc Med* (2009.0) **163** 731-8. DOI: 10.1001/archpediatrics.2009.78
48. Mcmurray RG. **The influence of physical activity, socioeconomic status, and ethnicity on the weight status of adolescents**. *Obesity* (2012.0) **8** 130-139. DOI: 10.1038/oby.2000.14
49. 49.Dinsa, et al., Obesity and socioeconomic status in developing countries: A systematic review. Obesity Reviews An Official Journal of the International Association for the Study of Obesity, 2012.
50. Jafarirad S, Keshavarz S, Khalilian A. **The relationship between socioeconomic factors and bodymass index (BMI) in adolescent girls of Sari**. *Prev Med* (2006.0) **16** 254-273
51. Mclaren L. **Socioeconomic Status and Obesity**. *Epidemiol Rev* (2012.0) **29** 29-48. DOI: 10.1093/epirev/mxm001
52. Currie CE. **Indicators of socioeconomic status for adolescents: the WHO Health Behaviour in School-aged Children Survey**. *Health Educ Res* (1997.0) **3** 385-397. DOI: 10.1093/her/12.3.385
53. 53.Roemer, M.I., Assessing the Quality of the March Current Population Survey and the Survey of Income and Program Participation Income Estimates, 1990–1996. unpublished paper, 2000.
54. Winkleby MA. **Socioeconomic status and health: how education, income, and occupation contribute to risk factors for cardiovascular disease**. *Am J Public Health* (1992.0) **82** 816-820. DOI: 10.2105/AJPH.82.6.816
55. Galobardes B. **Indicators of socioeconomic position (part 1)**. *J Epidemiol Community Health* (2006.0) **60** 7-12. DOI: 10.1136/jech.2004.023531
56. Gomez G. **Socioeconomic status impact on diet quality and body mass index in eight latin american countries: elans study results**. *Nutrients* (2021.0) **13** 2404. DOI: 10.3390/nu13072404
57. Ana BA. **The Relationship of Socioeconomic Status with Body Mass Index Depends on the Socioeconomic Measure Used**. *Obesity* (2018.0) **26** 176-184. DOI: 10.1002/oby.22042
58. Costa-Font J, Gil J. **Intergenerational and socioeconomic gradients of child obesity**. *Soc Sci Med* (2013.0) **93** 29-37. DOI: 10.1016/j.socscimed.2013.05.035
59. 59.Jeffery, et al., Awareness of body weight by mothers and their children: repeated measures in a single cohort (EarlyBird 64). Child Care Health Dev. 2015;41(3):434–42.
60. Orth U. **Development of domain-specific self-evaluations: a meta-analysis of longitudinal studies**. *J Pers Soc Psychol* (2021.0) **120** 145-172. DOI: 10.1037/pspp0000378
61. Telama R. **Secular trends in youth physical activity and parents' socioeconomic status from 1977 to 2005**. *Pediatr Exerc Sci* (2009.0) **21** 462-474. DOI: 10.1123/pes.21.4.462
62. 62.Lu C, Stol R. Factors of physical activity among Chinese children and adolescents: a systematic review. Int J Behav Nutr Phys Act. 2017;14(1):1–10.
63. Yi X. **Weight status, physical fitness, and health-related quality of life among chinese adolescents: a cross-sectional study**. *Int J Environ Res Public Health* (2019.0) **16** 2271-. DOI: 10.3390/ijerph16132271
64. Janssen I, Leblanc AG. **Systematic review of the health benefits of physical activity and fitness in school-aged children and youth**. *Int J Behav Nutr Phys Act* (2010.0) **7** 40-40. DOI: 10.1186/1479-5868-7-40
65. Cameron M. **The Role of Overweight and Obesity in Perceived Risk Factors for Cancer: Implications for Education**. *J Cancer Educ Official J Am Assoc Cancer Educ* (2010.0) **25** 506-511. DOI: 10.1007/s13187-010-0085-y
66. 66.Olds, T.S., C.A. Maher, and M. Lisa, Sleep Duration or Bedtime? Exploring the Relationship between Sleep Habits and Weight Status and Activity Patterns. Sleep. 2011;34 (10):1299–307.
67. Thumann BF. **Associations between sleep duration and insulin resistance in European children and adolescents considering the mediating role of abdominal obesity**. *PLoS ONE* (2020.0) **15** e0235049. DOI: 10.1371/journal.pone.0235049
|
---
title: ADSC-derived exosomes attenuate myocardial infarction injury by promoting miR-205-mediated
cardiac angiogenesis
authors:
- Tingting Wang
- Tao Li
- Xiaolin Niu
- Lang Hu
- Jin Cheng
- Dong Guo
- He Ren
- Ran Zhao
- Zhaole Ji
- Pengyun Liu
- Yan Li
- Yanjie Guo
journal: Biology Direct
year: 2023
pmcid: PMC9972746
doi: 10.1186/s13062-023-00361-1
license: CC BY 4.0
---
# ADSC-derived exosomes attenuate myocardial infarction injury by promoting miR-205-mediated cardiac angiogenesis
## Abstract
### Background
Acute myocardial infarction is a major health problem and is the leading cause of death worldwide. Myocardial apoptosis induced by myocardial infarction injury is involved in the pathophysiology of heart failure. Therapeutic stem cell therapy has the potential to be an effective and favorable treatment for ischemic heart disease. Exosomes derived from stem cells have been shown to effectively repair MI injury-induced cardiomyocyte damage. However, the cardioprotective benefits of adipose tissue-derived mesenchymal stem cell (ADSC)-Exos remain unknown. This study aimed to investigate the protective effects of exosomes from ADSC on the hearts of MI-treated mice and to explore the underlying mechanisms.
### Methods
Cellular and molecular mechanisms were investigated using cultured ADSCs. On C57BL/6J mice, we performed myocardial MI or sham operations and assessed cardiac function, fibrosis, and angiogenesis 4 weeks later. Mice were intramyocardially injected with ADSC-Exos or vehicle-treated ADSCs after 25 min following the MI operation.
### Results
Echocardiographic experiments showed that ADSC-Exos could significantly improve left ventricular ejection fraction, whereas ADSC-Exos administration could significantly alleviate MI-induced cardiac fibrosis. Additionally, ADSC-Exos treatment has been shown to reduce cardiomyocyte apoptosis while increasing angiogenesis. Molecular experiments found that exosomes extracted from ADSCs can promote the proliferation and migration of microvascular endothelial cells, facilitate angiogenesis, and inhibit cardiomyocytes apoptosis through miRNA-205. We then transferred isolated exosomes from ADSCs into MI-induced mice and observed decreased cardiac fibrosis, increased angiogenesis, and improved cardiac function. We also observed increased apoptosis and decreased expression of hypoxia-inducible factor-1α and vascular endothelial growth factor in HMEC-1 transfected with a miRNA-205 inhibitor.
### Conclusion
In summary, these findings show that ADSC-Exos can alleviate cardiac injury and promote cardiac function recovery in MI-treated mice via the miRNA-205 signaling pathway. ADSC-Exos containing miRNA205 have a promising therapeutic potential in MI-induced cardiac injury.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13062-023-00361-1.
## Background
Acute myocardial infarction (AMI) is the leading cause of death in both developed and developing countries [1]. The adoption of optimal primary percutaneous coronary intervention and early thrombus treatment has recently resulted in the reduced size of myocardial infarction areas and fewer MI-related deaths [2, [3]. However, myocardial infarction (MI) injury, which includes increased oxidative stress and inflammation, still induces acute or chronic loss of cardiomyocytes, eventually leading to impaired cardiac function and heart failure [2, [4]. Stem cell therapy for the regeneration of damaged cardiomyocytes has received much attention recently, and diverse cell types have been employed in stem cell therapy. Adipose-derived stem cells (ADSCs) are more easily and abundantly obtained by minimally invasive procedures than bone marrow mesenchymal stem cells (MSCs) [5]. Apart from this, ADSCs have been shown to play a vital role in repairing damaged cardiomyocytes, making them the most promising therapeutic candidate [6].
ADSCs administration has been shown to reduce myocardial infarct size and improve cardiac function [4]. Currently, ADSC-based therapy for infarcted myocardium mainly comprises intracoronary or trans-endocardial injection, direct intramyocardial injection, and other invasive techniques [2, [7]. Various studies have shown that the invasive injection of ADSCs results in limited retention of stem cells in the myocardium, limiting the beneficial effects on infarcted cardiomyocytes [8]. Although the protective effect of ADSC-based therapy in the myocardium after cardiac MI injury has been widely reported, the underlying mechanism by which ADSCs improve cardiac injury after MI remains unclear. Exosomes are membrane lipid vesicles with a diameter of 30–100 nm. Exosomes secreted by stem cells are the most effective paracrine components for active cell-to-cell communication, and they show remarkable therapeutic potential for repairing damaged tissue [6, [9, [10]. Moreover, it has been suggested that exosomes derived from stem cells can effectively repair damaged cardiomyocytes after MI injury [9, [11]. Therefore, ADSCs and ADSC-Exos have potential clinical applications.
Exosomes contain a diverse range of biomolecules, including DNA, mRNAs, miRNAs, proteins, and lipids, with miRNAs being the most abundant [12]. MiRNAs are small (∼22 nucleotides) non-coding RNAs [6] that are involved in cell differentiation, proliferation, and apoptosis [13]. They have been shown to regulate the expressions of multiple mRNAs and contribute to intracellular communication. They also play an important role in the progression of some diseases such as immune system modulation and metastasis progression in cancer [14]. Recent studies have shown that miRNA expression is associated with cardiac events, and changes in miRNA expression can give rise to heart diseases such as MI and heart failure [15, [16]. MiRNAs in exosomes may have therapeutic effects in MI injury. Previous studies have demonstrated that miRNA-205 regulation can inhibit apoptosis [17]. Various myocardial cell injuries can cause apoptosis, which can lead to heart failure [18]. Thus, miRNA-205 is a potential therapeutic target to reduce myocardial damage through the inhibition of myocardial apoptosis.
This study aimed to investigate the protective effects of ADSC-Exos on the hearts of MI mice and to explore the underlying mechanisms. Our results indicate that ADSC-exos attenuated cardiac injury and promoted cardiac functional recovery. We also found that ADSC-exos can promote microvascular endothelial cell proliferation, facilitate angiogenesis, and inhibit cardiomyocyte apoptosis through the miRNA-205 signaling pathway. In summary, our data provide strong evidence that ADSC-Exos containing miRNA205 is beneficial for MI injury and has clinical applications.
## Mouse MI model
Male mice were anesthetized with isoflurane (1–$2\%$) (8–12 weeks), the hearts of the mouse were rapidly squeezed out of the chest cavity through the left thoracic incision. In order to induce myocardial infarction, we used silk thread (6–0) to ligate the left anterior descending (LAD) coronary artery. Whitening of ischemic area and changes of ECG are important indicators of successful operation. The sham operated control mice received the same procedure without coronary artery ligation [19].
## Animal study protocol
We purchased 30 8–12 weeks old male C57BL/6 wild-type mice (body weight: 25–30 gm) from the Laboratory Animal Center of the Fourth Military Medical University. The mice were anesthetized on a C57BL/6 background with $2\%$ isoflurane. After 25 min, ADSC-Exos (100 μg protein, 50 μL) was administered evenly intramuscularly into five locations along the anterior wall of the left ventricle’s border zone. The slipknot was released after 40 min to reperfuse the myocardium. All animal experiments were approved by the Animal Care and Use Committee of the Fourth Military Medical University and followed the National Institutes of Health guidelines for the use of laboratory animals (National Institutes of Health Pub. No. 85–23, Revised 2011). The hearts were collected after 4 weeks and fixed with paraformaldehyde or further analysis.
## Isolation of neonatal rat cardiomyocytes (NRCMs) and detection of apoptotic cardiomyocytes
NRCM was isolated from C57BL/6 wild-type mice 1–2 days old. Simply, the heart tissue was washed with PBS three times to remove blood. Then, the hearts were cut into small pieces and digested with type I collagenase solution (1 mg/ml, Thermo Fisher Scientific, Waltham, MA, USA) for 5 to 6 times. Finally, complete medium was added to terminate the digestion process. Because the attachment time of cardiomyocytes was different from fibroblasts, the differential attachment method was used to remove fibroblasts as much as possible. The isolated primary cardiomyocytes were cultured in normal medium for 48 h. Before inducing hypoxia in cardiomyocytes, the medium was replaced with a sugar-free and serum-free medium to simulate nutrient deprivation. Moreover, the cardiomyocytes were placed in a hypoxic chamber ($1\%$ O2, $5\%$ CO2, and $94\%$ N2.) for further culturing 9 h. The apoptotis of cardiomyocytes was evaluated by flow cytometry, the images were analyzed by image J software [19].
## ADSC preparation
ADSCs were extracted according to the method described previously [2]. Under anesthesia, mice inguinal subcutaneous fat was harvested. The adipose tissue was washed several times with sterile phosphate-buffered saline (PBS, Sigma) and then the blood vessels in the adipose tissue were removed with the aid of a dissecting microscope. The remaining adipose tissue was digested with $0.1\%$ type I collagenase (catalog number 17018029, ThermoFisher Scientific, USA) at 37 °C for 60 min and then centrifuged at 1000 g for 10 min, non-adherent cells were removed 48 h after the cells were plated. Then, ADSCs were cultured in Dulbecco’s Modified Eagle Medium (DMEM, Gibco) containing $20\%$ Fetal Bovine Serum (FBS) and penicillin/streptomycin at 37 °C in a humidified atmosphere containing $5\%$ CO2. Cells from passage 2 were used for all experiments.
## Cell culture
Microvascular endothelial cells were purchased from the American Type Culture Collection (ATCC). Cell culture medium contains DMEM medium (Life Technologies, Grand Island, NY, USA) and $10\%$ heat-inactivated fetal bovine serum (Hyclone, UT, USA). All the cells were incubated in a 37 °C humidified incubator containing $5\%$ CO2.
Before induction of hypoxia in HMEC-1 cells, the medium was replaced with a sugar-free and serum-free medium to simulate nutrient deprivation. In addition, the HMEC-1 cells were placed in a hypoxic chamber ($1\%$ O2, $5\%$ CO2, and $94\%$ N2) for further 2 h culture [20].
## Echocardiography
Cardiac function of the mice subjected to I/R after 6 h and 4 weeks were evaluated by echocardiography, as previously described [2]. Mice were anesthetized by inhalation of 1–$2\%$ isoflurane, and transthoracic two-dimensional exercise mode echocardiography (VisualSonics) was performed. The Vevo770 software program (VisualSonics) was used to collect and analyze LV end-systolic dimensions (LVESD), LV end-diastolic dimensions (LVEDD) and LV ejection fraction (LVEF) parameters.
## HE and masson staining
Mice were executed and the hearts were isolated. For histological analysis of angiogenesis and fibrosis, HE and Masson staining were performed, a total of 10 Sects. ( 7–10 um thick) per heart were prepared [2]. HE staining of the sections were performed according to the manufacturer's instruction. Masson’s trichrome was used to evaluate fibrosis in post-I/R mice hearts. The percentage of fibrotic area to total heart represents myocardium fibrosis in post-MI mice hearts. Myocardial fibrosis was quantified by means of Image-*Pro plus* 6.0 software (Media Cybernetics).
## Immunofluorescence staining
All the sections were blocked with $1\%$ goat serum albumin for 1 h and then incubated with mouse monoclonal anti-CD31 primary antibody (Ab955, 1:200; Abcam) at 4 °C overnight. The sections were then stained with rabbit anti-mouse secondary antibody (1:1000; Abcam) for 1 h at room temperature. The tissue slices were washed and mounted with medium containing DAPI. All slices were observed by the Olympus FV1000 laser confocal microscope.
## Western blot analysis
Protein was extracted from heart or cultured microvascular endothelial cells and ADSCs according to standard Invitrogen protocols (Invitrogen, Carlsbad, CA, USA) as previously described [2]. Protein quantitation was modified by Bradford assay (Bio-Rad Laboratories, Hercules, CA, USA) and then separated by SDSPAGE with primary antibodies against The blots were incubated with primary antibodies as follows: HIF-1α(ab179483; Abcam), VEGF (ab32152; Abcam), CD63 (ab217345; Abcam), CD9 (ab223052; Abcam), TSG101 (ab235011; Abcam), β-actin (ab8226;Abcam), Caspase3 (ab184787; Abcam) overnight at 4 °C. The blots were visualized using a chemiluminescence system (Amersham Bioscience, Buchinghamshire, UK). Anti-β actin antibody (Proteintech, IL, United States) was used as a loading control. The signals were quantified by Image J software.
## Extraction and characterization of ADSC-exos
Cultured ADSCs were plated at 5 × 105 cells in a 10-cm dish. The culture medium was collected and centrifuged at 13,000 g for 30 min to remove cells and cell debris. According to the manufacturer's instructions (System Biosciences, CA, United States), 2 mL of ExoQuick-TCTM exosome precipitation solution was used to isolate ADSC-Exo from 10 mL of culture medium. After overnight incubation at 4 °C, the mixture was centrifuged at 10,000 g for 30 min at 4 °C. After being washed, the exosomes were centrifuged at 10,000 g for 15 min at 4 °C. Suspended the purified ADSC-Exos with 100 µL PBS and stored at − 80 °C for further study. The protein extracted from the exosomes was quantified using the Bradford method. And then biomarker CD63 and CD9 were used to characterize the purified ADSC-Exos. The morphology of ADSC-Exo was detected through the transmission electron microscope (Hitachi, Tokyo, Japan). The size of ADSC-Exo was evaluated by Nanoparticle tracking analysis (NTA) analysis.
## Wound healing assay
Wound healing was used to measure proliferation, microvascular endothelial cells were plated at 3,000 cells/well in 96-well plates and treated with ADSC-Exos or vehicle for 3 days. Then the treated microvascular endothelial cells were seeded onto Culture Insert 2 Well in -Dish 35 mm (no. 81176, Ibidi). After administration, cells were cultured in serum-free medium overnight. Then removed the culture insert to create an ~ 500um cell-free gap, and covered the dish with culture medium. Cellular migration was visualized at the indicated time points. The width of the open area at each time point versus the width at time 0 was used to determine the extent of wound healing ratio.
## Assessment of apoptosis
TdT-Mediated dUTP Nick End-Labeling (TUNEL) Assay kit (In Situ Cell Death Detection Kit, Roche, CA, USA) was used to detect the apoptosis of cardiomyocytes in MI-treated mice. The percentage of TUNEL-positive nuclei relative to the total nucleus represents the apoptosis level of cardiomyocytes. In addition, the apoptotic microvascular endothelial cells were evaluated using flow cytometry.
## ADMSC oxidative damage measurement
Oxidative stress is characterized by the production of reactive oxygen species (ROS). They represent the injury of ADSCs in the setting of oxidation stress. The intracellular ROS production of ADSCs was assessed using 10 μM dihydroethidium (DHE; Invitrogen, San Diego, CA, USA). The ADSCs were treated with DHE, incubated in a dark environment, 37 °C and viewed using confocal laser microscopy (Olympus).
## Cell viability assessment
Cells were harvested 24 h after reoxygenation and incubated in a culture medium containing 10 μL of Cell Counting Kit-8 (CCK-8; Sigma Aldrich, Darmstadt, Germany) solution for another 2 h. Subsequently, the optical density was measured at a wavelength of 450 nm, and the cell proliferation rate was calculated.
## Statistical analysis
All data are presented as mean ± the standard error of the mean. Graphpad Prism 8.0 software was used for statistical analyses. For the analysis of two groups, unpaired two-tailed Student t-tests were conducted. When more than two groups were compared, one-way analysis of variance with post hoc analysis was performed. P values of 0.05 were considered statistically significant.
## Isolation and characterization of exosomes derived from ADSCs
ADSCs were isolated from the inguinal fat tissue of C57BL/6 wild-type mice. Passage 2 ADSCs were used in this experiment. We tested ADSCs using the expression levels of MSC surface marker such as CD29, CD31, CD34, and CD44 (Fig. 1A). The exosomes from ADSCs were observed as typical cup-shaped structures using transmission electron microscopy (TEM) (Fig. 1C). Nanoparticle Tracking Analysis (NTA) was performed to analyze the size distribution of exosomes [6]. They were approximately 100 nm in size (Fig. 1B). Furthermore, western blot analysis demonstrated that exosomes derived from ADSCs were positive for exosome-specific markers Alix, TSG101, CD81, and CD63 (Fig. 1D–E).Fig. 1Isolation and characterization of exosomes derived from ADSCs A. The expression of relevant biomarkers of ADSCs (CD29, CD44, CD31 and CD34) were measured by flow cytometry; B. The concentration and size distribution of ADSC-Exo was detected by Nanoparticle tracking analysis (NTA); C. The morphology of ADSC-Exo was characterized by transmission electron microscope; C–E The relative expression of exosomes’ marker proteins (CD63, TSG101 and CD9) evaluated by Western blot;
## Intravenous injection of ADSCs-Exos can reduce infarction area and improve post-MI cardiac function and remodeling
The infarcted myocardium can regenerate itself through excessive extracellular matrix (ECM) deposition, which replaces the dead cardiomyocytes and leads to scar formation. Myocardial fibrosis also occurs in most cardiac pathologic conditions and is associated with poor cardiac outcomes. Notably, myocardial fibrosis leads to increased cardiac remodeling and reduced ventricular compliance, all of which contribute to the progression of heart failure [21]. To investigate the beneficial role of exosomes in MI, purified ADSC-Exos were intramyocardially injected into the border zone of infarcted mice hearts after 25 min [6]. The echocardiography results showed that in MI mice, the ejection fraction (EF) and fractional shortening (FS) were significantly lower than in the sham group (Fig. 2A–C). In contrast to MI mice, ADSC-Exos administration improved the EF and FS (Fig. 2A–C). Moreover, the effects of ADSC-exo pretreated with miR-205 inhibitor in mice were evaluated. The results showed that compared with ADSC-Exo-treated MI mice, the left ventricular ejection fraction (EF) and fractional shortening (FS) in the ADSC-Exo + miR-205 inhibitor treated MI mice were significantly decreased (Additional file 1: Figure S1A-B). Masson trichrome staining showed that, compared with the sham group, the area of myocardial fibrosis in the MI group was significantly increased. Furthermore, the intravenous delivery of ADSC-Exos significantly reduced the infarct size and fibrosis area compared to the MI group (Fig. 2D-F). These results indicate that ADSC-Exos play a protective role in MI, to some extent, ADSC-Exo can prevent myocardial MI injury through miR-205 in vivo. Fig. 2Intravenous injection of ADSCs-Exos can reduce infarction area and improve post-MI cardiac function and remodeling A. Echocardiography was used to evaluate cardiac function in control mice, MI-treated mice, and ADSC-Exo-treated MI mice; B-C. Representative analysis of left ventricular ejection fraction (EF) and fractional shortening (FS), compared with MI-treated mice, the EF and FS in the ADSC-Exo-treated MI mice were significantly increased; D. Masson staining for proportion of collagen in MI mice; E–F. Quantitative analysis of myocardial infarction area and the ration of fibrosis area and infarction area. Data were presented as Mean ± SEM, $$n = 8$$–10 mice. ** $P \leq 0.05$, *$P \leq 0.05$
## Intravenous delivery of ADSCs-Exos promotes the survival of cardiomyocytes after MI
Studies have shown that acute and chronic loss of cardiomyocytes leads to pathological left ventricular remodeling and cardiac dysfunction, leading to the progression of heart failure [4]. It has been reported that the excessive production of ROS during MI and the ischemic environment increases cardiomyocytes apoptosis [22, [23]. Therefore, it is important to improve the ischemic or oxidative stress conditions to protect these cardiomyocytes from apoptosis [24].
Apoptosis of cardiomyocytes was evaluated using TUNEL staining. The number of TUNEL-positive cells in the MI areas was significantly higher than in the sham group (Fig. 3A–B). Compared with the MI group, the delivery of ADSC-Exos significantly reduced cardiomyocytes apoptosis (Fig. 3A–B). The production of ROS in ischemic heart tissue was tested using DHE immunofluorescence staining. The results showed marked red fluorescent protein (RFP+) fluorescence in the MI group. Consistent with previous reports, the group administered intravenously with ADSC-Exos had reduced RFP+ fluorescence compared with the MI group (Fig. 3C–D).Fig. 3Intravenous delivery of ADSCs-Exos promotes the survival of cardiomyocytes after MI A. Representative apoptotic cardiomyocytes revealed by TUNEL staining; B. Quantitative analysis of the ratio of TUNEL-positive cardiomyocytes; C-D. The production of ROS in ischemic heart tissue was tested by DHE immunofluorescence staining. Data were presented as Mean ± SEM, $$n = 8$$–10 mice. ** $P \leq 0.05$, *$P \leq 0.05$
## Intravenous delivery of ADSCs-Exosomes promotes angiogenesis and microvascular endothelial cells proliferation
Angiogenesis has been reported to be a key component in the process of wound healing [25]. It is necessary for myocardial regeneration after MI [26]. The pathophysiology of MI involves the activation of hypoxia-inducible factor (HIF)-1α proteins and the release of vascular endothelial growth factor (VEGF) [27]. VEGF has also been shown to have angiogenic activity [28]. Various studies found that angiogenesis due to stem cell therapy preserved cardiac function following MI [29]. To explore the underlying protective mechanisms of ADSC-Exos in reducing infarct size, we assessed angiogenesis using CD31 immunofluorescence (Fig. 4A) and hematoxylin and eosin (HE) staining (Fig. 4C). The numbers of neovessels in the MI areas of hearts from the MI group did not differ significantly compared with the sham group (Fig. 4A and C). In contrast, the injection of ADSC-Exos significantly increased the number of neovessels compared with both sham and MI groups (both $P \leq 0.01$; Fig. 4A and C). Moreover, the expressions of activated angiogenic markers HIF-1α and VEGF were also evaluated using western blot analysis. The results showed that the expressions of HIF-1α and VEGF were higher in the MI group than in the sham group (Fig. 4B; D–E). Furthermore, compared with the MI group, the injection of ADSC-Exos significantly increased the expression of HIF-1α and VEGF (Fig. 4B; D–E). These results demonstrate that injection of ADSC-Exos increases angiogenesis, resulting in a cardioprotective role after MI.Fig. 4Intravenous delivery of ADSCs-Exosomes promotes angiogenesis and microvascular endothelial cells proliferation. A. Representative HE staining of neovessels in the hearts from the sham group, MI group and MI + ADSC-exo group; B. The expression of angiogenic marker HIF-1α and VEGF were evaluated by western blot; C. Fluorescent immunostaining of plaque sections with anti-CD31 antibody; D-E. Quantitative analysis of angiogenic marker HIF-1α and VEGF expression. Data were presented as Mean ± SEM, $$n = 8$$–10 mice. ** $P \leq 0.05$, *$P \leq 0.05$
## miRNA-205 was involved in the ADSC-Exos-mediated promotion of the proliferation of microvascular endothelial cells
ADSCs have been demonstrated to activate blood vessel formation, thus providing a promising future for therapeutic angiogenesis [30]. In recent angiogenesis studies, ADSCs have often been co-cultured with human microvascular endothelial cells (HMEC-1) to modulate endothelial cells and induce angiogenesis by promoting tube formation [31]. To identify the molecular mechanisms underlying the effects of ADSC-Exos on microvascular endothelial cell proliferation, metabolomics analysis was used to analyze miRNA levels in ADSC-Exos. The result showed that miRNA-205 was highly upregulated under an ischemic and hypoxic environment (Fig. 5A–B). To further confirm that the ADSCs and HMEC-1 communicate via miRNA-205, cy3-labeled miRNA-205 was added to the medium of ADSC cells, then they were co-cultured with microvascular endothelial cells (Fig. 5C). Immunofluorescence staining confirmed that HMEC-1 was labeled with miRNA-205-Cy3 (Fig. 5D). In addition, a negative control Cy3-labeled scrambled RNA (Cy3-ctrl) were also constructed and transfected to ADSC. Cy3 signal were successfully detected in ADSC cells (as shown in Additional file 2: Fig. S2A). Furthermore, we used quantitative real-time polymerase chain reaction to analyze miRNA-205 levels in ADSC-Exos-treated microvascular endothelial cells. The relative expression of miRNA-205 was upregulated more than twofold (Fig. 5E). These results suggest that miRNA-205 is involved in the ADSCs-Exos-mediated promotion of microvascular endothelial cell proliferation, thus promoting angiogenesis. Fig. 5miRNA-205 was involved in the ADSC-Exos-mediated promotion of the proliferation of microvascular endothelial cells. A. The level of miR-205 in ADSCs-exosomes was evaluated by metabolomics analysis; B. The level of miR-205 in ADSCs-exosomes was determined by RT-qPCR; C. ADSC cells treated with cy3 labeled miR-205 were co-cultured with microvascular endothelial cells. D. Cy3 labeled miR-205 (red) were present in microvascular endothelial cells. E. Relative level of miR-205 in ADSCs-exosomes was determined by RT-qPCR. Data were presented as Mean ± SEM, $$n = 6$$ independent experiment. ** $P \leq 0.05$
## Intravenously injected ADSCs-exosomes promoted hypoxia-treated HMEC-1 survival and angiogenesis through miR-205 after MI
To further investigate ADSC-Exos containing miR-205 play a critical role in HMEC-1 survival and the formation of neovessels. We performed a pretreatment of ADSCs with miR-205 mimics inhibitor. The result showed that ADSC-Exos markedly alleviated the impairment by hypoxia treatment, they promoted HMEC-1 survival (Fig. 6A). However, the protective effect of ADSC-Exos was significantly inhibited after treated with miR-205 mimics inhibitor (Fig. 6A). Similarly, flow cytometry assay also showed markedly reduced apoptosis of HMEC-1 with ADSC-Exos administration and critical increased apoptosis transfection with miR-205 mimics inhibitor (Fig. 6B–C). Additionally, activated angiogenic protein HIF-1α and VEGF were also measured by western blot (Fig. 6D). The result also confirmed that increased expression of HIF-1α and VEGF were found after adding ADSC-Exos, while transfection with miR-205 mimics inhibitor significantly decreased the expression of HIF-1α and VEGF (Fig. 6D–F). Taken together, these findings indicate that ADSCs-Exo containing miR-205 play a crucial role in promoting HMEC-1 survival and angiogenesis in MI.Fig. 6Intravenously injected ADSCs-exosomes promoted hypoxia-treated HMEC-1 survival and angiogenesis through miR-205 after MI. A. Effect of ADSC-Exo containing miR-205 on HMEC-1 viability after hypoxia injury was measured by MTT assay; B. Evaluation of HMEC-1 proliferation by flow cytometry is shown; C. Quantitative analysis of HMEC-1 proliferation; D. The level of angiogenic marker HIF-1α and VEGF were evaluated by Western blot analysis; E–F. Quantitative analysis of HIF-1α and VEGF expression. Data were presented as Mean ± SEM, $$n = 6$$ independent experiment. ** $P \leq 0.05$
## Intravenously injected ADSCs-exosomes prevented the apoptotic rate of hypoxia-treated HMEC-1 cells through miR-205 after MI
To further investigate the protective mechanism of ADSC-Exos against myocardial ischemic injury, a miRNA-205 inhibitor was used to confirm the effects of miRNA-205 on HMEC-1 cells apoptosis. HMEC-1 cells were subjected to hypoxia treatment for 2 h to mimic myocardial ischemic injury in vitro. The levels of apoptosis in cells were evaluated using flow cytometry (Fig. 7A–B). The result showed that transfection with ADSC-Exos significantly reduced the apoptotic rate of hypoxia-treated HMEC-1 compared to that in the control group (Fig. 7A–B). However, the miRNA-205 inhibitor significantly increased the level of apoptosis in HMEC-1 (Fig. 7A–B). In addition, the expression levels of apoptotic proteins such as caspase-3 were measured (Fig. 7C). Western blot analysis indicated that hypoxia treatment significantly increased the levels of caspase-3, and transfection with miRNA-205 inhibitor further promoted the expression of caspase-3 (Fig. 7C–D). However, ADSC-Exos markedly reversed the increase in the level of caspase-3 (Fig. 7C–D). Furthermore, in order to elucidate the protective effects of miR-205 on cardiomyocytes, the level of apoptotic neonatal cardiomyocytes in vitro undergoing hypoxia were evaluated by flow cytometry. However, the result showed that ADSC-exo with and without miR-205 inhibitor had no protective effect on neonatal cardiomyocytes apoptosis undergoing hypoxia environment (Additional file 3: Fig. S3A–B). These results of the scratch experiment showed that ADSC-Exos improved the migratory ability of HMEC-1, which was also blunted by the miRNA-205 inhibitor (Fig. 7E). These findings indicate that ADSC-Exos protect against hypoxia-induced HMEC-1 cells apoptosis and promote HMEC-1 cells migration via miRNA-205.Fig. 7Intravenously injected ADSCs-exosomes prevented the apoptotic rate of hypoxia-treated HMEC-1 cells through miR-205 after MI. A. The evaluation of HMEC-1 apoptosis was measured by flow cytometry; B. Quantitative analysis of the apoptotic ratio of HMEC-1 by flow cytometry; C. The level of caspase-3 was evaluated by Western blot analysis; D. Quantitative analysis of caspase-3 expression. E. Effect of ADSC-Exo containing miR-205 on microvascular endothelial cells migration was detected by Wound-Healing Assay. Mean ± SEM, $$n = 6$$ independent experiment. ** $P \leq 0.05$
## Discussion
The incidence of AMI has been reported to have increased rapidly in China in recent decades [1]. Although timely thrombolysis and percutaneous coronary intervention (PCI) are effective therapeutic strategies for patients with MI in terms of reducing the size of the infarcted area and myocardial ischemic injury [32], it remains the leading cause of global mortality [33]. Massive cardiomyocytes loss due to increased oxidative stress, inflammation, and induced myocardial apoptosis is still irreversible [6]. Various studies found that ADSCs are potential strategies for the treatment of ischemic heart disease [2]. However, the intravenous injection of ADSCs resulted in limited myocardial stem cell retention and survival [8, [34]. In this study, we found that ADSC-Exos decreased myocardial apoptosis and increased angiogenesis, thus contributing to cardiac function recovery after MI. We have also demonstrated that the cardioprotective effects of ADSC-Exos are achieved through large amounts of miRNA-205.
Since cardiomyocytes have little regenerative ability, reducing the apoptosis of cardiomyocytes after an injury has a promising therapeutic potential [35]. ADSCs are a type of MSC that can be easily obtained from a stromal vascular fraction (SVF) within adipose tissues [36]. ADSCs differ from other MSCs in that they are more readily available, have a high proliferation potential, have extraordinary self-renewal ability, and can secrete nutritional factors and extracellular vesicles, making them the ideal treatment candidate for cardiac function recovery [37]. Various studies have indicated that ADSCs play a central role in chemoattractant and promote angiogenesis through VEGF expression, thus contributing to cardiac tissue regeneration [38, [39]. Other studies have also indicated ADSCs can secrete many anti‐apoptotic, pro‐angiogenic, and anti‐inflammatory cytokines and growth factors, thus contributing to inhibiting adverse cardiac remodeling and improving ventricular function and myocardial vascularization in the infarcted myocardium [8]. Although the intravenous administration of ADSCs immediately showed better benefits after the reperfusion, the intravenous delivery method, the ideal concentration, and timing for cell administration remain unexamined [8]. Furthermore, the beneficial effects of stem cells were affected due to the poor survival, retention, and engraftment of MSCs following transplantation [40]. Although there are various invasive strategies to deliver ADSCs such as catheter-based delivery (intracoronary or trans-endocardial injection) or surgical delivery (direct intramyocardial injection) in injured myocardium, the retention of ADSCs was limited, and abundant ADSCs were distributed to the lungs [7, [8, [34]. As a result, it is critical to address the issue of ADSC redistribution to the lungs and improve the limited cardiac ADSC retention. Exosome-containing short RNAs released by MSCs can regulate the microenvironment in stem cells, which is dependent on the balance between differentiation and proliferation of stem cells [41]. These findings could help shed light on the regulatory mechanism that controls MSC paracrine activity, which is responsible for the tissue-specific regenerative characteristics of MSCs [41]. In this study, MI injury exacerbated cardiac dysfunction and promoted cardiac fibrosis. Furthermore, MI surgery significantly increased cardiomyocytes apoptosis, while ADSC-Exos administration markedly reversed these effects. ADSC-Exos reduces cardiomyocytes loss by inhibiting myocardial apoptosis and can be beneficial for MI-induced damage.
In recent years, exosomes have attracted much attention and they are a type of extracellular vesicle that range from 30 to 100 nm [42]. Exosomes contain biomolecules such as proteins, nucleic acids (DNA, mRNA, miRNA), lipids, and enzymes. They play important roles in cell-to-cell communication by transporting these biomolecules across various cells [42]. Adipose tissue has been demonstrated to play a central role in wound healing and tissue regeneration. Both ADSCs and ADSC-Exos are very important derivatives of fat tissues [42]. Researchers have found that although ADSC-Exos have no differentiation ability, they can mimic the capacity of ADSCs by lowering injury and inflammation damage, making them a suitable therapeutic target for damaged tissue regeneration and repair [42]. Furthermore, ADSC-Exos are small non-living substances with production and delivery advantages, making them a promising candidate for biological products. Most importantly, ADSC-Exos may be a safer therapeutic agent than ADSCs [42]. Additionally, the use of ADSC-Exos may effectively eliminate the issues that come with ADSC administration, such as limited ADSC survival, intensive immune rejection, functional inactivation, and unfavorable differentiation [43]. ADSC-Exos have been shown to offer therapeutic potential for targeted drug delivery and to be a promising candidate for regenerative medicine in skin healing and various reconstructive operations [44]. According to research, exosomes play an important role in immune response, antigen presentation, tumor cell migration and proliferation, apoptosis, and autophagy, thus they participate in the pathophysiology of many diseases [12]. When the homeostasis of the microenvironment of the tissue is disrupted by harmful stimuli such as disease or injury, MSC-derived exosomes play an important role in homeostasis [14]. We found that ADSC-Exos can inhibit cardiomyocyte apoptosis and promote angiogenesis, thus contributing to improving cardiac function in MI-treated mice. Based on these findings, ADSC-Exos has therapeutic potential to improve myocardial function.
Emerging evidence suggests that miRNAs play an important role in information transferring between cells [14]. Exosomal miRNAs are the most abundant molecules found in exosomes [12]. It is increasingly considered that miRNAs contribute to increased self-renewal of stem cells, and promote their differentiation and pluripotency [45]. Additionally, miRNAs play a central role in regulating the proliferation, differentiation, and survival of MSCs [46]. Exosomes as information carriers are thought to play a key role in miRNA-mediated cell-to-cell communication, according to growing evidence [14]. The diverse functions of exosomal miRNAs were observed in many physiological and pathological processes, such as inflammation, cell migration, proliferation, apoptosis, autophagy, and epithelial-mesenchymal transition [12]. There is increasing evidence showing that dysregulation of exosomal miRNAs occurs in various pathophysiological processes including atherosclerosis, acute coronary syndrome, heart failure, myocardial ischemia–reperfusion injury, and pulmonary hypertension [12, [47]. In these studies, miRNA-205 was demonstrated to be involved in the induction of inflammation and atherosclerosis in vascular endothelial cells by targeting the tissue inhibitor of metalloproteinase-3, which interferes with miRNA-205 expression and thus plays a protective role in vascular endothelial cells [18]. Studies have shown that the expression level of miRNA-205 is associated with the inhibition of apoptosis [17]. It has also been suggested that MI and cardiomyopathy can aggravate myocardial apoptosis and contribute to the progression of heart failure. Therefore, miRNA-205 has therapeutic potential to alleviate myocardial damage. In our study, we demonstrated that exosomes containing miRNA-205 can regulate myocardial apoptosis, ameliorate MI injury, and improve cardiac function. We also found that exosomes derived from ADSCs containing miRNA-205 can promote the proliferation and migration of microvascular endothelial cells, facilitate angiogenesis, and inhibit cardiomyocyte apoptosis. We demonstrated that ADSC-Exos containing miRNA-205 is a key mediator between ADSCs and microvascular endothelial cells, thereby regulating cardiac function recovery. Based on these findings, we concluded that ADSC-Exos containing miRNA205 have promising therapeutic potential in MI-induced heart injury.
## Conclusion
In summary, we comprehensively investigated the functional role and molecular mechanisms of ADSC-Exos containing miRNA-205 in MI injury. Our findings suggest that ADSC-Exos containing miRNA-205 reduces myocardial fibrosis and inhibits myocardial apoptosis, both of which are important for restoring cardiac function in mice with MI injury. In addition, we also found that ADSC-Exos containing miRNA-205 can promote angiogenesis. Therefore, the results of the present study provide basic evidence for the application of ADSC-Exos in clinical treatments for MI.
## Supplementary Information
Additional file 1: Figure 1 Intravenous injection of ADSCs-Exos pretreated with miR-205 inhibitor can aggravate cardiac function in post-MI mice A. Echocardiography was used to evaluate cardiac function in ADSC-Exo-treated MI mice and ADSC-Exo+miR-205 inhibitor treated MI mice; B. Representative analysis of left ventricular ejection fraction (EF) and fractional shortening (FS), compared with ADSC-Exo-treated MI mice, the EF and FS in the ADSC-Exo+miR-205 inhibitor treated MI mice were significantly decreased. Data were presented as Mean± SEM, $$n = 8$$-10 mice. * $P \leq 0.05.$Additional file 2: Figure 2 Cy3-labelled miRNA205 are kept by endothelial cellsAdditional file 3: Figure 3 ADSC-exo with and without miR-205 inhibitor has no effect on the apoptosis of neonatal cardiomyocytes A. Representative apoptotic neonatal cardiomyocytes revealed by Flow cytometry; B. Quantitative analysis of the ratio of apoptotic cardiomyocytes. Data were presented as Mean± SEM, $$n = 6$$ independent experiment. * $P \leq 0.05.$
## References
1. Xu H, Yang Y, Wang C, Yang J, Li W. **Association of hospital-level differences in care with outcomes among patients with acute ST-segment elevation myocardial infarction in china**. *JAMA Netw Open* (2020) **3** e2021677. DOI: 10.1001/jamanetworkopen.2020.21677
2. He Y, Guo Y, Xia Y, Guo Y, Wang R. **Resistin promotes cardiac homing of mesenchymal stem cells and functional recovery after myocardial ischemia-reperfusion via the ERK1/2-MMP-9 pathway**. *Am J Physiol Heart Circ Physiol* (2019) **316** H233-H244. DOI: 10.1152/ajpheart.00457.2018
3. Reed GW, Rossi JE, Cannon CP. **Acute myocardial infarction**. *Lancet* (2017) **389** 197-210. DOI: 10.1016/S0140-6736(16)30677-8
4. Wang WE, Yang D, Li L, Wang W, Peng Y. **Prolyl hydroxylase domain protein 2 silencing enhances the survival and paracrine function of transplanted adipose-derived stem cells in infarcted myocardium**. *Circ Res* (2013) **113** 288-300. DOI: 10.1161/CIRCRESAHA.113.300929
5. Lee RH, Kim B, Choi I, Kim H, Choi HS. **Characterization and expression analysis of mesenchymal stem cells from human bone marrow and adipose tissue**. *Cell Physiol Biochem* (2004) **14** 311-324. DOI: 10.1159/000080341
6. Lai TC, Lee TL, Chang YC, Chen YC, Lin SR. **MicroRNA-221/222 mediates ADSC-exosome-Induced cardioprotection against Ischemia/reperfusion by targeting PUMA and ETS-1**. *Front Cell Dev Biol* (2020) **8** 569150. DOI: 10.3389/fcell.2020.569150
7. Epstein SE, Luger D, Lipinski MJ. **Paracrine-Mediated systemic anti-inflammatory activity of intravenously administered mesenchymal stem cells: a transformative strategy for cardiac stem cell therapeutics**. *Circ Res* (2017) **121** 1044-1046. DOI: 10.1161/CIRCRESAHA.117.311925
8. Bobi J, Solanes N, Fernandez-Jimenez R, Galan-Arriola C, Dantas AP. **Intracoronary administration of allogeneic adipose tissue-derived mesenchymal stem cells improves myocardial perfusion but not left ventricle function, in a translational model of acute myocardial infarction**. *J Am Heart Assoc* (2017). DOI: 10.1161/JAHA.117.005771
9. Hu L, Wang J, Zhou X, Xiong Z, Zhao J. **Exosomes derived from human adipose mensenchymal stem cells accelerates cutaneous wound healing via optimizing the characteristics of fibroblasts**. *Sci Rep* (2016) **6** 32993. DOI: 10.1038/srep32993
10. Lai RC, Arslan F, Lee MM, Sze NS, Choo A. **Exosome secreted by MSC reduces myocardial ischemia/reperfusion injury**. *Stem Cell Res* (2010) **4** 214-222. DOI: 10.1016/j.scr.2009.12.003
11. Sahoo S, Losordo DW. **Exosomes and cardiac repair after myocardial infarction**. *Circ Res* (2014) **114** 333-344. DOI: 10.1161/CIRCRESAHA.114.300639
12. Zheng D, Huo M, Li B, Wang W, Piao H. **The role of exosomes and exosomal MicroRNA in cardiovascular disease**. *Front Cell Dev Biol* (2020) **8** 616161. DOI: 10.3389/fcell.2020.616161
13. Bartel DP. **MicroRNAs: genomics, biogenesis, mechanism, and function**. *Cell* (2004) **116** 281-297. DOI: 10.1016/S0092-8674(04)00045-5
14. Asgarpour K, Shojaei Z, Amiri F, Ai J, Mahjoubin-Tehran M. **Exosomal microRNAs derived from mesenchymal stem cells: Cell-to-cell messages**. *Cell Commun Signal* (2020) **18** 149. DOI: 10.1186/s12964-020-00650-6
15. Abdellatif M. **Differential expression of microRNAs in different disease states**. *Circ Res* (2012) **110** 638-650. DOI: 10.1161/CIRCRESAHA.111.247437
16. Yang B, Lin H, Xiao J, Lu Y, Luo X. **The muscle-specific microRNA miR-1 regulates cardiac arrhythmogenic potential by targeting GJA1 and KCNJ2**. *Nat Med* (2007) **13** 486-491. DOI: 10.1038/nm1569
17. Zhang G, Hou X, Li Y, Zhao M. **MiR-205 inhibits cell apoptosis by targeting phosphatase and tensin homolog deleted on chromosome ten in endometrial cancer Ishikawa cells**. *BMC Cancer* (2014) **14** 440. DOI: 10.1186/1471-2407-14-440
18. Xuan Y, Liu S, Li Y, Dong J, Luo J. **Shortterm vagus nerve stimulation reduces myocardial apoptosis by downregulating microRNA205 in rats with chronic heart failure**. *Mol Med Rep* (2017) **16** 5847-5854. DOI: 10.3892/mmr.2017.7344
19. Qi B, Song L, Hu L, Guo D, Ren G. **Cardiac-specific overexpression of Ndufs1 ameliorates cardiac dysfunction after myocardial infarction by alleviating mitochondrial dysfunction and apoptosis**. *Exp Mol Med* (2022) **54** 946-960. DOI: 10.1038/s12276-022-00800-5
20. Yang Y, Wang J, Zhang Y, Hu X, Li L, Chen P. **Hypoxic tubular epithelial cells regulate the angiogenesis of HMEC-1 cells via mediation of Rab7/MMP-2 axis**. *Aging (Albany NY)* (2021) **13** 23769-23779. DOI: 10.18632/aging.203648
21. Gonzalez A, Schelbert EB, Diez J, Butler J. **Myocardial interstitial fibrosis in heart failure: biological and translational perspectives**. *J Am Coll Cardiol* (2018) **71** 1696-1706. DOI: 10.1016/j.jacc.2018.02.021
22. Shi RZ, Li QP. **Improving outcome of transplanted mesenchymal stem cells for ischemic heart disease**. *Biochem Biophys Res Commun* (2008) **376** 247-250. DOI: 10.1016/j.bbrc.2008.09.004
23. Frangogiannis NG, Smith CW, Entman ML. **The inflammatory response in myocardial infarction**. *Cardiovasc Res* (2002) **53** 31-47. DOI: 10.1016/S0008-6363(01)00434-5
24. Zhou H, Yang J, Xin T, Li D, Guo J. **Exendin-4 protects adipose-derived mesenchymal stem cells from apoptosis induced by hydrogen peroxide through the PI3K/Akt-Sfrp2 pathways**. *Free Radic Biol Med* (2014) **77** 363-375. DOI: 10.1016/j.freeradbiomed.2014.09.033
25. Okonkwo UA, Dipietro LA. **Diabetes and wound angiogenesis**. *Int J Mol Sci* (2017). DOI: 10.3390/ijms18071419
26. Cochain C, Channon KM, Silvestre JS. **Angiogenesis in the infarcted myocardium**. *Antioxid Redox Signal* (2013) **18** 1100-1113. DOI: 10.1089/ars.2012.4849
27. Lee SH, Wolf PL, Escudero R, Deutsch R, Jamieson SW, Thistlethwaite PA. **Early expression of angiogenesis factors in acute myocardial ischemia and infarction**. *N Engl J Med* (2000) **342** 626-633. DOI: 10.1056/NEJM200003023420904
28. Tonnesen MG, Feng X, Clark RA. **Angiogenesis in wound healing**. *J Investig Dermatol Symp Proc* (2000) **5** 40-46. DOI: 10.1046/j.1087-0024.2000.00014.x
29. van der Laan AM, Piek JJ, van Royen N. **Targeting angiogenesis to restore the microcirculation after reperfused MI**. *Nat Rev Cardiol* (2009) **6** 515-523. DOI: 10.1038/nrcardio.2009.103
30. Rautiainen S, Laaksonen T, Koivuniemi R. **Angiogenic effects and crosstalk of adipose-derived mesenchymal stem/stromal cells and their extracellular vesicles with endothelial cells**. *Int J Mol Sci* (2021). DOI: 10.3390/ijms221910890
31. Pill K, Melke J, Muhleder S, Pultar M, Rohringer S. **Microvascular networks from endothelial cells and mesenchymal stromal cells from adipose tissue and bone marrow: a comparison**. *Front Bioeng Biotechnol* (2018) **6** 156. DOI: 10.3389/fbioe.2018.00156
32. Hausenloy DJ, Yellon DM. **Myocardial ischemia-reperfusion injury: a neglected therapeutic target**. *J Clin Invest* (2013) **123** 92-100. DOI: 10.1172/JCI62874
33. Cho HM, Cho JY. **Cardiomyocyte death and genome-edited stem cell therapy for ischemic heart disease**. *Stem Cell Rev Rep* (2021) **17** 1264-1279. DOI: 10.1007/s12015-020-10096-5
34. Hong SJ, Hou D, Brinton TJ, Johnstone B, Feng D. **Intracoronary and retrograde coronary venous myocardial delivery of adipose-derived stem cells in swine infarction lead to transient myocardial trapping with predominant pulmonary redistribution**. *Catheter Cardiovasc Interv* (2014) **83** E17-E25. DOI: 10.1002/ccd.24659
35. Brown DI, Griendling KK. **Regulation of signal transduction by reactive oxygen species in the cardiovascular system**. *Circ Res* (2015) **116** 531-549. DOI: 10.1161/CIRCRESAHA.116.303584
36. Kim H, Lee BK. **Anti-Inflammatory effect of adipose-derived stromal vascular fraction on osteoarthritic temporomandibular joint synoviocytes**. *Tissue Eng Regen Med* (2020) **17** 351-362. DOI: 10.1007/s13770-020-00268-2
37. Mazini L, Rochette L, Amine M, Malka G. **Regenerative capacity of adipose derived stem cells (ADSCs), comparison with mesenchymal stem cells (MSCs)**. *Int J Mol Sci* (2019). DOI: 10.3390/ijms20102523
38. Bajek A, Gurtowska N, Olkowska J, Kazmierski L, Maj M, Drewa T. **Adipose-derived stem cells as a tool in cell-based therapies**. *Arch Immunol Ther Exp (Warsz)* (2016) **64** 443-454. DOI: 10.1007/s00005-016-0394-x
39. Chen J, Gu Z, Wu M, Yang Y, Zhang J. **C-reactive protein can upregulate VEGF expression to promote ADSC-induced angiogenesis by activating HIF-1alpha via CD64/PI3k/Akt and MAPK/ERK signaling pathways**. *Stem Cell Res Ther* (2016) **7** 114. DOI: 10.1186/s13287-016-0377-1
40. Hu X, Chen P, Wu Y, Wang K, Xu Y. **MiR-211/STAT5A signaling modulates migration of mesenchymal stem cells to improve its therapeutic efficacy**. *Stem Cells* (2016) **34** 1846-1858. DOI: 10.1002/stem.2391
41. Baglio SR, Rooijers K, Koppers-Lalic D, Verweij FJ, Perez LM. **Human bone marrow- and adipose-mesenchymal stem cells secrete exosomes enriched in distinctive miRNA and tRNA species**. *Stem Cell Res Ther* (2015) **6** 127. DOI: 10.1186/s13287-015-0116-z
42. Xiong M, Zhang Q, Hu W, Zhao C, Lv W. **Exosomes from adipose-derived stem cells: the emerging roles and applications in tissue regeneration of plastic and cosmetic surgery**. *Front Cell Dev Biol* (2020) **8** 574223. DOI: 10.3389/fcell.2020.574223
43. Figueroa FE, Cuenca MJ, La Cava A. **Novel approaches to lupus drug discovery using stem cell therapy. Role of mesenchymal-stem-cell-secreted factors**. *Expert Opin Drug Discov* (2014) **9** 555-566. DOI: 10.1517/17460441.2014.897692
44. Mehryab F, Rabbani S, Shahhosseini S, Shekari F, Fatahi Y, Baharvand H, Haeri A. **Exosomes as a next-generation drug delivery system: an update on drug loading approaches, characterization, and clinical application challenges**. *Acta Biomater* (2020) **113** 42-62. DOI: 10.1016/j.actbio.2020.06.036
45. Heinrich EM, Dimmeler S. **MicroRNAs and stem cells: Control of pluripotency, reprogramming, and lineage commitment**. *Circ Res* (2012) **110** 1014-1022. DOI: 10.1161/CIRCRESAHA.111.243394
46. Clark EA, Kalomoiris S, Nolta JA, Fierro FA. **Concise review: MicroRNA function in multipotent mesenchymal stromal cells**. *Stem Cells* (2014) **32** 1074-1082. DOI: 10.1002/stem.1623
47. Barraclough JY, Joan M, Joglekar MV, Hardikar AA, Patel S. **MicroRNAs as prognostic markers in acute coronary syndrome patients-A systematic review**. *Cells* (2019). DOI: 10.3390/cells8121572
|
---
title: Cross-sectional analysis of potential risk factors of the pineal gland calcification
authors:
- Nazanin Jalali
- Mohammadrasoul Dehghani Firouzabadi
- Ali Mirshekar
- Parvin Khalili
- Amir reza Ravangard
- Jafar Ahmadi
- Pooya Saeed Askari
- Zahra Jalali
journal: BMC Endocrine Disorders
year: 2023
pmcid: PMC9972749
doi: 10.1186/s12902-023-01301-w
license: CC BY 4.0
---
# Cross-sectional analysis of potential risk factors of the pineal gland calcification
## Abstract
The Pineal gland (PG) is the site of production of melatonin as an important central hormone in the body. It is not known yet whether PG calcification (PGC) is an age-associated physiological process or a pathologic condition caused by lifestyle-factors and metabolic-dysregulations.
Here, we performed a cross-sectional analysis on 586 patients referred to have Computed Tomographic (CT) scans (above 15 years old), in the Ali Ebne Abi Taleb hospital radiology center in 2017–2018. Based on the CT-scans of the brain, the presence of PGC was recorded and a score of scale 0 to 6 (PGC_score) was calculated for its intensity based on the volume and the Hounsfield units of the calcified pineal. Logistic and ordered logistic regression tests were employed to determine potential risk factor of PGC and higher PGC_score, respectively, testing the factors age, sex, history of cardiovascular and metabolic diseases, smoking and opioid use. We found male sex (OR: 2.30 ($95\%$ CI:1.39–3.82) and smoking cigarettes (OR: 4.47 ($95\%$ CI:1.01–19.78)) as the main potential risk factors for the pineal gland calcification. For PGC_score, we found age to be dose-dependently associated with PGC_score only in patients aged below 63 (p-trend < 0.001). Stratifying for age, in patients < 63 years old, we found age, male sex (positive association) and dyslipidemia (negative association) as the main significantly associated factors of PGC_score. On the contrary, in patients aged > = 63, cigarette smoking was the only significantly associated factor of higher PGC_score.
In conclusion, our results indicate that at ages below 63, age, male sex and blood lipid are the main associated factors of higher PGC, but at ages above that, the lifestyle factor smoking is significantly associated with higher pineal gland calcification.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12902-023-01301-w.
## Introduction
The pineal gland as the main part of the epithalamus, is known to secrete melatonin as the direct regulator of [1] circadian rhythms in humans [2]. In addition, melatonin has been reported to be involved in neuroprotection against oxidative stress, inflammation, amyloid effects, and apoptosis [3, 4] and its dysregulation has been implicated in several neurodegenerative disorders [3, 5, 6], and stroke [7, 8]. The Pineal gland has a high rate of calcification in the human body forming deposits of magnesium and calcium around corpora arenacea [9]. There have been reports which suggested that the level of melatonin secretion is not affected by pineal calcification and consider this phenomenon as a physiologic process and not associated with aging and disease [10, 11]. On the other hand, some studies have suggested that the pineal gland calcification (PGC) is an age-related pathological process, and the level of 6-sulfatoxymelatonin, the main metabolic form of melatonin, is directly dependent on the size of uncalcified pineal tissue [12, 13].
The chemistry of PGC and the factors which predict the level of pineal calcification are poorly understood and require further studies to determine the main contributing demographic and pathologic factors in PGC induction. There are reports that suggest reduced melatonin levels in smoking and opioid using individuals [14–18]. But studies which investigated smoking and opioid use in relation to the pineal gland calcification are lacking.
Here, we conducted a cross-sectional study on a population of patients referred to Ali Ebne Abi Taleb radiology center in years 2017–2018, assessing the association of PGC and PGC score with the demographic, lifestyle (smoking and opioid use) and history of metabolic and cerebrovascular diseases. To the best of our knowledge this is the first report assessing smoking and opioid use in relation to the pineal gland calcification.
## Subjects, study design and ethical considerations
In this cross-sectional analysis, 691 patients ($58.4\%$ male, above 15 years old) with a brain CT-scan at the radiology center of the Ali Ebne Abi Taleb hospital at 2017–2018 were reviewed. Patients with low quality CT-scan, pineal tumors or trauma and individuals with incomplete medical records were excluded from our analysis. All procedures of data collection were conducted under the supervision of the Ethics Committee of Rafsanjan University of Medical Sciences (Ethical codes: IR.RUMS.REC.1397.227). The confidentiality of the personal data of participants were ensured by all necessary measures.
## DATA collection and measurements
The archived medical records of the included patients were used to obtain information on their medical history and demographic features including age, sex, history of CVA and current diabetes, hypertension, dyslipidemia, smoking and regular use of opioids (opium, heroin and methadone).
All CT-scans have been performed by SIEMENS machine (SIEMENS Company, Germany) in Axial Plane with a slice thickness of 5 mm without any gap between them. All CT scans were read independently by a neurologist (NJ) and a radiologist (AM)(supplemental Fig. 1). Disagreements were resolved by discussions and consensus. Calcification volume was estimated by measuring length, width and height of the calcified pineal. Patients were categorized to calcified and non-calcified pineal gland groups (PGC). Additionally, they were graded according to the maximal density in Hounsfield units (HU) of the calcified portion of the gland. As suggested by Kunz et al. [ 19], HU were graded on a five-point Likert scale (0: HU ≤ 49, 1: HU 50–150, 2: 151–250, 3: 251–350, and 4: HU ≥ 351 (HU-Kunz). The calcified volume was categorized to three levels as 0 (if no PGC) and two levels 1 and 2 based on the median volume of calcified pineal glands in the study population. Then, both scores were summed for a total degree of calcification that ranged from 0 to 6 (PGC-score). Categorized PGC_score (cPGC_score) was coded dividing individuals to four groups based on their PGC-score (cPGC_score) as follows: group1:PGC_score 0, group2: PGC_score 1 and 2, group3: PGC_score 3 and 4, group 4: PGC_score 5 and 6. Age was divided to groups by its 10 quantiles as follows: 16–25, 26–34, 35–44, 45–55, 56–62, 63–69, 70–75, 76–80, 81–85, 86–98.Fig. 1Mean categorized PGC_scores by age group. Data are shown as mean ± $95\%$ Cls
## Statistical analyses
When there was an expected frequency of at least 5 in $80\%$ of the cells, the chi2 test was used for the categorical variables. Otherwise, a Fisher's exact test was used. The normality of the continuous variables was assessed using skewness and kurtosis statistics. All continuous variables in our analysis displayed a normal distribution (age); therefore, independent t-test was used to analyze them. Subject matter knowledge and related epidemiological literature were used for recognition of potential risk factors. In order to find the risk factors in determination of PGC and PGC score, logistic and ordered logistic regression analyses were used respectively at unadjusted and multivariate level to evaluate the potential associated parameters among the following factors: age, sex, cigarette smoking, opioid regular use, history of diabetes Mellitus, hypertension, hyperlipidemia and previous CVA.
Factors which showed p-value < 0.2 in the unadjusted analysis, were entered in the respective adjusted logistic or ordered logistic models. The proportional odds ratio assumption was tested with the Brant test. Statistical analyses were performed in Stata software (version 14.1, Stata Corporation, TX, USA). All p-values were two-sided. The p-values < 0.05 and the $95\%$ confidence intervals not including 1 were considered as statistically significant.
## Results
Table 1 depicts the basic characteristics of the population study. 691 patients were assessed by reading brain CT and medical records. 586 patients were entered in to our analyses after exclusion of patients with low quality CT-scan, pineal tumors or pineal trauma and individuals with incomplete medical records. $84.47\%$ of patients were positive for PGC, and the highest percentage of subjects ($28.50\%$) were calculated a PGC-score of 2 in a scale from 0 to 6. The mean age of PGC (58.90 ± 22.27) and non-PGC cases (55.63 ± 23.28) did not differ significantly (p-value = 0.209). The chi2 test, displayed a significant association between male sex and PGC (p-value < 0.001). Additionally, cigarette smoking and regular opioid use displayed a significant association with PGC (respective p-value:0.001, 0.023).Table 1Baseline characteristics of the study population categorized by PGCNumber (%)Non PGCPGCTotalp-valueSex (%) < 0.001* Male37(10.77)306(89.21)343 (58.43) Female54(22.13)190(77.87)244 (41.57)CVA history0.902* No66(15.42)362(84.58)428 (77.26) Yes20(15.87)106(84.13)126 (22.74)Diabetes Mellitus0.336* No63(14.72)365(85.28)428 (77.26) Yes23(18.25)103(81.75)126 (222.74)Hyperlipidemia0.686* No75(15.24)417(84.76)492 (88.49) Yes11(17.19)53(82.81)64 (11.51)Hypertension0.944* No54(15.61)292(84.39)346 (62.45) Yes32(15.38)176(84.62)208 (37.55)Cigarette moking0.001* No78(17.77)361(82.23)439 (87.10) Yes2(3.08)63(96.92)65 (12.90)Opioid regular use0.023* No70(17.86)322(82.14)392 (77.78 Yes10(8.93)102(91.07)112 (22.22)PGC-score 091 (15.53)091 (15.53) 1019 (3.24)19 (3.24) 20167 (28.50)167 (28.50) 30125 (21.33)125 (21.33) 4076 (12.97)76 (12.97) 5058 (9.90)58 (9.90) 6050 (8.53)50 (8.53)Mean ± SD Age55.63 ± 23.2858.90 ± 22.270.209**Data are given as Mean ± SD or absolute number n (percentage)* Chi2 test** Independent t-test According to the logistic and ordered logistic regression analysis, significant factors related to PGC by univariate logistic regression were found to be male sex, cigarette smoking and regular opioid use. Male sex was associated with more than twice higher odds of PGC (OR: 2.35 ($95\%$ CI: 1.49–3.70), p-value < 0.001), smoking cigarettes was associated with more than 6 times higher odds ratio of PGC (OR: 6.80 ($95\%$ CI: 1.63- 28.40), p-value < 0.01), and opioid use was associated with $121\%$ higher odds ratio of PGC (OR: 2.21 ($95\%$ CI: 1.10–4.46), p-value: 0.02). We next performed multivariate logistic analysis adjusting for factors which displayed an association p-value lower than 0.2 in the unadjusted model. Adjusting for sex, cigarette smoking, and opioid use, we found male sex and smoking cigarettes as the significant risk factors for PGC (male sex adjusted OR: 2.30 ($95\%$ CI: 1.39–3.82), p-value = 0.001; Cigarette smoking adjusted OR: 4.47 ($95\%$ CI: 1.01–19.78), p-value = 0.048). We did not find a significant association between opioid use and PGC in the adjusted logistic model (see Table 2).Table 2Estimated unadjusted and adjusted odds ratios for PGC as predicted by demographic factors and medical historyUnadjusted modelAdjusted modelOR ($95\%$ CI)p-valueOR ($95\%$ CI)p-valuePGC#Age1.00 (0.99–1.01)0.21Male sex2.35 (1.49–3.70) < 0.0012.30 (1.39–3.82)0.001 aHistory of CVA0.96 (0.56–1.66)0.90Diabetes Mellitus0.77 (0.45–1.30)0.33HLP0.86 (0.43–1.73)0.68Hypertension1.01 (0.63–1.63)0.94Cigarette smoking6.8 (1.63- 28.40) < 0.014.47 (1.01–19.78)0.048aOpioid use2.21 (1.10–4.46)0.021.32 (0.62–2.77)0.46aMen Age1.00 (0.98–1.01)0.593 History of CVA0.63 (0.27–1.44)0.278 Diabetes Mellitus0.59 (0.26–1.34)0.213 HLP1.60 (0.36–7.09)0.530 Hypertension0.83 (0.38–1.80)0.647 Cigarette smoking7.44 (0.99–55.98)0.0517.44 (0.99–55.98)0.051 Opioid use1.67 (0.65–4.26)0.283Women Age1.02 (1.00–1.03)0.0121.02 (1.00–1.036)0.012 History of CVA1.48 (0.71–3.11)0.293 Diabetes Mellitus1.08 (0.54–2.16)0.818 HLP0.080 (0.35–1.85)0.617 Hypertension1.54 (0.82–2.86)0.173 Cigarette smoking2.84 (0.35–23.03)0.326 Opioid use1.97 (0.65–5.97)0.230Categorized PGC score## Age1.00 (0.99–1.01)0.21 Male sex2.34 (1.48–3.69) < 0.0011.72 (1.23–2.39)0.001 b History of CVA0.91 (0.63–1.30)0.617 Diabetes Mellitus1.21 (0.84–1.74)0.300 HLP0.87 (0.54–1.39)0.566 Hypertension0.940 (0.689–1.28)0.698 Cigarette smoking1.84 (1.15- 2.93)0.0101.56 (0.97–2.51)0.062b Opioid use1.24 (0.85–1.81)0.247Men Age1.00 (0.99–1.01)0.0951.00 (0.99–1.01)0.702c History of CVA0.78 (0.47–1.28)0.335 Diabetes Mellitus1.05 (0.62- 1.75)0.852 HLP0.85 (0.43–1.67)0.649 Hypertension1.07 (0.68–1.66)0.763 Cigarette smoking1.85 (1.088–3.16)0.0231.76 (1.02–3.02)0.039c Opioid use1.17(0.74- 1.86)0.484Women Age1.00 (0.99- 1.01)0.263 History of CVA1.19(0.71–2.01)0.495 Diabetes Mellitus1.61 (0.95–2.72)0.0751.61 (0.95–2.72)0.075 HLP0.99 (0.51- 1.92)0.995 Hypertension1.05 (0.66–1.67)0.804 Cigarette smoking0.88 (0.30–2.53)0.814 Opioid use1.958(0.48–1.90)0.905Age < 63 Male sex1.69 (1.09–2.62)0.0172.82 (1.72–4.62) < 0.001d Age1.02 (1.00–1.03)0.0021.03 (1.01–1.05) < 0.001d History of CVA1.38 (0.70–2.72)0.34 Diabetes Mellitus1.13 (0.60–2.13)0.68 HLP0.44 (0.17–1.11)0.0830.33 (0.13–0.87)0.025d Hypertension1.30 (0.78–2.16)0.309 Cigarette smoking1.24 (0.62–2.46)0.535Opioid use1.32 (0.75–2.32)0.33Age > = 63 Male sex1.72 (1.13–2.62)0.0111.39 (0.88–2.19)0.152e Age0.99 (0.96–1.01)0.42 History of CVA0.74 (0.47–1.16)0.190 Diabetes Mellitus1.26 (0.79–2.00)0.32 HLP1.11 (0.63–1.94)0.703 Hypertension0.73 (0.47–1.11)0.145 Cigarette smoking2.57 (1.36–4.85)0.0032.31 (1.20–4.42)0.011e Opioid use1.18 (0.71–1.96)0.504 Cigarette smoking (in men only)2.94 (1.40–6.18)0.004 Cigarette smoking (in women only)1.05 (0.26–4.23)0.94#logistic regression analysis##Ordered logistic regression analysisa Adjusted for sex, cigarette smoking and regular opioid useb Adjusted for sex and cigarette smokingc Adjusted for age and cigarette smokingd Adjusted for age decile, sex and hyperlipidemiae Adjusted for sex and cigarette smoking We next used sensitivity analysis by sex-stratification to investigate gender-specific associations of PGC with different factors. In men an odds ratio of 7.44 ($95\%$ CI: 0.99–55.985), p-value = 0.051) was observed for PGC in association with cigarette smoking. In female subjects age displayed a significant association with PGC (OR: 1.02 ($95\%$ CI: 1.00–1.036), p-value = 0.012).
Performing ordered logistic regression analysis on the potential risk factors for the categorized PGC-score (cPGC_score), in the unadjusted model, male sex and cigarette smoking displayed a statistically significant association with cPGC_score (respective p-value: < 0.001, 0.01). In the multivariate ordered logistic test, male sex showed a significant association with cPGC_score (adjusted OR: 1.72 ($95\%$ CI: 1.23–2.39), p-value: 0.001). In addition to adjusting for gender, we performed a sensitivity analysis by sex-stratification. In the unadjusted ordered logistic model in men cigarette smoking displayed a significant association with cPGC_score (p-value:0.23). Also, in the multivariate analysis in men only, cigarette smoking was found to be significantly associated with higher cPGC_score (adjusted OR: 1.76 ($95\%$ CI: 1.02–3.02), p-value: 0.039). Performing the same analysis in women, we did not find a statistically significant association between cigarette smoking and higher cPGC-score (adjusted OR: 0.88 ($95\%$ CI: 0.30–2.53), p-value: 0.814).
Figure 1 indicates the mean categorized PGC_scores for the 10 quantiles of the age. Based on this graph, until the 5th decile of age (below 63 years old in our population), there was an increasing trend of PGC_score by age. Therefore, we added ordered logistic analysis stratified by age (below age 63 and above 63) for different potential risk factors (Table 2). Our results indicated that in patients aged < 63 years: age (adjusted OR: 1.03 ($95\%$ CI: 1.01–1.05), p-value < 0.001) and male sex (adjusted OR: 2.82 ($95\%$ CI: 1.72–4.62), p-value < 0.001) were the two main positive associated factors of higher PGC_score, and hyperlipidemia (adjusted OR: 0.33 ($95\%$ CI: 0.13–0.87), p-value = 0.025) was the main factor negatively associated with higher PGC_score. A dose–response linear trend was observed for the categorized PGC_score and age deciles below 63 (p-trend < 0.001) (Table 3). On the contrary, in the patients aged 63 and above, the only significant associated factor of PGC_score was found to be cigarette smoking (adjusted OR: 2.31($95\%$ CI: 1.20–4.42), p-value = 0.011). Additionally, when performed separated analysis for men and women, we found that the association of cigarette smoking and PGC_score in patients aged 63 and above, is only significant in male objects (OR: 2.94 ($95\%$ CI: 1.40–6.18), p-value: 0.004). The gender differential results may be probably driven from residual confounding from gender or its interaction effects with smoking. Additionally, the reason may be the small number of smoking women compared to men (supplemental Table 1).Table 3Estimated unadjusted and adjusted odds ratios for categorized PGC_score as predicted by 10 quantiles of ageUnadjusted ModelAdjusted ModelOR ($95\%$ CI)p-valueOR ($95\%$ CI)p-valueCategorized PGC_score##Age < 63 years oldLinearp-Trend < 0.001a1st decile (16–25)ReferenceReference2nd decile (26–34)1.19 (0.62–2.26)0.5881.49(0.75–2.93)0.2473rd decile (35–44)2.00 (1.04–3.85)0.0352.79 (1.36–5.72)0.0054th decile (45–55)2.17 (1.15- 4.10)0.0163.30 (1.62–6.70)0.0015th decile (56–62)2.17 (1.14–4.17)0.0183.59 (1.74–7.40)0.001##Ordered logistic regression analysisa Adjusted for age_decile, sex and hyperlipidemia
## Discussion
We performed a cross-sectional study to investigate the association of demographic and personal habits with the pineal gland calcification. We found that the male sex is one of the factors significantly associated with PGC. Future studies are required to investigate the underlying reason for this gender difference in risk of pineal calcification.
In addition to male sex, our adjusted logistic analysis suggests cigarette smoking as a potent factor associated with increased odds of PGC (OR: 4.47 ($95\%$ CI: 1.01–19.78), cPGC_score in men (OR: 1.76 ($95\%$ CI: 1.02–3.02)), and cPGC_score in patients > = 63 years (OR: 2.31($95\%$ CI: 1.20–4.42)). We did not find any association between opioid regular use and PGC in the adjusted regression analyses. To the best of our knowledge, no previous study has assessed the connection of smoking and opioid use with PGC which is a unique character and strength of the present study. There are previously published evidences that support a link between smoking and opioid use with decreased melatonin levels [14–18]. Our results do not indicate opioid use effect on melatonin to occur through inducing PGC. Previous studies showing the effect of smoking on melatonin levels, have indicated changes in the pharmacokinetic parameters of melatonin such as a lowered Cmax (serum maximum concentration) when exogenous melatonin was injected. This study showed a pharmacokinetic effect of smoking for removal of melatonin from the body as the underlying mechanism for this effect, and suggested an impact of smoking independent of the pineal gland activity [20]. Our results showed an association between smoking and the pineal gland calcification. We propose future studies to investigate whether the decreasing effect of smoking on melatonin levels may be mediated at least partially by increasing the pineal gland calcification.
Smoking has been shown by several previous studies to be a risk factor for vascular calcification in different tissues [21–24]. The suggested underlying mechanisms are the smoking-induced oxidative stress and alterations in the vesicular trafficking in the vascular smooth muscle cells [20]. Future studies are required to ask whether smoking may induce pineal gland calcification via similar mechanisms.
There has been variation in the results of the former reports assessing whether the pineal gland calcification is a function of age or not. Some previous studies support a direct association between aging and PGC in all ages in human and animal studies [25, 26], proposing PGC is an inevitable process of aging; while some other studies found that the increase in PGC by age is observed only by certain age (60 years old), and above this age the correlation disappears or is reversed [27]). Our results conform to the later, showing an age-dependent increase in PGC_score only in patients aged below 63. In younger individuals, previous studies have shown that PGC is not observed before age 5, but shows an age-dependent increase from age older than 5 to 20 years old [28, 29]. Here, we have assessed PGC in patients above 15 years old, and we observed a significant association between PGC and age only in patients younger than 63 years old.
Given that the gold standard method in diagnosing the pineal gland calcifications (PGC) can only be achieved by postmortem investigation of the pineal gland, we propose future anatomo-histological studies on postmortem biopsy samples to assess the association of smoking and PGC. Previous post mortem pineal gland studies found pineal calcification at the highest rate in the age group of 46–65 years old, but no differences between genders were observed [30–32].
Some previous studies have suggested calcification as a commonly dominant feature of cystic pineal glands [33–39]. Future studies are required to assess whether there is a relationship between the risk of pineal gland calcification and cysts.
One limitation of our study is the lack of information on some of the potential risk factors of the pineal gland calcification, such as the body mass index, alcohol consumption, diet, physical activity and sunlight exposure. However, a complete medical history record for each of patients was available providing valuable information on the current diseases of the patients including diabetes mellitus, hyperlipidemia, hypertension and cerebrovascular diseases. The other limitation of the present study is the lack of information on the start age of the above-mentioned diseases or duration of smoking and opioid addiction or the dosage of their use.
In conclusion, we found that until age 63, age and male sex are the two potential associated risk factors for pineal gland calcification, and above this age smoking cigarettes may be a risk factor for PGC, which warrants further investigation in the future.
## Supplementary Information
Additional file 1. Additional file 2.
## Disclosure Statement
The authors have nothing to disclose.
## References
1. Ekström P, Meissl H. **Evolution of photosensory pineal organs in new light: the fate of neuroendocrine photoreceptors**. *Philos Trans R Soc Lond B Biol Sci* (2003.0) **358** 1679-1700. DOI: 10.1098/rstb.2003.1303
2. 2.Aulinas A. Physiology of the pineal gland and melatonin. Endotext. 2019.
3. Asefy Z, Khusro A, Mammadova S, Hoseinnejhad S, Eftekhari A, Alghamdi S. **Melatonin hormone as a therapeutic weapon against neurodegenerative diseases**. *Cell Mol Biol (Noisy-le-grand)* (2021.0) **67** 99-106. DOI: 10.14715/cmb/2021.67.3.13
4. Wongprayoon P, Govitrapong P. **Melatonin receptor as a drug target for neuroprotection**. *Curr Mol Pharmacol* (2021.0) **14** 150-164. DOI: 10.2174/1874467213666200421160835
5. Cardinali DP. **Melatonin: Clinical Perspectives in Neurodegeneration**. *Front Endocrinol* (2019.0) **10** 480. DOI: 10.3389/fendo.2019.00480
6. Olakowska E, Marcol W, Kotulska K, Lewin-Kowalik J. **The role of melatonin in the neurodegenerative diseases**. *Bratisl Lek Listy* (2005.0) **106** 171-174. PMID: 16080363
7. Yawoot N, Govitrapong P, Tocharus C, Tocharus J. **Ischemic stroke, obesity, and the anti-inflammatory role of melatonin**. *BioFactors (Oxford, England)* (2021.0) **47** 41-58. DOI: 10.1002/biof.1690
8. Andrabi SS, Parvez S, Tabassum H. **Melatonin and Ischemic Stroke: Mechanistic Roles and Action**. *Adv Pharmacol Sci* (2015.0) **2015** 384750. DOI: 10.1155/2015/384750
9. Vigh B, Szél A, Debreceni K, Fejér Z, e Silva MM, Teichmann IV. **Comparative histology of pineal calcification**. *Histol Histopathol* (1998.0) **13** 851-70. PMID: 9690142
10. Bojkowski C, Arendt J. **Factors influencing urinary 6-sulphatoxymelatonin, a major melatonin metabolite, in normal human subjects**. *Clin Endocrinol* (1990.0) **33** 435-444. DOI: 10.1111/j.1365-2265.1990.tb03882.x
11. 11.Commentz J, Fischer P, Stegner H, Winkler P, Helmke K, Willig R, editors. Pineal calcification does not affect melatonin production. Journal of Neural Transmission; 1986: SPRINGER-VERLAG WIEN SACHSENPLATZ 4–6, PO BOX 89, A-1201 VIENNA, AUSTRIA.
12. Bojkowski CJ, Arendt J, Shih M, Markey SJCc. **Melatonin secretion in humans assessed by measuring its metabolite, 6-sulfatoxymelatonin**. *Clin Chem* (1987.0) **33** 1343-8. DOI: 10.1093/clinchem/33.8.1343
13. Patel S, Rahmani B, Gandhi J, Seyam O, Joshi G, Reid I. **Revisiting the pineal gland: a review of calcification, masses, precocious puberty, and melatonin functions**. *Int J Neurosci* (2020.0) **130** 464-475. DOI: 10.1080/00207454.2019.1692838
14. 14.Liu Q, Su LY, Sun C, Jiao L, Miao Y, Xu M, et al. Melatonin alleviates morphine analgesic tolerance in mice by decreasing NLRP3 inflammasome activation. Redox biology. 2020;34:101560. Epub 2020/05/16. 10.1016/j.redox.2020.101560. PubMed PMID: 32413745; PubMed Central PMCID: PMCPMC7225735.
15. 15.Khazaie H, Ahmadi HR, Kiani A, Ghadami MR. Circadian melatonin profile in opium and amphetamine dependent patients: A preliminary study. Neurobiology of sleep and circadian rhythms. 2019;7:100046. Epub 2019/08/30. 10.1016/j.nbscr.2019.100046. PubMed PMID: 31463419; PubMed Central PMCID: PMCPMC6710474.
16. Li RS, Takeda T, Ohshima T, Yamada H, Ishii Y. **Metabolomic profiling of brain tissues of mice chronically exposed to heroin**. *Drug metabolism and pharmacokinetics* (2017.0) **32** 108-11. DOI: 10.1016/j.dmpk.2016.10.410
17. 17.Ursing C, von Bahr C, Brismar K, Röjdmark S. Influence of cigarette smoking on melatonin levels in man. European journal of clinical pharmacology. 2005;61(3):197–201. Epub 2005/04/13. doi: 10.1007/s00228-005-0908-7. PubMed PMID: 15824912.
18. Ozguner F, Koyu A, Cesur G. **Active smoking causes oxidative stress and decreases blood melatonin levels**. *Toxicol Ind Health* (2005.0) **21** 21-6. DOI: 10.1191/0748233705th211oa
19. Kunz D, Schmitz S, Mahlberg R, Mohr A, Stöter C, Wolf KJ. **A new concept for melatonin deficit: on pineal calcification and melatonin excretion**. *Neuropsychopharmacology* (1999.0) **21** 765-72. DOI: 10.1016/s0893-133x(99)00069-x
20. Petsophonsakul P, Burgmaier M, Willems B, Heeneman S, Stadler N, Gremse F. **Nicotine promotes vascular calcification via intracellular Ca2+-mediated, Nox5-induced oxidative stress, and extracellular vesicle release in vascular smooth muscle cells**. *Cardiovasc Res* (2022.0) **118** 2196-2210. DOI: 10.1093/cvr/cvab244.PubMedPMID:34273166;PubMedCentralPMCID:PMCPMC9302892
21. Chen HY, Engert JC. **Risk factors for valvular calcification**. *Curr Opin Endocrinol Diabetes Obes* (2019.0) **26** 96-102. DOI: 10.1097/med.0000000000000471
22. Babic M, Schuchardt M, Tölle M, van der Giet M. **In times of tobacco-free nicotine consumption: The influence of nicotine on vascular calcification**. *Eur J Clin Invest* (2019.0) **49(** e13077. DOI: 10.1111/eci.13077
23. van der Toorn JE, Rueda-Ochoa OL, van der Schaft N, Vernooij MW, Ikram MA, Bos D. **Arterial calcification at multiple sites: sex-specific cardiovascular risk profiles and mortality risk—the Rotterdam Study**. *BMC Med* (2020.0) **18** 263. DOI: 10.1186/s12916-020-01722-7
24. Ahmed MA, Parwani D, Mahawar A, Gorantla VR. **Subclavian Artery Calcification: A Narrative Review**. *Cureus* (2022.0) **14** e23312. DOI: 10.7759/cureus.23312
25. Macpherson P, Matheson M. **Comparison of calcification of pineal, habenular commissure and choroid plexus on plain films and computed tomography**. *Neuroradiology* (1979.0) **18** 67-72. DOI: 10.1007/BF00344824
26. Schmid HA, Raykhtsaum G. **Age-related differences in the structure of human pineal calcium deposits: Results of transmission electron microscopy and mineralographic microanalysis**. *J Pineal Res* (1995.0) **18** 12-20. DOI: 10.1111/j.1600-079X.1995.tb00134.x
27. Mohammed KA, Adjei Boakye E, Ismail HA, Geneus CJ, Tobo BB, Buchanan PM. **Pineal gland calcification in Kurdistan: a cross-sectional study of 480 roentgenograms**. *PLoS ONE* (2016.0) **11** e0159239. DOI: 10.1371/journal.pone.0159239
28. Zimmerman RA, Bilaniuk LT. **Age-related incidence of pineal calcification detected by computed tomography**. *Radiology* (1982.0) **142** 659-62. DOI: 10.1148/radiology.142.3.7063680
29. 29.Sumida M, Barkovich AJ, Newton TH, editors. Development of the pineal gland: measurement with MR imaging. Proceedings of the XV Symposium Neuroradiologicum; 1995 1995//; Berlin, Heidelberg: Springer Berlin Heidelberg.
30. Gheban BA, Colosi HA, Gheban-Rosca IA, Georgiu C, Gheban D, Crisan D. **Digital histological morphometry of the human pineal gland in a postmortem study, with endocrine and neurological clinical implications**. *Anat Histol Embryol* (2023.0) **52** 12-20. DOI: 10.1111/ahe.12828
31. 31.Gheban BA, Colosi HA, Gheban-Rosca IA, Pop B, Domsa AT, Georgiu C, et al. Age-Related Changes of the Pineal Gland in Humans: A Digital Anatomo-Histological Morphometric Study on Autopsy Cases with Comparison to Predigital-Era Studies. Medicina (Kaunas). 2021;57(4). Epub 20210415. 10.3390/medicina57040383. PubMed PMID: 33921100; PubMed Central PMCID: PMCPMC8071372.
32. 32.Harisha K, Arava S, Singh S, Pariplavi M. A morphological study of human pineal gland in post-mortem cases. Morphologie. 2022. Epub 20220630. 10.1016/j.morpho.2022.06.095. PubMed PMID: 35786527.
33. Arslan E, Usul H, Baykal S, Acar E, Eyuboglu EE, Reis A. **Massive congenital intracranial immature teratoma of the lateral ventricle with retro-orbital extension: a case report and review of the literature**. *Pediatr Neurosurg* (2007.0) **43** 338-342. DOI: 10.1159/000103319
34. Baykaner MK, Ergun E, Cemil B, Bayik P, Emmez H. **A mature cystic teratoma in pineal region mimicking parietal encephalocele in a newborn**. *Childs Nerv Syst* (2007.0) **23** 573-6. DOI: 10.1007/s00381-006-0240-2
35. Canan A, Gulsevin T, Nejat A, Tezer K, Sule Y, Meryem T. **Neonatal intracranial teratoma**. *Brain Dev* (2000.0) **22** 340-342. DOI: 10.1016/s0387-7604(00)00128-5
36. Fain JS, Tomlinson FH, Scheithauer BW, Parisi JE, Fletcher GP, Kelly PJ. **Symptomatic glial cysts of the pineal gland**. *J Neurosurg* (1994.0) **80** 454-460. DOI: 10.3171/jns.1994.80.3.0454
37. Fleege MA, Miller GM, Fletcher GP, Fain JS, Scheithauer BW. **Benign glial cysts of the pineal gland: unusual imaging characteristics with histologic correlation**. *AJNR Am J Neuroradiol* (1994.0) **15** 161-6. PMID: 8141049
38. Jinkins JR, Xiong L, Reiter RJ. **The midline pineal "eye": MR and CT characteristics of the pineal gland with and without benign cyst formation**. *J Pineal Res* (1995.0) **19** 64-71. DOI: 10.1111/j.1600-079x.1995.tb00172.x
39. Liu Z, Lv X, Wang W, An J, Duan F, Feng X. **Imaging characteristics of primary intracranial teratoma**. *Acta Radiol* (2014.0) **55** 874-81. DOI: 10.1177/0284185113507824
|
---
title: Apolipoprotein C3 is negatively associated with estrogen and mediates the protective
effect of estrogen on hypertriglyceridemia in obese adults
authors:
- Jinman Li
- Honglin Sun
- Ying Wang
- Jia Liu
- Guang Wang
journal: Lipids in Health and Disease
year: 2023
pmcid: PMC9972754
doi: 10.1186/s12944-023-01797-0
license: CC BY 4.0
---
# Apolipoprotein C3 is negatively associated with estrogen and mediates the protective effect of estrogen on hypertriglyceridemia in obese adults
## Abstract
### Background
Both estrogen and apolipoprotein C3 (ApoC3) play crucial roles in lipid metabolism. But the link between them remains unclear, and it is unknown whether estrogen regulates triglyceride (TG) levels via ApoC3. Researchers hypothesized that estrogen exerts a regulatory effect on ApoC3 metabolism, and that this regulation could play a significant role in lipid metabolism. To explore this potential link, the present investigation aimed to examine the associations between estradiol (E2), ApoC3, and TG levels in both males and females.
### Methods
A total of 519 obese people (133 males and 386 premenopausal females) were recruited. Based on their TG levels, the participants were split into two groups [hypertriglyceridemia (HTG) group: TG ≥ 1.7 mmol/L; control group: TG < 1.7 mmol/L]. Serum ApoC3, E2, and TG levels were measured and compared in those two groups for both sexes separately. To ascertain the connection among E2, ApoC3, and TG, linear regression and mediation analysis were used.
### Results
Participants in the HTG group presented higher levels of ApoC3 ($P \leq 0.001$). In contrast, they tend to have lower E2 levels than the control. Linear regression analysis proposed that in both sexes, E2 was negatively associated with ApoC3 levels. The relationship remained significant after adjustment for confounding factors (male: standardized β = -0.144, t = -2.392, $P \leq 0.05$; female: standardized β = -0.077, t = -2.360, $P \leq 0.001$). Furthermore, mediation analysis revealed the relationship between reduced E2 levels and elevated TG levels is directly mediated by ApoC3.
### Conclusions
In obese men and premenopausal women, ApoC3 was negatively and linearly correlated with serum E2 levels. The findings showed that estrogen may suppress ApoC3 expression and thus lower TG levels.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12944-023-01797-0.
## Background
Numerous clinical studies have shown that premenopausal women are less likely to develop coronary artery disease, diabetes, obesity, and dyslipidaemia than men [1]. In comparison to men, women have acute myocardial infarction about a decade later [2]. Furthermore, at the same age, women are $50\%$ less likely than males to have acute cardiovascular disease (CVD) [2, 3]. Endogenous estrogen is thought to be a mechanism for differentiating this risk. It can regulate lipid metabolism and control serum lipoprotein levels [4]. Elevated endogenous estrogen levels are significantly associated with reduced low-density lipoprotein (LDL) and low triglyceride (TG) levels, which are strongly related to CVD and metabolic syndrome [3, 5]. This effect is particularly prominent in obese people, who have higher risks of developing diseases [6–8].
Estradiol (E2) is a steroidal estrogen [9]. The biosynthesis of E2 is a multi-step process that involves the conversion of cholesterol into pregnenolone, a 19-carbon steroid hormone [10]. Pregnenolone is then metabolized into testosterone, which is further converted into the primary estrogens, estrone and 17β-estradiol [10]. E2 is the most potent form of mammalian estrogenic steroids [9].
Apolipoprotein C3 (ApoC3) has recently been a hot topic of research. It is increasingly considered a vital metabolic regulator of human triglyceride-rich lipoprotein (TRL) [11]. ApoC3 is mainly synthesized in the liver [12]. It not only inhibits the hydrolysis of TRL by controlling lipoprotein lipase but also suppresses the uptake of TRL residues by the liver [13]. Furthermore, high concentrations of ApoC3 affect the activity of hepatic lipase [14], which leads to impaired conversion of very-low-density lipoproteins (VLDL) to intermediate-density lipoproteins (IDL) and LDL [15]. All of these factors can contribute to the accumulation of atherogenic VLDL and chylomicron residues [16]. A growing number of trials have demonstrated that lower ApoC3 levels could reduce the risk of CVD [17]. In comparison to younger women, older women, particularly postmenopausal women, showed greater levels of ApoC3 [18]. Additionally, researchers noticed that men had higher levels of ApoC3 than women [18, 19].
Since both estrogen and ApoC3 play critical roles in lipid metabolism, the paper hypothesises that estrogen affects ApoC3 metabolism. However, few studies have focused on the correlation between estrogen and ApoC3 levels in men and premenopausal women, which would have enormous implications for the general population.
The current study evaluated the potential sex-specific relationship between E2 and metabolic parameters in obese people.
## Research population
This cross-sectional study included obese patients (BMI ≧ 30.0 kg/m2) who underwent routine medical checkups at the Beijing Chaoyang Hospital from June 2017 to March 2021. All included women were premenopausal. Individuals with major chronic illnesses such as severe CVD, liver or renal function impairment, systemic inflammatory disease, infectious disease, or cancer were excluded. The exclusion criteria also included using any medication that affects estrogen, glucose, or lipids; missing detailed data; or outliers. Ultimately, 519 participants were recruited. The Ethics Committee of Beijing Chaoyang Hospital, Capital Medical University, approved the study protocol. Written informed consent was received by all subjects before the study.
## Anthropometric and biochemical measurements
To gather information on the patients' health and medications, researchers employed a standard questionnaire. Height, weight, waist circumference (WC), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were measured at baseline. A stationary stadiometer with a movable headboard was used to measure height to the closest 0.1 cm. Weight was accurately measured to the closest 0.1 kg on the weighing scale while participants were clothed (without shoes) and indoors. WC was surveyed at the narrowest part of the torso to the nearest 0.1 cm by trained staff using tape measures. Blood pressure was measured twice after 10 min of lying down, and the average of the two results was taken as the patient's blood pressure level. The formula for calculating BMI was BMI = [weight (kg)/height2 (m2)].
Samples of venous blood were taken after an overnight fast. And at -80 °C, the samples were stored. Since previous study has suggested that there is no significant difference in TG and apolipoprotein B levels between the luteal and follicular phases of non-menopausal women, based on statistical analysis of the data [20]. All females had their blood drawn outside of their menstrual period. Total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), ApoC3, and apolipoprotein C2 (ApoC2) levels were assessed by colorimetric enzymatic assays using an autoanalyzer (Hitachi 7170). hypertriglyceridemia (HTG) is defined as TG ≧ 1.7 mmol/L as recommended [21]. Dyslipidemia was defined as HDL-C < 1.0 mmol/L, LDL-C ≧ 3.37 mmol/L, TC ≧ 5.2 mmol/L, TG ≧ 1.7 mmol/L, or a self-reported previous diagnosis of hyperlipidemia. E2, total testosterone (TT), and progesterone (P) were measured by chemiluminescent immunoassay using the Kikuchi 1000 immunoassay (Siemens). Fasting blood glucose (FBG), fasting insulin (FINS), and C-peptide levels were tested at Beijing Chao-yang Hospital’s central chemistry laboratory. The glucose oxidase method was used to measure plasma FBG, whereas the chemiluminescence method was used to measure FINS.
## Statistical analysis
The statistics software IBM SPSS, version 26, was used to conduct the study's statistical analysis. To explore the gender specificity of ApoC3 levels, researchers performed separate analyses for male and female participants. To examine the normality of the variables, the Shapiro–Wilk test was used. The skewed distribution of the data required log-transformation for TG, ApoC3, E2, and TT. The t test was used to analyse continuous parameters having a normal distribution. The results are displayed as the mean ± standard deviation. nonparametric tests were used to analyse continuous parameters with nonnormal distributions. The outcomes are expressed as medians and upper and lower quartiles. Data for categorical variables are expressed as numbers (%). Gender-specific Spearman and Pearson correlation analyses were performed to assess the relationship between ApoC3 (dependent variable) and sex hormones (independent variable). Linear regression analysis was used to assess the correlations, and $95\%$ confidence intervals (CI) were used for statistical inference. The significant statistical threshold was established at 0.05. Standardized coefficients β and t values were used to describe the results. Variables with no covariance were selected for adjustment. Model 1 was unadjusted, model 2 had age and BMI adjustments, whereas model 3 had adjustments for age, ApoC2, BMI, FBG, and C-peptide. Finally, mediation analysis was utilized to investigate the part that ApoC3 played in the association between E2 and TG after controlling for age, ApoC2, BMI, C-peptide, and FBG levels. First, TG was considered the outcome, and E2 had a coefficient, c, as the total effect on TG (TG = c × E2 + control variables + e1). Then, ApoC3 was added to the model as a mediator (TG = c′ × E2 + b × mediator + control variable + e2). Finally, regression analysis with ApoC3 and E2 (mediators = a × E2 + control variable + e3) was performed. The mediation impact was not recorded if ‘c’, ‘a’, or ‘b’ was insignificant. If ‘c’’ is nonsignificant, a fully mediated effect is considered [22, 23]. All models were revalidated by bootstrap testing.
## Clinical characteristics of study participants in males and females
The baseline characteristics of the 519 participants (133 males and 386 females) with HTG and without HTG are presented in Table 1.Table 1Baseline characteristics of males and females with and without hypertriglyceridemia (HTG)VariablesNon-HTG ($$n = 191$$)HTG ($$n = 228$$)P valueMaleN6370–Age, y31.65 ± 9.6331.94 ± 7.770.847SP, mmHg132 ± 16.08126.26 ± 10.60.215BP, mmHg81.38 ± 7.4683.16 ± 7.20.478BMI, kg/m244.85 ± 8.8441.92 ± 7.130.036WC, cm129.91 ± 15.02126 ± 15.340.164FBG, mmol/L5.98 ± 1.337.4 ± 3.820.004FINS, uIU/mL34.98 ± 21.3538.26 ± 23.440.417C-Peptide, ng/mL4.63 ± 1.495.23 ± 1.830.048TC, mmol/L4.63 ± 0.85.43 ± 1.02 < 0.001TG, mmol/L1.24 ± 0.263.93 ± 4.16 < 0.001HDL-C, mmol/L0.97 ± 0.150.98 ± 0.180.709LDL-C, mmol/L3.14 ± 0.653.37 ± 0.750.06ApoC3, mg/dL7.03 ± 1.5815.77 ± 8.75 < 0.001ApoC2, mg/dL2.75 ± 0.925.19 ± 1.48 < 0.001TT, nmol/L8.97 ± 4.178.9 ± 2.970.563E2, pmol/L198.94 ± 86.53180.18 ± 63.920.249PRG, ng/mL0.60 (0.44, 0.89)0.61 (0.46, 0.78)0.924OC, ug/L20.01 ± 6.6822.27 ± 7.730.185FemaleN128158–Age, y32.25 ± 8.2333.04 ± 7.740.346SBP, mmHg127.57 ± 13.88130.2 ± 16.210.309DBP, mmHg82.57 ± 10.2584.91 ± 11.50.212BMI, kg/m238.08 ± 6.2538 ± 6.20.907WC, cm111.17 ± 14.65112.56 ± 13.560.371FBG, mmol/L5.68 ± 1.316.83 ± 2.79 < 0.001FINS, uIU/mL27.16 ± 16.3530.41 ± 16.220.061C-Peptide, ng/mL3.84 ± 1.234.38 ± 1.43 < 0.001TC, mmol/L4.6 ± 0.745.12 ± 0.87 < 0.001TG, mmol/L1.23 ± 0.32.58 ± 1.29 < 0.001HDL-C, mmol/L1.16 ± 0.291.04 ± 0.19 < 0.001LDL-C, mmol/L2.95 ± 0.563.29 ± 0.67 < 0.001ApoC3, mg/dL8.09 ± 2.0513.05 ± 4.37 < 0.001ApoC2, mg/dL3.04 ± 0.944.94 ± 1.75 < 0.001TT, nmol/L2.1 ± 0.951.99 ± 0.970.157E2, pmol/L345.4 ± 263.14303.82 ± 294.210.047PRG, ng/mL0.79 (0.57, 9.39)0.71 (0.47, 1.40)0.171OC, ug/L21.08 ± 7.3920.45 ± 8.680.299Data are presented as the mean ± SD or median (upper and lower quartiles) or number. ApoC2, ApoC3, E2, TG, and TES were log-transformed due to a skewed distribution. ApoC2 apolipoprotein C2, ApoC3 apolipoprotein C3, BMI body mass index, DBP diastolic blood pressure, E2 estradiol, FINS fasting insulin, FBG fasting blood glucose, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, OC osteocalcin, SBP systolic blood pressure, TC total cholesterol, TT testosterone, TG triglycerides, PRG progesterone, WC waist circumference In comparison to controls, the HTG group had higher FBG and C-peptide levels among both sexes (all $P \leq 0.05$). In terms of indicators associated with lipid metabolism, the HTG group had higher levels of TC, ApoC2, and ApoC3 (all $P \leq 0.001$) across both sexes. In females, LDL-C levels were greater in the HTG group whereas HDL-C levels were lower (all $P \leq 0.001$). Females with HTG showed lower E2 levels than the control group ($P \leq 0.05$). Men with HTG tended to have lower levels of E2 and LDL-C, even though the difference was not statistically significant (E2: $$P \leq 0.249$$; LDL-C: $$P \leq 0.06$$). Males and females in the two groups did not greatly vary in terms of age, blood pressure, waist size, or osteocalcin (OC) values. According to earlier studies, estrogen consistently adversely affects TG levels [3], whereas ApoC3 positively regulates TG levels [11]. It is certainly worthwhile to study the relationship between estrogen and ApoC3 levels.
## The correlations between ApoC3 and clinical parameters in all participants
To investigate the link between circulating ApoC3 levels and clinical parameters, researchers categorized the sample by sex and carried out separate bivariate correlation analyses (Table 2).Table 2The correlation between ApoC3 and clinical parameters in all participantsApoC3 (Male)ApoC3 (Female)Age0.110.079BMI-0.29**-0.102*WC-0.196-0.055FBG0.311**0.270**C-peptide0.1350.089TC0.504**0.474**TG0.942**0.846**HDL-C0.0660.032LDL-C0.207*0.334**ApoC20.767**0.795**E2-0.206*-0.175**TT0.038-0.064PRG0.015-0.205*ApoC2 apolipoprotein C2, ApoC3 apolipoprotein C3, BMI body mass index, E2 estradiol, FINS fasting insulin, FBG fasting blood glucose, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol, TC total cholesterol, TT testosterone, TG triglycerides, PRG progesterone, WC waist circumferenceApoC2, ApoC3, E2, TG, and TT were log-transformed due to a skewed distribution. The relationship between PRG and ApoC3 was established by Spearman correlation analysis. Pearson correlation analysis was used to describe the association between ApoC3 and other markers*$P \leq 0.05$**$P \leq 0.01$ ApoC3 was positively correlated with the levels of ApoC2, FBG, LDL-C, TC, and TG. Similar correlations were observed in the gender-separated analysis (all $P \leq 0.01$). ApoC3 levels were shown to be adversely linked with E2 (male: r = -0.206, female: r = -0.175, all $P \leq 0.05$) and PRG (male: r = -0.015, $$P \leq 0.919$$, female: r = -0.205, $P \leq 0.05$). According to the findings, HTG and ApoC3 levels are positively correlated. In addition, ApoC3 levels were inversely correlated with E2 levels in both males (Fig. 1) and females (Fig. 2).Fig. 1The correlation between ApoC3 levels (dependent variable) and E2 levels (independent variables) among males with obesity. Legends: ApoC3 and E2 were log-transformed due to a skewed distribution. ApoC3 was negatively correlated with the levels of E2 in males (r = -0.206, $P \leq 0.05$)Fig. 2The correlation between ApoC3 levels (dependent variable) and E2 levels (independent variables) among premenopausal females with obesity. Legends: ApoC3 and E2 were log-transformed due to a skewed distribution. ApoC3 was negatively correlated with the levels of E2 in premenopausal females (r = -0.175, $P \leq 0.001$) The relationship between ApoC2 and clinical indicators was evaluated, too. The results are displayed in the supplementary chart (Supplementary Table 1). E2 is not related to ApoC2 levels.
## Association of ApoC3 with serum E2 levels by linear regression analysis
To further explore the relationship between ApoC3 and E2 levels, a linear regression analysis was employed (Table 3).Table 3Linear regression analysis for the association between ApoC3 levels (dependent variable) and E2 levels (independent variables) among individuals with obesityStandardized βtP value$95\%$CIMale Model 1-0.206-2.3070.023-0.510, -0.039 Model 2-0.137-1.5100.134-0.422, + 0.057 Model 3-0.144-2.3920.018-0.353, -0.033Female Model 1-0.175-3.3710.001-0.151, -0.040 Model 2-0.180-3.4550.001-0.154, -0.042 Model 3-0.077-2.3600.019-0.077, -0.007ApoC2, ApoC3, and E2 were log-transformed due to a skewed distributionModel 1: Crude modelModel 2: adjusted for age and BMIModel 3: adjusted for age, BMI, FBG, C-peptide, and ApoC2 Serum E2 levels were shown to be inversely associated with ApoC3 levels. This negative association remained after correcting for all nonlinear confounding variables. This relationship was observed in both sexes. However, the association was stronger among males (male: standardized β = -0.144, t = -2.392, $P \leq 0.05$; female: standardized β = -0.077, t = -2.360, $P \leq 0.001$).
## Association of ApoC3 with serum E2 levels by linear regression analysis in participants with or without dyslipidemia
It is fruitful to look into the relationship between ApoC3 and E2 among participants with dyslipidemia because estrogen and blood lipid metabolism are closely related. Participants with or without dyslipidemia were subjected to subgroup analysis by researchers. In both males and females with dyslipidemia, E2 was shown to be inversely correlated with ApoC3 in Supplementary Table 2 (male: standardized β = -0.195, t = -2.036, $P \leq 0.05$; female: standardized β = -0.150, t = -2.364, $P \leq 0.05$). The relationship still existed after correcting for confounding variables (male: standardized β = -0.167, t = -2.688, $P \leq 0.01$; female: standardized β = -0.089, t = -2.331, $P \leq 0.05$). This negative correlation, though, was not significant in the control group for either gender.
## Serial mediation model for a hypothesized pathway to a hypertriglyceridemia event in both sexes
In both men and women, ApoC3 significantly mediated the relationship between E2 and TGs, accounting for $100\%$ of the total effect. Mediation analysis supported the hypothesis that decreased E2 levels led to elevated TG levels by upregulating ApoC3 expression directly (Fig. 3).Fig. 3Serial mediation model for a hypothesized pathway to a hypertriglyceridemia event in both sexes. Legends: *In this* figure, c represents the total effect of the E2 level on hypertriglyceridemia, and c' is the residual effect of the E2 level on hypertriglyceridemia (independent of mediating effects). All analyses incorporated age, BMI, FBG, C-peptide, and ApoC2 as covariates. †$$P \leq 0.898$$, ‡$$P \leq 0.401$$, *$P \leq 0.05$, **$P \leq 0.01$ The outcomes of the mediation analysis were validated by bootstrapping analysis, and the results were consistent with those of the stepwise method.
## Discussion
This study investigated the association between serum E2 and ApoC3 levels among individuals with obesity from China. It found that ApoC3 was negatively associated with E2 levels in both men and premenopausal women. The mediation analysis indicated that decreased E2 levels led to increased TG levels by increasing ApoC3 levels, by a straight pathway. This finding implied that increased TG levels due to decreasing serum E2 levels may be mediated by ApoC3. This may be one of the pathways through which estrogen affects CVD and other metabolic diseases.
Extensive epidemiological evidence and basic studies have suggested that elevated levels of ApoC3 are strongly associated with CVD [17, 24]. Apoc3 is a secreted glycoprotein generated by the liver that plays a critical role in TRL metabolism [24]. It has been shown to enhance VLDL production from isolated hepatocyte cultures [25]. ApoC3 can cause severe HTG by inhibiting LPL activity [13]. In addition, ApoC3 could inhibit TRL lipoprotein clearance by the liver [26, 27]. Therefore, VLDL and celiac particles would accumulate. It was reported that the loss of function of ApoC3 could confer cardiovascular protection [28–30]. Consistent with this, our study showed that the HTG group had higher ApoC3 levels than the control group, among individuals with obesity. This effect of ApoC3 on lipid metabolism increases the risk of CVD.
Estrogen regulates lipid metabolism in a significant way, and its fluctuations in perimenopausal and postmenopausal women can lead to dyslipidaemia, such as elevated TGs [4]. Studies have reported that TG levels are significantly higher in postmenopausal women than in premenopausal women [31] and that TG levels are significantly lower after treatment with transdermal E2 [32]. There is no clear mechanism to explain how inhibition is mediated. Scholars proposed that estrogen can work directly in the liver to reduce TG content via estrogen receptor (ER) [33]. Animal experiments have evidenced that the ability of hepatic steatosis was lost with the absence of hepatic ER after estrogen reduction [32]. Estrogen reduces de novo fat synthesis in the liver by maintaining acetyl-coa carboxylase (ACC) phosphorylation [34] and promoting free fatty acids (FA) oxidation [35], too. Estrogen also promotes the uptake of FA in adipose tissue [36] and accelerates FA consumption in muscle tissue [37], thus limiting FA transport to the liver and reducing TG production at the source. These effects lead to a decrease in TGs production. In addition, estrogen can regulate serum lipoprotein levels [38]. Therefore, the study speculate that estrogen's inhibitory effect on triglycerides is mediated through the inhibition of ApoC3. Few studies have focused on the correlation between estrogen and ApoC3.
In our study, the HTG group had lower E2 levels in females, as described above. However, this difference was not significant in men. The results could be attributed to interference from confounding factors. After adjusting for confounding factors, a robust association was observed between E2 and TG levels, in both sexes. In order to comprehensively examine the correlation between E2 and ApoC3, we conducted an analysis of this relationship in both normolipidemic and hyperlipidemic patients. In males and females with dyslipidemia, a significant inverse correlation was observed between E2 and ApoC3. Conversely, in the control group, this negative correlation did not reach statistical significance for either gender. The present findings suggest that the regulatory impact of E2 on ApoC3 expression may be selectively induced in the context of dyslipidemia. Nevertheless, additional investigations are warranted to more fully characterize the complex interplay between these factors and the underlying biological pathways involved.
Individuals with obesity (BMI ≥ 30.0 kg/m2) have an elevated risk of developing cardiovascular and metabolic disorders [39]. Therefore, to achieve greater precision in our findings, the researchers conducted a cross-sectional analysis specifically among individuals with obesity.
The present investigation demonstrated a consistent linear association between serum E2 levels and ApoC3 in both men and premenopausal women, which persisted even after adjustment for confounding variables. This relationship was further observed to be more prominent among individuals with dyslipidemia. Additionally, the results of the mediation analysis supported the hypothesis that estrogen may mitigate TG levels by suppressing ApoC3 expression.
For the first time, the current study proposed a link between estrogen, ApoC3, and TGs in premenopausal women and men. This offers fresh perspectives on heart disease treatment and prevention in the future. To uncover the underlying mechanisms, additional research is required.
## Comparisons with other studies and what does the current work add to the existing knowledge
Prior research on estrogen concentrated on TC, TG, and LDL-C, which are common clinical markers of lipid metabolism. Few research has been conducted to investigate the link between estrogen and ApoC3. According to our clinical research, estrogen may suppress the expression of ApoC3, which would lower TG levels.
## Study advantages and limitations
This study's innovation is its main strength. We used mediation analysis to show how estrogen, ApoC3, and TGs are related. The study's strengths also include standardised measurement laboratory data and thorough information on drug intake.
Several limitations exist in this research. First, blood was gathered before the women's periods because E2 levels might fluctuate over the menstrual cycle. However, the menstrual cycle was not standardized, which may have resulted in some bias. Furthermore, this present trial is a small sample cross-sectional study involving Chinese individuals. This may restrict the generalizability of current findings to populations in other locations; hence, more research with large samples in multiregional cohorts is needed.
## Conclusion
In summary, ApoC3 was negatively and linearly associated with serum E2 levels in men and premenopausal women with obesity. This implies that estrogen may suppress ApoC3 expression and thus lower TG levels. Our findings provide new insights into the prevention and management of heart disease in the future.
## Supplementary Information
Additional file 1. Supplementary Table 1. The correlation between ApoC2 and clinical parameters among all participants. Additional file 2. Supplementary Table 2. Liner regressionanalysis for the association of ApoC3 levels (dependent variable) and E2 levels(independent variables) with or without dyslipidemia.
## References
1. Miller VM, Duckles SP. **Vascular actions of estrogens: functional implications**. *Pharmacol Rev* (2008) **60** 210-241. DOI: 10.1124/pr.107.08002
2. Freedman DS, Otvos JD, Jeyarajah EJ, Shalaurova I, Cupples LA, Parise H. **Sex and age differences in lipoprotein subclasses measured by nuclear magnetic resonance spectroscopy: the Framingham study**. *Clin Chem* (2004) **50** 1189-1200. DOI: 10.1373/clinchem.2004.032763
3. Roger VL, Go AS, Lloyd-Jones DM, Adams RJ, Berry JD, Brown TM. **Heart disease and stroke statistics–2011 update: a report from the American heart association**. *Circulation* (2011) **123** e18-e209. DOI: 10.1161/CIR.0b013e3182009701
4. Morselli E, Santos RS, Criollo A, Nelson MD, Palmer BF, Clegg DJ. **The effects of oestrogens and their receptors on cardiometabolic health**. *Nat Rev Endocrinol* (2017) **13** 352-364. DOI: 10.1038/nrendo.2017.12
5. Fukui M, Kitagawa Y, Kamiuchi K, Hasegawa G, Yoshikawa T, Nakamura N. **Association between serum estradiol concentrations and carotid atherosclerosis in men with type 2 diabetes mellitus**. *Metab* (2008) **57** 285-289. DOI: 10.1016/j.metabol.2007.09.014
6. Luo F, Huang W-Y, Guo Y, Ruan G-Y, Peng R, Li X-P. **17β-estradiol lowers triglycerides in adipocytes via estrogen receptor α and it may be attenuated by inflammation**. *Lipids Health Dis* (2017) **16** 182. DOI: 10.1186/s12944-017-0575-6
7. Ting W-J, Huang C-Y, Jiang C-H, Lin Y-M, Chung L-C, Shen C-Y. **Treatment with 17β-Estradiol reduced body weight and the risk of cardiovascular disease in a high-fat diet-induced animal model of obesity**. *Int J Mol Sci* (2017) **18** 629. DOI: 10.3390/ijms18030629
8. Manrique-Acevedo C, Chinnakotla B, Padilla J, Martinez-Lemus LA, Gozal D. **Obesity and cardiovascular disease in women**. *Int J Obes (Lond)* (2020) **44** 1210-1226. DOI: 10.1038/s41366-020-0548-0
9. Hammes SR, Levin ER. **Impact of estrogens in males and androgens in females**. *J Clin Invest* (2019) **129** 1818-1826. DOI: 10.1172/JCI125755
10. Xu Z-H, Xu X-H, Pan D, Liu T-Y, Yuan M-Z, Jiang S. **Effect of estradiol on penile erection: a cross-sectional study**. *Transl Androl Urol* (2019) **8** 574-582. DOI: 10.21037/tau.2019.10.15
11. van Capelleveen JC, BernelotMoens SJ, Yang X, Kastelein JJP, Wareham NJ, Zwinderman AH. **Apolipoprotein C-III levels and incident coronary artery disease risk: The EPIC-Norfolk prospective population study**. *Arterioscler Thromb Vasc Biol* (2017) **37** 1206-1212. DOI: 10.1161/ATVBAHA.117.309007
12. Morze J, Koch M, Aroner SA, Budoff M, McClelland RL, Mukamal KJ. **Associations of HDL subspecies defined by ApoC3 with non-alcoholic fatty liver disease: the multi-ethnic study of atherosclerosis**. *J Clin Med* (2020) **9** 3522. DOI: 10.3390/jcm9113522
13. Ramms B, Gordts PLSM. **Apolipoprotein C-III in triglyceride-rich lipoprotein metabolism**. *Curr Opi Lipidol* (2018) **29** 171-179. DOI: 10.1097/MOL.0000000000000502
14. Akoumianakis I, Zvintzou E, Kypreos K, Filippatos TD. **ANGPTL3 and Apolipoprotein C-III as novel lipid-lowering targets**. *Curr Atheroscler Rep* (2021) **23** 20. DOI: 10.1007/s11883-021-00914-7
15. Gaudet D, Alexander VJ, Baker BF, Brisson D, Tremblay K, Singleton W. **Antisense Inhibition of Apolipoprotein C-III in Patients with Hypertriglyceridemia**. *N Engl J Med* (2015) **373** 438-447. DOI: 10.1056/NEJMoa1400283
16. Mendivil CO, Rimm EB, Furtado J, Chiuve SE, Sacks FM. **Low-density lipoproteins containing apolipoprotein C-III and the risk of coronary heart disease**. *Circulation* (2011) **124** 2065-2072. DOI: 10.1161/CIRCULATIONAHA.111.056986
17. Pechlaner R, Tsimikas S, Yin X, Willeit P, Baig F, Santer P. **Very-low-density lipoprotein-associated apolipoproteins predict cardiovascular events and are lowered by inhibition of APOC-III**. *J Am Coll Cardiol* (2017) **69** 789-800. DOI: 10.1016/j.jacc.2016.11.065
18. Khoo C, Campos H, Judge H, Sacks FM. **Effects of estrogenic oral contraceptives on the lipoprotein B particle system defined by apolipoproteins E and C-III content**. *J Lipid Res* (1999) **40** 202-212. DOI: 10.1016/S0022-2275(20)33358-7
19. Onat A, Hergenç G, Sansoy V, Fobker M, Ceyhan K, Toprak S. **Apolipoprotein C-III, a strong discriminant of coronary risk in men and a determinant of the metabolic syndrome in both genders**. *Atherosclerosis* (2003) **168** 81-89. DOI: 10.1016/S0021-9150(03)00025-X
20. Tzeravini E, Tentolouris A, Eleftheriadou I, Chaviaras N, Kolovou G, Apostolidou-Kiouti F. **Comparison of postprandial serum triglyceride and apolipoprotein B concentrations between the two phases of menstrual cycle in healthy women**. *Curr Vasc Pharmacol* (2021) **19** 411-422. DOI: 10.2174/1573406416666200611105113
21. Oh RC, Trivette ET, Westerfield KL. **Management of hypertriglyceridemia: common questions and answers**. *Am Fam Physician* (2020) **102** 347-354. PMID: 32931217
22. Shpitser I. **Counterfactual graphical models for longitudinal mediation analysis with unobserved confounding**. *Cogn Sci* (2013) **37** 1011-1035. DOI: 10.1111/cogs.12058
23. Lyu L, Yu J, Liu Y, He S, Zhao Y, Qi M. **High hemoglobin glycation index is associated with telomere attrition independent of HbA1c, mediated by TNFα**. *J Clin Endocrinol Metab* (2022) **107** 462-473. DOI: 10.1210/clinem/dgab703
24. Guo M, Xu Y, Dong Z, Zhou Z, Cong N, Gao M. **Inactivation of ApoC3 by CRISPR/Cas9 protects against atherosclerosis in hamsters**. *Circ Res* (2020) **127** 1456-1458. DOI: 10.1161/CIRCRESAHA.120.317686
25. Sundaram M, Zhong S, Bou Khalil M, Links PH, Zhao Y, Iqbal J. **Expression of apolipoprotein C-III in McA-RH7777 cells enhances VLDL assembly and secretion under lipid-rich conditions**. *J Lipid Res* (2010) **51** 150-161. DOI: 10.1194/jlr.M900346-JLR200
26. Sacks FM. **The crucial roles of apolipoproteins E and C-III in apoB lipoprotein metabolism in normolipidemia and hypertriglyceridemia**. *Curr Opin Lipidol* (2015) **26** 56-63. DOI: 10.1097/MOL.0000000000000146
27. Gordts PLSM, Nock R, Son N-H, Ramms B, Lew I, Gonzales JC. **ApoC-III inhibits clearance of triglyceride-rich lipoproteins through LDL family receptors**. *J Clin Invest* (2016) **126** 2855-2866. DOI: 10.1172/JCI86610
28. Crosby J, Peloso GM, Auer PL. **Loss-of-function mutations in APOC3, triglycerides, and coronary disease**. *N Engl J Med* (2014) **371** 22-31. DOI: 10.1056/NEJMoa1307095
29. Jorgensen AB, Frikke-Schmidt R, Nordestgaard BG, Tybjaerg-Hansen A. **Loss-of-function mutations in APOC3 and risk of ischemic vascular disease**. *N Engl J Med* (2014) **371** 32-41. DOI: 10.1056/NEJMoa1308027
30. Natarajan P, Kohli P, Baber U, Nguyen KH, Sartori S, Reilly DF. **Association of APOC3 loss-of-function mutations with plasma lipids and subclinical atherosclerosis: the multi-ethnic bioimage study**. *J Am Coll Cardiol* (2015) **66** 2053-2055. DOI: 10.1016/j.jacc.2015.08.866
31. Guo W, Fu J, Chen X, Gao B, Fu Z, Fan H. **The effects of estrogen on serum level and hepatocyte expression of PCSK9**. *Metabolism* (2015) **64** 554-560. DOI: 10.1016/j.metabol.2015.01.009
32. Balci H, Altunyurt S, Acar B, Fadiloglu M, Kirkali G, Onvural B. **Effects of transdermal estrogen replacement therapy on plasma levels of nitric oxide and plasma lipids in postmenopausal women**. *Maturitas* (2005) **50** 289-293. DOI: 10.1016/j.maturitas.2004.07.002
33. Palmisano BT, Zhu L, Stafford JM. **Role of estrogens in the regulation of liver lipid metabolism**. *Adv Exp Med Biol* (2017) **1043** 227-256. DOI: 10.1007/978-3-319-70178-3_12
34. Cole LK, Jacobs RL, Vance DE. **Tamoxifen induces triacylglycerol accumulation in the mouse liver by activation of fatty acid synthesis**. *Hepatology* (2010) **52** 1258-1265. DOI: 10.1002/hep.23813
35. Camporez JPG, Jornayvaz FR, Lee H-Y, Kanda S, Guigni BA, Kahn M. **Cellular mechanism by which estradiol protects female ovariectomized mice from high-fat diet-induced hepatic and muscle insulin resistance**. *Endocrinology* (2013) **154** 1021-1028. DOI: 10.1210/en.2012-1989
36. Newell-Fugate AE. **The role of sex steroids in white adipose tissue adipocyte function**. *Reproduction* (2017) **153** R133-R149. DOI: 10.1530/REP-16-0417
37. Huss JM, Torra IP, Staels B, Giguère V, Kelly DP. **Estrogen-related receptor alpha directs peroxisome proliferator-activated receptor alpha signaling in the transcriptional control of energy metabolism in cardiac and skeletal muscle**. *Mol Cell Biol* (2004) **24** 9079-9091. DOI: 10.1128/MCB.24.20.9079-9091.2004
38. Ko S-H, Jung Y. **Energy metabolism changes and dysregulated lipid metabolism in postmenopausal women**. *Nutrients* (2021) **13** 4556. DOI: 10.3390/nu13124556
39. Piché M-E, Tchernof A, Després J-P. **Obesity phenotypes, diabetes, and cardiovascular diseases**. *Circ Res* (2020) **126** 1477-1500. DOI: 10.1161/CIRCRESAHA.120.316101
|
---
title: Girl predominance in trampoline-related forearm shaft fractures and their increasing
incidence since 2000
authors:
- Markus Stöckell
- Ella Pikkarainen
- Tytti Pokka
- Juha-Jaakko Sinikumpu
journal: BMC Musculoskeletal Disorders
year: 2023
pmcid: PMC9972755
doi: 10.1186/s12891-023-06241-z
license: CC BY 4.0
---
# Girl predominance in trampoline-related forearm shaft fractures and their increasing incidence since 2000
## Abstract
### Background
There are reports of increasing incidence of forearm shaft fractures in children. Their treatment has been preferably nonoperative but surgical fixation has gained popularity due to elastic stable intramedullary nailing. We aimed to study whether the incidence of pediatric both-bone forearm shaft fractures and their operative care have changed since year 2000. Trampoline injuries, in particular, and their treatment, re-displacement and short-term outcomes were the secondary outcomes of the study.
### Methods
A population-based study in the geographic catchment area of Oulu University Hospital district in 20-years of time period (2000 – 2019) was performed. Altogether 481 diaphyseal both-bone forearm fractures in children (< 16 years) were included. Age- and sex-related incidence rates were determined, by using the official numbers of the population-in-risk by Statistics Finland. Trampoline jumping and other types of injury were reviewed, as well as particulars of treatment and outcomes.
### Results
The incidence of diaphyseal both-bone forearm fractures increased from $\frac{9.4}{100}$ 000 in 2000–2001 to $\frac{41.7}{100}$ 000 in 2018–2019 ($P \leq 0.001$). Surgical treatment increased respectively (from $\frac{8.8}{100}$ 000 in 2000–2001 to $\frac{35.3}{100}$ 000 in 2018–2019, $P \leq 0.0001$). Trampoline injuries explained one in three ($29\%$) of all fractures; they increased from $0\%$ in 2000–2001 to $36.6\%$ in 2018–2019 ($P \leq 0.001$). During the last four years of the study (2016–2019), most trampoline-related injuries occurred among girls ($61.2\%$), compared to boys ($38.8\%$) ($$P \leq 0.031$$). Trampoline-related injuries comprised $46.9\%$ of all fractures in girls, compared to $26.0\%$ among boys (Diff. $20.8\%$, $4.7\%$ to $36.1\%$, $$P \leq 0.009$$). The mean age of the patients elevated from 6.4 years (2000–2001) to 8.6 years (2018–2019) ($$P \leq 0.015$$). Boys predominated ($69.6\%$) in 2000–2009 but during the last ten years, there was no statistical difference in distribution between the genders (males $54.6\%$, $$P \leq 0.11$$).
### Conclusions
During the twenty-year’s of study period, the incidence of pediatric diaphyseal forearm fractures increased fivefold. Trampolining was the most usual single reason for the fractures. More attention should be focused to increase the safety of trampoline jumping, in particular among the girls.
## Background
Approximately one-third of children experience at least one fracture before the age of 17 [1]. Typical injury mechanism for pediatric fractures is fall, while sport related injuries, playground injuries and traffic accidents are usual types of injuries. Forearm shaft fractures comprise up to $15\%$ of all pediatric fractures needing in-hospital treatment [2]. They are challenging to treat, while bone healing is poor as compared to other pediatric fractures and they can result in permanent disability or morbidity [3]. The incidence of forearm shaft fractures has increased four-fold during the first decade of 2000’s [4]. At that time period trampolining caused approximately $25\%$ of all forearm shaft fractures, being the most important single reason for the fracture [4]. *In* general, trampoline-related injuries in children have become a great challenge, resulting in the high need for preventive interventions [5].
Pediatric forearm shaft fractures are preferable treated with closed reduction and immobilization by a cast [6]. There has been an increasing trend towards surgical intervention, probably as a cause of high ($30\%$) rate of re-reduction during the nonoperative cast treatment [7]. Less follow-up visits and radiographs are needed after operative vs. nonoperative treatment. Several studies indicate that operative care results in good functional, radiological and cosmetical outcomes [8, 9]. Elastic stable intramedullary nailing (ESIN) is the preferred method for osteosynthesis. Postoperative immobilization is seldom needed after ESIN, and surgical wounds are small. Plate and screw fixation is an option in older children [10, 11] because of their higher risk of complications such as nonunion and need of re-reduction [7, 11]. Plate and screw fixation is feasible method in treating meta-diaphyseal forearm fractures, too.
Given that pediatric forearm shaft fractures have increased with high rate during the first decade of the 2000’s, the authors’ aimed to investigate if the incidence of both-bone forearm shaft fractures has persisted in increasing trend. Incidence of diaphyseal forearm fractures in an unselected normal children population (< 16-years’ of age) was the principal aim of the study. Another aims were to investigate trampoline jumping as a background factor of these fractures, and their treatment trends, other injuries, re-displacement and short-term outcomes.
## Methods
This is a population-based cross-section study in a geographical catchment area, where all patients aged less than 16 years with a both-bone diaphyseal middle third forearm fracture during 2000–2019 were included. In total, 481 children and adolescents comprised to the study population. The study center is located at Northern Finland, and it is the only round-the-clock pediatric trauma unit at the region. Regardless on the potential primary treatment at a primary health care unit of few isolated fractures, the clinical and radiographic follow-up of the patients has occurred in the study institution, resulting in that all potential patients were enrolled to the analyses regardless on the place of primary visit. The participation was taken to be inclusive. Information about the patients, injury types, treatment and follow-up were gathered from the hospital data base, while International Classification of Diseases (ICD), version 10, was used for data collecting. All radiographs of the patients were reviewed to confirm the diagnosis. Non-residents were excluded from the study population to avoid bias in the incidence rate. The age-matching population-in-the-risk in the study area was determined by using official descriptive of the population by Statistics Finland. The children population in the study area varied between 84 333 and 88 093 during the study time.
All radiographs associated to the injury were primarily reviewed by a radiologist on-duty and re-reviewed by a clinician not only to confirm the fractures and the eligibility of the patients but also to confirm that only diaphyseal middle third both-bone fractures were included to the data. Displacement and angular deformity of the fractures were analyzed. Fractures with disruption of distal radioulnar joint (Galeazzi fracture and analogous) or radiocapitellar joint (Monteggia fracture and analogous) were excluded. Open fractures were included to the data. Operation reports and post-operative clinical notes were analyzed to find out the recovery of the patients. Treatment was classified as a closed non-invasive treatment (reduction under general anaesthesia and casting or immobilization in-situ) or operative treatment (closed or open reduction and internal fixation by using any method of surgical stabilization).
## Statistics
Incidences were reported per 100 000 age-related persons. Differences between the groups were tested by independent-samples t-test for continuous variables. Chi-squared test was used to test the difference in distributions of the categorical variables. Standardized normal distribution (SND) test was used to analyze the difference of two independent proportions; 2 years’ of groups were used to get satisfactory number of cases, except from genders of the patients with trampoline fractures, when four years of periods were used to get satisfactory groups. Linear trend test was performed to determine the potential change in time. The differences between annual incidence densities during the first two and the last two study years (2000–1 vs. 2018–19) was tested by the chi-square test. Further, descriptive of the study cohort during the first and the last decades were aimed to be presented to present the potential rough trends of the fractures. The estimation in the amount of annual diaphyseal fracture increase was calculated by exponential regression analysis. P-value < 0.05 was considered statistically significant, taken that all values were two-tailed, and $95\%$ of confidence intervals were reported when applicable. Statistical analyses were performed using StatsDirect Statistical Software version 3.2.8 and SPSS Statistical Package (version 27.0, IBM Corporation, 2020).
## Descriptivies of the study cases
There were 481 patients. Altogether 288 ($59.9\%$) were males ($P \leq 0.001$). The mean age of the boys was 8.4 (Standard deviation (SD) + 3.6) and girls 8.8 (SD + 3.3). The mean age elevated from 6.4 years in 2000–1 (SD + 3.9) to 9.0 in 2018–19 (SD + 3.3) (Fig. 1). The proportion of the girls increased from $30.4\%$ in the first decade (2000–9) to $45.4\%$ in the last decade (2010–2019) ($$P \leq 0.002$$) in SND test. Fig. 1The mean age of pediatric patients with diaphyseal both-bone fracture by gender The majority ($$n = 432$$, $90\%$) of the fractures were angulated. Shortening was found in $24\%$ ($$n = 114$$) and multifragmentation in $5\%$ ($$n = 24$$) of the patients. The rate of open fractures was $8\%$ ($$n = 38$$) (Table 1).Table 1Descriptives of pediatric diaphyseal both-bone forearm fractures in the first decade and the second decade of the study period2000–20092010–2019AllNumber of cases168313481Gender Male117 ($69.6\%$)171 ($54.6\%$)288 ($59.9\%$) Female51 ($30.4\%$)142 ($45.4\%$)193 ($40.1\%$)Fracture characteristics Open fracture15 ($8.9\%$)23 ($7.3\%$)38 ($7.9\%$) Comminuted fracture8 ($4.8\%$)16 ($5.1\%$)24 ($5.0\%$) Angular deformity137 ($81.5\%$)295 ($94.2\%$)432 ($89.8\%$) Complete displacement21 ($12.5\%$)93 ($29.7\%$)114 ($23.7\%$)
## Incidence
The mean annual incidence of forearm shaft fractures was $\frac{27.8}{100.000}$ during the entire study period. The incidence increased from $\frac{9.4}{100}$ 000 in 2000–2001 to $\frac{41.7}{100}$ 000 in 2018–2019 (Difference (Diff.) $\frac{32.3}{100.000}$, $95\%$ CI $\frac{22.1}{100.000}$ to 44/$100.000\%$, $P \leq 0.001$). There was an increase in the incidence of surgical procedures from $\frac{8.8}{100}$ 000 in 2000–2001 to $\frac{35.3}{100}$ 000 in 2018–2019 (Diff. $\frac{26.5}{100.000}$, $95\%$ CI $\frac{17}{100.000}$ to $\frac{37.3}{100.000}$, $P \leq 0.001$) (Fig. 2).Fig. 2The age-related incidence of pediatric diaphyseal both-bone forearm fractures (bars) and the incidence of surgical procedures for them (line)
## Trampoline injuries
Trampoline jumping was associated with every three ($29\%$, $$n = 138$$) of all fractures. Trampoline injuries increased from $0\%$ in 2000–1 to $36.6\%$ in 2018–2019 (Diff. $36.6\%$, $95\%$ CI $16.0\%$ to $48.3\%$, $$P \leq 001$$) (Fig. 3). During the last four years of the study (2016–2019), most trampoline-related injuries occurred among girls ($61.2\%$), compared to boys ($38.8\%$) ($$P \leq 0.031$$). In total, trampoline-related injuries comprised $46.9\%$ of all fractures in girls, compared to $26.0\%$ among boys (Diff. $20.8\%$, $4.7\%$ to $36.1\%$, $$P \leq 0.009$$). Other recreational reasons of the fractures are described in the Table 2.Fig. 3The crude incidence of trampoline related pediatric diaphyseal both-bone forearm fractures and all pediatric diaphyseal both-bone forearm fractures during the study period 2000–2019Table 2Injury mechanisms and recreational activities of the children patients, who suffered from diaphyseal both-bone forearm fractures2000–12002–32004–52006–72008–92010–112012–132014–152016–172018–19AllaInjury mechanism Fall on the same plane$25\%$ [4]$33.3\%$ [11]$20\%$ [6]$18.8\%$ [6]$17.9\%$ [10]$19.1\%$ [9]$18.6\%$ [11]$21.4\%$ [15]$21.5\%$ [14]$29.6\%$ [21]$22.3\%$ [107] Fall between planes$31.3\%$ [5]$18.2\%$ [6]$13.3\%$ [4]$18.8\%$ [6]$26.8\%$ [15]$29.8\%$ [14]$23.7\%$ [14]$34.3\%$ [24]$24.6\%$ [16]$19.7\%$ [14]$24.6\%$ [118] Fall on the ice/snow$0\%$ [0]$6.1\%$ [2]$6.7\%$ [2]$3.1\%$ [1]$1.8\%$ [1]$4.3\%$ [2]$6.8\%$ [4]$5.7\%$ [4]$3.1\%$ [2]$1.4\%$ [1]$4\%$ [19] Fall at the playground$25\%$ [4]$30.3\%$ [10]$33.3\%$ [10]$50\%$ [16]$42.9\%$ [24]$42.6\%$ [20]$37.3\%$ [22]$31.4\%$ [22]$36.9\%$ [24]$43.7\%$ [31]$38.2\%$ [183] Traffic injury$12.5\%$ [2]$3\%$ [1]$20\%$ [6]$3.1\%$ [1]$3.6\%$ [2]$2.1\%$ [2]$6.8\%$ [4]$1.4\%$ [1]$1.5\%$ [1]$0\%$ [0]$4\%$ [19] Other injury$6.3\%$ [1]$9.1\%$ [3]$6.7\%$ [2]$6.3\%$ [2]$7.1\%$ [4]$2.1\%$ [1]$6.8\%$ [4]$5.7\%$ [4]$12.3\%$ [8]$5.6\%$ [4]$6.9\%$ [33]Recreational causes Organized sports (soccer, ice hockey, etc.)$18.8\%$ [3]$15.2\%$ [5]$3.3\%$ [1]$9.4\%$ [3]$8.9\%$ [5]$14.9\%$ [7]$11.9\%$ [7]$17.1\%$ [12]$12.3\%$ [8]$18.3\%$ [13]$13.4\%$ [64] Slalom, cross-country skiing, snowboarding$0\%$ [0]$6.1\%$ [2]$6.7\%$ [2]$3.1\%$ [1]$1.8\%$ [1]$2.1\%$ [1]$3.4\%$ [2]$2.9\%$ [2]$1.5\%$ [1]$0\%$ [0]$2.5\%$ [12] Bicycling, skateboarding, roller-skating$6.3\%$ [1]$15.2\%$ [5]$20\%$ [6]$0\%$ [0]$8.9\%$ [5]$10.6\%$ [5]$10.2\%$ [6]$5.7\%$ [4]$6.2\%$ [4]$5.6\%$ [4]$8.4\%$ [40] Trampoline$0\%$ [0]$15.2\%$ [5]$23.3\%$ [7]$40.6\%$ [13]$30.4\%$ [17]$29.8\%$ [14]$27.1\%$ [16]$24.3\%$ [17]$35.4\%$ [23]$36.6\%$ [26]$28.8\%$ [138] Other playground device, swing, etc$6.3\%$ [1]$9.1\%$ [3]$10\%$ [3]$9.4\%$ [3]$12.5\%$ [7]$17\%$ [8]$15.3\%$ [9]$15.5\%$ [11]$6.2\%$ [4]$11.3\%$ [8]$11.9\%$ [57] Indoor play$12.5\%$ [2]$9.1\%$ [3]$6.7\%$ [2]$9.4\%$ [3]$8.9\%$ [5]$6.4\%$ [3]$6.8\%$ [4]$7.1\%$ [5]$9.2\%$ [6]$5.6\%$ [4]$7.7\%$ [37] Motor vehicle$6.3\%$ [1]$0\%$ [0]$0\%$ [0]$9.4\%$ [3]$3.6\%$ [2]$4.3\%$ [2]$3.4\%$ [2]$1.4\%$ [1]$1.5\%$ [1]$0\%$ [0]$2.5\%$ [12] Falling from ladder, roof, etc$25\%$ [4]$6.1\%$ [2]$6.7\%$ [2]$3.1\%$ [1]$8.9\%$ [5]$8.5\%$ [4]$8.5\%$ [5]$8.6\%$ [6]$10.8\%$ [7]$5.6\%$ [4]$8.4\%$ [40] Falling when running$25\%$ [4]$24.2\%$ [8]$20\%$ [6]$15.6\%$ [5]$12.5\%$ [7]$6.4\%$ [3]$8.5\%$ [5]$11.4\%$ [8]$12.3\%$ [8]$14.1\%$ [10]$13.4\%$ [64] Falling from stairs$0\%$ [0]$0\%$ [0]$0\%$ [0]$0\%$ [0]$0\%$ [0]$0\%$ [0]$3.4\%$ [2]$2.9\%$ [2]$3.1\%$ [2]$1.4\%$ [1]$1.5\%$ [7] Other reason$0\%$ [0]$0\%$ [0]$3.3\%$ [1]$0\%$ [0]$3.6\%$ [2]$0\%$ [0]$1.7\%$ [1]$2.9\%$ [2]$1.5\%$ [1]$1.4\%$ [1]$1.7\%$ [8]aTotal number of cases 479 (two cases missing data)
## Surgical treatment
The majority ($$n = 272$$, $56.5\%$) of the patients were treated nonoperatively, without surgical fixation, by using cast immobilization with/without previous closed reduction. Nevertheless, there was an increase in surgical care during the study period from $12.5\%$ in 2000–2001 to $39.4\%$ in 2018–2019, (Diff. $26.9\%$, $1.2\%$ to $42.9\%$, $$P \leq 0.027$$) (Table 3).Table 3Operative treatment and surgical procedures for the pediatric diaphyseal both-bone forearm fractures2000–12002–32004–52006–72008–92010–112012–132014–152016–172018–19AllP-value**Operative activity (% of all patients and number) Procedure at ORa (operated)$93.8\%$ [15]$93.9\%$ [31]$93.3\%$ [28]$90.6\%$ [29]$96.5\%$ [55]$80.9\%$ [38]$88.1\%$ [52]$83.1\%$ [59]$81.5\%$ [53]$84.5\%$ [60]$87.3\%$ [420] Not admitted to OR (not operated)$6.3\%$ [1]$6.1\%$ [2]$6.7\%$ [2]$9.4\%$ [3]$3.5\%$ [2]$19.1\%$ [9]$11.9\%$ [7]$16.9\%$ [12]$18.5\%$ [12]$15.5\%$ [11]$12.7\%$ [61]0.007Surgical procedure (% of all operated and number) Closed or open reduction & internal fixation$13.3\%$ [2]$45.2\%$ [14]$35.7\%$ [10]$55.2\%$ [16]$54.5\%$ [30]$63.2\%$ [24]$59.6\%$ [31]$47.5\%$ [28]$49.1\%$ [26]$46.7\%$ [28]$49.8\%$ [209] Closed treatment$86.7\%$ [13]$54.8\%$ [17]$64.3\%$ [18]$44.8\%$ [13]$45.5\%$ [25]$36.8\%$ [14]$40.4\%$ [21]$52.5\%$ [31]$50.9\%$ [27]$53.3\%$ [32]$50.2\%$ [211]0.23aOperating room**Trend test
## Re-operation rate
The need of unplanned operation due to loss of reduction was $26.1\%$ ($$n = 71$$/272) among the cases who were treated non-invasively primarily. In comparison, the need of re-operation for any reason was found in nine cases ($4.3\%$, $$n = 9$$/209) among them who were operatively treated primarily ($P \leq 0.001$).
## Discussion
The main finding of this comprehensive population-based research of childhood forearm shaft fractures was that the incidence of pediatric forearm shaft fractures has increased fivefold during 20-years of period in 2000–2019. The incidence was 8.2 per 100 000 children-in-risk in the beginning of the study, and 40.3 per 100 000 at the end of the study. Trampoline jumping was the most common ($29\%$) single recreational cause of the fractures. Trampoline-related injuries became more frequent and comprised $37\%$ of all fractures at the end of the study (2018–2019). During the last four years of the research, trampoline-injuries happened predominantly in girls ($61\%$), which is an important finding. This finding is opposite to many other childhood fractures, while boys usually predominate in most childhood fractures. The previous literature mainly indicate that boys suffer more usually from forearm fractures, too [6, 12–14]. There is no simple explanation for the predomination of the girls in trampoline-related fractures during the end of the research. However, many reasons for the increasing interest in trampoline jumping by girls can be considered. Trampoline jumping is traditionally associated with gymnastics, which again is traditionally more popular sport among girls than boys. Another explanation may be that indoor trampoline parks have gained popularity among both boys and girls. Indoor trampoline parks with great and powerful high-quality trampolines, compared to conventional backyard trampolines, may predispose children to injuries, especially when playing tricks or somersaults [15]. Furthermore, social media is suggested as a promotor for still more challenging tricks on the trampoline, while video recording of the tricks can incite a child to exceed his/her skills. Given that girls in particular are skilled and active in social media, it is reasonable to hypothesize the use of social media is associated with increased forearm shaft fractures particularly among girls. Nevertheless, that hypothesis needs more research in the future.
A substantial increase in the incidence of both-bone forearm shaft fractures has been reported previously, concerning the beginning of the twenty-first century [4, 13, 14, 16]. However, it has been unclear if such high increase in the incidence could still continue. Our findings suggest that the incidence of forearm shaft fractures continued to increase during the entire 20 years’ of study period, since 2000, albeit the trend became more gentle towards the end of the study. Given that the overall incidence of pediatric fractures has decreased or held stable, such an increasing trend is particularly important [14]. In addition, the forearm shaft fractures in children are one of those that present a real risk of disturbed bone healing and long-term sequelae [3]. Overall complication rate after forearm shaft fractures is 9–$22\%$ [17–20], which justify high preventive interventions to avoid these fractures.
*In* general, there are many factors that have been assumed to explain pediatric fractures. The association between low bone mineral density and increased risk of forearm and wrist fractures has been shown in several studies [21–23]. For instance, low milk consumption and poor calcium intake are associated to low bone mineral density and to a higher risk for fractures [24]. Deficiency of vitamin D was associated with increased risk of forearm fractures as well [22]. Obesity is increasing among children worldwide [25]. Overweight has been reported to be a risk factor for pediatric fractures [22, 26]. Children usually fall against the out-stretched upper extremity, and weight has a solid association with the trauma energy during falling. Increased physical activity has also been associated with increased risk of fractures [12], which is reasonable. Participating in organized sports has increased in the study country recently [27], which have increased the potential injuries for forearm shaft fractures.
In this study, most patients were treated under general anaesthesia in the operating room. The need of surgical intervention increased during the 20 year period. However, the increase was in relation to the increasing number of fractures per se, meaning that no change in treatment paradigm was happening. Therefore, the threshold to treat the patients in the operation theatre had been relatively stable during the study time. However, it has been suggested that there is an increasing trend towards invasive treatment such as elastic stable intramedullary nailing in the treatment of both-bone forearm shaft fractures [17, 28, 29]. This is opposite to the previous reports which have stated that surgical fixation has become more popular as an alternative to nonoperative care, due to high occurrence rate of redisplacement, especially in older children [6, 30]. However, in our study, increase in the surgical treatment of forearm shaft fractures was found but it was depending on the increased frequency of the fractures and not on the change in treatment strategy from nonoperative to operative care. If surgical stabilization in needed, ESIN is a preferred method. It is minimal invasive technique, and the surgical wounds are far from the fracture site, which will enhance the healing process. In addition, it produces decent stability [10] resulting in good functional and cosmetical outcomes [31–33], without routineous postoperative cast immobilization [34]. However, our current research suggests that nonoperative treatment had remained as the preferred option over surgical treatment, and principally, osteosynthesis would be reserved for older children, beyond the age of 10 or more, and to the cases, whose remodeling potential is low [30, 35, 36].
There are several strengths in this study. The research was performed in a catchment area of about 88.000 children and the enrolment can be taken as inclusive. There are few private clinics in the area that may have treated some isolated pediatric forearm shaft fractures, but the number of these potential patients were considered to be infinitesimally small; the study institution is the only round-the-clock pediatric trauma unit, familiarized with ESIN, and the unit where follow-up of the children fracture patients is performed. Non-residents were excluded from the data and the official numbers of children-in-risk was precise and certain. As another strength, radiographs were obtained from all patients, and close radiographic particulars of the cases were reviewed. As limitations, this is retrospective study and the decision to operate or not has not been randomized. The decision of treatment was made by a treating surgeon-on-call individually for every patient. Another weakness is that at least two-years’ of time periods were needed to get satisfactory groups for analyses. Differences in genders (girls vs boys) in a subgroup of trampoline fractures only was analyzed in 4-years’ of time periods, to get satisfactory groups for analyses. However, classification of the cases in two to four years’ of study groups worked properly, answering to the study aims with satisfactory accuracy.
## Conclusion
As a conclusion, we found fivefold increase in the incidence of pediatric diaphyseal both-bone forearm fractures during the last twenty years (2000–2019). Trampoline-related fractures increased, comprising $37\%$ of all forearm shaft fractures at the end of the study. Girls predominated in trampoline-related fractures, which is new finding and justify further research.
## References
1. Cooper C, Dennison EM, Leufkens HGM. **Epidemiology of childhood fractures in britain: a study using the general practice research database**. *J Bone Miner Res* (2004) **19** 1976-1981. DOI: 10.1359/jbmr.040902
2. Körner D, Gonser CE, Bahrs C, Hemmann P. **Change in paediatric upper extremity fracture incidences in German hospitals from 2002 to 2017: an epidemiological study**. *Arch Orthop Trauma Surg* (2020) **140** 887-894. DOI: 10.1007/s00402-019-03321-5
3. Sinikumpu J-J, Victorzon S, Antila E. **Nonoperatively treated forearm shaft fractures in children show good long-term recovery: A population-based matched case-control study with mean 11 years of follow-up**. *Acta Orthop* (2014) **85** 620-625. DOI: 10.3109/17453674.2014.961867
4. Sinikumpu J-J, Lautamo A, Pokka T, Serlo W. **The increasing incidence of paediatric diaphyseal both-bone forearm fractures and their internal fixation during the last decade**. *Injury* (2012) **43** 362-366. DOI: 10.1016/j.injury.2011.11.006
5. Hadley-Miller N, Carry PM, Brazell CJ. **Trends in trampoline fractures: 2008–2017**. *Pediatrics* (2020) **145** e20190889. DOI: 10.1542/PEDS.2019-0889
6. Alrashedan BS, Jawadi AH, Alsayegh SO. **Outcome of diaphyseal pediatric forearm fractures following non-surgical treatment in a Level I Trauma Center**. *Int J Health Sci (Qassim)* (2018) **12** 60-65. PMID: 30202409
7. Sinikumpu J-J, Lautamo A, Pokka T, Serlo W. **Complications and radiographic outcome of children’s both-bone diaphyseal forearm fractures after invasive and non-invasive treatment**. *Injury* (2013) **44** 431-436. DOI: 10.1016/j.injury.2012.08.032
8. Antabak A, Luetic T, Ivo S. **Treatment outcomes of both-bone diaphyseal paediatric forearm fractures**. *Injury* (2013) **44** 11. DOI: 10.1016/S0020-1383(13)70190-6
9. Fernandez F, Langendörfer M, Wirth T, Eberhardt O. **Failures and complications in intramedullary nailing of children’s forearm fractures**. *J Child Orthop* (2010) **4** 159-167. DOI: 10.1007/s11832-010-0245-y
10. Wall L. **Staying Out of Trouble Performing Intramedullary Nailing of Forearm Fractures**. *J Pediatr Orthop* (2016) **36** S71-S73. DOI: 10.1097/BPO.0000000000000760
11. Freese KP, Faulk LW, Palmer C. **A comparison of fixation methods in adolescent patients with diaphyseal forearm fractures**. *Injury* (2018) **49** 2053-2057. DOI: 10.1016/j.injury.2018.08.023
12. Valerio G, Gallè F, Mancusi C. **Pattern of fractures across pediatric age groups: analysis of individual and lifestyle factors**. *BMC Public Health* (2010) **10** 656. DOI: 10.1186/1471-2458-10-656
13. Khosla S, Melton LJ, Dekutoski MB. **Incidence of Childhood Distal Forearm Fractures Over 30 Years: A Population-Based Study**. *JAMA* (2003) **290** 1479-1485. DOI: 10.1001/jama.290.11.1479
14. Mäyränpää MK, Mäkitie O, Kallio PE. **Decreasing incidence and changing pattern of childhood fractures: A population-based study**. *J Bone Miner Res* (2010) **25** 2752-2759. DOI: 10.1002/jbmr.155
15. Mulligan CS, Adams S, Brown J. **Paediatric injury from indoor trampoline centres**. *Inj Prev* (2017) **23** 352-354. DOI: 10.1136/injuryprev-2016-042071
16. Bergman E, Lempesis V, Nilsson J-Å. **Time trends in pediatric fractures in a Swedish city from 1950 to 2016**. *Acta Orthop* (2020) **91** 598-604. DOI: 10.1080/17453674.2020.1783484
17. Flynn JM, Jones KJ, Garner MR, Goebel J. **Eleven Years Experience in the Operative Management of Pediatric Forearm Fractures**. *J Pediatr Orthop* (2010) **30** 313-319. DOI: 10.1097/BPO.0b013e3181d98f2c
18. Pogorelić Z, Gulin M, Jukić M. **Elastic stable intramedullary nailing for treatment of pediatric forearm fractures: A 15-year single centre retrospective study of 173 cases**. *Acta Orthop Traumatol Turc* (2020) **54** 378-384. DOI: 10.5152/j.aott.2020.19128
19. Kruppa C, Bunge P, Schildhauer TA, Dudda M. **Low complication rate of elastic stable intramedullary nailing (ESIN) of pediatric forearm fractures: A retrospective study of 202 cases**. *Medicine (Baltimore)* (2017) **96** e6669. DOI: 10.1097/MD.0000000000006669
20. Rokaya PK, Rawal M, Khan JA, Giri PK. **Elastic stable intramedullary nailing in pediatric diaphyseal forearm bone fracture**. *Int J Res Orthop* (2017) **3** 190-196. DOI: 10.18203/issn.2455-4510.IntJResOrthop20170455
21. Ma D, Jones G. **The Association between Bone Mineral Density, Metacarpal Morphometry, and Upper Limb Fractures in Children: A Population-Based Case-Control Study**. *J Clin Endocrinol Metab* (2003) **88** 1486-1491. DOI: 10.1210/jc.2002-021682
22. Ryan LM, Teach SJ, Singer SA. **Bone mineral density and vitamin D status among African American children with forearm fractures**. *Pediatrics* (2012) **130** 553. DOI: 10.1542/peds.2012-0134
23. Goulding A, Cannan R, Williams SM. **Bone Mineral Density in Girls with Forearm Fractures**. *J Bone Miner Res* (1998) **13** 143-148. DOI: 10.1359/jbmr.1998.13.1.143
24. Goulding A, Rockell JEP, Black RE. **Children who avoid drinking cow’s milk are at increased risk for prepubertal bone fractures**. *J Am Diet Assoc* (2004) **104** 250-253. DOI: 10.1016/j.jada.2003.11.008
25. Ng M, Fleming T, Robinson M. **Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013**. *Lancet* (2014) **384** 766-781. DOI: 10.1016/S0140-6736(14)60460-8
26. Valerio G, Gallè F, Mancusi C. **Prevalence of overweight in children with bone fractures: a case control study**. *BMC Pediatr* (2012) **12** 166. DOI: 10.1186/1471-2431-12-166
27. Mathisen FKS, Kokko S, Tynjälä J. **Leisure-time physical activity and participation in organized sports: Changes from 1985 to 2014 in Finland and Norway**. *Scand J Med Sci Sports* (2019) **29** 1232-1242. DOI: 10.1111/sms.13431
28. Helenius I, Lamberg T, Kääriäinen S. **Operative Treatment of Fractures in Children Is Increasing: A Population-Based Study from Finland**. *J Bone Joint Surg Am* (2009) **91** 2612-2616. DOI: 10.2106/JBJS.H.01519
29. Cruz AI, Kleiner JE, DeFroda SF. **Increasing rates of surgical treatment for paediatric diaphyseal forearm fractures: a National Database Study from 2000 to 2012**. *J Child Orthop* (2017) **11** 201-209. DOI: 10.1302/1863-2548.11.170017
30. Asadollahi S, Pourali M, Heidari K. **Predictive factors for re-displacement in diaphyseal forearm fractures in children-role of radiographic indices**. *Acta Orthop* (2017) **88** 101-108. DOI: 10.1080/17453674.2016.1255784
31. Sahu B, Mishra A, Tudu B. **Management of pediatric both-bone forearm fractures by titanium elastic nailing system: A prospective study of 40 cases**. *J Orthop Trauma* (2018) **10** 103-106
32. Naorem K. **Treatment of forearm fractures in children and adolescents with titanium elastic nails (Tens): A prospective study**. *J Orthop* (2018) **4** 939-942. DOI: 10.22271/ortho.2018.v4.i2n.133
33. Kapila R, Sharma R, Chugh A, Goyal M. **Evaluation of Clinical Outcomes of Management of Paediatric Bone Forearm Fractures using Titanium Elastic Nailing System: A Prospective Study of 50 Cases**. *J Clin Diagn Res* (2016) **10** RC12-RC15
34. Salunkhe RM, Deshmukh A, Kumar S, Kandari A. **Conservative vs. surgical intervention in diaphyseal forearm fractures in age group 1 to 15 years: A prospective study**. *Int J Orthop Sci* (2018) **4** 251-254. DOI: 10.22271/ortho.2018.v4.i3e.43
35. Arora R, Mishra P, Aggarwal AN. **Factors responsible for redisplacement of pediatric forearm fractures treated by closed reduction and cast role of casting indices and three point index**. *Indian J Orthop* (2018) **52** 536-547. DOI: 10.4103/ortho.IJOrtho_382_17
36. Bowman EN, Mehlman CT, Lindsell CJ, Tamai J. **Nonoperative treatment of both-bone forearm shaft fractures in children: predictors of early radiographic failure**. *J Pediatr Orthop* (2011) **31** 23-32. DOI: 10.1097/BPO.0b013e318203205b
|
---
title: 'In it for the long haul: the complexities of managing overweight in family
practice: qualitative thematic analysis from the Health eLiteracy for Prevention
in General Practice (HeLP-GP) trial'
authors:
- Katrina Paine
- Sharon Parker
- Elizabeth Denney-Wilson
- Jane Lloyd
- Sue Randall
- Carmel McNamara
- Don Nutbeam
- Richard Osborne
- Shoko Saito
- Mark Harris
journal: BMC Primary Care
year: 2023
pmcid: PMC9972770
doi: 10.1186/s12875-023-01995-w
license: CC BY 4.0
---
# In it for the long haul: the complexities of managing overweight in family practice: qualitative thematic analysis from the Health eLiteracy for Prevention in General Practice (HeLP-GP) trial
## Abstract
### Background
Australia has one of the highest rates of overweight and obesity in the developed world, and this increasing prevalence and associated chronic disease morbidity reinforces the importance of understanding the attitudes, views, and experiences of patients and health providers towards weight management interventions and programs. The purpose of this study was to investigate patients, family practitioners and family practice nurses’ perceptions and views regarding the receipt or delivery of weight management within the context of the HeLP-GP intervention.
### Methods
A nested qualitative study design including semi-structured interviews with family practitioners ($$n = 8$$), family practice nurses ($$n = 4$$), and patients ($$n = 25$$) attending family practices in New South Wales ($$n = 2$$) and South Australia ($$n = 2$$). The patient interviews sought specific feedback about each aspect of the intervention and the provider interviews sought to elicit their understanding and opinions of the strategies underpinning the intervention as well as general perceptions about providing weight management to their patients. Interviews were recorded and transcribed verbatim, and coding and management conducted using NVivo 12 Pro. We analysed the interview data using thematic analysis.
### Results
Our study identified three key themes: long-term trusting and supportive relationships (being ‘in it for the long haul’); initiating conversations and understanding motivations; and ensuring access to multi-modal weight management options that acknowledge differing levels of health literacy. The three themes infer that weight management in family practice with patients who are overweight or obese is challenged by the complexity of the task and the perceived motivation of patients. It needs to be facilitated by positive open communication and programs tailored to patient needs, preferences, and health literacy to be successful.
### Conclusions
Providing positive weight management in family practice requires ongoing commitment and an open and trusting therapeutic relationship between providers and patients. Behaviour change can be achieved through timely and considered interactions that target individual preferences, are tailored to health literacy, and are consistent and positive in their messaging. Ongoing support of family practices is required through funding and policy changes and additional avenues for referral and adjunctive interventions are required to provide comprehensive weight management within this setting.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12875-023-01995-w.
## Background
Most of the world’s population live in countries where overweight and obesity kills more people than underweight [1]. Australia has one of the highest rates of overweight and obesity in the developed world [2] with rates doubling over the past two decades, and $67\%$ of adults affected [2, 3]. The increasing prevalence of overweight and obesity in Australia and associated chronic disease morbidity reinforces the importance of understanding the attitudes, views, and experiences of patients and health providers towards weight management interventions and programs.
Within Australia, general practice (GP) or family practice is an important contributor to treatment and prevention of overweight and obesity. The term family practice is used in this publication as this term has wider recognition from an international perspective. Participants attending primary care view their family medical practitioner (FP) and family practice nurse (FPN) as having a key role in managing obesity [4, 5]. We know from previous research however that within this setting weight, body mass index (BMI) and waist circumference (WC) are infrequently assessed [6–8], and opportunities to provide comprehensive weight management advice is often missed [6–8]. Moreover, there are challenges in delivering preventive care for weight management reflecting its complex, variable, and time-consuming nature.
Obesity is a chronic and relapsing disease [9]. Medically the concept of weight homeostasis with upregulation of physiological pathways leading to hunger and a diminution of energy means that for many people, returning to a starting weight is likely [10]. We also live in a obesogenic environment characterised by continuous access to high-energy foods combined with reduced obligations for physical activity [11]. Obesity and the associated comorbidities come with significant psychosocial burden including stigma, depression and anxiety, eating disorders, substance abuse, poor body image and poor self-esteem [12]. Addressing weight in family practice is also influenced by provider skill, willingness and interest [13], as well as patient motivation, health literacy, and personal and social circumstances [14]. In addition, the structure of the Medicare Benefits Schedule (MBS) – Australia’s national health insurance scheme – provides better remuneration for multiple standard consultations compared to a single longer consultation, and there is a lack of specific incentives to help overweight and obese patients achieve healthy weight [15, 16].
The Health eLiteracy for Prevention in General Practice (HeLP-GP) cluster randomised controlled trial was conducted 2017-2020. Twenty-two family practices in lower socioeconomic areas of Sydney, New South Wales (NSW) and Adelaide, South Australia (SA) consented to participate and 11 were allocated to the intervention and 11 to usual care. Patients of participating practices were flagged at presentation using Doctors Control Panel (DCP) and approached to consent. In total 215 participants were recruited to the study (120 to the intervention group and 95 to the control group). The main outcomes of this study have been reported in Parker et al. [ 17]. This nested study used qualitative interviews to elicit patient and provider perceptions and views regarding the receipt or delivery of weight management within the context of the HeLP-GP intervention. It aimed to deepen our understanding of the study implementation and outcomes, as well as to provide practical insights to guide future interventions of this type.
## Context
The HeLP-GP intervention supported overweight and obese participants by providing a FPN-led tailored health check based on the 5As model of patient-centred care: Ask, Advise, Assess, Assist, and Arrange [18], combined with a purpose-built lifestyle app (mysnapp) and/or referral to a free telephone coaching service (Get Healthy1) [19]. This combination intervention aimed to assist participants to improve their diet, increase their level of physical activity, and improve their general health.
Eligible participants were 40-74 years of age, overweight or obese (BMI ≥ 28), had their weight and blood pressure (BP) recorded within the previous 12 months, and had access to a smart phone or tablet device [19]. Participants had to be able to speak and read either English, Arabic, Chinese, or Vietnamese.
The HeLP-GP study (including the qualitative study) was approved by the University of New South Wales Human Research Ethics Committee (HC17474) and ratified by the University of Adelaide Human Research Ethics committee. All experiments were performed in accordance with relevant guidelines and regulations. This trial is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12617001508369, date registered $\frac{26}{10}$/2017). http://www.ANZCTR.org.au/ACTRN12617001508369.aspx
## Qualitative sample selection and recruitment
Invitations to participate were extended to four of eleven intervention practices (two from each participating state). FPs and FPNs were eligible for interview if they had been actively involved in delivering the HeLP-GP intervention and patients were approached only if they had attended the health check. Patients were invited to participate at the routine 6-month follow-up. The size of the practice (< 5 or ≥ 5 FPs) and the gender and age of patients were used to maximise our chances of recruiting a more diverse sample.
## Development of the interview guides
Three semi-structured interview guides were developed for each participant cohort (i.e., patients, FPs and FPNs). These guides were designed by a working group comprising trial investigators and research staff and were based on the findings of a preliminary study [20]. The interview guides were tailored for the participant group. Specifically, the wording of questions were refined and simplified based on feedback from the piloting phase. Some of the questions were broken into simpler questions to elicit a clearer response from the participant. The participant interviews sought specific feedback about each aspect of the intervention (i.e. mysnapp or Get Healthy telephone coaching) and provider interviews included questions to gauge their understanding and opinions of the underpinning strategies embedded in the intervention (the 5As and teach-back models). To frame the interview context, they all began with questions about preventive care to elicit participants’ understandings of the concept and general viewpoints about receiving or delivering preventive care. After piloting the interview guides on eight people, refinements were made based in this feedback.
The final conversation guides (see Appendixes A, B and C) followed a traditional structure characterised by a small number of open-ended questions with a series of specifically designed prompts. The rationale for conducting semi-structured interviews was that we considered the format to encourage interviewees to relax and open up, affording them a sense of primary control (as opposed to researcher control) over the issues and concerns given focus. Reporting in this study was guided by the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist [21] as seen in Appendix D.
## Data collection
Interviews occurred between 3 and 6 months post-intervention to ensure interviewees had time to engage fully in the interventions yet were sufficiently recent to assist recall. Consent for interview (including audio-recording) was obtained verbally at the time of interview. All interviews were conducted between July 2019 and April 2020. Patient interviews were conducted over the telephone. An interpreter was offered if the patient spoke primarily a language other than English. The average duration of patient interviews was 23 minutes (range 11-59 minutes). Although this average duration is relatively short for telephone-based interviews, it proved sufficient for data collection from patients given the highly focused aim of the research. No other methods to interview the patient participants was offered. All interviews were undertaken by two research staff to ensure consistency in the interview methods. Face-to-face interviews with FPs and FPNs were conducted at the practice by Authors CM or KP. The average duration of these interviews was 23 minutes (range 9-38 minutes).
## Data management and analysis
Interviews were recorded and transcribed verbatim, then imported to NVivo 12 Pro. We analysed the interview data using the thematic analysis method proposed by Braun and Clarke [22]. Initially, transcripts were coded by two independent researchers (Authors KP and SS) and reviewed with the aim to identify and discuss any coding discrepancies. A coding framework was created to define/describe each code. Throughout the analysis the codes were cross-checked and any interpretive differences (e.g. omissions or commissions of concepts/ themes) were resolved by agreement following a review by Authors KP and SP. An expert research working group (which met fortnightly) discussed the codes and emergent themes (Appendix E). The research working group consisted of Author ED-W, a health expert in mixed methods research in public health; Author SR, a health expert in qualitative research in primary health; Author DN, an expert in mixed methods research in social science and public health, and Author MH an academic general practitioner with expertise in primary health care and primary health care research.
## Results
The final sample for the provider interviews included 8 FPs and 4 FPNs (Table 1).Table 1Practice and provider characteristicsID No. Practice DetailsSize of practiceNo. of FPs interviewedAge range (years)FP gender (M/F)No. of FPNs interviewedFPN gender (M/F)Age range (years)17< 5 FPs21 = 65+ 1 = 55-64M, M1MUnknown18≥5 FPs145-55F1F20-3422≥5 FPs31 = unknown2 = 35-44M, M, M1F45-5425≥5 FPs21 = 44-641 = 65+M, M1F35-44 Twenty-five patients were interviewed. Participants in the qualitative study had broadly similar demographics to those in the intervention arm of the main study Tables 2 and 3. Most were born in Australia ($68\%$), and all chose to be interviewed in English. Within our sample roughly the same were male and female although in two practices we recruited no men for the qualitative interview. However, recruited numbers were lower in these two practices overall. Table 2Patient characteristicsPatients Interviewed for the Qualitative StudyPractice ID.No. of patients interviewed (%) at baselineNo. of females interviewed (%)No. of males interviewed (%)Mean age/age range of interviewees$\frac{1714}{35}$ [40]$\frac{6}{19}$ [31]$\frac{8}{16}$ [50]56 [48-68]$\frac{186}{22}$ [27]$\frac{1}{11}$ [9]$\frac{5}{10}$ [24]56 [46-72]$\frac{221}{7}$ [29]½ [50]$\frac{0}{5}$ [0]58 [48-68]$\frac{254}{11}$ [36]$\frac{4}{6}$ [100]068 [61-72]Total25 [34]12 [46]13 [52]58 [48-72]NSW Intervention20 [20]7 [7]13 [13]56 [46-72]SA Intervention6 [26]5 [20]0 [0]63 [48-72]Table 3Baseline characteristics (intervention group): Full cohort and qualitative cohortVariablesResponsesFull cohort (Intervention) n (%)Qualitative cohortn (%)No.12125Age, mean (SD)59.0 (8.8)57.8 (8.9)GenderFemale60 (49.6)13 (52.0)Male61 (50.4)12 (48.0)Place of birthAustralia66 (54.5)17 (68.0)Overseas55 (45.5)8 (32.0)Primary language at homeEnglish97 (80.2)23 (92.0)Other27 (19.8)2 (8.0)StateNSW100 (82.6)20 (80.0)SA20 (17.4)5 (20.0) We identified the following key themes from our data. Although we address these themes separately in the following section, substantial crossover and inter-relationships between themes were identified. Figure 1 shows the three key themes and identifies the inter-relationships with the subthemes. Fig. 1Three key themes and their inter-relationships with the five subthemes
## Long-term trusting and supportive relationships (being ‘in it for the long haul’)
This theme relates to the complexity of weight management and the gains made through interactions based on trust and empathy. Providers exhibited frustration and found engaging patients difficult, particularly with what they perceived to be entrenched and unmoving patterns of thinking and behaviour. The repetitiveness of trying to engage patients with weight management strategies was frequently perceived as requiring an investment they could not always offer, taking up time they did not often have, or as unfulfilling and a wasted effort:*Weight is* one of the most frustrating things you deal with. … Probably around two to 5 % (of patients) will lose weight over two years. They’ll lose it quickly. But then they put it back on again. ( FP, Male, 65+).
Likewise, patients expressed recurrent experiences when managing weight: … ….. I’m a Weight Watchers veteran, you know. I’ve done Weight Watchers on and off for years, and been on diets for years... (Patient, Female, 54).
Despite seeing weight management and prevention as part of their clinical role, providers felt overwhelmed by the extent of the task within their practice and the expectations of patients. This created a reluctance to explore weight issues with some patients, particularly in the context of prioritising the patient’s presenting condition:… … I would say more than $50\%$ of our patients have an overweight or obesity problem …... But of course, when you bring it up, people say, oh okay doctor, how am I going to lose weight? So, suddenly it’s my fault, my problem, and I’ve got to make them lose weight, and so I can see why some doctors don’t bring it up. And then half an hour later you’re going round and round in circles with somebody because they say they can’t. But it has to be brought up. If you’ve already dealt with five different issues in five different parts of the body and then you’re suddenly telling them how to lose weight, well … (FP, Male, 55-64).
There was a tendency for providers to express their interactions with patients using negative language rather than providing examples of ‘success’ and also to place the onus of weight management onto patients, or to focus blame on the patient’s lack of enthusiasm or inability to follow through or ‘stick to a program’:I find the ones that have the overweight issues, the story’s always the same: ‘I don’t want to move’, ‘It hurts’; and I realise I’m overweight but, um, it can’t hurt this bad, and things like that. All the excuses. ( FPN, Female, 35-44).
Similar to clinicians, patients’ attitudes and perspectives were moulded by past experiences and the feelings that manifest because of those experiences. Some expressed frustration with health professional they had perceived to treat them poorly, provide guidance lacking instruction, or not provide suitable tools to help them follow through:They do assessment and they say, do diet and exercise, and you relapse and don’t do it. They say to stay away from these foods, and if you don’t, you know, it’s useless even going to the FP. ( Patient, Male, 67).
Patients were more likely to be responsive to weight management advice when they perceived their provider to be trustworthy, non-judgemental, empathetic, and respectful. Within this study, this was true regardless of the provider (FPNs, FPs, health coaches etc.). When patients felt supported and comfortable in their interactions, they used words such as ‘openness’, ‘information sharing’, ‘honest’, ‘nurtured’, and ‘supported’. In addition, they were more inclined to engage in positive dialogue and weight management and lifestyle activities. This positive milieu was one in which the patient and provider could jointly plan or set goals and patients reflect on achievements/failures without fearing reprisal or judgement. Well, the guy (Get Healthy coach) had rung me weekly at first and then fortnightly. Now it’s about every third week, but honestly, I look forward to the calls because I can say to him, ‘Oh, I’ve lost this much’ or “I’ve lost weight around …’ you know, that sort of thing. But it just gave me someone to look forward to telling what I’ve done. It’s just really exciting when you’ve got the phone call and he’d be advising me on what I should do and what I could do and asking me questions about myself. ( Patient, Female, 63).
Patients who did not experience this safe and supportive relationship tended to express negative emotions. They described feeling alienated within the interaction (despite the content), misunderstood, and not listened to. These patients often discontinued using the program and exhibited reduced enthusiasm and engagement:My FP sent me to someone about going on a diet. I thought, ‘Yeah, I will go and see what it’s like, and yeah, I didn’t like it. He tried to tell me to pick and eat just one piece of fruit and that just turned me off. … So, he wasn’t listening to me (Patient, Female, 57).
Patients preferred open lines of communication and mutual respect. These conditions emerged as the cornerstones of constructive dialogue and empowered patients to make their own health decisions and to be responsible for their actions:I feel they’re (FP and FPN) … incredibly courteous in the way they deal with me. I guess they speak to me very openly and I understand what they’re saying. I really appreciate that I’m part of it. They have stuff that they know, but they involve me in that information [so] I can have some ownership of it I don’t like being told what to do. They don’t tell me what to do, they give me information and we talk about it. I love that. ( Patient, Female, 67).
Many patients described consistent and long-standing relationships with their FPs (sometimes decades). This strengthened their belief that the health information and healthcare they received were both accurate and appropriate and these patients were more likely to join the study because the FP had suggested it:He [FP] generally looks at what I go there for, but he also weighs me, and we talk, just in general, not always about what I go there for. … He was the one that put me forward for this study. ( Patient, Male, 53).
Flexibility and perseverance were other characteristics promoting good interactions between providers and patients. In addition, the ability to engage in innovative interactions produced positive outcomes. Sometimes frequent and multiple approaches were needed to engage patients appropriately and to identify different interventions to best fit with their lifestyles. A FPN and FP, respectively, described their experiences with patients:I think he was feeling like it was such a rigid thing. I said, no, we don’t have to do it in that timeframe, we can do it whenever. … I really had to think outside the box to find a way to make it work for him and so, that really challenged me actually. ( FPN, Female, 45-54).Depending on how sensitive they are. If they are then I back off, if not I can try to tweak the conversation a bit and ask more in terms of the more general stuff, like, what they feel about their eating, or what they think they could improve on, rather than direct questions. ( FP, Male, Unknown).
## Initiating conversations and understanding motivations
Open discussions of weight between patients and clinicians resulted in a minefield of mixed opinions, sentiments, and pre-conceived beliefs. Patients and clinicians alike identified barriers to having conversations about weight:Some patients don’t really like us to point out that they are obese or they’re overweight. They’re not quite comfortable talking about their diet or their exercise so we just try to tell them but some of them just don’t take the advice. ( FPN, Female, 45-54).
For many patients, it was not the conversation that was most important but the way in which the message was conveyed:It just depends on how it’s delivered to you, or how someone brings it up as a subject. I think that’s going to be the problem with a lot of people; you know, ‘are you saying I’m fat?’...So, it’s got to be delivered in such a way that I think the person needs to think that it’s of value to them, rather than you are being nagged to death. ( Patient, Female, 54).
For some patients, timing of the conversation was key. When preoccupied with other things such as illness they often did not want to think or talk about their weight. If the timing was right, however, the message was likely to be more impactful:Well, I found it was very useful that he mentioned it. It got me thinking [that] if I keep going, putting more weight, I won’t be able to move or walk. It’s true. I will need a walker if I keep going with my weight …. ( Patient, Female, 69).
Clinicians were more likely to initiate weight conversations, and approaches ranged from harsh and direct, introducing the topic sensitively, or being opportunistic. Clinicians frequently focused on risk assessment indicators (weight, calculating BMI, BP, or blood glucose level) to segue into discussions about lifestyle alterations. They tended to feel more comfortable if there was a physical imperative to intervene:I had a patient … in his early 40s where we do a health assessment and diabetes risk assessment............. So, I first did the risk assessment, and then said, ‘look you know, if you reduce your waistline from 112 to 107, and if you do regular exercise, your risk is reduced by $50\%$.’ Thankfully, he said straight away, ‘I know what needs to be changed, it’s just a bit of my eating habit.’ He has a sugar addiction; coke, soft drinks, and other things. So, he took it quite well (FP, Male, Unknown).
Clinicians were also cognisant of the negative impact that stigma and judgement may have in their interactions with patients over weight management. Where present, these sentiments can drive a wedge between otherwise collaborating partners. It was regarded as important that clinicians remain supportive, accept failure in their patients, and are prepared to persevere when things do not go as planned. As one FP so eloquently stated:No-one likes being told what to do. You don’t tell them; you just help them to make their decision and you be there on their side in their journey. If they have failed, you don’t criticise them, but actually help them to stand up again and keep on going. The moment you start criticising; you know, you should have stayed on it, or you shouldn’t have done that, that’s the end of it. They will stop coming back to you, or they will come back, but they will no longer talk about those things. Such a judgmental attitude, … we all are told and taught [to avoid it], but still you see it a lot, even in the health field, … which is a bit sad. ( FP, Male, Unknown).
Low patient motivation as a barrier to weight management is widely reported. A provider’s perception of patient motivation, however, may not always align to that of the patient. Finding this shared ground can be the difference between a negative and a positive interaction, and any subsequent impact of this on communication may be the difference between pessimism and collaboration. Exploring opportunities to find this balance is essential:What I have personally experienced is, if the patient is very much motivated, then referring makes a really big impact. For example, doing the care plans and referrals for dietician and physios is when the person comes with that motivation and mindset; ‘yes, I want to make the change’. This is different to where the person is not that motivated, but opportunistically you have to bring it up. You discuss [weight] and try to explain the importance of weight loss and positive lifestyle changes and tell them that you are eligible for the care plan and Medicare funded program to get input from a dietician and physiotherapy. They will say, ‘yeah, yeah, I’ll do it, because I don’t have to pay.’ They’ll buy that thinking. ( FP, Male, Unknown).
Clinicians who were inclined to perceive patients as ‘unmotivated’ and ‘not prepared to change’ were less likely to engage with, or encourage, patients in weight management strategies. Overall, providers expressed an imperative to discuss weight with their patients. They understood patients will vary in their responses, that some will be more open than others, and, at times, the message will fall on deaf ears. At other times, however, the message might strike a chord and lead to something more positive:It’s a mixed bag, yes. A lot of them [patients] are very keen and a lot of them need persuasion. Some are reluctant, and some say I know I’m overweight, you don’t need to tell me that or whatever, but most are very engaged and realise that it is very important. So, I’m happy to discuss that [weight] in the beginning. I might list some of the benefits from weight reduction and exercise, some things that they might not be aware of; for example, cancer risk reductions, things like that. I’m trying to get them more on board to try and sell the strategy to them. ( FP, Male, 65+).
Patients also acknowledged that being motivated to change their thinking or behaviour was necessary, and was a major factor in their attitude towards weight loss:It’s got to come from within if I want to lose weight. It’s got to be something I want to do. I mean, people telling you to lose weight, it’s like my wife telling me lose weight, and I don’t always listen to her. I’ll be honest. I mean, people can talk to you every day nonstop, but if you don’t want to listen, no offence, you know you’re not going to change. You’ve got to want to change. ( Patient, Male, 62).
Motivation is not static and sometimes small gains can be seen as large gains by patients. In turn, this can lead to further motivation:My biggest issue is my weight and when I started doing this is when I finished work. In your mind’s eye you always lose lots and lots of weight but of course it doesn’t happen like that. I may not have lost a lot of weight, but I haven’t put it on either. So, for me, that’s a positive. ( Patient, Female, 62).
Some patients found motivation from participating in programs which emphasised consistency and monitoring. For one patient, anticipation of a call from the health coach provided considerable impetus towards meeting objectives and being ‘true to self’:I sort of fell off the wagon a little bit. She called me on Wednesday, I can’t lie to her, you know. So, I’ve got to work harder in trying to lose weight and trying to do everything right before I speak to her so I’m not lying. It’s nothing really big, but it’s still a motivator. ( Patient, Male, 53).
The patient negativity that providers experience may be compounded because they do not have the skills to adapt their practices to elevate and/or engage the patient. In essence, the negativity is often reciprocal leading to minimal positive results around weight management. One FPN described working with a patient to identify solutions and how this could increase confidence and lead to enhanced patient motivation:So, for a lot of people there’s, ‘I can’t do it because … ’, so, they didn’t have the skills to work around that or have as many options perhaps as I might. So you say, ‘if you did this then and you could actually do it at night or with a friend or … have an exercise physiologist to tie it altogether. Or I’ve found that they just actually needed some physio first. Okay, so confidence is the issue, so let’s get that worked out and then we can do the rest … …... some people just needed that little push to say, ‘yes, I’ve been talking about this and thinking about this for ages. Now that you’ve talked about it, we’ll do it.’ ( FPN, Female, 45-54).
## Ensuring access to multi-modal weight management options that acknowledge differing levels of health literacy
Within this trial patients could take up two additional programs. The lifestyle app allowed them to set diet and lifestyle goals and then monitor their progress against these goals, whereas the telephone coaching option provided up to 10 coaching calls with a trained coach. There was, as expected, mixed feedback about these programs, with perhaps a slight preference for the coaching over the mobile app-based alternative.
Some simply did not engage with the app or found it too difficult to use. Others found it impersonal, or perceived it as not being interactive enough:Well, I basically thought it (mysnapp) was a waste of time. It’s just progress reporting and what you’ve actually done for the week. It’s a report card. ( Patient, Male, 72).
Conversely, some patients liked the app because it provided a constructive way to keep track of their diet and exercise, along with their progress around these goals:It [app] was just a good way of keeping track and make sure how many times a week you just done your bit of exercise. Make sure you’re keeping track of having your proper meals and how many serves of fruit or stuff like that you’re having. ( Patient, Male, 53).
Those who indicated a preference for the coaching service saw it as an opportunity to access regular contact and support:The phone calls meant more to me than the app because it was somebody actually encouraging you and listening to your story. ( Patient, Female, 63).
Whereas for others, the telephone coaching was too impersonal:I spent my working life on phones and emails, and I really just find them a bit – it overwhelms me. I hate email. … I’m retired and I only work part-time. I try and keep my life very, very simple. I like face-to-face. ( Patient, Female, 67).
Some providers preferred to refer patients rather than try to provide in-house management. This was an acknowledgement that they did not possess the skill set required to comprehensively manage the problem or, in the case of referral (particularly to a dietician or a sports physiologist), it was regarded as giving the patient access to the expertise they needed and would most benefit them.
Medication and bariatric surgery as potential and viable treatment options for weight management were raised by clinicians and patients alike. FPs were more inclined to report that their patients deferred to these options in lieu of trying weight loss programs or lifestyle changes. They were also cautious in case their patients became too focused on a ‘quick fix’ without thinking through the consequences:I’ve been having to advise my patients who are having bariatric surgery that this is only an adjunct. I say, ‘if you think that that will lose your weight automatically, it won’t.’ I think they’re all now hitting the gastric bypass which seems to be the most effective. But I might have seen two or three who, even with bariatric surgery, have kept their weight down, but over two or three years they haven’t. I’m happy to send people off [for surgery]. Let’s face it, when you’ve got a BMI of 45, you’re not going lose weight and so I think the surgery is reasonable. But I’m now really vetting them very hard. ( FP, Male, 65+).
It was evident from our data that, individually, patients developed firm preferences regarding approaches to weight management. They may have a general preference for exercise over diet, or develop preferences based on their social circumstances or previous experiences (both positive and negative). This preference might also relate to ‘personality’ and the degree to which the patient is ‘open’ to trying something new. Therefore, an intervention or program that is acceptable to, or which works for, one patient will not necessarily be acceptable to, or work for, another.
Our results also indicate that clinician awareness of each patient’s baseline understanding of their health is important, as is their understanding of each patient’s preferences for education, information, and instruction. In turn, some providers enjoyed assisting patients to improve their health literacy and considered it clearly within their remit:It’s amazing how people are really illiterate about their health. Often, we take it for granted because, the thing is, it’s in our head. Because it’s in our head, you think, you know, it’s in the other person’s head as well, but there is a big bridge to cross. That’s why I love general practice, you know, because we get these opportunities to know the patient dynamics and how they think and how to transfer that information across. Because patients often don’t say, ‘I don’t know doctor.’ ( FP, Male, 35-44).
Many providers, however, found low levels of health literacy among patients to be challenging and too time intensive: I guess you have got to try to work in with general patients, try and talk to them at their level. Sometimes it’s hard to work out, you know, what someone’s level of literacy is. ( FP, Male, 55-64).I wouldn’t be able to go in-depth in terms of health literacy or consultation because we have limited time and we have to cover a whole lot of things, as in terms of health literacy, I don’t do a whole load of … information. … We didn’t spend that much time on it. ( FPN, Male, Unknown).
Patients with a more sophisticated understanding of their circumstances and health status did not appreciate being handed simple information on weight management, particularly if it was generic and did not resonate with them. They wanted new, innovative, and useable information that increased their knowledge of weight management strategies. In addition, many patients expressed a desire to improve their knowledge as they equated this with greater empowerment to take charge of their own care:I’m an avid reader anyway because he (FP) would always say things like, “Do you want the long version or the short version” to me because he knew I would read it. And so I look things up myself as well. ( Patient, Female, 71).It’s always good to gain new information and knowledge. … There might be a bit of curiosity to spend time … to get back into my fitness and that type of stuff. So, I think that was the main reasons to see if there were any new kits, tools, or strategies, or things like that that I should be focussing on that are out there. ( Patient, Male, 49).
## Discussion
This qualitative study indicates that delivering positive weight management in family practice with patients who are overweight or obese is challenged by the complexity of the task and the perceived motivation of patients. It is however facilitated by positive open communication and programs tailored to patient needs, preferences, and health literacy. The content of our themes firmly align with the findings of many other studies on obesity that identify the importance of positive patient-provider communication/relationships [23–25], recognise the impact of motivation in weight management and weight loss treatment [23, 26], acknowledge the importance of recognising individual health literacy levels and pitching education at that level [27, 28], and value tailoring programs to the patient’s particular needs [29].
Our study shows that clinicians can be very influential, in the context of a sustained, open, trusting, and therapeutic relationship. Primary care providers, whether FPs or FPNs, need enthusiasm, dedication, and to spend sufficient time with patients to understand their underlying concerns and tap into personal motivators effectively. This was reiterated throughout our study, where long-standing and person-centred relationships (interpersonal continuity) fostered an environment where weight could be better addressed in the context of preventive care [30, 31]. We know that patients often seek out and trust the advice they receive from their primary care providers [32, 33]. Patients in our study were more likely to be responsive to weight management advice if they perceived their provider as trustworthy, non-judgemental, empathetic, and respectful. Moreover, they reported being more inclined to engage in positive dialogue and attempt weight management and lifestyle behaviour change activities when interacting with a provider with whom they did not fear reprisal or judgment.
Concepts such as ‘patient noncompliance’ and low motivation often focus on patient failure, and the association between patient motivation and behaviour change is widely reported [34–36]. This was also evident within our study. FPs and FPNs often perceived their patients as lacking in insight, unmotivated, or unwilling. They often described them as presenting entrenched patterns of thinking and behaviour that were resistant to intervention. Conversely, some patients described being sensitive to feeling judged and stigmatised due to their weight. Understanding that patient readiness to change may alter over time, appreciating they may be influenced by past failures, and helping patients anticipate relapse can often improve patient satisfaction and lower clinician frustration during this process [37, 38]. Our study indicated that unless programs are perceived as relevant and valuable, patients are unlikely to try them or stay engaged with them. Moreover, personal circumstances (e.g. lack of time, financial stress, etc.) and other psychosocial factors will impact patient motivation and willingness and if not adequately addressed or accounted for, the patient is less likely to exhibit readiness to change. Adequately addressing psychosocial issues within this setting is challenging. Many people experience eating disorders, self-sabotaging behaviours, and poor body image. Equally many may suffer occupational and family stress, medical disorders and depression and/or anxiety, all of which can impede an individual from reaching and/or sustaining their weight loss goals. Currently FPs are encouraged to utilise multidisciplinary services such as exercise physiology, psychology, dietitians, and health coaches and consider therapeutic and surgical options after fully assessing patients [39].
Communication and clinical relationships that support continued collaboration between the patient and provider are valuable in supporting weight management [40]. However, they require personalised interventions to be successful [41]. As El Ghoch and Fakhoury [42] have commented, patients who are overweight or obese “know what to do, but also need to know how”. Many patients expressed that they found clear communication and a supportive clinical relationship with their health providers allowed them to ask more tailored questions on how they could manage their weight.
Identifying each patient’s level of understanding of their weight and health needs should inform the types of patient education materials, aids (e.g. apps) or health coaching being offered. Tailoring materials and programs to the patient’s readiness, circumstances, and health literacy may also help to increase patient motivation for behaviour change [20, 43]. Within this study, having informative and relevant instructions and education materials was perceived by providers as integral to guiding patients to follow through with advice, as was the availability of options and pathways for the patient and provider. Our results suggest that to achieve better results, patients need: a) individualised, achievable programs, b) programs that are assessed regularly and adapted according to the patient’s needs, and c) support from providers whom they respect and can impart the correct information effectively, manage their expectations and behaviours, and help them to stay motivated to change.
## Implications for family practice
While the views of patients and providers canvassed in this study further support the role of family practitioners in weight management and long-term continuity of care, they also highlight the significant challenges inherent in this endeavour. Family practice is an appropriate setting to address weight management over the long term, yet FPs/ FPNs may not be the only people to deliver weight loss interventions. Patients may access weight loss interventions through organisations such as Weight Watchers, formalised weight loss programs (e.g. Get Healthy), and via the Allied Health sector such as pharmacists and dieticians. Positioning family practice, and FPs specifically, as wholly responsible for guiding and managing patient weight may therefore be misplaced. It is reasonable to view family practice as the ‘starting point’ for weight management, but with recognition that additional supports in the form of referral, adjunctive medication, and surgery are viable options for some patients. Indeed, referral is a strategy used and preferred by many FPs [44, 45]. The family practice setting supports the development of longer-term therapeutic relationships with patients, which is further facilitated by good-quality communication between general practices and referral services. One alternative model is ‘shared care’ in which GPs and specialist services contemporaneously share in the care of patients supported by an understanding of each other’s roles, and effective mechanisms for communication and information sharing. Within our study, referring a patient on was a viable option for FPs who didn’t feel they had the depth of knowledge, skill or time to provide optimal treatment and advice to their patients. There is however a lack of clear referral pathways or management options for patients who are willing to engage in weight management [46], and inequity in access to coordinated surgical and specialist care [10]. As such, FPs and FPNs have a personal and continuing role in maintaining continuity of care even after referral. As a result, patients need to view the care provided by the referral services and the FP as consistent and reinforcing. A respectful and trusting provider-patient relationships underpins effective weight management. These relationships are facilitated by providers with the communication skills to initiate and guide ‘difficult conversations’ about weight and weight management with their patients, and the ability to build rapport with patients and to provide appropriate support. Although patients’ expectations vary, positive lifestyle messages skilfully delivered at the right time and with the right sentiments can have a notable impact on patient motivation. In turn, this can lead to enhanced patient engagement in behaviour change and/or acceptance of a referral to another provider or service (e.g. a dietician, health coach, or exercise physiologist).
A primary challenge for FPs and FPNs in implementing weight management programs, including patient education on the health risks of their lifestyle behaviours, is the need to manage time constraints and balance competing demands [33]. Put simply, if family practices are not funded adequately, they do not have the time to provide effective education and support. Family practices that support and nurture patients provide them with the capability and opportunity to achieve a healthy weight [47]. FPs and FPNs need to stay involved in their patient’s weight management journey, even though this is often difficult due to time, capacity, and/or funding constraints. Even if patients do not accept help at first, family practices need to prioritise weight management, demonstrate empathy towards the patient’s situation, attempt to impart the correct information, and keep checking in with their patients to assess whether their acceptance for, or readiness to change has shifted. These practices should all be performed while reiterating the need for the patient to continue to attend the family practice.
## Study strengths and limitations
The findings reported here should be considered within the strengths and limitations of the study. Notably, these findings emerged in the context of a quantitative randomised controlled trial where participants in the intervention group ‘selected themselves’ for participation in the qualitative phase. Although our patient sample was broadly representative of the intervention group, all were interviewed in English and two thirds were born in Australia, so they are not necessarily representative of the general population. All interviews were conducted by telephone and were reasonably short (average 23 minutes), however these were highly focused and were offered via telephone due to the geographic dispersion of the participants. Performing the patient interviews face-to-face or via a video and audio-based communication software may have provided more non-verbal communication data which was not available through phone interviews. It is possible that patients who agreed to be in the qualitative study were somewhat more engaged or had undergone a better experience with the intervention. Our sample also was drawn from four urban/urban fringe practices in Adelaide and Sydney and therefore may not be generalisable to all general practices. These factors must be considered when interpreting the implications of the findings for practice and, ultimately, the generalisability of any practice recommendations. We received various responses from patients, both positive and negative, which indicates that our sample included patients across the weight loss spectrum.
It is generally accepted that qualitative research permits the utilisation of relatively smaller samples [48]. Our sample of FPs and FPNs was drawn from two states but only from four family practices. Because we approached practices that had been more successful in recruiting patients, they potentially represent higher performing intervention practices generally. We should not assume that the views of these providers are therefore indicative of the views of all FPs and FPNs. We do note, however, that the themes identified from the provider data align well with the findings of other qualitative studies which included interviews with providers for their views and perceptions related to weight management in family practice.
## Conclusion
Providing weight management within the environment of family practice is complex. Targeting obesity with individuals requires commitment and a good therapeutic relationship, and an acceptance that in some cases only small gains will be achieved. Ongoing support of family practice is required through funding and policy changes if they are to provide comprehensive weight management to patients that is both timely and effective. The programs that are more likely to work; namely, those that seek to engage patients and target motivations should be highly individualised and tailored, relevant to each patient, regularly monitored by health providers, and delivered within an environment that fosters mutual respect and trust. When targeting behaviour change, family practitioners must therefore ensure that all communications with patients are tailored to the level of health literacy of the patient, as well as consistent and positive in their messaging. This will help to engage the patient and foster supportive relationships while acknowledging obesity as a chronic relapsing condition with dynamic influences.
## Supplementary Information
Additional file 1.
## References
1. 1.
Obesity and overweight [https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight#:~:text=In%202016%2C%20more%20than%201.9%20billion%20adults%20aged%2018%20years,women)%20were%20obese%20in%202016.]
2. 2.
Overweight & obesity: Overview. [https://www.aihw.gov.au/reports-data/behaviours-risk-factors/overweight-obesity/overview.]
3. Kristen G, Opie Cynthia a. **Wright Julian: preferred language regarding overweight and obesity in general practice: a survey of predominantly rural Australian adults**. *Aust J Primary Health* (2018.0) **24** 391-397. DOI: 10.1071/PY18023
4. Buffart LM, Allman-Farinelli M, King LA, van der Ploeg HP, Smith BJ, Kurko J, Bauman AE. **Are general practitioners ready and willing to tackle obesity management?**. *Obes Res Clin Pract* (2008.0) **2** 189-194. DOI: 10.1016/j.orcp.2008.04.008
5. Malatzky C, Glenister K. **Talking about overweight and obesity in rural Australian general practice**. *Health Social Care Comm* (2019.0) **27** 599-608. DOI: 10.1111/hsc.12672
6. Mazza D, McCarthy E, Carey M, Turner L, Harris M. **“90% of the time, it’s not just weight”: general practitioner and practice staff perspectives regarding the barriers and enablers to obesity guideline implementation**. *Obes Res Clin Pract* (2019.0) **13** 398-403. DOI: 10.1016/j.orcp.2019.04.001
7. Turner LR, Harris MF, Mazza D. **Obesity management in general practice: does current practice match guideline recommendations?**. *Med J Aust* (2015.0) **202** 370-372. DOI: 10.5694/mja14.00998
8. Verberne L, Nielen MMJ, Leemrijse CJ, Verheij RA, Friele R. **Recording of weight in electronic health records: an observational study in general practice**. *BMC Fam Pract* (2018.0) **19** 174. DOI: 10.1186/s12875-018-0863-x
9. Critchlow N, Rosenberg G, Rumgay H, Petty R. **J V: weight assessment and the provision of weight management advice in primary care: a cross-sectional survey of self-reported practice among general practitioners and practice nurses in the United Kingdom**. *BMC Fam Pract* (2020.0) **21** 111. DOI: 10.1186/s12875-020-01184-z
10. Aly A, Talbot ML, Brown WA. **Bariatric surgery: a call for greater access to coordinated surgical and specialist care in the public health system**. *Med J Aust* (2022.0) **217** 228-231. DOI: 10.5694/mja2.51673
11. Proietto J. **Why is treating obesity so difficult? Justification for the role of bariatric surgery**. *Med J Aust* (2011.0) **195** 144-146. DOI: 10.5694/j.1326-5377.2011.tb03242.x
12. Sarwer DB, Polonsky HM. **The psychosocial burden of obesity**. *Endocrinol Metab Clin N Am* (2016.0) **45** 677-688. DOI: 10.1016/j.ecl.2016.04.016
13. 13.Forgione N, Deed G, Kilov G, Rigas G. Managing obesity in primary care: breaking down the barriers. Adv Ther. 2018;35.
14. Ruelaz AR, Diefenbach P, Simon B, Lanto A, Arterburn D, Shekelle PG. **Perceived barriers to weight management in primary care - perspectives of patients and providers**. *J Gen Intern Med* (2007.0) **22** 518-522. DOI: 10.1007/s11606-007-0125-4
15. Ananthakumar T, Jones NR, Hinton L, Aveyard P. **Clinical encounters about obesity: systematic review of patients' perspectives**. *Clinical Obesity* (2020.0) **10** n/a. DOI: 10.1111/cob.12347
16. Faruqi N, Spooner C, Joshi C, Lloyd J, Dennis S, Stocks N, Taggart J, Harris MF. **Primary health care-level interventions targeting health literacy and their effect on weight loss: a systematic review**. *BMC Obesity* (2015.0) **2** 6-6. DOI: 10.1186/s40608-015-0035-7
17. Parker SM, Barr M, Stocks N, Denney-Wilson E, Zwar N, Karnon J, Kabir A, Nutbeam D, Roseleur J, Liaw S-T. **Preventing chronic disease in overweight and obese patients with low health literacy using eHealth and teamwork in primary healthcare (HeLP-GP): a cluster randomised controlled trial**. *BMJ Open* (2022.0) **12** e060393. DOI: 10.1136/bmjopen-2021-060393
18. Glasgow RE, Emont S, Miller DC. **Assessing delivery of the five ‘as’ for patient-centered counseling**. *Health Promot Int* (2006.0) **21** 245-255. DOI: 10.1093/heapro/dal017
19. Parker SM, Stocks N, Nutbeam D, Thomas L, Denney-Wilson E, Zwar N, Karnon J, Lloyd J, Noakes M, Liaw S-T. **Preventing chronic disease in patients with low health literacy using eHealth and teamwork in primary healthcare: protocol for a cluster randomised controlled trial**. *BMJ Open* (2018.0) **8** e023239. DOI: 10.1136/bmjopen-2018-023239
20. Faruqi N, Stocks N, Spooner C, el Haddad N, Harris MF. **Research protocol: management of obesity in patients with low health literacy in primary health care**. *BMC obesity* (2015.0) **2** 5-5. DOI: 10.1186/s40608-015-0036-6
21. Tong A, Sainsbury P, Craig J. **Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups**. *Int J Qual Health Care* (2007.0) **19** 349-357. DOI: 10.1093/intqhc/mzm042
22. Braun V, Clarke V. **Using thematic analysis in psychology**. *Qual Res Psychol* (2006.0) **2** 77-101. DOI: 10.1191/1478088706qp063oa
23. Walker RE, Kusch J, Fink JT, Nelson DA, Morris G, Skalla J, Cisler RA. **Facilitating factors and barriers to weight Management in Women: physician perspectives**. *J Patient-Centered Res Rev* (2018.0) **5** 18-27. DOI: 10.17294/2330-0698.1495
24. Albury C, Strain WD, Brocq SL, Logue J, Lloyd C, Tahrani A. **The importance of language in engagement between health-care professionals and people living with obesity: a joint consensus statement**. *Lancet Diab Endocrinol* (2020.0) **8** 447-455. DOI: 10.1016/S2213-8587(20)30102-9
25. Glenister KM, Malatzky CAR, Wright J. **Barriers to effective conversations regarding overweight and obesity in regional Victoria**. *Aust Fam Physician* (2017.0) **46** 769-773. PMID: 29036779
26. LaRose JG, Guthrie KM, Lanoye A, Tate DF, Robichaud E, Caccavale LJ, Wing RR. **A mixed methods approach to improving recruitment and engagement of emerging adults in behavioural weight loss programs**. *Obes Sci Pract* (2016.0) **2** 341-354. DOI: 10.1002/osp4.71
27. James DCS, Harville C, Efunbumi O, Martin MY. **Health literacy issues surrounding weight management among African American women: a mixed methods study**. *J Hum Nutr Diet* (2015.0) **28** 41-49. DOI: 10.1111/jhn.12239
28. Joshi C, Jayasinghe UW, Parker S, Mar CD, Russell G, Lloyd J, Mazza D, Denney-Wilson E, Van Driel M, Taylor R. **Does health literacy affect patients' receipt of preventative primary care? A multilevel analysis**. *BMC Fam Pract* (2014.0) **15** 171. DOI: 10.1186/s12875-014-0171-z
29. Enwald HPK. **Huotari M-LA: preventing the obesity epidemic by second generation tailored health communication: an interdisciplinary review**. *J Med Internet Res* (2010.0) **12** e24. DOI: 10.2196/jmir.1409
30. Saultz JW, Lochner J. **Interpersonal continuity of care and care outcomes: a critical review**. *Ann Fam Med* (2005.0) **3** 159-166. DOI: 10.1370/afm.285
31. Sturgiss EA, Elmitt N, Haelser E, Van Weel C, Douglas KA. **Role of the family doctor in the management of adults with obesity: a scoping review**. *BMJ Open* (2018.0) **8** e019367. DOI: 10.1136/bmjopen-2017-019367
32. Jarlenski MP, Gudzune KA, Bennett WL, Cooper LA, Bleich SN. **Insurance coverage for weight loss: overweight adults' views**. *Am J Prev Med* (2013.0) **44** 453-458. DOI: 10.1016/j.amepre.2013.01.021
33. Soleymani T, Daniel S, Garvey WT. **Weight maintenance: challenges, tools and strategies for primary care physicians**. *Obes Rev* (2016.0) **17** 81-93. DOI: 10.1111/obr.12322
34. Lundahl B, Moleni T, Burke BL, Butters R, Tollefson D, Butler C, Rollnick S. **Motivational interviewing in medical care settings: a systematic review and meta-analysis of randomized controlled trials**. *Patient Educ Couns* (2013.0) **93** 157-168. DOI: 10.1016/j.pec.2013.07.012
35. Ward SH, Gray AM, Paranjape A. **African Americans’ perceptions of physician attempts to address obesity in the primary care setting**. *J Gen Intern Med* (2009.0) **24** 579-584. DOI: 10.1007/s11606-009-0922-z
36. Zimmerman GL, Olsen CG, Bosworth MF. **A 'Stages of Change' approach to helping patients change behavior**. *Am Fam Physician* (2000.0) **61** 1409-1416. PMID: 10735346
37. Asselin J, Osunlana AM, Ogunleye AA, Sharma AM, Campbell-Scherer D. **Missing an opportunity: the embedded nature of weight management in primary care**. *Clin Obes* (2015.0) **5** 325-332. DOI: 10.1111/cob.12115
38. Burgess E, Hassmén P, Pumpa KL. **Determinants of adherence to lifestyle intervention in adults with obesity: a systematic review: adherence to lifestyle intervention in obesity**. *Clin Obes* (2017.0) **7** 123-135. DOI: 10.1111/cob.12183
39. Markovic TP, Proietto J, Dixon JB, Rigas G, Deed G, Hamdorf JM, Bessell E, Kizirian N, Andrikopoulos S, Colagiuri S. **The Australian obesity management algorithm: a simple tool to guide the management of obesity in primary care**. *Obes Res Clin Pract* (2022.0) **16** 353-363. DOI: 10.1016/j.orcp.2022.08.003
40. Asselin J, Osunlana AM, Ogunleye AA, Sharma AM, Campbell-Scherer D. **Challenges in interdisciplinary weight management in primary care: lessons learned from the 5As team study**. *Clin Obes* (2016.0) **6** 124-132. DOI: 10.1111/cob.12133
41. Kreidieh D, Itani L, El Kassas G, El Masri D, Calugi S, Dalle Grave R, El Ghoch M. **Long-term lifestyle-modification programs for overweight and obesity Management in the Arab States: systematic review and Meta-analysis**. *Curr Diabetes Rev* (2018.0) **14** 550-558. DOI: 10.2174/1573399813666170619085756
42. El Ghoch M, Fakhoury R. **Challenges and new directions in obesity management:: Lifestyle modification programmes, pharmacotherapy and bariatric surgery**. *J Popul Ther Clin Pharmacol* (2019.0) **26** e1-e4. DOI: 10.15586/jptcp.v26i2.599
43. Pietrabissa G, Manzoni GM, Corti S, Vegliante N, Molinari E, Castelnuovo G. **Addressing motivation in globesity treatment: a new challenge for clinical psychology**. *Front Psychol* (2012.0) **3** 317. DOI: 10.3389/fpsyg.2012.00317
44. Booth HP, Prevost AT, Gulliford MC. **Access to weight reduction interventions for overweight and obese patients in UK primary care: population-based cohort study**. *BMJ Open* (2015.0) **5** e006642. DOI: 10.1136/bmjopen-2014-006642
45. Edshteyn I, Uduhiri KA, Rhodes KL, Sherin KM. **Practice policy statement: integrating effective weight management into practice**. *Am J Prev Med* (2016.0) **51** 542-548. DOI: 10.1016/j.amepre.2016.05.010
46. Iwanowski K. *Weight Management in General Practice –phase 1 report. Healthy Weight GP Project* (2020.0)
47. Stubbs RJ, Lavin JH. **The challenges of implementing behaviour changes that lead to sustained weight management**. *Nutr Bull* (2013.0) **38** 5-22. DOI: 10.1111/nbu.12002
48. Benton T. *Philosophical foundations of the three sociologies (RLE social Theory)* (2014.0)
|
---
title: Lactoferrin suppresses the progression of colon cancer under hyperglycemia
by targeting WTAP/m6A/NT5DC3/HKDC1 axis
authors:
- Huiying Li
- Chaonan Li
- Boyang Zhang
- Hongpeng Jiang
journal: Journal of Translational Medicine
year: 2023
pmcid: PMC9972781
doi: 10.1186/s12967-023-03983-1
license: CC BY 4.0
---
# Lactoferrin suppresses the progression of colon cancer under hyperglycemia by targeting WTAP/m6A/NT5DC3/HKDC1 axis
## Abstract
### Background
Although the relationship between type 2 diabetes (T2D) and the increased risk of colorectal carcinogenesis is widely defined in clinical studies, the therapeutic methods and molecular mechanism of T2D-induced colon cancer and how does hyperglycemia affect the progression is still unknown. Here, we studied the function of lactoferrin (LF) in suppressing the progression of colon cancer in T2D mice, and uncovered the related molecular mechanisms in DNA 5mC and RNA m6A levels.
### Methods
We examined the effects of LF ($50\%$ iron saturation) on the migration and invasion of colon tumor cells under high concentration of glucose. Then, transcriptomics and DNA methylation profilings of colon tumor cells was co-analyzed to screen out the special gene (NT5DC3), and the expression level of NT5DC3 in 75 clinical blood samples was detected by q-PCR and western blot, to investigate whether NT5DC3 was a biomarker to distinguish T2D patients and T2D-induced colon cancer patients from healthy volunteers. Futhermore, in T2D mouse with xenografted colon tumor models, the inhibitory effects of LF and NT5DC3 protein on colon tumors were investigated. In addition, epigenetic alterations were measured to examine the 5mC/m6A modification sites of NT5DC3 regulated by LF. Utilizing siRNA fragments of eight m6A-related genes, the special gene (WTAP) regulating m6A of NT5DC was proved, and the effect of LF on WTAP/NT5DC3/HKDC1 axis was finally evaluated.
### Results
A special gene NT5DC3 was screened out through co-analysis of transcriptomics and DNA methylation profiling, and HKDC1 might be a downstream sensor of NT5DC3. Mechanistically, LF-dependent cellular DNA 5mC and RNA m6A profiling remodeling transcriptionally regulate NT5DC3 expression. WTAP plays a key role in regulating NT5DC3 m6A modification and subsequently controls NT5DC3 downstream target HKDC1 expression. Moreover, co-treatment of lactoferrin and NT5DC3 protein restrains the growth of colon tumors by altering the aberrant epigenetic markers. Strikingly, clinical blood samples analysis demonstrates NT5DC3 protein expression is required to direct the distinction of T2D or T2D-induced colon cancer with healthy humans.
### Conclusions
Together, this study reveals that lactoferrin acts as a major factor to repress the progression of colon cancer under hyperglycemia, thus, significantly expanding the landscape of natural dietary mediated tumor suppression.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12967-023-03983-1.
## Introduction
Epidemiological and clinical evidence suggests that type 2 diabetes (T2D) is linked to an increased risk of cancer, especially colon cancer [1–4]. Several meta-analyses reveal that the patients with T2D have up to $40\%$ higher risks of suffering bladder, breast, colorectal, and kidney cancers than the patients without T2D [1–4]. Interestingly, research is shedding light on the potential biological links between these two diseases, with some studies suggesting that hyperglycemia might be one of the possible reasons directly linking diabetes and cancer [5]. Hyperglycemic conditions directly reprogram the epigenome, in which DNA hydroxymethylation and protein phosphorylation are mediated as a key switch and regulatory pathway that connects diabetes to cancer [6]. DNA methylation and hydroxymethylation are critical epigenetic processes involved in regulating the expression of tumor suppressor genes, as well as other cancer-related genes that are frequently perturbed in tumor cells [7]. In addition to cancer, alterations in the DNA methylation profile also have been identified in T2D patients [8]. Furthermore, as the most abundant and reversible modification happens at RNA, N6-methyladenosine (m6A) emerges as a new type of epigenetic regulation in recent years. A number of studies have shown that aberrancies in m6A are associated with both cancer and diabetes, and in several diabetic patients, the mRNA level of METTL3 is proved to increase when compared with the healthy volunteers [9]. The integration of DNA/RNA methylation data has further elucidated the pathways of cancer signaling networks [10–12]. A wholistic insight that highlights and prioritizes DNA 5mC/RNA m6A is required to further unravel the biological complexity of the relationship between T2D and colon cancer.
The coexistence of diabetes and cancer has been found to increase individual mortality, yet some cancer therapeutics can result in transient or permanent damage [13, 14]. The targeting therapies of hormone-based drugs (such as glucocorticoids), tyrosine kinase inhibitors (nilotinib and pazopanib), and mTOR inhibitors (everolimus and temsirolimus), for instance, have been connected to the development of high rates of hyperglycemia. However, antidiabetic therapies, such as metformin, also show implications as targets for the treatment of cancer [15]. Hence, additional studies should be performed to optimize cancer-targeting therapy strategies in patients with diabetes. There is currently increasing interest in the use of natural compounds (diet therapy) to address this issue. One prospective cohort study, based on 136,384 individuals in 21 countries on five continents, reports the association of dairy consumption with a lower risk of mortality [16], and an increased consumption of dairy products has also been linked to a significant reduction in colon cancer, and the relative risk of T2D is reportedly nearly $10\%$ lower in people with a high milk intake [17]. Thus, whether special bioactive components in dairy products play roles in the progression of T2D-colon cancer, deserves our further research.
Lactoferrin (LF), one of the active biological factors in milk, has recently been considered as a nutraceutical protein [18]. Due to the two Fe3 + binding sites in its structure, lactoferrin can be divided into the apo-type (without iron atom), a single-type (binding with 1 iron atom), and the holo-type (binding with 2 iron atoms), which have been proved to be tightly related to its bioactivities including anti-inflammation, anti-oxidation, anti-virus and anti-tumor [19–21]. LF is shown to improve the insulin-signaling response, used as an unexpected application in a potential treatment for diabetes [22]. Studies using both in vitro and in vivo models have verified that LF has beneficial effects in cancer treatment, therein suggesting its anti-tumor properties [23]. Recently, the antitumor activities of LF were proved in a human colon tumor model and HT29 tumor-bearing nude mice via the inhibition of tumor angiogenesis and metastasis signal pathways [20]. However, there were few studies that proved lactoferrin with $50\%$ iron saturation suppressed the progression of colon cancer under a high concentration of glucose. Here, we demonstrate the potential advantage of LF (with $50\%$ iron saturation) in the suppression of colon cancer in T2D mice. This study reveals the underlying mechanism by which LF-dependent DNA 5mC and RNA m6A remodeling in epigenetically regulating the NT5DC3/HKDC1 axis, and subsequently inhibiting colon cancer progression under hyperglycemia.
## Development of colon cancer in T2D mice model
To set up the in vivo colon tumor model in T2D mice, we conducted a xenograft model screen by using cancer cells derived from different organs, including gastric cancer cell line MGC803, colon cancer cell lines HT29, HCT116, and SW620. The tumor formation time and tumor weight in both T2D and non-diabetic BALB/c nude mice were determined. Though the diabetic mice were found to be at a comparatively higher risk of developing tumors, there were significant differences in the tumor formation: the tumor formation time for HT29 cells (17.2 ± 1.4 d in normal mice and 11.4 ± 1.5 d in diabetic mice) and HCT116 cells (18.4 ± 1.7 d in normal mice and 13.8 ± 1.8 d in diabetic mice) was significantly shorter than other cell lines (MGC803: 20.6 ± 2.1 d in normal mice and 16.8 ± 2.3 d in diabetic mice; SW620: 19.0 ± 2.3 d in normal mice and 14.4 ± 0.7 d in diabetic mice. As shown in Fig. 1A, B and Additional file 1: Fig. S1A, tumors formed by the colon cancer cell lines (especially HT29 cells and HCT116 cells) in the diabetic mice were notably different from those in the non-diabetic mice ($P \leq 0.05$), indicating that diabetes is more likely to induce colon tumors in vivo. Therefore, the HT29 cells were used for the following in vitro and in vivo studies. Fig. 1The effect of LF in regulating NT5DC3 and HKDC1. A Comparison of four xenografted tumors dissected from non-diabetic and diabetic mice in the BALB/c nude mouse model. B Average tumor weight in non-diabetic and diabetic mice implanted with four types of cancer cells. Data are presented as mean ± SD, * $P \leq 0.05$ compared with the HT29 group ($$n = 5$$). The corresponding mean weight in HT29 and HCT116 groups exceeded the others as well. C Volcano plot of identified DEGs. A total of 5,585 differentially expressed genes (DEGs, P value < 0.01, and with a fold change < 0.5 or > 2) were identified among HT29 colon cancer cells and NCM460 normal human colon cells, of which 2,534 were up-regulated, while 3,051 were down-regulated (Supplementary *Transcriptomics analysis* data in Data and Code Availability section). D Normalized mRNA levels of NT5DC3 and HKDC1 in HT29 cells treated with normal-glucose (2 g·L−1) or high-glucose (5 g·L−1) medium, in 6 types of cells; 1–6 indicated GES1 cells, MGC803 cells, NCM460 cells, HT29 cells, HK2 cells, and SW839 cells, respectively, * $P \leq 0.05$ ($$n = 3$$). E Protein levels of NT5DC3 and HKDC1 in 6 types of cells under normal glucose, * $P \leq 0.05$ ($$n = 3$$). F Protein levels of NT5DC3 and HKDC1 in 6 types of cells under a high glucose condition, * $P \leq 0.05$ ($$n = 3$$). G Protein levels of NT5DC3 and HKDC1 in HT29 cells under normal glucose and high glucose, * $P \leq 0.05$ ($$n = 3$$). H Normalized mRNA levels of NT5DC3 and HKDC1 under normal glucose (2 g·L−1) or high glucose (5 g·L−1) in NCM460 cells and HT29 cells, * $P \leq 0.05$ ($$n = 3$$). I NT5DC3 and HKDC1 protein levels under normal glucose (2 g·L−1) or high glucose (5 g·L−1), in NCM460 cells and HT29 cells, * $P \leq 0.05$ ($$n = 3$$). J Protein expressions of NT5DC3 and HKDC1 in HT29 cells treated with NT5DC3 siRNA, *$P \leq 0.05$ ($$n = 3$$). K Protein expressions of NT5DC3 and HKDC1 in HT29 cells treated with HKDC1 siRNA, *$P \leq 0.05$ ($$n = 3$$) As the closed links among diabetes, hyperglycemic, and colon cancer, the cellular glucose concentration could be critical in the progression of colon cancer. By assessing cell viability and cell apoptosis assays, we explored the proper concentrations of glucose in HT29 cells for further in vitro analysis. According to two principles: [1], the cell viability was above $80\%$ (Additional file 1: Fig. S1B); [2], the apoptosis rate was below $10\%$ (Additional file 1: Fig. S1C), 2 g·L−1 (11.1 mM) and 5 g·L−1 (27.8 mM) were selected as the normal glucose and high glucose concentrations for the following experiments, respectively.
We next accessed the in vitro wound healing-based cell migration assay and transwell-based cell invasion assay to test whether LF acts as the inhibitor in a HT29 colon cancer model. As shown in Additional file 1: Fig. S1D and E, LF treatment showed obviously suppressive ability in migration and invasion activities of HT29 cells (comparing with the control, $P \leq 0.05$), further validating the potential anti-malignancy effect of LF on colon cancer. Taken together, we screened HT29 colon cancer cells as the working model for colon cancer development under a high concentration of glucose (hyperglycemia) and verified the potent function of LF.
## High throughput sequencing identified NT5DC3 as the key factor in the progression of colon cancer under a high concentration of glucose
To evaluate the inner mechanism of the progression of colon cancer under a high concentration of glucose, we performed high throughput RNA sequencing to screen potential candidates that involved in colon carcinogenesis. The GO classification and pathway enrichment of the DEGs were illustrated in Fig. 1C and Additional file 2: Fig. S2A and B. We analyzed the potential cancer-related as well as glucose metabolism-related gene candidates, together with the published information regarding both colon cancer and diabetes in the GEO database [24]. Ultimately, our focus was narrowed to 5'-nucleotidase domain-containing protein 3 (NT5DC3), which was significantly reduced (log2FC = − 1.22) in the HT29 cells. NT5DC3 has been previously reported to be altered in colon cancer [25]. Excitingly, the following epigenetic assessments in the present study further accounted for the dys-expression of NT5DC3 and its key role in the progression of colon cancer under hyperglycemia. DNA methylation microarray showed that LF caused a dramatic alteration of the DNA methylation profiling in the HT29 cells, with 11,038 hyper-methylated genes and 9595 hypo-methylated genes (Additional file 2: Fig. S2C–E). The function enrichment of the differentially methylated genes (DMGs) further represented their potential correlations with T2D (Additional file 2: Fig. S2F). Based on the co-analysis of transcriptomics (Supplementary transcriptomics screening data) and DNA methylation detection (Supplementary DNA methylation profiling data), we found that LF could increase the mRNA expression level of NT5DC3, intriguingly, LF could also decrease the DNA methylation level of NT5DC3 (comparing with the control, $P \leq 0.05$). Considering the anti-tumor effect of NT5DC3 gene in tumor models especially in colon cancer has not be clearly uncovered yet, the study on this novel factor might be beneficial to colon cancer pretreatment in clinical area, therefore, we primarily defined NT5DC3 as the candidate tumor suppressor in inhibiting colon cancer and preliminarily identified the regulation of LF on NT5DC3 (Additional file 2: Fig. S2C–E). These data indicate that NT5DC3 is a bona fide downstream target of LF and suggest that NT5DC3 might be a potential biomarker in colon tumor progression under hyperglycemia.
## NT5DC3 and HKDC1 are regulated by LF
Hexokinase domain component 1 (HKDC1) is reported to play a critical role in the maintenance of glucose homeostasis and there is increasing evidence to suggest that its overexpression may contribute to several types of cancers [26]. To this end, we examined the mRNA and protein levels of NT5DC3 and HKDC1 under normal or high glucose conditions in different types of cells. NT5DC3 expression levels were down-regulated, while HKDC1 expression levels were significantly up-regulated in HT29 cells under a high-glucose treatment ($P \leq 0.05$), compared to the cells under normal-glucose conditions (Fig. 1D–G). Furthermore, we observed that co-treatment of LF could neutralize the differential expression of NT5DC3 and HKDC1 induced by glucose concentration (comparing with control, $P \leq 0.05$) (Fig. 1H, I), suggesting the important mechanism underlying LF’s anti-tumor activity via the regulation of NT5DC3 and HKDC1. Moreover, LF-dependent up-regulating of NT5DC3 and down-regulating of HKDC1 expression provided strong evidence that NT5DC3 and HKDC1 might play pivotal roles in the induction of T2D to colon tumor which could be relieved by LF. To exclude the possibility that LF-induced NT5DC3 and HKDC1 expression is caused by indirect effects, we utilized siRNA-mediated inactivation of NT5DC3 and HKDC1 in HT29 cells and validated the relationship between the two factors. As shown in Fig. 1J, the level of HKDC1 increased sharply after transfection of NT5DC3 siRNA under both normal glucose and high glucose ($P \leq 0.05$), and LF failed to decrease its expression. As expected, after HKDC1 knockdown, the level of NT5DC3 showed no change in comparison to the control group, while LF up-regulated its expression (Fig. 1K), indicating that LF could suppress the expression of HKDC1 by up-regulating its upstream factor NT5DC3. These data demonstrate that aberrant expression altering of NT5DC3 and HKDC1 induced by intracellular glucose could be directly modulated by LF, underscoring the potential role of LF in colon cancer therapy.
## LF inhibits colon tumor development in T2D mice
As the LF-dependent NT5DC3/HKDC1 expression alteration in colon cancer, we sought to determine the relevance of LF and downstream targets NT5DC3/HKDC1 in HT29 tumor model in vivo. To this end, we assessed the HT29 implantation tumor model in immune-competent C57BL/6 mouse by LF, NT5DC3 protein, or HKDC1 antibody or combination treatment both in normal and diabetic mice. The workflow of mice model construction was demonstrated in Fig. 2A. As expected, in the average tumor weight of the C57BL/6 diabetic mice was larger than the corresponding one of C57BL/6 normal mice, the one of control mice was 4.9 ± 0.4 g and the one of diabetic mice was 5.6 ± 0.6 g, indicating that HT29 tumors grow faster under a high glucose environment (Fig. 2B). We observed single LF, NT5DC3 protein, or HKDC1 antibody treatment could partially suppress the tumor growth. However, the tumor growth was significantly inhibited under double combination or triple combination treatment (comparing with the untreated groups, $P \leq 0.05$) (Fig. 2B, D). To exclude the possibility that the immune system could affect the result of implantation, this assay was also performed in immune-deficient BALB/c nude mice. As is shown in Fig. 2C and E, the tumor average weight of BALB/c control mice was 4.8 ± 0.3 g and the one of diabetic mice was 5.4 ± 0.5 g, indicating that HT29 tumors consistently demonstrated stronger growing ability under a high glucose environment (Fig. 2C). Further, LF and NT5DC3 protein combination significantly inhibited the development of HT29 tumors (comparing with the untreated groups, $P \leq 0.05$), underscoring the tumor suppressor NT5DC3 corroborates with LF to anti-tumor roles mainly through the activation of the NT5DC3 level in diabetic mouse models (Fig. 2C, E). These results provided evidences that the high risk of diabetic mice in developing colon cancer could be suppressed by LF and NT5DC3.Fig. 2HT29 xenograft tumors in two mouse models. A The workflow of HT29 xenograft tumors in mouse models. B In the C57BL/6 mouse model, comparison of diabetic mice and normal mice, and tumors dissected from diabetic mice and normal mice implanted with HT29 cells, which were treated with lactoferrin (LF), NT5DC3 protein (NT), HKDC1 antibody (HK), or their combinations. C In BALB/c nude mouse model, comparison of diabetic mice and normal mice, and tumors dissected from diabetic mice and normal mice implanted with HT29 cells, which were treated with LF, NT, or their combinations. D The average tumor weights of diabetic mice and normal mice in the C57BL/6 mouse model. E The average tumor weights of diabetic mice and normal mice in the BALB/c nude mouse model. The tumor group stands for the control. The above data (D, E) are presented as mean ± SD, *$P \leq 0.05$ compared with the control ($$n = 5$$)
## LF suppresses 5mC and m6A of NT5DC3
To elucidate the epigenetic regulation of NT5DC3 in the progression of colon cancer under hyperglycemia condition, DNA 5mC of NT5DC3 on genome level and RNA m6A modification of NT5DC3 on transcript level were detected. Cells were treated under different concentrations of glucose with or without LF (normal, high, normal + LF, high + LF, high transfer to normal, and high transfer to normal + LF), and the ratio of DNA 5mC/C or RNA m6A/A was determined by the liquid chromatography-tandem mass spectrometry/mass spectrometry (LC–MS/MS) technique. As shown in Fig. 3A and B, the ratio of DNA 5mC/C or RNA m6A/A was upregulated under a high glucose treatment in comparison to normal glucose conditions (lane 1 vs. 3) and downregulated after the transfer from high to normal glucose conditions (lane 3 vs. 5), and there were significant statistical differences ($P \leq 0.05$). However, the ratio was not affected by LF treatment under normal conditions (lane 1 vs. 2). Conversely, there was obvious decreasing ratio of DNA 5mC/C or RNA m6A/A under a high glucose conditions by LF treatment (lane 3 vs. 4, and lane 5 vs 6, $P \leq 0.05$), suggesting LF is an unexpected modulator in the global epigenome. Further, we found the knockdown of NT5DC3 had no obvious effect on the ratio of DNA 5mC/C or RNA m6A/A, further indicating NT5DC3 was the downstream of LF-dependent regulation ($P \leq 0.05$) (Fig. 3C, D).Fig. 3The total 5mC and m6A levels, as well as SAM/SAH ratio detected by MS. A The levels of 5mC/C under different concentrations of glucose. B The levels of m6A/A under different concentrations of glucose. C The levels of 5mC/C with NT5DC3 siRNA treatment. D The levels of m6A/A with NT5DC3 siRNA treatment. E The ratios of SAM/SAH under different concentrations of glucose. F The ratios of SAM/SAH with NT5DC3 siRNA treatment. N stands for normal-glucose (2 g·L−1), H stands for high-glucose (5 g·L−1), LF stands for lactoferrin, H-N stands for the transfer from high-glucose to normal-glucose. The above data are presented as mean ± SD, *$P \leq 0.05$ compared with the normal, & $P \leq 0.05$ compared with LF treatment group ($$n = 3$$) S-Adenosylmethionine (SAM) is the methyl donor for the biological methylation modifications which regulates the functions of nucleic acids and proteins [2, 27]. Methylation consumes SAM and converts the byproduct, S-adenosylhomocysteine (SAH) [28]. Thus, the methylation index (SAM/SAH ratio) is commonly considered an indicator of cellular methylation potential, if the ratio is lower, the methylation level usually increases [28]. To this end, SAM and SAH concentrations were analyzed to confirm the tight relationship with 5mC and m6A, SAM/SAH ratio is negative correlated with the methylation degree of 5mC and m6A. The results showed that the SAM/SAH ratio had the same pattern as the ratio of DNA 5mC/C or RNA m6A/A shown above, SAM/SAH ratio was sharply lower under high concentration of glucose when compared with the control group ($P \leq 0.05$), indicating that the methylation degree was upregulated under hyperglycimia, and LF could increase the SAM/SAH ratio significantly, that was to say, LF took effects in downregulating 5mC and m6A methylation levels ($P \leq 0.05$) (Fig. 3E, F). Whereas NT5DC3 knockdown still had no influence on SAM/SAH ratio (Fig. 3F). These data demonstrate the epigenetic regulation of cellular glucose concentration and LF is mediated by SAM/SAH dependent DNA 5mC or RNA m6A proportion, suggesting a high concentration of glucose-induced DNA or RNA methylation could be alleviated by LF treatment.
## The mechanism of the epigenetic regulation of NT5DC3 by LF
It is well defined that 5mC DNA methylation modifications are catalyzed by the participation of the DNMT [29]. Thus, we firstly detected the expression level of DNMT using the same condition, as shown in Fig. 4A and B, DNMT level was upregulated under a high glucose treatment which could be inhibited by LF ($P \leq 0.05$). In conformity with this result, the NT5DC3 CpG island 1 level was changed at the same pattern ($P \leq 0.05$) (Fig. 4C, D). On the other hand, the expression levels of RNA methylation-related genes (METTL3, METTL14, WTAP, FTO, ALKBH5, YTHDF1, YTHDF2 and YTHDF3) were also detected by qPCR analysis. All the eight m6A-related genes were observed to change significantly under a high glucose in comparison to the normal group, the levels of m6A eraser genes (FTO and ALKBH5) were downregulated, the levels of m6A writer genes (METTL3, METTL14 and WTAP) and m6A reader genes (YTHDF1, YTHDF2 and YTHDF3) were all upregulated under the high glucose conditions ($P \leq 0.05$), indicating that m6A was activated under hyperglycemia (Fig. 4E, F). However, LF could upregulate the levels of m6A eraser genes and downregulate the levels of m6A writer genes and reader genes under the high glucose conditions ($P \leq 0.05$), but not under normal glucose conditions, further verifying that LF selectively decreased the degree of m6A under hyperglycemia (Fig. 4E, F). Furthermore, the RNA m6A of NT5DC3 level was obviously upregulated under a high glucose in comparison to the normal group, and LF significantly attenuated the NT5DC3 m6A level at site 2309 ($P \leq 0.05$) (Fig. 4G, H). These results demonstrate LF could affect the 5mC and m6A machine genes’ expression to control the methylation status of NT5DC3.Fig. 4The levels of DNA/RNA methylation-related genes, as well as the normalized NT5DC3 (5mC CpG island 1/m6A 2309 site). A The levels of DNMT under different concentrations of glucose. B The levels of DNMT with NT5DC3 siRNA treatment. C The levels of NT5DC3 (5mC CpG island 1) under different concentrations of glucose. D The levels of NT5DC3 (5mC CpG island 1) with NT5DC3 siRNA treatment. E The levels of eight m6A related genes (METTL3, METTL14, WTAP, FTO, ALKBH5, YTHDF1, YTHDF2 and YTHDF3) under different concentrations of glucose. F The levels of eight m6A- related genes with NT5DC3 siRNA treatment. This part also verified the results in (E). G The levels of NT5DC3 (m6A 2309) under different concentrations of glucose. H The levels of NT5DC3 (m6A 2309) with NT5DC3 siRNA treatment. I The levels of NT5DC3 (m6A 2309) with the treatment of eight genes siRNA fragments, respectively. J The protein levels of NT5DC3 and HKDC1 with the treatment of eight genes siRNA fragments, respectively. N stands for normal-glucose (2 g·L−1), H stands for high-glucose (5 g·L−1), LF stands for lactoferrin, H-N stands for the transfer from high-glucose to normal-glucose. The above data are presented as mean ± SD, *$P \leq 0.05$ compared with the control, & $P \leq 0.05$ compared with LF treatment group ($$n = 3$$).
Next, to determine which gene was specifically responsible for the NT5DC3 m6A level modulating at site 2309, we performed an m6A related siRNA knockdown screen to identify the NT5DC3 regulator: [1] MS analysis to test the NT5DC3 2309 m6A level; [2] Western blot assay to test NT5DC3 protein level. Interestingly, the knockdown of WTAP could abrogate the changing of the NT5DC3 m6A or NT5DC3 protein levels after LF treatment under a high glucose conditions, and ALKBH5 siRNA group (panel 5), YTHDF1 siRNA group (panel 6) and YTHDF3 siRNA group (panel 8) showed the similar pattern of changes as WTAP siRNA group indicating that these m6A-related genes could be regulated by LF under hyperglycemia, and WTAP showed the stongest changes ($P \leq 0.05$) (Fig. 4I, J). The data suggested that these genes especially WTAP might be required for LF dependent m6A (2309 site) of NT5DC3 and subsequently the protein expression of NT5DC3. As for HKDC1 protein, we found that its expression level was reversely regulated in comparison to NT5DC3, in both the WTAP siRNA group and LF treatment group ($P \leq 0.05$) (Fig. 4J), further proving that LF could suppress the progression of colon cancer under a high concentration of glucose through regulating the WTAP/m6A/ NT5DC3/HKDC1 axis.
To exclude the possibility that this regulation mechanism was cell line-specific, we performed the same experiment in HCT116 cell line, which had the same growth ability as HT29 cells in diabetic induced tumors. As was shown in Additional file 3: Fig. S3A–C, the ratio of DNA 5mC, RNA m6A, and SAM/SAH increased significantly under a high glucose condition in comparison with the normal conditions which were suppressed by LF treatment but not NT5DC3 knockdown ($P \leq 0.05$). Further, we found that DNMT or WTAP were consistently required for DNA 5mC or RNA m6A regulation, respectively (Additional file 3: Fig. S3D–I). These data indicate that epigenetic regulation of NT5DC3 by LF through DNMT-mediated DNA 5mC or WTAP-mediated RNA m6A has broad relevance in colon cancer cells.
## The epigenetic regulation of NT5DC3 and potential consequences in vivo
We further investigated the epigenetic regulation of NT5DC3 in vivo. To this end, the ratios of 5mC/C, m6A/A, or SAM/SAH at the tumor tissue or normal precancerous tissue in the C57BL/6 mice were measured. As expected, in comparison to normal precancerous tissues, all these tested ratios increased in tumor tissues, whereas LF significantly downregulated them ($P \leq 0.05$), indicating that the ratios of 5mC/C, m6A/A, SAM/SAH were consistently controlled by glucose and LF in vivo (Fig. 5A–C). Moreover, 5mC (NT5DC3 CpG island 1) and m6A (NT5DC3 2309 site) in mice tumor tissues were higher than the ones in precancerous tissues but downregulated by LF treatment as well ($P \leq 0.05$). These data demonstrate that regulation of NT5DC3 by 5mC and m6A is conserved from mice to humans, underlying the importance of this instinct regulation mechanism in vivo. Fig. 5Mythelation detection of NT5DC3. A The 5mC/C in C57BL/6 mice paracancerous tissue and tumor tissue samples by MS. B The m6A/A in C57BL/6 mice paracancerous tissue and tumor tissue samples by MS. C The ratio of SAM/SAH in C57BL/6 mice paracancerous tissue and tumor tissue samples by MS. D The levels of NT5DC3 (5mC CpG island 1) in C57BL/6 mice paracancerous tissue and tumor tissue samples by q-PCR. E The levels of NT5DC3 (m6A 2309) in C57BL/6 mice paracancerous tissue and tumor tissue samples by q-PCR. Tumor stands for the control group, LF stands for lactoferrin group. The above data are presented as mean ± SD, *$P \leq 0.05$ compared with the normal, and & $P \leq 0.05$ compared with tumor group ($$n = 3$$)
## NT5DC3 expression is the biomarker for T2D or T2D/colon cancer coexistence patients
Because the above data indicate that NT5DC3 regulation plays a critical role in colon cancer development in T2D mice in response to LF, it is very likely that NT5DC3 could be the biomarker for T2D-induced colon cancer. To corroborate this finding, we collected 75 clinical blood samples from healthy people ($$n = 30$$), T2D patients ($$n = 30$$), and T2D/colon cancer coexistence patients ($$n = 15$$). Then, we tested the mRNA and protein levels of NT5DC3 and found a decrease of NT5DC3 in T2D patients in comparison to the healthy people group, further confirming that the expression levels of NT5DC3 reflect the blood glucose concentration (Fig. 6A, B). Notably, the NT5DC3 expression showed a dramatic reduction in the clinical T2D/colon cancer coexistence group which was consistent with our above finding ($P \leq 0.05$). Moreover, the NT5DC3 5mC at CpG island 1 or NT5DC3 m6A 2309 site methylation levels among these three groups were increased in gradient ($P \leq 0.05$), further indicating the aberrant epigenetic profiling occurs during T2D or colon cancer progression in T2D patients (Fig. 6C). These results suggest that the NT5DC3 mRNA and protein levels are a bona fide biomarker in distinguishing healthy volunteers and T2D patients, and even T2D/colon cancer coexistence patients. These results also demonstrate the potentness of monitoring NT5DC3 in the prognosis of T2D or T2D-induced colon cancer. In the clinical field, whether NT5DC3 could be a marker in diagnosing T2D patients who have a higher risk of getting colon cancer required more patient surveys and sample verification. Fig. 6Detection of NT5DC3 in human blood samples. A The mRNA level of NT5DC3 by q-PCR. B The protein level of NT5DC3 by western blotting. C The 5mC (CpG island 1) and m6A (2309 site) of NT5DC3 in blood samples. N1-30 stands for the healthy volunteer group, T2D1-30 and D1-30 stand for the T2D patient group. T2D-cc1-15 and T2D-c1-15 stand for the T2D-induced colon cancer patient group. The above data are presented as mean ± SD, *$P \leq 0.05$ compared with the healthy volunteer group ($$n = 75$$)
## Discussion
NT5DC3, a member of the 5'-nucleotidase domain-containing family, codes for a largely uncharacterized transmembrane protein [30]. The aberrant expression of 5ʹ-nucleotidase domain-containing family members is reportedly associated with abnormalities, cancer, endocrine system disorders, and organismal injury [30]. In this study, we found NT5DC3 as a key target of diabetes and colon cancer under hyperglycemia. Our identification and development of colon cancer cells (HT29 and HCT116 cell lines) induced mouse tumor models provide a platform to investigate the effect of natural dietary component LF. On the other hand, based on the co-analysis of transcriptomics and DNA methylation detection, we defined NT5DC3 as the tumor suppressor in inhibiting colon cancer and preliminarily identified the regulation of LF on NT5DC3.
Several studies have demonstrated that LF can be applied as a candidate treatment for diabetes or to efficiently inhibit the growth of tumors and reduce susceptibility to cancer [4, 19–23]. Notably, the methylation of the promotor and the first intronic region of LF, associated with an increased incidence of cancer, has been observed in cancers of the breast, lung, prostate, etc. [ 31–33]. Alterations of the methylation profile have been described in T2D [8], however, information regarding the relationship between LF, methylation, and T2D remains limited. In the present study, LF was found to decrease 5mC and m6A methylation of NT5DC3 in colon tumor cells and xenografted colon tumors implanted in T2D mice, thereby suggesting its potential to suppress the progression of colon cancer under a high concentration of glucose (hyperglycemia).
By using epigenetic and biochemistry techniques, we successfully discovered and verified the modification sites of NT5DC3 regulated by LF. LF was proved to destroy the bridge between T2D and colon cancer through the suppression of epigenetic modification levels of 5mC (CpG island 1) and m6A [2309] of NT5DC3. We also found that WTAP played a key role in m6A [2309] of NT5DC3, which subsequently affect the expression levels of NT5DC3/HKDC1. WTAP-mediated m6A methylation has a crucial role in tumorigenesis. WTAP acts as a sensitive marker in gastrointestinal cancer [34], non-small cell lung cancer [35], breast cancer [36], etc. It is reported that WTAP involves in hepatocellular carcinoma (HCC) oncogenesis via regulation of the HuR-ETS1-p21/p27 axis [37]. Overexpression of WTAP facilitates renal cell carcinoma by stabilizing CDK2 transcript [38], and WTAP promotes the invasiveness of glioblastoma through enhancing the activity of EGFR [39]. Moreover, WTAP cooperates with METTL3 and METTL14 to promote cell cycle transition in the MCE of adipocyte differentiation [40]. Those findings highlight WTAP as a potential therapeutic target for cancer treatment, however, little has been known about the molecular function of WTAP in the progression of colon tumors under a high concentration of glucose. Our study uncovered that WTAP participated in m6A [2309] of NT5DC3 to suppress NT5DC3 expression thus, LF-dependent WTAP expression inhibition exhibited a novel pathway to suppress the progression of colon tumor under a high concentration of glucose. However, how LF inhibits WTAP expression needs further elucidation. These discoveries about the mechanism of NT5DC3 in suppressing colon cancer progression under hyperglycemia, combined with the positive regulation of LF on NT5DC3, will expand current knowledge of the functions of bioactive proteins within different research layers. Moreover, the results from clinical blood samples defined NT5DC3 as a sensitive biomarker to distinguish T2D patients from healthy volunteers, and the expression levels of NT5DC3 in the patients who had the coexistence of colon cancer and T2D were continuously lower than the ones in T2D patients. Further investigation should be conducted to ascertain whether this mechanism holds true in more clinical T2D-induced colon tumor patients, or whether LF inhibits the progression of the two diseases via acting on WTAP/m6A/NT5DC3/HKDC1 axis in the clinical area. Based on a large number of toxicological experiments, LF is regarded as GRAS (generally recognized as safe) by FDA, thus, the dosage of 250 mg·kg−1 body weight (b.w.) ( as 3.1 μM·kg−1 b.w.) used in the present study might be completely accepted in future clinical treatments, especially in the prevention from T2D to colon cancer, which also deserves our further investigation.
In brief, we revealed that lactoferrin acts as a suppressor in the progression of colon cancer under a high concentration of glucose, by which epigenetically regulating glucose-dependent DNMT/5mC or WTAP/m6A/NT5DC3/HKDC1 axis. Lactoferrin could regulate WTAP and inhibit m6A of NT5DC3 at 2309 site, and subsequently affect the expression levels of NT5DC3/HKDC1 proteins in vitro and in vivo. Our study reveals that lactoferrin and its downstream target NT5DC3 represent a new light in clinical T2D-induced colon cancer prognosis and treatment, underscoring the potential application of natural dietary mediated tumor suppression. If possible, the measurement of NT5DC3 expression level might be developed as a sensitive biomarker in clinical prediction models, to distinguish whether T2D patients are susceptible to developing colon cancer.
## CRediT authorship contribution statements
HYL, HPJ, XCZ, BYZ– Planning and execution of research work. HPJ and XCZ– Experimental design and statistical analysis of research work. HYL and BYZ–Supervision and interpretation. All authors read and approved the final manuscript.
## Ethics declaration
The animal experiments were approved by the Ethics Committee of Chinese Academy of Agriculture Sciences (Beijing, China; Permission No. IAS2020-90). All patients submitted the informed written consent to utilize biological specimens for investigational procedures, according to the Ethics Committee approval of Beijing Friendship Hospital, Capital Medical University (Permission No. 2020-P2-175-02, as Supplementary Ethics Committee approval in Data and Code Availability section).
## Cell culture and viability detection
GES1 cells, MGC803 cells, HK2 cells, SW839 cell, NCM460 cells, HT29 cells, HCT116 cells, and SW620 cells were cultured in RPMI1640 medium (containing 2 g·L−1 (11.1 mM) glucose) with $10\%$ FBS, at 37 °C in $5\%$ CO2 and $95\%$ saturated atmospheric humidity. The cells were plated into 96-well plates (1 × 104 cells/100 μL medium) and incubated for 24 h, then the medium was replaced with 100 μL fresh medium containing different concentrations of glucose (0, 1, 2, 3, 4, 5, 6, 7 and 8 g·L−1, also as 0, 5.6, 11.1, 16.7, 22.2, 27.8, 33.3, 38.9 and 44.4 mM, respectively). Then the cells were cultured for another 48 h, and the CCK-8 kit was applied to measure cell viability. The absorbance at 490 nm was determined by a Microplate Reader (Thermo Fisher Scientific). The cell viability = (Value test − Value blank) / (Value control − Value blank) × $100\%$. The dosages with the viabilities greater than $80\%$ meanwhile significantly different from the control ($P \leq 0.05$) were chosen as the appropriate concentrations of glucose used in the following experiments.
## Cell apoptosis assay
Cells grew in 6-well plates treated with a growth medium containing increasing concentrations of glucose (1, 2, 3, 4, 5, and 6 g·L−1, respectively). The cells were collected and resuspended in 250 μL binding buffer, then treated with 15 μL FITC-Annexin V buffer and 30 μL propidium iodide (PI) buffer (10 g·L−1, as 15.0 mM), gently vortexed and incubated for 15 min in the dark at 25 °C. Subsequently, 300 μL binding buffer was added into each tube and then analyzed by flow cytometry (BD) within 1 h.
## Preparation of lactoferrin with approximate 50% iron saturation
Lactoferrin with $100\%$ iron saturation (Holo-LF) was purchased from Sigma (USA). 11 g Holo-LF was added with 300 mL citrate buffer (pH = 5), and adjusted PH value to 8.2, to promise iron saturation as $49\%$-$51\%$ according to the fitting equation ($Y = 0.0643$*2.27045X, Y stands for LF iron saturation, X stands for PH value). Then the solution was concentrated and desalted through a 30 kDa ultrafiltration membrane. When the conductivity was less than 1.5 ms·cm−1, the volume was concentrated 5 times and lyophilized (Chinese Invention patent application, Application publication number: CN 110655570 A).
## Cell invasion and migration analyses
In the migration test, the upper chambers were seeded with 5 × 103 cells in 200 μL serum-free medium containing normal or high glucose, respectively, and 400 μL of medium (2 g·L−1 or 5 g·L−1 glucose, accordingly) with $15\%$ FBS was added to the lower chambers. Lactoferrin with approximate $50\%$ iron saturation (LF, 0.5 g·L−1, as 6.3 μM) prepared above was added to the upper chamber and cocultured for 24 h, then cell invasion was detected by calculating three random captured pictures [20].
In the invasion test, HT29 cells were plated in a 6-well plate and incubated for 24 h to achieve a cell density greater than $90\%$. A single lesion with a width of approximately 5.0 mm was scratched across the cell monolayer by mechanical scraping. The cells were then incubated with LF (0.5 g·L−1) dissolved in a normal (2 g·L−1) or high glucose (5 g·L−1) medium. The width of the scratch wound was photographed and scanned again 24 h later, and the recovery rate was measured [20].
## mRNA sequencing and DNA methylation profiling
Total RNA (1 μg) extracted from each cell sample was firstly utilized to poly-d(A)-RNA isolation with NEBNext Magnetic Oligo d(T)25 Beads (NEB), and then used for mRNA library preparation with an RNA Library Prep Kit for Illumina (NEB) according to the instruction procedures. All of the libraries were subjected to 150 bp pair-end sequencing on an Illumina HiSeq2000 platform. After sequencing, trimmed and cleansed reads were analyzed by using the Bowtie2 suite to align to the hg19 reference genome and count normalized transcript abundance. Differentially expressed genes (DEGs) were calculated using the DESeq2 package and further analyzed based on GO biological processes, molecular functions, and the KEGG pathway. DNA methylation profiling was studied using the Infinium Human Methylation EPIC Bead Chip. In brief, 6 samples of 500 ng genomic DNA isolated from each treatment (HT29 cells as control group, HT29 cells treated with 0.5 g·L−1 LF in high glucose medium as the LF group, 4 biological repetitions) were treated with EZ DNA methylation kit (Zymo Research), the targets were prepared, labeled and hybridized with the kits and reagents indicated by the Infinium HD Methylation Assay Protocol Guide (15019519B). Processed methylation chip after single base extension and staining was scanned using an iScan reader (Illumina). Generated microarray data were analyzed using Genome Studio software v2011.1 (Illumina), and for quality control, methylation measures with a detection p-value > 0.05 and samples with a CpG coverage < $95\%$ were removed. After initial normalization using internal controls in the Genome Studio software, the methylation levels of CpG sites were calculated as β-values (β = Intensity (methylated)/intensity (methylated + unmethylated)). The data were further normalized and the differential DNA methylation was assessed using the IMA 3.12-R package. P-values were calculated by t-test corrected for multiple hypotheses testing by the Benjamini–Hochberg method in combination with the Illumina custom false discovery rate (FDR) model (www.tandfonline.com). A threshold for differential DNA methylation was set at FDR-corrected P-value lower than 0.05. The functions of the associated genes were further studied based on GO and KEGG pathway analysis using KOBAS.
## Animal models
The animal experiments were approved by the Ethics Committee of Chinese Academy of Agriculture Sciences (Beijing, China; permission number: IAS2020-90), conforming to internationally accepted principles in the care and use of experimental animals (NRC, 2011). All surgical procedures were performed under sodium pentobarbital anesthesia, and all efforts were made to minimize the suffering of the mice.
In C57BL/6 mouse model, 80 male mice (18–22 g) were randomly divided into two parts (16 groups in total): groups in the first part were normal control without any treatment, tumor control treated with HT29 cells implantation, tumor control + LF, tumor control + NT5DC3 protein (Novus Biologicals, USA), tumor control + HKDC1 antibody (Abcam, USA), tumor control + LF + NT5DC3 protein, tumor control + LF + HKDC1 antibody, tumor control + LF + NT5DC3 protein + HKDC1 antibody; while groups in the second part included diabetic mice, diabetic mice implanted with HT29 tumor (dia-tumor mice), dia-tumor mice + LF, dia-tumor mice + NT5DC3 protein, dia-tumor mice + HKDC1 antibody, dia-tumor mice + LF + NT5DC3 protein, dia-tumor mice + LF + HKDC1 antibody, dia-tumor mice + LF + NT5DC3 protein + HKDC1 antibody. The 40 diabetic mice were fed with a high-fat diet for 30 days continuously, and the mice were intraperitoneally administered with streptozocin (STZ, 100 mg·kg−1 (0.38 mM·kg−1) b.w.) once on the 31st day. Then the indicators including fasting blood glucose detection (FBG), oral glucose tolerance test (OGTT), glycated serum protein (GSP) and serum insulin (INS) of mice were detected on the 33rd day to confirm the successful model construction (data in Additional file 7: Table S4). 1 × 108 HT29 cells in 150 μL matrigel medium (BD) were subcutaneously injected into the back of each mouse, and when the tumors volume reached 100–120 mm3, the total 80 mice were treated with LF (250 mg·kg−1 b.w.), NT5DC3 protein (50 mg·kg−1 (0.94 mM·kg−1) b.w.), HKDC1 antibody (50 mg·kg−1 (0.42 mM·kg−1) b.w.), or their combinations, respectively. LF was orally administered by gavage, while NT5DC3 protein or HKDC1 antibody was injected through the tail vein every two days at the same time. All the mice were sacrificed on the 28th day, and the tumors were weighed.
In BALB/c nude mouse model, 50 male BALB/c nude mice (18–22 g) were randomly divided into two parts (10 groups in total): groups in the first part were normal control without any treatment, tumor control treated with HT29 cells implantation, tumor control + LF, tumor control + NT5DC3 protein, tumor control + LF + NT5DC3 protein; while groups in the second part included diabetic mice, diabetic mice implanted with HT29 tumor (dia-tumor mice), dia-tumor mice + LF, dia-tumor mice + NT5DC3 protein, dia-tumor mice + LF + NT5DC3 protein. The diabetic mice model was constructed and protein treatments were performed as described in the C57BL/6 mouse model. All the mice were sacrificed on the 28th day, and the tumors were weighed.
In the two mouse models, tumor diameters were detected with a caliper every 4 days, and tumor volume was calculated using the following formula: tumor volume (mm3) = 0.5 × length (mm) × width (mm)2. Individual tumor suppression rate (%) = (the average tumor weight in the control group − the individual tumor weight in the LF treatment groups) / the average tumor weight in the control group × $100\%$, as the average tumor weight in the control group was calculated by each tumor weight in the control group. Relative tumor volume (RTV, %) = detected volume / volume before dosing × $100\%$ [20].
## The 5mC detection and m6A detection
According to the method [41], 1 µg DNA was denatured at 100 °C for 5 min and subsequently chilled at 4 °C for 10 min. One-tenth volume of 0.15 M ammonium acetate (pH 7.5) and 2.5 units of DNase I (TransGen) were added, then the mixture was incubated at 37 °C for 4 h. 2 units of Alkaline Phosphatase (TaKaRa) was added into the solution and incubated for an additional 3 h at 37 °C. Thereafter, the mixture was incubated for 12 h at 37 °C with 40 units of Exonuclease I (TaKaRa). The complete lysis mixture was placed in a refrigerator at 4 °C for LC–MS/MS detection [42]. 1 μg genomic DNA was hydrolyzed by utilizing a DNA degrease Plus™ kit following the manufacturer’s instructions and according to the previous method [43]. In both test samples and standards, the hydrolyzed DNA was analyzed by liquid chromatography-electrospray ionization tandem mass spectrometry with multiple reaction monitoring (LC–ESI–MS/MS-MRM), and the MRM method was applied to monitor three transitions for each analysis, the experiment parameters in the 5mC/m6A detection by MS were demonstrated in Additional file 4: Table S1. Finally, the total amount of 5mC in test samples from the 5mC MRM peak area was calculated by dividing the sum of the 5mC and cytosine peak areas (5mC/C) [43].
Purified total mRNA (200 ng) was digested to its constituent mono-nucleosides according to previous method [42]. MRM mode was applied for the UPLC-MS/MS analysis through monitoring transition pairs in the Additional file 4: Table S1.
## Detection of 5mC- and m6A-related genes
100 ng total RNA from cells samples or blood samples was extracted, the total RNA samples were transcribed into cDNA (42 °C for 10 min, 65 °C for 10 s, stored at 4 °C) by PrimeScript™ RT reagent Kit (TaKaRa). Primers of evaluated genes including NT5DC3, HKDC1, Homo sapiens DNA methyltransferase 1 (DNMT), methyltransferase-like 3 (METTL3), methyltransferase-like 14 (METTL14), Wilms' tumor 1-associated protein (WTAP), fat mass, and obesity-associated factor (FTO), AlkB homologue 5 (ALKBH5), YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), YTH N6-methyladenosine RNA binding protein 2 (YTHDF2), YTH N6-methyladenosine RNA binding protein 3 (YTHDF3) and GAPDH, as well as siRNA fragments of these genes, were outlined in Additional file 5: Table S2, and GAPDH was utilized as the internal reference to assure the equal loadings. qRT-PCR was performed using 96-well microwell plates in a total volume of 20 μL, containing 1 μL template cDNA (10 ng·μL−1), 0.5 μL forward primer (10 μM), 0.5 μL reverse primer (10 μM), 10 μL of TB Green® Fast qPCR Mix (TaKaRa). The q-PCR reactions were performed at 95 ℃ for 3 min, followed by 40 cycles of 95 ℃ for 10 s, 60 ℃ for 30 s by using two-step qRT-PCR. All qRT-PCR reactions were performed.
## Methylation sites verified by SELECT qPCR
DNA methylation region of NT5DC3 (5mC, CpG island 1) determination and primer design principles: DNA sequence of NT5DC3 (GenBank Reference Sequence: NM_001031701.3) was harvested using NCBI website and MethPrimer (http://www.urogene.org/cgi-bin/methprimer/methprimer.cgi) was utilized for CpG island prediction (CpG island 1: 47–295 bp) and primer design. When DNA is subjected to bisulfite conversion, the bisulfite-sensitive unmodified cytosines (C) are converted to uracils (U) and further replaced by thymidines (T) in PCR amplification, while the methylated cytosines (5mC) could survive the bisulfite conversion and remain unchanged. From the primer candidates provided by MethPrimer, one pair with a high GC content and an annealing temperature close to 60 °C was chosen as the general primers for assessing the methylation status of the predicted CpG island 1 of NT5DC3 (Additional file 5: Table S2).
Protocols of DNA methylation site (5mC CpG island 1) detection: Genomic DNA (gDNA) was extracted using a DNeasy Blood & Tissue Kit (QIAGEN). 1 μg gDNA of each sample, respectively, was subjected to bisulfite conversion using a DNA bisulfite conversion kit (QIAGEN) following the manufacturers' instructions. A thermocycler was used for the conversion with the following procedure: 95 °C for 10 min and 64 °C for 60 min. After bisulfite conversion, gDNA was purified and used for PCR analysis. The specific primers used were listed in Additional file 5: Table S2. The methylation rate was calculated using the ΔΔCt method: methylation rate (%) = $100\%$/2ΔΔCt.
RNA methylation site of NT5DC3 (m6A, 2309) determination and primer design principles: DNA sequence of NT5DC3 (GenBank Reference Sequence: NM_001031701.3) was firstly harvested using the NCBI website, and three kinds of sequence that could undergo m6A methylation, namely GGACU(T), GAACU(T) and GAACA, were screened. Furthermore, the m6AVar database (http://m6avar.renlab.org/index.html) was utilized for the m6A site prediction, and chr12:101471023(+), namely m6A 2309, was finally chosen for the following investigation (labeled as site X). The nearest adenine (A) on the 5’ upstream of and at least six bases away from the site X was labeled as site N. The RNA methylation-specific primers for both site X and N were designed, respectively. Site X was regarded methylated if the Ct value in site X PCR detection was larger than that in site N detection.
## Protocols of RNA methylation site (m6A 2309) detection
NT5DC3-2309 methylation in RNA level was detected mainly through three steps by SELECT qPCR, which were conducted by following Xiao’s protocol and previous references [42, 44, 45]. Firstly, the total RNA (1500 ng) was mixed with 40 nM up primers (Additional file 5: Table S2), 40 nM down primers (Additional file 5: Table S2), and 5 μM dNTP (NEB) in 1.7 μL 10 × CutSmart buffer (NEB), 20 μL total volume. Then the RNA and primers were incubated as the reference [46] introduced, then 20 μL qPCR reaction system was set up and contained 5 μL of the final reaction mixture, 200 nM SELECT primers (see NT5DC3 select in Additional file 5: Table S2), and TB Green® Fast qPCR Mix (TaKaRa). SELECT qPCR was performed with the following program: 95 °C for 5 min; 95 °C for 10 s, 60 °C for 35 s, 40 cycles in total; 95 °C for 15 s; 60 °C for 1 min; 95 °C for 15 s; then hold at 4 °C. Ct values of samples were normalized to their corresponding Ct values of GAPDH.
## SAM, SAH detection by MS
SAM and SAH were quantified by LC–MS/MS as described previously [27, 47], with minor modifications to run on UPLC coupled to a XEVO TQ-S micro mass spectrometer. Cell samples were washed three times with ice PBS, followed by bead-beating in $80\%$ methanol: water (LC–MS grade methanol, Thermo Fisher Scientific) at − 20 °C. The extraction mixture was verting for 10 s, and then ultrasonication for 30 min, and centrifuged for 15 min at 12,000 g·min−1. The supernatants were transferred to an autosampler vial, and 5 µL of the mixture was then used for UPLC-MS/MS analysis (Waters). The separation was performed on a BEH Amide column (130 Å, 1.7 µm, 1 mm × 100 mm, 1/pkg, Waters, USA) for nucleosides. Mobile phases consisted of: (A) $100\%$ water, containing $0.1\%$ formic acid, and (B) $100\%$ acetonitrile containing $0.1\%$ formic acid. The following gradient and mass spectrometer operation were applied as reference described [48, 49]. The precursor → product transitions for SAM (m/z 399.3 → 250.3) and SAH (m/z 385.3 → 136.3) were monitored.
## Western Blotting analysis
HT29 or NCM460 cells were lysed by RIPA buffer, and the total protein concentration was measured using a BCA kit (Beyotime). The antibodies included: anti-human NT5DC3 (PA5-70919, Invitrogen), anti-human HKDC1(PA5-35894, Invitrogen), and anti-human β-actin (PAB0865, Enzo Life Sciences). For western blotting, the primary antibodies were diluted at 1:1000; the second antibodies were used at 1:3000 dilution. The signals were captured and analyzed by Clinx ChemiCapture software (Clinx).
## Clinical samples collection
From July 2020 to October 2020, 30 patients with T2D and 15 T2D/colon cancer coexsitence patients (colon cancer was diagnosed two years after the T2D diagnosis) were enrolled in the present study. Patients' characteristics are outlined in Additional file 6: Table S3. All patients submitted the informed written consent to utilize biological specimens for investigational procedures, according to the Ethics Committee approval of Beijing Friendship Hospital, Capital Medical University (Permission No. 2020-P2-175–02, as Supplementary Ethics Committee approval in Data and Code Availability section). Blood samples of the patients were taken upon admission on the morning, and the patients did not take any food or water 12 h strictly before the blood was sampled. As the normal control group, 30 healthy people were selected and enrolled in the study. Their blood samples were collected upon admission in the morning. The blood samples of each patient were divided into two parts, 4 mL was collected into RNAase-free tubes (BD) containing preservation solution for DNA/RNA isolation, 2 mL was collected into aseptic anticoagulant tubes (BD) for protein detection.
400 μL lysis buffer was added into each 200 μL blood sample and vortexed for 30 s, which was centrifuged (10,000 rpm, 1 min) later, and the supernatant was removed. The nuclear precipitation was treated with several types of buffer and the total genome DNA was extracted, and the DNA level of NT5DC3 was measured by qPCR, as mentioned above. DNA methylation (CpG island 1) and RNA methylation (2309 site) of NT5DC3 were measured by SELECT-qPCR, according to the protocol described above.
100 μL RIPA lysis buffer (with protease inhibitors) was added into each 10 μL blood sample and vortexed for 5 min, then 30 μL 5 × loading buffer was added and boiled for 15 min, and then the protein level of NT5DC3 was detected by western blotting.
## Statistical analysis
All data were presented as means ± standard deviation (SD) and analyzed using SPSS 19.0 and GraphPad Prism 6.0 software (GraphPad Inc., San Diego, CA). Statistical analyses were conducted between two groups (control group vs. treatment groups, single treatment group vs. two treatments group) using a Student’s t-test. P values < 0.05 were considered to be statistically significant.
## Supplementary Information
Additional file 1: Figure S1. Selection of tumor cells and glucose concentrations. A) Tumor formation time in non-diabetic and diabetic mice implanted with four types of cancer cells. The formation time of the HT29 and HCT116 cells-formed tumors was the shortest one. Data are presented as mean ± SD, * $P \leq 0.05$ compared with the HT29 group ($$n = 5$$). B) Cell viabilities of HT29 cells in DMEM containing different concentrations of glucose (0, 1, 2, 3, 4, 5, 6, 7, 8 g·L−1). Data are presented as mean ± SD, * $P \leq 0.05$ compared with the control group (0 g·L−1) ($$n = 3$$). C) *Cell apoptosis* of HT29 cells in DMEM containing different concentrations of glucose (0, 1, 2, 3, 4, 5, 6 g·L−1). Data are presented as mean ± SD, * $P \leq 0.05$ compared with 1 g·L−1 glucose group or 6 g·L−1 glucose group ($$n = 3$$). D) Microscopy photographs (200 ×) showing LF inhibition of wound healing in HT29 cells cultured under both normal and high concentrations of glucose and the recovery rate of each scratch width under each treatment condition. Data are presented as mean ± SD, * $P \leq 0.05$ compared with control group, # $P \leq 0.05$ compared with high-glucose group ($$n = 3$$). At the same time-point of 24 h, the recovery of the scratch width of the high glucose (5 g·L−1)-cultured HT29 cells exceeded that in cells cultured under the normal condition (2 g·L−1), thus indicating that high glucose facilitated the migration of cancer cells. E) Microscopy photographs (200 ×) showing LF inhibition of the migration of HT29 cells under each treatment condition, and the quantification of migrated cells. Data are presented as mean ± SD, * $P \leq 0.05$ compared with control group, # $P \leq 0.05$ compared with high-glucose group ($$n = 3$$). LF (0.5 g·L−1) significantly suppressed cell migration under both culture conditions, indicating that the high glucose promoted the invasion of HT29 cancer cells, which could be notably mitigated by the LF.Additional file 2: Figure S2. mRNA sequencing and DNA methylation detection. A) In mRNA sequencing, the functional classification of identified transcriptome-differentially expressed gene deep analysis (DEGs) by Gene Ontology database, through comparing normal colon epithelial cells (NCM460) and colon cancer cells (HT29). B) In mRNA sequencing, the KEGG pathway enrichment through comparing NCM460 cells and HT29 cells. C) Distribution map of differentially methylated loci, here, HT29 cells as the control group, HT29 cells treated with 0.5 g·L−1 lactoferrin in high glucose medium as the LF group (Supplementary DNA methylation profiling data in Data and Code Availability section) D) In methylation assay, the overview of differentially methylated genes (DMGs) in LF group, including 11,038 hyper-methylated genes and 9,595 hypo-methylated genes, when compared with HT29 control group. E) In methylation assay, the cluster heatmap of DMGs through comparing NCM460 cells and HT29 cells. F) In methylation assay, the DMG-related disease enrichment through comparing HT29 cells and the cells treated with LF. Based on the raw data analysis of Supplementary DNA methylation profiling data and results shown in (C-E), intriguingly, LF was found to cause a hypo-methylation of NT5DC3. These data indicate that NT5DC3 is a bona fide downstream target of LF and suggest that NT5DC3 might be a potential biomarker in colon tumor progression under hyperglycemia. Additional file 3: Figure S3. The total 5mC/m6A and SAM/SAH ratio detected by MS, as well as the role of WTAP in regulating NT5DC3 m6A. A) The levels of 5mC/C under different concentrations of glucose. B) The levels of m6A/A under different concentrations of glucose. C) The ratios of SAM/SAH under different concentrations of glucose. D) The normalized levels of DNMT with NT5DC3 siRNA treatment. E) The levels of NT5DC3 (5mC CpG island 1) with NT5DC3 siRNA treatment. F) The normalized levels of WTAP with NT5DC3 siRNA treatment. G) The levels of NT5DC3 (m6A 2309) with NT5DC3 siRNA treatment. H) The levels of NT5DC3 (m6A 2309) with WTAP siRNA treatment. I) The levels of NT5DC3 and HKDC1 proteins with WTAP siRNA treatment. N stands for normal-glucose (2 g·L−1), H stands for high-glucose (5 g·L−1), LF stands for lactoferrin, H-N stands for the transfer from high-glucose to normal-glucose. The above data are presented as mean ± SD, * $P \leq 0.05$ compared with the control, & $P \leq 0.05$ compared with LF treatment group ($$n = 3$$).Additional file 4: Table S1. Parameters in the 5mC/m6A detection by MS.Additional file 5: Table S2. Sequence of primers used for qPCR, Select qPCR (m6A) and DNA methylation. Additional file 6: Table S3. Characteristics of patients and healthy human subjects. Additional file 7: Table S4. Type 2 diabetes indicators of mice.
## References
1. Hua F, Yu JJ, Hu ZW. **Diabetes and cancer, common threads and missing links**. *Cancer Lett* (2016) **374** 54-61. DOI: 10.1016/j.canlet.2016.02.006
2. Chang CH, Lin JW, Wu LC, Lai MS, Chuang LM, Chan KA. **Association of thiazolidinediones with liver cancer and colorectal cancer in type 2 diabetes mellitus**. *Hepatology* (2012) **55** 1462-1472. DOI: 10.1002/hep.25509
3. Shi Y, Hu FB. **The global implications of diabetes and cancer**. *Lancet* (2014) **383** 1947-1948. DOI: 10.1016/S0140-6736(14)60886-2
4. Johnson JA, Carstensen B, Witte D, Bowker SL, Lipscombe L, Renehan AG. **Diabetes and cancer (1): evaluating the temporal relationship between type 2 diabetes and cancer incidence**. *Diabetologia* (2012) **55** 1607-1618. DOI: 10.1007/s00125-012-2525-1
5. Giovannucci E, Harlan DM, Archer MC, Bergenstal RM, Gapstur SM, Habel LA, Pollak M, Regensteiner JG, Yee D. **Diabetes and cancer: a consensus report**. *Diabetes Care* (2010) **33** 1674-1685. DOI: 10.2337/dc10-0666
6. Wu D, Hu D, Chen H, Shi G, Fetahu IS, Wu F, Rabidou K, Fang R, Tan L, Xu S. **Glucose-regulated phosphorylation of TET2 by AMPK reveals a pathway linking diabetes to cancer**. *Nature* (2018) **559** 637-641. DOI: 10.1038/s41586-018-0350-5
7. Julien SG, Dubé N, Hardy S, Tremblay ML. **Inside the human cancer tyrosine phosphatome**. *Nat Rev Cancer* (2011) **11** 35-49. DOI: 10.1038/nrc2980
8. Volkmar M, Dedeurwaerder S, Cunha DA, Ndlovu MN, Defrance M, Deplus R, Calonne E, Volkmar U, Igoillo-Esteve M, Naamane N. **DNA methylation profiling identifies epigenetic dysregulation in pancreatic islets from type 2 diabetic patients**. *Embo J* (2012) **31** 1405-1426. DOI: 10.1038/emboj.2011.503
9. Yang Y, Shen F, Huang W, Qin S, Huang JT, Sergi C, Yuan BF, Liu SM. **Glucose is involved in the dynamic regulation of m6A in patients with type 2 diabetes**. *J Clin Endocrinol Metab* (2019) **104** 665-673. DOI: 10.1210/jc.2018-00619
10. Grimes M, Hall B, Foltz L, Levy T, Rikova K, Gaiser J, Cook W, Smirnova E, Wheeler T, Clark NR. **Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks**. *Sci Signal* (2018). DOI: 10.1126/scisignal.aaq1087
11. Tan CS, Bodenmiller B, Pasculescu A, Jovanovic M, Hengartner MO, Jørgensen C, Bader GD, Aebersold R, Pawson T, Linding R. **Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases**. *Sci Signal* (2009) **2** ra39. DOI: 10.1126/scisignal.2000316
12. Ying H, DePinho RA. **Cancer signaling: when phosphorylation meets methylation**. *Cell Res* (2014) **24** 1282-1283. DOI: 10.1038/cr.2014.103
13. Shlomai G, Neel B, LeRoith D, Gallagher EJ. **Type 2 diabetes mellitus and cancer: the role of pharmacotherapy**. *J Clin Oncol* (2016) **34** 4261-4269. DOI: 10.1200/JCO.2016.67.4044
14. García-Jiménez C, Gutiérrez-Salmerón M, Chocarro-Calvo A, García-Martinez JM, Castaño A, De la Vieja A. **From obesity to diabetes and cancer: epidemiological links and role of therapies**. *Br J Cancer* (2016) **114** 716-722. DOI: 10.1038/bjc.2016.37
15. Pernicova I, Korbonits M. **Metformin–mode of action and clinical implications for diabetes and cancer**. *Nat Rev Endocrinol* (2014) **10** 143-156. DOI: 10.1038/nrendo.2013.256
16. Dehghan M, Mente A, Rangarajan S, Sheridan P, Mohan V, Iqbal R, Gupta R, Lear S, Wentzel-Viljoen E, Avezum A. **Association of dairy intake with cardiovascular disease and mortality in 21 countries from five continents (PURE): a prospective cohort study**. *Lancet* (2018) **392** 2288-2297. DOI: 10.1016/S0140-6736(18)31812-9
17. Elwood PC, Givens DI, Beswick AD, Fehily AM, Pickering JE, Gallacher J. **The survival advantage of milk and dairy consumption: an overview of evidence from cohort studies of vascular diseases, diabetes and cancer**. *J Am Coll Nutr* (2008) **27** 723s-734s. DOI: 10.1080/07315724.2008.10719750
18. Brimelow RE, West NP, Williams LT, Cripps AW, Cox AJ. **A role for whey-derived lactoferrin and immunoglobulins in the attenuation of obesity-related inflammation and disease**. *Crit Rev Food Sci Nutr* (2017) **57** 1593-1602. DOI: 10.1080/10408398.2014.995264
19. González-Chávez SA, Arévalo-Gallegos S, Rascón-Cruz Q. **Lactoferrin: structure, function and applications**. *Int J Antimicrob Agents* (2009) **33** e301-308
20. Li HY, Yang HG, Li P, Wang YZ, Huang GX, Xing L, Wang JQ, Zheng N. **Effect of heat treatment on the antitumor activity of lactoferrin in human colon tumor (HT29) model**. *J Agric Food Chem* (2019) **67** 140-147. DOI: 10.1021/acs.jafc.8b05131
21. Okubo K, Kamiya M, Urano Y, Nishi H, Herter JM, Mayadas T, Hirohama D, Suzuki K, Kawakami H, Tanaka M. **Lactoferrin suppresses neutrophil extracellular traps release in inflammation**. *EBioMedicine* (2016) **10** 204-215. DOI: 10.1016/j.ebiom.2016.07.012
22. Mayeur S, Spahis S, Pouliot Y, Levy E. **Lactoferrin, a pleiotropic protein in health and disease**. *Antioxid Redox Signal* (2016) **24** 813-836. DOI: 10.1089/ars.2015.6458
23. Gibbons JA, Kanwar RK, Kanwar JR. **Lactoferrin and cancer in different cancer models**. *Front Biosci (Schol Ed)* (2011) **3** 1080-1088. DOI: 10.2741/212
24. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M. **NCBI GEO: archive for functional genomics data sets—update**. *Nucleic Acids Res* (2013) **41** D991-995. DOI: 10.1093/nar/gks1193
25. Galamb O, Kalmár A, Barták BK, Patai ÁV, Leiszter K, Péterfia B, Wichmann B, Valcz G, Veres G, Tulassay Z, Molnár B. **Aging related methylation influences the gene expression of key control genes in colorectal cancer and adenoma**. *World J Gastroenterol* (2016) **22** 10325-10340. DOI: 10.3748/wjg.v22.i47.10325
26. Ludvik AE, Pusec CM, Priyadarshini M, Angueira AR, Guo C, Lo A, Hershenhouse KS, Yang GY, Ding X, Reddy TE. **HKDC1 is a novel hexokinase involved in whole-body glucose use**. *Endocrinology* (2016) **157** 3452-3461. DOI: 10.1210/en.2016-1288
27. Mentch SJ, Mehrmohamadi M, Huang L, Liu X, Gupta D, Mattocks D, Gómez Padilla P, Ables G, Bamman MM, Thalacker-Mercer AE. **Histone methylation dynamics and gene regulation occur through the sensing of one-carbon metabolism**. *Cell Metab* (2015) **22** 861-873. DOI: 10.1016/j.cmet.2015.08.024
28. Weickhmann AK, Keller H, Wurm JP, Strebitzer E, Juen MA, Kremser J, Weinberg Z, Kreutz C, Duchardt-Ferner E, Wöhnert J. **The structure of the SAM/SAH-binding riboswitch**. *Nucleic Acids Res* (2019) **47** 2654-2665. DOI: 10.1093/nar/gky1283
29. Goll MG, Kirpekar F, Maggert KA, Yoder JA, Hsieh CL, Zhang X, Golic KG, Jacobsen SE, Bestor TH. **Methylation of tRNAAsp by the DNA methyltransferase homolog Dnmt2**. *Science* (2006) **311** 395-398. DOI: 10.1126/science.1120976
30. Chen YJ, Chang WA, Huang MS, Chen CH, Wang KY, Hsu YL, Kuo PL. **Identification of novel genes in aging osteoblasts using next-generation sequencing and bioinformatics**. *Oncotarget* (2017) **8** 113598-113613. DOI: 10.18632/oncotarget.22748
31. Iijima H, Tomizawa Y, Iwasaki Y, Sato K, Sunaga N, Dobashi K, Saito R, Nakajima T, Minna JD, Mori M. **Genetic and epigenetic inactivation of LTF gene at 3p21.3 in lung cancers**. *Int J Cancer* (2006) **118** 797-801. DOI: 10.1002/ijc.21462
32. Liu YH, Huang AT, Teng CT. **Polymorphism and altered methylation of the lactoferrin gene in normal leukocytes, leukemic cells, and breast cancer**. *Cancer Res* (1991) **51** 3037-3043. PMID: 1674448
33. Shaheduzzaman S, Vishwanath A, Furusato B, Cullen J, Chen Y, Bañez L, Nau M, Ravindranath L, Kim KH, Mohammed A. **Silencing of Lactotransferrin expression by methylation in prostate cancer progression**. *Cancer Biol Ther* (2007) **6** 1088-1095. DOI: 10.4161/cbt.6.7.4327
34. Miao Y, Su B, Tang X, Wang J, Quan W, Chen Y, Mi D. **Construction and validation of m(6)A RNA methylation regulators associated prognostic model for gastrointestinal cancer**. *IET Syst Biol* (2022) **16** 59-71. DOI: 10.1049/syb2.12040
35. Cheng H, Wang S, Li Z, Ma Y, Song Y. **ING2-WTAP is a potential therapeutic target in non-small cell lung cancer**. *Biochem Biophys Res Commun* (2022) **02** 31-38. DOI: 10.1016/j.bbrc.2022.02.041
36. Wang C, Tang C, Wang Y, Huang B, Shao J. **Upregulated WTAP expression appears to both promote breast cancer growth and inhibit lymph node metastasis**. *Sci Rep* (2022) **12** 1023. DOI: 10.1038/s41598-022-05035-y
37. Chen Y, Peng C, Chen J, Chen D, Yang B, He B, Hu W, Zhang Y, Liu H, Dai L. **WTAP facilitates progression of hepatocellular carcinoma via m6A-HuR-dependent epigenetic silencing of ETS1**. *Mol Cancer* (2019) **18** 127. DOI: 10.1186/s12943-019-1053-8
38. Tang J, Wang F, Cheng G, Si S, Sun X, Han J, Yu H, Zhang W, Lv Q, Wei JF, Yang H. **Wilms' tumor 1-associating protein promotes renal cell carcinoma proliferation by regulating CDK2 mRNA stability**. *J Exp Clin Cancer Res* (2018) **37** 40. DOI: 10.1186/s13046-018-0706-6
39. Jin DI, Lee SW, Han ME, Kim HJ, Seo SA, Hur GY, Jung S, Kim BS, Oh SO. **Expression and roles of Wilms' tumor 1-associating protein in glioblastoma**. *Cancer Sci* (2012) **103** 2102-2109. DOI: 10.1111/cas.12022
40. Kobayashi M, Ohsugi M, Sasako T, Awazawa M, Umehara T, Iwane A, Kobayashi N, Okazaki Y, Kubota N, Suzuki R. **The RNA methyltransferase complex of WTAP, METTL3, and METTL14 regulates mitotic clonal expansion in adipogenesis**. *Mol Cell Biol* (2018). DOI: 10.1128/MCB.00116-18
41. Friso S, Choi SW, Dolnikowski GG, Selhub J. **A method to assess genomic DNA methylation using high-performance liquid chromatography/electrospray ionization mass spectrometry**. *Anal Chem* (2002) **74** 4526-4531. DOI: 10.1021/ac020050h
42. Liu R, Zhao F, Wei J, Yu P, Zhang J, Wang Y, Li Z, Zhang J, Zhang X, Tian X. **Determination of five nucleosides by LC-MS/MS and the application of the method to quantify N(6)-methyladenosine level in liver messenger ribonucleic acid of an acetaminophen-induced hepatotoxicity mouse model**. *J Sep Sci* (2019) **42** 2668-2678. PMID: 31197931
43. Fernandez AF, Valledor L, Vallejo F, Cañal MJ, Fraga MF. **Quantification of global DNA methylation levels by mass spectrometry**. *Methods Mol Biol* (2018) **1708** 49-58. DOI: 10.1007/978-1-4939-7481-8_3
44. Li H, Yang H, Li P, Li M, Yao Q, Min L, Zhang Y, Wang J, Zheng N. **Maillard reaction products with furan ring, like furosine, cause kidney injury through triggering ferroptosis pathway**. *Food Chem* (2020) **319** 126368. DOI: 10.1016/j.foodchem.2020.126368
45. Xiao Y, Wang Y, Tang Q, Wei L, Zhang X, Jia G. **An elongation- and ligation-based qPCR amplification method for the radiolabeling-free detection of locus-specific N(6)-methyladenosine modification**. *Angew Chem Int Ed Engl* (2018) **57** 15995-16000. DOI: 10.1002/anie.201807942
46. Mo J, Chen Z, Qin S, Li S, Liu C, Zhang L, Ran R, Kong Y, Wang F, Liu S. **TRADES: targeted RNA demethylation by SunTag System**. *Adv Sci (Weinh)* (2020) **7** 2001402. DOI: 10.1002/advs.202001402
47. Gajovic N, Jurisevic M, Pantic J, Radosavljevic G, Arsenijevic N, Lukic ML, Jovanovic I. **Attenuation of NK cells facilitates mammary tumor growth in streptozotocin-induced diabetes in mice**. *Endocr Relat Cancer* (2018) **25** 493-507. DOI: 10.1530/ERC-17-0529
48. Burren KA, Mills K, Copp AJ, Greene ND. **Quantitative analysis of s-adenosylmethionine and s-adenosylhomocysteine in neurulation-stage mouse embryos by liquid chromatography tandem mass spectrometry**. *J Chromatogr B Anal Technol Biomed Life Sci* (2006) **844** 112-118. DOI: 10.1016/j.jchromb.2006.07.012
49. Krijt J, Dutá A, Kozich V. **Determination of S-adenosylmethionine and S-adenosylhomocysteine by LC-MS/MS and evaluation of their stability in mice tissues**. *J Chromatogr B Anal Technol Biomed Life Sci* (2009) **877** 2061-2066. DOI: 10.1016/j.jchromb.2009.05.039
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---
title: Associations between sex differences, eating disorder behaviors, physical and
mental health, and self-harm among Chinese adolescents
authors:
- Yuanyuan Wang
- Zhihao Ma
- Su Lu
- Zhizhou Duan
- Amanda Wilson
- Yinwei Jia
- Yong Yang
- Runsen Chen
journal: Journal of Eating Disorders
year: 2023
pmcid: PMC9972801
doi: 10.1186/s40337-023-00754-7
license: CC BY 4.0
---
# Associations between sex differences, eating disorder behaviors, physical and mental health, and self-harm among Chinese adolescents
## Abstract
### Background and aim
Eating Disorders (ED) result in impaired well-being, but there exist an insufficient number of studies that have focused on the influence of sex and sexual orientation disparities within ED behaviors. Thus, we aimed to investigate ED behaviors among male and female adolescents with different sexual orientations in a school sample to understand prevalence and correlates of different ED behaviors.
### Method
Data was analysed from 11,440 Chinese school adolescents with a mean age of 14.74 years (SD = 1.46). Reported data was gathered on sociodemographic information including sexual orientation, ED behaviors, health factors (reported health, cognitive function), mental health factors (depression, anxiety, suicidal ideation, non-suicidal self-injurious behavior), and social functioning (school bully victimization, and school bully perpetration). Logistic regression models were used to estimate the associations with ED behaviors, using the heterosexual orientation as the reference group as they are the majority.
### Results
Compared to female adolescents, male adolescents reported lower anxiety symptoms (t = − 12.39, $p \leq 0.001$, Cohen’s d = − 0.233), were more likely to be the perpetrator of school bullying (χ2 = 190.61, $p \leq 0.001$, φ = 0.129), and reported a lower likelihood of taking dietary restriction (χ2 = 290.08, $p \leq 0.001$, φ = 0.160). Overall, the prevalence of dietary restriction presented sex disparities. Adolescents who reported no sexual attraction were less likely to engage in ED behaviors. Using heterosexual orientation as the reference group, the group who reported no sexual attraction was associated with lower risk in dietary restriction and purging in both male and female adolescents. Using the heterosexual orientation as the reference group, female sexual minority groups were at high risk of ED behaviors, with bisexual orientation and gay/lesbian orientation having a higher likelihood of engaging in objective binge eating.
### Conclusions
The results revealed significant sex and sexual orientation differences of ED behaviors. The study suggests that adolescents is a period of sexuality development and could be critical for understanding adolescents’ eating behaviors. It is important to guide adolescents to healthy eating during their development and considerations should be made by clinicians when creating interventions for ED behaviors among the different sex and sexual orientation groups.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40337-023-00754-7.
## Summary
Poor reported health status, cognitive deficits, suicidal ideation, self-harm, mood disorders, and school bullying experiences were associated with ED behaviors among adolescents. Also, female sexual minority groups had a stronger association with ED behaviors. We recommended to implement tailored prevention strategies that are different for each of the adolescent groups studied, and that this needs to be specific to the different ED behaviors discussed.
## Introduction
Eating Disorders (ED) are psychiatric disorders characterised by pathological eating behaviors, which can lead to impaired physical health, psychosocial functions, and medical complications [68]. ED such as objective binge eating or extreme dietary restriction have been significantly associated with functional impairment in both work and life due to the impact that ED’s can have on mental and physical health [45, 64]. The prevalence of ED has increased in the recent half century, and researchers have recommended that eating habits should be routinely assessed as a part of general health assessments [68]. Adolescence is a period of body development, that results in adolescents being vulnerable to EDs, and the peak of onset is 15–19 years of age, which has arguably been linked to the onset of puberty’s hormonal and weight related changes [53]. In a longitudinal study over 30-years, sex differences were found in the psychological impact of the onset of puberty relative to peers, where late onset puberty was associated with abnormal eating attitudes and behavior among adolescent males but a protective factor for females; likewise earlier onset puberty was an associated factor for females, but there was no significant association for males [31]. There are also sex differences in current research looking at 14–19-year-olds that suggests being a victim of bullying and reported distress are two specific associations for developing ED during adolescence, with sex differences being reported where $19.4\%$ of males and $44.6\%$ of females met the threshold for ED, with adolescent females reporting worse peer relationships than males [13].
## Sex and ED behaviors
It is well-documented that there are sex differences in ED behaviors. Research has shown that males are more likely to perceive overeating, while females are more likely to perceive loss of control while eating [65]. Previous research in Australia suggests that when compared to females, objective binge eating has a greater impairment on males’ mental health, and that weight concerns have a greater impairment on females’ mental health [44]. Neumark-Sztainer and colleagues [49] further found that concerns about weight, weight related teasing, and dieting predicted ED behaviors in female adolescents, and concern over weight and weight control behaviors predicted these behaviors in male adolescents. The weight related concerns vary in males and females, where males are socialized to value muscularity and female are socialized to prefer a slim figure [68].
## Sexual orientation and ED behaviors
Besides the sex differences, research also indicates that there are eating behavior disparities among different sexual orientation groups [7]. Limited research has focused on different ED behaviors when considering sexual orientation, particularly for adolescent males and females, as sexual minority youth also are at a greater risk of EDs [2, 47]. A literature review does suggest that adolescents who are Lesbian, Gay, or Bisexual (LGB) are more prone to eating disorders than heterosexual adolescents, with the literature on lesbian adolescents being inconsistent in their findings. However, overall individual factors are considered related to the Minority Stress Theory [51]. In the Minority Stress Theory LGB populations perceive stressors including distal stress, such as discrimination and victimization, proximal stress, such as internalised homophobic stigma, and disclosure stress, such as the stress of concealment of sexual orientation, as well as social violence and victimization [51]. In an expansion of the theory researchers indicate that the ED behaviors reported by sexual minorities could be perpetuated by minority stress and discrimination [47, 51]. Research on ED among the LGB population has shown that there are more ED behaviors among gay males when compared to heterosexual males [1]. Compared to heterosexual counterparts, sexual minority youth perceive ED behaviors more frequently and they have a significantly higher prevalence of ED than heterosexuals [47, 77]. A recent meta-analysis revealed that compared to heterosexual females, a higher number ED behaviors were found among sexual minority female, with higher occurrences of binge eating and purging specifically [43]. Studies further show that sexual orientation could modify related ED behavior in male and females [7, 8, 21]. Bisexual female and gay males are found to have significantly higher body weight dissatisfaction than heterosexual participants, lesbians, and bisexual males [42]. Thus, those disparities in body weight dissatisfaction may consequently lead to disparities in ED behaviors among different sexual orientation groups. In addition, research has indicated that sexual orientation discrimination was related to similar or increased ED in lesbian youth, when compared to heterosexual females, as they perceive less familial social support, which is related to negative affect and social anxiety [40]. The research on sexuality and ED behaviors factor in that identifying as lesbian or a bisexual females may affect the eating behaviors of adolescents and young adults. For example, research suggests that lesbian or bisexual females face the same pressures to conform to heteronormative beauty standards such as thin and curvaceous, which are seen as more desirable to certain sexual partners and have higher rates of body satisfaction [58]. In contrast, when compared with heterosexual females, lesbians are considered significantly more likely to be overweight [3, 66]. These socio-cultural standards of sexual attraction and the reality of their body related features could impact on eating behaviors among different sexual orientations, however further research is needed before any conclusion can be made. It is also important to consider the negative social construction that comes with being a sexual minority, in concordance with the Chinese traditional cultural values [20, 74, 75], which could affect the bodily perception of young sexual minority people and consequently lead to their distorted perceptions of body shape resulting in ED behaviors.
## Other variables associated with ED behaviors
Beside the disparities from sex and sexual orientation, there are variables proposed in previous studies that are closely associated with ED behaviors. Specifically, suicidal ideation and non-suicidal self-harm, reported cognitive deficits, anxiety symptoms, depressive symptoms, health status, school bullying experiences, and culture. These variables will be discussed in turn.
A previous meta-analysis showed that compared with the general population, people with anorexic tendencies are eighteen times more likely to die from suicide, and five times more likely to die prematurely from any other cause [26]. Thus, research on Anorexia Nervosa related concepts, such as dietary restriction, in adolescents is necessary. Furthermore, in a regression model study, with 82 adolescents, binge eating did not predict suicide where restrictive eating did [73]. It has been further suggested there is a high comorbidity of non-suicidal self-harm and ED behaviors [11], however the literature on adolescents predominantly focuses on adolescent females. In a sample of 47 adolescent females, who were admitted to hospital for Anorexia Nervosa, self-harm was found in $47\%$ of the young females [28]. In contrast, a study of 189 female adolescent inpatients, showed a higher tendency of non-suicidal self-harm in the binge-purge group when compared to the restrictive eating group, with various personality factors influencing the prevalence [4]. The research in this area is still in its infancy with the protocol recently published for a mixed methods Self-Harm in Eating Disorders (SHINE) study [30], suggesting that understanding the risk of non-suicidal self-harm in sexual minority adolescent populations is also required.
Cognitive deficits, or mental impairments like an attention disorder, have also been linked to ED, with research suggesting that having a cognitive deficit can increase the risk of developing an ED [32]. This predisposition is also found in other studies, with a study of high-risk versus low-risk adolescents with ED finding that the high-risk group had deficits, specifically those with a higher risk of bulimia being more impulsive and having more theory of mind deficits when compared to those with anorexia [48]. Overall though there is a paucity of research on cognitive deficits and ED, but it is widely accepted the two concepts are related. The opposite is found when looking at common comorbid psychiatric problems, such as anxiety and depressive symptoms and ED, where there is a dearth of research. In a longitudinal study of adolescent boys and girls, having anxiety symptoms as a preadolescent can predict ED and the longitudinal effects of anxiety and ED can persist for 15 years after follow-up [70]. Not only can anxiety symptoms predict ED in adolescents but so can depressive symptoms, where being a female child predicts anorexia symptoms, and no known factors predicted the development of bulimia [5]. In another longitudinal study spanning a 15 year follow up period, depressive symptoms were predicted by dissatisfaction with one’s body, including ED outcomes like dieting, unhealthy weight control, and binge eating, particularly amongst female adolescents [54]. Similar to anxiety and depressive symptoms, the Health Questionnaire Short Form-36 has been widely studied in adolescents with ED, and is the most common measure of what we have referred to as health status in those with ED [15]. Though there is a dearth of research on anxiety and depressive symptoms and research using the health status survey, this has yet to be explored within sexual minority adolescents. The same is found with school bullying research, while it is concluded in a systematic review that bullying does predict ED [14], this has yet to be understood in sexual minority adolescents. Similar to the West, Chinese culture promotes fitness and shames obesity, with obesity signifying someone is unhealthy, ugly, clumsy, and/or stupid, irrespective of sex [34], which is fatphobic and highly stigmatizing. Though again the Chinese cultural context lacks explanation in most papers on Chinese adolescents with ED.
This study therefore aimed to explore the ED behaviors in adolescents and aimed to investigate the associated factors and health burden in a Chinese adolescent population, which could be used to inform future clinical interventions.
## Procedures and sample
Data for this study was cross-sectional and collected in Suzhou, China between June 2019 and July 2019 [75]. The participants were recruited in eighteen local secondary schools (grades 7–11). School teachers aided in the recruitment of participants. It was made clear to potential participants that participation was voluntary and that there were no adverse consequences for declining to participate or if later they withdrew from the study. The Ethics Committee of Suzhou Guangji Hospital approved the study protocol. A total of 12,354 adolescents completed the survey, with a response rate of $83.2\%$. All eligible samples ($$n = 11$$,440) provided their birth-assigned sex (the biological sex), gender identity, and what sex they were sexually attracted to [76]. A total of 914 students were excluded from analysis including: 227 students who did not provide a valid response on age (missing, below 10 years, and above 20 years were treated as invalid), 234 students who did not further provided their reported sex, and 453 students who could not further be identified as a certain type of sexual orientation.
## Measures
Socio-demographic information included: age, birth assigned sex, residence, whether they had any siblings, grade, and school residential status.
## Sexual orientation
The student’s sexual orientation was measured by two questions: “*What is* your reported sex (choosing from male or female)?” and “which sex are you sexually attracted to (choosing from male, female, both, or none)?”. Those male adolescents who were attracted to male were identified as gay adolescents; those female adolescents who were attracted to female were identified as lesbian adolescents; those who were attracted to opposite sex were identified as heterosexual adolescents; those who were attracted to both males and females were identified as bisexual adolescents; and those who were attracted to none were identified as reporting no sexual attraction. The measurement of sexual orientation was also adopted based on previous studies [75, 76].
## Eating disorder behaviors
Dietary restriction, purging, subjective binge eating, and objective binge eating were assessed by four related questions adapted from the Mini International Neuropsychiatric Interview—Anorexia Nervosa Criteria [55], which is used to screen ED in both clinical and epidemiological settings [16, 60, 61]. Previous studies [18, 60] use this inventory to measure diverse ED symptoms established according to DSM-IV axis I criteria (American Psychiatric Association, 1994). There is little evidence to suggest the changes between the DSM-IV and DSM-5 are significant, with research showing little change in a community sample (similar to the sample in this study) even with the introduction of Binge Eating Disorder, which is summed to the conservative changes between ED in the two versions [25]. Main changes from DSM-IV to DSM-5 result in a reduction of eating disorders not otherwise specified [6], a group excluded in this study, therefore the resource available (DSM-IV) was utilised. Dietary restriction was defined based on whether adolescents tried to avoid weight gain in the past three month (0 = no, 1 = yes). Purging was defined based on whether adolescents made themselves vomit or use laxatives to avoid weight increase after eating (0 = no, 1 = yes). Subjective binge eating and objective binge eating were measured by two items [7, 22]. Binary yes/no questions were asked about eating too much food in a short period of time and feeling out of control. Students were categorized as subjective binge eating if they ate too much food in a short period of time and felt under control. Students were categorized as having reported binge eating if they chose yes on either item.
## Suicidal ideation and non-suicidal self-harm
Suicidal ideation was measured by the item “have you had any suicidal ideation in the last month?”, with the option to choose from “no” or “yes”. Non-suicidal self-harm was measured by the item “I have tried to hurt myself deliberately without intention to kill myself in the last month”, choosing from “no” or “yes”. The two items have been used widely in previous studies [38, 39, 56].
## Reported cognitive deficits
The Reported Deficits Questionnaire (PDQ-5) was used to assess reported cognitive deficits, which can capture attention, memory, and executive function related-essential cognitive dysfunction [46]. PDQ-5 is a 5-item Likert scale and each item provides the option to choose from 0 (none) to 4 (very frequently). A higher mean score indicates more severe reported cognitive deficits. The Chinese versions of the PDQ-5 has good validity and reliability [19] and has been used previously with Chinese adolescents [9]. The Cronbach’s alpha was 0.78 in this survey.
## Anxiety symptoms
The Generalized Anxiety Disorder-7 (GAD-7) was used to screen for anxiety symptoms [62]. The GAD-7 is a 7-item, 4-point Likert scale, with the total scores ranging 0 from 21. A higher mean score indicates a higher level of anxiety symptoms and it has confirmed validity and reliability in Chinese populations [79], including adolescents [10, 78, 79] Cronbach’s alpha was 0.94 in this survey.
## Depression symptoms
The Patient Health Questionnaire-9 (PHQ-9) was used to screen for depression symptoms in this study [29]. The PHQ-9 consists of 9 items, and each item is scored from 0 (not at all) to 3 (nearly every day). A higher mean score indicates a higher level of depressive symptoms. The Chinese version of PHQ-9 has confirmed good validity and reliability [73]. Studies focused on adolescent populations also employ the Chinese version of PHQ-9 [78, 80]. The Cronbach’s alpha was 0.93 in this study.
## Reported health status
Reported health status was assessed by a single item adapted from the 36-Item Short -Form Survey (36-SF) [33]. The item asked, “*In* general, how what do you feel your health status is?”, and participants choose from excellent (scored 1) to poor (scored 5).
## School bullying experiences
Previous studies revealed that traumatic events in the school environment, especially school bullying experiences, were associated with ED [35, 36, 52, 67]. The school bullying experiences was measured by two questions: “Have you ever been bullied at school in this academic year?” and “Have you ever bullied or laughed at others in this academic year?”. Participants were coded as victims of school bullying if they answered “yes” to the first question. If the second question had a response of “yes”, the participants were coded as perpetrators of school bullying. The single item measurements of both school bullying victimization and perpetration have been widely used in large sample epidemiological surveys [23, 24].
## Statistical analysis
Basic socio-demographic variables, health related factors and status were depicted as Number (%) for categorical variables and Mean (SD) for continuous variables. All variables included in the current study (e.g., socio-demographic variables, sexual orientation, covariate variables, and ED behaviors) were compared between male and female adolescents using chi-square test with φ as the effect size for categorical variables, and t-test with Cohen’s d as the effect size for continuous variables. Binary logistic regressions were used to explore whether sexual orientation contributed to ED behaviors among students, with ED behaviors as the dependent variable, and socio-demographic characteristics and health related factors as the independent variables. Since heterosexuals are usually the majority group, we decided that the heterosexual orientation group would act as the reference group. Moreover, we conduct binary logistic regression to test the associations between the independent variables (excluding sexual orientation) and ED (results are presented in Additional file 1: Tables S1–S16 in the Appendix). The binary logistic regression models were run for both male and female adolescents separately. All analyses were conducted by Stata/SE 16.1 software and statical significance was set at the 0.05 (two-tailed) level in this study. We use the term “associations” in this study to mean the “correlate shown to precede the outcome”, instead of strictly indicating the causality. In addition, the male or female term used in sections of the analysis and results refers to one’s biological sex.
## Characteristics of the participants
The characteristics of the participants are shown in Table 1 and the mean age was 14.74 (SD = 1.46). Compared to female adolescents, male adolescents reported decreased anxiety symptoms (t = − 12.39, $p \leq 0.001$, Cohen’s d = − 0.233). Moreover, male adolescents were more likely to be the perpetrator in school bullying (χ2 = 190.61, $p \leq 0.001$, φ = 0.129). Additionally, male adolescents reported a lower likelihood of taking dietary restrictions (χ2 = 290.08, $p \leq 0.001$, φ = 0.160).Table 1Basic socio-demographic characteristics and health related features of samplesVariablesTotal ($$n = 11$$,440)Male adolescents ($$n = 6145$$)Female adolescents ($$n = 5295$$)χ2/TbPEffect size (Cohen's d/ φ)Mean ± SDN (%)Mean ± SDN (%)Mean ± SDN (%)Age14.74 ± 1.46–14.74 ± 1.45–14.75 ± 1.47– − 0.370.709 − 0.007Household Registration Residencea10.640.0010.032 Rural–4207 (39.35)–2175 (37.92)–2032 (41.01) Urban–6484 (60.65)–3561 (62.08)–2923 (58.99)Being the Only Childa76.81 < 0.0010.083 No–7383 (65.89)–3742 (62.24)–3641 (70.11) Yes–3822 (34.11)–2270 (37.76)–1552 (29.89)Gradea5.250.0220.021 Junior middle school–8579 (75.03)–4662 (75.89)–3917 (74.03) Senior middle school–2855 (24.97)–1481 (24.11)–1374 (25.97)School residential statusa5.910.0150.023 Board at school–2900 (25.74)–1502 (24.81)–1398 (26.82) *Attend a* day school–8365 (74.26)–4551 (75.19)–3814 (73.18)Sexual orientation318.59 < 0.0010.096 Heterosexual–4418 (38.62)–2767 (45.03)–1651 (31.18) Gay/Lesbian–653 (5.71)–241 (3.92)–412 (7.78) Bisexual–1524 (13.32)–628 (10.22)–896 (16.92) Reporting no sexual attraction–4845 (42.35)–2509 (40.83)–2336 (44.12)Suicidal Ideation98.99 < 0.0010.093 No–9749 (85.22)–5425 (88.28)–4324 (81.66) Yes–1691 (14.78)–720 (11.72)–971 (18.34)Non-suicidal self-Harm85.68 < 0.0010.087 No–10,061 (87.95)–5565 (90.56)–4496 (84.91) Yes–1379 (12.05)–580 (9.44)–799 (15.09)Perceived cognitive deficitsa1.23 ± 0.85–1.17 ± 0.86–1.30 ± 0.84– − 8.15 < 0.001 − 0.153Anxiety symptomsa0.74 ± 0.72–0.66 ± 0.70–0.82 ± 0.73– − 12.39 < 0.001 − 0.233Depression symptomsa0.59 ± 0.68–0.54 ± 0.65–0.65 ± 0.70– − 8.89 < 0.001 − 0.167Poor self-rated health statusa2.09 ± 0.81–2.02 ± 0.83–2.18 ± 0.77– − 10.54 < 0.001 − 0.199School bullying victima No–10,549 (92.41)–5556 (90.68)–4993 (94.42)56.65 < 0.0010.070 Yes–866 (7.59)–571 (9.32)–295 (5.58)School bullying perpetratora No–9583 (83.77)–4876 (79.35)–4707 (88.90)190.61 < 0.0010.129 Yes–1857 (16.23)–1269 (20.65)–588 (11.10)Dietary restrictiona290.08 < 0.0010.160 No–6735 (59.03)–4063 (66.31)–2672 (50.59) Yes–4674 (40.97)–2064 (33.69)–2610 (49.41)Purginga0.660.4150.008 No–10,644 (93.69)–5719 (93.86)–4925 (93.49) Yes–717 (6.31)–374 (6.14)–343 (6.51)Subjective Binginga42.51 < 0.0010.061 No–10,122 (88.61)–5325 (86.81)–4797 (90.70) Yes–1301 (11.39)–809 (13.19)–492 (9.30)Binge Eatinga0.110.7380.003 No–8629 (75.54)–4626 (75.42)–4003 (75.69) Yes–2794 (24.46)–1508 (24.58)–1286 (24.31)aMissing data were not entered statistical analysisbχ2 tests or T tests were used to test the differences between male and female adolescents
## Associations between sexual orientation and ED behaviors
Tables 2, 3, 4, and 5 reveal the results of how sexual orientation is associated with ED behaviors for male and female adolescents separately. After controlling for the socio-demographic variables and associations, the binary logistic regression models revealed that, among male adolescents, being of an reporting no sexual attraction (heterosexual as ref.) was associated with lower likelihood of dietary restriction (OR 0.835; $95\%$ CI 0.731–0.954; $p \leq 0.01$, Table 2), purging (OR 0.709; $95\%$ CI 0.536–0.937; $p \leq 0.05$, Table 3), and binge eating (OR 0.852; $95\%$ CI 0.731–0.993; $p \leq 0.05$, Table 5).Table 2Risk factors and association with dietary restriction in school adolescentsMale adolescentsFemale adolescentsOdds ratioStd. errZ valueP value[$95\%$ conf. interval]Odds ratioStd. errZ valueP value[$95\%$ conf. interval]Sexual orientation (Heterosexual = 0)Gay/Lesbian0.8270.132 − 1.1950.2320.605–1.1291.1360.1391.0450.296Bisexual0.9140.093 − 0.8770.3800.748–1.1170.9570.089 − 0.4720.6370.798–1.148Reporting no sexual attraction sexuality0.835**0.057 − 2.6580.0080.731–0.9540.695***0.050 − 5.0470.0000.604–0.801Age0.9820.022 − 0.8230.4110.940–1.0261.077***0.0243.3310.0011.031–1.124Household registration residence (Urban = 0)0.9830.060 − 0.2800.7790.872–1.1080.9460.059 − 0.8890.3740.837–1.069Being the only child (No = 0)1.0110.0610.1760.8600.897–1.1390.9440.063 − 0.8660.3870.828–1.076Suicidal ideation (No = 0)1.1610.1191.4510.1470.949–1.4191.320**0.1272.8920.0041.094–1.593Non-suicidal self-harm (No = 0)1.1050.1210.9100.3630.891–1.3711.566***0.1554.5360.0001.290–1.901Perceived cognitive deficits0.9300.036 − 1.8420.0650.862–1.0051.164***0.0493.5790.0001.071–1.265Anxiety symptoms1.0560.0780.7350.4620.914–1.2191.1390.0831.8010.0720.989–1.313Depression symptoms1.1750.0971.9510.0510.999–1.3821.0070.0840.0890.9290.856–1.186Poor self-rated health status1.405***0.0529.2390.0001.307–1.5101.327***0.0566.7460.0001.222–1.440School bullying victim (No = 0)1.1870.1221.6730.0940.971–1.4511.1310.1610.8660.3870.856–1.495School bullying perpetrator (No = 0)1.0650.0800.8490.3960.920–1.2331.2070.1251.8240.0680.986–1.477Constant0.326***0.110 − 3.3260.0010.169–0.6310.137***0.046 − 5.9560.0000.072–0.264Observations54414772DF1414Chi2171.6353.7Log likelihood − 3386 − 3130***$p \leq 0.001$, **$p \leq 0.01$Table 3Risk factors and association with purging in school adolescentsMale adolescentsFemale adolescentsOdds ratioStd. errZ valueP value[$95\%$ conf. interval]Odds ratioStd. errZ valueP value[$95\%$ conf. interval]Sexual orientation (Heterosexual = 0)Gay/Lesbian0.8420.251 − 0.5750.5650.469–1.5120.7550.175 − 1.2140.2250.480–1.189Bisexual1.0950.2030.4900.6240.762–1.5730.9280.151 − 0.4600.6450.674–1.277Reporting no sexual attraction0.709*0.101 − 2.4170.0160.536–0.9370.557***0.086 − 3.7880.0000.411–0.754Age0.9750.042 − 0.5740.5660.896–1.0621.103*0.0472.3050.0211.015–1.199Household registration residence (Urban = 0)1.1610.1411.2250.2210.914–1.4731.1560.1451.1510.2500.903–1.479Being the only child (No = 0)0.9230.115 − 0.6420.5210.724–1.1781.0660.1420.4760.6340.820–1.384Suicidal ideation (No = 0)1.425*0.2462.0510.0401.016–2.0001.2460.2021.3590.1740.907–1.711Non-suicidal self-harm (No = 0)1.3680.2461.7480.0800.963–1.9451.513**0.2352.6700.0081.116–2.051Perceived cognitive deficits0.9470.071 − 0.7280.4670.816–1.0971.0850.0871.0080.3130.926–1.270Anxiety symptoms1.2010.1651.3310.1830.917–1.5721.2510.1631.7210.0850.969–1.615Depression symptoms1.3080.1971.7860.0740.974–1.7571.372*0.1992.1880.0291.034–1.823Poor self-rated health status1.287***0.0913.5580.0001.120–1.4791.410***0.1233.9260.0001.188–1.674School bullying victim (No = 0)1.2710.2261.3520.1760.898–1.8011.2680.2671.1260.2600.839–1.917School bullying perpetrator (No = 0)1.1100.1550.7460.4560.844–1.4601.565**0.2542.7630.0061.139–2.150Constant0.037***0.025 − 4.9860.0000.010–0.1360.004***0.002 − 8.5900.0000.001–0.013Observations54164758DF1414Chi2117.1232.4Log likelihood − 1138 − 1019***$p \leq 0.001$, **$p \leq 0.01$, *$p \leq 0.05$Table 4Risk factors and association with subjective binging in school adolescentsMale adolescentsFemale adolescentsOdds ratioStd. errZ valueP value[$95\%$ conf. interval]Odds ratioStd. errZ valueP value[$95\%$ conf. interval]Sexual orientation (Heterosexual = 0)Gay/Lesbian0.9230.192 − 0.3840.7010.614–1.3881.0050.1980.0260.9790.683–1.479Bisexual1.2130.1571.4970.1340.942–1.5631.3090.1841.9120.0560.993–1.724Reporting no sexual attraction0.8530.081 − 1.6620.0960.708–1.0290.8870.111 − 0.9560.3390.694–1.134Age1.0190.0300.6170.5370.961–1.0801.097**0.0392.6130.0091.023–1.175Household registration residence (Urban = 0)1.0400.0870.4760.6340.884–1.2250.9900.103 − 0.0920.9270.807–1.215Being the only child (No = 0)0.9520.080 − 0.5830.5600.808–1.1221.0280.1140.2520.8010.827–1.278Suicidal ideation (No = 0)1.0230.1350.1750.8610.790–1.3260.8810.129 − 0.8690.3850.661–1.173Non-suicidal self-harm (No = 0)0.9870.140 − 0.0890.9290.748–1.3040.8850.131 − 0.8260.4090.663–1.182Perceived cognitive deficits1.240***0.0624.2790.0001.124–1.3691.278***0.0853.6770.0001.121–1.457Anxiety symptoms1.1660.1121.6090.1080.967–1.4070.8870.100 − 1.0670.2860.711–1.106Depression symptoms1.1250.1201.1060.2690.913–1.3881.690***0.2104.2160.0001.324–2.158Poor self-rated health status1.0670.0531.3180.1870.969–1.1761.0970.0781.3160.1880.956–1.260School bullying victim (No = 0)1.0100.1340.0780.9380.778–1.3121.1490.2280.7030.4820.780–1.694School bullying perpetrator (No = 0)1.493***0.1404.2860.0001.243–1.7941.370*0.2002.1570.0311.029–1.824Constant0.063***0.029 − 6.0890.0000.026–0.1530.011***0.006 − 8.3720.0000.004–0.031Observations54434776DF1414Chi2131.6141.2Log likelihood − 2100 − 1418***$p \leq 0.001$, **$p \leq 0.01$, *$p \leq 0.05$Table 5Risk factors and association with binge Eeting in school adolescentsMale adolescentsFemale adolescentsOdds ratioStd. errZ valueP value[$95\%$ Conf. Interval]Odds ratioStd. ErrZ valueP value[$95\%$ conf. interval]Sexual orientation (Heterosexual = 0)Gay/Lesbian1.0830.1830.4700.6380.777–1.5091.377*0.1942.2670.0231.044–1.815Bisexual1.0980.1220.8370.4020.883–1.3651.401**0.1503.1530.0021.136–1.727Reporting no sexual attraction0.852*0.067 − 2.0430.0410.731–0.9930.9960.091 − 0.0450.9640.833–1.191Age0.9830.025 − 0.6830.4950.936–1.0331.062*0.0282.2780.0231.008–1.119Household registration residence (Urban = 0)1.0420.0720.6020.5470.910–1.1940.9910.076 − 0.1180.9060.853–1.151Being the only child (No = 0)0.8920.062 − 1.6440.1000.778–1.0221.0390.0850.4710.6370.886–1.219Suicidal ideation (No = 0)1.1220.1221.0600.2890.907–1.3881.0270.1070.2550.7990.837–1.260Non-suicidal self-harm (No = 0)1.1920.1381.5120.1310.949–1.4951.310**0.1372.5790.0101.067–1.609Perceived cognitive deficits1.405***0.0598.0850.0001.294–1.5251.726***0.08611.0130.0001.566–1.902Anxiety symptoms1.1080.0881.2910.1970.948–1.2941.0570.0870.6710.5020.899–1.242Depression symptoms1.445***0.1284.1730.0001.216–1.7181.700***0.1575.7280.0001.418–2.038Poor self-rated health status1.220***0.0504.8260.0001.125–1.3221.218***0.0623.8560.0001.102–1.347School bullying victim (No = 0)1.240*0.1351.9730.0481.001–1.5341.3150.1991.8110.0700.978–1.769School bullying perpetrator (No = 0)1.601***0.1256.0060.0001.373–1.8671.754***0.1925.1290.0001.415–2.174Constant0.120***0.046 − 5.5740.0000.057–0.2530.019***0.008 − 9.7790.0000.009–0.042Observations54434776DF1414Chi2502.3765.6Log likelihood − 2773 − 2269***$p \leq 0.001$, **$p \leq 0.01$, *$p \leq 0.05$ As for female adolescents, having an reporting no sexual attraction (heterosexual as reference) was significantly associated with being less likely to have dietary restriction (OR 0.695; $95\%$ CI 0.604–0.801; $p \leq 0.001$, Table 2) and purging (OR 0.557; $95\%$ CI 0.411–0.754; $p \leq 0.001$, Table 3). Bisexual female (heterosexual as reference) was positively correlated with objective binge eating (OR 1.401; $95\%$ CI 1.136–1.727, $p \leq 0.01$, Table 5). Moreover, female adolescents who were lesbians (heterosexual as reference) had a higher likelihood of objective binge eating (OR 1.377; $95\%$ CI 1.044–1.815, $p \leq 0.05$, Table 5).
## Associations between suicidal ideation and non-suicidal self-harm and ED behaviors
Among male adolescents, suicidal ideation was positively associated with purging (OR 1.425; $95\%$ CI 1.016–2.000; $p \leq 0.05$, Table 3). As for female adolescents, suicidal ideation was associated with dietary restrictions (OR 1.320; $95\%$ CI 1.094–1.593; $p \leq 0.01$, Table 2), non-suicidal self-harm was associated with dietary restriction (OR 1.566; $95\%$ CI 1.290–1.901; $p \leq 0.001$, Table 2), purging (OR 1.513; $95\%$ CI 1.116–2.051; $p \leq 0.01$, Table 3), and objective binge eating (OR 1.310; $95\%$ CI 1.067–1.609; $p \leq 0.01$, Table 5).
## Associations between reported cognitive deficits and ED behaviors
Among male adolescents, reported cognitive deficits were associated with subjective binge eating (OR 1.240; $95\%$ CI 1.124–1.369; $p \leq 0.001$, Table 4) and objective binge eating (OR 1.405; $95\%$ CI 1.294–1.525; $p \leq 0.001$, Table 5). As for female adolescents, reported cognitive deficits were associated with dietary restriction (OR 1.164; $95\%$ CI 1.071–1.265; $p \leq 0.001$, Table 2), subjective binge eating (OR 1.278; $95\%$ CI 1.121–1.457; $p \leq 0.001$, Table 4), and objective binge eating (OR 1.726; $95\%$ CI 1.566–1.902; $p \leq 0.001$, Table 5).
## Associations between anxiety and depressive symptoms and ED behaviors
Results revealed that, the severity of anxiety was not associated with ED behaviors for both male and female adolescents. Among male adolescents, severity of depression was associated with objective binge eating (OR 1.445; $95\%$ CI 1.216–1.718; $p \leq 0.001$, Table 5). As for female adolescents, severity of depression was positively associated with the likelihood of purging (OR 1.372; $95\%$ CI 1.034–1.823; $p \leq 0.05$, Table 3), subjective binge eating (OR 1.690; $95\%$ CI 1.324–2.158; $p \leq 0.001$, Table 4), and objective binge eating (OR 1.700; $95\%$ CI 1.418–2.038; $p \leq 0.001$, Table 5).
## Associations between reported health and ED behaviors
Among male adolescents, poorer self-rated health status was positively associated with dietary restriction (OR 1.405; $95\%$ CI 1.307–1.510; $p \leq 0.001$, Table 2), purging (OR 1.287; $95\%$ CI 1.120–1.479; $p \leq 0.001$, Table 3), and objective binge eating (OR 1.220; $95\%$ CI 1.125–1.322; $p \leq 0.001$, Table 5). As for female adolescents, poor reported health status was associated with dietary restriction (OR 1.327; $95\%$ CI 1.222–1.440; $p \leq 0.001$, Table 2), purging (OR 1.410; $95\%$ CI 1.188–1.674; $p \leq 0.001$, Table 3), and objective binge eating (OR 1.218; $95\%$ CI 1.102–1.347; $p \leq 0.001$, Table 5).
## Associations between school bullying and ED behaviors
Among male adolescents, being a perpetrator of school bullying was associated with subjective binge eating (OR 1.493; $95\%$ CI 1.243–1.794; $p \leq 0.001$, Table 4) and objective binge eating (OR 1.601; $95\%$ CI 1.373–1.867; $p \leq 0.001$, Table 5). Being a victim of school bullying was associated with objective binge eating (OR 1.240; $95\%$ CI 1.001–1.534; $p \leq 0.05$, Table 5). As for female adolescents, being a perpetrator of school bullying was positively associated with the likelihood of purging (OR 1.565; $95\%$ CI 1.139–2.150; $p \leq 0.01$, Table 3), subjective binge eating (OR 1.370; $95\%$ CI 1.029–1.824; $p \leq 0.05$), and objective binge eating (OR 1.754; $95\%$ CI 1.415–2.174; $p \leq 0.001$, Table 5).
## Discussion
This is the first comprehensive assessment of the associations across different ED behaviors in Chinese adolescents, which compared psychiatric symptoms and cognitive deficits between male and female adolescents in China. There is insufficient understanding on the underlying pathological mechanism of ED, particularly in sexual minority youth [26]. The lack of insight into associations for ED limits the intervention and prevention available [27, 59]. The results of this study begin to understand ED behaviors from a more comprehensive perspective, and it could provide useful information for clinical interventions and prevention methods. Our results revealed multiple associations with ED behaviors, which covered aspects of sexual orientation, suicidal ideation, self-harm, mental health symptoms, cognitive deficits, reported health status, and school bullying experiences.
## Associations between sexual orientation and ED
Significant differences in ED behaviors were found across different sexual orientations. Compared with the heterosexual orientation, being an reporting no sexual attraction was associated with less ED behaviors in both male and female adolescents in terms of both purging and dietary restriction. Female sexual minority groups demonstrated a higher risks of ED behaviors, with bisexual and lesbian female adolescents more likely to engage in objective binge eating. This is the first study to identify adolescents with an reporting no sexual attraction having less risk of engaging in ED behaviors, in both male and female adolescents. Noticeably, the female sexual minority groups demonstrated a higher risk of ED behaviors, with bisexual female adolescents more likely to engage in subjective binge eating and objective binge eating, and lesbian adolescents more likely to engage in objective binge eating. Unlike the comparison between general males and females, in which female adolescents showed significantly higher scores in dietary restriction, female sexual minority groups tended to behave in the opposite way by engaging in overeating and binge eating. This is inconsistent with previous finding that suggest when compared with heterosexual female adolescents, lesbian or bisexual female adolescents are just as likely as heterosexual female adolescents to feel pressure to conform to the ideal beauty standard [3, 66]. It is consistent though in confirming that lesbian adolescents are more likely to be obese due to overeating and binge eating than their heterosexual counterparts [66]. However, it is still important to be aware of the inconsistent research results in lesbian adolescents in relation to their body image and eating issues [71]. Such as, when other researchers indicate that lesbians are less invested in appearance and maintaining weight, and less concerned with dieting and thinness than heterosexual females or gay males [70], when the literature is still in its infancy.
## Associations between other factors and ED
Having depressive symptoms was pervasive in females across purging, subjective binge eating, and objective binge eating, with the only reported risk in males being objective binge eating. The results indicated that it is possible that female adolescents used ED behaviors as a maladaptive way of coping with depressive symptoms. Research has shown that depression is positively and directly associated with emotional eating, and depression is indirectly related to emotional eating via both alexithymia and impulsivity [50]. That is, there are also potential mediating pathways between depression and ED behaviors. The cultural ideal body image of thinness can cause depression among females [41], which could lead to continuation of ED behaviors. In China, slim female beauty has become a cultural preference and a fashionable female aesthetic standard [37]. This social pressure on this ideal female body could cause more distress for adolescent females, which could lead to an increase in ED behaviors. The study found that cognitive deficits were significantly associated with subjective binge eating and objective binge eating, this was true for both male and female adolescents. Although the role of cognitive deficits in the development of ED requires further research, researchers have proposed that cognitive deficits could pre-exist and underlie the aetiology of ED [32]. This study supports the evidence that it is necessary to investigate neuropsychological deficits as a potential association with ED onset, in relation to sex and sexual orientation.
A previous study found that objective binge eating was associated with greater impairment in males than females, and purging was associated with general health impairment for females, but higher general health for males [44]. Researchers proposed that males and females might have different perceptions about purging, and males may perceive the health benefits of purging to feel cleansed [44]. However, unlike the previous study, this study found that both objective binge eating and purging were associated with a poorer reported health status for both male and female adolescents. The shared protective factors in terms of purging were reporting no sexual attraction in male or female adolescents.
Previous study showed that there was an association between being the victims of bullying and binge-eating/purging [35]. Moreover, research also indicated that perpetrators of bullying also have adverse health outcomes [57]. Bullying is said to predict ED behaviors in both victims and perpetrators [12]. Our results were consistent with previous findings, while it also identified sex differences. Being a perpetrator of school bullying was significantly related with three ED behavior outcomes in female adolescents including purging, subjective binge eating, and binge eating. Being a school bullying perpetrator was also significantly related with subjective binge eating and binge eating in male adolescents. Across all the ED behaviors, binge eating was closely related with school bullying, with bullying perpetrators in both sexes showing a significant correlation with binge eating and being a bullying victim in male adolescents was significantly correlated with binge eating. The nature of the link between being bullied and ED behaviors is not clear [67]. Adolescent could get teased due to overweight body shaming, which could be caused by binge eating. It is also possible that adolescent use binge eating as way of coping with the stress caused by bullying.
## Limitations
There are several limitations that need to be considered when interpreting the results. First, due to the cross-sectional nature of the survey, no causal relationships of ED behaviors can be established. Future longitudinal studies are required to investigate the causes of ED behaviors in adolescents. Second, in the economic well-developed southern region of China, ED behaviors might be different from other regions in China that are more rural. The associations of ED behaviors identified in this study may therefore not represent other adolescents in economically deprived areas. Third, due to the limited resources, the current study did not measure all the associations with ED behaviors. For example, previous research suggests that low BMI was the most important predictor for onset of Anorexia Nervosa [63], and the researchers did not measure the BMI, which is known to be contested. Fourth, we did not include gender diverse adolescents in the current study as previous research suggests that transgender individuals may experience specific body related image dissatisfaction, which could be closely associated with EDs [47]. Moreover, issues related to being an intersex or transgender adolescent were not included in the current study, and is a point for future research. Fifth, the study investigated ED behaviors using scale measurement with a yes/no responses rather than a nuanced questionnaire or relying on a clinical diagnosis of ED. Thus, our results have limitations in capturing the granularity of ED behaviors. Future studies could adopt a more nuanced questionnaire design or focus on those diagnosed with ED, which could result in further impairments for adolescents. Sixth, given the sample analysed were quite young, the prevalence of sexual orientation ambiguity was high, which may lead to potential measurement error in identifying the group of reporting no sexual attraction adolescents. Thus, we cannot confirm the sexual orientation of these adolescents.
## Conclusion
Considering the unclear aetiology of ED and the high morbidity and mortality rates, the detection of ED at early stage is critical [17]. The research was conducted with a large school sample to identify associations between ED behaviors, and overeating, purging, and dietary restrictions. This is one of the first comprehensive studies to explore multiple ED behaviors in Chinese adolescents considering both sex and sexual orientation. In conclusion, this study revealed that the female sexual minority groups had the most associations with ED behaviors, and the reporting no sexual attraction adolescents tended to be less likely to be associated with ED behaviors. The findings could be used to provide rigorous evidence for future prevention measures and interventions in high-risk groups. We recommended clinicians implement tailored prevention strategies that are different for each of the adolescent groups studied, and that this needs to be specific to the different ED behaviors discussed.
## Supplementary Information
Additional file 1. Risk factors and association with eating disorder behaviors in school adolescents.
## References
1. 1.Arcelus J, Fernádndez-Aranda F, Bouman WP Eating disorders and disordered eating in the LGBTQ population 2018.
2. Arikawa AY, Ross J, Wright L, Elmore M, Gonzalez AM, Wallace TC. **Results of an online survey about food insecurity and eating disorder behaviors administered to a volunteer sample of self-described LGBTQ+ young adults aged 18–35 years**. *J Acad Nutr Diet* (2021.0) **121** 1231-1241. DOI: 10.1016/j.jand.2020.09.032
3. Boehmer U, Bowen DJ, Bauer GR. **Overweight and obesity in sexual-minority women: evidence from population-based data**. *Am J Public Health* (2007.0) **97** 1134-1140. DOI: 10.2105/AJPH.2006.088419
4. Buelens T, Luyckx K, Verschueren M, Schoevaerts K, Dierckx E, Depestele L, Claes L. **Temperament and character traits of female eating disorder patients with (out) non-suicidal self-injury**. *J Clin Med* (2020.0) **9** 1207. DOI: 10.3390/jcm9041207
5. Bufferd SJ, Levinson CA, Olino TM, Dougherty LR, Dyson MW, Carlson GA, Klein DN. **Temperament and psychopathology in early childhood predict body dissatisfaction and eating disorder symptoms in adolescence**. *Behav Res Ther* (2022.0) **151** 104039. DOI: 10.1016/j.brat.2022.104039
6. Call C, Walsh BT, Attia E. **From DSM-IV to DSM-5: changes to eating disorder diagnoses**. *Curr Opin Psychiatry* (2013.0) **26** 532-536. DOI: 10.1097/YCO.0b013e328365a321
7. Calzo JP, Austin SB, Micali N. **Sexual orientation disparities in eating disorder symptoms among adolescent boys and girls in the UK**. *Eur Child Adolesc Psychiatry* (2018.0) **27** 1483-1490. DOI: 10.1007/s00787-018-1145-9
8. Carlat DJ, Camargo CA, Herzog DB. **Eating disorders in males: a report on 135 patients**. *Am J Psychiatry* (1997.0) **154** 1127-1132. DOI: 10.1176/ajp.154.8.1127
9. Chen H, Hu X, Gao J, Han H, Wang X, Xue C. **early effects of repetitive transcranial magnetic stimulation combined with sertraline in adolescents with first-episode major depressive disorder**. *Front Psychiatry* (2022.0) **13** 853961. DOI: 10.3389/fpsyt.2022.853961
10. Chen X, Qi H, Liu R, Feng Y, Li W, Xiang M, Cheung T, Jackson T, Wang G, Xiang YT. **Depression, anxiety and associated factors among Chinese adolescents during the COVID-19 outbreak: a comparison of two cross-sectional studies**. *Transl Psychiatry* (2021.0) **11** 148. DOI: 10.1038/s41398-021-01271-4
11. 11.Claes L, Muehlenkamp JJ Non-suicidal self-injury and eating disorders: Dimensions of self-harm. In Non-suicidal self-injury in eating disorders. Springer; 2014. pp. 3–18.
12. Copeland WE, Bulik CM, Zucker N, Wolke D, Lereya ST, Costello EJ. **Does childhood bullying predict eating disorder symptoms? A prospective, longitudinal analysis**. *Int J Eat Disord* (2015.0) **48** 1141-1149. DOI: 10.1002/eat.22459
13. D’Anna G, Lazzeretti M, Castellini G, Ricca V, Cassioli E, Rossi E, Silvestri C, Voller F. **Risk of eating disorders in a representative sample of Italian adolescents: prevalence and association with self-reported interpersonal factors**. *Eat Weight Disord-Stud Anorex Bulim Obes* (2022.0) **27** 701-708. DOI: 10.1007/s40519-021-01214-4
14. Day S, Bussey K, Trompeter N, Mitchison D. **The impact of teasing and bullying victimization on disordered eating and body image disturbance among adolescents: a systematic review**. *Trauma Violence Abuse* (2022.0) **23** 985-1006. DOI: 10.1177/1524838020985534
15. Dobrescu SR, Dinkler L, Gillberg C, Råstam M, Gillberg C, Wentz E. **Anorexia nervosa: 30-year outcome**. *Br J Psychiatry* (2020.0) **216** 97-104. DOI: 10.1192/bjp.2019.113
16. Faravelli C, Ravaldi C, Truglia E, Zucchi T, Cosci F, Ricca V. **Clinical epidemiology of eating disorders: results from the Sesto Fiorentino study**. *Psychother Psychosom* (2006.0) **75** 376-383. DOI: 10.1159/000095444
17. Ghaderi A, Scott B. **Prevalence, incidence and prospective risk factors for eating disorders**. *Acta Psychiatr Scand* (2001.0) **104** 122-130. DOI: 10.1034/j.1600-0447.2001.00298.x
18. Godart NT, Flament MF, Curt F, Perdereau F, Lang F, Venisse JL, Halfon O, Bizouard P, Loas G, Corcos M. **Anxiety disorders in subjects seeking treatment for eating disorders: a DSM-IV controlled study**. *Psychiatry Res* (2003.0) **117** 245-258. DOI: 10.1016/S0165-1781(03)00038-6
19. Hou Y, Yao S, Hu S, Zhou Q, Han H, Yu X, McIntyre RS, Shi C. **PSYCHOMETRIC properties of the Chinese version of the THINC-it tool for cognitive symptoms in patients with major depressive disorder**. *J Affect Disord* (2020.0) **273** 586-591. DOI: 10.1016/j.jad.2020.03.146
20. Hu X, Wang Y. **LGB identity among young Chinese: the influence of traditional culture**. *J Homosex* (2013.0) **60** 667-684. DOI: 10.1080/00918369.2013.773815
21. Kane GD. **Revisiting gay men's body image issues: exposing the fault lines**. *Rev Gen Psychol* (2010.0) **14** 311-317. DOI: 10.1037/a0020982
22. Kann L, McManus T, Harris WA, Shanklin SL, Flint KH, Queen B, Lowry R, Chyen D, Whittle L, Thornton J, Lim C, Bradford D, Yamakawa Y, Leon M, Brener N, Ethier KA. **Youth risk behavior surveillance: United States, 2017**. *MMWR Surveill Summ* (2018.0) **67** 1-114. DOI: 10.15585/mmwr.ss6708a1
23. Karna A, Voeten M, Little TD, Poskiparta E, Alanen E, Salmivalli C. **Going to scale: a nonrandomized nationwide trial of the KiVa antibullying program for grades 1–9**. *J Consult Clin Psychol* (2011.0) **79** 796-805. DOI: 10.1037/a0025740
24. Karna A, Voeten M, Little TD, Poskiparta E, Kaljonen A, Salmivalli C. **A large-scale evaluation of the KiVa antibullying program: grades 4–6**. *Child Dev* (2011.0) **82** 311-330. DOI: 10.1111/j.1467-8624.2010.01557.x
25. Keel PK, Brown TA, Holm-Denoma J, Bodell LP. **Comparison of DSM-IV versus proposed DSM-5 diagnostic criteria for eating disorders: Reduction of eating disorder not otherwise specified and validity**. *Int J Eat Disord* (2011.0) **44** 553-560. DOI: 10.1002/eat.20892
26. Keshaviah A, Edkins K, Hastings ER, Krishna M, Franko DL, Herzog DB, Thomas JJ, Murray HB, Eddy KT. **Re-examining premature mortality in anorexia nervosa: a meta-analysis redux**. *Compr Psychiatry* (2014.0) **55** 1773-1784. DOI: 10.1016/j.comppsych.2014.07.017
27. Kotlicka-Antczak M, Podgorski M, Oliver D, Maric NP, Valmaggia L, Fusar-Poli P. **Worldwide implementation of clinical services for the prevention of psychosis: the IEPA early intervention in mental health survey**. *Early Interv Psychiatry* (2020.0) **14** 741-750. DOI: 10.1111/eip.12950
28. Koutek J, Kocourkova J, Dudova I. **Suicidal behavior and self-harm in girls with eating disorders**. *Neuropsychiatr Dis Treat* (2016.0) **12** 787. PMID: 27114709
29. Kroenke K, Spitzer RL, Williams JB. **The PHQ-9: validity of a brief depression severity measure**. *J Gen Intern Med* (2001.0) **16** 606-613. DOI: 10.1046/j.1525-1497.2001.016009606.x
30. Lavis A, McNeil S, Bould H, Winston A, Reid K, Easter CL, Pendrous R, Michail M. **Self-harm in eating disorders (SHINE): a mixed-methods exploratory study**. *BMJ Open* (2022.0) **12** e065065. DOI: 10.1136/bmjopen-2022-065065
31. Le Grange D, O'Connor M, Hughes EK, Macdonald J, Little K, Olsson CA. **Developmental antecedents of abnormal eating attitudes and behaviors in adolescence**. *Int J Eat Disord* (2014.0) **47** 813-824. DOI: 10.1002/eat.22331
32. Lena SM, Fiocco AJ, Leyenaar JK. **The role of cognitive deficits in the development of eating disorders**. *Neuropsychol Rev* (2004.0) **14** 99-113. DOI: 10.1023/b:nerv.0000028081.40907.de
33. Li L, Wang HM, Shen Y. **Chinese SF-36 health survey: translation, cultural adaptation, validation, and normalisation**. *J Epidemiol Commun Health* (2003.0) **57** 259-263. DOI: 10.1136/jech.57.4.259
34. Li Y. **Linking body esteem to eating disorders among adolescents: a moderated mediation model**. *J Health Psychol* (2020.0) **25** 1755-1770. DOI: 10.1177/1359105319886048
35. Lie SØ, Bulik CM, Andreassen OA, Rø Ø, Bang L. **The association between bullying and eating disorders: a case–control study**. *Int J Eat Disord* (2021.0) **54** 1405-1414. DOI: 10.1002/eat.23522
36. Lie SØ, Rø Ø, Bang L. **Is bullying and teasing associated with eating disorders? A systematic review and meta-analysis**. *Int J Eat Disord* (2019.0) **52** 497-514. DOI: 10.1002/eat.23035
37. Liu X, Chen G, Yan J, Luo J. **Weight status and bullying behaviors among Chinese school-aged children**. *Child Abuse Negl* (2016.0) **52** 11-19. DOI: 10.1016/j.chiabu.2015.12.010
38. Liu X, Chen H, Bo QG, Fan F, Jia CX. **Poor sleep quality and nightmares are associated with non-suicidal self-injury in adolescents**. *Eur Child Adolesc Psychiatry* (2017.0) **26** 271-279. DOI: 10.1007/s00787-016-0885-7
39. Liu X, Liu ZZ, Chen RH, Cheng XZ, Bo QG, Wang ZY, Yang Y, Fan F, Jia CX. **Nightmares are associated with future suicide attempt and non-suicidal self-injury in adolescents**. *J Clin Psychiatry* (2019.0). DOI: 10.4088/JCP.18m12181
40. Mason TB, Lewis RJ, Heron KE. **Indirect pathways connecting sexual orientation and weight discrimination to disordered eating among young adult lesbians**. *Psychol Sex Orientat Gend Divers* (2017.0) **4** 193. DOI: 10.1037/sgd0000220
41. McCarthy M. **The thin ideal, depression and eating disorders in women**. *Behav Res Ther* (1990.0) **28** 205-214. DOI: 10.1016/0005-7967(90)90003-2
42. Meneguzzo P, Collantoni E, Bonello E, Vergine M, Behrens SC, Tenconi E, Favaro A. **The role of sexual orientation in the relationships between body perception, body weight dissatisfaction, physical comparison, and eating psychopathology in the cisgender population**. *Eat Weight Disord-Stud Anorex Bulim Obes* (2021.0) **26** 1985-2000. DOI: 10.1007/s40519-020-01047-7
43. Meneguzzo P, Collantoni E, Gallicchio D, Busetto P, Solmi M, Santonastaso P, Favaro A. **Eating disorders symptoms in sexual minority women: a systematic review**. *Eur Eat Disord Rev* (2018.0) **26** 275-292. DOI: 10.1002/erv.2601
44. Mitchison D, Mond J, Slewa-Younan S, Hay P. **Sex differences in health-related quality of life impairment associated with eating disorder features: a general population study**. *Int J Eat Disord* (2013.0) **46** 375-380. DOI: 10.1002/eat.22097
45. Mond JM, Hay PJ. **Functional impairment associated with bulimic behaviors in a community sample of men and women**. *Int J Eat Disord* (2007.0) **40** 391-398. DOI: 10.1002/eat.20380
46. Moscovici M, Wnuk S, Okrainec A, Hawa R, Sockalingam S. **Psychosocial predictors of cognition in bariatric surgery**. *Psychosomatics* (2019.0) **60** 164-171. DOI: 10.1016/j.psym.2018.06.010
47. Nagata JM, Ganson KT, Austin SB. **Emerging trends in eating disorders among sexual and gender minorities**. *Curr Opin Psychiatry* (2020.0) **33** 562. DOI: 10.1097/YCO.0000000000000645
48. Naor-Ziv R, Glicksohn J. **Investigating cognitive deficits as risk factors for developing eating disorders during adolescence**. *Dev Neuropsychol* (2016.0) **41** 107-124. DOI: 10.1080/87565641.2016.1170129
49. Neumark-Sztainer DR, Wall MM, Haines JI, Story MT, Sherwood NE, van den Berg PA. **Shared risk and protective factors for overweight and disordered eating in adolescents**. *Am J Prev Med* (2007.0) **33** 359-369. DOI: 10.1016/j.amepre.2007.07.031
50. Ouwens MA, van Strien T, van Leeuwe JF. **Possible pathways between depression, emotional and external eating**. *A Struct Eq Model Appetite* (2009.0) **53** 245-248
51. Parker LL, Harriger JA. **Eating disorders and disordered eating behaviors in the LGBT population: a review of the literature**. *J Eat Disord* (2020.0) **8** 1-20. DOI: 10.1186/s40337-020-00327-y
52. Quintero-Jurado J, Moratto-Vásquez N, Caicedo-Velasquez B, Cárdenas-Zuluaga N, Espelage DL. **Association between school bullying, suicidal ideation, and eating disorders among school-aged children from antioquia, Colombia**. *Trends Psychol* (2021.0) **30** 58-72. DOI: 10.1007/s43076-021-00101-2
53. Rapee RM, Oar EL, Johnco CJ, Forbes MK, Fardouly J, Magson NR, Richardson CE. **Adolescent development and risk for the onset of social-emotional disorders: a review and conceptual model**. *Behav Res Ther* (2019.0) **123** 103501. DOI: 10.1016/j.brat.2019.103501
54. Sharpe H, Griffiths S, Choo TH, Eisenberg ME, Mitchison D, Wall M, Neumark-Sztainer D. **The relative importance of dissatisfaction, overvaluation and preoccupation with weight and shape for predicting onset of disordered eating behaviors and depressive symptoms over 15 years**. *Int J Eat Disord* (2018.0) **51** 1168-1175. DOI: 10.1002/eat.22936
55. Sheehan DV, Lecrubier Y, Sheehan KH, Amorim P, Janavs J, Weiller E, Hergueta T, Baker R, Dunbar GC. **The Mini-International Neuropsychiatric Interview (MINI): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10**. *J Clin Psychiatry* (1998.0) **59** 22-33. PMID: 9881538
56. Silverman MM, Berman AL, Sanddal ND, OCarroll PW, Joiner TE. **Rebuilding the tower of Babel: a revised nomenclature for the study of suicide and suicidal behaviors. Part 2: Suicide-related ideations, communications, and behaviors**. *Suicide Life Threat Behav* (2007.0) **37** 264-277. DOI: 10.1521/suli.2007.37.3.264
57. Skilbred-Fjeld S, Reme SE, Mossige S. **Cyberbullying involvement and mental health problems among late adolescents**. *Cyberpsychol: J Psychosoc Res Cyberspace* (2020.0) **14** 5. DOI: 10.5817/CP2020-1-5
58. Smith ML, Telford E, Tree JJ. **Body image and sexual orientation: the experiences of lesbian and bisexual women**. *J Health Psychol* (2019.0) **24** 1178-1190. DOI: 10.1177/1359105317694486
59. Solmi M, Radua J, Stubbs B, Ricca V, Moretti D, Busatta D, Carvalho AF, Dragioti E, Favaro A, Monteleone AM, Shin JI, Fusar-Poli P, Castellini G. **Risk factors for eating disorders: an umbrella review of published meta-analyses**. *Braz J Psychiatry* (2020.0). DOI: 10.1590/1516-4446-2020-1099
60. Speranza M, Corcos M, Godart N, Loas G, Guilbaud O, Jeammet P, Flament M. **Obsessive compulsive disorders in eating disorders**. *Eat Behav* (2001.0) **2** 193-207. DOI: 10.1016/S1471-0153(01)00035-6
61. Speranza M, Loas G, Wallier J, Corcos M. **Predictive value of alexithymia in patients with eating disorders: a 3-year prospective study**. *J Psychosom Res* (2007.0) **63** 365-371. DOI: 10.1016/j.jpsychores.2007.03.008
62. Spitzer RL, Kroenke K, Williams JB, Löwe B. **A brief measure for assessing generalized anxiety disorder: the GAD-7**. *Arch Intern Med* (2006.0) **166** 1092-1097. DOI: 10.1001/archinte.166.10.1092
63. Stice E, Desjardins CD. **Interactions between risk factors in the prediction of onset of eating disorders: exploratory hypothesis generating analyses**. *Beh Res Therapy* (2018.0) **105** 52-62. DOI: 10.1016/j.brat.2018.03.005
64. Striegel RH, Bedrosian R, Wang C, Schwartz S. **Why men should be included in research on binge eating: results from a comparison of psychosocial impairment in men and women**. *Int J Eat Disord* (2012.0) **45** 233-240. DOI: 10.1002/eat.20962
65. Striegel-Moore RH, Rosselli F, Perrin N, DeBar L, Wilson GT, May A, Kraemer HC. **Gender difference in the prevalence of eating disorder symptoms**. *Int J Eat Disord* (2009.0) **42** 471-474. DOI: 10.1002/eat.20625
66. Struble CB, Lindley LL, Montgomery K, Hardin J, Burcin M. **Overweight and obesity in lesbian and bisexual college women**. *J Am Coll Health* (2010.0) **59** 51-56. DOI: 10.1080/07448481.2010.483703
67. Sweetingham R, Waller G. **Childhood experiences of being bullied and teased in the eating disorders**. *Eur Eat Disord Rev: Prof J Eat Disord Assoc* (2008.0) **16** 401-407. DOI: 10.1002/erv.839
68. Treasure J, Duarte TA, Schmidt U. **Eating disorders**. *Lancet* (2020.0) **395** 899-911. DOI: 10.1016/S0140-6736(20)30059-3
69. van Eeden AE, Oldehinkel AJ, van Hoeken D, Hoek HW. **Risk factors in preadolescent boys and girls for the development of eating pathology in young adulthood**. *Int J Eat Disord* (2021.0) **54** 1147-1159. DOI: 10.1002/eat.23496
70. Wagenbach P. **Lesbian body image and eating issues**. *J Psychol Hum Sex* (2004.0) **15** 205-227. DOI: 10.1300/J056v15n04_04
71. 71.Wagenbach P Lesbian body image and eating issues. Lesbian and Bisexual Women's Mental Health 2014. pp. 219–242.
72. Wang SB, Mancuso CJ, Jo J, Keshishian AC, Becker KR, Plessow F, Izquierdo AM, Slattery M, Franko DL, Misra M. **Restrictive eating, but not binge eating or purging, predicts suicidal ideation in adolescents and young adults with low-weight eating disorders**. *Int J Eat Disord* (2020.0) **53** 472-477. DOI: 10.1002/eat.23210
73. Wang W, Bian Q, Zhao Y, Li X, Wang W, Du J, Zhang G, Zhou Q, Zhao M. **Reliability and validity of the Chinese version of the Patient Health Questionnaire (PHQ-9) in the general population**. *Gen Hosp Psychiatry* (2014.0) **36** 539-544. DOI: 10.1016/j.genhosppsych.2014.05.021
74. Wang Y, Hu Z, Peng K, Xin Y, Yang Y, Drescher J, Chen R. **Discrimination against LGBT populations in China**. *Lancet Public Health* (2019.0) **4** e440-e441. DOI: 10.1016/S2468-2667(19)30153-7
75. Wang Y, Yu H, Yang Y, Li R, Wilson A, Wang S, Drescher J, Chen R. **The victim-bully cycle of sexual minority school adolescents in China: prevalence and the association of mood problems and coping strategies**. *Epidemiol Psychiatr Sci* (2020.0) **29** e179. DOI: 10.1017/S2045796020000918
76. Wang Y, Zhang Y, Wang G, Duan Z, Wilson A, Yang Y, Sun S, Chen R. **Latent profile analysis to identify subgroups of Chinese sexual minority adolescents at risk of suicidality**. *Prev Med* (2022.0) **157** 107007. DOI: 10.1016/j.ypmed.2022.107007
77. Watson RJ, Adjei J, Saewyc E, Homma Y, Goodenow C. **Trends and disparities in disordered eating among heterosexual and sexual minority adolescents**. *Int J Eat Disord* (2017.0) **50** 22-31. DOI: 10.1002/eat.22576
78. Zhang W, Pu J, He R, Yu M, Xu L, He X, Chen Z, Gan Z, Liu K, Tan Y, Xiang B. **Demographic characteristics, family environment and psychosocial factors affecting internet addiction in Chinese adolescents**. *J Affect Disord* (2022.0) **315** 130-138. DOI: 10.1016/j.jad.2022.07.053
79. Zhou SJ, Zhang LG, Wang LL, Guo ZC, Wang JQ, Chen JC, Liu M, Chen X, Chen JX. **Prevalence and socio-demographic correlates of psychological health problems in Chinese adolescents during the outbreak of COVID-19**. *Eur Child Adolesc Psychiatry* (2020.0) **29** 749-758. DOI: 10.1007/s00787-020-01541-4
80. Zhou X, Zhen R. **Posttraumatic stress symptoms between parents and their children following a natural disaster: an integrated model under a dyadic approach**. *Psychol Trauma* (2022.0) **14** 141-150. DOI: 10.1037/tra0001082
|
---
title: 'Longitudinal risk of death, hospitalizations for atrial fibrillation, and
cardiovascular events following catheter ablation of atrial fibrillation: a cohort
study'
authors:
- Linh Ngo
- Richard Woodman
- Russell Denman
- Tomos E Walters
- Ian A Yang
- Isuru Ranasinghe
journal: European Heart Journal. Quality of Care & Clinical Outcomes
year: 2022
pmcid: PMC9972809
doi: 10.1093/ehjqcco/qcac024
license: CC BY 4.0
---
# Longitudinal risk of death, hospitalizations for atrial fibrillation, and cardiovascular events following catheter ablation of atrial fibrillation: a cohort study
## Abstract
### Aims
Population studies reporting contemporary long-term outcomes following catheter ablation of atrial fibrillation (AF) are sparse.
We evaluated long-term clinical outcomes following AF ablation and examined variation in outcomes by age, sex, and the presence of heart failure.
### Methods and results
We identified 30 601 unique patients (mean age 62.7 ± 11.8 years, $30.0\%$ female) undergoing AF ablation from 2008 to 2017 in Australia and New Zealand using nationwide hospitalization data. The primary outcomes were all-cause mortality and rehospitalizations for AF or flutter, repeat AF ablation, and cardioversion. Secondary outcomes were rehospitalizations for other cardiovascular events. During 124 858.7 person-years of follow-up, 1900 patients died (incidence rate $\frac{1.5}{100}$ person-years) with a survival probability of $93.0\%$ ($95\%$ confidence interval (CI) 92.6–$93.4\%$) by 5 years and $84.0\%$ ($95\%$ CI 82.4–$85.5\%$) by 10 years. Rehospitalizations for AF or flutter ($\frac{13.3}{100}$ person-years), repeat ablation ($\frac{5.9}{100}$ person-years), and cardioversion ($\frac{4.5}{100}$ person-years) were common, with respective cumulative incidence of $49.4\%$ ($95\%$ CI 48.4–$50.4\%$), $28.1\%$ ($95\%$ CI 27.2–$29.0\%$), and $24.4\%$ ($95\%$ CI 21.5–$27.5\%$) at 10 years post-ablation. Rehospitalizations for stroke ($\frac{0.7}{100}$ person-years), heart failure ($\frac{1.1}{100}$ person-years), acute myocardial infarction ($\frac{0.4}{100}$ person-years), syncope ($\frac{0.6}{100}$ person-years), other arrhythmias ($\frac{2.5}{100}$ person-years), and new cardiac device implantation ($\frac{2.0}{100}$ person-years) occurred less frequently. Elderly patients and those with comorbid heart failure had worse survival but were less likely to undergo repeat ablation, while long-term outcomes were comparable between the sexes.
### Conclusion
Patients undergoing AF ablations had good long-term survival, a low incidence of rehospitalizations for stroke or heart failure, and about half remained free of rehospitalizations for AF or flutter, including for repeat AF ablation, or cardioversion.
## Graphical Abstract
Graphical Abstract
## Introduction
Nearly one in four men and one in five women after the age of 40 will have atrial fibrillation (AF), the most common sustained heart rhythm disorder.1,2 Besides the high prevalence, AF is associated with a higher risk of adverse outcomes, including death, stroke, heart failure, and acute myocardial infarction.3 While medical therapy has low efficacy in restoring normal sinus rhythm,4 catheter ablation is a more effective option to terminate AF and improve symptoms.5,6 Moreover, AF patients with comorbid heart failure who undergo ablation experience a survival benefit in addition to symptoms relief when compared with medical therapy.7 Although the benefits of catheter ablation in maintaining sinus rhythm have been well established in clinical trials, long-term clinical sequelae of patients undergoing AF ablation in actual clinical practice are less well known. Published studies mostly report arrhythmia-free survival rate and are often derived from selected populations and experienced ablation centres,6,8,9 making their results less representative of outcomes in mainstream clinical practice. Furthermore, while the frequently used definition of arrhythmia occurrence (AF lasting >30 s)10 is useful in clinical trial settings, less is known about the incidence of clinically meaningful episodes such as those warranting hospitalization or requiring repeat ablation or cardioversion. Moreover, data are lacking about the risk of other equally important cardiovascular outcomes such as mortality, stroke, and heart failure, which is crucial for patients and clinicians seeking to better understand long-term outcomes of AF ablation. Population studies can provide unbiased estimates of these long-term outcomes, yet existing literature is sparse and limited by short follow-up time (up to 1 year)11–13 or a focus on specific populations.14–16 Accordingly, we used population-wide data to examine long-term outcomes of patients undergoing AF ablation in Australia and New Zealand (ANZ) from 2008 to 2017. Specifically, we evaluated the longitudinal risk of all-cause mortality, rehospitalizations for AF or flutter, repeat AF ablation, cardioversion, and other cardiovascular events, including hospitalizations for stroke or transient ischaemic attack (TIA) and heart failure. We also examined how the primary outcomes varied by age, sex, and the presence of comorbid heart failure as this subgroup of AF patients has been shown to experience survival benefits with catheter ablation compared with medical therapy.7
## Data source
We used hospitalization data from all public and most ($80\%$) private hospitals recorded in the Admitted Patient Collection (APC) from each state and territory in Australia and the equivalent New Zealand National Minimum Dataset (Hospital Events). Data were missing from private hospitals in New Zealand and the Australian states of South Australia, Tasmania, and Northern Territory, whose total population accounted for <$10\%$ of the total Australian population. A standard set of variables is recorded for each admission (both inpatient and outpatient visits), including patient demographic characteristics, the primary diagnosis and up to 50 secondary diagnoses, all procedures performed, and the patient's status at discharge. In both countries, diagnoses are coded using the International Classification of Diseases, 10th Revision, Australian Modification (ICD-10-AM), and procedures were coded using the Australian Classification of Health Interventions (ACHI). Accuracy of diagnosis and procedure coding is reported to exceed $85\%$ when compared with medical records.17,18 In Australia, each encounter was linked with subsequent records within the APC and to each region's Birth, Death, and Marriages Registry by using probabilistic matching using multiple patient identifiers with reported accuracy exceeding $99\%$.19 In New Zealand, all patient records are linked nationally using a unique National Health Index number, and all deaths are recorded in the National Health Index Sociodemographic Profile. These linkages allowed the capture of rehospitalizations to any hospital within each region and all deaths occurring in hospital or in the community.
## Study cohort
The use of administrative data to identify patients undergoing AF ablations has been described previously.20,21 In brief, we included patients aged ≥18 years hospitalized with a primary diagnosis of AF and a procedure code for catheter ablation from 2008 to 2017 and excluded those who [1] had secondary diagnoses of other arrhythmias to ensure that the ablation was solely for AF; [2] had a previous or current cardiac device implantation; [3] had open (surgical) ablation; and [4] were discharged against medical advice (see the supplementary material online, Table S1 for all ICD-10-AM and ACHI codes used to identify the study cohort). The identification of AF ablation in administrative data has been validated to be highly accurate ($100\%$ specificity and $87.3\%$ sensitivity).22 For patients with multiple admissions during the study period, the first episode was considered the index hospitalization, with all subsequent events considered an outcome (rehospitalization for AF).
## Study outcomes
The primary outcomes were [1] all-cause mortality and [2] rehospitalizations for atrial arrhythmias (AF or flutter), repeat AF ablation, and cardioversion. Secondary outcomes included relevant cardiovascular events, including hospitalizations for stroke or TIA, heart failure, acute myocardial infarction, syncope, arrhythmias other than AF or flutter (bradycardia and tachycardia, all types of heart block), and new cardiac device (pacemaker or defibrillator) implantation (see the Supplementary material online, Table S1 for more details).
## Statistical analysis
We presented data as frequencies and percentages for categorical variables and mean ± standard deviation (SD) or median and interquartile range (IQR) for continuous variables. Differences between groups were evaluated using χ2 or Fisher's exact test for categorical variables and Student’s t-test or Mann–Whitney U test for continuous variables where appropriate. For patients who were rehospitalized multiple times, only the first episode was counted. Comorbidities were derived using the Condition Categories system, that groups selected secondary diagnoses from the index hospitalization and all diagnoses (both primary and secondary) from hospitalizations in the previous 12 months into 180 meaningful clinical conditions.23 To estimate the incidence rate, which reflects the number of new events that occurred in a given period, we divided the number of events by the patient time at risk and reported results as the number of events per 100 person-years (PY). Patients were considered at risk until they died, experienced a non-fatal outcome, or survived until the end of the study period (1 January 2018). We used the Kaplan–Meier method to estimate the survival probability and reported results as percentages with the respective $95\%$ confidence interval (CI). For non-fatal outcomes, we estimated the cumulative incidence, which reflects the proportion of patients experiencing the event over a given period by using Fine and Gray's method of competing risk survival analysis (subdistribution hazard) with death being the competing event.24 To examine variation in outcomes by age, sex, and comorbid heart failure, we used Cox regression survival analysis to adjust for baseline differences in patient and procedural characteristics. Simple Cox regression analysis was used with the outcome of death, while for non-fatal outcomes, separate competing risk models using Fine and Gray's method were developed with death being the competing event.25 Results are reported as hazard ratio (HR) for mortality or subdistribution HR (sHR) for non-fatal outcomes with the corresponding $95\%$ CI. Candidate variables considered for these models included patient demographic characteristics (age, sex, and presenting region), procedural characteristics (elective admission, ablation of both atria, and ablation in a private hospital), and various cardiovascular and non-cardiovascular comorbidities that may be associated with long-term outcomes.
All analyses were performed using Stata version 16.0 (StataCorp LLC, College Station, TX) and a two-tailed P-value < 0.05 was considered statistically significant. This study was approved by the respective Human Research Ethics Committees of each state and territory in Australia. Data from New Zealand were obtained under a data user agreement with the New Zealand Ministry of Health. A waiver of informed consent was provided for the use of deidentified data.
## Results
From 2008 to 2017, 45 398 patients were hospitalized with a primary diagnosis of AF and a procedure code for catheter ablation, of whom 30 601 unique patients met selection criteria and were included in the final study cohort (Figure 1).
**Figure 1:** *Patient selection flow diagram. AF, atrial fibrillation.*
## Cohort characteristics
The mean age was 62.7 (±11.8) years with $46.1\%$ aged ≥65 years and the cohort was predominantly ($70.0\%$) male (Table 1). The median length of stay was 1.0 day (IQR 1.0–2.0 days) and $94.0\%$ of AF ablations were performed during an elective (planned) hospitalization. Nearly half of the study cohort ($49.1\%$) had been hospitalized with a primary diagnosis of AF or flutter in the previous year, but rates of comorbidities were generally low, with hypertension ($13.4\%$) and diabetes ($10.1\%$) being the most common cardiac and non-cardiac comorbidities, respectively. The median estimated CHA2DS2-VASc score (thromboembolic risk score for AF patients in which a point each is given for the presence of congestive heart failure [C], hypertension [H], age 65-74 years old [A], diabetes [D], vascular disease (VASc) and female sex and 2 points each are given for age ≥75 years old and history of stroke [S]) was 1 (IQR 0–2) with $90.1\%$ with a score ≤2.
**Table 1**
| Unnamed: 0 | Unnamed: 1 | All-cause mortality | All-cause mortality.1 | All-cause mortality.2 | Rehospitalizations for AF or flutter | Rehospitalizations for AF or flutter.1 | Rehospitalizations for AF or flutter.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Overall (N = 30 601) n (%) | Survived (N = 28 701) n (%) | Died (N = 1900) n (%) | P value | Not rehospitalized (N = 19 479) n (%) | Rehospitalized (N = 11 114) n (%) | P- value |
| Demographic characteristics | | | | | | | |
| Age (mean ± SD) | 62.7 ± 11.8 | 62.0 ± 11.5 | 72.4 ± 10.8 | <0.001 | 63.3 ± 12.1 | 61.5 ± 11.0 | <0.001 |
| Age group | | | | | | | |
| 18–34 | 642 (2.1) | 631 (2.2) | 11 (0.6) | <0.001 | 427 (2.2) | 215 (1.9) | <0.001 |
| 35–49 | 3316 (10.8) | 3261 (11.4) | 55 (2.9) | — | 1993 (10.2) | 1323 (11.9) | — |
| 50–64 | 12 540 (41.0) | 12 203 (42.5) | 337 (17.7) | — | 7536 (38.7) | 5002 (45.0) | — |
| 65–79 | 12 270 (40.1) | 11 278 (39.3) | 992 (52.2) | — | 8053 (41.3) | 4212 (37.9) | — |
| ≥80 | 1833 (6.0) | 1328 (4.6) | 505 (26.6) | — | 1470 (7.6) | 362 (3.3) | — |
| Female (%) | 9180 (30.0) | 8588 (29.9) | 592 (31.2) | 0.255 | 5825 (29.9) | 3351 (30.2) | 0.650 |
| Median length of stay (IQR) | 1.0 (1.0–2.0) | 1.0 (1.0–2.0) | 1.0 (1.0–3.0) | <0.001 | 1.0 (1.0–2.0) | 1.0 (1.0–2.0) | <0.001 |
| Presenting region | | | | | | | |
| NZ | 2572 (8.4) | 2439 (8.5) | 133 (7.0) | <0.001 | 1700 (8.7) | 871 (7.8) | <0.001 |
| ACT/NSW | 8203 (26.8) | 7672 (26.7) | 531 (28.0) | — | 5226 (26.8) | 2974 (26.8) | — |
| SA/NT | 2130 (7.0) | 1932 (6.7) | 198 (10.4) | — | 1388 (7.1) | 741 (6.7) | — |
| QLD | 6152 (20.1) | 5765 (20.1) | 387 (20.4) | — | 3687 (18.9) | 2465 (22.2) | — |
| TAS | 0 (0.0) | 0 (0.0) | 0 (0.0) | — | 0 (0.0) | 0 (0.0) | — |
| VIC | 7433 (24.3) | 7035 (24.5) | 398 (21.0) | — | 5121 (26.3) | 2309 (20.8) | — |
| WA | 4111 (13.4) | 3858 (13.4) | 253 (13.3) | — | 2357 (12.1) | 1754 (15.8) | — |
| Elective (scheduled) procedure | 28 755 (94.0) | 27 089 (94.4) | 1666 (87.7) | <0.001 | 18 270 (93.8) | 10 478 (94.3) | 0.087 |
| Private hospital | 19 272 (63.0) | 18 208 (63.4) | 1064 (56.0) | <0.001 | 12 158 (62.4) | 7111 (64.0) | 0.006 |
| CHA2DS2-VASc scorea (median, IQR) | 1 (0-2) | 1 (0-1) | 2 (1-3) | <0.001 | 1 (0-2) | 1 (0-1) | <0.001 |
| 0 (n, %) | 13 012 (42.5) | 12 818 (44.7) | 194 (10.2) | <0.001 | 7916 (40.6) | 5095 (45.8) | <0.001 |
| 1 (n, %) | 9236 (30.2) | 8807 (30.7) | 429 (22.6) | — | 5803 (29.8) | 3430 (30.9) | — |
| ≥2 (n, %) | 8353 (27.3) | 7076 (24.7) | 1277 (67.2) | — | 5760 (29.6) | 2589 (23.3) | — |
| Cardiac history | | | | | | | |
| Hypertension | 4098 (13.4) | 3518 (12.3) | 580 (30.5) | <0.001 | 2520 (12.9) | 1575 (14.2) | 0.002 |
| Heart failure | 3123 (10.2) | 2574 (9.0) | 549 (28.9) | <0.001 | 2046 (10.5) | 1073 (9.7) | 0.018 |
| Valvular and rheumatic heart disease | 1319 (4.3) | 1149 (4.0) | 170 (9.0) | <0.001 | 784 (4.0) | 535 (4.8) | 0.001 |
| Coronary artery disease | 3294 (10.8) | 2858 (10.0) | 436 (23.0) | <0.001 | 2136 (11.0) | 1155 (10.4) | 0.120 |
| Vascular disease | 514 (1.7) | 426 (1.5) | 88 (4.6) | <0.001 | 353 (1.8) | 161 (1.5) | 0.017 |
| History of hospitalization for AF or flutter | 15 035 (49.1) | 14 117 (49.9) | 918 (48.3) | 0.462 | 8861 (45.5) | 6171 (55.5) | <0.001 |
| Non-cardiac comorbidities | | | | | | | |
| Diabetes mellitus | 3101 (10.1) | 2780 (9.7) | 321 (16.9) | <0.001 | 2221 (11.4) | 879 (7.9) | <0.001 |
| Chronic lung diseases | 1365 (4.5) | 1060 (3.7) | 305 (16.1) | <0.001 | 924 (4.7) | 439 (4.0) | 0.001 |
| Chronic kidney disease | 1072 (3.5) | 811 (2.8) | 261 (13.7) | <0.001 | 790 (4.1) | 281 (2.5) | <0.001 |
| Stroke or TIA | 425 (1.4) | 374 (1.3) | 51 (2.7) | <0.001 | 261 (1.3) | 164 (1.5) | 0.329 |
| Haematological disorders | 1369 (4.5) | 1086 (3.8) | 283 (14.9) | <0.001 | 863 (4.4) | 505 (4.5) | 0.644 |
| Pneumonia | 667 (2.2) | 511 (1.8) | 156 (8.2) | <0.001 | 477 (2.5) | 188 (1.7) | <0.001 |
| Musculoskeletal and connective tissue disorders | 2214 (7.2) | 1981 (6.9) | 233 (12.3) | <0.001 | 1379 (7.1) | 833 (7.5) | 0.177 |
| Dementia and senility | 55 (0.2) | 41 (0.1) | 14 (0.7) | <0.001 | 44 (0.2) | 11 (0.1) | 0.012 |
| Major cancer | 229 (0.8) | 148 (0.5) | 81 (4.3) | <0.001 | 182 (0.9) | 47 (0.4) | <0.001 |
| End-stage liver disease | 39 (0.1) | 30 (0.1) | 9 (0.5) | 0.001 | 27 (0.1) | 12 (0.1) | 0.470 |
| Drug or alcohol abuse, psychosis, or dependence | 458 (1.5) | 383 (1.3) | 75 (4.0) | <0.001 | 304 (1.6) | 153 (1.4) | 0.202 |
| Psychiatric disorders | 447 (1.5) | 376 (1.3) | 71 (3.7) | <0.001 | 291 (1.5) | 156 (1.4) | 0.527 |
| Neurological disorders and paralysis | 326 (1.1) | 275 (1.0) | 51 (2.7) | <0.001 | 211 (1.1) | 115 (1.0) | 0.691 |
| Skin ulcers | 99 (0.3) | 55 (0.2) | 44 (2.3) | <0.001 | 79 (0.4) | 20 (0.2) | 0.001 |
| Urinary tract disorders and incontinence | 1283 (4.2) | 1081 (3.8) | 202 (10.6) | <0.001 | 874 (4.5) | 408 (3.7) | 0.001 |
During 124 858.7 person-years of follow-up (median follow-up time of 3.8 years, IQR: 1.7–6.2 years), 1900 patients died. Compared with those who survived, deceased patients were older (72.4 vs. 62.0 years), less likely to have an elective index hospitalization ($87.7\%$ vs. $94.4\%$), and had a higher frequency of comorbidities such as hypertension ($30.5\%$ vs. $12.3\%$), heart failure ($28.9\%$ vs. $9.0\%$), coronary artery disease ($23.0\%$ vs. $10.0\%$), diabetes ($16.9\%$ vs. $9.7\%$), and a higher CHA2DS2-VASc score (median score of 2 vs. 1) (all P-values < 0.001).
A total of 30 593 patients survived the index hospitalizations, among whom 11 114 were rehospitalized for AF or flutter during follow-up. Rehospitalized patients were younger (61.5 vs. 63.3 years), had higher rate of hypertension ($14.2\%$ vs. $12.9\%$), and were more likely to be hospitalized for AF or flutter in the previous year ($55.5\%$ vs. $45.5\%$) compared with those not rehospitalized.
## All-cause mortality
The overall incidence rate of all-cause death was $\frac{1.5}{100}$ PY (Table 2 and Figure 2), which increased from $\frac{1.2}{100}$ PY in the first year to $\frac{1.5}{100}$ PY in 1–5 years and reached $\frac{2.0}{100}$ PY at 5–10 years after the index ablation. This corresponded to a survival probability of $98.8\%$ ($95\%$ CI 98.6–$98.9\%$) at 1 year, $93.0\%$ ($95\%$ CI 92.6–$93.4\%$) at 5 years, and $84.0\%$ ($95\%$ CI 82.4–$85.5\%$) at 10-year, respectively.
**Figure 2:** *Long-term outcomes following catheter ablation of atrial fibrillation. (A) Survival probability following catheter ablation of atrial fibrillation. (B) Cumulative incidence of rehospitalizations for AF or flutter, and related procedures (repeat AF ablation and cardioversion). (C) Cumulative incidence of rehospitalizations for other cardiovascular events. AF, atrial fibrillation; AFL, atrial flutter; AMI, acute myocardial infarction; CI, confidence interval; and TIA, transient ischaemic attack.* TABLE_PLACEHOLDER:Table 2
## Rehospitalization for atrial fibrillation or flutter
A total of 11 114 patients experienced at least one acute (unplanned) or elective (planned) rehospitalization for AF or flutter (incidence rate $\frac{13.3}{100}$ PY). The incidence rate peaked during the first year ($\frac{29.0}{100}$ PY) and rapidly declined to $\frac{7.8}{100}$ PY and $\frac{3.9}{100}$ PY at 1–5 years and 5–10 years post-ablation, respectively. The cumulative incidence of rehospitalizations for atrial arrhythmias was $23.8\%$ ($95\%$ CI 23.4–$24.3\%$) at 1-year, and $49.4\%$ ($95\%$ CI 48.4–$50.4\%$) at 10 years. Of these 11 114 patients, 5148 ($46.3\%$) had an acute (unplanned) admission for AF or flutter with an overall incidence rate of $\frac{4.8}{100}$ PY and a cumulative incidence of $24.8\%$ ($95\%$ CI 24.0–$25.7\%$).
## Repeat atrial fibrillation ablation and cardioversion
Among the 11 114 patients rehospitalized for AF or flutter, a subset of 6001 patients underwent repeat AF ablation (incidence rate $\frac{5.9}{100}$ PY) and 4811 patients received cardioversion (incidence rate $\frac{4.5}{100}$ PY). The incidence rate for repeat ablation was highest in the first year ($\frac{12.2}{100}$ PY), and then decreased to $\frac{1.7}{100}$ PY in years 5–10 post ablation. The 10-year cumulative incidence was $28.1\%$, ($95\%$ CI 27.2–$29.0\%$). Similarly, there was a rapid decline in the incidence rate of cardioversion for AF or flutter from $\frac{10.6}{100}$ PY in the first year to $\frac{1.6}{100}$ PY in years 5–10 post-ablation with an overall cumulative incidence of $24.4\%$ ($95\%$ CI 21.5–$27.5\%$).
## Rehospitalizations for other cardiovascular events
Overall, the incidence rates of other cardiovascular events were low. Specifically, rehospitalizations for stroke or TIA occurred at an incidence rate of $\frac{0.7}{100}$ PY with a cumulative incidence of $6.6\%$ ($95\%$ CI 5.8–$7.5\%$). Similarly, the incidence rate for heart failure ($\frac{1.1}{100}$ PY) and acute myocardial infarction ($\frac{0.4}{100}$ PY) was low with a cumulative incidence of $8.5\%$ ($95\%$ CI 7.9–$9.2\%$) and $3.7\%$ ($95\%$ CI 3.3–$4.2\%$), respectively. Rehospitalizations for syncope occurred with an incidence rate of $\frac{0.6}{100}$ PY with a cumulative incidence of $4.3\%$ ($95\%$ CI 4.0–$4.7\%$). The estimates for rehospitalizations for arrythmias other than AF or flutter were $\frac{2.5}{100}$ PY and $15.9\%$ ($95\%$ CI 15.0–$16.7\%$) respectively. A total of $14.5\%$ (13.6–$15.3\%$) of patients received either a pacemaker or defibrillator (incidence rate of $\frac{2.0}{100}$ PY) during the 10-year period. The cumulative incidence was higher among patients with comorbid heart failure ($19.8\%$, $95\%$ CI 17.6–$22.0\%$) compared with those without ($13.9\%$, $95\%$ CI 13.0–$14.8\%$).
## Variation in primary outcomes by age, sex, and comorbid heart failure
The unadjusted analysis showed survival probability declined in elderly patients and those with comorbid heart failure but was comparable between sexes (Figure 3A). Long term cumulative incidence of rehospitalizations for AF or flutter was lower among patients ≥80 years but was comparable between subgroups regardless of sex or the presence of heart failure (Figure 3B). Similarly, cumulative incidence of repeat ablation was lowest in the oldest age group and comparable between sex but was lower in patients with comorbid heart failure compared with those without heart failure (Figure 3C).
**Figure 3:** *Long-term outcomes following catheter ablation of atrial fibrillation by age, sex, and comorbid heart failure. (A) Long-term survival probability following catheter ablation of atrial fibrillation based on (1) Age group. (2) Sex (3) The presence of comorbid heart failure. (B): Long-term risk of rehospitalizations for recurrent atrial arrhythmias (atrial fibrillation or flutter) following catheter ablation of atrial fibrillation based on (1) Age group, (2) Sex (3) The presence of comorbid heart failure. (C) Long-term risk of repeat AF ablation following catheter ablation of atrial fibrillation based on (1) Age group, (2) Sex, (3) The presence of comorbid heart failure. AF, atrial fibrillation; AFL, atrial flutter; and y, years old.*
After adjusting for differences in other patient characteristics, female sex was associated with better survival (HR 0.83, $95\%$ CI 0.75–0.92) while older age (HR for each decade increase in age: 2.40, $95\%$ CI 2.28–2.52) and heart failure (HR 1.90, $95\%$ CI 1.69–2.14) was associated with worse long-term survival (Supplementary material online, Figure S1). After accounting for the competing risk of death, older age was associated with lower adjusted hazard of rehospitalizations for AF or flutter (sHR 0.93, $95\%$ CI 0.91–0.94) but higher hazard of repeat AF ablation (sHR 1.35, $95\%$ CI 1.16–1.57). Female sex was associated with higher hazard of being rehospitalized for AF or flutter (sHR 1.06, $95\%$ CI 1.02–1.11) but hazard of repeat ablation was similar between sex (sHR 0.95, $95\%$ CI 0.69–1.31), while heart failure had no significant relationship with these outcomes (Supplementary material online, Figures S2 and S3).
## Discussion
We found that patients undergoing AF ablations in ANZ had a good long-term clinical outcome with a high survival probability and a low incidence of clinical sequelae of AF such as rehospitalizations for stroke or TIA, heart failure, and acute myocardial infarction. Furthermore, about half of the patients did not experience rehospitalization for AF or flutter, including for repeat AF ablation or cardioversion. Collectively, these findings suggest a good prognosis after AF ablation but also suggest the need for additional measures to reduce the burden of AF as nearly $50\%$ of patients required hospitalization for AF or flutter in the 10 years post-ablation.
Population studies play an important role in providing outcome data in clinical practice but existing studies primarily focus on short-term outcomes, typically up to 1 year post-ablation,11–13 while long-term outcome data mostly come from Asian countries14,15 or selected populations such as those who had cardioversion before ablation.16 Our study provided long-term outcomes from an unselected, contemporary cohort in ANZ, irrespective of age or payer. Our population was older than that in Korea and Taiwan (mean age of 51–57 years)14,15 but comparable with Denmark (mean age 65.5 years)16 and North America (60.0–65.3 years).11–13 Despite these differences, the incidence rate of death was consistent among studies ($\frac{1.1}{100}$ PY)14–16 but nearly double what is reported in large, multicentre clinical trials such as the Catheter Ablation vs. Antiarrhythmic Drug Therapy for Atrial Fibrillation (CABANA) trial ($\frac{0.63}{100}$ PY).6 Similarly, rates of rehospitalizations for stroke or TIA (0.5–$\frac{0.7}{100}$ patient-years)14–16 and heart failure (0.7–$\frac{1.2}{100}$ patient-years)14–16 were consistent among population studies but higher than those reported in the CABANA trial ($\frac{0.01}{100}$ PY for disabling stroke). Collectively, patients undergoing AF ablation in clinical practice appear to have a relatively low incidence of death and hospitalizations for stroke or heart failure, although the risks are greater than those reported in clinical trials.
We also extend the literature by reporting the longitudinal risk of hospitalization for recurrent AF or flutter and the need for repeat intervention-outcomes of critical importance that are frequently sought by patients and clinicians but rarely reported by other population studies.14–16 Although the definition of AF recurrence as an episode lasting >30 s is commonly used in clinical trials and recommended by management guidelines,10 measuring events that require hospital admission might better reflect the direct burden these patients face. Indeed, Terricabras and colleagues have shown that regardless of AF recurrence status, catheter ablation significantly reduces the AF burden and improves quality of life, suggesting that AF burden might be a more relevant outcome measure for patients undergoing AF ablation.26 Encouragingly, we found that just over $50\%$ of patients were free of hospital admission for AF or flutter at 10 years post-ablation, and the incidence during the first year was only $23.8\%$, significantly lower than the $49.1\%$ rate in the year preceding the ablation. Given the near $50\%$ incidence of arrhythmia recurrence (defined as AF >30 s)10 at four years post-ablation reported in the CABANA trial,6 our finding ($39.4\%$ patients rehospitalized for AF or flutter at 4 years) suggests that $80\%$ of these recurrences are likely severe enough to warrant rehospitalization. We also found that $28.1\%$ of our patients required at least one repeat ablation, which is lower than the cumulative incidence ($57.6\%$) reported in another population study.16 Nevertheless, the incidence rate of repeat AF ablation in our cohort was more than double that of the CABANA trial (5.9 vs. $\frac{2.6}{100}$ patient-years),8 suggesting that in clinical practice, patients may require more frequent ablation to achieve outcomes comparable with the trial setting.
Another important observation is a comparable adjusted likelihood of repeat ablation in patients with heart failure despite the higher mortality risk in this subgroup. The literature has consistently shown better outcomes in heart failure patients undergoing AF ablations27 with the Catheter *Ablation versus* Standard Conventional Therapy in Patients with Left Ventricular Dysfunction and Atrial Fibrillation (CASTLE-AF) trial of AF ablation in those with severe heart failure (ejection fraction ≤$35\%$) reporting a nearly $50\%$ lower hazard of death compared with medical therapy.7 More importantly, this trial had a higher incidence of repeat ablation than that in our study ($24.5\%$ at 3.15 years vs. $14.8\%$ at 3 years).7 This raises the possibility that AF ablation may be underutilized in patients with heart failure who might benefit most from the procedure.
Our findings provide important prognostic information for patients and clinicians who seek to be better informed about the long-term clinical outcomes of ablation. These data are reassuring as the incidence rates of untoward outcomes such as death, stroke or TIA, heart failure or acute myocardial infarction after ablation were relatively low. Nevertheless, nearly $50\%$ of patients required hospitalization for AF or flutter and repeat ablation, suggesting that additional measures are required to minimize the AF burden. There are several strategies proven to minimize AF recurrence and improve ablation outcomes such as weight loss,28 reducing alcohol consumption,29 and optimal management of AF risk factors like comorbid hypertension, diabetes, and sleep apnoea.30 Systematic implementation of these strategies may further improve ablation outcomes, decrease the disease burden, and reduce the need of hospital admission or repeat procedures.
Several limitations should be considered when interpreting our results. First, we used routinely collected administrative data that are generally less granular than those collected specifically for research purposes. However, validation studies have shown good accuracy of coded data,17,18 linkages of health records,19 and identification of AF ablation in administrative data.22 Although the algorithm to identify AF ablation in administrative data may not capture all procedures as the sensitivity is not $100\%$, this approach has $100\%$ specificity and has been widely used by other studies using hospitalization data.31,32 Second, we could not estimate the rate of atrial arrhythmia recurrence (defined as any atrial arrhythmia lasting >30 s10) as not all recurrences required hospital admission. Visits to the emergency department due to AF or flutter or other cardiovascular diseases after ablation that did not lead to hospitalization were also not counted. Instead, we focused on AF or flutter hospitalizations and other events that required hospital admission—events that are most concerning for patients and clinicians and have the greatest impact on health care resources. Third, patients undergoing AF ablation in Australia and New Zealand were relatively young and had low rates of comorbidities and therefore, our results may not reflect the outcomes of AF ablations in older and sicker patients. Fourth, this study did not seek to compare outcomes of patients undergoing ablation with those of patients who did not as many AF patients may present to general practitioners (not recorded in hospitalization data), while all AF ablations are performed in-hospital. Fifth, the inherent differences in study design and patient selection criteria must be considered when comparing our results with those in clinical trials.6,7 Nevertheless, such a comparison was helpful to better understand the outcomes of AF ablation in clinical practice. Finally, several variables that may influence long-term outcomes were not collected in these data sets and were therefore not adjusted for, such as type of AF, type of ablation energy or lesions performed, operator experience, cardiac function, and medications used.
## Conclusions
Patients undergoing catheter ablation of atrial fibrillation have a good long-term prognosis with $84.0\%$ surviving by 10 years and relatively low incidence of sequelae such as stroke and heart failure. Furthermore, about half of the patients remained free of rehospitalizations for AF or flutter, repeat ablation, or cardioversion. Nevertheless, additional measures such as weight loss, alcohol abstinence, and better management of comorbidities are necessary to reduce the residual burden of AF post-ablation.
## Funding
National Heart Foundation of Australia (ID 101186).
## Disclosures
L.N. was supported by the Hospital Research Foundation Postgraduate Scholarship and a Research Training Program Scholarship from The University of Queensland. I.R. was supported by the National Heart Foundation of Australia Future Leader Fellowships (ID 101 186). R.D. has received speaking honoraria from Medtronic. The remaining authors have no conflict of interest.
## Unknown
Conflict of interest: None beyond the stated disclosures.
## Data availability
The data underlying this article were provided by a third party, including the Data Custodian Units of each state and territory in Australia, under ethics approval and the New Zealand Ministry of *Health via* a data user agreement. The data can be accessed upon request to the third party.
## References
1. James SL, Abate D, Abate KH, Abay SM, Abbafati C, Abbasi N. **Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990**. *Lancet* (2018) **392** 1789-1858
2. Lloyd-Jones DM, Wang TJ, Leip EP, Larson MG, Levy D, Vasan RS. **Lifetime risk for development of atrial fibrillation: the Framingham Heart study**. *Circulation* (2004) **110** 1042-1046. PMID: 15313941
3. Staerk L, Sherer JA, Ko D, Benjamin EJ, Helm RH. **Atrial fibrillation: epidemiology, pathophysiology, and clinical outcomes**. *Circ Res* (2017) **120** 1501-1517. PMID: 28450367
4. Calkins H, Reynolds MR, Spector P, Sondhi M, Xu Y, Martin A. **Treatment of atrial fibrillation with antiarrhythmic drugs or radiofrequency ablation: two systematic literature reviews and meta-analyses**. *Circ Arrhythm Electrophysiol* (2009) **2** 349-361. PMID: 19808490
5. Pappone C, Augello G, Sala S, Gugliotta F, Vicedomini G, Gulletta S. **A randomized trial of circumferential pulmonary vein ablation versus antiarrhythmic drug therapy in paroxysmal atrial fibrillation: the APAF study**. *J Am Coll Cardiol* (2006) **48** 2340-2347. PMID: 17161267
6. Packer DL, Mark DB, Robb RA, Monahan KH, Bahnson TD, Poole JE. **Effect of catheter ablation vs. antiarrhythmic drug therapy on mortality, stroke, bleeding, and cardiac arrest among patients with atrial fibrillation: the CABANA randomized clinical trial**. *JAMA* (2019) **321** 1261-1274. PMID: 30874766
7. Marrouche NF, Brachmann J, Andresen D, Siebels J, Boersma L, Jordaens L. **Catheter ablation for atrial fibrillation with heart failure**. *N Engl J Med* (2018) **378** 417-427. PMID: 29385358
8. Nielsen JC, Johannessen A, Raatikainen P, Hindricks G, Walfridsson H, Pehrson SM. **Long-term efficacy of catheter ablation as first-line therapy for paroxysmal atrial fibrillation: 5-year outcome in a randomised clinical trial**. *Heart* (2017) **103** 368-376. PMID: 27566295
9. Scaglione M, Gallo C, Battaglia A, Sardi D, Gaido L, Anselmino M. **Long-term progression from paroxysmal to permanent atrial fibrillation following transcatheter ablation in a large single-center experience**. *Heart Rhythm* (2014) **11** 777-782. PMID: 24561164
10. Calkins H, Hindricks G, Cappato R, Kim YH, Saad EB, Aguinaga L. **2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement on catheter and surgical ablation of atrial fibrillation: executive summary**. *J Arrhythm* (2017) **33** 369-409. PMID: 29021841
11. Patil N, Arora S, Davis L, Akoum NW, Chung MK, Sridhar AR. **Incidence and predictors of 30-day acute cerebrovascular accidents post atrial fibrillation catheter ablation (from the Nationwide Readmissions Database**. *Am J Cardiol* (2021) **138** 61-65. PMID: 33058801
12. Guo J, Nayak HM, Besser SA, Beaser A, Aziz Z, Broman M. **Impact of atrial fibrillation ablation on recurrent hospitalization: a nationwide cohort study**. *JACC Clin Electrophysiol* (2019) **5** 330-339. PMID: 30898236
13. Arora S, Lahewala S, Tripathi B, Mehta V, Kumar V, Chandramohan D. **Causes and predictors of readmission in patients with atrial fibrillation undergoing catheter ablation: a national population-based cohort study**. *J Am Heart Assoc* (2018) **7** e009294. PMID: 29907655
14. Yang PS, Sung JH, Jang E, Yu HT, Kim TH, Uhm JS. **Catheter ablation improves mortality and other outcomes in real-world patients with atrial fibrillation**. *J Am Heart Assoc* (2020) **9** e015740. PMID: 32427022
15. Chang CH, Lin JW, Chiu FC, Caffrey JL, Wu LC, Lai MS. **Effect of radiofrequency catheter ablation for atrial fibrillation on morbidity and mortality: a nationwide cohort study and propensity score analysis**. *Circ Arrhythm Electrophysiol* (2014) **7** 76-82. PMID: 24446025
16. Modin D, Claggett B, Gislason G, Hansen ML, Worck R, Johannessen A. **Catheter ablation for atrial fibrillation is associated with lower incidence of heart failure and death**. *Europace* (2020) **22** 74-83. PMID: 31595294
17. Henderson T, Shepheard J, Sundararajan V. **Quality of diagnosis and procedure coding in ICD-10 administrative data**. *Med Care* (2006) **44** 1011-1019. PMID: 17063133
18. Briffa T, Hung J, Knuiman M, McQuillan B, Chew DP, Eikelboom J. **Trends in incidence and prevalence of hospitalization for atrial fibrillation and associated mortality in Western Australia, 1995**. *Int J Cardiol* (2016) **208** 19-25
19. Holman CD, Bass AJ, Rouse IL, Hobbs MS. **Population-based linkage of health records in Western Australia: development of a health services research linked database**. *Aust N Z J Public Health* (1999) **23** 453-459. PMID: 10575763
20. Ngo L, Ali A, Ganesan A, Woodman R, Adams R, Ranasinghe I. **Gender differences in complications following catheter ablation of atrial fibrillation**. *Eur Heart J Qual Care Clin Outcomes* (2021) **7** 458-467. PMID: 33963402
21. Ngo L, Ali A, Ganesan A, Woodman RJ, Adams R, Ranasinghe I. **Utilisation and safety of catheter ablation of atrial fibrillation in public and private sector hospitals**. *BMC Health Serv Res* (2021) **21** 883. PMID: 34454482
22. Singh SM, Webster L, Calzavara A, Wijeysundera HC. **Validation of algorithms to identify invasive electrophysiology procedures using administrative data in Ontario, Canada**. *Med Care* (2017) **55** e44-e50. PMID: 25415560
23. Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Lezzoni LI. **Risk adjustment of Medicare capitation payments using the CMS-HCC model**. *Health Care Financ Rev* (2004) **25** 119-141. PMID: 15493448
24. Fine JP, Gray RJ. **A proportional hazards model for the subdistribution of a competing risk**. *J Am Statist Assoc* (1999) **94** 496-509
25. Dignam JJ, Zhang Q, Kocherginsky M. **The use and interpretation of competing risks regression models**. *Clin Cancer Res* (2012) **18** 2301-2308. PMID: 22282466
26. Terricabras M, Mantovan R, Jiang CY, Betts TR, Chen J, Deisenhofer I. **Association between quality of life and procedural outcome after catheter ablation for atrial fibrillation: a secondary analysis of a randomized clinical trial**. *JAMA Netw Open* (2020) **3** e2025473. PMID: 33275151
27. Turagam MK, Garg J, Whang W, Sartori S, Koruth JS, Miller MA. **Catheter ablation of atrial fibrillation in patients with heart failure: a meta-analysis of randomized controlled trials**. *Ann Intern Med* (2019) **170** 41-50. PMID: 30583296
28. Peigh G, Wasserlauf J, Vogel K, Kaplan RM, Pfenniger A, Marks D. **Impact of pre-ablation weight loss on the success of catheter ablation for atrial fibrillation**. *J Cardiovasc Electrophysiol* (2021) **32** 2097-2104. PMID: 34191371
29. Takahashi Y, Nitta J, Kobori A, Sakamoto Y, Nagata Y, Tanimoto K. **Alcohol consumption reduction and clinical outcomes of catheter ablation for atrial fibrillation**. *Circ Arrhythm Electrophysiol* (2021) **14** e009770. PMID: 33999699
30. Pathak RK, Middeldorp ME, Lau DH, Mehta AB, Mahajan R, Twomey D. **Aggressive risk factor reduction study for atrial fibrillation and implications for the outcome of ablation: the ARREST-AF cohort study**. *J Am Coll Cardiol* (2014) **64** 2222-2231. PMID: 25456757
31. Deshmukh A, Patel NJ, Pant S, Shah N, Chothani A, Mehta K. **In-hospital complications associated with catheter ablation of atrial fibrillation in the United States between 2000 and 2010: analysis of 93 ,801 procedures**. *Circulation* (2013) **128** 2104-2112. PMID: 24061087
32. Tripathi B, Arora S, Kumar V, Abdelrahman M, Lahewala S, Dave M. **Temporal trends of in-hospital complications associated with catheter ablation of atrial fibrillation in the United States: an update from Nationwide Inpatient Sample database (2011**. *J Cardiovasc Electrophysiol* (2018) **29** 715-724
33. Hindricks G, Potpara T, Dagres N, Arbelo E, Bax JJ, Blomström-Lundqvist C. **2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio Thoracic Surgery (EACTS): the Task Force for the diagnosis and management of atrial fibrillation of the European Society of Cardiology (ESC) developed with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC**. *Eur Heart J* (2021) **42** 373-498. PMID: 32860505
|
---
title: 'Eating disorders in sexual minority adolescents and young adults: examining
clinical characteristics and psychiatric co-morbidities in an inpatient medical
setting'
authors:
- Anita V. Chaphekar
- Amanda Downey
- Andrea K. Garber
- Mikayla Kuykendall
- Paola Bojorquez-Ramirez
- Kyle T. Ganson
- Sara M. Buckelew
- Jason M. Nagata
journal: Journal of Eating Disorders
year: 2023
pmcid: PMC9972849
doi: 10.1186/s40337-023-00756-5
license: CC BY 4.0
---
# Eating disorders in sexual minority adolescents and young adults: examining clinical characteristics and psychiatric co-morbidities in an inpatient medical setting
## Abstract
### Background
Sexual minority adolescents and young adults are at higher risk of eating disorders compared to heterosexual peers. However, little is known about the clinical and psychiatric presentation of this population requiring inpatient medical stabilization. Given the increased risk for eating disorder behaviors in sexual minority individuals amidst increased rates of medical hospitalizations secondary to eating disorders, it is important to understand presenting characteristics of this population. The objectives of this study were to [1] describe the clinical characteristics of sexual minority adolescents and young adults with eating disorders admitted for medical instability and [2] compare psychiatric co-morbidities and suicidality of sexual minority adolescents and young adults to heterosexual peers.
### Methods
A retrospective chart review was conducted of 601 patients admitted to a large inpatient eating disorders medical stabilization unit between 2012 and 2020. Data collected included demographics, medical data including vital signs, and psychiatric characteristics. Chi square or t-tests were used to examine potential differences in clinical characteristics and psychiatric co-morbidities between groups. Modified Poisson regression was used to assess associations between sexual orientation and psychiatric co-morbidities.
### Results
Over one fifth ($21.1\%$, $$n = 103$$) of our inpatient sample identified as a sexual minority individual. The average age of participants was 15.6 years (2.7). Sexual minority adolescents and young adults had higher percent median body mass index compared to heterosexual peers and yet equally severe vital sign instability on admission. Sexual minority adolescents and young adults were almost 1.5 times more likely to have a psychiatric comorbidity with higher rates of depression, anxiety, and post-traumatic stress disorder. Sexual minority adolescents and young adults were approximately two times more likely to have a history of self-injurious behaviors and/or suicidality.
### Conclusions
Sexual minority adolescents and young adults with eating disorders have equally severe vital sign instability despite higher percent median body mass index on admission for medical stabilization. Sexual minority adolescents and young adults hospitalized for medical complications of eating disorders are far more likely to have an additional mental health disorder and a history of self-harm and/or suicidality, which may portend a less favorable long-term prognosis.
## Plain English summary
Eating disorders are highly prevalent among sexual minority individuals, including those who identify as gay, lesbian, or bisexual. Little is known about the medical and psychiatric profile of sexual minority adolescents and young adults with eating disorders who require inpatient hospitalization for medical stabilization. This study examines the clinical characteristics and psychiatric comorbidities of sexual minority adolescents and young adults compared to heterosexual peers hospitalized for medical complications of eating disorders from 2012-2020. The average age of our participants was 15 years old. The majority of participants were assigned female at birth and were White/Caucasian. We found that sexual minority adolescents and young adults were just as medically compromised as heterosexual peers and more likely to have a co-morbid diagnosis of depression, anxiety, and/ or post-traumatic stress disorder. Additionally, sexual minority individuals were more likely to have a history of suicidal ideation and self-injurious behavior. These findings may have important implications for long-term prognosis and potential for recovery.
## Background
Despite advances in nutritional rehabilitation and evidence-based psychological interventions in the treatment of eating disorders, the incidence of eating disorders in adolescents and young adults (AYA) continues to rise [1, 2]. Dangerously low body weight, hemodynamic instability, and electrolyte abnormalities secondary to an eating disorder can yield serious medical complications requiring hospitalization; however, there is a paucity of research on these medical complications in sexual minority populations with eating disorders [3]. Individuals identifying as gay, lesbian, and bisexual are at increased risk for eating disorders and disordered eating behaviors [4, 5]. Studies have reported up to 4 times higher odds of sexual minority individuals experiencing eating disorders in their lifetime and 1.5 times higher odds of having disordered eating behaviors [4–6]. While the majority of this research has been in the adult population, studies have shown an increased prevalence of eating disorder behaviors such as fasting ($24.8\%$) and purging (16–$17\%$) in sexual minority adolescents compared to heterosexual peers [5, 7].
AYA with eating disorders are highly likely to have an additional co-morbid psychiatric disorder [2]. More than half of individuals with anorexia nervosa (AN) and bulimia nervosa (BN) have a comorbid psychiatric disorder [2]. While AN and BN have different comorbidity profiles, depression, anxiety, post-traumatic stress disorder (PTSD) and attention deficit hyperactivity disorder (ADHD) are highly prevalent among all eating disorder diagnoses [2, 8, 9]. Additionally, sexual minority individuals, including children and adolescents, experience higher rates of mental health conditions compared to heterosexual individuals [10–12]. One study examining depressive symptoms among high school students across the United States reported a prevalence of $60.4\%$ in sexual minority adolescents compared to $26.4\%$ in heterosexual peers [11]. Presence of a comorbid psychiatric disorder portends negative long-term outcomes for patients with eating disorders [2, 8, 9, 13, 14]. This is of utmost concern in sexual minority adolescents and young adults (SM AYA), a population which already has higher rates of preoccupation with body weight and appearance, weight control behaviors, and mental health conditions compared to heterosexual peers [10, 15–23].
Elevated rates of self-injurious and suicidal behaviors are common, with suicide now being the second leading cause of mortality among patients with AN [24]. Approximately half of adolescent patients with AN struggle with suicidal ideation and more than one-third of adolescents with BN have attempted suicide [2]. Specifically, in the SM AYA population, studies report sexual orientation as a risk factor for suicide attempts and describe SM individuals as being at a higher risk for suicide attempt [20]. As SM AYA are more likely to have a history of suicide attempt and experience more suicidal thoughts and behaviors as compared to heterosexual peers, SM AYA with eating disorders may be especially vulnerable to mental health comorbidities and unfavorable long-term outcomes.
To our knowledge, studies have not examined the clinical and psychiatric characteristics of SM AYA with eating disorders requiring inpatient medical stabilization. As hospitalization rates for eating disorders are on the rise and SM AYA have an increased risk for both eating disorders and mental health conditions, it is imperative to study this population to identify significant characteristics which could inform future care. This exploratory study aims to describe the clinical characteristics of SM AYA. We will also compare psychiatric co-morbidities and suicidality of SM AYA to their heterosexual peers admitted for medical stabilization. Given the mental health disparities in sexual minority individuals described above, we hypothesize that higher rates of suicidality and comorbid mental health diagnoses will be observed among SM AYA compared to heterosexual peers in our inpatient sample.
## Study design, participants, and study setting
A retrospective chart review was conducted of 601 adolescents and young adults, aged 9 to 25 years, admitted to a large inpatient medical stabilization unit at the University of California, San Francisco (UCSF) between May 2012 and August 2020. This timeframe was chosen as electronic data prior to May 2012 was not available. Patients were admitted for bradycardia, hypotension, orthostasis, rapid weight loss or extremely low body weight, and electrolyte abnormalities per Society of Adolescent Health and Medicine indications for supporting hospitalization in an adolescent with an eating disorder [3]. The goal of hospitalization is medical stabilization through nutritional rehabilitation. Refeeding and electrolyte monitoring protocols have been described in detail elsewhere [25]. Our inpatient program has an interdisciplinary team comprised of physicians, dietitians, psychologists, and social workers with eating disorder expertise that meet with each patient.
## Measurements
Data was collected as a part of a larger UCSF eating disorder medical registry including age, vital signs, height, weight, laboratory values, sex assigned at birth, sexual orientation, eating disorder diagnosis, co-morbid psychiatric diagnoses, and suicidality or self-injurious behavior. The Institutional Review Board of the University of California, San Francisco, has approved the use of the eating disorder medical registry. Height, weight, and laboratory evaluation were measured within 24 h of admission. Body mass index (BMI) and median BMI (mBMI) were calculated using height and weight [26]. Heart rate and blood pressure nadirs were collected during the entirety of hospitalization. Procedures for vital sign measures, along with the protocols for electrolyte monitoring and replacement and weight assessments have been previously published [25, 27]. Length of hospitalization was measured in days and determined by subtracting discharge date from admission date.
Sexual orientation data were self-reported as part of the physicians’ history and physical at time of hospital admission and/or in the electronic medical record under “Sexual Orientation and Gender Identity”. SM AYA were grouped as one category/variable and defined as “lesbian”, “gay”, “bisexual”, “queer”, “unsure/questioning”, “pansexual”, or “asexual”. This is consistent with other studies that include “unsure” in the sexual minority category [28]. Participants with stated sexual orientation as “straight” were included in the heterosexual group. For the purposes of this study, gender identity (cisgender, transgender, non-binary) was excluded from data collection and analysis.
Eating disorder diagnosis was classified into three categories: AN, other specified feeding and eating disorders (OSFED) which includes atypical anorexia nervosa, and other. ‘ Other’ included avoidant restrictive food intake disorder (ARFID), BN, and unspecified feeding or eating disorder. These diagnoses were grouped together to allow for a sufficient sample for analysis. A psychologist or psychiatrist gave participants an eating disorder diagnosis following psychological evaluation during inpatient admission per Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [29]. The study team reviewed charts and reclassified diagnoses per DSM-5 criteria for patients hospitalized prior to the release of DSM-5 in 2013. Specifically, participants' electronic medical records were reviewed and those with a diagnosis of eating disorder not otherwise specified, a diagnosis no longer present in DSM-5, were found to meet DSM-5 criteria for unspecified feeding and eating disorder ($$n = 2$$).
Psychiatric diagnoses for participants included self-reported pre-existing psychiatric diagnoses and psychiatric diagnoses made during hospitalization by psychologists. Specific diagnoses included depression, anxiety, obsessive compulsive disorder (OCD), PTSD, and ADHD. For the purposes of this study, our category of depression included the specific DSM-IV and DSM-5 diagnoses of major depressive disorder, depressive disorder not otherwise specified, and unspecified depression. The anxiety category included generalized anxiety disorder, social anxiety disorder, anxiety not otherwise specified, and unspecified anxiety. Depression and anxiety diagnoses were not reclassified if participants were diagnosed using DSM-IV criteria. History of suicidal ideation, history of suicide attempt, self-injurious behavior, passive suicidal ideation during hospitalization, and active suicidal ideation during hospitalization were collected from the participant’s history and physical note or from the psychological assessment during hospitalization.
## Statistical analyses
Participants with missing sexual orientation data or without a DSM-5 eating disorder diagnosis were excluded from the study ($$n = 138$$). The final analytic sample consisted of 463 patients. All three categories of eating disorder diagnosis (as described above) were included in analysis. Fisher’s exact, Chi square or t-tests were used to examine potential differences in clinical characteristics and psychiatric co-morbidities between SM and heterosexual AYA. Sexual orientation was dichotomized. Modified Poisson regression analyses were conducted and transformed to risk ratios to examine associations between sexual orientation and psychiatric co-morbidities [30]. Models were adjusted for age, sex assigned at birth, and eating disorder diagnosis. Analyses used Stata 17 (Stata Corp LP, College Station, TX).
## Demographics, length of hospitalization, and medical data
Of the 463 participants, 99 ($21\%$) identified as a SM and 364 ($79\%$) identified as heterosexual (Table 1). 388 were assigned female at birth and 75 were assigned male at birth. The mean age of participants was 15.6 years (2.7) and $63\%$ were White or Caucasian (Table 1).Table 1Demographics, length of hospitalization, and medical characteristics by sexual orientationaCharacteristicSexual OrientationTotal ($$n = 463$$)bHeterosexual Individuals ($$n = 364$$)cSexual Minority Individuals ($$n = 99$$)cp-ValuedAge, years15.60 ± 2.6815.67 ± 2.5415.34 ± 3.130.286Sex assigned at birth0.063 Female388 (83.80)299 (82.14)89 (89.90) Male75 (16.20)65 (17.86)10 (10.10)Race, N (%)0.133 White or Caucasian291 (62.85)239 (65.66)52 (52.53) Asian or NHOPIe42 (9.07)28 (7.69)14 (14.14) Black or African American13 (2.81)10 (2.75)3 (3.03) Other99 (21.38)74 (20.33)25 (25.25) Unknown/Declined18 (3.89)13 (4.92)5 (5.05)Ethnicity, N (%)0.628 Hispanic88 (19.01)70 (19.23)18 (18.18) Non-Hispanic351 (75.81)277 (76.10)74 (74.75) Unknown/Declined24 (5.18)17 (4.67)7 (7.07)Length of hospitalization stay (days)9.93 ± 6.4710.18 ± 6.909.01 ± 4.440.111BMI kg/m217.83 ± 2.8217.72 ± 2.7718.21 ± 3.010.132Percent median body mass indexf88.11 ± 13.8187.31 + 13.2691.09 + 15.380.016Vital signs Pulse (beats per minute) nadir during hospitalization45.73 ± 10.6145.98 ± 10.6644.82 ± 10.450.333 Systolic Pressure (mmHg) nadir during hospitalization89.92 ± 9.6383.74 ± 9.4684.57 ± 10.27 Diastolic Pressure (mmHg) nadir during hospitalization45.47 ± 7.2245.35 ± 7.1545.89 ± 7.50Electrolyte analysis at admissiong Sodium (135–145 mmol/L)138.09 ± 10.64138.12 ± 11.93137.92 ± 2.460.860 Potassium (3.5–5.0 mmol/L)3.88 ± 0.6813.89 ± 0.0363.84 ± 0.0650.499 Magnesium (1.7–2.2 mg/dL)2.13 ± 0.3942.13 ± 0.4342.15 ± 0.1920.720 Phosphorous (2.9–5.0 mg/dL)4.05 ± 2.604.08 ± 2.913.93 ± 0.6090.612Other laboratory evaluation at admissiong Triglycerides (< 200 mg/dL)46.56 ± 33.2445.99 ± 34.2348.65 ± 29.360.507 Cholesterol (< 170 mg/dL)166.11 ± 41.36166.04 ± 41.86166.37 ± 39.650.948 Hemoglobin (12–17 g/dL)12.96 ± 1.2412.95 ± 1.2513.02 ± 1.220.652 Hematocrit (36–$52\%$)38.36 ± 3.5338.31 ± 3.5738.59 ± 3.400.498 Creatinine (0.45–1.08 mg/dL)0.78 ± 1.840.80 ± 2.070.71 ± 0.160.676 *Blood urea* nitrogen (7–21 mg/dL)11.91 ± 6.4411.79 ± 6.74122.35 ± 6.440.453 Thyroid stimulating hormone (0.45–4.33 mIU/L)1.97 ± 1.442.03 ± 1.471.72 ± 1.270.065 Free thyroxine (10–18 pmol/L)12.00 ± 1.8212.04 ± 1.7711.84 ± 2.010.353 Aspartate transaminase (13–35 U/L)28.84 ± 35.1529.69 ± 39.0625.65 ± 11.860.328 Alanine transaminase (8–24 U/L)22.07 ± 28.8721.94 ± 30.6222.57 ± 21.320.852 Albumin (3.5–5.0 g/dL)4.32 ± 0.424.30 ± 0.404.38 ± 0.480.122Bold face indicates significant p valueaTable values are mean ± SD for continuous variables and n (column %) for categorical variablesbTotal number of participants with eating disorder diagnosis and sexual orientation listed in chartcPercentages may not sum to $100\%$ due to roundingdT-test was used for continuous variables. Chi Square or Fisher's exact test if n < 5 was used for categorical variableseNHOPI = Native Hawaiian and Other Pacific Islandersf50th percentile body mass index for age and sexgReference range includes ranges for males and females over and under the age of 18 years The average body mass index (BMI) at time of admission of the participants was 17.8 kg/m2 (2.8) (Table 1). The average percent median BMI (%mBMI) of the participants was $88.11\%$ (13.8) which is consistent with mild malnutrition [31]. SM individuals had a higher %mBMI on admission of $91\%$ compared to their heterosexual peers who had %mBMI of $87\%$ ($$p \leq 0.016$$). Heart rate and blood pressure nadirs did not differ between groups (Table 1). Laboratory evaluation was comparable between groups (Table 1). The average length of hospitalization between SM and heterosexual AYA did not differ (9.0 ± 4.4 days vs. 10.2 ± 6.9 days).
## Eating disorder diagnoses and psychiatric co-morbidities:
There was no statistical difference observed in eating disorder diagnoses between SM AYA and heterosexual AYA (Table 2). SM AYA had significantly higher percentages of depression, anxiety, and PTSD compared to heterosexual peers ($$p \leq 0.003$$, $$p \leq 0.008$$, and $p \leq 0.001$ respectively) (Table 2). SM AYA were more likely to have a psychiatric co-morbidity ($69\%$ versus $48\%$, $p \leq 0.001$; RR = 1.46, $95\%$ CI [1.09, 1.93], $$p \leq 0.009$$) and more likely to be taking psychiatric medication (RR = 1.56, $95\%$ CI [1.12, 2.21], $$p \leq 0.012$$) (Table 3). SM AYA were more likely to have a diagnosis of depression (RR = 1.59, $95\%$ CI [1.10, 2.29], $$p \leq 0.014$$), anxiety (RR = 1.57, $95\%$ CI [1.08, 2.29], $$p \leq 0.019$$), and PTSD (RR = 4.88, $95\%$ CI [1.94, 12.26], $$p \leq 0.001$$) compared to heterosexual peers when adjusted for age, sex, and eating disorder diagnosis (Table 3).Table 2Eating disorder diagnosis and psychiatric comorbidities for adolescents and young adults hospitalized for complications of malnutrition by sexual orientationaCharacteristicSexual orientationTotal ($$n = 463$$)bHeterosexual ($$n = 364$$)cSexual Minority ($$n = 99$$)cp-ValuedEating disorder diagnosis0.147 Anorexia nervosae58.10 [269]60.44 [220]49.49 [49] Other Specified Feeding and Eating Disorder (OSFED)f31.97 [148]30.22 [110]38.38 [38] Otherg9.94 [46]9.34 [34]12.12 [12]Presence of other psychiatric diagnosish < 0.001 Yes52.48 [243]48.08 [175]68.69 [68] No47.52 [220]51.92 [189]31.31 [31]Specific psychiatric diagnosis Depressioni30.24 [140]26.92 [98]42.42 [42]0.003 Anxietyj28.73 [133]25.82 [94]39.39 [39]0.008 Obsessive–compulsive disorder (OCD)5.18 [24]5.22 [19]5.05 [5]0.946 Post-traumatic stress disorder (PTSD)4.10 [19]2.20 [8]11.11 [11] < 0.001 Attention deficit hyperactivity disorder (ADHD)3.02 [14]3.02 [11]3.03 [3]0.997Suicide or self-harmk During hospitalization7.99 [37]6.59 [24]13.13 [13]0.033 Prior to hospitalization22.25 [103]18.13 [66]37.37 [37] < 0.001Bold face indicates significant p valueaTable values are % (N)bTotal number of participants with eating disorder diagnosis and sexual orientation listed in chartcPercentages may not sum to $100\%$ due to roundingdT-test was used for continuous variables. Chi Square (or Fisher's exact test if n < 5) was used for categorical variableseAnorexia nervosa includes both restricting subtype and binge/purge subtypefOSFED includes Atypical Anorexia Nervosa and Purging DisordergOther includes Avoidant Restrictive Food Intake Disorder, Bulimia nervosa, and Unspecified Eating DisorderhPresence of psychiatric diagnosis other than eating disorderiDepression includes major depressive disorder, depressive disorder not otherwise specified, and unspecified depressionjAnxiety includes generalized anxiety disorder, social anxiety disorder, anxiety not otherwise specified, and unspecified anxietykSuicide includes suicide attempt, active suicidal ideation, and passive suicidal ideationTable 3Associations between psychiatric co-morbidities and sexual orientationaOutcomeSexual Minority, RR ($95\%$ CI)bpPresence of psychiatric co-morbidity1.46 (1.09, 1.93)0.009Psychiatric medication use1.56 (1.12, 2.21)0.012Depression1.59 (1.10, 2.29)0.014Anxiety1.57 (1.08, 2.29)0.019Obsessive–compulsive disorder (OCD)1.01 (0.13, 2.38)0.980Post-traumatic stress disorder (PTSD)4.88 (1.94, 12.26)0.001Attention deficit hyperactivity disorder (ADHD)1.14 (0.31, 4.16)0.844Suicidality during hospitalization1.80 (0.90, 3.56)0.095History of self-harm or suicidality1.94 (1.29, 2.92)0.001Bold face indicates significant p valueaAbbreviated output of modified Poisson regression analyses adjusted for age, sex assigned at birth, and eating disorder diagnosisbHeterosexual participants are reference category SM AYA were more likely to have a history of self-injurious behavior or suicidality compared to heterosexual peers (RR = 1.94, $95\%$ CI [1.29, 2.92], $$p \leq 0.001$$) (Table 3). There was no difference between groups in active suicidality during hospitalization (Table 3).
## Discussion
Despite the high prevalence of eating disorders among SM AYA, no studies to our knowledge have examined the medical and psychiatric characteristics of this population hospitalized for medical instability. Previous studies have described higher BMIs among SM individuals, specifically those assigned female at birth [32, 33]. Our study demonstrates that SM AYA presented with equally severe vital sign instability despite having a higher %mBMI on admission compared to heterosexual peers. This suggests that higher weight is not protective [34]. This research further highlights the need for medical providers caring for SM AYA to understand that they may be medically unstable at any weight, even one presumed to be in a "normal" or higher range.
Our finding that SM AYA with eating disorders have greater psychiatric comorbidities and higher prior history of suicidality compared to heterosexual peers suggests the need for a psychologic assessment and ongoing mental health support in this population. The higher rates of depression, anxiety, and PTSD in our inpatient population has also been seen in SM AYA seeking treatment in residential and outpatient treatment programs for eating disorders [35]. While this study did not explore causative factors for this increased mental health burden, minority stress theory details the stigma-related stressors associated with higher rates of psychopathology, including eating disorder behavior, in sexual minority individuals [16, 36, 37]. Given that psychiatric comorbidities portend worsened eating disorder outcomes, our findings underscore the importance of psychological support for SM AYA admitted for medical stabilization.
This study is limited by its retrospective and observational nature, which precludes causal inferences. Data was collected from a tertiary care hospital in San Francisco, California and may not be generalizable to other inpatient populations. Pre-existing psychiatric history and suicidality were often collected by self-report, introducing recall bias and heterogeneity into diagnostic reporting. Although we focused on sexual orientation for this analysis, future studies could also assess gender identity, which may influence preoccupation with body weight and appearance in SM AYA [23]. Additionally, our study is limited in examining the impact of socioeconomic factors as we do not have information about income, education, and/or housing available for our participants. Future studies should examine the relationship between socioeconomic status, sexual orientation, and eating disorder behaviors.
Strengths of our study include 8 years of clinical data that was collected by a multi-disciplinary team with expertise in eating disorders including physicians, dietitians, and psychologists. It is noteworthy that over $20\%$ of our study population identified as a sexual minority individual which is greater than the United States population of sexual minority youth at approximately $16\%$ [38].
## Conclusions
SM AYA with eating disorders present with higher %mBMI but are equally medically compromised on inpatient admission for medical stabilization as their heterosexual peers. Additionally, SM AYA have more mental health comorbidity and suicidality. By describing the clinical and psychiatric characteristics of this population, clinicians can better tailor affirming, individualized eating disorder treatment for SM AYA with eating disorders that recognizes their increased mental health burden to ensure equitable health outcomes.
## References
1. Lin JA, Hartman-Munick SM, Kells MR, Milliren CE, Slater WA, Woods ER. **The impact of the COVID-19 pandemic on the number of adolescents/young adults seeking eating disorder-related care**. *J Adolesc Health* (2021.0) **69** 660-663. DOI: 10.1016/j.jadohealth.2021.05.019
2. Herpertz-Dahlmann B. **Adolescent eating disorders: update on definitions, symptomatology, epidemiology, and comorbidity**. *Child Adolesc Psychiatr Clin N Am* (2015.0) **24** 177-196. DOI: 10.1016/j.chc.2014.08.003
3. Golden NH, Katzman DK, Sawyer SM, Ornstein RM, Rome ES. **Position Paper of the Society for Adolescent Health and Medicine: medical management of restrictive eating disorders in adolescents and young adults**. *J Adolesc Health.* (2015.0) **56** 121-5. DOI: 10.1016/j.jadohealth.2014.10.259
4. Kamody RC, Grilo CM, Udo T. **Disparities in DSM-5 defined eating disorders by sexual orientation among U.S. adults**. *Int J Eat Disord* (2020.0) **53** 278-87. DOI: 10.1002/eat.23193
5. Nagata JM, Ganson KT, Austin SB. **Emerging trends in eating disorders among sexual and gender minorities**. *Curr Opin Psychiatry* (2020.0) **33** 562-567. DOI: 10.1097/YCO.0000000000000645
6. Nagata JM, Garber AK, Tabler JL, Murray SB, Bibbins-Domingo K. **Prevalence and correlates of disordered eating behaviors among young adults with overweight or obesity**. *J Gen Intern Med* (2018.0) **33** 1337-1343. DOI: 10.1007/s11606-018-4465-z
7. Calzo JP, Turner BC, Marro R, Phillips GL. **Alcohol use and disordered eating in a us sample of heterosexual and sexual minority adolescents**. *J Am Acad Child Adolesc Psychiatry* (2019.0) **58** 200-210. DOI: 10.1016/j.jaac.2018.09.437
8. Salbach-Andrae H, Lenz K, Simmendinger N, Klinkowski N, Lehmkuhl U, Pfeiffer E. **Psychiatric comorbidities among female adolescents with anorexia nervosa**. *Child Psychiatry Hum Dev* (2008.0) **39** 261-272. DOI: 10.1007/s10578-007-0086-1
9. Swanson SA, Crow SJ, Le Grange D, Swendsen J, Merikangas KR. **Prevalence and correlates of eating disorders in adolescents**. *Arch Gen Psychiatry* (2011.0) **68** 714-723. DOI: 10.1001/archgenpsychiatry.2011.22
10. Blashill AJ, Calzo JP. **Sexual minority children: Mood disorders and suicidality disparities**. *J Affect Disord* (2019.0) **1** 96-98. DOI: 10.1016/j.jad.2018.12.040
11. Zaza S, Kann L, Barrios LC. **Lesbian, gay, and bisexual adolescents**. *JAMA* (2016.0) **316** 2355-2356. DOI: 10.1001/jama.2016.11683
12. Kerridge BT, Pickering RP, Saha TD, Ruan WJ, Chou SP, Zhang H. **Prevalence, sociodemographic correlates and DSM-5 substance use disorders and other psychiatric disorders among sexual minorities in the United States**. *Drug Alcohol Depend* (2017.0) **1** 82-92. DOI: 10.1016/j.drugalcdep.2016.10.038
13. Fennig S, Hadas A. **Suicidal behavior and depression in adolescents with eating disorders**. *Nord J Psychiatry* (2010.0) **64** 32-39. DOI: 10.3109/08039480903265751
14. Bühren K, Schwarte R, Fluck F, Timmesfeld N, Krei M, Egberts K. **Comorbid psychiatric disorders in female adolescents with first-onset anorexia nervosa**. *Eur Eat Disord Rev* (2014.0) **22** 39-44. DOI: 10.1002/erv.2254
15. Austin SB, Ziyadeh N, Kahn JA, Camargo CA, Colditz GA, Field AE. **Sexual orientation, weight concerns, and eating-disordered behaviors in adolescent girls and boys**. *J Am Acad Child Adolesc Psychiatry* (2004.0) **43** 1115-1123. DOI: 10.1097/01.chi.0000131139.93862.10
16. Watson RJ, Adjei J, Saewyc E, Homma Y, Goodenow C. **Trends and disparities in disordered eating among heterosexual and sexual minority adolescents**. *Int J Eat Disord* (2017.0) **50** 22-31. DOI: 10.1002/eat.22576
17. 17.Institute of Medicine (US) Committee on Lesbian, Gay, Bisexual, and Transgender Health Issues and Research Gaps and Opportunities. The Health of Lesbian, Gay, Bisexual, and Transgender People: Building a Foundation for Better Understanding [Internet]. Washington (DC): National Academies Press (US); 2011 [cited 2022 Dec 27]. (The National Academies Collection: Reports funded by National Institutes of Health). Available from: http://www.ncbi.nlm.nih.gov/books/NBK64806/.
18. Austin SB, Nelson LA, Birkett MA, Calzo JP, Everett B. **Eating disorder symptoms and obesity at the intersections of gender, ethnicity, and sexual orientation in US high school students**. *Am J Public Health* (2013.0) **103** e16-22. DOI: 10.2105/AJPH.2012.301150
19. Calzo JP, Austin SB, Micali N. **Sexual orientation disparities in eating disorder symptoms among adolescent boys and girls in the UK**. *Eur Child Adolesc Psychiatry* (2018.0) **27** 1483-1490. DOI: 10.1007/s00787-018-1145-9
20. Miranda-Mendizábal A, Castellví P, Parés-Badell O, Almenara J, Alonso I, Blasco MJ. **Sexual orientation and suicidal behaviour in adolescents and young adults: systematic review and meta-analysis**. *Br J Psychiatry* (2017.0) **211** 77-87. DOI: 10.1192/bjp.bp.116.196345
21. Stone DM, Luo F, Ouyang L, Lippy C, Hertz MF, Crosby AE. **Sexual orientation and suicide ideation, plans, attempts, and medically serious attempts: evidence from local Youth Risk Behavior Surveys, 2001–2009**. *Am J Public Health* (2014.0) **104** 262-271. DOI: 10.2105/AJPH.2013.301383
22. Austin SB, Ziyadeh NJ, Corliss HL, Rosario M, Wypij D, Haines J. **Sexual orientation disparities in purging and binge eating from early to late adolescence**. *J Adolesc Health* (2009.0) **45** 238-245. DOI: 10.1016/j.jadohealth.2009.02.001
23. McClain Z, Peebles R. **Body image and eating disorders among lesbian, gay, bisexual, and transgender youth**. *Pediatr Clin North Am* (2016.0) **63** 1079-1090. DOI: 10.1016/j.pcl.2016.07.008
24. Goldstein A, Gvion Y. **Socio-demographic and psychological risk factors for suicidal behavior among individuals with anorexia and bulimia nervosa: a systematic review**. *J Affect Disord* (2019.0) **15** 1149-1167. DOI: 10.1016/j.jad.2018.12.015
25. Garber AK, Cheng J, Accurso EC, Adams SH, Buckelew SM, Kapphahn CJ. **Short-term outcomes of the study of refeeding to optimize inpatient gains for patients with anorexia nervosa**. *JAMA Pediatr* (2021.0) **175** 1-9. DOI: 10.1001/jamapediatrics.2020.3359
26. 26.Growth Charts - Clinical Growth Charts [Internet]. 2022 [cited 2022 Dec 27]. Available from: https://www.cdc.gov/growthcharts/clinical_charts.htm.
27. Nagata JM, Bojorquez-Ramirez P, Nguyen A, Ganson KT, Machen VI, Cattle CJ. **Sex differences in refeeding among hospitalized adolescents and young adults with eating disorders**. *Int J Eat Disord* (2022.0) **55** 247-253. DOI: 10.1002/eat.23660
28. Zullig KJ, Matthews-Ewald MR, Valois RF. **Relationship between disordered eating and self-identified sexual minority youth in a sample of public high school adolescents**. *Eat Weight Disord* (2019.0) **24** 565-573. DOI: 10.1007/s40519-017-0389-6
29. 29.Diagnostic and statistical manual of mental disorders: DSM-5.
30. Zou G. **A modified poisson regression approach to prospective studies with binary data**. *Am J Epidemiol* (2004.0) **159** 702-706. DOI: 10.1093/aje/kwh090
31. White JV, Guenter P, Jensen G, Malone A, Schofield M. **Consensus statement of the Academy of Nutrition and Dietetics/American Society for Parenteral and Enteral Nutrition: characteristics recommended for the identification and documentation of adult malnutrition (undernutrition)**. *J Acad Nutr Diet.* (2012.0) **112** 730-8. DOI: 10.1016/j.jand.2012.03.012
32. Eliason MJ, Ingraham N, Fogel SC, McElroy JA, Lorvick J, Mauery DR. **A systematic review of the literature on weight in sexual minority women**. *Womens Health Issues* (2015.0) **25** 162-175. DOI: 10.1016/j.whi.2014.12.001
33. Austin SB, Ziyadeh NJ, Corliss HL, Haines J, Rockett HR, Wypij D. **Sexual orientation disparities in weight status in adolescence: findings from a prospective study**. *Obesity* (2009.0) **17** 1776-1782. DOI: 10.1038/oby.2009.72
34. Garber AK, Cheng J, Accurso EC, Adams SH, Buckelew SM, Kapphahn CJ. **Weight loss and illness severity in adolescents with atypical anorexia nervosa**. *Pediatrics* (2019.0) **144** e20192339. DOI: 10.1542/peds.2019-2339
35. Mensinger JL, Granche JL, Cox SA, Henretty JR. **Sexual and gender minority individuals report higher rates of abuse and more severe eating disorder symptoms than cisgender heterosexual individuals at admission to eating disorder treatment**. *Int J Eat Disord* (2020.0) **53** 541-554. DOI: 10.1002/eat.23257
36. Meyer IH. **Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence**. *Psychol Bull* (2003.0) **129** 674-697. DOI: 10.1037/0033-2909.129.5.674
37. Hatzenbuehler ML, Pachankis JE. **Stigma and minority stress as social determinants of health among lesbian, gay, bisexual, and transgender youth: research evidence and clinical implications**. *Pediatr Clin North Am* (2016.0) **63** 985-997. DOI: 10.1016/j.pcl.2016.07.003
38. 38.YRBSS | DASH | CDC [Internet]. 2022 [cited 2022 Dec 27]. Available from: https://www.cdc.gov/healthyyouth/data/yrbs/index.htm
|
---
title: 'Impact on habitual crossfit participant''s exercise behavior, health, and
well‐being: A cross‐sectional survey of UK COVID‐19 lockdowns'
authors:
- Athalie Redwood‐Brown
- Jennifer Wilson
- Paul Felton
journal: Health Science Reports
year: 2023
pmcid: PMC9972867
doi: 10.1002/hsr2.1140
license: CC BY 4.0
---
# Impact on habitual crossfit participant's exercise behavior, health, and well‐being: A cross‐sectional survey of UK COVID‐19 lockdowns
## Abstract
### Background and Aims
The period between March 2020 and March 2021 saw an unprecedented change to everyday life due to the COVID‐19 pandemic. This included the closure of businesses in the health and fitness sector. Such closures impacted people in several ways; increasing stress, reducing mental well‐being, and decreasing motivation to exercise. The purpose of this study was to evaluate the effect of UK lockdowns on the behavior, motives, and general health & well‐being of CrossFit™ gym members in the United Kingdom.
### Methods
A cross‐sectional study was conducted on 757 CrossFit™ participants (height 1.71 ± 0.10 m; weight 76.4 ± 16.1 kg; body mass index [BMI]: 26.1 ± 4.7 kg/m²) using an online survey, which included questions pertaining to COVID‐19, lockdown behaviors, motivation, health, and well‐being. Participants also reported on their training background and exercise habits during lockdown restrictions.
### Results
Differences were observed in levels of exercise ($$p \leq 0.004$$), motivation to train at home ($p \leq 0.001$), and the feeling of being more stressed during the second lockdown compared with the first lockdown ($$p \leq 0.008$$). It was also highlighted that motivation to exercise was lower and stress levels significantly higher, in the 18–24 and 25–34 age groups compared with older ages groups.
### Conclusion
This study found that exercise behavior, motivation, and stress levels were significantly impacted by the second government‐imposed lockdown. It is argued that these factors need to be addressed in planning for future National lockdowns to maintain the health and well‐being of UK residents, especially in younger adults.
## INTRODUCTION
There is an abundance of research linking regular exercise with improved mental and physical health. 1, 2 This relationship is further strengthened by research such as Weinstein et al., 1 who investigated the relationship between exercise/physical activity withdrawal and mental health. They found withdrawal from exercise for as little as 2 weeks resulted in symptoms of depression, fatigue, tension, and decreased self‐esteem. Similar feelings were also reported by Antunes and colleagues 3 who found increases in negative mood following exercise deprivation in exercise addicted subjects.
Over the last 3 years, we have seen several disruptions to everyday life due to government‐imposed lockdowns. One of the major consequences of this has been the reduction in physical activity undertaken, ultimately leading to decreases in mental and physical health, and increases in unhealthy behaviors 4, 5, 6, 7, 8, 9 such as poor sleeping patterns/modified sleeping behavior, 6 poor nutritional choices, 7 and increased alcohol consumption. 8 Studies reporting on the health of the general population during the first UK lockdown reported increased sedentary behavior, greater disease risk, and increased negative psychological characteristics. 10, 11, 12, 13, 14, 15 These characteristics include posttraumatic stress symptoms, anger, infection fears, and boredom 16 many of which were considered a consequence of financial struggles, frustration, and inadequate supplies of essential items and services during the lockdown period. 16 For those who exercise consistently as part of an active lifestyle, gym closures and competition cancellations have been associated with additional stress, anxiety, frustration, and depression, namely related to the removal of social support and the change to normal training/exercise routines. 17, 18, 19, 20 This reduction in physical activity due to gym closures not only facilitates sedentary behavior but also reduces social interaction which for a lot of individuals impacts mental health and well‐being, particularly as many participate to reduce their symptoms of anxiety and depression. 15, 19 Some preliminary investigations have demonstrated that if exercise behaviors are maintained during lockdown periods, the impact on mental health is limited, 18 while others have noted a decline in mental health, suggesting that further investigation is needed. 20 However, it is evident that habitually trained individuals from community focused exercise programs are more likely to maintain exercise habits during nationally imposed lockdowns and report fewer changes to mental health as a result. 18 Community‐focused programs, such as CrossFit™ are generally associated with high levels of retention and adherence compared with other forms of fitness training, 21 particularly when the methodology seeks to increases exercise enjoyment; provides challenge and satisfaction; and enhances goal achievement, elements associated with positive mental health. 22 To understand the impact of restricted exercise access, for population groups such as this, further investigation is required. Specifically, the impact of successive lockdowns on habitual community focused/group exercise and health. Recent investigations by Ocobock and Hejtmanek 20 support the notion that individuals who participate in CrossFit™ are less likely to be impacted by lockdown, compared with other forms of exercise. However, it is not yet known how multiple lockdown periods may impact participants who are more socially invested in their physical training regimes and thus, could be more vulnerable when normal training regimes are significantly disrupted. 21 No investigations to date have compared the outcomes of two successive lockdowns, or the long‐term impact on psychological well‐being in habitually trained individuals therefore, this area warrants further exploration.
## Aims and objectives
Subsequently, the aims of this study were to investigate the effects of consecutive lockdown periods in a habitually trained population. To inform findings, this study will focus on habitually trained CrossFit™ athletes, where typical levels of retention and adherence are known to be high compared with other forms of fitness training. 18 Specifically, the study will investigate motivations, exercise behaviors, and general well‐being between the two lockdowns. It is proposed that these findings may help to evidence the importance of gyms and thus their essential status in future lockdowns or prolonged periods of imposed closure.
## Participants
A total of 757 CrossFit™ participants residing in the United Kingdom were recruited via email invitation sent to 650 UK CrossFit™ affiliate gyms or social media advertisement (Twitter, Facebook, and Instagram) to voluntarily participate in this study. Each participant fully completed an online survey hosted by Survey Monkey which controlled for repeat participants (https://www.surveymonkey.co.uk/) within a 2‐month period (January 8–March 31, 2021). Study details were explained to each participant and informed consent was gathered in accordance with guidelines approved by Nottingham Trent Universities noninvasive Research Ethics Committee. No incentives were offered for participation, nor were there any penalties for not participating (researchers were blind to participation). Responses were removed from analysis if the participant did not reside in the United Kingdom or the survey was incomplete (104 in total).
## Survey design
The survey (Appendix 1 available at request from corresponding author) adopted was specifically designed for this study and centered on a self‐reported structure. The questionnaire consisted of five parts. The first part gathered demographic information and consisted of eight questions regarding participant's gender, age, body height, body weight, ethnicity, and training location. The second part of the survey focused on the participants’ training background and consisted of four questions designed using a deductive approach to collect information regarding their training years, the type of gym they train at; the number of minutes exercised per week, and how many CrossFit™ sessions they participate in per week. The third part of the questionnaire focused on participants' behavior, motives, and general health and well‐being during lockdown periods. Participants were asked 12 questions designed using a deductive approach to collate data on whether they felt more stressed, more relaxed, less motivated to train, and exercised less during the second lockdown compared with the first lockdown. Participants were also asked to rank their main motives for participation in CrossFit™, as well as those they had missed most due to the lockdown restrictions. The motives derived using a deductive approach were fitness, mental health, general health, strength, time out, weight, performance, and toning. The fourth part of the survey asked participants about medical conditions and their severity. The final part of the survey asked participants whether they or the people around them had contracted COVID‐19, and the severity of the virus. 17 In total, there were 5 parts, and a total of 39 questions.
The number of questions for each section was kept to a minimum to reduce response bias associated with boredom and increase validity. For the third section, where participants were asked to respond to statements focused on how the two UK lockdowns had affected their motivations, behaviors, and general health and well‐being, a five‐point Likert‐type scale was adopted to elicit the strength of the participants’ agreement, as follows: 1 = strongly agree; 2 = agree; 3 = neither agree nor disagree; 4 = disagree; 5 = strongly disagree. Each statement was kept as short as possible to improve the validity of the responses. In this section, participants were also asked to rank the order of potential motivations for participation in CrossFit™ by ranking them in order from 1 to 8.
## Data processing
All data collected from this study was downloaded and imported into SPSS v.27 (IMB) for processing and statistical analysis where an alpha value of 0.05 was used to determine significance. Data were grouped using three polytomous questions from the survey describing gender, ethnicity, and age. While a further grouping variable was determined by grouping participants based on their body mass index (BMI): underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), Obese I (30.0–34.9 kg/m2), Obese II (35–39.9 kg/m2), Obese III (> 40 kg/m2).
## Statistical analysis
To investigate the motives for CrossFit™ participation the responses to questions regarding participants' main motives were analyzed. For all statistical analysis, an alpha level threshold of 0.05 was used to determine significance. To establish if a difference in rank existed across the motives, a two‐tailed Friedman's two‐way analysis of variance by Ranks was performed with two‐tailed Bonferroni adjusted Wilcoxon Signed Rank tests (Power = 0.80; α = 0.05; |p | = 0.03; $$n = 767$$). To investigate the effect of lockdown on a habitually trained CrossFit™ population the responses to questions comparing levels of exercise, motivation, stress, and relaxation, between two scenarios: normal versus lockdown; and first lockdown and second lockdown; were analyzed. To determine differences within each of the groups (defined above), a two‐tailed Mann–Whitney U test was performed if there were two subgroups (gender), and a two‐tailed Kruskal–Wallis H test with Bonferroni adjusted Mann–Whitney U tests employed if there were more than two subgroups (ethnicity, age, and BMI). If the differences were significant between subgroups, effect sizes (ES) were calculated and post hoc power analyses were performed to determine the achieved power of each post hoc test. ES were classified as negligible (0.00–0.001), weak (0.01–0.04), moderate (0.04–0.16), relatively strong (0.16–0.36), strong (0.36–0.64), and very strong (0.64–1.00).
A full STROBE cross‐sectional checklist available at from the corresponding author, detailing further details of the methodological design can be seen in Appendix 2 available at request from corresponding author.
## Participant characteristics
A total of 757 participants (height: 1.71 ± 0.10 m; weight: 76.4 ± 16.1 kg; BMI: 26.1 ± 4.7 kg/m2) completed the online survey in full. There was a slight skew in gender within the respondents with $59.2\%$ identifying as female compared with $40.8\%$ as male (Table 1). Most participants, $73.2\%$, were aged between 25 and 44 years, with $36.7\%$ aged 25–34 years, and $36.5\%$ aged 35–44 years (Table 1). Furthermore, a heavy ethical bias in responders was observed with $93.9\%$ identifying as White compared with $1.7\%$ Asian, $3.3\%$ Mixed, and $1\%$ Other (Table 1).
**Table 1**
| Category | n | % |
| --- | --- | --- |
| Gender | | |
| Male | 309.0 | 40.8 |
| Female | 448.0 | 59.2 |
| Age (years) | | |
| 18–24 | 44.0 | 5.8 |
| 25–34 | 278.0 | 36.7 |
| 35–44 | 276.0 | 36.5 |
| 45–54 | 131.0 | 17.3 |
| 55–64 | 21.0 | 2.8 |
| 65+ | 7.0 | 0.9 |
| Ethnicity | | |
| White | 711.0 | 93.9 |
| Asian | 13.0 | 1.7 |
| Mixed | 25.0 | 3.3 |
| Other | 8.0 | 1.1 |
| Body mass ranking | | |
| Underweight | 7.0 | 0.9 |
| Normal | 338.0 | 45.1 |
| Overweight | 306.0 | 40.8 |
| Obese 1 | 68.0 | 9.1 |
| Obese 2 | 16.0 | 2.1 |
| Obese 3 | 15.0 | 2.0 |
## CrossFit™ participation and training experience
Participants reported their current training from <1 year experience to 5+ years. Almost half of the participants reported training for over 5 years and only $8\%$ had less than 1 year's experience (see Table 2). In terms of specific CrossFit™ training, participants reported less years' experience with only $21\%$ having 5+ years in a CrossFit™ gym. However, $94\%$ of all respondents reported attending at least three CrossFit™ sessions per week with $13\%$ attending six sessions and $11\%$ attending every day (see Table 2).
**Table 2**
| Category | n | % |
| --- | --- | --- |
| Training age | | |
| <1 year experience | 61 | 8.0 |
| 1–2 years experience | 91 | 12.0 |
| 2–3 years experience | 105 | 14.0 |
| 3–4 years experience | 91 | 12.0 |
| 4–5 years experience | 61 | 8.0 |
| 5 years + | 348 | 46.0 |
| Minutes of exercise per week | | |
| < 60 min | 8 | 1.0 |
| 60–120 min | 45 | 6.0 |
| 120–180 min | 121 | 16.0 |
| 180–240 min | 174 | 23.0 |
| 240+ mins | 409 | 54.0 |
| CrossFit™ training age | | |
| < 1 year | 167 | 22.0 |
| 1–2 years | 151 | 20.0 |
| 2–3 years | 136 | 18.0 |
| 3–4 years | 91 | 12.0 |
| 4–5 years | 53 | 7.0 |
| 5+ years | 159 | 21.0 |
| Number of CrossFit™ sessions per week | Number of CrossFit™ sessions per week | |
| 1 p/w | 8 | 1.0 |
| 2 p/w | 37 | 5.0 |
| 3 p/w | 167 | 22.0 |
| 4 p/w | 167 | 22.0 |
| 5 p/w | 197 | 26.0 |
| 6 p/w | 98 | 13.0 |
| 7 + p/w | 83 | 11.0 |
## Motives for CrossFit™ participation
Participants were asked to rank their main motives for attending the box in order from 1 = most important, 8 = least important (Figure 1). Results showed that CrossFit™ participants ranked fitness (2.55 ± 1.72) as their main motive for attending the box, however, mental health (2.92 ± 2.02) was ranked a close second, with general health benefits (3.71 ± 1.83) being ranked the third most important motive, strength (4.14 ± 1.63) ranked fourth, time out (5.43 ± 1.95) fifth, weight management/loss (5.46 ± 2.15) sixth, athletic performance (5.66 ± 2.28) seventh, and finally the motive which CrossFit™ participants ranked the least important was toning (6.13 ± 1.66).
**Figure 1:** *Mean rank and distribution of the ranking of motives for attending the box of CrossFit™ gym members in the United Kingdom (1 = most important, 8 = least important).*
There was a significant difference found in the distribution of ranks given to the motives for CrossFit Participation (χ 2[7] = 1571.5, $p \leq 0.001$; Figure 1). Post hoc analysis with a Bonferroni correction applied identified significant differences (Ζ = −20.77 to −3.91, $p \leq 0.003$; Figure 1) in the ranking of fitness, mental health, general health benefits, strength, and toning with all other motives, the strength of the ES of these differences were moderate to very strong (0.14–0.75). No significant differences were found (Z = −2.09 to −0.605, $p \leq 0.05$; Figure 1) in the rank between the following motives: time out; weight management/loss, and athletic performance, with the ES classified as weak (0.02–0.08).
## Effect of a second lockdown on exercise levels, motivation to train, and feelings of stress
Respondents reported that during lockdown they exercised less and had less motivation to train at home compared with before lockdown (Table 3). They also reported feeling more stressed during lockdown compared with pre‐lockdown (Table 3). These responses were common across genders, ethnicity, and BMI groups with no significant differences found in the strength of agreement between the subgroups. Significant differences were found however, across the age subgroups regarding a lower motivation to train at home (χ 2[4] = 35.45, $p \leq 0.001$; Figure 2A) and feeling more stressed (χ 2[4] = 16.84, $$p \leq 0.002$$; Figure 2B) compared with pre‐lockdown. No significant differences were found regarding the strength of agreement on exercising less due to lockdown across the age subgroups.
Post hoc tests investigating the strength of agreement on the lack of motivation to train at home during lockdown highlighted significant differences between the following subgroups: 18–24 versus 35–24 ($U = 4157$, $$p \leq 0.006$$; ES: 0.13; Power = 0.12), 18–24 versus 45–54 ($U = 1796.5$, $$p \leq 0.002$$; ES: 0.14; Power = 0.12), 18–24 versus 55–64 ($U = 237$ $$p \leq 0.009$$; ES: 0.12; Power = 0.07); and 25–34 versus 45–54 ($U = 4157$, $$p \leq 0.006$$; ES: 0.13; Power = 0.25). No further significant differences between age subgroups were identified. Despite the low reliability of these findings due to the large differences in subgroup population, there is some evidence that the younger age groups (18–24 and 25–34) more strongly agreed that their motivation levels to train at home during lockdown were less compared with pre‐lockdown, than the older age subgroups. Although the difference in median between the over 65 age group (which indicated they neither agreed nor disagreed that their motivation levels to train at home were less during lockdown compared with pre‐lockdown) and the other age groups was visibly different, no significant differences were observed likely due to the small population ($$n = 7$$) and large variation in responses.
Similarly, post hoc tests investigating the strength of agreement regarding feeling more stressed during lockdown highlighted significant differences between the 25–34 and 45–54 age subgroups ($U = 37184$, $$p \leq 0.004$$; ES = 0.13; Power = 0.22). Despite the low reliability of these findings due to the large differences in subgroup population, there is some evidence that the younger age group (25–34) felt more stressed during lockdown than the older age group (45–54). Again, despite the median indicating the over 65 age group neither agreed nor disagreed that they felt more stressed during lockdown compared with pre‐lockdown, no significant differences were observed.
## Effect of second lockdown on first lockdown exercise levels, motivation to train, and feelings of stress
Respondents demonstrated a level of agreement that during the second lockdown they exercised less and had less motivation to train at home compared with the first lockdown (Table 4). They also agreed that they felt more stressed during the second lockdown compared with the first (Table 4). These findings were again common across genders, ethnicity, and BMI groups with no significant differences found in the strength of agreement between the subgroups. Significant differences were found across age subgroups regarding the strength of agreement relating to exercising less (χ 2[4] = 13.77, $$p \leq 0.008$$; Figure 3A), the motivation to train at home being less (χ 2[4] = 20.03, $p \leq 0.001$; Figure 3B) and the feeling of being more stressed (χ 2[4] = 12.90, $$p \leq 0.01$$; Figure 3C) during the second lockdown compared with the first lockdown.
Despite there being a significant effect of age on all three factors globally, post hoc tests were unable to reveal significant differences between any of the subgroups when the alpha value had been adjusted for multiple comparisons using Bonferroni's correction. This is likely due to the low power of each statistical test due to the large differences in each subgroup population size. Despite differences in the median between age groups within each factor, especially in the older age groups (55–64 and 65 +), observation of the interquartile range within each group (Figure 3) highlights a large variation in responses.
## DISCUSSION
The purpose of this article was to evaluate the effects of the National UK lockdowns on the behavior, motive, and general health and well‐being of habitually trained CrossFit™ participants. Specifically, emotional stress and exercise behaviors were investigated to determine how this group of individuals responded to a second lockdown period. Despite a recent publication 18 indicating that exercise habits remained unchanged during the first UK National lockdown, the current study found that habitually trained CrossFit™ participants were more likely to self‐report greater levels of emotional stress and decreased motivation to exercise during the second UK national lockdown. In contrast to previous studies which have prominently investigated lockdowns in isolation and/or focused on untrained individuals 8, 16 the current study suggests that the second lockdown was detrimental to both the mental and physical health of those who meet or exceed typical exercise guidelines and thus regard it as key to their lifestyle.
## Stress
Periods of lockdown, such as those experienced in the United Kingdom have previously been associated with increased stress, anxiety, depression, anger, and confusion. 19 *Stress is* reportedly an outcome of economic and financial uncertainty, and a disruption to, or change to normal routine. 22, 23, 24, 25 The removal of favored activities, or inability to visit “favorite places” has also been associated with increased feelings of stress in habitual exercisers. 19 In the current study, stress was reported as greater in the second lockdown compared with the first, specifically participants were generally less stressed and experienced more positive mental and physical well‐being during the first lockdown compared with the second. Interestingly the youngest age group (18–24 years) were more likely to report increased stress during the second UK national lockdown, which is somewhat concerning.
A possible explanation for the increase in stress could relate to the reasons participants gave for their consistent participation in CrossFit™. Several participants in the current study ranked mental health as one of the highest reasons for participation, indicating that managing mental health through consistent exercise is an essential for many and as such, restricting habitual exercise patterns could be synonymous with increased feelings of stress. This relationship is also highlighted in previous literature 24 and has also been associated more strongly with younger adults. Namely as the disruption to their exercise routine results in a more significant change to their everyday life compared with their older counterparts, due to the reduction in social interaction, worry relating to the pandemic and a loss of local support network. 23, 24, 25 Furthermore, reductions in habitual exercise are often associated with feelings of guilt thus it may be evident that participants were experiencing emotional distress/guilt associated with a reduction to their normal exercise habits. 19 No further differences were noted in genders, despite contemporary literature indicating that younger females are at greater risk of pandemic induced anxiety and depression. 2, 26 Typically, the CrossFit™ program is thought to enhance aspects of psychological health through creating a social support network. 21 High levels of motivation to exercise are considered to occur in tandem with social support and a sense of belongingness. 27, 28, 29 The results of the current study indicate that even the localized online support of a CrossFit™ community could not offset the increased feelings of stress in habitually trained individuals during the second lockdown period, when access to the gym was restricted. Previous research 18 would suggest that habitually trained CrossFit™ participants were more protected in their mental health during lockdown periods, particularly when compared with other forms of exercise. 20 The current study confirms that this is not the case when multiple lockdowns occur suggesting that cessation from community‐focused activity, and prolonged lockdown periods are a risk to mental health.
An inverse relationship between positive healthy behaviors and emotional distress has been identified. 1, 19, 23, 24, 25 *This is* supported by results of the current study. Thus, it is evident that government‐imposed lockdowns can have a significant impact on the psychological welfare of its constituents when normal healthy behaviors and routines are disrupted. 29
## Exercise behavior
Studies have reported numerous behavioral changes in UK residents following imposed restrictions. 6, 30 These include changes to eating habits, alcohol consumption, 30, 31 and observed exercise habits, 30 though no studies to date have investigated the impact of a lockdown in habitually trained athletes, let alone across different age groups. The current study indicates that the younger respondents (18–24 and 25–34 years) were less motivated to exercise, reporting the greatest reduction in motivation during the second lockdown compared with their older counterparts. Recently studies 32 have hypothesized that older age groups maybe more likely to continue exercising, due to perceptions of COVID‐19 infection risk however no conclusions in the literature have been found as to why younger participants were impacted more readily by lockdown periods and potentially points to the importance of a social network for younger generations.
Other reasons for changing exercise behavior may include access to equipment and a suitable training environment. During lockdown, individuals were required to exercise at home in either indoor living space or outdoors. Many habitual exercisers were able to mitigate feelings of stress during initial lockdown periods, by modifying their habitual exercise practice when favored facilities were inaccessible. 19 However, it is documented that habitual exercise behaviors change during colder months in tandem with exercise motivation, despite the increased risk of chronic disease and illness during this period. 33 It seems reasonable to suggest that lockdown periods during the colder, darker months may have impacted individual desire to exercise, thereby decreasing overall participation rates.
The outcome of this, and the associated loss of exercise behaviors is likely to translate to health and disease, as evidenced by reports noting increases in obesity/BMI levels during the pandemic. 12, 14 decreased motivation to eat healthy food, 31 decreased motivation to exercise, 7 decreased mental health, 1, 2, 3 and increased alcohol consumption. 8 Previous research into CrossFit™ participation has found associated increases in mental and physical resilience, 27 as well as a reduction in perceived severity of COVID‐19 symptoms. 18 Yet even in habitually trained CrossFit™ participants, frequent government‐imposed restrictions can have harmful effects on healthy behaviors which would otherwise lead to enhanced physical and mental well‐being. The findings in the current study support the argument that fitness facilities should operate as an essential business, with reduced capacity, to support the ongoing physical and mental health of members during lockdown periods and reduce the mirage of associated health decline related to such restrictions.
## Strengths & limitations
While the current study provides a comprehensive analysis of exercise behaviors during the second government‐imposed lockdown, it does have several limitations. First, the survey did not consider additional variables that may have caused stress and or decreases in motivation during the lockdown periods such as loss of work/income, fear of illness, and so on. Although this may have influenced participants responses to questions related to stress, motivation, and behaviors, the nature of field work such as this means to control all external factors would be beyond the scope of the data collection methods. As the aim of the study was to compare the two UK lockdowns a number of these confounding variables would be present across both periods. The survey also specifically asked about stress, motivation, behavior, and so on, related to a lack of physical activity, which was highlighted at the start of the questions. Second, participants were recruited via social media platforms to complete an online questionnaire. Therefore, the sample may be biased toward those who have previously used similar technologies. In addition, a further bias may exist within the findings due to the unequal size of the groups when participants were grouped based on age, ethnicity, BMI or training experience and participation, which may limit the power of the statistical approach adopted. However, the strategy for recruitment and participant was the most feasible at the time of completion. Furthermore, the recruitment strategy also relied on self‐selection to participate and therefore there may be some evidence of bias to those interested in the effect that national UK lockdown had on CrossFit™ participation. Nonetheless, this was a particular area of interest for researchers, therefore it did not seem appropriate to recruit beyond the CrossFit™ community.
## PERSPECTIVE
Overall, the purpose of this study was to investigate the effects of consecutive lockdown periods in a habitually trained population. Although previous research has highlighted no impact on habitual exercisers, results from the current study found that the behavior, motives, and general health of members were impacted by the second lockdown, with a more significant impact on the younger age groups. These individuals (18–24 year age group) reported lower motivation to exercise and increased stress levels during the second lockdown, indicating their potential risk of decreased physical and psychological well‐being. This was attributed to the decrease in social interaction, which is vital to younger age groups, especially when their social network revolves around their physical activity habits. It is argued that these factors need to be addressed in planning for future national lockdowns, and consideration should be given to recognizing fitness facilities as essential practices within healthcare during imposed restrictions or prolonged periods of imposed closure especially given the link between physical activity and COVID‐19 severity. 18
## AUTHOR CONTRIBUTIONS
Athalie Redwood‐Brown: Conceptualization; investigation; methodology; project administration; writing — original draft; writing — review & editing. Jennifer Wilson: Conceptualization; methodology; writing — original draft; writing — review & editing. Paul Felton: Formal analysis; writing — review & editing.
## CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
## TRANSPARENCY STATEMENT
The lead author Athalie Redwood Brown affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
## ETHICS STATEMENT
The study protocol was reviewed and approved by Nottingham Trent Universities Noninvasive Ethics Committee. Before completing the survey, participants were given details on the purpose and aims of the study and were requested to give their informed consent. All authors have read and approved the final version of the manuscript and had full access to all the data in this study and take complete responsibility for the integrity of the data and the accuracy of the data analysis.
## DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
## References
1. Weinstein AA, Koehmstedt C, Kop WJ. **Mental health consequences of exercise withdrawal: a systematic review**. *Gen Hosp Psychistry* (2017) **49** 11-18
2. Vella SA, Aidman E, Teychenne M. **Optimising the effects of physical activity on mental health and wellbeing: a joint consensus statement from sports Medicine Australia and the Australian psychological society**. *J Sci Med Sport* (2023). DOI: 10.1016/j.jsams.2023.01.001
3. Antunes HK, Leite GS, Lee KS. **Exercise deprivation increases negative mood in exercise‐addicted subjects and modifies their biochemical markers**. *Physiol Behav* (2016) **156** 182-190. PMID: 26812592
4. Pellegrini M, Ponzo V, Rosato R. **Changes in weight and nutritional habits in adults with obesity during the “lockdown” period caused by the COVID‐19 virus emergency**. *Nutrients* (2020) **12**. DOI: 10.3390/nu12072016
5. Lim MA, Pranata R. **Sports activities during any pandemic lockdown**. *Ir J Med Sci* (2020) **1971** 1-5
6. Ingram J, Maciejewski G, Hand CJ. **Corrigendum: changes in diet, sleep, and physical activity are associated with differences in negative mood during COVID‐19 lockdown**. *Front Psychol* (2020) **11**. DOI: 10.3389/fpsyg.2020.605118
7. Robinson E, Boyland E, Chisholm A. **Obesity, eating behavior and physical activity during COVID‐19 lockdown: a study of UK adults**. *Appetite* (2021) **156**. PMID: 33038479
8. Arora T, Grey I. **Health behaviour changes during COVID‐19 and the potential consequences: a mini‐review**. *J Health Psychol* (2020) **25** 1155-1163. PMID: 32551944
9. Morrey LB, Roberts WO, Wichser L. **Exercise‐related mental health problems and solutions during the COVID‐19 pandemic**. *Curr Sports Med Rep* (2020) **19** 194-195. DOI: 10.1249/JSR.0000000000000725
10. Li Q, Guan X, Wu P. **Early transmission dynamics in Wuhan, China, of novel coronavirus‐infected pneumonia**. *N Engl J Med* (2020) **382** 1199-1207. DOI: 10.1056/NEJMoa2001316
11. Chow N, Fleming‐Dutra K, Gierke R. **Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019—United States, February 12–March 28, 2020**. *MMWR Morb Mortal Wkly Rep* (2020) **69** 382-386. PMID: 32240123
12. Rogers NT, Waterlow NR, Brindle H. **Behavioral change towards reduced intensity physical activity is disproportionately prevalent among adults with serious health issues or self‐perception of high risk during the UK COVID‐19 lockdown**. *Front Public Health* (2020) **8**. DOI: 10.1016/j.jsams.2023.01.001
13. Sothern MS, Loftin M, Suskind RM, Udall JN, Blecker U. **The health benefits of physical activity in children and adolescents: implications for chronic disease prevention**. *Eur J Pediatr* (1999) **158** 271-274. DOI: 10.1007/s004310051070
14. Lamy E, Viegas C, Rocha A. **Changes in food behavior during the first lockdown of COVID‐19 pandemic: a multi‐country study about changes in eating habits, motivations, and food‐related behaviors**. *Food Qual Pref* (2022) **99**
15. Hull JH, Loosemore M, Schwellnus M. **Respiratory health in athletes: facing the COVID‐19 challenge**. *Lancet Respir Med* (2020) **8** 557-558. DOI: 10.1016/S2213-2600(20)30175-2
16. Jiménez‐Pavón D, Carbonell‐Baeza A, Lavie CJ. **Physical exercise as therapy to fight against the mental and physical consequences of COVID‐19 quarantine: special focus in older people**. *Prog Cardiovasc Dis* (2020) **63** 386-388. PMID: 32220590
17. Toresdahl BG, Asif IM. **Coronavirus disease 2019 (COVID‐19): considerations for the competitive athlete**. *Sports Health Multidiscip Approach* (2020) **12** 221-224. DOI: 10.1177/1941738120918876
18. Redwood‐Brown A, Ralston GW, Wilson J. **Incidence, severity, and perceived susceptibility of COVID‐19 in the UK CrossFit population**. *BMC Sports Sci Med Rehabil* (2021) **13** 106. PMID: 34488886
19. Ronkainen NJ, Pesola AJ, Tikkanen O, Brand R. **Continuity and discontinuity of sport and exercise type during the COVID‐19 pandemic. an exploratory study of effects on mood**. *Front Psychol* (2021) **12**. DOI: 10.3389/fpsyg.2021.622876
20. Ocobock C, Hejtmanek KR. **Missing the gym: COVID‐19 stay‐at‐home orders and formal fitness communities**. *Anthropol Now* (2022) **14** 112-126
21. Whiteman‐Sandland J, Hawkins J, Clayton D. **The role of social capital and community belongingness for exercise adherence: an exploratory study of the CrossFit gym model**. *J Health Psychol* (2018) **23** 1545-1556. PMID: 27553606
22. Gianzina EA, Kassotaki OA. **The benefits and risks of the high‐intensity CrossFit training**. *Sport Sci Health* (2019) **15** 21-33
23. Kumar A, Nayar KR. **COVID 19 and its mental health consequences**. *J Ment Health* (2021) **30** 1-2. DOI: 10.1080/09638237.2020.1757052
24. Pieh C, Budimir S, Probst T. **The effect of age, gender, income, work, and physical activity on mental health during coronavirus disease (COVID‐19) lockdown in Austria**. *J Psychosom Res* (2020) **136**. PMID: 32682159
25. Mayorga NA, Smit T, Garey L, Gold AK, Otto MW, Zvolensky MJ. **Evaluating the interactive effect of COVID‐19 worry and loneliness on mental health among young adults**. *Cognit Ther Res* (2022) **46** 11-19. DOI: 10.1007/s10608-021-10252-2
26. Stanton R, To QG, Khalesi S. **Depression, anxiety, and stress during COVID‐19: associations with changes in physical activity, sleep, tobacco, and alcohol use in Australian adults**. *Int J Environ Res Public Health* (2020) **17** 4065. PMID: 32517294
27. Bycura D, Feito Y, Prather C. **Motivational factors in CrossFit® training participation**. *Health Behav Policy Rev* (2017) **4** 539-550
28. Ryan Shuda M, Feito Y. **Challenge, commitment, community, and empowerment: factors that promote the adoption of CrossFit as a training program**. *Transformation* (2017) **1** 1-14
29. Mikkelsen K, Stojanovska L, Polenakovic M, Bosevski M, Apostolopoulos V. **Exercise and mental health**. *Maturitas* (2017) **106** 48-56. DOI: 10.1016/j.maturitas.2017.09.003
30. Ammar A, Brach M, Trabelsi K. **Effects of COVID‐19 home confinement on eating behaviour and physical activity: results of the ECLB‐COVID19 international online survey**. *Nutrients* (2020) **12** 1583. PMID: 32481594
31. Snuggs S, McGregor S. **Food & meal decision making in lockdown: how and who has Covid‐19 affected?**. *Food Qual Pref* (2021) **89**
32. Smith L, Jacob L, Butler L. **Prevalence and correlates of physical activity in a sample of UK adults observing social distancing during the COVID‐19 pandemic**. *BMJ Open Sport Exerc Med* (2020) **6**. DOI: 10.1136/bmjsem-2020-000850
33. Sarker HN. **Common winter illness—a seasonal suffering‐an editorial**. *Planet* (2021) **50**
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---
title: Inhibition of the mitochondrial pyruvate carrier simultaneously mitigates hyperinflammation
and hyperglycemia in COVID-19
authors:
- Bibo Zhu
- Xiaoqin Wei
- Harish Narasimhan
- Wei Qian
- Ruixuan Zhang
- In Su Cheon
- Yue Wu
- Chaofan Li
- Russell G. Jones
- Mark H. Kaplan
- Robert A. Vassallo
- Thomas J. Braciale
- Lindsay Somerville
- Jerry R. Colca
- Akhilesh Pandey
- Patrick E. H. Jackson
- Barbara J. Mann
- Connie M. Krawczyk
- Jeffrey M. Sturek
- Jie Sun
journal: Science Immunology
year: 2023
pmcid: PMC9972900
doi: 10.1126/sciimmunol.adf0348
license: CC BY 4.0
---
# Inhibition of the mitochondrial pyruvate carrier simultaneously mitigates hyperinflammation and hyperglycemia in COVID-19
## Abstract
The relationship between diabetes and COVID-19 is bi-directional: while individuals with diabetes and high blood glucose (hyperglycemia) are predisposed to severe COVID-19, SARS-CoV-2 infection can also cause hyperglycemia and exacerbate underlying metabolic syndrome. Therefore, interventions capable of breaking the network of SARS-CoV-2 infection, hyperglycemia, and hyper-inflammation, all factors that drive COVID-19 pathophysiology, are urgently needed. Here, we show that genetic ablation or pharmacological inhibition of mitochondrial pyruvate carrier (MPC) attenuates severe disease following influenza or SARS-CoV-2 pneumonia. MPC inhibition using a second-generation insulin sensitizer, MSDC-0602 K (MSDC), dampened pulmonary inflammation and promoted lung recovery, while concurrently reducing blood glucose levels and hyperlipidemia following viral pneumonia in obese mice. Mechanistically, MPC inhibition enhanced mitochondrial fitness and destabilized HIF-1α, leading to dampened virus-induced inflammatory responses in both murine and human lung macrophages. We further showed that MSDC enhanced responses to nirmatrelvir (the antiviral component of Paxlovid) to provide high levels of protection against severe host disease development following SARS-CoV-2 infection and suppressed cellular inflammation in human COVID-19 lung autopsies, demonstrating its translational potential for treating severe COVID-19. Collectively, we uncover a metabolic pathway that simultaneously modulates pulmonary inflammation, tissue recovery, and host metabolic health, presenting a synergistic therapeutic strategy to treat severe COVID-19, particularly in patients with underlying metabolic disease.
## INTRODUCTION
Despite the development of many vaccines and highly successful vaccination campaigns, respiratory viruses such as influenza and SARS-CoV-2 continue to present a significant public health burden [1]. The ever-present threat of respiratory viral infections is a result of non-sterilizing immunity induced by vaccination [2, 3], as well as the constant emergence of new viral variants [1, 4]. Respiratory viral infections are particularly dangerous to individuals with underlying metabolic syndrome, most notably insulin resistance associated with obesity and diabetes. Indeed, hyperglycemia (high blood sugar levels) is common in hospitalized COVID-19 patients and is strongly associated with worse outcomes following SARS-CoV-2 infection [5, 6]. Conversely, SARS-CoV-2 promotes insulin resistance and beta cell dysfunction, inducing hyperglycemia in a significant proportion of patients without history of metabolic disease (7–10).
Immunological features of severe COVID-19 are linked to exuberant inflammation in the respiratory tract, driven by profound immune dysregulation (11–14). Thus, it is unsurprising that therapeutics targeting hyper-inflammation have shown potential in ameliorating severe COVID-19 [15]. Indeed, the anti-inflammatory steroidal agent dexamethasone remains the most effective treatment for patients with severe COVID-19 with hypoxemia [16], with the restrictive therapeutic window of antiviral drugs in the early stage of infection [17, 18]. However, the efficacy of dexamethasone and other anti-inflammatory treatments (such as tocilizumab; anti-IL-6) are still limited [18]. Furthermore, dexamethasone treatment often results in hyperglycemia, complicating its use in patients with underlying metabolic disease- a population exhibiting increased risk of severe COVID-19 complications [19, 20]. Thus, there is a clinical need to develop interventions capable of simultaneously mitigating hyper-inflammation and hyperglycemia, to address two distinct processes dysregulated in COVID-19 patients with metabolic syndrome.
Pyruvate is the end-product of glycolysis that can either be reduced to lactate in the cytosol or used as a fuel for oxidative metabolism in the mitochondria. Glycolysis is well established to have a critical role in macrophage activation and inflammatory responses [21]. However, the contribution of pyruvate oxidation in tricarboxylic acid (TCA) cycle to macrophage function and inflammation is not well understood. Pyruvate is imported into the mitochondria via the mitochondrial pyruvate carrier (MPC), a protein known to maintain TCA cycle flux [22]. Pyruvate fuels the TCA cycle to support anabolic metabolism and energy production. Pyruvate conversion to Acetyl-CoA supports Acetyl-CoA-dependent reactions including fatty acid oxidation and protein acetylation. Notably, MPC-mediated pyruvate metabolism has emerged as an important player in the physiological and pathophysiological processes underlying type-2 diabetes [23, 24]. In this report, we found that inhibition of MPC activity dampened exaggerated pulmonary inflammation and concurrently promoted host metabolic health, thereby diminishing host morbidity and mortality following influenza or SARS-CoV-2 infection in obese mice. Furthermore, MPC inhibition decreased cellular inflammation in human COVID-19 lung autopsy tissue. MPC inhibition synergized with the antiviral component of Paxlovid, nirmatrelvir, to markedly lower host mortality and lung injury in SARS-CoV-2-infected obese mice. Our results suggest that MPC inhibitor MSDC-0602 K (MSDC), a second-generation insulin sensitizer with an outstanding safety profile, has potential as a therapeutic for treating severe COVID-19, particularly in patients with underlying metabolic disease.
## Genetic ablation or pharmacological inhibition of MPC function improves disease outcome following influenza or SARS-CoV-2 infection
Excessive lung macrophage activation can initiate and contribute to unrestrained inflammation through release of various pro-inflammatory mediators that lead to the recruitment of inflammatory immune cells to the lung following respiratory viral infection such as SARS-CoV-2 [12, 25, 26]. Glycolysis is known to support macrophage activation and inflammation, but the importance of glucose oxidation via pyruvate translocation into the mitochondria to macrophage inflammatory responses in vivo is understudied (Fig. 1A). We found that mice bearing myeloid-specific deletion of the mitochondrial pyruvate transporter 2 (MPC2ΔLyz2), which forms the heteromeric MPC complex with MPC1, had decreased host morbidity and mortality following sub-lethal or lethal doses of H1N1 influenza A virus (IAV) infection (Fig. 1, B and C). MPC2ΔLyz2 mouse lungs showed comparable viral titers compared to wild type (WT) littermates but exhibited greatly diminished inflammatory gene expression in the lungs and decreased pro-inflammatory cytokine levels in bronchoalveolar lavage (BAL) fluid at 4 days post infeciton (d.p.i) (Fig. 1D; and fig. S1, A to C). MPC2ΔLyz2 mice also showed lowered accumulation of inflammatory monocytes (Ly6c+) and neutrophils in the lung at 4 d.p.i, which are major contributors of pulmonary immune pathology following respiratory viral infection (27–29) (fig. S1, D and E). Additionally, lung histologic tissue inflammation, disrupted alveolar areas, and total BAL protein content were all reduced in MPC2ΔLyz2 mice at 14 d.p.i (fig. S1, F and G), suggesting MPC depletion mitigates lung injury in response to IAV infection. Further, alveolar type II cell (ATII)-specific genes (such as Abca3 and Sftpb) were enhanced in MPC2ΔLyz2 mice at 14 days post infection (fig. S1H), suggesting MPC depletion promotes tissue recovery. These data together suggest that genetic ablation of MPC function in myeloid cells reduces the severity of diseases following IAV pneumonia and that glucose-fueled mitochondrial metabolism likely supports pulmonary inflammation.
**Fig. 1.:** *Disruption of MPC-mediated oxidative pyruvate metabolism mitigates IAV- or SARS-CoV-2-induced pathogenesis.(A) Diagram of pyruvate metabolic pathways. (B to D) MPC2fl/fl and MPC2ΔLyz2 mice were infected with sub-lethal (B and D) and lethal (C) doses of IAV, respectively. Host morbidity (B) and mortality (C) were monitored. (D) BAL cytokine levels at 4 d.p.i. (n = 9). (E) Schematic diagram for viral infected C57BL/6 WT mice with vehicle or MSDC-0602 k (MSDC) treatment. (F to I) Mice were infected with sub-lethal (F, H, and I) and lethal (G) doses of IAV. Host morbidity (F) and mortality (G) were monitored. (H) RNA-seq analysis of lungs at 4 d.p.i. (n = 3). GSEA of inflammatory response gene set shown. (I) H&E staining of lung section (n = 4–5) at 14 d.p.i. Scale bar, 200 μm (I). (J to N) Mice were infected with SARS-CoV-2 MA10 virus. (J) Schematic diagram for viral infected C57BL/6 WT mice with vehicle or MSDC treatment. (K) Host morbidity was monitored. (L) H&E staining of lung section (n = 5) at 8 d.p.i. Scale bar, 200 μm. (M) BAL viral titers were measured at the indicated time points (n = 5). n.d., not detected. (N) BAL cytokine levels at 8 d.p.i. (n = 9–10). (O) MPC2fl/fl and MPC2ΔLyz2 mice were infected with SARS-CoV-2 MA10. Host morbidity was monitored. Representative (I and L) or pooled data (B, C, D, F, G, K, N and O) from at least two independent experiments. Data are presented as means ± SEM. *, p < 0.05; **, p < 0.01; ***, p < 0.001. The p value was determined by multiple t-tests (B, F, K and O), Logrank test (C and G) or a two-tailed Student’s t-test (D, M and N).*
To determine whether pharmacological targeting of MPC could lead to attenuated viral pathology in the respiratory tract, we infected WT mice with IAV and then treated the mice with MSDC-0602 k (MSDC), which is a MPC inhibitor and also a second-generation insulin-sensitizing thiazolidinedione (TZD) that has a superior safety profile compared with first generation TZDs (Fig. 1E) [24]. Intraperitoneal administration of MSDC from one day post IAV infection resulted in decreased host morbidity and mortality without affecting the kinetics of viral replication (Fig. 1, F and G; and fig. S1, I and J). MSDC-treated mice exhibited decreased BAL cytokine levels, diminished monocyte and neutrophil infiltration (fig. S1, K and L) and reduced lung inflammatory responses 4 d.p.i (Fig. 1H; and fig. S2, A to E). MSDC treatment also enhanced inflammation resolution, decreased lung damage and promoted ATII cell regeneration at 14 d.p.i (Fig. 1I; and fig. S2, F to H). Importantly, we did not observe a significant reduction in the magnitude of adaptive T cell response in MSDC-treated animals at 6 d.p.i, indicating its mode of action is primarily to dampen exuberant innate inflammation without interfering in antiviral adaptive immunity necessary for preventing viral dissemination and persistence (fig. S2, I to K).
Similar to IAV infection, SARS-CoV-2 infection results in profound inflammation in the lower respiratory tract [11]. Since MSDC reduced pulmonary inflammation in IAV-infected hosts, we tested whether MSDC could dampen SARS-CoV-2-induced lung inflammation and host diseases. We challenged WT mice with a mouse-adapted SARS-CoV-2 strain MA10, which induces acute lung damage and pneumonia in mice similar to human COVID-19 [30], and then treated mice with vehicle or MSDC (Fig. 1J). MSDC treatment enhanced host weight recovery (Fig. 1K) and diminished pathological changes in the lungs at 8 d.p.i (Fig. 1L; and fig. S2L). This was accompanied by reduced accumulation of inflammatory monocytes and neutrophils as well as pro-inflammatory cytokine levels in the BAL, without altering viral titers (Fig. 1, M and N; and fig. S2, M and N). ATII cell loss is a prominent feature of COVID-19 [31], and ATII regeneration is vital for lung recovery from viral pneumonia [32]. We found that MSDC treatment promoted ATII cell recovery in SARS-CoV-2-infected lungs at 8 d.p.i (fig. S2, O and P). Collectively, these data suggest that MSDC ameliorates pulmonary inflammation and promotes tissue recovery following SARS-CoV-2 infection.
Next, we determined whether MSDC delivered after 1 or 2 days following SARS-CoV-2 infection would still be effective. In WT mice, MSDC treatment from 1 day post-infection decreased morbidity and the efficacy diminished with treatment starting from 2 days post-infection (fig. S2, Q and R). These data suggest that the drug would be more efficacious if delivered early after viral infection, although the time “day 1” here would likely be analogous to several days after infection in human due to the delivery of a large amount of virus directly into the lung in this model. To determine whether myeloid deficiency of MPC ameliorates host morbidity, we infected MPC2ΔLyz2 mice with SARS-CoV-2 MA10 and determined host weight loss after infection. Consistent with MSDC-treated mice, myeloid MPC-deficient mice showed diminished host weight loss (Fig. 1O).
SARS-CoV-2 infection shows an age-dependent increase in disease severity [33, 34]. Parallel results were found in mice that became progressively more susceptible to mouse-adapted SARS-CoV-2 infection correlating with aging [30, 35]. We evaluated the efficacy of MSDC in aged (13–14-month-old) C57BL/6 female mice regarding SARS-CoV-2 infection (fig. S3A). In aged mice infected with SARS-CoV-2 MA10, MSDC treatment significantly reduced host mortality, with concomitant effects on weight loss (fig. S3, B and C). Additionally, MSDC diminished pathological changes in the lungs of surviving mice at 10 d.p.i (fig. S3D). These results showed that MSDC ameliorated SARS-CoV-2-induced disease in older mice.
## Murine and human lung macrophages are prominent targets of MSDC
Single cell RNA-seq (scRNAseq) analysis revealed that lungs isolated from MSDC-treated mice exhibited relatively higher proportions of lung structural and resident immune cells including alveolar epithelial cells, endothelial cells and alveolar macrophages (AMs), but diminished infiltrating immune cells such as neutrophils, monocytes, proliferating T and plasmacytoid dendritic cells following IAV infection at 4 d.p.i (Fig. 2, A and B; and fig. S4, A and B). Consistently, MSDC-treated lungs showed enrichment of gene sets associated with wound healing, epithelial regeneration and fatty acid oxidation, while demonstrating downregulation of inflammation-associated gene sets (Fig. 2C; and fig. S4, C to E). Monocyte and macrophage-mediated inflammatory responses are considered as a major driver of respiratory viral pathogenesis [12, 26]. We delineated 8 subsets of monocyte and macrophage populations in the infected lungs, and MSDC treatment diminished the presence of Ly6C+ inflammatory monocytes, monocyte-derived macrophages (MdM) and inflammatory alveolar macrophage (AM) subsets (Fig. 2D; and fig. S4F). Notably, MSDC markedly inhibited the expression of a large number of inflammatory associated genes/sets within the total AM population, but had less prominent inhibitory effects on the inflammatory responses of monocytes or MdM (Fig. 2E; and fig. S4, G to J). RT-PCR analysis of inflammatory gene expression by sorted AMs, CD11b+ macrophage and monocytes further confirmed that MSDC exhibited marked anti-inflammatory effects on AMs, but with more modest effects on CD11b+ macrophage and monocytes at 4 d.p.i (Fig. 2F; and fig. S5A). MSDC also had modest effects in suppressing bone-marrow derived macrophages (BMDM) inflammation following Poly IC stimulation in vitro, compared to those of AMs (fig. S5B). RNA-seq analysis found that MSDC promoted fatty acid degradation, oxidative phosphorylation, PPAR signaling, and inhibited genes associated with interferon signaling, cytokine responses and monocyte chemotaxis in AMs with or without Poly IC treatment (Fig. 2G; and fig. S5, C to E), indicating that MSDC inhibition of MPC function profoundly altered the balance of anti-viral versus pro-inflammatory AM status. MSDC also exhibited marked anti-inflammatory effects on human AMs but showed relatively moderate immunoinhibitory effects on blood monocytes or monocyte-derived macrophages (Fig. 2H). Importantly, MSDC inhibitory effects on AM inflammatory responses was abrogated in MPC deficiency, confirming that MSDC inhibits AM inflammation via MPC (fig. S5F). Notably, while not responding to MSDC treatment, MPC-deficient-AMs showed slightly higher baseline expression of Il6 compared to MSDC-treated AMs (fig. S5F), indicating a potential off-target effect of MSDC in this setting. Together, these data indicate that lung macrophages, but not circulating monocytes, are the prominent targets of MSDC during respiratory viral infection, consistent with a recent finding that MPC function is dispensable for BMDM inflammatory responses following LPS stimulation [36].
**Fig. 2.:** *Murine and human lung macrophages are prominent target of MPC inhibition by MSDC.(A to E) scRNA-seq analysis of lungs from IAV-infected C57BL/6 WT mice with vehicle or MSDC treatment at 4 d.p.i. Lung cells were pooled from three individual mice from each group. (A) UMAP plot visualization of lung cells from vehicle- or MSDC-treated mice. (B) The relative contributions of indicated clusters by each group. (C) Dot plot showing enrichment of Gene Ontology biological processes pathways enriched in MSDC-treated lungs. The color of the dots represents the adjusted P value. Dot size represents the enrichment score. (D) UMAP showing clusters of monocytes and macrophages from (A) in vehicle- or MSDC- treated lung cells (upper panel). The percentages of each cluster in each studied subject was shown on the lower panel. (E) Volcano plot showing the differentially expressed genes in AMs (cluster 2, 4, 6 and 7) of vehicle (blue) and MSDC (red) treated mice. (F) The mRNA levels of Tnf, Il1b and Ccl2 in AMs, CD11b+ macrophages (Mac) and monocytes (Mono) sorted from lungs at 4 d.p.i. (G) RNA-seq analysis of mouse AMs stimulated with or without Poly IC in the presence of vehicle or MSDC overnight in vitro. Heatmap of K-means clustering of differentially expressed genes and KEGG enrichment analysis. (H) The mRNA levels of TNF, IL1B and CCL2 in human AMs, monocyte-derived macrophages (MdM), and monocytes (Mono) stimulated with or without Poly IC in the presence of vehicle or MSDC overnight in vitro. Data are presented as means ± SEM. *, p < 0.05; **, p < 0.01; ***, p < 0.001. The p value was determined by multiple t tests (F) and one-way ANOVA (H).*
## MPC inhibition by MSDC selectively reduces HIF-1α levels in lung macrophages
To explore the molecular mechanisms by which MPC inhibition by MSDC suppressed lung macrophage inflammatory responses, we performed Western blot analysis probing HIF-1α, NF-kB and STAT-1 activation, which are known to promote macrophage inflammatory responses [37, 38], following Poly IC treatment. Poly IC stimulation led to the accumulation of HIF-1α protein and higher NF-kB p65 and STAT-1 phosphorylation in AMs, and MSDC treatment inhibited HIF-1α levels but not NF-kB and STAT-1 activation (Fig. 3, A and B). In contrast, MSDC did not interfere with HIF-1α expression, p65 phosphorylation or STAT-1 activation in BMDM, consistent with its moderate effects on BMDMs (Fig. 3C). Of note, MSDC did not affect Hif1a mRNA levels, and MPC2 deficiency in AMs recapitulated the MSDC effects on HIF-1α protein levels (fig. S6, A and B). Additionally, MSDC treatment decreased HIF-1α levels in AMs at day 4 post IAV infection in vivo (Fig. 3D). Consistent with the diminished HIF-1α protein levels following MSDC treatment, MSDC treatment in Poly IC-stimulated AMs showed diminished enrichment of hypoxia-associated genes (Fig. 3E). Importantly, MSDC suppressed HIF-1α accumulation in human primary AMs following Poly IC treatment (Fig. 3F). Furthermore, in vitro treatment with Molidustat and Roxadustat, two HIF-1α stabilizers that promote HIF-1α accumulation in AMs, abrogated the suppressive effects of MSDC on AM inflammatory responses (Fig. 3G; and fig. S6C).
**Fig. 3.:** *MPC inhibition dampens HIF-1α levels in lung macrophages.(A to C) Immunoblot analysis of indicated total or phosphorylated protein levels in mouse AMs (A) and BMDM (C) stimulated with or without Poly IC in the presence of vehicle or MSDC in vitro. (B) Quantification of HIF-1α levels in (A) of three experiments were shown. (D) Flow cytometry analysis of HIF-1α levels in AMs from naïve or IAV-infected wt mice treated with vehicle or MSDC at 4 d.p.i. (n = 5). (E) GSEA of hypoxia gene set shown for Poly IC stimulated AMs with vehicle or MSDC treatment. (F) Immunoblot analysis of HIF-1α in human AMs stimulated with or without Poly IC in the presence of vehicle or MSDC overnight in vitro (left). Quantitation on the right (n = 3 donors). (G) Ccl2 and Tnf levels in mouse AMs under indicated conditions overnight in vitro. Representative immunoblots (A, C and F) were from three independent experiments. Data are presented as means ± SEM. *, p < 0.05. The p value was determined by a two-tailed Student’s t-test (B, D, and F) and one-way ANOVA (G).*
UK5099 is a well-known MPC small molecule inhibitor [39], although a recent manuscript has also identified off-target effects [36]. We examined AM inflammation as well as HIF-1α expression in the presence of UK5099. MSDC and UK5099 both reduced Poly IC-induced inflammatory gene expression as well as HIF-1α level in AMs in vitro (fig. S6, D and E). To this end, our data from MSDC-treated MPC2-deficient AMs confirmed that the effects of MSDC on macrophage HIF-1α expression as well as inflammatory responses are largely dependent on MPC function (fig. S6B). Together, these data suggest that MSDC inhibits MPC function and promotes HIF-1α instability, thereby inhibiting lung macrophage inflammatory responses following viral stimuli.
## MPC inhibition promotes mitochondrial fitness and diminishes HIF-1α-stabilizing metabolites
Viral stimuli have been demonstrated to inhibit macrophage mitochondrial metabolism, facilitating macrophage-mediated inflammatory responses [34, 40]. MSDC treatment enhanced maximal oxygen consumption rate (OCR) and respiratory reserve in AMs, but not BMDMs, following stimulation with Poly IC (Fig. 4A; and fig. S7A). Consistent with these observations, fewer depolarized mitochondria were seen in MSDC-treated AMs but not BMDM populations (Fig. 4B; and fig. S7B). Additionally, MSDC treatment increased respiratory reserve and mitochondrial fitness in AMs at 4 days post IAV infection in vivo (Fig. 4, C and D). These data suggest that MSDC promotes mitochondrial respiration in lung macrophages. In support of this conclusion, AMs, but not lung monocytes, exhibited enrichment of genes associated with mitochondrial oxidative phosphorylation in vivo during infection after MSDC treatment (fig. S7, C and D). MSDC treatment caused enhanced extracellular acidification rate (ECAR) potentially due to the increased lactic acid accumulation after the blockade of pyruvate translocation into mitochondria both in AMs and BMDMs (fig. S7, E and F) [36, 41].
**Fig. 4.:** *MPC inhibition promotes mitochondrial fitness and diminishes HIF-1α stabilizing metabolites in lung macrophages.(A and B) AMs were stimulated with or without Poly IC in the presence of vehicle or MSDC overnight in vitro. (A) OCR of AMs and quantification of respiratory reverse. (B) Flow cytometry showing mitochondrial mass by Mitotracker green versus Mitotracker deep red in AMs on the left and quantification on the right. (C and D) OCR (C) or mitochondrial mass (D) in AMs from naïve or IAV-infected wt mice treated with vehicle or MSDC at 4 d.p.i. (n = 5). (E) Heatmap showing TCA cycle metabolites measured in AMs (n = 3). (F) Succinate to Ketoglutarate (α-KG) ratios in (E). (G) Acetyl-CoA concentrations in BMDM and AMs treated with vehicle or MSDC overnight in vitro. (H) BMDM and AMs were stimulated with Poly IC in the presence of vehicle or MSDC overnight in vitro. MG132 were added 4 h before cell harvesting. Coimmunoprecipitation (IP) was performed with anti-HIF-1α antibodies followed by immunoblot analysis of HIF-1α and HIF-1α acetylation (Ace-K, with anti-ace-lysine antibodies) levels. Quantification of HIF-1α acetylation was shown. (I and J) AMs were stimulated with or without Poly IC in the presence of vehicle, MSDC, dimethyl succinate (DMS), or sodium acetate (SDA) overnight in vitro. (I) HIF-1α protein levels in AMs. (J) Tnf and Ccl2 gene expression in AMs. Representative immunoblots (H and I) were from three independent experiments. Data are presented as means ± SEM. *, p < 0.05; **, p < 0.01; ***, p < 0.001. The p value was determined by a two-tailed Student’s t-test (A, D, F, G, and H) and one-way ANOVA (B and J).*
Pyruvate is oxidized in the TCA cycle following its translocation into mitochondria [22]. We therefore measured TCA metabolites following in vitro. Poly IC stimulation in the presence or absence of MSDC. Notably, Poly IC greatly promoted the accumulation of succinate in AMs and BMDMs, which was diminished in the presence of MSDC in AMs but not BMDMs (Fig. 4E; and fig. S7G). High succinate/α-KG ratio is an indication of reduced complex II activity of the electron transport train (ETC). MSDC treatment also reduced the succinate/α-KG ratio in AMs but not BMDMs, consistent with improved mitochondrial respiration (Fig. 4F; and fig. S7H). HIF-1α protein can be stabilized by both succinate and acetylation [42, 43]. We measured Acetyl-CoA levels and found that MSDC reduced Acetyl-CoA accumulation in AMs, but not BMDMs (Fig. 4G). Reduced Acetyl-CoA levels could affect gene expression by reducing histone acetylation, however, MSDC treatment did not markedly suppress the acetylation of total H3 or H3K27 (fig. S7I). Rather, diminished Acetyl-CoA in AMs was associated with decreased HIF-1α acetylation (Fig. 4H) [42, 43]. We next sought to determine whether exogenous succinate or Acetyl-CoA could promote AM HIF-1α levels and promote their inflammatory responses following MSDC treatment. We treated Poly IC- and/or MSDC-stimulated AMs with sodium acetate (SDA) or dimethyl succinate (DMS) to boost intracellular Acetyl-CoA and succinate, respectively. Exogenous SDA or DMS treatment promoted HIF-1α accumulation in AMs and enhanced Tnf and Ccl2 expression even in the presence of MSDC (Fig. 4, I and J), indicating that diminished succinate and/or Acetyl-CoA levels likely contribute to decreased HIF-1α following MSDC treatment in AMs.
Previous studies in T cells and other cell types have found that inhibition of pyruvate translocation led to increased glutamine and lipid incorporation into the mitochondria [44]. Next, we sought to investigate whether increased mitochondrial respiration in AMs following disruption of pyruvate metabolism by MSDC could be due to the increased utilization of fatty acid or glutamine oxidation. We performed Seahorse assays in AMs in the presence of glutaminase inhibitor BPTES and/or carnitine palmitoyltransferase-1 inhibitor etomoxir in the context of MPC inhibition in vitro. MSDC treatment increased oxidative phosphorylation (OXPHOS) and mitochondrial fitness compared to vehicle treatment; however, single and particularly the combined treatment of BPTES and/or etomoxir inhibited OXPHOS and mitochondrial fitness compared to MSDC alone treated cells (fig. S8). These data suggest that the increased mitochondrial fitness in AMs following MPC inhibition is likely due to the compensatory effects of glutamate and/or fatty acid oxidation.
Previously, we have shown that genetic HIF-1α deficiency in AMs or inhibition of HIF-1α stability by LW6 administration in vivo diminished lung inflammation following IAV infection [38]. Similarly, we found that LW6 treatment decreased host morbidity and inflammation following SARS-CoV-2 infection (fig. S9, A to F), suggesting that increased HIF-1α expression is a primary driver of pulmonary inflammatory responses. HIF-1α is known to promote IL-1β production in macrophages, and IL-1β release is a major contributor of pulmonary inflammation during COVID-19 [42, 45, 46]. Next, we sought to determine whether inhibition of HIF-1α-dependent IL-1β contributed to decreased macrophage inflammation by MSDC. We treated WT or Il1b-deficient AMs with MSDC or LW6 in the presence or absence of Poly IC. MSDC or LW6 diminished proinflammatory cytokine expression in both WT or Il1b-deficient AMs following Poly IC stimulation (fig. S9G), suggesting that the decreased inflammatory capacity of AMs by MSDC or HIF-1α inhibitor treatment is independent of IL-1β in vitro. Consistently, blockade of IL-1β did not significantly decrease host morbidity as seen in MSDC- or LW6-treated mice in vivo (fig. S9H). Therefore, we conclude that the diminished macrophage inflammation following MSDC treatment is unlikely to be due to diminished IL-1β production after treatment. Together, these data suggest that MPC inhibition by MSDC improves mitochondrial metabolism and diminishes the accumulation of metabolites capable of stabilizing HIF-1α, thereby suppressing the inflammatory responses of lung macrophages following respiratory viral infection.
## MSDC promotes metabolic health and concurrently suppresses pulmonary hyper inflammation
Respiratory virus infections are particularly dangerous to people who have underlying metabolic syndromes, most notably insulin resistance with obesity and diabetes [5, 47]. This paradigm is observed in models where, compared to lean mice, obese mice also showed increased morbidity and mortality following IAV or SARS-CoV-2 infection (48–50). Since MSDC has been found safe and effective in lowering glycemia and liver steatosis [24], and dampening IAV-induced inflammation in lean host (Fig. 1), we tested the therapeutic efficacy of MSDC in ameliorating IAV pneumonia in obese mouse models (Fig. 5A). High fat diet (HFD)-induced obese (DIO) mice showed higher levels of blood glucose and total cholesterol at 5 d.p.i compared to lean mice, while MSDC administration improved glucose tolerance and lowered total cholesterol in the blood compared to vehicle-treated DIO mice (Fig. 5, B and C). Furthermore, MSDC-treated mice had decreased cytokine levels in the BAL and a marked reduction in expression of multiple pro-inflammatory genes in the lung (Fig. 5D; and fig. S10, A and B), but similar viral titers compared to vehicle-treated mice at 5 d.p.i (fig. S10C). MSDC treatment also promoted lung inflammation resolution and recovery as evidenced by less disrupted lung tissue areas, diminished BAL protein levels and enhanced expression of ATII-specific genes at the recovery stage following MSDC treatment (15 d.p.i.) ( fig. S10, D to F). Similar phenotypes of MSDC treatment were observed in IAV-infected leptin receptor mutant (db/db) mice (fig. S11), indicating that MSDC can simultaneously promote metabolic heath and dampen lung inflammation following IAV infection in both genetically- or diet-induced obesity models. Consistent with these observations, MSDC treatment reduced host mortality following IAV infection in DIO mice (Fig. 5E).
**Fig. 5.:** *MSDC treatment simultaneously promotes host metabolic health, dampens pulmonary inflammation and enhances tissue recovery.(A) Schematic diagram for treatment of IAV-infected DIO mice. (B to E) DIO mice were infected with sub-lethal (B to D) or lethal (E) doses of IAV and treated with vehicle or MSDC. (B) Blood glucose concentration measured by an i.p. glucose tolerance test at 5 d.p.i. (n = 9–10). (C) Total cholesterol concentrations in the blood at 5 d.p.i. (n = 4–5). (D) BAL cytokine levels at 5 d.p.i. (n = 13–14). (E) Host mortality was monitored. (F to K) DIO mice were infected with SARS-CoV-2 MA10 virus and treated with vehicle or MSDC. (F) Schematic diagram. (G) BAL cytokine levels at 5 d.p.i. (n = 5). (H) Blood glucose and total cholesterol levels were measured at 5 d.p.i. (n = 5). (I) H&E staining of lung section (n = 5) and quantification of pathological lesions at 5 d.p.i. Scale bar, 200 μm. HM, hyaline membranes. (J) Fluorescence microscopy images of PDPN, proSP-C and DAPI staining in fixed lung tissues at 5 d.p.i (n = 5). Scale bar, 50 μm. Quantification of proSP-C+ cell number was performed using at least 10 random fields (10x) of alveolar space per mouse lung. (K) Host mortality was monitored. Representative (I and J) or pooled data (B, D, E and K) from at least two independent experiments. Data are presented as means ± SEM. *, p < 0.05; **, p < 0.01. The p value was determined by Logrank test (E and K), one-way ANOVA (B), or a two-tailed Student’s t-test (C, D and G to J).*
Emerging evidence has suggested that obesity predisposes hosts to severe COVID-19 following SARS-CoV-2 infection [5, 6, 47]. Our data also indicated that DIO mice showed increased host mortality than lean mice following SARS-CoV-2 infection (fig. S12A). To this end, we examined the therapeutic potential of MSDC in mitigating severe diseases following SARS-CoV-2 MA10 infection in obese mice (Fig. 5F). MSDC-treated DIO mice had lower levels of pro-inflammatory cytokines, which was accompanied with decreased numbers of inflammatory monocytes and neutrophils in the BAL at 5 d.p.i (Fig. 5G; and fig. S12, B and C), suggesting MPC inhibition dampened SARS-CoV-2-induced pulmonary inflammation. MSDC administration also decreased glucose and cholesterol levels in the blood at 5 d.p.i (Fig. 5H), indicating that MSDC ameliorated metabolic conditions in obese mice after SARS-CoV-2 infection. Additionally, MSDC treatment lowered lung inflammatory cytokine expression, BAL protein levels and reduced disrupted alveolar areas by lung histopathological analysis without affecting viral gene expression (Fig. 5I; and fig. S12, D and E). MSDC-treated lungs showed increased ATII gene expression and elevated levels of ATII cell presence at 5 d.p.i (Fig. 5J; and fig. S12F), which suggest that MSDC can potently enhance lung recovery and regeneration following SARS-CoV-2 infection in obese hosts. Consequently, SARS-CoV-2-induced lethality was partially abrogated following MSDC treatment at 6 h or 1 day post-infection, while most of the vehicle-treated DIO mice had succumbed to SARS-CoV-2 infection (Fig. 5K; and fig. S12G). These data suggest that MPC inhibition by MSDC diminishes pulmonary hyperinflammation while concurrently promoting metabolic health following respiratory viral pneumonia in hosts with underlying metabolic conditions.
## MSDC diminishes cellular inflammation in COVID-19 lung autopsies and enhances response to anti-viral therapy
We next sought to further explore the translational potential of MSDC in treating COVID-19, particularly in patients with metabolic conditions. Infection of macrophages by SARS-CoV-2 has emerged as an important contributor to COVID-19 associated inflammation [45, 51]. We thus examined whether MSDC could diminish human lung macrophage inflammatory responses following SARS-CoV-2 infection. SARS-CoV-2 infection caused markedly elevated inflammatory gene expression in AMs isolated from two of three healthy donors, but MSDC treatment markedly inhibited a large number of inflammatory genes upregulated following SARS-CoV-2 infection in AMs (Fig. 6A; and fig. S13, A and B). We next examined whether MSDC could dampen lung inflammatory responses in COVID-19 patients to determine its potential as a treatment for severe COVID-19. To this end, we incubated total lung cells from seven deceased COVID-19 patients with or without MSDC, and determined TNF, CCL2, CCL4 and CXCL10 expression in the lung (Fig. 6B; and fig. S13C). Notably, MSDC treatment inhibited the expression of these inflammatory genes in cells from the lungs of COVID-19 patients (Fig. 6B; and fig. S13C), demonstrating the potential of MSDC in treating exuberant pulmonary inflammation in severe COVID-19.
**Fig. 6.:** *MSDC diminishes COVID-19-associated lung inflammation and exhibits synergy with anti-viral therapy.(A) Human AMs from BAL of non-infectious donors were infected with or without SARS-CoV-2 in the presence of vehicle or MSDC (n = 3 donors) for 48 h in vitro. Nanostring analysis of inflammatory genes in AMs. (B) Cytokine expression in lung cells from COVID-19 patient autopsies following vehicle or MSDC treatment overnight ex vivo (n = 7 subjects). (C and D) DIO mice were infected with SARS-CoV-2 MA10 virus and treated with vehicle, Nirmatrelvir, or Nirmatrelvir plus MSDC. (C) Host mortality was monitored and survival rate is shown. (D) H&E staining of lung section (n = 5) and quantification of pathological lesions at 21 d.p.i. Scale bar, 200 μm. HM, hyaline membranes. Representative (D) or pooled data (B and C) from at least two independent experiments. Data are presented as means ± SEM. *, p < 0.05; **, p < 0.01; ***, p < 0.001. The p value was determined by paired t test (B), Logrank test (C), and a two-tailed Student’s t-test (D).*
Finally, since many patients at-risk for severe disease from COVID-19 are now likely to be treated with anti-viral drugs such as Paxlovid, we examined whether MSDC could work with anti-viral therapies to provide an added level of protection against severe COVID-19, in this setting. We treated SARS-CoV-2 MA10-infected DIO mice with nirmatrelvir (the anti-viral component of Paxlovid) at 6 h post-infection in the absence or presence of MSDC (Fig. 6C). Notably, MSDC plus nirmatrelvir treatment protected the majority of mice from death following SARS-CoV-2 infection in obese mice, whereas nirmatrelvir alone appeared less efficacious (Fig. 6C). Furthermore, lungs from mice treated with MSDC plus nirmatrelvir showed less tissue inflammation and alveolar disruption compared to mice treated with nirmatrelvir alone (Fig. 6D), indicating that MSDC in combination with antiviral therapy can mitigate severe COVID-19 in at-risk hosts. Taken together, these data support a potential role for MSDC in the treatment of COVID-19 in patients with metabolic conditions, particularly when combined with anti-viral therapy such as nirmatrelvir.
## DISCUSSION
Lung macrophage populations are heterogeneous immune sentinel cells, critical for antiviral innate immunity and tissue recovery following respiratory viral infections [52]. Conversely, macrophage-derived inflammatory and/or injurious mediators also contribute to excessive pulmonary inflammation and collateral tissue injury following viral infections [12, 38, 53]. Indeed, the aberrant activation of monocytes or resident macrophages is considered a key driver of virus-induced inflammation in severe COVID-19 [12, 51, 54]. However, specific pathways and/or mediators that can be targeted to dampen exuberant macrophage-mediated lung inflammation without compromising beneficial antiviral immunity remain largely unknown. Here, we showed that mitochondrial pyruvate translocation is selectively required for detrimental lung macrophage-mediated inflammatory responses following viral infections including SARS-CoV-2. Moreover, this pathway can be safely targeted to improve outcomes following severe viral pneumonia using the second-generation TZD MSDC (fig. S14).
AMs are among the first immune cells responding to viral infections. The inflammatory mediators produced by AMs not only directly contribute to pulmonary inflammation, but could also indirectly augment the overall levels of inflammation by recruiting inflammatory immune cells such as monocytes and neutrophils following infection [25, 38, 55]. Thus, the inhibition of AM inflammatory responses by MSDC has the potential to directly and/or indirectly dampen the pathogenic inflammation after respiratory viral infection. Additionally, we did observe modest inhibitory effects of MSDC on MdM and monocyte inflammatory responses in vivo. Thus, MSDC may function to inhibit the inflammatory responses of both resident and recruited macrophages to mitigate severe disease development post viral infection.
While glycolysis is well-established to be involved in inflammatory responses for both monocytes and macrophages [56], MPC-dependent pyruvate oxidation appears to be preferentially required for pulmonary macrophage-mediated inflammation by stabilizing HIF-1α. This distinct feature of MPC-dependent inflammatory activity in lung macrophages may be particularly meaningful and exploited to dampen pulmonary inflammation. Unlike corticosteroid treatment such as dexamethasone that induces a systemic anti-inflammatory state often resulting in secondary infections and complications [19, 57], the immunosuppressive effect of the MPC inhibitor may be limited to the respiratory tract. Interestingly, inhibition of pyruvate flux into mitochondria can lead to compensatory metabolic reactions including increased fatty acid and/or glutamine oxidation in T and other cell types [44], which is concordant with our transcriptomic analysis and increased mitochondrial respiration. Notably, fatty acid and glutamine oxidative metabolism is known to promote pro-recovery M2-like macrophages [58], consistent with the observation that MSDC treatment enhanced lung recovery and regeneration following viral clearance. Thus, MSDC may also serve as a pro-reparative therapeutic in the clinic by augmenting lung tissue recovery (such as ATII cell replenishment) following COVID-19 lung injury.
Growing evidence indicates that SARS-CoV-2 induces mitochondrial dysfunction in immune cells. Acute SARS-CoV-2 infection resulted in rapid mitochondrial dysfunction in both CD4 and CD8 T cells, which compromised "T cell" functionality contributing to suppressed "T cell" immune responses to viral infection [59]. Patients with SARS-CoV-2 infection displayed depolarized mitochondria and abnormal mitochondrial ultrastructure in monocytes, which was correlated with enhanced inflammatory responses [60]. Recently, targeted transcriptome analysis also revealed impairment of mitochondrial OXPHOS and anti-oxidant gene expression in autopsy samples, which was associated with enhanced HIF-1α stabilization [61, 62]. Thus, means that can promote mitochondrial metabolic fitness may be promising for developing novel therapeutic avenue for COVID-19. In line with this notion, our study also showed compromised mitochondrial respiration and increased HIF-1α expression in AMs after viral infection in vivo [40], and the inhibition of pyruvate metabolism by MSDC enhanced mitochondrial OXPHOS and fitness, which was associated with the reduction of proinflammatory cytokines.
Obesity and/or diabetes greatly increases the risk of severe disease following IAV or SARS-CoV-2 infections [5, 63]. Correction of insulin resistance and hyper-glycemia using insulin sensitizer drugs has been suggested for the management of diabetic patients with COVID-19 [5]. Metformin, the most prescribed anti-diabetic drug, has been suggested as a repurposed drug for COVID-19 due to its anti-inflammatory properties [64, 65]. Nevertheless, metformin failed to provide significant clinical benefits in randomized placebo-controlled clinical trials [66, 67], and the efficacy of other anti-diabetic drugs such as Glucagon-like peptide-1 receptor agonists (GLP1-RAs) also remains controversial [68]. Therefore, interventions capable of circumventing the deadly cycle of viral infection, hyperglycemia and hyper-inflammation are critical for improving treatment of severe COVID-19 patients with history of metabolic disease. *First* generation TZDs, including Pioglitazone and Rosiglitazone, are effective in treating type 2 diabetes [69], but induce considerable side effects, resulting in their restriction and removal from the market in many countries. MSDC is a second-generation insulin sensitizer, maintaining all the pharmacological benefits of first generation TZDs, without the potential for edema and exhibiting an outstanding safety profile as per a recent multicenter, double-blinded, randomized controlled trial [24]. Importantly, our data also shows that MSDC treatment is effective when combined with a current standard-of-care antiviral therapy.
We have shown that MSDC can simultaneously dampen hyperglycemia and hyperinflammation in obese hosts. Of note, hyperglycemia per se predisposes the host to more severe disease development following viral infection including SARS-CoV-2. Therefore, we have not assessed the relative contribution of hyperglycemia versus macrophage inflammatory activities in driving the severity of respiratory viral infection during obesity. Future experiments utilizing MPC myeloid conditional deficient mice under the obesity setting would be ideal to study this question. Additionally, our data showed that inhibition of glutaminase and/or carnitine palmitoyltransferase-1 disrupted elevated mitochondrial metabolism in response to MSDC treatment, suggesting that the increased fitness and respiration of mitochondria after MPC inhibition is likely due to the increased glutamine and/or fatty acid oxidation. However, such a model can not be definitively established without analyzing the metabolic flux using specific isotope tracers. Unfortunately, such tracing experiments remain impractical due to requiring a large number of primary tissue macrophages. Nevertheless, our data have uncovered a metabolic pathway that concurrently modulates macrophage inflammation, lung recovery and host metabolic health, and suggest a potentially viable therapeutic agent that may be combined with existing anti-viral agents to treat severe COVID-19 in patients with underlying metabolic disease.
## Study design
The aim of this study was to determine the therapeutic potential of mitochondrial pyruvate carrier inhibitor MSDC-0602 K (MSDC) in treating severe viral infection including SARS-CoV-2 infection in normal and obese hosts. We examined the efficacy of MSDC in dampening host diseases and promoting metabolic health following influenza virus and mouse-adapted SARS-CoV-2 MA10 virus infection in lean and obese mouse models. ScRNA-seq, bulk RNA-seq, metabolic analysis and flow cytometry were used to uncover the cellular, molecular and metabolic mechanisms by which MSDC regulates lung macrophage inflammatory process following influenza or SARS-CoV-2 infection. Lastly, we tested the potential roles of MSDC in regulating SARS-CoV-2-induced inflammation in humans by culturing SARS-CoV-2-infected primary human lung macrophages and human COVID-19 lung autopsy samples in vitro with or without MSDC. Viral infections in mice were ended upon mouse sacrifice at indicated days after infection. *In* general, experiments were conducted in replicates and the number of mice used in the studies were included in figure legends.
## Ethics and biosafety
The study involving human participants was reviewed and approved by Mayo Clinic Institutional Review Boards (IRB# 19–012187). All animal experiments were performed in animal housing facilities at the Mayo Clinic (Rochester, MN) or the University of Virginia (UVA, Charlottesville, VA). Sex matched and age matched adult mice of both sexes unless otherwise specified were used in the experiments. The animal experiments were approved by the the Mayo Clinic or UVA Institutional Animal Care and Use Committees (IACUC). All work with SARS-CoV-2 infection was approved for use under ABSL3/BSL3 conditions, and was performed with approved standard operating procedures and safety conditions by UVA Institutional Review Board.
## Mouse and infection
WT C57BL/6 (Cat# 000664), Lyz2-cre (Cat# 004781), and Mpc2fl/fl (Cat# 032118) mice were purchased from the Jackson Laboratory and bred in house. Mpc2ΔLyz2 mice were generated by crossing Mpc2fl/fl mice with Lyz2-cre mice. High fat diet-induced obese (DIO) male mice on C57BL/6 background (with $60\%$ Kcal fat chow for 20 weeks) (Cat# 380050) and age/sex-matched control mice (Cat# 380056) were purchased from the Jackson Laboratory and bred in house for another 2 weeks with $60\%$ Kcal fat chow (Research Diets, Cat# D12492) or normal chow before experiments. All mice housed in a specific pathogen-free environment. For host morbidity experiments following regular dose of influenza A virus (IAV) infection, influenza A/PR$\frac{8}{34}$ strain was diluted in FBS-free DMEM media on ice and inoculated in anesthetized mice through intranasal route. For host mortality experiments following high dose (2.5 folds of the sublethal dose, lethal) of IAV infection, the outcome was determined based on the humane endpoint (more than $30\%$ weight loss or moribund) or deaths before humane sacrifice as described before [38]. For SARS-CoV-2 MA10 infection, mice were infected with 105 PFU (for 9–12 weeks old WT C57BL/6 mice), 8x104 PFU (for aged C57BL/6 mice) or 104 PFU (for 26–28 weeks old DIO mice) of MA10 intranasally under anesthesia. Body weight was monitored daily for virus infected mice.
## MPC inhibitor MSDC-0602 k treatment in vivo
MSDC-0602 k (MSDC) was kindly provided by Cirius Therapeutics, which was developed for nonalcoholic steatohepatitis (NASH). MSDC was dissolved in DMSO. For treatment of IAV-infected WT C57BL/6 lean mice or DIO mice, mice were administered by intraperitoneal (for C57BL/6 lean mice) or oral gavage (for DIO mice) daily with either $5\%$ DMSO as vehicle or 30 mg/kg MSDC in 200 μl PBS from 1 d.p.i to 8 d.p.i. unless otherwise specified. For treatment of SARS-CoV-2 MA10-infected WT C57BL/6 mice, aged C57BL/6 mice or DIO mice, mice were administered by intraperitoneal (for C57BL/6 mice or aged C57BL/6 mice) or oral gavage (for DIO mice) daily with either $5\%$ DMSO as vehicle or 30 mg/kg MSDC in 200 μl PBS from 6 hours post infection to 7 d.p.i. unless otherwise specified. Since DIO mice have greatly enhanced morbidity and mortality after infection, we reasoned that for DIO mice, i.p. injection of 200ul liquid daily is a huge burden, therefore, we chose to use oral gavage. For treatment of SARS-CoV-2 MA10-infected DIO mice with Nirmatrelvir, Nirmatrelvir (PF-07321332) was purchased from MedChemExpress (Cat# HY-138687). The compounds were dissolved in DMSO and formulated as 40 mg/ml in corn oil containing $10\%$ DMSO. DIO mice were administered by oral gavage with either $10\%$ DMSO as vehicle, 300 mg/kg Nirmatrelvir or 300 mg/kg *Nirmatrelvir plus* 30 mg/kg MSDC. The treatment of Nirmatrelvir or MSDC was initiated at 6 h post MA10 infection, and continued twice daily for a total of 3 days for Nirmatrelvir or continued once daily for a total of 7 days for MSDC, respectively. For treatment of SARS-CoV-2 MA10-infected WT C57BL/6 mice, mice were administered by intraperitoneal with 400 μg IgG (BioXcell, Cat# BE0091) or Anti-IL-1β (BioXcell, Cat# BE0246) antibodies at day 1 and day 3 post infection. Mice were monitored for body weight change. At indicated time points, a subset of mice was euthanized and lung or BAL samples were collected for inflammation and titre analysis. Another subset of mice were euthanized and half of each lung lobe was taken for histopathology and were fixed in $10\%$ phosphate buffered formalin before paraffin embedding and sectioning, and half of lung lobe was taken for further analysis.
## Human AM culture and treatment in vitro
For human AMs, we selected donors without a history of immunosuppression and chemo or radiotherapies, and are free of inflammation or pulmonary infection. The study was reviewed and approved by the Institutional Review Board (IRB# 19–012187) at Mayo Clinic. All participants provided written informed consent prior to sample collection and subsequent analysis.
Human AMs were obtained from Broncho-alveolar lavage (BAL) of adult donors undergoing flexible bronchoscopy as described before [38]. About 100 to 200 ml of saline were instilled in 20-ml aliquots until 60 ml of lavage fluid was obtained. The specimen was placed on ice and immediately hand carried to laboratory for cell isolation. AMs were purified by adherence for 2 h in complete medium (RPMI-1640, $10\%$ FBS, $1\%$ Pen/Strep/glutamate) at 37 °C and $5\%$ CO2. The non-adherent cells were washed off with warm PBS. The remaining adherent cells were cultured in complete medium supplemented with 50 ng/ml recombinant human GM-CSF (Biolegend, Cat# 572903) and M-CSF (Biolegend, Cat# 574804). For Poly IC treatment, AMs were pre-treated with DMSO (vehicle) or MSDC (10 μM) for two hours, then, cells were stimulated with or without Poly IC (5 μg/mL) for 24 hours and were analyzed by quantitative RT-PCR.
For SARS-CoV-2 infection, AMs were pre-treated with DMSO (vehicle) or MSDC (10 μM) for two hours. Subsequently, cells were washed with cold PBS and challenged with or without 1 MOI of SARS-CoV-2 virus for one hour, and then cultured in the presence of DMSO or MSDC (10 μM) for 48 hours. Cells were analyzed by quantitative RT-PCR or Nanostring.
## Human lung tissue specimens
Lung autopsy samples from 7 individuals who died from COVID-19 were obtained from Mayo Clinic Department of Pathology. Informed consent was obtained from relatives of study participants. Lung tissue specimens were obtained within 24 h of autopsy and immediately processed for single cell suspension. For lung cells treatment ex vivo, the cells were treated with DMSO (vehicle) or MSDC (10 μM) in complete medium supplemented with 50 ng/ml recombinant human GM-CSF and M-CSF for 24 h. Cells were analyzed by quantitative RT-PCR.
## Glucose tolerance test
Glucose tolerance test was performed as described before [70]. Briefly, DIO mice were weighed and fasted overnight at 4 d.p.i. Then the mice were injected intraperitoneally with 1 g/kg D-glucose in $0.9\%$ NaCl immediately after collecting blood from tail vein ($T = 0$) at 5 d.p.i. Subsequently, the blood was obtained at 0.5, 1, 2 and 4 h post injection from tail vein. Blood glucose was measured with 2 μl of serum from each blood sample at indicated time points by colorimetric glucose assay kit (Abcam, Cat# ab65333) according to manufacturer’s instructions.
## Total cholesterol detection
Total cholesterol concentrations in each blood sample were measured with colorimetric cholesterol assay kit (Abcam, Cat# ab65390) according to manufacturer’s instructions. Intra-assay C.V.’s was $0.8\%$, $0.8\%$ and $0.7\%$ at 85, 178 and 340 mg/dL respectively.
## Mouse AM culture and treatment in vitro
Mouse AMs were obtained from BAL as described previously [26]. Briefly, alveolar lavages were pooled from BAL washes (PBS with 2 mM EDTA). AMs were purified by adherence for 2 h in complete medium (RPMI-1640, $10\%$ FBS, $1\%$ Pen/Strep/glutamate) at 37°C and $5\%$ CO2. The non-adherent cells were washed off with warm PBS. The remaining adherent cells were cultured in complete medium supplemented with 10 ng/ml recombinant murine GM-CSF (Biolegend, Cat# 576304). For Poly IC treatment, AMs were pre-treated with DMSO (vehicle), MSDC (10 μM), UK5099 (20 μM, Selleckchem), BPTES (10 μM, TargetMol), or Etomoxir (20 μM, Sigma-Aldrich) for two hours, then, cells were stimulated with or without Poly IC (5 μg/mL) for 24 hours and were analyzed by quantitative RT-PCR. For HIF-1α stabilizers treatment, AMs were pre-treated with DMSO (vehicle) or MSDC (10 μM) with or without the HIF-1α stabilizers, 100 μM Molidustata (MedChemExpress, Cat# HY-12654) or 100 μM Roxadustat (MedChemExpress, Cat# HY-13426) for two hours. Subsequently, cells were stimulated with or without Poly IC (5 μg/mL) for 24 hours. For boost intracellular Acetyl-CoA and succinate assay, AMs were pre-treated with DMSO (vehicle) or MSDC (10 μM) with or without 20 mM sodium acetate (Sigma, Cat# S8625) or 20 mM dimethyl succinate (Sigma, Cat# W239607) for two hours. Subsequently, cells were stimulated with or without Poly IC (5 μg/mL) for 24 hours. Cells were analyzed by quantitative RT-PCR or Western blot.
## Acetyl-Coenzyme A measurement
Mouse AMs or BMDMs were pre-treated with DMSO (vehicle) or MSDC (10 μM) for two hours, then, cells were stimulated with Poly IC (5 μg/mL) for 24 hours. The concentration of acetyl-CoA is quantified by BioVision’s PicoProbe Acetyl CoA Assay Kit (Cat# MAK039), according to the protocols provided by the manufacturer.
## Bulk RNA sequencing
Total RNA from lungs of IAV-infected mice and in vitro cultured AMs were used for bulk RNA sequencing. After quality control, high quality (Agilent Bioanalyzer RIN >7.0) total RNA was used to generate the RNA sequencing library. cDNA synthesis, end-repair, A-base addition, and ligation of the Illumina indexed adapters were performed according to the TruSeq RNA Sample Prep Kit v2 (Illumina, San Diego, CA). The concentration and size distribution of the completed libraries was determined using an Agilent Bioanalyzer DNA 1000 chip (Santa Clara, CA) and Qubit fluorometry (Invitrogen, Carlsbad, CA). Paired-end libraries were sequenced on an Illumina HiSeq 4000 following Illumina’s standard protocol using the Illumina cBot and HiSeq $\frac{3000}{4000}$ PE Cluster Kit. Base-calling was performed using Illumina’s RTA software (version 2.5.2). Paired-end RNA-seq reads were aligned to the mouse reference genome (GRCm38/mm10) using RNA-seq spliced read mapper Tophat2 (v2.2.1). Pre- and post-alignment quality controls, gene level raw read count and normalized read count (i.e. FPKM) were performed using RSeQC package (v2.3.6) with NCBI mouse RefSeq gene model. Differential expression for each gene between various groups specified in the text was identified on basis of the results of DESeq2 Wald tests. For visualization, data were logarithmic transformed, and genes that exhibited log2 fold change values >2 and log10 $P \leq 25$ between compared groups were highlighted. For functional analysis, gene set enrichment analysis (GSEA) [71] was applied to identify enriched gene sets from MSigDB, using a weighted enrichment statistic and a log2 ratio metric for ranking genes. The Bulk RNA sequencing was conducted once using multiple biological samples per group (as indicated in figures).
## Single-cell RNA sequencing
C57BL/6 WT mice were infected with ~200 PFU IAV and treated with vehicle or MSDC for 3 days. Lung cells were pooled from three individual mice from each group at 4 d.p.i, and subjected to scRNA-seq analysis. Single-cell libraries were prepared using the Chromium Single Cell 5’ Reagent Kit (10x Genomics) following manufacturer’s instruction. Paired-end sequencing was performed using an DNBSEQ-G400 in rapid-run mode. scRNA-seq data were aligned and quantified using 10x Genomics Cell Ranger Software Suite. Subsequently, the doublet cells cells were removed by the package “scDblFinder”. Remaining cells were analyzed using “Seurat” (version 4.1.1). The following criteria were applied for quality control: gene number > 200, UMI count >1,000 and mitochondrial gene percentage < $5\%$. The workflow included normalization, dimension reduction, and clustering, as well as identification of marker genes for clusters and differentially expressed genes. Gene set enrichment analysis (GSEA) [71] analysis is based on the results of FindAllMarkers with the package of clusterProfiler [72].
## Metabolic analysis
Real-time oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of AMs or BMDM were measured using a Seahorse XFp Analyzers (Seahorse Bioscience) [38]. 1 × 105 a.m. or BMDM were seeded into each well of Seahorse XFp Cell Culture Miniplates, and pre-treated with DMSO (vehicle) or MSDC (10 μM) for two hours, then, cells were stimulated with or without Poly IC (5 μg/mL) for overnight at 37°C and $5\%$ CO2. On the following day, the cells were washed twice and incubated at 37°C for 1 hr. in the absence of CO2 in unbuffered assay medium (pH = 7.4, Agilent Technologies) with 10 mM glucose for mitochondrial stress test (or without glucose for glycolytic stress test). OCR and ECAR were measured under basal conditions and after the addition of the following compounds: 1 μM oligomycin, 1.5 μM FCCP (carbonyl cyanide-4-(trifluoromethoxy) phenylhydrazone), 0.5 μM rotenone +0.5 μM antimycin, 10 mM glucose, and 50 mM 2-DG (2-deoxy-D-glucose) (all compounds obtained from Sigma) as indicated. Data were analyzed with Wave Desktop software version 2.6 (Agilent Technologies).
## Measurement of mitochondrial mass
1 × 105 a.m. or BMDM were seeded into 24 well plates and pre-treated with DMSO (vehicle) or MSDC (10 μM) for two hours, then, cells were stimulated with or without Poly IC (5 μg/mL) for overnight at 37°C and $5\%$ CO2. On the following day, the cells were washed and rinsed and incubated with Mitotracker deep red (Invitrogen, Cat# M22426) and Mitotracker green (Invitrogen, Cat# M7514) at 50 nM for 30 min at 37°C. Then, cells were washed twice with PBS and lifted off the plates for flow cytometry.
## Metabolite analysis
For TCA-analytes testing as described before [73], 2 million of mouse AMs or BMDMs were treated with DMSO (vehicle) or MSDC (10 μM) in the presence or absence of Poly IC in vitro. The cell pellets were lyzed in 50 μl of acidified 1X PBS after spiking in 15 μl of internal solution containing U-13C labeled analytes. The proteins were removed by adding 260 μl of chilled methanol and acetonitrile solution to the sample mixture. After drying the supernatant in the speed vac, the sample was derivatized with ethoxime and then with MtBSTFA +$1\%$ tBDMCS (N-Methyl-N-(t-Butyldimethylsilyl)-Trifluoroacetamide +$1\%$ t-Butyldimethylchlorosilane) before it was analyzed on an Agilent 5975C GC/MS (gas chromatography/mass spectrometry) under electron impact and single ion monitoring conditions. Concentrations of lactic acid (m/z 261.2), fummaric acid (m/z 287.1), succinic acid (m/z 289.1), oxaloacetic acid (m/z 346.2), ketoglutaric acid (m/z 360.2), malic acid (m/z 419.3), aspartic acid (m/z 418.2), 2-hydroxyglutaratic acid (m/z 433.2), cis aconitic acid (m/z 459.3), citric acid (m/z 591.4), and isocitric acid (m/z 591.4), glutamic acid (m/z 432.4) were measured against a 7-point calibration curves that underwent the same derivatization.
## Flow cytometry analysis
Fluorescence-conjugated flow cytometry antibodies (Abs) were purchased from Biolegend and BD Biosciences. Cell suspensions were stained with the appropriate antibody cocktail in flow cytometry buffer at 4°C for 30 min. The cell populations were defined based on following cell surface markers as described previously [38]: AMs (CD11c+ Siglec F+ CD11blow CD64+ MerTK+), Neutrophils (CD11b+ Ly6G+), total CD11b+ Monocyte/Macrophage population (Ly6G− Siglec F− CD11b+), inflammatory Monocytes (Ly6G− Siglec F− CD11b+ Ly6C+), interstitial macrophages (Siglec F− CD11b+ CD64+ MerTK+), NP366 tetramer+ cells (CD8+ NP366-tet+), PA224 tetramer+ cells (CD8+ PA224-tet+). The information for those Abs as follows: anti-SiglecF-BV421 (BD Biosciences, clone E50–2440, Cat# 562681), anti-CD11b-PerCP-Cy5.5 (Biolegend, clone M$\frac{1}{70}$, Cat# 101228), anti-CD11c-BV510 (Biolegend, clone N418, Cat# 117338), anti-Ly6G-PE-Cy7 (Biolegend, clone 1A8, Cat# 127618), anti-Ly6C-BV711 (Biolegend, clone HK1.4, Cat# 128037), anti-CD64-PE (Biolegend, clone X54–$\frac{5}{7.1}$, Cat# 139304), anti-MerTK-APC (Biolegend, clone 2B10C42, Cat# 151508), anti-CD4-BV510 (Biolegend, clone RM4–5, Cat# 100559), anti-CD8-PerCP-Cy5.5 (Biolegend, clone YTS156.7.7, Cat# 126610), Influenza NP366 Tetramer (NIH Tetramer Facility, Cat# H-2D(b) ASNENMETM), Influenza PA224 Tetramer (NIH Tetramer Facility, Cat# H-2D(b) SSLENFRAYV). The dilution of surface staining Abs was 1:200. Staining samples were collected on FACS Attune or FACS Attune NXT flow cytometer (Life technologies) and analyzed using Flow Jo software (Tree Star).
For intracellular staining, cell suspensions were stained for surface marker at 4°C for 30 min. Cells were washed twice with FACS buffer (PBS, 2 mM EDTA, $2\%$ FBS, $0.09\%$ Sodium Azide), prior to fixation and permeabilization with the Foxp3 transcription factor staining buffer set (eBioscience) for 1 h at RT in the dark. Cells were washed twice with perm wash buffer (eBioscience), and stained with Abs against HIF-1α (clone 241812, R&D Systems, Cat# IC1935P) and control immunoglobulin (Biolegend) for at least 30 min at RT, and washed twice with perm wash. Samples were processed with flow cytometer.
## Statistical analysis
Data are mean ± SEM of values from individual mice (in vivo experiments). Unpaired two-tailed Student’s t-test (two group comparison), one-way ANOVA (multiple group comparison), Multiple t-tests (weight loss) or Logrank test (survival study) were used to determine statistical significance by GraphPad Prism software. We consider P values <0.05 as significant. *, $p \leq 0.05$; **, $p \leq 0.01.$
## Unknown
Funding: This work is in part supported by the US National Institutes of Health grants AI147394, AG069264, AI 112844 and AI 154598 to J.S; Interdisciplinary Training Program in Immunology T32 AI007496 to H.N. Competing Interests: J.S. is a consultant of TeneoFour company. J.C. is an employee of Cirius Therapeutics. R.G.J. is a scientific advisor for Servier and Agios Pharmaceuticals and is a member of the Scientific Advisory Board of Immunomet Therapeutics. R.G.J. has equity interest in Immunomet Therapeutics. The University of Virginia has filed a provisional patent application on the use of MSDC in treating severe viral pneumonia. Data and materials availability: Bulk RNA-seq and scRNA-seq datasets are available under GEO accession number GSE181776, GSE181793 and GSE181798. All data needed to evaluate the conclusions in the paper are present in the paper or the Supplementary Materials. Author Contributions: J.S. conceived the project. B.Z., X. W., H.N., W.Q., R.Z, I.S.C., Y.W., C.L. designed and performed all experiments and analyzed data. R.G.J., M.H.K., R.A.V., T.J.B., W.A.P., L.S., J.R.C., A.P., P.E.H.J., B.M., C.M.K., J.M.S. contributed to analysis or provided critical reagents. B.Z., X.W. and J.S. wrote the manuscript with input from all authors. View/request a protocol for this paper from Bio-protocol.
## References
1. Tregoning J. S., Flight K. E., Higham S. L., Wang Z., Pierce B. F.. **Progress of the COVID-19 vaccine effort: Viruses, vaccines and variants versus efficacy, effectiveness and escape**. *Nat. Rev. Immunol.* (2021.0) **21** 626-636. PMID: 34373623
2. Topol E. J., Iwasaki A.. **Operation nasal vaccine-lightning speed to counter COVID-19**. *Sci. Immunol.* (2022.0) **7** eadd9947. PMID: 35862488
3. Tang J., Zeng C., Cox T. M., Li C., Son Y. M., Cheon I. S., Wu Y., Behl S., Taylor J. J., Chakaraborty R., Johnson A. J., Shiavo D. N., Utz J. P., Reisenauer J. S., Midthun D. E., Mullon J. J., Edell E. S., Alameh M. G., Borish L., Teague W. G., Kaplan M. H., Weissman D., Kern R., Hu H., Vassallo R., Liu S.-L., Sun J.. **Respiratory mucosal immunity against SARS-CoV-2 after mRNA vaccination**. *Sci. Immunol.* (2022.0) eadd4853. PMID: 35857583
4. Wei C. J., Crank M. C., Shiver J., Graham B. S., Mascola J. R., Nabel G. J.. **Next-generation influenza vaccines: Opportunities and challenges**. *Nat. Rev. Drug Discov.* (2020.0) **19** 239-252. PMID: 32060419
5. Lim S., Bae J. H., Kwon H. S., Nauck M. A.. **COVID-19 and diabetes mellitus: From pathophysiology to clinical management**. *Nat. Rev. Endocrinol.* (2021.0) **17** 11-30. PMID: 33188364
6. Khunti K., del Prato S., Mathieu C., Kahn S. E., Gabbay R. A., Buse J. B.. **COVID-19, hyperglycemia, and new-onset diabetes**. *Diabetes Care* (2021.0) **44** 2645-2655. PMID: 34625431
7. Wu C.-T., Lidsky P. V., Xiao Y., Lee I. T., Cheng R., Nakayama T., Jiang S., Demeter J., Bevacqua R. J., Chang C. A., Whitener R. L., Stalder A. K., Zhu B., Chen H., Goltsev Y., Tzankov A., Nayak J. V., Nolan G. P., Matter M. S., Andino R., Jackson P. K.. **SARS-CoV-2 infects human pancreatic β cells and elicits β cell impairment**. *Cell Metab.* (2021.0) **33** 1565-1576.e5. PMID: 34081912
8. Reiterer M., Rajan M., Gómez-Banoy N., Lau J. D., Gomez-Escobar L. G., Ma L., Gilani A., Alvarez-Mulett S., Sholle E. T., Chandar V., Bram Y., Hoffman K., Bhardwaj P., Piloco P., Rubio-Navarro A., Uhl S., Carrau L., Houhgton S., Redmond D., Shukla A. P., Goyal P., Brown K. A., tenOever B. R., Alonso L. C., Schwartz R. E., Schenck E. J., Safford M. M., Lo J. C.. **Hyperglycemia in acute COVID-19 is characterized by insulin resistance and adipose tissue infectivity by SARS-CoV-2**. *Cell. Metab.* (2021.0) **33** 2174-2188.e5. PMID: 34599884
9. Montefusco L., Ben Nasr M., D’Addio F., Loretelli C., Rossi A., Pastore I., Daniele G., Abdelsalam A., Maestroni A., Dell’Acqua M., Ippolito E., Assi E., Usuelli V., Seelam A. J., Fiorina R. M., Chebat E., Morpurgo P., Lunati M. E., Bolla A. M., Finzi G., Abdi R., Bonventre J. V., Rusconi S., Riva A., Corradi D., Santus P., Nebuloni M., Folli F., Zuccotti G. V., Galli M., Fiorina P.. **Acute and long-term disruption of glycometabolic control after SARS-CoV-2 infection**. *Nat. Metab.* (2021.0) **3** 774-785. PMID: 34035524
10. Müller J. A., Groß R., Conzelmann C., Krüger J., Merle U., Steinhart J., Weil T., Koepke L., Bozzo C. P., Read C., Fois G., Eiseler T., Gehrmann J., van Vuuren J., Wessbecher I. M., Frick M., Costa I. G., Breunig M., Grüner B., Peters L., Schuster M., Liebau S., Seufferlein T., Stenger S., Stenzinger A., Mac Donald P. E., Kirchhoff F., Sparrer K. M. J., Walther P., Lickert H., Barth T. F. E., Wagner M., Münch J., Heller S., Kleger A.. **SARS-CoV-2 infects and replicates in cells of the human endocrine and exocrine pancreas**. *Nat. Metab.* (2021.0) **3** 149-165. PMID: 33536639
11. Tay M. Z., Poh C. M., Renia L., MacAry P. A., Ng L. F. P.. **The trinity of COVID-19: Immunity, inflammation and intervention**. *Nat. Rev. Immunol.* (2020.0) **20** 363-374. PMID: 32346093
12. Merad M., Martin J. C.. **Pathological inflammation in patients with COVID-19: A key role for monocytes and macrophages**. *Nat. Rev. Immunol.* (2020.0) **20** 355-362. PMID: 32376901
13. Merad M., Blish C. A., Sallusto F., Iwasaki A.. **The immunology and immunopathology of COVID-19**. *Science* (2022.0) **375** 1122-1127. PMID: 35271343
14. Mettelman R. C., Allen E. K., Thomas P. G.. **Mucosal immune responses to infection and vaccination in the respiratory tract**. *Immunity* (2022.0) **55** 749-780. PMID: 35545027
15. Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., Zhang L., Fan G., Xu J., Gu X., Cheng Z., Yu T., Xia J., Wei Y., Wu W., Xie X., Yin W., Li H., Liu M., Xiao Y., Gao H., Guo L., Xie J., Wang G., Jiang R., Gao Z., Jin Q., Wang J., Cao B.. **Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China**. *Lancet* (2020.0) **395** 497-506. PMID: 31986264
16. **Dexamethasone in hospitalized patients with covid-19**. *N. Engl. J. Med.* (2021.0) **384** 693-704. PMID: 32678530
17. Vegivinti C. T. R., Evanson K. W., Lyons H., Akosman I., Barrett A., Hardy N., Kane B., Keesari P. R., Pulakurthi Y. S., Sheffels E., Balasubramanian P., Chibbar R., Chittajallu S., Cowie K., Karon J., Siegel L., Tarchand R., Zinn C., Gupta N., Kallmes K. M., Saravu K., Touchette J.. **Efficacy of antiviral therapies for COVID-19: A systematic review of randomized controlled trials**. *BMC Infect. Dis.* (2022.0) **22** 107. PMID: 35100985
18. Robinson P. C., Liew D. F. L., Tanner H. L., Grainger J. R., Dwek R. A., Reisler R. B., Steinman L., Feldmann M., Ho L. P., Hussell T., Moss P., Richards D., Zitzmann N.. **COVID-19 therapeutics: Challenges and directions for the future**. *Proc. Natl. Acad. Sci. U.S.A.* (2022.0) **119** e2119893119. PMID: 35385354
19. Cai J., Li H., Zhang C., Chen Z., Liu H., Lei F., Qin J.-J., Liu Y.-M., Zhou F., Song X., Zhou J., Zhao Y.-C., Wu B., He M., Yang H., Zhu L., Zhang P., Ji Y.-X., Zhao G.-N., Lu Z., Liu L., Mao W., Liao X., Lu H., Wang D., Xia X., Huang X., Wei X., Xia J., Zhang B.-H., Yuan Y., She Z.-G., Xu Q., Ma X., Wang Y., Yang J., Zhang X., Zhang X.-J., Li H.. **The neutrophil-to-lymphocyte ratio determines clinical efficacy of corticosteroid therapy in patients with COVID-19**. *Cell Metab.* (2021.0) **33** 258-269.e3. PMID: 33421384
20. Russell C. D., Millar J. E., Baillie J. K.. **Clinical evidence does not support corticosteroid treatment for 2019-nCoV lung injury**. *Lancet* (2020.0) **395** 473-475. PMID: 32043983
21. Ryan D. G., O'Neill L. A. J.. **Krebs cycle reborn in macrophage immunometabolism**. *Annu. Rev. Immunol.* (2020.0) **38** 289-313. PMID: 31986069
22. McCommis K. S., Finck B. N.. **Mitochondrial pyruvate transport: A historical perspective and future research directions**. *Biochem. J.* (2015.0) **466** 443-454. PMID: 25748677
23. McCommis K. S., Chen Z., Fu X., McDonald W. G., Colca J. R., Kletzien R. F., Burgess S. C., Finck B. N.. **Loss of mitochondrial pyruvate carrier 2 in the liver leads to defects in gluconeogenesis and compensation via pyruvate-alanine cycling**. *Cell Metab.* (2015.0) **22** 682-694. PMID: 26344101
24. Harrison S. A., Alkhouri N., Davison B. A., Sanyal A., Edwards C., Colca J. R., Lee B. H., Loomba R., Cusi K., Kolterman O., Cotter G., Dittrich H. C.. **Insulin sensitizer MSDC-0602K in non-alcoholic steatohepatitis: A randomized, double-blind, placebo-controlled phase IIb study**. *J. Hepatol.* (2020.0) **72** 613-626. PMID: 31697972
25. Gwyer Findlay E., Hussell T.. **Macrophage-mediated inflammation and disease: A focus on the lung**. *Mediators Inflamm.* (2012.0) **2012** 140937. PMID: 23304058
26. Huang S., Zhu B., Cheon I. S., Goplen N. P., Jiang L., Zhang R., Peebles R. S., Mack M., Kaplan M. H., Limper A. H., Sun J.. **PPAR-γ in macrophages limits pulmonary inflammation and promotes host recovery following respiratory viral infection**. *J. Virol.* (2019.0) **93** e00030-19. PMID: 30787149
27. Channappanavar R., Fehr A. R., Vijay R., Mack M., Zhao J., Meyerholz D. K., Perlman S.. **Dysregulated type I interferon and inflammatory monocyte-macrophage responses cause lethal pneumonia in SARS-CoV-infected mice**. *Cell Host Microbe* (2016.0) **19** 181-193. PMID: 26867177
28. Newton A. H., Cardani A., Braciale T. J.. **The host immune response in respiratory virus infection: Balancing virus clearance and immunopathology**. *Semin. Immunopathol.* (2016.0) **38** 471-482. PMID: 26965109
29. Zhu B., Zhang R., Li C., Jiang L., Xiang M., Ye Z., Kita H., Melnick A. M., Dent A. L., Sun J.. **BCL6 modulates tissue neutrophil survival and exacerbates pulmonary inflammation following influenza virus infection**. *Proc. Natl. Acad. Sci. U.S.A.* (2019.0) **116** 11888-11893. PMID: 31138703
30. Leist S. R., Dinnon K. H., Schäfer A., Tse L. V., Okuda K., Hou Y. J., West A., Edwards C. E., Sanders W., Fritch E. J., Gully K. L., Scobey T., Brown A. J., Sheahan T. P., Moorman N. J., Boucher R. C., Gralinski L. E., Montgomery S. A., Baric R. S.. **A mouse-adapted SARS-CoV-2 induces acute lung injury and mortality in standard laboratory mice**. *Cell* (2020.0) **183** 1070-1085.e12. PMID: 33031744
31. Melms J. C., Biermann J., Huang H., Wang Y., Nair A., Tagore S., Katsyv I., Rendeiro A. F., Amin A. D., Schapiro D., Frangieh C. J., Luoma A. M., Filliol A., Fang Y., Ravichandran H., Clausi M. G., Alba G. A., Rogava M., Chen S. W., Ho P., Montoro D. T., Kornberg A. E., Han A. S., Bakhoum M. F., Anandasabapathy N., Suárez-Fariñas M., Bakhoum S. F., Bram Y., Borczuk A., Guo X. V., Lefkowitch J. H., Marboe C., Lagana S. M., del Portillo A., Tsai E. J., Zorn E., Markowitz G. S., Schwabe R. F., Schwartz R. E., Elemento O., Saqi A., Hibshoosh H., Que J., Izar B.. **A molecular single-cell lung atlas of lethal COVID-19**. *Nature* (2021.0) **595** 114-119. PMID: 33915568
32. Basil M. C., Katzen J., Engler A. E., Guo M., Herriges M. J., Kathiriya J. J., Windmueller R., Ysasi A. B., Zacharias W. J., Chapman H. A., Kotton D. N., Rock J. R., Snoeck H. W., Vunjak-Novakovic G., Whitsett J. A., Morrisey E. E.. **The cellular and physiological basis for lung repair and regeneration: Past, present, and future**. *Cell Stem Cell* (2020.0) **26** 482-502. PMID: 32243808
33. Channappanavar R., Perlman S.. **Age-related susceptibility to coronavirus infections: Role of impaired and dysregulated host immunity**. *J. Clin. Invest.* (2020.0) **130** 6204-6213. PMID: 33085654
34. Wu Y., Goplen N. P., Sun J.. **Aging and respiratory viral infection: From acute morbidity to chronic sequelae**. *Cell Biosci.* (2021.0) **11** 112. PMID: 34158111
35. Wong L. R., Zheng J., Wilhelmsen K., Li K., Ortiz M. E., Schnicker N. J., Thurman A., Pezzulo A. A., Szachowicz P. J., Li P., Pan R., Klumpp K., Aswad F., Rebo J., Narumiya S., Murakami M., Zuniga S., Sola I., Enjuanes L., Meyerholz D. K., Fortney K., Mc Cray P. B., Perlman S.. **Eicosanoid signalling blockade protects middle-aged mice from severe COVID-19**. *Nature* (2022.0) **605** 146-151. PMID: 35314834
36. 36L. Ran, S. Zhang, P. Zhao, J. Zhou, H. Gan, R. Jeon, Q. Li, J. Herrmann, F. Wang, UK5099 inhibits macrophage activation independent of mitochondrial pyruvate carrier mediated metabolism. Research Square (2021); 10.21.203/rs.3.rs-888191/v1.. DOI: 10.21.203/rs.3.rs-888191/v1
37. Mills E. L., Kelly B., Logan A., Varma M., Bryant C. E., Tourlomousis P., Däbritz J. H. M., Gottlieb E., Latorre I., Corr S. C., Manus G. M., Ryan D., Jacobs H. T., Szibor M., Xavier R. J., Braun T., Frezza C., Murphy M. P., O’Neill Luke A. **Succinate dehydrogenase supports metabolic repurposing of mitochondria to drive inflammatory macrophages**. *Cell* (2016.0) **167** 457-470.e13. PMID: 27667687
38. Zhu B., Wu Y., Huang S., Zhang R., Son Y. M., Li C., Cheon I. S., Gao X., Wang M., Chen Y., Zhou X., Nguyen Q., Phan A. T., Behl S., Taketo M. M., Mack M., Shapiro V. S., Zeng H., Ebihara H., Mullon J. J., Edell E. S., Reisenauer J. S., Demirel N., Kern R. M., Chakraborty R., Cui W., Kaplan M. H., Zhou X., Goldrath A. W., Sun J.. **Uncoupling of macrophage inflammation from self-renewal modulates host recovery from respiratory viral infection**. *Immunity* (2021.0) **54** 1200-1218.e9. PMID: 33951416
39. Halestrap A. P.. **The mitochondrial pyruvate carrier. Kinetics and specificity for substrates and inhibitors**. *Biochem. J* (1975.0) **148** 85-96. PMID: 1156402
40. Gao X., Zhu B., Wu Y., Li C., Zhou X., Tang J., Sun J.. **TFAM-dependent mitochondrial metabolism is required for alveolar macrophage maintenance and homeostasis**. *J. Immunol.* (2022.0) **208** 1456-1466. PMID: 35165165
41. Buyse C., Joudiou N., Corbet C., Feron O., Mignion L., Flament J., Gallez B.. **Impact of inhibition of the mitochondrial pyruvate carrier on the tumor extracellular pH as measured by CEST-MRI**. *Cancers (Basel)* (2021.0) **13** 4278. PMID: 34503089
42. Tannahill G. M., Curtis A. M., Adamik J., Palsson-McDermott E. M., McGettrick A. F., Goel G., Frezza C., Bernard N. J., Kelly B., Foley N. H., Zheng L., Gardet A., Tong Z., Jany S. S., Corr S. C., Haneklaus M., Caffrey B. E., Pierce K., Walmsley S., Beasley F. C., Cummins E., Nizet V., Whyte M., Taylor C. T., Lin H., Masters S. L., Gottlieb E., Kelly V. P., Clish C., Auron P. E., Xavier R. J., O’Neill L. A. J.. **Succinate is an inflammatory signal that induces IL-1β through HIF-1α**. *Nature* (2013.0) **496** 238-242. PMID: 23535595
43. Geng H., Liu Q., Xue C., David L. L., Beer T. M., Thomas G. V., Dai M. S., Qian D. Z.. **HIF1α protein stability is increased by acetylation at lysine 709**. *J. Biol. Chem.* (2012.0) **287** 35496-35505. PMID: 22908229
44. Wenes M., Jaccard A., Wyss T., Maldonado-Pérez N., Teoh S. T., Lepez A., Renaud F., Franco F., Waridel P., Maroun C. Y., Tschumi B., Dumauthioz N., Zhang L., Donda A., Martín F., Migliorini D., Lunt S. Y., Ho P.-C., Romero P.. **The mitochondrial pyruvate carrier regulates memory T cell differentiation and antitumor function**. *Cell. Metab.* (2022.0) **34** 731-746 e739. PMID: 35452600
45. Sefik E., Qu R., Junqueira C., Kaffe E., Mirza H., Zhao J., Brewer J. R., Han A., Steach H. R., Israelow B., Blackburn H. N., Velazquez S. E., Chen Y. G., Halene S., Iwasaki A., Meffre E., Nussenzweig M., Lieberman J., Wilen C. B., Kluger Y., Flavell R. A.. **Inflammasome activation in infected macrophages drives COVID-19 pathology**. *Nature* (2022.0) **606** 585-593. PMID: 35483404
46. Paludan S. R., Mogensen T. H.. **Innate immunological pathways in COVID-19 pathogenesis**. *Sci. Immunol.* (2022.0) **7** eabm5505. PMID: 34995097
47. Jain S., Chaves S. S.. **Obesity and influenza**. *Clin. Infect. Dis.* (2011.0) **53** 422-424. PMID: 21844025
48. Rai P., Chuong C., LeRoith T., Smyth J. W., Panov J., Levi M., Kehn-Hall K., Duggal N. K., Lucarelli J. W.. **Adenovirus transduction to express human ACE2 causes obesity-specific morbidity in mice, impeding studies on the effect of host nutritional status on SARS-CoV-2 pathogenesis**. *Virology* (2021.0) **563** 98-106. PMID: 34509029
49. Huang S., Jiang L., Cheon I. S., Sun J.. **Targeting peroxisome proliferator-activated receptor-gamma decreases host mortality after influenza infection in obese mice**. *Viral Immunol.* (2019.0) **32** 161-169. PMID: 31009317
50. Zhang Y. N., Zhang Z.-R., Zhang H.-Q., Li X.-D., Li J.-Q., Zhang Q.-Y., Liu J., Li Q., Deng C.-L., Shi Z.-L., Yuan Z.-M., Ye H.-Q., Zhang B.. **Increased morbidity of obese mice infected with mouse-adapted SARS-CoV-2**. *Cell Discov* (2021.0) **7** 74. PMID: 34433811
51. Junqueira C., Crespo Â., Ranjbar S., de Lacerda L. B., Lewandrowski M., Ingber J., Parry B., Ravid S., Clark S., Schrimpf M. R., Ho F., Beakes C., Margolin J., Russell N., Kays K., Boucau J., das Adhikari U., Vora S. M., Leger V., Gehrke L., Henderson L. A., Janssen E., Kwon D., Sander C., Abraham J., Goldberg M. B., Wu H., Mehta G., Bell S., Goldfeld A. E., Filbin M. R., Lieberman J.. **FcγR-mediated SARS-CoV-2 infection of monocytes activates inflammation**. *Nature* (2022.0) **606** 576-584. PMID: 35385861
52. Byrne A. J., Mathie S. A., Gregory L. G., Lloyd C. M.. **Pulmonary macrophages: Key players in the innate defence of the airways**. *Thorax* (2015.0) **70** 1189-1196. PMID: 26286722
53. Grant R. A., Morales-Nebreda L., Markov N. S., Swaminathan S., Querrey M., Guzman E. R., Abbott D. A., Donnelly H. K., Donayre A., Goldberg I. A., Klug Z. M., Borkowski N., Lu Z., Kihshen H., Politanska Y., Sichizya L., Kang M., Shilatifard A., Qi C., Lomasney J. W., Argento A. C., Kruser J. M., Malsin E. S., Pickens C. O., Smith S. B., Walter J. M., Pawlowski A. E., Schneider D., Nannapaneni P., Abdala-Valencia H., Bharat A., Gottardi C. J., Budinger G. R. S., Misharin A. V., Singer B. D., Wunderink R. G., Grant R. A., Morales-Nebreda L., Markov N. S., Swaminathan S., Querrey M., Guzman E. R., Abbott D. A., Donnelly H. K., Donayre A., Goldberg I. A., Klug Z. M., Borkowski N., Lu Z., Kihshen H., Politanska Y., Sichizya L., Kang M., Shilatifard A., Qi C., Lomasney J. W., Argento A. C., Kruser J. M., Malsin E. S., Pickens C. O., Smith S. B., Walter J. M., Pawlowski A. E., Schneider D., Nannapaneni P., Abdala-Valencia H., Bharat A., Gottardi C. J., Budinger G. R. S., Misharin A. V., Singer B. D., Wunderink R. G., Wagh A. A., Hauser A. R., Wolfe A. R., Thakrar A., Yeldandi A. V., Wang A. A., Levenson A. R., Joudi A. M., Tran B., Gao C. A., Kurihara C., Schroedl C. J., Horvath C. M., Meza D., Odell D. D., Kamp D. W., Winter D. R., Ozer E. A., Shanes E. D., Bartom E. T., Rendleman E. J., Leibenguth E. M., Wehbe F., Liu G. Y., Gadhvi G. T., Navarro H. T., Sznajder J. I., Dematte J. E., le J., Arnold J. M., du J. C., Coleman J., Bailey J. I., Deters J. S., Fiala J. A., Starren J., Ridge K. M., Secunda K., Aren K., Gates K. L., Todd K., Gradone L. D., Textor L. N., Wolfe L. F., Pesce L. L., Nunes Amaral L. A., Rosenbaum M. L., Kandpal M., Jain M., Sala M. A., Saine M., Carns M., Alexander M. J., Cuttica M. J., Prickett M. H., Khan N. H., Chandel N. S., Soulakis N. D., Rivas O. R., Seed P. C., Reyfman P. A., Go P. D., Sporn P. H. S., Cooper P. R., Tomic R., Patel R., Garza-Castillon R., Kalhan R., Morimoto R. I., Mylvaganam R. J., Kim S. S., Gatesy S. W. M., Thakkar S., Ben Maamar S., Han S. H., Rosenberg S. R., Nozick S., Green S. J., Russell S. R., Poor T. A., Zak T. J., Lombardo T. A., Stoeger T., Shamaly T., Ren Z.. **Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia**. *Nature* (2021.0) **590** 635-641. PMID: 33429418
54. Wong L. R., Perlman S.. **Immune dysregulation and immunopathology induced by SARS-CoV-2 and related coronaviruses—Are we our own worst enemy?**. *Nat. Rev. Immunol.* (2022.0) **22** 47-56. PMID: 34837062
55. Arish M., Qian W., Narasimhan H., Sun J.. **COVID-19 immunopathology: From acute diseases to chronic sequelae**. *J. Med. Virol.* (2022.0) **95** e28122. PMID: 36056655
56. Kelly B., O'Neill L. A.. **Metabolic reprogramming in macrophages and dendritic cells in innate immunity**. *Cell Res.* (2015.0) **25** 771-784. PMID: 26045163
57. van Paassen J., Vos J. S., Hoekstra E. M., Neumann K. M. I., Boot P. C., Arbous S. M.. **Corticosteroid use in COVID-19 patients: A systematic review and meta-analysis on clinical outcomes**. *Crit. Care* (2020.0) **24** 696. PMID: 33317589
58. Liu Y., Xu R., Gu H., Zhang E., Qu J., Cao W., Huang X., Yan H., He J., Cai Z.. **Metabolic reprogramming in macrophage responses**. *Biomark. Res.* (2021.0) **9** 1. PMID: 33407885
59. Mo Y., To K. K.-W., Zhou R., Liu L., Cao T., Huang H., Du Z., Lim C. Y. H., Yim L.-Y., Luk T.-Y., Chan J. M.-C., Chik T. S.-H., Lau D. P.-L., Tsang O. T.-Y., Tam A. R., Hung I. F.-N., Yuen K.-Y., Chen Z.. **Mitochondrial dysfunction associates with acute T lymphocytopenia and impaired functionality in COVID-19 patients**. *Front. Immunol.* (2021.0) **12** 799896. PMID: 35095881
60. Gibellini L., De Biasi S., Paolini A., Borella R., Boraldi F., Mattioli M., Tartaro D. L., Fidanza L., Caro-Maldonado A., Meschiari M., Iadisernia V., Bacca E., Riva G., Cicchetti L., Quaglino D., Guaraldi G., Busani S., Girardis M., Mussini C., Cossarizza A.. **Altered bioenergetics and mitochondrial dysfunction of monocytes in patients with COVID-19 pneumonia**. *EMBO Mol. Med.* (2020.0) **12** e13001. PMID: 33078545
61. Guarnieri J. W., Dybas J. M., Fazelinia H., Kim M. S., Frere J., Zhang Y., Albrecht Y. S., Murdock D. G., Angelin A., Singh L. N., Weiss S. L., Best S. M., Lott M. T., Cope H., Zaksas V., Saravia-Butler A., Meydan C., Foox J., Mozsary C., Kidane Y. H., Priebe W., Emmett M. R., Meller R., Singh U., Bram Y., tenOever B. R., Heise M. T., Moorman N. J., Madden E. A., Taft-Benz S. A., Anderson E. J., Sanders W. A., Dickmander R. J., Baxter V. K., Baylin S. B., Wurtele E. S., Moraes-Vieira P. M., Taylor D., Mason C. E., Schisler J. C., Schwartz R. E., Beheshti A., Wallace D. C.. **Targeted down regulation of core mitochondrial genes during SARS-CoV-2 infection**. *bioRxiv* (2022.0). DOI: 10.1101/2022.02.19.481089
62. Bhowal C., Ghosh S., Ghatak D., De R.. **Pathophysiological involvement of host mitochondria in SARS-CoV-2 infection that causes COVID-19: A comprehensive evidential insight**. *Mol. Cell. Biochem.* (2022.0) 1-19
63. Drucker D. J.. **Diabetes, obesity, metabolism, and SARS-CoV-2 infection: The end of the beginning**. *Cell Metab.* (2021.0) **33** 479-498. PMID: 33529600
64. Samuel S. M., Varghese E., Busselberg D.. **Therapeutic potential of metformin in COVID-19: Reasoning for its protective role**. *Trends Microbiol.* (2021.0) **29** 894-907. PMID: 33785249
65. Xian H., Liu Y., Nilsson A. R., Gatchalian R., Crother T. R., Tourtellotte W. G., Zhang Y., Aleman-Muench G. R., Lewis G., Chen W., Kang S., Luevanos M., Trudler D., Lipton S. A., Soroosh P., Teijaro J., de la Torre J. C., Arditi M., Karin M., Sanchez-Lopez E.. **Metformin inhibition of mitochondrial ATP and DNA synthesis abrogates NLRP3 inflammasome activation and pulmonary inflammation**. *Immunity* (2021.0) **54** 1463-1477.e11. PMID: 34115964
66. Reis G., dos Santos Moreira Silva E. A., Silva D. C. M., Thabane L., Milagres A. C., Ferreira T. S., dos Santos C. V. Q., de Figueiredo Neto A. D., Callegari E. D., Savassi L. C. M., Simplicio M. I. C., Ribeiro L. B., Oliveira R., Harari O., Bailey H., Forrest J. I., Glushchenko A., Sprague S., Kay P. M., Rayner C. R., Ruton H., Guyatt G. H., Mills E. J.. **Effect of early treatment with metformin on risk of emergency care and hospitalization among patients with COVID-19: The TOGETHER randomized platform clinical trial**. *Lancet Reg. Health Am.* (2022.0) **6** 100142. PMID: 34927127
67. Bramante C. T., Huling J. D., Tignanelli C. J., Buse J. B., Liebovitz D. M., Nicklas J. M., Cohen K., Puskarich M. A., Belani H. K., Proper J. L., Siegel L. K., Klatt N. R., Odde D. J., Luke D. G., Anderson B., Karger A. B., Ingraham N. E., Hartman K. M., Rao V., Hagen A. A., Patel B., Fenno S. L., Avula N., Reddy N. V., Erickson S. M., Lindberg S., Fricton R., Lee S., Zaman A., Saveraid H. G., Tordsen W. J., Pullen M. F., Biros M., Sherwood N. E., Thompson J. L., Boulware D. R., Murray T. A.. **Randomized trial of metformin, ivermectin, and fluvoxamine for covid-19**. *New Engl. J. Med.* (2022.0) **387** 599-610. PMID: 36070710
68. Lee J. H.. **Potential therapeutic effect of glucagon-like peptide-1 receptor agonists on COVID-19-induced pulmonary arterial hypertension**. *Med. Hypotheses* (2022.0) **158** 110739. PMID: 34916733
69. Lebovitz H. E.. **Thiazolidinediones: The forgotten diabetes medications**. *Curr. Diab. Rep.* (2019.0) **19** 151. PMID: 31776781
70. Kamm D. R., Pyles K. D., Sharpe M. C., Healy L. N., Colca J. R., McCommis K. S.. **Novel insulin sensitizer MSDC-0602K improves insulinemia and fatty liver disease in mice, alone and in combination with liraglutide**. *J. Biol. Chem.* (2021.0) **296** 100807. PMID: 34022222
71. Subramanian A., Tamayo P., Mootha V. K., Mukherjee S., Ebert B. L., Gillette M. A., Paulovich A., Pomeroy S. L., Golub T. R., Lander E. S., Mesirov J. P.. **Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles**. *Proc. Natl. Acad. Sci. U.S.A.* (2005.0) **102** 15545-15550. PMID: 16199517
72. Yu G., Wang L.-G., Han Y., He Q.-Y.. **clusterProfiler: An R package for comparing biological themes among gene clusters**. *OMICS* (2012.0) **16** 284-287. PMID: 22455463
73. Li C., Zhu B., Son Y. M., Wang Z., Jiang L., Xiang M., Ye Z., Beckermann K. E., Wu Y., Jenkins J. W., Siska P. J., Vincent B. G., Prakash Y. S., Peikert T., Edelson B. T., Taneja R., Kaplan M. H., Rathmell J. C., Dong H., Hitosugi T., Sun J.. **The transcription factor Bhlhe40 programs mitochondrial regulation of resident CD8**. *Immunity* (2019.0) **51** 491-507.e7. PMID: 31533057
|
---
title: Optimal Niacin Requirement of Oriental River Prawn Macrobrachium nipponense
as Determined by Growth, Energy Sensing, and Glycolipid Metabolism
authors:
- Jing-Wen Wang
- Yi-Cheng Che
- Miao Sun
- Yi-Qing Guo
- Bo Liu
- Xiang-Fei Li
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973127
doi: 10.1155/2022/8596427
license: CC BY 4.0
---
# Optimal Niacin Requirement of Oriental River Prawn Macrobrachium nipponense as Determined by Growth, Energy Sensing, and Glycolipid Metabolism
## Abstract
Niacin is indispensable for the growth and development of aquatic animals. However, the correlations between dietary niacin supplementations and the intermediary metabolism of crustaceans are still poorly elucidated. This study explored the effects of different dietary niacin levels on the growth, feed utilization, energy sensing, and glycolipid metabolism of oriental river prawn Macrobrachium nipponense. Prawns were fed with different experimental diets containing graded niacin levels (15.75, 37.62, 56.62, 97.78, 176.32, and 339.28 mg/kg, respectively) for 8 weeks. Weight gain, protein efficiency, feed intake, and hepatopancreas niacin contents all maximized in the 176.32 mg/kg group with significance noted with the control group ($P \leq 0.05$), whereas the opposite was true for feed conversion ratio. Hepatopancreas niacin concentrations increased significantly ($P \leq 0.05$) as dietary niacin levels increased, and peaked at the 339.28 mg/kg group. Hemolymph glucose, total cholesterol, and triglyceride concentrations all maximized in the 37.62 mg/kg group, while total protein concentration reached the highest value in the 176.32 mg/kg group. The hepatopancreas mRNA expression of AMP-activated protein kinase α and sirtuin 1 peaked at the 97.78 and 56.62 mg/kg group, respectively, and then both decreased as dietary niacin levels increased furtherly ($P \leq 0.05$). Hepatopancreas transcriptions of the genes related to glucose transportation, glycolysis, glycogenesis, and lipogenesis all increased with increasing niacin levels up to 176.32 mg/kg, but decreased significantly ($P \leq 0.05$) as dietary niacin levels increased furtherly. However, the transcriptions of the genes related to gluconeogenesis and fatty acid β-oxidation all decreased significantly ($P \leq 0.05$) as dietary niacin levels increased. Collectively, the optimum dietary niacin demand of oriental river prawn is 168.01-169.08 mg/kg. In addition, appropriate doses of niacin promoted the energy-sensing capability and glycolipid metabolism of this species.
## 1. Introduction
Niacin, also called nicotinic acid, vitamin B3, and the anti-leprosy factor, is an indispensable water-soluble vitamin for animals [1]. Being an important component of coenzyme I (NAD+) and II (NADP+), niacin participates in more than 300 redox reactions in vivo [2], especially in the transfer of both H+ and electron in the redox metabolism of carbohydrates, lipids, and proteins, thus playing crucial roles in energy metabolism and biosynthesis pathway [3]. Indeed, it has been reported that niacin can regulate the lipid metabolism of animals through various hormones and regulatory factors like the G protein-coupled receptor, diacylglycerol acyltransferase, silent information regulator 1 (SIRT1), and adenosine monophosphate-activated protein kinase (AMPK) [4]. In addition, niacin can govern the glucose metabolism by targeting SIRT1 and AMPK, which can consequently mediate the pathways of glycolysis, glycogenesis, gluconeogenesis, and so on [5]. However, unlike the case in mammals, aquatic animals lack the ability to convert tryptophan to niacin [6], thus often exhibiting niacin deficiency symptoms like anorexia, poor feed utilization, growth retardation, and increased mortality when fed niacin-deficient diets [7]. To date, the optimal niacin requirement has been evaluated in many fish species, like 28 mg/kg for carp Cyprinus carpio [8], 26 mg/kg for tilapia *Oreochromis mossambicus* [9], 7.4 mg/kg for channel catfish *Ictalurus punctatus* [10, 11], 33 mg/kg for rohu *Labeo rohita* and mrigal *Cirrhinus mrigala* [12], 30.62-31.25 mg/kg for blunt snout bream *Megalobrama amblycephala* [13], and 63-83 mg/kg for gilthead seabream *Sparus aurata* [14]. Unlikely, the optimal niacin requirement has been investigated only in several shrimp and prawn species, like 7.2 mg/kg for grass shrimp Penaeus monodon [15], 109.6 mg/kg for Pacific white shrimp Litopenaeus vannamei [16], 250 mg/kg for Indian white prawn *Penaeus indicus* [1], and 400 mg/kg for kuruma prawn *Penaeus japonicus* [17]. However, previous literatures generally focused on growth and feed utilization. The correlations between dietary niacin supplementation and the intermediary metabolism of aquatic species are still poorly elucidated.
The intermediary metabolism is controlled by a complex network composed of various nutrient sensors, among which AMPKα and SIRT1 have both attracted considerable attention [18]. The AMPKα-SIRT1 pathway is widely acknowledged as an energy sensing network, which governs the cellular energy balance [19]. Once activated, AMPKα could depress hepatic gluconeogenesis and lipogenesis, while stimulate the uptake of glucose and the β-oxidation of fatty acids, thus leading to the enhanced energy consumption [20]. These metabolic adjustments are also facilitated by SIRT1, since it and AMPKα can activate each other to form a positive feedback loop that precisely regulates the metabolic homeostasis [18]. Until now, the correlations between niacin and the energy sensing and glucose/lipid metabolism of aquatic animals are still poorly elucidated. To date, only one literature is accessible indicating that dietary nicotinamide supplementation benefits the glycolipid metabolism and glucose homeostasis of blunt snout bream offered a carbohydrate-enriched diet by mediating SIRT1 and its coactivators [21]. Nevertheless, the potential roles of niacin in the energy sensing and intermediary metabolism of crustaceans are still barely understood.
As an economically crucial freshwater prawn species, oriental river prawn Macrobrachium nipponense has been widely cultured in China. Due to the high protein and amino acid content and delicious flavor, aquaculture of this species has gained considerable attention in the east region of China [22]. Currently, the culture cost of this species is relatively high mainly due to the lack of high-efficiency artificial feed. This underscores the urgency of investigating the nutrients' demand of this species. Nowadays, the optimal requirements of macro-nutrients have been well characterized in oriental river prawn [23–28]. However, the vitamin requirement of this species has still been barely evaluated, which brings great difficulties to the development of nutritionally balanced diets for this species. Considering the important roles of niacin in energy metabolism, this study aimed to evaluate the optimal niacin requirements of oriental river prawn in terms of growth performance and feed utilization. Besides, the correlations between niacin doses and energy sensing and glucose/lipid metabolism were also elucidated. The present results could facilitate the development of high-efficiency diets for this species, as could lower the farming cost.
## 2.1. Experimental Diets
Firstly, a semi-purified basal diet was prepared using casein, gelatin, and fish meal as protein sources, fish oil and soybean oil as lipid sources, and corn starch as the sole carbohydrate source. The premix contained all the vitamins and minerals required by prawn except for niacin. Then, a total of 6 experimental diets (Table 1) were prepared by supplementing graded doses of niacin (0, 20, 40, 80, 160, and 320 mg/kg) to the basal diet, respectively. The actual dietary niacin concentrations were 15.75, 37.62, 56.62, 97.78, 176.32, and 339.28 mg/kg diet, respectively, quantified by the high-performance liquid chromatography (HPLC) method [29].
All raw materials were crushed through a 60-mm mesh, and were weighted according to the feed formula. Then, all the protein sources were mixed thoroughly with corn starch. The premix and niacin were both mixed by the gradual enlargement method to improve the efficiency of mixing. Other powder ingredients were then added for further mixing. After that, both soybean oil and fish oil were supplemented for a thorough mix. Last a certain proportion ($15\%$ of feed weight) of water was supplemented to turn the feed powder into dough, which was then pelleted by a double screw extrusion machine. The feed was then broken into a particle size of 1.0 mm, dried, and stored at -20°C for subsequent use.
## 2.2. Feeding Experiment
Healthy M. nipponense (Taihu No.1) were purchased from an experimental hatch farm near Tai Lake (Jiangsu province, China). Prawns were adapted to the culture conditions for 2 weeks by feeding the control diet in several indoor tanks. After that, 720 prawns (average weight: 0.18 g) were collected and divided into 24 indoor tanks (30 prawns per tank) randomly with 4 replicates for each group. Prawns were fed at 8:00, 12:00, and 16:00 h each day for 8 weeks. After 2 h of feeding, a 45.5 cm long siphon drain was used to clear the uneaten feed and feces with the feed intake recorded. Dead prawns were also collected and weighted. During the feeding trial, several artificial water grasses were placed in the tanks for shielding. The water temperature and dissolved oxygen were maintained at 26.0 ± 0.5°C and above 5 mg/L, respectively. In addition, $\frac{1}{3}$ of water was replaced every two days to meet the basic demand of prawns for water quality.
## 2.3. Sampling
Prawns were subjected to a 24 h fast to empty the intestinal content when the feeding trial terminated. Then, the number and weight of prawns within each tank were recorded. Six prawns were randomly selected from each tank for hemolymph and hepatopancreas. Briefly, 1 mL aseptic syringe was used to extract hemolymph from the pericardial sinus of the posterior edge of the breastplate. Then, samples were placed into Eppendorf tubes and subjected to a centrifugation (3,500 rpm at 4°C for 10 min) with the supernatant kept in the refrigerator at -80°C. Thereafter, the hepatopancreas was separated and subjected to liquid nitrogen frozen followed by preservation at -80°C.
## 2.4.1. Growth Performance Determination
The calculation formulas of the growth performance parameters were presented as follows: [1]Weight gainWG,%=W2−W1/W1∗100,Specific growth rate SGR,%/d=ln W2−lnW1/T∗100,Survival ratioSR,%=Nt/N0∗100,Feed conversion ratioFCR=W3/W2−W1,Protein efficiency ratioPER=W2−W1/W3∗CP∗100,where W1 and W2 is the initial and final body weight, respectively, W3 is total feed intake, *Nt is* final prawn number, N0 is initial prawn number, T is the culturing days, and CP is the protein content in the diets.
## 2.4.2. Determination of Proximate Composition and Niacin Contents
The proximate composition of the experimental diets was determined by the following methods: moisture by oven drying at 105°C for a constant weight; crude protein (N ×6.25) by the Kjeldahl System; crude lipid by ether extraction; and ash by combustion in a muffle furnace (550°C for 5 h).
The HPLC method was adopted to quantify the hepatopancreas and dietary niacin contents [29]. In short, approximately 0.5 g of hepatopancreas or diets was homogenized with hydrochloric acid (3 mL, 0.01 mol/L) followed by the protein precipitation using trichloroacetic acid (1 mL, $10\%$). The mixture was then subjected to a 10,260 rpm centrifugation for 10 min at 4°C. The supernatant was purified with ether for three times. Then, the ether was vaporized, followed by a filtration using a 0.45 μm membrane. The supernatant was then analyzed by a HPLC device (HP1100, America). The sample was derivatized with a mixture consisted of potassium ferricyanide ($0.1\%$) and sodium hydroxide ($15\%$). The mobile phase was performed using a mixture containing potassium hydrogen phosphate (25 mmol/L) and methanol (v:v, 85: 15) under a 1.0 mL/min flow rate. Then, a fluorescence detector (excitation wave length: 360 nm, emission wave length: 425 nm) was used to quantify the niacin content.
## 2.4.3. Hemolymph Metabolites Detection
Hemolymph total protein (TP), glucose (GLU), triglyceride (TG), and total cholesterol (TC) concentrations were all quantified by the OLYMPUSAU600 biochemical analyzer using the bicinchoninic acid assay [30], the glucose oxidase method [31], and the glycerol-3-phosphate oxidase p-aminophenol method (for TG) and the cholesterol oxidase-peroxidase coupling method (for TC) [32], respectively.
## 2.4.4. Transcriptional Analysis
Real-time fluorescence quantitative polymerase chain reaction (PCR) was adopted to determine the relative transcriptions of the genes involved in energy sensing and glycolipid metabolism in the hepatopancreas of prawns. Briefly, the total RNA of hepatopancreas was extracted by the Trizol reagent. After verifying its purity, the synthesis of cDNA was conducted using a Prime Script™ RT-PCR kit, and was amplified by a SYBR® Premix Ex Taq ™ II kit. Primers were designed according to the available cDNA sequences of this species from previous literatures and Genbank (Table 2), and were synthesized later. The reaction system (22.5 μL) included 2 μL of cDNA template, 10 μL of 2 × QuantiNova SYBR® Green PCR Master mix, 0.5 μL of upstream and downstream primers, respectively, and 9.5 μL of ddH2O. Briefly, the PCR reaction conditions were followed by a total of 40 cycles: pre-denaturation at 95°C for 1 min, denaturation at 95°C for 10 s, and extension at 60°C for 15 s. Then, the 2-ΔΔCt method [33] was adopted to determine the relative transcriptions of genes using β-actin as the internal reference [34].
## 2.5. Statistics
Data were tested for homogeneity (the Shapiro-Wilk test) and normality (Levene's test) before the conduction of one-way analysis of variance (ANOVA). Then, the means were ranked by Turkey's multiple range test if significance ($P \leq 0.05$) was noted. The orthogonal polynomial contrasts were also adopted to evaluate the types (namely, linear, quadratic, or cubic) of significance [35] with the data shown as means ± S.E.M. Statistical significance was set as $P \leq 0.05$, and the extreme significance was set as $P \leq 0.001.$ In addition, the broken-line regression analysis (between weight gain and hepatopancreas niacin contents against dietary niacin levels, respectively) was also adopted to quantify the optimum niacin demand of oriental river prawn.
## 3.1. Growth and Feed Utilization
There was no significance noted in SR ($P \leq 0.05$) among all the treatments (Table 3). However, WG, SGR, feed intake (FI), FCR, and PER were all significantly ($P \leq 0.05$) affected in a linear pattern. Besides, FCR was also significantly ($P \leq 0.05$) affected in a quadratic pattern. Significantly ($P \leq 0.05$) high values of WG, SGR, FI, and PER were noted in the 176.32 mg/kg group compared with the control group, while the opposite was true for FCR. The optimum dietary niacin demand of oriental river prawn was 169.08 mg/kg (Figure 1) according to the broken-line regression analysis of WG against dietary niacin levels.
## 3.2. Hepatopancreas Niacin Contents and Hemolymph Metabolites Concentrations
Hepatopancreas niacin content was significantly ($P \leq 0.001$) affected in a linear pattern (Table 4). It increased significantly ($P \leq 0.05$) with increasing niacin levels up to 176.32 mg/kg, then plateaued as niacin levels increased furtherly ($P \leq 0.05$). The optimum dietary niacin demand of oriental river prawn was 168.01 mg/kg (Figure 2) according to the broken-line regression analysis of hepatopancreas niacin contents against dietary niacin levels. Additionally, the GLU, TP, and TG concentrations were all significantly ($P \leq 0.05$) affected in a linear pattern, while a significant ($P \leq 0.05$) linear and cubic pattern was observed in the TC concentration. The GLU concentration of the 37.62 mg/kg group was significantly ($P \leq 0.05$) higher than those of the 176.32 and 339.28 mg/kg groups, but showed no statistical difference ($P \leq 0.05$) with those of the rest groups. The TG concentration of the 37.62 mg/kg group was significantly ($P \leq 0.05$) higher than those of the 56.62 and 339.28 mg/kg groups, but showed no statistical difference ($P \leq 0.05$) with those of the other groups. A significantly ($P \leq 0.05$) high value of TP concentration was noted in the 176.32 mg/kg group compared with the 15.75 mg/kg group. The TC concentration of the 37.62 mg/kg group was significantly ($P \leq 0.05$) higher than those of the other groups except for the 56.62 mg/kg group.
## 3.3. The Transcriptions of Energy Sensing-Related Genes
The transcription of ampkα was significantly ($P \leq 0.001$) affected in a cubic pattern, while a significant ($P \leq 0.05$) linear, quadratic, and cubic pattern was noted in that of sirt1 (Figure 3). The ampkα transcription of the 56.62 mg/kg group was significantly ($P \leq 0.05$) higher than that of the 339.28 mg/kg group, but showed no statistical difference ($P \leq 0.05$) with those of the rest groups. The sirt1 transcription of the 37.62 mg/kg group was significantly ($P \leq 0.05$) lower than that of the 97.78 mg/kg group, but showed no statistical difference ($P \leq 0.05$) with those of the other groups.
## 3.4. Relative Transcriptions of the Genes Related to Glycolipid Metabolism
The transcriptions of glut2, hk, pk, gs, and fas were all significantly ($P \leq 0.05$) affected in a quadratic and cubic pattern, while a significant ($P \leq 0.001$) linear pattern was noted in those of pc, pepck, g6p, and hsl (Figures 4 and 5). Besides, the transcriptions of acc and cptI were both significantly ($P \leq 0.05$) affected in a cubic pattern. The hepatopancreas transcriptions of glut2, hk, gs, pk, and fas all increased with increasing niacin levels up to 176.32 mg/kg, but decreased significantly ($P \leq 0.05$) as dietary niacin levels increased furtherly. A similar result was also noted in the transcription of acc, which maximized in the 97.78 mg/kg group. However, the transcriptions of pc, pepck, g6p, cptI, and hsl all decreased significantly ($P \leq 0.05$) as dietary niacin levels increased.
## 4. Discussion
As a vital vitamin, niacin is crucial for the normal growth of aquatic animals. An inadequate intake of this nutrient inevitably induces a series of deficiency symptoms in aquatic organisms. The WG, SGR, and PER of oriental river prawn in this study all improved remarkably as dietary niacin levels increased, and all maximized in the 176.32 mg/kg group. However, the opposite was true for FCR. Supportively, similar results were also reported in other aquatic species like grass shrimp and Indian carps (namely, C. mrigala and L. rohita) [15, 36]. According to a previous literature, niacin is essential for the metabolism of macro-nutrients, and its supplementation at suitable doses could improve the feed utilization and growth performance of prawns [1]. The optimum niacin demand of oriental river prawn is 169.08 mg/kg as determined by the regression analysis of WG against dietary niacin levels. The higher niacin requirement of prawn, compared with most fish species, was justifiable. Generally, unlike fish, crustaceans eat slowly. The feed particles will be immersed in water for a long time before being eaten, thus resulting in the increased dissolution rate of water-soluble vitamins. In addition, the leaking of water-soluble vitamins would be further accelerated by the pellet-eating process of crustaceans [3]. This inevitably leads to the higher niacin demand of crustaceans compared with fish. A growth retardation coupled with the poor survival rate and feed efficiency of oriental river prawn was noted in the control group in this study, but no other obvious deficiency symptoms were noticed, as was similar to that reported in grass shrimp [15]. In addition, high doses of niacin (namely, 339.28 mg/kg) also resulted in the retarded growth of oriental river prawn coupled with the poor feed efficiency, although no significance was observed. Supportively, an excessive intake of niacin generally resulted in an inhibition of hematopoietic and immune functions in aquatic animals [13], as would consequently result in the poor growth performance of prawn. However, further validations are needed.
Currently, hepatopancreas niacin contents are commonly determined to evaluate the optimum niacin demand of aquatic species apart from growth performance and nutrients utilization [13, 37]. The hepatopancreas niacin contents of oriental river prawn in this study generally increased as dietary niacin levels increased from 15.75 to 176.32 mg/kg, but plateaued with furthering increasing niacin doses. This indicated a saturation of niacin deposition in the hepatopancreas of prawn offered a high dose of niacin (namely, 339.28 mg/kg). Supportively, supra-optimal dietary niacin levels also resulted in a saturated hepatopancreas niacin content in the Indian white prawn coupled with the retarded growth and low survival rate [1].
Niacin is also closely involved in the metabolism of macro-nutrients in aquatic animals [38]. Accordingly, the effects of niacin on the hemolymph metabolites concentrations of oriental river prawn were investigated in this study. The concentrations of glucose, protein, and total cholesterol all increased first then decreased with increasing dietary niacin levels, while that of triglyceride generally decreased. It has been demonstrated in human medicine that niacin has dual functions. For example, it can prevent pellagra at low doses (0.3 mg/kg per day), but could largely affect the lipid metabolism after a high intake (7-60 mg/kg per day) [39]. Similarly, diets supplemented with a certain amount of niacin also remarkably increased the serum total cholesterol content of adult *Gift tilapia* (Oreochromis niloticus), but decreased the triglyceride content [40]. Generally, niacin can be converted into nicotinoylglycine assisted by coenzyme A, thus preventing stem cells from using coenzyme A to synthesize cholesterol [41]. This would undoubtedly lead to the decreased cholesterol content with the increasing niacin supplementations. In addition, the increased glucose and protein concentrations might be indicative of an enhanced carbohydrate and protein utilization by oriental river prawn fed appropriate niacin levels. However, the underlying mechanisms are still unknown. Further studies investigating the effects of niacin on the digestive and absorptive functions of prawn are needed.
In the present study, dietary supplementation of niacin at 56.62 and 97.78 mg/kg up-regulated the transcriptions of ampkα and sirt1, respectively, suggesting that niacin could promote the energy sensing capability of oriental river prawn. Supportively, as an evolutionary conserved serine/threonine protein kinase, AMPKα governs the cellular energy homeostasis, and is generally regarded as a key energy sensor [42]. In addition, as a NAD+-dependent histone deacetylase, SIRT1 also plays important regulatory roles in energy metabolism [19]. Both AMPKα and SIRT1 can activate each other to form a positive feedback loop, thus governing intracellular energy metabolism [43]. Indeed, niacin supplementation has been demonstrated to increase the cellular production of NAD+, which can consequently activate SIRT1 in fish [21]. Then, SIRT1 can deacetylate liver kinase B1 (LKB1) at Lys48, thus triggering its activation. Being an upstream kinase, LKB1 can in turn activate AMPKα by phosphorylating Thr172 in the catalytic subunit of AMPKα [44], thus mediating intermediary metabolism. However, further studies at the translational and post-translational levels are needed to validate this.
Once activated, both AMPKα and SIRT1 could increase the energy expenditure by adjusting glycolipid metabolism, thus maintaining energy homeostasis [19]. In this study, high doses of niacin (176.32 mg/kg) remarkably up-regulated the expressions of glut2, pk, hk, and gs in the hepatopancreas of oriental river prawn at the transcriptional level, but decreased those of pepck, pc, and g6p. These results indicated that niacin could enhance the glucose transport, glycolysis, and glycogen synthesis of this species when supplemented at appropriate doses, but depress the gluconeogenesis pathway. It is widely acknowledged that [1] GLUT2 is a bidirectional glucose transporter, ensuring the movement of glucose into and out of hepatocytes [45]; [2] both PK and HK govern the glycolysis pathway, while PEPCK is the rate-limiting enzyme of the gluconeogenesis pathway, which also involves PC and G6Pase [46]; [3] and GS governs the glycogenesis pathway as a rate-limiting enzyme [47]. This was in line with the results noted in the hemolymph glucose concentrations of oriental river prawn in this study. In addition, appropriate doses of niacin also remarkably up-regulated the expressions of both fas and acc at the transcriptional level, but inhibited those of both cptI and hsl, suggesting that niacin supplementation could promote fatty acids synthesis in oriental river prawn, while inhibit lipid mobilization. Supportively, both ACC and FAS play crucial roles in the biosynthesis of fatty acids, while both CPTI and HSL are closely involved in the β-oxidation of fatty acids [47]. This result was consistent with those noted in the hemolymph TG and TC concentrations, which showed extremely low values in the control group. Based on these results, it can be speculated that niacin might stimulate the glucose uptake by hepatocytes from hemolymph in oriental river prawn assisted by GLUT2 [21]. Then, the increased glucose flux could up-regulate the glycolysis pathway, as consequently enhance glycogenesis, while suppress gluconeogenesis [48]. Meanwhile, the increased glucose uptake could enhance fatty acids synthesis, while depress the β-oxidation of fatty acids [49]. However, the processes mentioned above were all inhibited by the highest niacin doses (339.28 mg/kg), suggesting that excessive niacin intake might result in the disorders of glycolipid metabolism of oriental river prawn, as needs further validations.
## 5. Conclusion
Collectively, the present study demonstrated that dietary supplementation of niacin enhanced the growth performance and nutrients utilization of oriental river prawn. In addition, appropriate doses of niacin promoted the energy-sensing capability and glycolipid metabolism of this species. The optimum niacin demand of oriental river prawn was 168.01-169.08 mg/kg based on the regression analysis of weight gain and hepatopancreas niacin content.
## Data Availability
The data sets generated and/or analyzed during the current study are available from the corresponding author on request.
## Ethical Approval
This study was approved by the Animal Care and Use Committee of Nanjing Agricultural University (Nanjing, China) (permit number: SYXK (Su) 2011–0036).
## Conflicts of Interest
The authors declare that they have no conflict of interest.
## Authors' Contributions
Jing-Wen Wang was in charge of feeding trials, data analysis, and writing the article. Yi-Cheng Che was in charge of data analysis and writing the article. Miao Sun was in charge of the purchase of reagents and other operation assistance, and the guidance of analysis tools. Yi-Qing Guo was in charge of data analysis assistance. Bo Liu, who is the host of this project, was in charge of experimental guidance and provided funding. Xiang-Fei Li, who is the main participant of this project, contributed significantly to the conception and design of the experiment, and provided writing advice.
## References
1. Boonyaratpalin M.. **Nutrition of**. (1998) **6** 69-78. DOI: 10.1080/10641269891314203
2. Foster J. W., Park Y., Penfound T., Fenger T., Spector M. P.. **Regulation of NAD metabolism in Salmonella typhimurium: molecular sequence analysis of the bifunctional nadR regulator and the nadA-pnuC operon**. (1990) **172** 4187-4196. DOI: 10.1128/jb.172.8.4187-4196.1990
3. 3National Research Council (NRC)
Nutrient Requirements of Fish and Shrimp
2011WashingtonNAP. (2011)
4. Long F., Zhai M. R., Zou B. C., Chen C. B., Xu L. J.. **Effect of niacin on lopid metabolism and its mechanisms of action**. (2022) **3** 134-139
5. Lage R., Diéguez C., Vidal-Puig A., López M.. **AMPK: a metabolic gauge regulating whole-body energy homeostasis**. (2008) **14** 539-549. DOI: 10.1016/j.molmed.2008.09.007
6. Mohamed J. S., Ibrahim A.. **Quantifying the dietary niacin requirement of the**. (2001) **32** 157-162. DOI: 10.1046/j.1365-2109.2001.00530.x
7. Woodward B.. **Riboflavin requirement for growth, tissue saturation and maximal flavin-dependent enzyme activity in young rainbow trout (**. (1985) **115** 78-84. DOI: 10.1093/jn/115.1.78
8. Aoe H., Masuda I., Takada T.. **Water-soluble vitamin requirements of Carp-III**. (1967) **33** 681-685. DOI: 10.2331/suisan.33.681
9. Shiau S. Y., Suen G. S.. **Estimation of the niacin requirements for tilapia fed diets containing glucose or dextrin**. (1992) **122** 2030-2036. DOI: 10.1093/jn/122.10.2030
10. Ng W. K., Keembiyehetty C. N., Wilson R. P.. **Bioavailability of niacin from feed ingredients commonly used in feeds for channel catfish,**. (1998) **161** 393-404. DOI: 10.1016/S0044-8486(97)00287-1
11. Ng W. K., Serrini G., Zhang Z., Wilson R. P.. **Niacin requirement and inability of tryptophan to act as a precursor of NAD**. (1997) **152** 273-285. DOI: 10.1016/S0044-8486(96)01510-4
12. Abidi S. F., Khan M. A.. **Dietary tryptophan requirement of fingerling rohu,**. (2010) **41** 700-709. DOI: 10.1111/j.1749-7345.2010.00412.x
13. Li X. F., Wang T. J., Qian Y., Jiang G. Z., Zhang D. D., Liu W. B.. **Dietary niacin requirement of juvenile blunt snout bream Megalobrama amblycephala based on a dose–response study**. (2017) **23** 1410-1417. DOI: 10.1111/anu.12516
14. Morris P. C., Davies S. J.. **The requirement of the gilthead seabream (**. (1995) **61** 437-443. DOI: 10.1017/S1357729800013989
15. Shiau S. Y., Suen G. S.. **The dietary requirement of juvenile grass shrimp (**. (1994) **125** 139-145. DOI: 10.1016/0044-8486(94)90290-9
16. Xia M. H., Huang X. L., Wang H. L., Jin M., Li M., Zhou Q. C.. **Dietary niacin levels in practical diets for**. (2015) **21** 853-860. DOI: 10.1111/anu.12210
17. 17National Research Council (NRC)
Nutrient Requirement of Warmwater Fishes
1983WashingtonNAP. (1983)
18. Rodgers J. T., Lerin C., Haas W., Gygi S. P., Spiegelman B. M., Puigserver P.. **Nutrient control of glucose homeostasis through a complex of PGC-1**. (2005) **434** 113-118. DOI: 10.1038/nature03354
19. Cantó C., Auwerx J.. **PGC-1alpha, SIRT1 and AMPK, an energy sensing network that controls energy expenditure**. (2009) **20** 98-105. DOI: 10.1097/MOL.0b013e328328d0a4
20. Viollet B., Lantier L., Devin-Leclerc J.. **Targeting the AMPK pathway for the treatment of type 2 diabetes**. (2009) **14** 3380-3400. DOI: 10.2741/3460
21. Shi H. J., Li X. F., Xu C.. **Nicotinamide improves the growth performance, intermediary metabolism and glucose homeostasis of blunt snout bream**. (2020) **26** 1311-1328. DOI: 10.1111/anu.13088
22. Fu H., Jiang S., Xiong Y.. **Current status and prospects of farming the giant river prawn (**. (2012) **43** 993-998. DOI: 10.1111/j.1365-2109.2011.03085.x
23. Ding Z. L., Chen L. Q., Du Z. Y., Jiang H. B., Sun S. M., Li E. C.. **A mixture of fish oil and soybean oil as a dietary lipid source prevents precocity and promotes growth in juvenile**. (2014) **45** 1567-1572. DOI: 10.1111/are.12088
24. Ding Z. L., Kong Y. Q., Li J. F.. **Growth and metabolic responses of juvenile**. (2017) **23** 1136-1144. DOI: 10.1111/anu.12482
25. Ding Z. L., Kong Y. Q., Qi C. L., Liu Y., Zhang Y. X., Ye J. Y.. **The alleviative effects of taurine supplementation on growth, antioxidant enzyme activities, hepatopancreas morphology and mRNA expression of heat shock proteins in freshwater prawn**. (2021) **27** 2195-2204. DOI: 10.1111/anu.13354
26. Sun C. X., Shan F., Liu M. Y.. **High-fat-diet-induced oxidative stress in giant freshwater prawn (**. (2022) **11** p. 228. DOI: 10.3390/antiox11020228
27. Sun M., Li X. F., Ge Y. P., Zhang L., Liu B., Liu W. B.. **Dietary thiamine requirement and its effects on glycolipid metabolism in oriental river prawn (**. (2022) **550, article 737824**. DOI: 10.1016/j.aquaculture.2021.737824
28. Wang W. L., Li L. Q., Huang X. X., Zhu Y. M., Kuang X. X., Yi G. F.. **Effects of dietary protein levels on the growth, digestive enzyme activity and fecundity in the oriental river prawn,**. (2022) **53** 2886-2894. DOI: 10.1111/are.15803
29. Sander S., Hahn A., Stein J., Rehner G.. **Comparative studies on the high-performance liquid chromatographic determination of thiamine and its phosphate esters with chloroethylthiamine as an internal standard using pre- and post-column derivatization procedures**. (1991) **558** 115-124. DOI: 10.1016/0021-9673(91)80116-X
30. Smith P. K., Krohn R. I., Hermanson G. T.. **Measurement of protein using bicinchoninic acid**. (1985) **150** 76-85. DOI: 10.1016/0003-2697(85)90442-7
31. Asadi F., Hallajian A., Asadian P., Shahriari A., Pourkabir M.. **Serum lipid, free fatty acid, and proteins in juvenile sturgeons: Acipenser persicus and Acipenser stellatus**. (2009) **18** 287-289. DOI: 10.1007/s00580-008-0797-0
32. Mcnamara J. R., Schaefer E. J.. **Automated enzymatic standardized lipid analyses for plasma and lipoprotein fractions**. (1987) **166** 1-8. DOI: 10.1016/0009-8981(87)90188-4
33. Livak K. J., Schmittgen T. D.. **Analysis of relative gene expression data using real-time quantitative PCR and the 2**. (2001) **25** 402-408. DOI: 10.1006/meth.2001.1262
34. Luo N., Ding Z. L., Kong Y. Q.. **An evaluation of increasing linolenic acid level in the diet of**. (2018) **24** 758-767. DOI: 10.1111/anu.12604
35. Poppi D. A., Moore S. S., Glencross B. D.. **Redefining the requirement for total sulfur amino acids in the diet of barramundi (**. (2017) **471** 213-222. DOI: 10.1016/j.aquaculture.2017.01.009
36. Ahmed I.. **Effect of dietary niacin on growth and body composition of two Indian major carps rohu,**. (2010) **19** 567-584. DOI: 10.1007/s10499-010-9373-0
37. Zehra S., Khan M. A.. **Dietary niacin requirement of fingerling**. (2018) **34** 929-936. DOI: 10.1111/jai.13604
38. Luo D., Gao Y. F., Lu Y. Y.. **Niacin alters the ruminal microbial composition of cattle under high- concentrate condition**. (2017) **3** 180-185. DOI: 10.1016/j.aninu.2017.04.005
39. Zak A., Zeman M., Vecka M., Tvrzicka E.. **Nicotinic acid: an unjustly neglected remedy**. (2006) **145** 825-831. PMID: 17168412
40. Jiang M., Huang F., Wen H.. **Dietary niacin requirement of gift tilapia,**. (2014) **45** 333-341. DOI: 10.1111/jwas.12119
41. Wang T. J.. **Study on nicotinic acid requirement and anti-stress regulation ability of juvenile blunt snout bream (**. (2014)
42. Foretz M., Viollet B.. **Regulation of hepatic metabolism by AMPK**. (2011) **54** 827-829. DOI: 10.1016/j.jhep.2010.09.014
43. Price N. L., Gomes A. P., Ling A. J. Y.. **SIRT1 is required for AMPK activation and the beneficial effects of resveratrol on mitochondrial function**. (2012) **15** 675-690. DOI: 10.1016/j.cmet.2012.04.003
44. Kuno A., Horio Y.. **SIRT1: a novel target for the treatment of muscular dystrophies**. (2016) **2016** 11. DOI: 10.1155/2016/6714686
45. Narasimhan A., Chinnaiyan M., Karundevi B.. **Ferulic acid regulates hepatic GLUT2 gene expression in high fat and fructose- induced type-2 diabetic adult male rat**. (2015) **761** 391-397. DOI: 10.1016/j.ejphar.2015.04.043
46. Beale E. G., Harvey B. J., Forest C.. **PCK1 and PCK2 as candidate diabetes and obesity genes**. (2007) **48** 89-95. DOI: 10.1007/s12013-007-0025-6
47. Enes P., Panserat S., Kaushik S., Oliva-Teles A.. **Nutritional regulation of hepatic glucose metabolism in fish**. (2009) **35** 519-539. DOI: 10.1007/s10695-008-9259-5
48. Hemre G. I., Mommsen T. P., Krogdahl A.. **Carbohydrates in fish nutrition: effects on growth, glucose metabolism and hepatic enzymes**. (2002) **8** 175-194. DOI: 10.1046/j.1365-2095.2002.00200.x
49. Kamalam B. S., Medale F., Panserat S.. **Utilisation of dietary carbohydrates in farmed fishes: new insights on influencing factors, biological limitations and future strategies**. (2017) **467** 3-27. DOI: 10.1016/j.aquaculture.2016.02.007
50. Sun S. M., Xuan F. J., Fu H. T., Zhu J., Ge X. P., Gu Z. M.. **Transciptomic and histological analysis of hepatopancreas, muscle and gill tissues of oriental river prawn (Macrobrachium nipponense) in response to chronic hypoxia**. (2015) **16** p. 491. DOI: 10.1186/s12864-015-1701-3
|
---
title: Effect of Dietary Administration of Green Macroalgae (Ulva intestinalis) on
Mucosal and Systemic Immune Parameters, Antioxidant Defence, and Related Gene Expression
in Zebrafish (Danio rerio)
authors:
- Elaheh Rouhani
- Roghieh Safari
- Mohammad Reza Imanpour
- Seyed Hossein Hoseinifar
- Metin Yazici
- Ehab El-Haroun
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973132
doi: 10.1155/2022/7693468
license: CC BY 4.0
---
# Effect of Dietary Administration of Green Macroalgae (Ulva intestinalis) on Mucosal and Systemic Immune Parameters, Antioxidant Defence, and Related Gene Expression in Zebrafish (Danio rerio)
## Abstract
This study investigated the effects of adding green macroalgae gutweed (Ulva intestinalis) powder to zebrafish (Danio rerio) feed at different levels on innate immune responses, antioxidant defence, and gene expression. A total of 600 zebrafish (0.3 ± 0.08 g) were randomly allocated to 12 aquariums in four treatments with three replicates (50 fish per aquarium). Zebrafish were fed with different levels of U. intestinalis powder 0, 0.25, 0.5, and $1\%$ for eight weeks. Whole-body extract (WBE) immune parameters including total protein level, globulin level, and lysozyme activity were evaluated and revealed statistically significant increased in all U. intestinalis supplemented groups compared to the control ($P \leq 0.05$). However, mucus immune parameters (total protein, globulin, and lysozyme) were statistically different in only $1\%$ gutweed supplemented groups from other groups. While glutathione peroxidase (GPx) and superoxide dismutase (SOD) increased with the addition of gutweed ($P \leq 0.05$), catalase (CAT) did not change ($P \leq 0.05$). The study results showed that dietary gutweed remarkably upregulated immune-related genes such as lysozyme (Lyz) and Interleukin 1 beta (IL-1β). Antioxidant-related genes (SOD and CAT) and growth-related genes, including growth hormone (GH) and insulin-like growth factor-I (IGF-1), were remarkably upregulated with gutweed treatment ($P \leq 0.05$). In conclusion, dietary U. intestinalis showed beneficial effects on immunity, and same effects were observed in case of antioxidant and growth related genes expression in zebrafish.
## 1. Introduction
Studies on the determination of alternative feed additives for the aquaculture sector are increasing. The probiotics [1], prebiotics, synbiotics, organic acid [2], and medicinal herb [3] are among the alternative feed additives which showed beneficial effects in aquaculture [4, 5]. Seaweed or seaweed-derived ingredients recieved increasing attention as alternative feed additives in fish feed formulations [6, 7], as they are highly nutritious [8] and have exceptionally versatile properties [9]. Seaweeds (macroalgae) have widely been used as functional feed additives to improve fish growth [10, 11], nutrient utilisation, stress response [11], fish health, and disease resistance [12] as well as skin pigmentation in ornamental fish [11].
Recently, macroalgae and their extracts have received worldwide attention due to their bioactive compounds [10, 13], including polysaccharides [14], proteins, polyunsaturated fatty acids [15], pigments, polyphenols, minerals, vitamins, and plant growth hormones [16]. However, seaweed components may vary considerably depending on the species [17], time of harvesting, habitat, and external conditions including water temperature, nutrient concentration, and light intensity in the water [9].
The green macroalgae (Chlorophyta) comprises a minor part (<$1\%$) of total algae production ([18, 19]. However, they are rich in beneficial compounds such as polysaccharides, particularly sulfated proteins, amino acids, chlorophylls and carotenoids, polyunsaturated fatty acids, minerals, and vitamins with medicinal and health-promoting effects [13, 20]. U.intestinalis, known as gutweed and grass kelp, is found naturally throughout the world [21, 22]. It has been reported that Ulva sp. and its derivatives such as ulvan had immunostimulatory effects and increased disease resistance in various fish and shrimp species [23], including gilthead sea bream (Sparus aurata), *European sea* bass (Dicentrarchus labrax) [6], Pacific white shrimp (Litopenaeus vannamei) [10], and banana shrimp (Fenneropenaeus merguiensis) [24] diets. On the other hand, different studies have shown that the extracts derived from plants, seaweed, and microalgae can neutralise free radicals in fish tissues and, therefore, can be used as natural sources of antioxidants [11].
Zebrafish (Danio rerio) has been used as a suitable model fish in feeding and immunity studies due to their short life cycle, low cost, easy manipulation, and physiological similarity to most farm species [25, 26]. Therefore, research in zebrafish nutrition can allow essential contributions in many areas of research for finfish aquaculture [27, 28].
Previous studies revealed that some green macroalgae may be used as feed additives in fish [7] and crustacean diets [10]. However, in these studies, the effects of U. intestinalis on skin mucus immunity have not been investigated. Morever, as far as we know, the effects of “U. intestinalis” at the molecular level on growth, antioxidant, and immunity have not been explored in fish. Therefore, a holistic approach was exhibited in this study, and the effects of U. intestinalis on skin mucus immunity, nonspecific immunity, and antioxidant enzyme activities were investigated, as well as the effects of growth, antioxidant response, and immune defence were examined at the molecular level in fish.
## 2.1. Preparing of U. Intestinalis for Diet
U. intestinalis specimens were handpicked from the shores of Iskenderun Bay at a depth of 0-20 m Iskenderun, Hatay, Turkey (36.26.49 N 35.52.24 E). The collected macroalgae were prepared for use by washing, drying, and storing in cooler containers at the Iskenderun Technical University Algal Biotechnology Laboratory [29]. U. intestinalis was first washed thoroughly with ambient water to remove foreign substances such as sand and adhering substances and brought to the laboratory environment in sterile polyethylene bags. To remove epiphytic organisms and necrotic particles on the samples, they were washed with distilled water in the Algal Biotechnology Laboratory. A shaded area in the laboratory environment that was not exposed to the sun for the drying process was used. Identification studies of macroalgae were carried out in Algal Biotechnology Laboratory based on the schemes reported in the literature and using the Ckx41sf model stereo inverted light microscope from Olympus. The macroalgae, which were ground with a properly dried homogeniser, were stored at +4°C until they were added to the feed [30, 31].
## 2.2. Preparation of Diets and Experimental Design
Table 1 indicates the proximate analysis of the basal diet, a commercial diet (BioMar SAS, Nersac, France). The experimental diets were formed by adding the crude U. intestinalis powders, which were ground to obtain fine powders, to the control diet at a ratio of 0, 0.25, 0.5, and $1\%$, as described earlier [17]. In the process of preparing the experimental diets, the basal diet was pulverized, and algae powders were added to correspond diet at the expense of cellulose (to equalize the total energy content). Then, it was mixed thoroughly by means of a blender; after that, water was added to form a mixture. Finally, it was repelleted via a meat grinder, air-dried, ground, and sieved to obtain a suitable size (1 mm). The prepared diets were stored at 4°C in sealed and labelled packages until the study was conducted.
## 2.3. Fish Rearing Conditions and Feeding
600 six-week-old zebrafish used in the study were procured from Ornamental Fish farm (Golestan Province, Iran) and transported in polyethylene bags with oxygen-filled water to the GUASNR (Gorgan University of Agricultural Sciences and Natural Resources) Zebra Lab. Fish were adapted for two weeks. During this period, fish were fed with a commercial diet (Biomar, France) three times a day. Then, zebrafish with approximate weight of 0.3 ± 0.08 g were randomly assigned into triplicate groups as follows: 0 (control; C), $0.25\%$, and $0.5\%$ and $1\%$ (each group contains 50 fish) in 100 L aquarium (half filled with water, 50 L) During the lasting eight weeks of the experiment, the fish were fed the experimental diets as ad libitum three times daily until they reached apparent satiation. In the study, in which the static culture system was used, continuous ventilation was provided using an air stone. Approximately $50\%$ of the total water in each aquarium was changed every two days to maintain water quality. During the trial, water quality parameters were measured regularly using a portable device (WTW, Munich, Germany); water temperature, pH, and dissolved oxygen were as follows, respectively, 24.0 ± 0.8°C, 7 ± 0.1, and 7.6 ± 0.2 mg L−1. The current study was carried out under the protocol approved by the committee of ethics of the faculty of sciences of the University of Tehran (357; 8 November 2000).All procedures were performed in compliance with relevant laws and institutional guidelines.
## 2.4.1. Sampling for Immunologic Parameters
As the fish were too small to collect blood, the protocol described in our previous study, Yousefi et al. [ 32], which was based on Holbech et al. [ 33], was followed to obtain WBE of nine fish per each treatment (0, 0.25, 0.5, and $1\%$ with U. intestinalis). Briefly, the heads and fins were cut. Then, by using a mortar filled with liquid nitrogen, the samples were crushed, and twice the tissue weight of homogenate, buffer was added as described by Holbech et al. [ 33]. The homogenate was centrifuged, and the supernatant was stored -80°C until used.
At the end of the experiment, mucus sampling was also done, as Hoseinifar et al. [ 34] stated. Nine 24 hours fasted fish were randomly selected from each treatment group (three samples per replicate; each replicate as a separate data). Fish exposed to 500 g L−1 of clove powder were transferred to a polyethylene bag containing 5 mL of 50 mM NaCl (Sigma, Steinheim, Germany) for 2 minutes. During this period, the fish were smoothly rubbed inside the zip bags. The obtained mucus samples were quickly transferred to 15 ml sterile tubes and centrifuged at 1500 g for 10 minutes at 4°C. Supernatants were kept at -80°C until used [35].
## 2.4.2. Assessment of Nonspecific Immune Response
This study measured lysozyme activity as well as levels of total protein and globulin to assess nonspecific immune parameters in mucus and WBE in the treatment groups. Total protein level in mucus samples and WBE was determined according to Lowry et al. [ 36] and Bradford [37], 2011's protocol. The method described by Guardiola et al. [ 38] was used to measure lysozyme activity in both skin mucus and WBE in zebrafish fed experimental diets, with some modifications. Briefly, equal volumes (100 μl) of mucus or WBE samples were mixed with the bacterial suspension of *Micrococcus luteus* (Sigma) and incubated at 30°C. The resulting decrease in optical density at 450 nm (OD450) was measured using a microplate reader (Benchmark, BioRad, USA) at 25°C for 15 minutes. The amount of sample that causes an absorbance decrease of 0.001 per minute is defined as one unit of lysozyme activity. The method described by [39] was used to determine the total globulin level of skin mucus and WBE. Briefly, total protein levels were measured both before and after precipitation of Ig molecules using a $12\%$ polyethylene glycol (Sigma) solution. The difference in protein content was considered as the globulin content of skin mucus and WBE.
## 2.5. Antioxidant Defence
The WBE catalase (CAT) activity was measured using a commercially available kit (ZellBio GmbH, Germany) following the instruction of the manufacturer, as we described in our previous paper [40]. As suggested by ([41]), a commercial kit (Zellbio®, Berlin, Germany) was employed to determine the WBE glutathione peroxidase (GPx) as well as superoxide dismutase (SOD).
## 2.6.1. Sampling
After 8 weeks of feeding study, nine zebrafish were randomly sampled from each aquarium (replicate) and anesthetized using 500 mg L−1 clove solution. Samples from the whole fish brain, liver, and intestinal tissues were rapidly removed. Samples from each replicate pooled [42] and held in liquid nitrogen in a deep freezer (-80°C) until further analysis.
## 2.6.2. RNA Extraction and cDNA Synthesis
Extraction of total RNA of samples from 100 mg tissue was done using Esterabad-Zistfan-Pishro-Azma. Then, the isolated RNA was treated with DNase I (Fermentas, Lithuania) to remove genomic DNA. NanoDrop (Nanodrop Technology, Wilmington, DE, USA) and $1.5\%$ Agarose gel were used to control the RNA concentration and quality in each sample. The transcription of total RNA (5 μl with approximate concentration of 500 ng) to cDNA was performed using the cDNA synthesis kit (Fermentas, Lithuania) in accordance with the company's recommended protocol [42].
## 2.6.3. Real-Time PCR
The primers used to detect immunity, antioxidant, and growth-related gene expression were shown in Table 2. Primers were designed in accordance with both GenBank sequences and Primer3 software as described by Safari et al. [ 35]. Quantitative real-time PCR (qPCR) assays were performed to study alteration in the expression of immune (IL-1β and lysozyme-C), antioxidant (SOD and CAT), and growth-related genes (GH and IGF-1). The expression of immune-related genes was measured in intestine, antioxidant, and IGF-1 in liver and GH in brain. The relative real-time PCR was fulfilled using an iCycler (BioRad, USA) and a SYBR Green qPCR Master Mix (Fermentase, Lithuania) as explained in our previous paper [43].
## 2.7. Statistical Analysis
The *Pfaffl formula* [44] was used to calculate relative gene expression. Analysis of the ratio between targets and housekeeping (β-actin) genes was performed with REST software. Using the Kolmogorov-Smirnov test, the normality of the distributions of both immune parameters and gene expression data was evaluated. The obtained data were subjected to one-way ANOVA with α = 0.05 and then Duncan's test. Data were reported as mean ± standard deviation (X ± S.D). Statistical analyses were performed with SPSS 19 software (SPSS, USA).
## 3.1. Immune Response
Table 3 represents nonspecific immune system parameters in skin mucus and WBE of zebrafish after an 8-week feeding trial. While the amount of mucosal total protein and mucosal globulin showed slight increases in the 0.25 and 0.5 supplemented groups, which were not statistically significant compared to control, and the increases in the $1\%$ supplemented group were statistically significant ($P \leq 0.05$). The lysozyme activity in skin mucus of $1\%$ U. intestinalis fed fish was significantly higher than other treatments ($P \leq 0.05$).
Immunological parameters, such as the level of total protein, globulin level, and lysozyme activity in the WBE, showed statistically significant increases in all U. intestinalis supplemented groups compared to the control group ($P \leq 0.05$). No statistical difference was observed among the groups with U. intestinalis supplementation ($P \leq 0.05$).
## 3.2. Effects on Antioxidant Enzyme Activities
The effects on antioxidant activity such as SOD, CAT, and GPx in zebrafish fed with U. intestinalis feed were evaluated, and the results are shown in Table 4. SOD exhibited significantly higher activity in $0.5\%$ and $1\%$ U. intestinalis supplemented groups than control and $0.25\%$ supplemented groups with the highest level in $1\%$ supplemented group ($P \leq 0.05$). Similarly, the activity of GPx in fish fed $1\%$ U. intestinalis diets was higher than the other treatments ($P \leq 0.05$). However, no significant difference was found in CAT activity ($P \leq 0.05$).
## 3.3. Gene Expressions
The effects on the expression of immune-related genes (Lyz and IL-1β), growth-related genes (GH and IGF-1), and antioxidant-related genes (SOD and CAT) of zebrafish which fed diets supplemented with U. intestinalis powder are shown in Figures 1–3, respectively.
Expression of Lyz and IL-1β genes showed significant differences in all U. intestinalis containing groups in related to the control group (Figure 1). Expression of IL-1β was significantly higher than that in fish fed 0.5 and $1\%$ U. intestinalis compared control and $0.25\%$ U. intestinalis groups (P ≤ 0.05). As for *Lyz* gene expression, the highest increase was obtained in the group with $1\%$ addition, and no difference was detected between the groups with $0.25\%$ and $0.5\%$ additions. The expression of the IL-1β gene was 5.04, 8.6, and 8.8, and *Lyz* gene was 5.7, 6.8, and 9.9 fold of control in U.intestinalis treated groups ($0.25\%$, $0.5\%$, and $1\%$), respectively, which showed dose-dependent reduction pattern ($P \leq 0.05$) (Figure 1).
Both antioxidant-related genes (SOD and CAT) were remarkably upregulated with U. intestinalis supplementation with respect to control (Figure 2). While the highest expressions' levels in SOD and CAT genes were observed in the $1\%$ U. intestinalis supplemented group, no difference was observed in the $0.25\%$ and $0.5\%$ supplemented groups. A dose-dependent upward pattern was observed in the expression of SOD and CAT. The expression of the SOD gene was 8.6, 9.4, and 10.94, and CAT gene was 6.76, 7.06, and 9.8 fold of control in U.intestinalis treated groups ($0.25\%$, $0.5\%$, and $1\%$) (Figure 2).
Similar to antioxidant and immune-related genes, growth-related genes (GH and IGF-1) were also upregulated in fish fed to all U. intestinalis containing diets compared to the control group (Figure 3). The expression of the GH gene was 10.03, 10.5, and 12.25, and IGF-1 gene was 8.01, 9.9, and 10.83 fold of control in U.intestinalis treated groups ($0.25\%$, $0.5\%$, and $1\%$), respectively. There was no difference in GH expressions between $0.25\%$ and $0.5\%$ U. intestinalis supplemented groups, and the highest expression level was observed with $1\%$ U. intestinalis.
## 4. Discussions
Macroalgae has received more attention due to its rich source of bioactive compounds [9]. Macroalgae and/or their extracts contribute to the improvement of the health status of aquatic animals and increase productivity, offering a great potential to the aquaculture sector, and providing healthy food for consumers [45]. In addition, it has been shown that macroalgae and some products obtained from macroalgae can improve some parameters of innate immune response such as serum lysozyme [46], alternative complement pathway [15], and phagocytic activity in cultured fish [47] and crustaceans [10, 24]. Although the exact mechanism is not known, it has been suggested that the effect of macroalgae to increase mucosal immunity may be more than systemic immunity [6]. Several studies have suggested that macroalgae and their extracts can be used as safer prophylactic and therapeutic alternatives to antibiotics in the control of infectious diseases affecting farmed fish [45] due to their strong antiviral [10] and antibacterial properties [48] against virus and some bacterial fish pathogens [9, 49].
Lysozyme, which has lytic activity against both gram-positive and gram-negative bacteria, has been proven to be involved in a wide variety of protective mechanisms such as activate the complement system and phagocytes and can be found in the mucus, lymphoid tissue, plasma, and other body fluids of fish. Therefore, lysozyme is a very important factor in determining the innate immunity of fish [15, 50]. In this study, feeding zebrafish with diets containing U. intestinalis powders showed a remarkable effect on lysozyme activity. In line with our work, Akbary and Aminikhoei [46] reported that the lysozyme activity of mullet fish fed $1\%$ supplemented water-soluble extract of *Ulva rigida* was remarkably higher than that of fish fed the control diet and rest of other treatment levels. In another study, sea bass fed with $2.5\%$ U. intestinalis diet showed significantly higher lysozyme activity than those fed with control and $7.5\%$ U. intestinalis added diets [51]. Since the skin mucus of fish contains many different biologically active molecules, it plays a vital role in preventing the entry of pathogens into the body and in immunity [50]. Martinez-Antequera et al. [ 6] reported that the inclusion of Ulva onhoi in feed resulted in an increase in skin mucus lysozyme activity and alkaline phosphatase in sea bream and sea bass. Additionally, Vazirzadeh et al. [ 52] stated that rainbow trout fed diets containing different marine algae such as $5\%$ and $10\%$ of Gracilariopsis persica, $5\%$ and $10\%$ of Hypnea flagelliformis, and $5\%$ of *Sargassum boveanum* and showed high lysozyme activity similar to the results in our study. Moreover, Liu et al. [ 24] suggested that Enteromorpha polysaccharides application can be used to support the shrimp immune system as it leads to an increase in serum phenoloxidase levels, lysozyme activity, and phagocytic activity in banana shrimps (Fenneropenaeus merguiensis).
Total proteins are generally considered as a clinical indicator of many conditions in fish, such as health, stress, and nutritional status [53]. In the present study, the amount of total protein in WBE and skin mucus increased in fish groups fed a $1\%$ supplement diet. In agreement with our findings, Harikrishnan et al. [ 49] reported that total protein amount increased in both L. rohita fed all ulvan supplemented diets challenged with F. columnaris and L. rohita fed ulvan supplemented diets unchallenged compared to control group. However, [54] suggested that the serum total protein level of tilapia is not affected by dietary levels of the green alga Ulva clathrata. Regarding the globulin level, the results in our study showed an increase similar to the results obtained by Hoseinifar et al. [ 17] by adding $1\%$ of Gracilaria to zebrafish feed.
Antioxidant enzymes such as SOD, CAT, and GPx are considered to be the first line of defence against the harmful effects of free radicals, which can be produced for various reasons and cause oxidative damage in body tissues [3, 55]. In the present research, it was observed that the addition of U. intestinalis to zebrafish diets caused an increase in SOD and GPx enzyme activities but did not cause a change in CAT enzyme activities. The results are in agreement with previous studies using various fish and shrimps and different macroalgae as feed additives. Akbary and Aminikhoei [46] in mullet fish showed that the best results in antioxidant enzyme activities such as superoxide dismutase, glutathione, and malondialdehyde in mullet, excluding CAT activity, were obtained from the water-soluble polysaccharide extract from the green algae *Ulva rigida* diet. Moreover, Liu et al. [ 24] showed that when they used polysaccharides from U. intestinalis (1 g kg−1) as feed additives, they effectively increased the activities of antioxidant enzymes in the hemolymph of Fenneropenaeus merguiensis, including total antioxidative capacity (T-AOC), SOD, GPx, and glutathione S-transferase (GST). It has also been shown that dichloromethane solvent extracts of U. intestinalis [16] and methanol extracts [56] showed good antioxidant activity in vitro conditions.
In contrast, unlike the present study, Guerreiro et al. [ 12] reported that there was no change in SOD and GPx antioxidant enzyme activities in the liver when they added *Chondrus crispus* and *Ulva lactuca* separately and as a mixture to their sea bream feed. Similarly, Peixoto reported that $7.5\%$ Gracilaria sp. or an equal amount of $7.5\%$ algae mixture (Gracilaria spp., Ulva spp., and Fucus spp.) added to the sea bass feed did not cause a change in antioxidant enzyme activities. In addition, Peasura et al. [ 57] showed that the addition of U. intestinalis did not cause a significant difference in the antioxidant enzyme activities of total glutathione (GT), glutathione peroxidase (GPx), and oxidised glutathione (GSSG) in the livers of sea bass.
In CAT activity, Peasura et al. [ 57], Peixoto et al. [ 51], Guerreiro et al. [ 12], Pezeshk et al. [ 11], and Akbary and Aminikhoei [46], who added U. rigida to mullet feeds, reported that it did not change in line with the current study. In contrast, Zhou et al. [ 47] reported that the polysaccharide obtained from Enteromorpha prolifera, which is one of the green algae, caused an increase in CAT antioxidant enzyme activity in the crucian carp *Carassius auratus* serum compared to the control group.
On the other hand, SOD antioxidant activities of fish fed with diets supplemented with macroalgae were also inconsistent with our results in some studies [11, 15]. Safavi et al. [ 15] showed that SOD activity in the liver of rainbow trout fed with 1.5 g kg−1 sulfated polysaccharides extracted from *Gracilariopsis persica* (SPG) and 0.5 g kg−1 sulfated polysaccharides extracted from U. intestinalis (SPU) for 8 weeks was significantly lower when compared with control group. Pezeshk et al. [ 11], on the other hand, reported that SOD activity was significantly reduced in U. intestinalis supplemented diets (Labidochromis caeruleus) compared to the control, in contrast to our study. The antioxidant capacity of macroalgae is attributed to the presence of antioxidant compounds such as carotenoids, certain polysaccharides, and polyphenols with scavenging activity, and they can neutralise these reactive oxygen species through their own oxidation due to their very high affinity for oxidative compounds [58]. Although most of the researchers suggested that there is a relationship between total phenolic content and antioxidant activity [40], some researchers claimed that they did not observe such a relationship [59, 60]. Ak and Turker [59], in their study with 5 macroalgae, reported that although they obtained the highest total phenolic content from *Cystoseira barbata* and the highest flavonoid activity from Enteromorpha intestinalis, they obtained the highest antioxidant activity from Scytosiphon lomentaria. It will be useful to carry out studies to shed light on this issue in future studies.
In the present study beside the levels of antioxidant enzymes activity, we checked the expression of antioxidant-related gene expression. This was performed to see if any upregulations occurred and either this upregulation is in line with elevation of enzyme activity. The inclusion of U. intestinalis in the zebrafish diets remarkably upregulated the SOD and CAT antioxidant-related gene expressions compared to control group. Similar results were observed in *Labeo rohita* fed with ulvan-containing diets [49] and in the hemocytes and gills of Pacific white shrimp (Litopenaeus vannamei) fed diet with hot water crude extract (HWCE) from U. intestinalis [10]. Also, Harikrishnan et al. [ 49] showed in their study that the antioxidant-related gene expressions such as SOD and GPx were remarkably upregulated in both challenged with F. columnaris and unchallenged groups fed with all ulvan supplementing diets (0, 25, 50, and 100 mg kg−1), except in challenged fish fed with 100 mg kg−1 ulvan diet when compared to control. Klongklaew reported that after the 21-day study, shrimp fed diets with 1 and 10 g kg−1U. intestinalis hot water crude extract (Ui-HWCE) showed higher expression levels than those fed diets supplemented with control and 5 g kg−1 Ui-HWCE. However, after 28 days, it was stated that all Ui-HWCE treatment groups (1, 5, and 10 g kg−1) exhibited significant SOD upregulation in the hemocytes compared to the control group ($P \leq 0.05$).
Molecular tools are increasingly used to evaluate the effects of various stress factors or nutrients on immunity, antioxidant, or growth in organisms at the molecular level [61–63]. In the present study, a significant amount of upregulation was detected in immune-related genes such as IL-1β (0.5-$1\%$) and lyz ($1\%$) in zebrafish fed with U. intestinalis added feed (Figure 1). IL-1β is an important cytokine involved in various cellular activities, including proliferation of T and B lymphocytes, and in the regulation of immune responses [57]. In the same context, Harikrishnan et al. [ 49] suggested that the expressions of IL-1β, lyz, and hepcidin cytokine genes were significantly upregulated in Labeo fish fed with ulvan enriched diet. In parallel with the findings of our study, Liu et al. [ 24] reported that when banana shrimp (F. merguiensis) were fed Enteromorpha polysaccharides additive diets at different rates for 42 days, lyz gene expression levels in the hepatopancreas, intestine, and gills were higher than the control group. However, in a recent study, Klongklaew et al. [ 10] reported that an upregulation of lyz gene expression was observed in in the hemocytes of Pacific white shrimp (L. vannamei) supplemented with 1 g kg−1 diet hot water crude extract from U. intestinalis (Ui-HWCE) after 28 days of feeding.
Seaweed has become a widely used method in fish diets, both as an immunostimulant and as a growth promoter [15]. Growth, which is coordinated by the GH-IGF system, is also affected by environmental factors such as temperature, photoperiod, and food availability [64, 65]. Since the growth rate of fish is a reflection of productivity and profitability in aquaculture, determining the effect of environmental and nutritional conditions on GH and IGF-I gene expression has significant potential in optimising fish health [66] and production [65, 67]. Therefore, it has been suggested that the GH-IGF-1 axis can be used as an indicator of growth performance and nutritional status in aquaculture [67]. Dietary U. intestinalis supplementation significantly increased the expression of growth-related genes (GH, IGF-1). Although, there is no report on the effects of dietary U. intestinalis on fish growth performance and related gene expression, it has been reported that green seaweeds (Ulva sp.) could improve growth performance in *Nile tilapia* [68]. Similar results have been reported by Mustafa and Nakagawa [69] in case of using small amount of seaweed in diet. Although there is no information available on the mode of action, Yone et al. [ 70] suggested that positive effect on growth performance can be due to an acceleration of nutrient absorption. Also, Nakagawa and Montgomery [71] stated that the seaweed's lipids comprise a wide range of fatty acids, including long-chain polyunsaturated important to neural function.
## 5. Conclusion
Green algae are renewable products with rich bioactive content that can be used as crude powder or by extracting them. In the current study, U. intestinalis was added to feed as crude powder and it was shown that it can be used as a feed additive. The best results achieved in $1\%$ inclusion treatment. However, in case of several parameters no peak value appeared. It can be concluded that the beneficial effect may continue to increase when the amount of gutweed powder added to the feed is further increased. It may therefore make sense to control higher inclusion levels to get the best dose. However, the mechanism of beneficial results obtained using crude macroalgae powder was not investigated in the present study. In future studies, the effects of adding high levels as a feed component on digestive enzyme activities and intestinal morphology in cultured fish should be investigated. In addition, since the mechanism of action in fish has not been fully elucidated, studies should be implemented to determine the bioactive ingredients and then to determine the optimal dose of both the raw powders and the extracted bioactive components.
## Data Availability
The data that support the findings of this study are available upon reasonable request to the corresponding authors.
## Ethical Approval
All experiments within the scope of the study were carried out according to the protocol determined by the ethics committee of Tehran University Faculty of Science (357; 8 November 2000). During the experiment, utmost care was taken into account to provide good environment for fish.
## Conflicts of Interest
The authors' declaration indicates no conflict of interest for this manuscript.
## References
1. Aski H. S., Hoseinifar S. H., Bayani M.. **The effects of dietary stachyose as prebiotic on immunity and antioxidant related genes’ expression and lipid metabolism in zebrafish (Danio rerio)**. (2022) **22** 1097-1104. DOI: 10.2478/aoas-2022-0019
2. Safari R., Hoseinifar S. H., Dadar M., Van Doan H.. **Enrichment of common carp (Cyprinus carpio) diet with malic acid: effects on skin mucosal immunity, antioxidant defence and growth performance**. (2021) **21** 561-573. DOI: 10.2478/aoas-2020-0092
3. Hoseinifar S. H., Jahazi M. A., Nikdehghan N., Van Doan H., Volpe M. G., Paolucci M.. **Effects of dietary polyphenols from agricultural by-products on mucosal and humoral immune and antioxidant responses of convict cichlid (Amatitlania nigrofasciata)**. (2020) **517** 1-7. DOI: 10.1016/j.aquaculture.2019.734790
4. Davies S. J., Soler-vila A., Fitzgerald R., Johnson M. P.. **Macroalgae as a sustainable aquafeed ingredient**. (2019) **11** 458-492. DOI: 10.1111/raq.12241
5. Lieke T., Meinelt T., Hoseinifar S. H., Pan B., Straus D. L., Steinberg C. E. W.. **Sustainable aquaculture requires environmental‐friendly treatment strategies for fish diseases**. (2020) **12** 943-965. DOI: 10.1111/raq.12365
6. Martinez-Antequera F. P., Martos-Sitcha J. A., Reyna J. M., Moyano F. J.. **Evaluation of the inclusion of the green seaweed Ulva ohnoi as an ingredient in feeds for gilthead sea bream (Sparus aurata) and European sea bass (Dicentrarchus labrax)**. (2021) **11** 1-19. DOI: 10.3390/ani11061684
7. Saez M. I., Vizcaino A., Galafat A., Anguís V., Fernandez-Diaz C., Balebona M. C.. **Assessment of long-term effects of the macroalgae _Ulva ohnoi_ included in diets on Senegalese sole (Solea senegalensis) fillet quality**. (2020) **47, article 101885**. DOI: 10.1016/j.algal.2020.101885
8. Lopes D., Melo T., Rey F.. **Valuing bioactive lipids from green, red and brown macroalgae from aquaculture, to foster functionality and biotechnological applications**. (2020) **25** p. 3883. DOI: 10.3390/molecules25173883
9. Naiel M. A. E., Alagawany M., Patra A. K., El-kholy A. I., Amer M. S., El-Hack M. E. A.. **Beneficial impacts and health benefits of macroalgae phenolic molecules on fish production**. (2021) **534** p. 736186. DOI: 10.1016/j.aquaculture.2020.736186
10. Klongklaew N., Praiboon J., Tamtin M., Srisapoome P.. **Chemical composition of a hot water crude extract (HWCE) from Ulva intestinalis and its potential effects on growth performance, immune responses, and resistance to white spot syndrome virus and yellowhead virus in Pacific white shrimp (Litopenaeus vannamei)**. (2021) **112** 8-22. DOI: 10.1016/j.fsi.2021.02.004
11. Pezeshk F., Babaei S., Kenari A. A., Hedayati M., Naseri M.. **The effect of supplementing diets with extracts derived from three different species of macroalgae on growth, thermal stress resistance, antioxidant enzyme activities and skin colour of electric yellow cichlid (Labidochromiscaeruleus)**. (2019) **25** 436-443. DOI: 10.1111/anu.12869
12. Guerreiro I., Magalhães R., Coutinho F., Couto A., Sousa S., Delerue-matos C.. **Evaluation of the seaweeds Chondrus crispus and Ulva lactuca as functional ingredients in gilthead seabream (Sparus aurata)**. (2019) **31** 2115-2124. DOI: 10.1007/s10811-018-1708-7
13. Ruslan F. S., Susanti D., Noor N. M., Iman N.. **Bioactive compounds , cosmeceutical and nutraceutical applications of green seaweed species (Chlorophyta)**. (2021) **16** 41-55. DOI: 10.15578/squalen.514
14. Pradhan B., Patra S., Behera C.. **Preliminary investigation of the antioxidant, anti-diabetic, and anti-inflammatory activity of Enteromorpha intestinalis extracts**. (2021) **26** p. 1171. DOI: 10.3390/molecules26041171
15. Safavi S. V., Kenari A. A., Tabarsa M., Esmaeili M.. **Effect of sulfated polysaccharides extracted from marine macroalgae (Ulva intestinalis and Gracilariopsis persica) on growth performance , fatty acid profile , and immune response of rainbow trout (Oncorhynchus mykiss)**. (2019) **31** 4021-4035. DOI: 10.1007/s10811-019-01902-w
16. Srikong W., Bovornreungroj N., Mittraparparthorn P., Bovornreungroj P.. **Antibacterial and antioxidant activities of differential solvent extractions from the green seaweed Ulva intestinalis**. (2017) **43** 88-95. DOI: 10.2306/scienceasia1513-1874.2017.43.088
17. Hoseinifar S. H., Yousefi S., Capillo G.. **Mucosal immune parameters, immune and antioxidant defence related genes expression and growth performance of zebrafish (Danio rerio) fed on Gracilaria gracilis powder**. (2018) **83** 232-237. DOI: 10.1016/j.fsi.2018.09.046
18. Moreira A., Cruz S., Marques R., Cartaxana P.. **The underexplored potential of green macroalgae in aquaculture**. (2022) **14** 5-26. DOI: 10.1111/raq.12580
19. Tabarsa M., You S. G., Dabaghian E. H., Surayot U.. **Water-soluble polysaccharides from Ulva intestinalis molecular properties, structural elucidation and immunomodulatory activities**. (2018) **26** 599-608. DOI: 10.1016/j.jfda.2017.07.016
20. Garcia-Poza S., Leandro A., Cotas C.. **The evolution road of seaweed aquaculture: cultivation technologies and the industry 4.0**. (2020) **17** 1-42. DOI: 10.3390/ijerph17186528
21. Wulanjati M. P., Indrianingsih A. W., Darsih C., Apriyana W.. **Antioxidant and antibacterial activity of ethanolic extract from Ulva sp**. (2020) **462**. DOI: 10.1088/1755-1315/462/1/012028
22. Zhong R., Wan X., Wang D.. **Polysaccharides from marine _Enteromorpha_ : structure and function**. (2020) **99** 11-20. DOI: 10.1016/j.tifs.2020.02.030
23. Lauzon Q. D., Serrano A. E.. **Ulvan extract from Enteromorpha intestinalis enhances immune responses in Litopenaeus vannamei and Penaeus monodon juveniles**. (2015) **7** 1-10
24. Liu W. C., Zhou S. H., Balasubramanian B., Zeng F. Y., Sun C. B., Pang H. Y.. **Dietary seaweed (Enteromorpha) polysaccharides improves growth performance involved in regulation of immune responses, intestinal morphology and microbial community in banana shrimp _Fenneropenaeus merguiensis_**. (2020) **104** 202-212. DOI: 10.1016/j.fsi.2020.05.079
25. Jørgensen L. V.. **Zebrafish as a model for fish diseases in aquaculture**. (2020) **9** 609-620. DOI: 10.3390/pathogens9080609
26. Lee-Estevez M., Cosson J., Ulloa-rodr P., Far J. G.. **Zebrafish as a useful model for immunological research with potential applications in aquaculture**. (2018) **10** 213-223. DOI: 10.1111/raq.12156
27. Ribas L., Piferrer F.. **The zebrafish (Danio rerio) as a model organism , with emphasis on applications for finfish aquaculture research**. (2014) **6** 209-240. DOI: 10.1111/raq.12041
28. Ulloa P. E., Medrano J. F., Feijoo C. G.. **Zebrafish as animal model for aquaculture nutrition research**. (2014) **5**. DOI: 10.3389/fgene.2014.00313
29. Aras A., Sayin S.. **Determination of potential of some marine macroalgae for future functional products**. (2020) **3** 22-35
30. Keskinkaya H. B., Akköz C., Güneş E., Fadhıl M. A., Okudan E. Ş., Karakurt S.. **Determination of antimicrobial activity of different extracts of Enteromorpha intestinalis (Linnaeus Nees.1820) against pathogenic microorganisms**. (2020) **1** 13-18
31. Ruangchuay R., Dahamat S., Chirapat A., Notoya M.. **Effects of culture conditions on the growth and reproduction of gut weed, Ulva intestinalis Linnaeus (Ulvales, Chlorophyta)**. (2012) **34** 501-507
32. Yousefi S., Hoseinifar S. H., Paknejad H., Hajimoradloo A.. **The effects of dietary supplement of galactooligosaccharide on innate immunity, immune related genes expression and growth performance in zebrafish (Danio rerio)**. (2018) **73** 192-196. DOI: 10.1016/j.fsi.2017.12.022
33. Holbech H., Andersen L., Petersen G. I., Korsgaard B., Pedersen K. L., Bjerregaard P.. **Development of an ELISA for vitellogenin in whole body homogenate of zebrafish (Danio rerio)**. (2001) **130** 119-131. DOI: 10.1016/S1532-0456(01)00229-0
34. Hoseinifar S. H., Hoseini S. M., Bagheri D.. **Effects of galactooligosaccharide and Pediococcus acidilactici on antioxidant defence and disease resistance of rainbow trout, Oncorhynchus mykiss**. (2017) **17** 217-227. DOI: 10.1515/aoas-2016-0024
35. Safari R., Hoseinifar S. H., Dadar M., Sattari M., Rahbar M.. **The effects of Coriandrum sativum L. as feed additive on mucosal immune parameters, antioxidant defence and, immune-related genes expression in zebrafish (Danio rerio)**. (2019) **50** 2621-2627. DOI: 10.1111/are.14218
36. Lowry O. H., Rosenbrough N. J., Farr A. L., Randall R. J.. **Protein measurement with the folin phenol reagent**. (1951) **193** 265-275. DOI: 10.1016/S0021-9258(19)52451-6
37. Bradford M. M.. **A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding**. (1976) **72** 248-254. DOI: 10.1016/0003-2697(76)90527-3
38. Guardiola F. A., Cuesta A., Arizcun M., Meseguer J., Esteban M. A.. **Comparative skin mucus and serum humoral defence mechanisms in the teleost gilthead seabream (Sparus aurata)**. (2014) **36** 545-551. DOI: 10.1016/j.fsi.2014.01.001
39. Siwicki A. K., Anderson D. P., Waluga J.. **Non-specific defence mechanisms assay in fish: II. Potential killing activity of neutrophils and macrophages, lysozyme activity in serum and organs**. (1993) 105-111
40. Safari R., Hoseinifar S. H., Imanpour M. R., Mazandarani M., Sanchouli H., Paolucci M.. **Effects of dietary polyphenols on mucosal and humoral immune responses, antioxidant defense and growth gene expression in beluga sturgeon (Huso huso)**. (2020) **528, article 735494**. DOI: 10.1016/j.aquaculture.2020.735494
41. Hoseinifar S. H., Shakouri M., Doan H.. **Dietary supplementation of lemon verbena (Aloysia citrodora) improved immunity, immune-related genes expression and antioxidant enzymes in rainbow trout (Oncorrhyncus mykiss)**. (2020) **99** 379-385. DOI: 10.1016/j.fsi.2020.02.006
42. Safari R., Hoseinifar S. H., Dadar M., Khalili M.. **Powder of the white bottom mushroom, Agaricus bisporus (Agaricomycetes), improved immunomodulatory and health-promoting effects of Lactobacillus casei in zebrafish (Danio rerio)**. (2018) **20** 695-704. DOI: 10.1615/IntJMedMushrooms.2018026551
43. Safari R., Hoseinifar S. H., Van Doan H., Dadar M.. **The effects of dietary Myrtle (Myrtus communis) on skin mucus immune parameters and mRNA levels of growth, antioxidant and immune related genes in zebrafish (Danio rerio)**. (2017) **66** 264-269. DOI: 10.1016/j.fsi.2017.05.007
44. Pfaffl M. W., Horgan G. H., Dempfle L.. **Relative expression software tool (REST(C)) for group-wise comparison and statistical analysis of relative expression results in real-time PCR**. (2002) **30** 36e-336. DOI: 10.1093/nar/30.9.e36
45. Mohan K., Ravichandran S., Muralisankar T.. **Application of marine-derived polysaccharides as immunostimulants in aquaculture: a review of current knowledge and further perspectives**. (2019) **86** 1177-1193. DOI: 10.1016/j.fsi.2018.12.072
46. Akbary P., Aminikhoei Z.. **Effect of water-soluble polysaccharide extract from the green alga Ulva rigida on growth performance, antioxidant enzyme activity, and immune stimulation of grey mullet Mugil cephalus**. (2018) **30** 1345-1353. DOI: 10.1007/s10811-017-1299-8
47. Zhou Z., Pan S., Wu S.. **Modulation of the growth performance, body composition and nonspecific immunity of crucian carp _Carassius auratus_ upon _Enteromorpha prolifera_ polysaccharide**. (2020) **147** 29-33. DOI: 10.1016/j.ijbiomac.2020.01.065
48. Razarinah W., Ross R., Rahim N. F. A., Faridon B. S., Radzun K. A.. **Antimicrobial activity of marine green algae extract against microbial pathogens**. (2018) **21** 43-47
49. Harikrishnan R., Devi G., Doan H., Balasundram C., Arockiaraj J., Jagruthi C.. **Efficacy of ulvan on immune response and immuno-antioxidant gene modulation in _Labeo rohita_ against columnaris disease**. (2021) **117** 262-273. DOI: 10.1016/j.fsi.2021.08.004
50. Esteban M. A.. **An overview of the immunological defenses in fish skin**. (2012) **2012, article 853470** 1-29. DOI: 10.5402/2012/853470
51. Peixoto M. J., Salas-Leitón E., Pereira L. F.. **Role of dietary seaweed supplementation on growth performance, digestive capacity and immune and stress responsiveness in European seabass (Dicentrarchus labrax)**. (2016) **3** 189-197. DOI: 10.1016/j.aqrep.2016.03.005
52. Vazirzadeh A., Marhamati A., Rabiee R., Faggio C.. **Immunomodulation , antioxidant enhancement and immune genes up-regulation in rainbow trout (Oncorhynchus mykiss) fed on seaweeds included diets**. (2020) **106** 852-858. DOI: 10.1016/j.fsi.2020.08.048
53. Ashouri G., Mahboobi Soofiani N., Hoseinifar S. H.. **Combined effects of dietary low molecular weight sodium alginate and _Pediococcus acidilactici_ MA18/5M on growth performance, haematological and innate immune responses of Asian sea bass (Lates calcalifer) juveniles**. (2018) **79** 34-41. DOI: 10.1016/j.fsi.2018.05.009
54. del Rocío Quezada-Rodríguez P., Fajer-Ávila E. J.. **The dietary effect of ulvan from Ulva clathrata on hematological-immunological parameters and growth of tilapia (Oreochromis niloticus)**. (2017) **29** 423-431. DOI: 10.1007/s10811-016-0903-7
55. Hoseinifar S. H., Ahmadi A., Khalili M., Raeisi M., Van Doan H., Caipang C. M.. **The study of antioxidant enzymes and immune-related genes expression in common carp (Cyprinus carpio) fingerlings fed different prebiotics**. (2017) **48** 5447-5454. DOI: 10.1111/are.13359
56. Aftabuddin S., Akter S., Hossen S., Rahman M. A.. **Antioxidant , antibacterial and cytotoxic activity of Caulerpa racemosa (Forsskal)J .Agardh and Ulva (Enteromorpha) intestinalis L**. (2020) **55** 237-244. DOI: 10.3329/bjsir.v55i4.50959
57. Peasura N., Laohakunjit N., Kerdchoechuen O., Vongsawasdi P., Chao L. K.. **Assessment of biochemical and immunomodulatory activity of sulphated polysaccharides from _Ulva intestinalis_**. (2016) **91** 269-277. DOI: 10.1016/j.ijbiomac.2016.05.062
58. Gomez-zavaglia A., Lage M. A. P., Jimenez-lopez C., Mejuto J. C., Simal-gandara J.. **The potential of seaweeds as a source of functional ingredients of prebiotic and antioxidant value**. (2019) **8** 406-430. DOI: 10.3390/antiox8090406
59. Ak I., Turker G.. **Antioxidant activity of five seaweed extracts**. (2018) **7** 149-155
60. Chernane H., Mansori M., Latique S., El Kaoua M.. **Evaluation of antioxidant capacity of methanol extract and its solvent fractions obtained from four Moroccan macro algae species**. (2014) **10** 35-48
61. Ahmadifar E., Fallah H. P., Yousefi M.. **The gene regulatory roles of herbal extracts on the growth, immune system, and reproduction of fish**. (2021) **11** p. 2167. DOI: 10.3390/ani11082167
62. Panserat S., Kaushik S. J.. **Regulation of gene expression by nutritional factors in fish**. (2010) **41** 751-762. DOI: 10.1111/j.1365-2109.2009.02173.x
63. Shukry M., El-kader M. F. A., Hendam B. M.. **Dietary Aspergillus oryzae modulates serum biochemical indices, immune responses, oxidative stress, and transcription of HSP70 and cytokine genes in Nile tilapia exposed to salinity stress**. (2021) **11** p. 1621. DOI: 10.3390/ani11061621
64. Bertucci J. I., Blanco A. M., Sundarrajan L., Rajeswari J. J., Velasco C., Unniappan S.. **Nutrient regulation of endocrine factors influencing feeding and growth in fish**. (2019) **10** 1-17. DOI: 10.3389/fendo.2019.00083
65. Moriyama S., Ayson F. G., Kawauchi H.. **Growth regulation by insulin-like growth factor-i in fish**. (2000) **64** 1553-1562. DOI: 10.1271/bbb.64.1553
66. Triantaphyllopoulos K. A., Cartas D., Miliou H.. **Factors influencing GH and IGF-1 gene expression on growth in teleost fish: how can aquaculture industry benefit?**. (2020) **12** 1637-1662. DOI: 10.1111/raq.12402
67. Dyer A. R., Barlow C. G., Bransden M. P.. **Correlation of plasma IGF-I concentrations and growth rate in aquacultured finfish: a tool for assessing the potential of new diets**. (2004) **236** 583-592. DOI: 10.1016/j.aquaculture.2003.12.025
68. El-Tawil N. E.. **Effects of green seaweeds (**. (2010) **5** 179-194
69. Mustafa M. G., Nakagawa H.. **A review: dietary benefits of algae as an additive in fish feed**. (1995) **47** 155-162
70. Yone Y., Furuichi M., Urano K.. **Effects of wakame Undaria pinnatifida and Ascophyllum nodosum on absorption of dietary nutrients, and blood sugar and plasma free amino-N levels of red sea bream**. (1986) **52** 1817-1819. DOI: 10.2331/suisan.52.1817
71. Nakagawa H., Montgomery W. L., Nakagawa H., Sato S., Gatlin D. M.. **Algae**. (2007) 133-168
|
---
title: Effects of Tributyrin Supplementation on Growth Performance, Intestinal Digestive
Enzyme Activity, Antioxidant Capacity, and Inflammation-Related Gene Expression
of Large Yellow Croaker (Larimichthys crocea) Fed with a High Level of Clostridium
autoethanogenum Protein
authors:
- Xiuneng Wang
- Min Wan
- Zhen Wang
- Haitao Zhang
- Si Zhu
- Xiufei Cao
- Ning Xu
- Jichang Zheng
- Xianyong Bu
- Wei Xu
- Kangsen Mai
- Qinghui Ai
journal: Aquaculture Nutrition
year: 2023
pmcid: PMC9973137
doi: 10.1155/2023/2687734
license: CC BY 4.0
---
# Effects of Tributyrin Supplementation on Growth Performance, Intestinal Digestive Enzyme Activity, Antioxidant Capacity, and Inflammation-Related Gene Expression of Large Yellow Croaker (Larimichthys crocea) Fed with a High Level of Clostridium autoethanogenum Protein
## Abstract
An 8-week growth experiment was conducted to investigate effects of tributyrin (TB) supplementation on growth performance, intestinal digestive enzyme activity, antioxidant capacity, and inflammation-related gene expression of juvenile large yellow croaker (Larimichthys crocea) (initial weight of 12.90 ± 0.02 g) fed diets with high level of *Clostridium autoethanogenum* protein (CAP). In the negative control diet, $40\%$ fish meal was used as the major source of protein (named as FM), while $45\%$ fish meal protein of FM was substituted with CAP (named as FC) to form a positive control diet. Based on the FC diet, grade levels of $0.05\%$, $0.1\%$, $0.2\%$, $0.4\%$, and $0.8\%$ tributyrin were added to formulate other five experimental diets. Results showed that fish fed diets with high levels of CAP significantly decreased the weight gain rate (WGR) and specific growth rate (SGR) compared with fish fed the FM diet ($P \leq 0.05$). WGR and SGR were significantly higher than in fish fed diets with $0.05\%$ and $0.1\%$ tributyrin that fed the FC diet ($P \leq 0.05$). Supplementation of $0.1\%$ tributyrin significantly elevated fish intestinal lipase and protease activities compared to FM and FC diets ($P \leq 0.05$). Meanwhile, compared to fish fed the FC diet, fish fed diets with $0.05\%$ and $0.1\%$ tributyrin showed remarkably higher intestinal total antioxidant capacity (T-AOC). Malondialdehyde (MDA) content in the intestine of fish fed diets with $0.05\%$-$0.4\%$ tributyrin was remarkably lower than those in the fish fed the FC diet ($P \leq 0.05$). The mRNA expressions of tumor necrosis factor α (tnfα), interleukin-1β (il-1β), interleukin-6 (il-6), and interferon γ (ifnγ) were significantly downregulated in fish fed diets with $0.05\%$-$0.2\%$ tributyrin, and the mRNA expression of il-10 was significantly upregulated in fish fed the $0.2\%$ tributyrin diet ($P \leq 0.05$). In regard to antioxidant genes, as the supplementation of tributyrin increased from $0.05\%$ to $0.8\%$, the mRNA expression of nuclear factor erythroid 2-related factor 2 (nrf2) demonstrated a trend of first rising and then decreasing. However, the mRNA expression of Kelch-like ECH-associated protein 1 (keap1) was remarkably lower in fish fed the FC diet than that fed diets with tributyrin supplementation ($P \leq 0.05$). Overall, fish fed tributyrin supplementation diets can ameliorate the negative effects induced by high proportion of CAP in diets, with an appropriate supplementation of $0.1\%$.
## 1. Introduction
As the major source of protein, fish meal is widely used in fish feed formulation for its nice palatability and balanced nutrition. However, the conflict between high demand for fish meal and shortage of fish meal supply has seriously restricted aquaculture development [1, 2]. The aquaculture feed industry has spared no effort to find alternative sources of proteins that are suitable, low-cost, and available for substituting fishmeal. Clostridium autoethanogenum protein (CAP) is a novel bacterial protein produced by *Clostridium autoethanogenum* (without toxic genes) [3] fermented with carbon monoxide (CO). As a type of single-cell protein (SCP), CAP has pleasant quality of high protein content and balanced profile of amino acids, showing great potential to replace fish meal. The studies on largemouth bass (Micropterus salmoides) (CAP replacement level ≤ $43\%$) and juvenile turbot (*Scophthalmus maximus* L.) (CAP replacement level ≤ $45\%$) suggested that an appropriate level of CAP replacing fish meal did not have a significant negative effect on growth performance [4, 5]. However, fish growth performance was compromised by a high level of CAP replacing fish meal in diets. For example, Jiang et al. [ 6] found that the Pacific white shrimp (Litopenaeus vannamei) growth was significantly reduced and intestinal morphology and immunity were negatively affected as the CAP replacement ratio was higher than $45\%$. Similarly, excessive CAP substitution (≥$45\%$) had a significant adverse impact on the growth of large yellow croaker (Larimichthys crocea) [7].
Due to the implementation of relevant policies to reduce or eliminate use of antibiotics, research on harmless additives has gradually received more attention [8]. Butyric acid, a type of short-chain fatty acid, is considered to be a promising feed additive owing to its positive role in production applications [9]. Butyrate was initially identified as the main energy source for the intestines [10]. Moreover, butyrate can regulate inflammation and intestinal barrier function through inhibition of histone deacetylases and interactions with G protein-coupled receptors [11]. The multiple beneficial effects on the intestine of butyrate are well documented, which proved that butyrate plays a regulatory role in immune regulation, enhances epithelial defense barrier, and ameliorates oxidative stress and mucosal inflammation, transepithelial fluid transport, intestinal motility, and visceral perception [12]. However, the unpleasant odor of butyrate limits the consumption of butyrate-containing feeds [13], and butyrate is often absorbed prematurely in the digestive tract rather than reach the colon to function [14]. For better application of butyrate in fish species, some production forms including butyric acid (BA), butyrate glycerides (BG), microencapsulated sodium butyrate (MSB), sodium butyrate (SB), and tributyrin (TB) were developed [15]. Tributyrin is composed of a glycerol backbone and three butyric acid lipid molecules, which have higher stability and produce more butyric acid in the gut compared to other butyrate forms [16]. Tributyrin has been proved to regulate gut health and inflammation. Previous studies found that tributyrin supplementation attenuated ethanol-induced intestinal inflammation in mice [17] and similar studies in piglets [18]. In regard to aquatic animals, studies also reported positive effects of tributyrin on growth performance, intestinal morphology, microbiota, and lipid metabolism of tributyrin in black sea bream (Acanthopagrus schlegelii) [19], snake head (Channa argus) [20], large yellow croaker [21], and yellow drum (Nibea albiflora) [22].
Large yellow croaker is the major economic fish widely farmed in southeast coastal areas of China [23]. To date, extensive studies have been conducted on large yellow croaker in the nutritional requirements and replacement of fish meal [24–26]. Based on the research on carnivorous fish including largemouth bass [4, 27], turbot [5], and large yellow croaker [7], we supposed that $45\%$ may be a high level of CAP replacement. To make CAP more widely applied, growth performance, intestinal digestive enzyme activity, antioxidant capacity, and inflammation-related gene expression were evaluated in large yellow croaker fed with different levels of tributyrin supplementation in a high-CAP diet.
## 2.1. Ethical Approval
The use and care of animals were approved by the Committee on the Ethics of Animal Experiments of Ocean University of China and followed by Management Rule of Laboratory Animals (Chinese Order NO.676 of the State Council, revised 1 March 2017).
## 2.2. Experimental Diets
Seven isonitrogenous and isolipid diets (containing $42\%$ crude protein and $12\%$ crude lipid) were formulated in this experiment. Fish oil and soybean lecithin were the main source of lipid, and bread flour was the main source of carbohydrate in diets. As the main protein source, the composition of CAP and fish meal was analyzed (Table 1). The negative control diet named as FM was designed with inclusion of $40\%$ fish meal, while CAP was used replacing $45\%$ fish meal protein in the positive control named as FC. The amino acid compositions of the FM and FC diets were analyzed (Table 2). Based on FC, other 5 experimental diets supplementing graded levels of $0.05\%$, $0.1\%$, $0.2\%$, $0.4\%$, and $0.8\%$ tributyrin (Shanghai Menon Animal Nutrition Technology Co., Ltd., Shanghai, China) were formulated (Table 3). In brief, the raw ingredients were thoroughly ground and sieved. The tributyrin was blended into bread flour as premix. All ingredients were mixed with liquid mixture of fish oil, soybean lecithin, and clean water to produce a stiff dough and then using a granulator to produce the pellets (3 mm × 5 mm). All pellets were dried at 50°C overnight then refrigerated at -20°C before use.
## 2.3. Feeding Procedure
All juvenile large yellow croakers were supplied by Aquatic Seeds Farm of the Marine and Fishery Science and Technology Innovation Base, Ningbo, China. Before the experiment, juveniles were fed commercial diets and acclimated in a floating sea cage (4 m × 8 m × 4 m) for 14 days. After acclimation, juveniles (initial body weight: 12.90 ± 0.02 g) were randomly apportioned to 21 sea cages (1 m × 1 m × 2 m). Each experimental diet was allocated to three cages containing 40 fish per cage. Juveniles were hand-fed two times a day (05:00 and 17:00) until visual apparent satiation for 8 weeks and growth with appropriate water environmental conditions (temperature: 18.4 to 24.2°C; dissolved oxygen level: 6.3 to 7.6 mg/L, and salinity: 26.5 to 29.3‰) during the experimental period.
## 2.4. Sampling
At the end of the experiment, juveniles per cage were anesthetized with eugenol (1: 10,000; Shanghai Reagent, China) after being starved for one day. The number of total fish in each cage was counted, and final body weight was recorded to detect survival rate and growth performance. The wet weight of the body, viscera, and liver was measured to calculate the morphological indexes. Four fish of each cage were randomly selected and refrigerated at -20°C for whole-body composition analysis. Six fish of each cage were sampled for serum samples and intestine samples for further analysis. Use a 1 mL syringe to collect blood from the fishtail vein, refrigerate at 4°C overnight, and then centrifuge (4,000 rpm for 15 min) to collect serum. Serum and intestine samples were put into liquid nitrogen immediately then stored at –80°C for further analysis.
## 2.5. Experimental Diets and Whole Fish Body Composition Analysis
Moisture, crude protein, and crude lipid in the whole fish body were analyzed by AOAC [1995]. Moisture was measured by drying samples at 110°C to a constant weight. Crude protein was measured based on the Kjeldahl method, and crude lipid was measured by the Soxhlet extraction method (FOSS, Soxtec 2050).
The freeze dryer (ALPHA 1–4 LD freeze dryer, Kleist, Germany) was used to dry the raw materials and experimental diets to a constant weight at –50°C. The automatic amino acid analyzer (L-8900, Hitachi) was used to determine the amino acid profiles of freeze-dried samples after acid hydrolysis (6 N HCl at 110°C for 24 h).
## 2.6. Serum Index Analysis
The serum alkaline phosphatase (ALP), alanine transaminase (ALT), and aspartate transaminase (AST) were detected by commercial kits provided by Nanjing Jiancheng Bioengineering Institute (Nanjing, China). The preparation and operation of the reagents were carried out strictly in accordance with the operating procedures of the specific kits.
## 2.7. Intestinal Digestive Enzymes and Antioxidant Activity
The intestinal digestive enzyme activity of amylase, trypsin, and lipase and the intestine antioxidant capacity index of total antioxidant capacity (T-AOC), superoxide dismutase (SOD), malondialdehyde (MDA), and catalase (CAT) were assessed by specific commercial kits. All kits were obtained from Nanjing Jiancheng Bioengineering Institute (Nanjing, Jiangsu, China). The preparation and operation of the reagents were carried out according to the operating procedures of the specific kits.
## 2.8. RNA Extraction and Real-Time Quantitative PCR
Total RNAs of intestine were extracted in strict accordance with the instruction of RNA isolation kit (Vazyme, Nanjing, China). A NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA) was used to detect the quality and concentration of RNA. HiScript II Q RT SuperMix for qPCR (+gDNA wiper) purchased from Vazyme (Nanjing, China) was used to reversely transcribe RNA to cDNA in strict accordance with the instruction of manufacturer. The CFX Connect Real-Time System (Bio-Rad) was used to analyze relative quantifies of target genes; then, a total volume of qPCR was carried out as follows: 1 μL cDNA primer, 2 μL cDNA product, 10 μL ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China), and 7 μL RNAase-free water. Primers used in this study are shown in Table 4. The RT-qPCR program was set according to previous research [28]. The amplification efficiency and specificity of the product were confirmed by a standard curve and melting curve analysis. *The* gene expression levels were analyzed using the 2−ΔΔCT methods.
## 2.9. Calculations and Statistical Methods
[1] Weight gain rateWGR,%=100×Wt−WoWo,Specific growth rateSGR,%/$d = 100$×LnWt−LnWod,Hepatosomatic indexHSI,%=100×liver weightbody weight,Viscerasomatic indexVSI,%=100×visceral weightbody weight,Feed intakeFI,%/day=100×Fd×Wt+Wo/2,Feed efficiency ratioFER=wet weight gainF,Protein efficiency ratePER,%=wet weight gaintotal protein fed,Survival rateSR,%=100×FNIN, where Wt was the final weight and Wo was the initial weight of fish in the cage, d was the experiment period, F was the total feed consumption (g, dry matter), and FN and IN were the final and initial numbers of fish.
SPSS Statistics 25.0 software (IBM, USA) was used to analyze the data by using one-way analysis of variance (ANOVA), and then, specific differences were assessed by Tukey's test. All the data were expressed as means ± SEM (standard error of the mean). The level of significant difference was indicated by $P \leq 0.05.$
## 3.1. Survival, Growth Performance, and Morphological Indexes
SR ranging from $0.05\%$ to $0.8\%$ tributyrin showed no significant difference ($P \leq 0.05$). WGR and SGR in fish fed the FC diet were significantly lower compared with fish fed the FM diet. The supplementation of $0.05\%$ and $0.1\%$ tributyrin significantly increased the WGR and SGR compared to fish fed with the FC diet ($P \leq 0.05$). No remarkable differences were observed in HSI, VSI, and FER in all treatments ($P \leq 0.05$) (Figure 1).
## 3.2. Body Composition
No remarkable differences were detected in the moisture and crude protein of whole fish body in all tributyrin treatments ($P \leq 0.05$). The highest whole body crude lipid content was observed in the fish fed with the FM diet, but not significantly different in all tributyrin treatments ($P \leq 0.05$) (Table 5).
## 3.3. Serum Biochemical Indexes
No remarkable differences were was observed in serum alkaline phosphatase (ALP) activity ($P \leq 0.05$). As the tributyrin level elevated from $0.05\%$ to $0.8\%$, serum alanine transaminase (ALT) activity showed a first decreasing then increasing trend. The activity of serum ALT in fish fed diets with 0.05-$0.2\%$ tributyrin was significantly lower than that that in fish fed the FC diet. The activity of serum AST in fish fed tributyrin supplementation diets had significantly lower than that that in fish fed the FC diet ($P \leq 0.05$) (Table 6).
## 3.4. Intestinal Digestive Enzyme Activity
No remarkable differences were detected in the activity of amylase in all tributyrin treatments ($P \leq 0.05$). As the tributyrin level elevated from $0.05\%$ to $0.8\%$, the activity of lipase showed increasing followed by decreasing trends. The activity of lipase was remarkedly higher in fish fed with $0.1\%$ tributyrin than in fish fed the FC diet ($P \leq 0.05$). The activity of trypsin was remarkedly higher in fish fed diets with $0.05\%$ and $0.1\%$ tributyrin compared to those fed FM and FC diets ($P \leq 0.05$) (Table 7).
## 3.5. Intestinal Antioxidant Capacity and the mRNA Expression of Antioxidant-Related Genes in the Intestine
No remarkable differences were detected in T-AOC, SOD, and CAT between fish fed with FM and FC diet ($P \leq 0.05$). The activity of T-AOC was significantly increased in fish fed with $0.05\%$ tributyrin than that in the FC diet, while the activity of SOD was significantly increased in fish fed with $0.1\%$ tributyrin than that in the FC diet ($P \leq 0.05$). The supplementation of $0.05\%$-$0.4\%$ tributyrin significantly decreased MDA content compared with fish fed with FC diet ($P \leq 0.05$). Moreover, the activity of CAT in fish fed the diet with $0.8\%$ tributyrin was notably decreased compared to those fed with FC diet ($P \leq 0.05$) (Table 8).
In regard to antioxidant-related genes, with the tributyrin level elevated from $0.05\%$ to $0.8\%$, mRNA expressions of nrf2 showed increasing followed by decreasing trends. $0.1\%$ tributyrin supplementation had notably increased the expression level of nrf2 ($P \leq 0.05$). Besides, the expression levels of keap1 were notably downregulated in fish fed with $0.1\%$-$0.8\%$ tributyrin diets compared with the FC diet ($P \leq 0.05$) (Figure 2).
## 3.6. The mRNA Expression of Inflammation-Related Genes in the Intestine
The expression of proinflammatory genes in fish fed the FC diet were upregulated compared to the FM diet. With the tributyrin level elevated from $0.05\%$ to $0.8\%$, mRNA expressions of tnf-α, il-1β, and ifn-γ showed decreasing trends followed by increasing trends ($P \leq 0.05$). Compared to the FC diet, mRNA expression of tnf-α and il-1β was remarkably downregulated in fish fed with tributyrin ($P \leq 0.05$). The mRNA expressions of ifn-γ and il-6 in fish fed the diet with 0.05-$0.2\%$ tributyrin were markedly lower than those in fish fed the FC diet ($P \leq 0.05$). Moreover, when the tributyrin level elevated from $0.05\%$ to $0.8\%$, mRNA expression of anti-inflammatory genes il-10 increased followed by decreased and was remarkably higher in fish fed with $0.2\%$ tributyrin diet than those fed the FC diet ($P \leq 0.05$) (Figure 3).
## 4. Discussion
Tributyrin had a positive effect on the growth performance of large yellow croaker. In this study, substituting $45\%$ of fish meal protein with CAP significantly degraded the growth of large yellow croaker, which indicated that high level of CAP replacing fish meal can have a negative effect on fish growth. This result ties well with previous studies in pacific white shrimp [6] and largemouth bass [27]. The decline in growth performance may be attributed to the lack of arginine in CAP and some unknown growth factors in fish meal [6]. However, the WGR and SGR were increased with the appropriate tributyrin supplementation ($0.05\%$ and $0.10\%$) in diet with high levels of CAP, indicating that the growth performance of large yellow croaker could be improved by tributyrin supplementation, which was consistent with previous research in our lab [21]. Similar results were found in marine fish including golden pompano [29], turbot [30], and sea bream [31]. A study in tawny puffer showed that WG, SGR, feed efficiency (FE), and protein efficiency ratio (PER) of fish were increased with the increasing TB level in diets [32]. In our study, tributyrin supplementation improved feed efficiency and protein utilization compared to FC, but did not reach a significant level. The difference could be due to different species, supplementation levels, and CAP replacement levels. Previous studies have shown that the supplementation of butyrate and its derivatives in the diets of animal production species including pigs [33], poultry [34], and ruminants [35, 36] facilitates the development of the gastrointestinal tract, promotes the digestion and absorption of nutrients, and improves the gut health of animals. The result of these improvements is often related to an observed increase in growth performance [13]. Our experiment also found that an appropriate amount of tributyrin can improve the digestive enzyme activity and antioxidant capacity of the intestine and reduce inflammation. However, dose-response experiments reported that high butyrate in diet had no positive effect or even had an adverse effect on the growth performance of fish [15]. A previous study on black sea bream showed that adverse effects on growth performance could be observed when the tributyrin supplementation reached $0.8\%$ in the diet [19]. In our study, a similar result was observed. Presumably, high levels of butyrate products have a strong odor and bitter taste [37], which may reduce fish feed intake and reduce fish growth performance.
Digestive enzyme activity affects the feed utilization of fish, which is an important factor in optimizing dietary formula [38]. Previous studies have demonstrated the substitution of SCP for fish meal seems to have little effect on the digestive enzyme activity of aquatic animals. In pacific white shrimp, the activities of digestive enzymes did not significantly affect when diet fish meal replaced by SCP [39]. In the present study, the digestive enzyme activities in fish fed with $45\%$ CAP were decreased without a significant difference compared with the FM diet. CAP has been proven to have good absorbing properties in fish. The apparent digestibility of CAP by largemouth bass was $82.77\%$, $87.44\%$, and $97.48\%$, respectively [40]. $45\%$ CAP substitution ratio may not be enough to inhibit digestion and absorption of large yellow croaker. However, tributyrin supplementation significantly increased digestive enzyme activities compared with the FC diet. In intrauterine retarded piglets, tributyrin supplementation significantly increased lipase activity in the ileum and trypsin activity in the ileum and jejunum [33]. It was also reported that dietary supplementation of 2.5 g/kg tributyrin increased the activities of lipase and protease in snakehead [20]. As the major energy source for intestinal epithelial, butyric acid plays a vital role in absorption, feed digestion, and promoting intestinal development [41]. Supplementation of tributyrin may participate in releasing butyrate, thus promoting the absorption of CAP nutrients by fish to improve growth performance to some extent.
Further investigation showed the effect of tributyrin on enhancing antioxidant capacity. Oxidative stress results from an imbalance between the generation of oxygen-derived radicals and the antioxidant defenses of organism. Organisms have evolved a system to prevent and repair the impact of oxidative stress. Prevention comes in the antioxidant form, which can be enzymatic [42]. Antioxidant capacity is a crucial index for evaluating the health and oxidative stress status of fish. Tributyrin supplementation reduced MDA content and increased the activities of T-AOC and SOD in intestine. Results in this study showed that $45\%$ CAP replacement of fish meal could cause slight oxidative damage to juvenile large yellow croaker intestine, and this negative effect could be alleviated by tributyrin supplementation. Moreover, at the gene level, our results indicated that the mRNA expression of the antioxidant-related gene nrf2 was remarkably upregulated in the $0.1\%$ tributyrin treatments, while the mRNA expression of keap1 was downregulated. A previous study in goats [43] lead the consisted result suggesting that butyrate may increase the antioxidant enzyme activity by regulating the Nrf2 signaling pathway, thus alleviating oxidative damage. Sodium butyrate supplementation can also enhance the intestinal physical barrier function of grass carp through the Nrf2 signal pathway [44].
Oxidative stress is usually accompanied by elevated levels of inflammation; appropriate amounts of inflammatory factors are involved in damaged tissue regeneration, which can repair and heal wounds. Severe inflammation will impair tissues and cells, causing various inflammatory diseases and seriously compromise the normal immune performance of fish [45, 46]. Therefore, intestinal inflammatory status was assessed in this study. The result indicated that high level of CAP replacing fish meal could upregulate the expression of proinflammatory genes and downregulate the expression of anti-inflammatory genes, suggesting that the immune function of the intestine may be impaired. Similar findings were observed in the study of Pacific shrimp, in which $30\%$ CAP replacement significantly upregulated the immune genes such as cox1 and cox2, and the damage appeared to be more severe when the replacement level reached $70\%$ [6]. Our results also found that tributyrin supplementation could remarkably suppress inflammation. The mRNA expression of genes related to anti-inflammatory cytokines il-10 was significantly elevated in fish fed the tributyrin treatment diets compared to the FC diet, while expression of pro-inflammatory cytokines (tnf-α, il-1β, il-6, and ifn-γ) showed a reverse trend. In accordance with findings in our study, mammals reported that tributyrin reduces content of TNF-α, IL-6, and IL-1β in macrophages of mice fed a high-fat diet [47]. Wang et al. [ 18] found that intestinal inflammation and oxidative stress could be attenuated by tributyrin supplementation in diquat-challenged pigs. The anti-inflammatory properties of butyrate have also been reported in aquatic animals. Ding et al. [ 9] found tributyrin could significantly reduce lipid peroxidation and intestinal protein carbonylation levels and degrade tnf-α and il-16 expression levels in the intestine of shrimp. A similar conclusion was found in grass carp [48] and common carp [49]. Moreover, the mechanism of this anti-inflammatory effect could be due to butyrate which plays an immunomodulatory role by inhibiting the activation of nuclear factor kappa B (NF-κB) signaling pathway and then suppressed the initiation and expression of downstream proinflammatory cytokines and a group of chemokine genes [48, 50, 51]. Previous studies indicated NF-κB activation may suppress the myosin light chain kinase (MLCK) expression, then enhance intestinal integrity, and reduce the permeability of the intestinal mucosal epithelium to pathogens to exert anti-inflammatory effects [22, 52].
## 5. Conclusion
In conclusion, this study indicated appropriate supplementation of tributyrin has a positive effect on the growth performance of large yellow croaker fed with a high level of CAP-replaced fish meal, which is mainly attributed to the enhancement of intestinal digestive enzyme activity and antioxidant capacity and reduced the inflammatory response of large yellow croaker. In terms of this study, the optimal tributyrin level was approximately $0.1\%$ with CAP replaced $45\%$ fish meal protein in a diet for juvenile large yellow croaker.
## Data Availability
The data that supported the findings of this study are available from the corresponding author upon reasonable request.
## Conflicts of Interest
There are no conflicts of interest to report.
## References
1. Montoya-Camacho N., Marquez-Ríos E., Castillo-Yáñez F. J.. **Advances in the use of alternative protein sources for tilapia feeding**. (2019) **11** 515-526. DOI: 10.1111/raq.12243
2. Panikkar P., Parakkandi J., Khan F.. **Use of black soldier fly (Hermetia illucens) prepupae reared on organic waste as feed or as an ingredient in a pellet-feed formulation for Nile tilapia (Oreochromis niloticus)**. (2022) **29** 72968-72978. DOI: 10.1007/s11356-022-20926-3
3. Utturkar S. M., Klingeman D. M., Bruno-Barcena J. M.. **Sequence data for**. (2015) **2**. DOI: 10.1038/sdata.2015.14
4. Yang P., Li X., Song B., He M., Wu C., Leng X.. **The potential of Clostridium autoethanogenum, a new single cell protein, in substituting fish meal in the diet of largemouth bass (Micropterus salmoides): growth, feed utilization and intestinal histology**. (2021) **8** 67-75. DOI: 10.1016/j.aaf.2021.03.003
5. Zheng J., Zhang W., Dan Z.. **Replacement of dietary fish meal with Clostridium autoethanogenum meal on growth performance, intestinal amino acids transporters, protein metabolism and hepatic lipid metabolism of juvenile turbot (**. (2022) **13** p. 981750. DOI: 10.3389/fphys.2022.981750
6. Jiang X., Yao W., Yang H., Tan S., Leng X., Li X.. **Dietary effects of**. (2021) **119** 635-644. DOI: 10.1016/j.fsi.2021.10.005
7. Wu Y., Tian S., Yuan J.. **Effects of**. (2022) **561** p. 738591. DOI: 10.1016/j.aquaculture.2022.738591
8. Khan S. H., Iqbal J.. **Recent advances in the role of organic acids in poultry nutrition**. (2016) **44** 359-369. DOI: 10.1080/09712119.2015.1079527
9. Ding Y., Liu X., Chen Z., Su C., Pan L.. **Effects of tributyrin on the growth performance, immune response and intestinal microbiota of shrimp (**. (2022) **559** p. 738370. DOI: 10.1016/j.aquaculture.2022.738370
10. Bergman E. N.. **Energy contributions of volatile fatty acids from the gastrointestinal tract in various species**. (1990) **70** 567-590. PMID: 2181501
11. Brahe L. K., Astrup A., Larsen L. H.. **Is butyrate the link between diet, intestinal microbiota and obesity-related metabolic diseases?**. (2013) **14** 950-959. DOI: 10.1111/obr.12068
12. Canani R. B., Costanzo M. D., Leone L., Pedata M., Meli R., Calignano A.. **Potential beneficial effects of butyrate in intestinal and extraintestinal diseases**. (2011) **17** 1519-1528. DOI: 10.3748/wjg.v17.i12.1519
13. Bedford A., Gong J.. **Implications of butyrate and its derivatives for gut health and animal production**. (2018) **4** 151-159. DOI: 10.1016/j.aninu.2017.08.010
14. Pituch A., Walkowiak J., Banaszkiewicz A.. **Butyric acid in functional constipation**. (2013) **8** 295-298. DOI: 10.5114/pg.2013.38731
15. Xie D., Dai Q., Xu C., Li Y.. **Dietary tributyrin modifies intestinal function by altering morphology, gene expression and microbiota profile in common carp (**. (2021) **27** 439-453. DOI: 10.1111/anu.13197
16. Lum J., Sygall R., Ros Felip J. M.. **Comparison of tributyrin and coated sodium butyrate as sources of butyric acid for improvement of growth performance in Ross 308 broilers**. (2018) **17** 290-294. DOI: 10.3923/ijps.2018.290.294
17. Cresci G. A., Bush K., Nagy L. E.. **Tributyrin supplementation protects mice from acute ethanol-induced gut injury**. (2014) **38** 1489-1501. DOI: 10.1111/acer.12428
18. Wang C., Cao S., Zhang Q.. **Dietary tributyrin attenuates intestinal inflammation, enhances mitochondrial function, and induces mitophagy in piglets challenged with diquat**. (2019) **67** 1409-1417. DOI: 10.1021/acs.jafc.8b06208
19. Volatiana J. A., Wang L., Gray N., Tong S., Zhang G., Shao Q.. **Tributyrin-supplemented high-soya bean meal diets of juvenile black sea bream,Acanthopagrus schlegelii: study on growth performance and intestinal morphology and structure**. (2020) **51** 135-146. DOI: 10.1111/are.14355
20. Hou Y., Hou Y., Yao L., Chen S., Fan J., Qian L.. **Effects of chromium yeast, tributyrin and bile acid on growth performance, digestion and metabolism ofChanna argus**. (2019) **50** 836-846. DOI: 10.1111/are.13954
21. Xu N., Ding T., Liu Y.. **Effects of dietary tributyrin on growth performance, body composition, serum biochemical indexes and lipid metabolism‐related genes expression of juvenile large yellow croaker (**. (2021) **27** 395-406. DOI: 10.1111/anu.13192
22. Zhu W., Tan P., Lou B., Chen R., Wang L., Xu D.. **Supplementation of a soybean oil-based diet with tributyrin influences growth, muscle composition, intestinal morphology, and expression of immune-related genes of juvenile yellow drum (Nibea albiflora Richardson, 1846)**. (2020) **28** 2027-2043. DOI: 10.1007/s10499-020-00572-7
23. Liu M., de Mitcheson Y. S.. **Profile of a fishery collapse: why mariculture failed to save the large yellow croaker**. (2008) **9** 219-242. DOI: 10.1111/j.1467-2979.2008.00278.x
24. Ai Q., Mai K., Tan B.. **Replacement of fish meal by meat and bone meal in diets for large yellow croaker,**. (2006) **260** 255-263. DOI: 10.1016/j.aquaculture.2006.06.043
25. Wei Y., Shen H., Xu W.. **Replacement of dietary fishmeal by Antarctic krill meal on growth performance, intestinal morphology, body composition and organoleptic quality of large yellow croaker**. (2019) **512** p. 734281. DOI: 10.1016/j.aquaculture.2019.734281
26. Zhang Z., Yuan J., Tian S.. **Effects of dietary vitamin E supplementation on growth, feed utilization and flesh quality of large yellow croaker**. (2022) **551** p. 737954. DOI: 10.1016/j.aquaculture.2022.737954
27. Lu Q., Xi L., Liu Y.. **Effects of dietary inclusion of Clostridium autoethanogenum protein on the growth performance and liver health of largemouth bass (**. (2021) **8**. DOI: 10.3389/fmars.2021.764964
28. Weng M., Zhang W., Zhang Z.. **Effects of dietary lysolecithin on growth performance, serum biochemical indexes, antioxidant capacity, lipid metabolism and inflammation-related genes expression of juvenile large yellow croaker (**. (2022) **128** 50-59. DOI: 10.1016/j.fsi.2022.07.020
29. Zhou C. P., Lin H. Z., Huang Z., Wang J., Wang Y., Yu W.. **Effect of dietary sodium butyrate on growth performance, enzyme activities and intestinal proliferation-related gene expression of juvenile golden pompanoTrachinotus ovatus**. (2019) **25** 1261-1271. DOI: 10.1111/anu.12940
30. Liu Y., Chen Z., Dai J.. **Sodium butyrate supplementation in high-soybean meal diets for turbot (**. (2019) **88** 65-75. DOI: 10.1016/j.fsi.2019.02.064
31. Robles R., Lozano A. B., Sevilla A., Marquez L., Nuez-Ortin W., Moyano F. J.. **Effect of partially protected butyrate used as feed additive on growth and intestinal metabolism in sea bream (Sparus aurata)**. (2013) **39** 1567-1580. DOI: 10.1007/s10695-013-9809-3
32. Zhai Q., Zhang C., Sun Y., Wang L., Song K., Zhang L.. **Effects of tributyrin and mannan-oligosaccharide on growth performance, body composition and intestinal health indices of tawny puffer (**. (2014) **26** 2197-2208
33. Dong L., Zhong X., He J.. **Supplementation of tributyrin improves the growth and intestinal digestive and barrier functions in intrauterine growth-restricted piglets**. (2016) **35** 399-407. DOI: 10.1016/j.clnu.2015.03.002
34. Hu Z., Guo Y.. **Effects of dietary sodium butyrate supplementation on the intestinal morphological structure, absorptive function and gut flora in chickens**. (2007) **132** 240-249. DOI: 10.1016/j.anifeedsci.2006.03.017
35. Gorka P., Kowalski Z. M., Pietrzak P., Kotunia A., Jagusiak W., Zabielski R.. **Is rumen development in newborn calves affected by different liquid feeds and small intestine development?**. (2011) **94** 3002-3013. DOI: 10.3168/jds.2010-3499
36. Cavini S., Iraira S., Siurana A., Foskolos A., Ferret A., Calsamiglia S.. **Effect of sodium butyrate administered in the concentrate on rumen development and productive performance of lambs in intensive production system during the suckling and the fattening periods**. (2015) **123** 212-217. DOI: 10.1016/j.smallrumres.2014.11.009
37. Donovan J. D., Cadwallader K. R., Lee Y.. **Volatile retention and morphological properties of microencapsulated tributyrin varied by wall material and drying method**. (2016) **81** E643-E650. DOI: 10.1111/1750-3841.13243
38. Lazzari R., Neto J., Pedron F., Loro V., Pretto A., Gioda C.. **Protein sources and digestive enzyme activities in jundiá (Rhamdia quelen)**. (2010) **67** 259-266. DOI: 10.1590/S0103-90162010000300002
39. Chen K., Sagada G., Xu B.. **Evaluation of methanotroph (Methylococcus capsulatus, Bath) bacteria meal as an alternative protein source for growth performance, digestive enzymes, and health status of Pacific white shrimp (**. (2022) **30** 1693-1710. DOI: 10.1007/s10499-022-00869-9
40. Shi Y., Zhu S., Tan B.. **Apparent digestibility of six new non-grain protein ingredients for largemouth bass (micropterus salmoides)**. (2022) **46** 1187-1196
41. Blottiere H. M., Buecher B., Galmiche J. P., Cherbut C.. **Molecular analysis of the effect of short-chain fatty acids on intestinal cell proliferation**. (2003) **62** 101-106. DOI: 10.1079/PNS2002215
42. Birnie-Gauvin K., Costantini D., Cooke S. J., Willmore W. G.. **A comparative and evolutionary approach to oxidative stress in fish: a review**. (2017) **18** 928-942. DOI: 10.1111/faf.12215
43. Ma N., Abaker J. A., Bilal M. S., Dai H., Shen X.. **Sodium butyrate improves antioxidant stability in sub-acute ruminal acidosis in dairy goats**. (2018) **14** p. 275. DOI: 10.1186/s12917-018-1591-0
44. Wu P., Tian L., Zhou X. Q.. **Sodium butyrate enhanced physical barrier function referring to Nrf2, JNK and MLCK signaling pathways in the intestine of young grass carp (**. (2018) **73** 121-132. DOI: 10.1016/j.fsi.2017.12.009
45. Yang J. H.. **Perfluorooctanoic acid induces peroxisomal fatty acid oxidation and cytokine expression in the liver of male Japanese medaka (**. (2010) **81** 548-552. DOI: 10.1016/j.chemosphere.2010.06.028
46. Zhang M., Zhou L., Xu Y.. **A STAT3 palmitoylation cycle promotes T**. (2020) **586** 434-439. DOI: 10.1038/s41586-020-2799-2
47. Vinolo M. A., Rodrigues H. G., Festuccia W. T.. **Tributyrin attenuates obesity-associated inflammation and insulin resistance in high-fat-fed mice**. (2012) **303** E272-E282. DOI: 10.1152/ajpendo.00053.2012
48. Tian L., Zhou X. Q., Jiang W. D.. **Sodium butyrate improved intestinal immune function associated with NF-**. (2017) **66** 548-563. DOI: 10.1016/j.fsi.2017.05.049
49. Liu W., Yang Y., Zhang J., Gatlin D. M., Ringo E., Zhou Z.. **Effects of dietary microencapsulated sodium butyrate on growth, intestinal mucosal morphology, immune response and adhesive bacteria in juvenile common carp (Cyprinus carpio) pre-fed with or without oxidised oil**. (2014) **112** 15-29. DOI: 10.1017/S0007114514000610
50. Andoh A., Fujiyama Y., Hata K.. **Counter-regulatory effect of sodium butyrate on tumor necrosis factor-alpha (TNF-**. (1999) **118** 23-29. DOI: 10.1046/j.1365-2249.1999.01038.x
51. de la Blétière D. R., Bourreille A., Leray V.. **Butyrate inhibits inflammatory responses through NF**. (2000) **47** 397-403. DOI: 10.1136/gut.47.3.397
52. Wu X., Wang L., Xie Q., Tan P.. **Effects of dietary sodium butyrate on growth, diet conversion, body chemical compositions and distal intestinal health in yellow drum (**. (2020) **51** 69-79. DOI: 10.1111/are.14348
53. Li X., Chen Q., Li Q.. **Effects of high levels of dietary linseed oil on the growth performance, antioxidant capacity, hepatic lipid metabolism, and expression of inflammatory genes in large yellow croaker (**. (2021) **12** p. 631850. DOI: 10.3389/fphys.2021.631850
54. Tan P., Ding Y., Li X., Dong X., Mai K., Ai Q.. **Nrf2 pathway in vegetable oil-induced inflammation of large yellow croaker (**. (2022) **127** 778-787. DOI: 10.1016/j.fsi.2022.05.046
|
---
title: Nutritional and Nonnutritional Content of Underexploited Edible Seaweeds
authors:
- Rabia Alghazeer
- Hesham El Fatah
- Salah Azwai
- Sana Elghmasi
- Maammar Sidati
- Ali El Fituri
- Ezdehar Althaluti
- Ftaim Gammoudi
- Ervia Yudiati
- Nadia Talouz
- Ghalia Shamlan
- Ammar AL-Farga
- Wafa S. Alansari
- Areej A. Eskandrani
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973140
doi: 10.1155/2022/8422414
license: CC BY 4.0
---
# Nutritional and Nonnutritional Content of Underexploited Edible Seaweeds
## Abstract
Macroalgae are a valuable source of highly bioactive primary and secondary metabolites that may have useful bioapplications. To investigate the nutritional and nonnutritional contents of underexploited edible seaweeds, proximate composition, including protein, fat, ash, vitamins A, C, and E, and niacin, as well as important phytochemicals, including polyphenols, tannins, flavonoids, alkaloids, sterols, saponins, and coumarins, were screened from algal species using spectrophotometric methods. Ash content ranged from 3.15–$25.23\%$ for green seaweeds, 5–$29.78\%$ for brown algae, and 7–$31.15\%$ for red algae. Crude protein content ranged between 5 and $9.8\%$ in Chlorophyta, 5 and $7.4\%$ in Rhodophyta, and between 4.6 and $6.2\%$ in Phaeophyceae. Crude carbohydrate contents ranged from 20 to $42\%$ for the collected seaweeds, where green algae had the highest content (22.5–$42\%$), followed by brown algae (21–$29.5\%$) and red algae (20–$29\%$). Lipid content was found to be low in all the studied taxa at approximately 1–$6\%$, except for *Caulerpa prolifera* (Chlorophyta), which had a noticeable higher lipid content at $12.41\%$. These results indicated that Phaeophyceae were enriched with a high phytochemical content, followed by that of Chlorophyta and Rhodophyta. The studied algal species contained a high amount of carbohydrate and protein, indicating that they could be considered as a healthy food source.
## 1. Introduction
Marine algae are a valuable source of highly bioactive primary and secondary metabolites that may have potential bioapplications in the development of new industrial, pharmaceutical, and food applications. Several active compounds from natural sources have shown reduced side effects and are of great interest because of their very low cytotoxicity [1].
The nutritional value of algae is very important as it has been used as a part of the diet in many countries, particularly those in Asia [2, 3]. The variety of chemical components in algae and their quantity depends on many factors such as species, maturity, and environmental conditions [4]. Algae are nutritionally important with a high level of vital nutrients, including polysaccharides, polyunsaturated fatty acids, proteins, and amino acids, as well as dietary fiber, vitamins, and minerals [5–7]. In addition, algae contain a wide variety of nutritional minerals, including iodine, potassium, calcium, magnesium, phosphorus, iron, and zinc [8]. One of the most valuable nutritional properties of algae is related to their high content of polysaccharide.
In addition to their nutritional value, seaweeds contain various nonnutritional compounds that recently have been the subject of considerable scientific and therapeutic interest [9]. The major bioactive compounds of marine algae include phenolics, phlorotannins, terpenes, terpenoids, alkaloids, tannins, and flavonoids [10, 11]. Algae also contain antioxidants, including polyphenols, carotenoids, and flavonoids [12, 13], while compounds, such as rutin, quercetin, and kaempferol, as well as flavonoids, have been identified in many algal species [14]. In addition, several marine algae have been assessed in in vitro and in vivo investigations for their anticancer activity [15, 16].
A Libyan study has reported the phytochemical analysis and antioxidant and antimicrobial effect of several seaweeds [17]; however, the availability of pharmaceutical data from seaweeds is still rare in comparison with that from plants. In this study, we undertook a qualitative and quantitative analysis of many of the nonnutritive and nutritive compounds in 20 different algal species collected from different areas of Libya.
## 2.1. Sample Collections
Twenty-four species were studied from three groups of algae. These included Chlorophyta (green algae): *Caulerpa prolifera* (collected in 2021 from Farwa Island, Zuwara, 90 km west of Tripoli) and Codium tomentosum, *Ulva compressa* (formerly Enteromorpha compressa), *Ulva intestinalis* (formerly Enteromorpha intestinalis), *Ulva linza* (Enteromorpha linza), Flabellia petiolata, Halimeda tuna, and Ulva lactuca; Phaeophyceae (brown algae): Cladostephus spongiosus, Cystoseira compressa, *Ericaria amentacea* (formerly Cystoseira stricta), Dictyota dichotoma, Halopteris scoparia, Padina pavonica, Petalonia fascia, and Sargassum hornschuchii; and Rhodophyta (red algae): Asparagopsis taxiformis, *Ceramium virgatum* (formerly Ceramium rubrum), Corallina officinalis, *Pterocladiella capillacea* (formerly Gelidium capillaceum), *Gracilariopsis longissima* (formerly Gracilaria verrucosa), Hypnea musciformis, Jania rubens, and *Osmundea pinnatifida* (formerly Laurencia pinnatifida) were collected on 2021 from the western coast of Libya (SA 01, N 32°53'45.47 E 13°21'3.16; SA 02, N 32°53'51.95 E 13°21'4.25; SA 03, N 32°53'54.19 E 13°20'54.10; SA 04, N 32°53'46.23 E 13°20'50.90) (Figure 1). The algal samples were taxonomically identified at the Marine Biology Research Center, Tajura, East of Tripoli, Libya.
The collected algae were cleaned with sea water to remove all the extraneous matter (epiphytes and necrotic parts) and brought to the laboratory in plastic bags. Thereafter, the algae were thoroughly washed with tap water, followed with distilled water before being dried at room temperature in the shade for 7–14 days. The dried samples were grounded thoroughly into fine powder using a kitchen blender. The powdered samples were then stored at 4°C.
## 2.2. Phytochemical Screening
The tested extracts were screened for sterols, alkaloids, phenolic compounds, tannins, saponins, flavonoids, glycosides, coumarins, and quinones. Phytochemical screening of the extracts was performed according to the standard method described by Harborne [18].
## 2.3. Proximate Analysis
Carbohydrate, protein, fat, ash, and moisture content were estimated according to the procedure of the Association of Official Analytical Chemists [19].
## 2.4. Determination of Vitamin Contents
Vitamin A, C, and E and niacin levels in the extracts were determined according to the methods described by Okwu and Ndu [20].
## 2.5. Quantitative Determination of Phytochemicals
Total phenolic content was estimated according to the Folin–Ciocalteu colorimetric method by Singleton et al. [ 21] using gallic acid as the standard. Total flavonoid content was estimated as described by Zhishen et al. [ 22] using rutin as the standard. Total tannin content was determined as detailed by Julkunen-Tiitto [23] using tannic acid as the standard. Total alkaloid content was determined as described by Shamsa et al. [ 24] and Sharief et al. [ 25] using atropine as the standard. Total coumarin content was estimated following the standard methods by Buragohain [26] and de Osório and Martins [27] using coumarin as the standard. Total steroid content was estimated according to Devanaboyina et al. [ 28] using cortisone as the standard.
## 3.1. Phytochemical Analysis
Important phytochemicals, such as polyphenols, tannins, flavonoids, alkaloids, sterols, saponins, and coumarins, were screened from algal species collected from the western coast of Libya. The phytochemical contents obtained from the extraction of the collected algae are shown in Table 1. This analysis showed that Phaeophyceae were highly enriched in phytochemicals, followed by Chlorophyta and Rhodophyta (Table 1).
## 3.2. Proximate Primary Composition
The proximate composition of the dried seaweeds collected from Tripoli coastline is shown in Figures 2 and 3, with the moisture and ash shown in Figure 2. In Chlorophyta, the moisture content of the collected macroalgae was between 40.50 and $92.61\%$. Ulva spp. had the lowest levels of moisture content of approximately 40–$47\%$ after drying, while C. tomentosum had the highest value at $92.6\%$. The brown seaweed C. spongiosus had the lowest moisture level ($39.77\%$) after drying, while D. dichotoma had the highest ($90.55\%$) in Phaeophyceae. In the Rhodophyta, J. rubens had the lowest moisture content ($36.56\%$), while A. taxiformis and O. pinnatifida had the highest content (93.57 and $93.82\%$, respectively). The ash content ranged from 3.15 to $25.23\%$ for green seaweeds, with C. tomentosum and C. prolifera having the lowest and the highest values, respectively. For brown algae, H. scoparia had the lowest ash content (approximately $5\%$), and C. spongiosus had the highest ($29.78\%$). For red algae, P. capillacea had the highest ash content ($31.15\%$), while C. officinalis and G. longissima had the lowest ($7\%$). We found that moisture contents were relatively high for most of the collected seaweeds. Wan et al. [ 29] observed similar results and determined that the moisture content from green, red, and brown species ranged from 64.9 to $94\%$. Lower residual moisture contents have been reported by other researchers using other methods such as oven-drying at 60°C or freeze-drying [30, 31]. The higher moisture content recorded in this study could be attributed to the drying method used for the algae (air-drying). Higher drying temperatures may reduce drying time and cost, but several compounds (e.g., vitamins, proteins, unsaturated fatty acids, phenols, and carotenoids) would be vulnerable to degradation during the drying process [32, 33]. The optimal method for drying the seaweeds should be used to obtain a high proximate composition, as the removal of water from seaweeds is a necessary step in maintaining their quality as a food or in their proximate composition [34]. The high ash content obtained in the collected seaweeds may be due to the collection of the algal samples during low-temperature seasons [35]. Furthermore, a high level of ash content is associated with the amount of mineral elements [34, 35].
The crude carbohydrate contents ranged from 20 to $42\%$ of the collected seaweeds where green algae had the highest content with 22.5–$42\%$, followed by brown algae and red algae with approximately 21–$29.5\%$ and 20–$29\%$, respectively (Figure 3). C. prolifera and U. linza showed the lowest and the highest values in Chlorophyta, respectively. There was little variation in the carbohydrate contents between the Phaeophyta and Rhodophyta, with C. compressa and P. capillacea having the lowest value of around $20\%$ and P. pavonica and J. rubens having the highest contents at $29\%$ in brown and red algae, respectively. High carbohydrate content was observed from macroalgal species in several studies [36, 37]. These relatively high carbohydrate contents in green algae suggest that they could be an important source of phycocolloids in food and industrial uses. These results were similarly observed in other studies [31, 38].
The crude protein content differed widely across groups of algae with low concentrations between 5 and $9.8\%$ in Chlorophyta, 5–$7.4\%$ in Rhodophyta, and $4.6\%$–$6.2\%$ in Phaeophyceae (Figure 3). Wells et al. [ 39] recorded that among the marine macroalgae, the red and green algae often contain high levels of protein (as % dry weight) in contrast to lower levels in most brown algae. The protein content was moderately low compared with those in other macroalgae and agrees with the results from other studies [31, 34, 38]. In contrast, Wan et al. [ 29] recorded the highest protein content in seaweeds from the Rhodophyta division, including C. crispus, Gracilariopsis, and Pyropia species. Small variations in the crude protein content of studied macroalgae could be because of similar environmental conditions and geographical collection sites [40, 41]. In addition, during seasons of nutrient limitation (for instance, the summer season in coastal waters) the protein content of macroalgal decreases, and the relative proportions of amino acids change [39, 42].
Macroalgal species have a relatively low lipid content with values of <$5\%$ w/dry weight [43]. Lipids in marine macrophytes are usually phospholipids and glycolipids [44]. Low lipid contents were observed in all the studied taxa at approximately $1\%$–$6\%$, except for C. prolifera, which had the highest lipid content at $12.405\%$ (Figure 3). In agreement with the observed results, Pirian et al. [ 35] stated that the higher lipid contents were associated with the green algae Caulerpa sertularioides, C. racemosa, and Bryopsis corticulans found in the Persian Gulf. For the brown algae, D. dichotoma had the highest lipid content at $6.50\%$ (Figure 3). These results were similar to those recorded by McDermid and Stuercke [45] who found that *Dictyota acutiloba* and *Dictyota sandvicensis* had a total lipid content (16.1 ± 0.1 and 20.2 ± $0.1\%$ dry weight). However, Miyashita et al. [ 46] stated that brown algal species found in temperate seas produced more lipids than those growing in tropical seas. Biancarosa et al. [ 47] also observed that brown species have a higher lipid content compared with those of green species.
## 3.3. Secondary Metabolite Composition
Algal seaweeds are rich in vitamins [39, 48]. Algae are a source of water-soluble vitamin B2 (riboflavin), B12 (cobalamin), and C (ascorbic acid) and lipid-soluble vitamin E (α-, β-, γ-, and δ-tocopherol, and α-, β-, γ-, and δ-tocotrienol) [29].
The results of this study showed that chlorophyte and Phaeophyceae are rich in vitamin A and C. The green algae F. petiolata had the highest vitamin A content in all studied taxa, whereas the red algae C. rubrum and H. musciformis had the lowest content (Figure 4). Higher values of vitamin A in green algae may be due to their rich β-carotene content (provitamin A) as compared with that in other algal groups [49].
Vitamin E from seaweeds can be especially important in aquaculture feeds as this can serve as an internal antioxidant [29]. We found that brown and green algae had a higher content of vitamin E as compared with that in red algae (Figure 4). F. petiolata and D. dichotoma had the highest content of vitamin E from chlorophyte and Phaeophyta, respectively, while the red seaweed *Corallina officinalis* had the lowest content of vitamin E. These results agreed with earlier reports that stated that brown algae contained higher levels of vitamin E content followed by green and red algae [50].
## 3.4. Phenolics
Polyphenols have been widely described in plants and algae, and phenolic compounds have gained a significant attention because of their biological effects: antioxidant, antiproliferative, antimicrobial, antiallergic, antidiabetic, and neuroprotective actions [51–53], while others are known for either or both their toxicological effects and antinutritional properties [29]. The phenolic compounds found in macroalgae vary from simple molecules, such as phenolic acids or flavonoids, to the more complex phlorotannin polymeric structures.
Algae phenolic concentration is dependent on several factors, such as species, seasonal variations, and environmental conditions [54]. Phenolic compounds are considered as one of the most effective antioxidants in marine algae [55, 56]. We found that phenols were relatively low in chlorophytes and rhodophytes, where U. lactuca and J. rubens had the lowest values (0.66 ± 0.03 and 0.54 ± 0.03 mg GAE/gdw) (Table 2), whereas C. prolifera and O. pinnatifida had the highest values (3.46 ± 0.22 and 3.35 ± 0.17 mg GAE/gdw) from green and red algae, respectively. Brown algae had a relatively higher content of phenols compared with those in green algae, ranging between 0.65 ± 0.05 mg GAE/gdw in *Ericaria amentacea* and 3.31 ± 0.10 mg GAE/gdw in D. dichotoma. The higher total phenolic content resulted in higher antioxidant capacity. These results agreed with Chia et al. [ 57] who recorded those brown seaweeds to have a higher content of phenolic compounds compared with that in green seaweeds and that this may be due to the presence of phlorotannins, bipolar polyphenols that are commonly found in brown seaweeds.
Flavonoids are one of the most diverse and widespread groups of natural products and are probably the most important natural phenolics. The flavonoid content in red seaweeds was low and ranged from 0.49 to 14.84 mg RE/gdw. In green seaweeds, the flavonoid content varies from 4.78 mg RE/gdw in U. lactuca to 29.11 mg RE/gdw in F. petiolata. The highest flavonoid content was found in brown algae, which ranged between 6.86 and 32.38 mg RE/gdw, where D. dichotoma had the highest value at 32.38 mg RE/gdw (Table 2). Although the samples were collected during the same season, there was significant difference in their flavonoid content. This change in flavonoid content may be due to the variation in physicochemical parameters, such as salinity among the collected stations or environmental conditions [58].
In regard to the alkaloid content of the collected algae, we found that a moderate to high content, ranging from 0.27 ± 0.08 to 3.05 ± 0.31 mg AE/gdw in green algae, 0.75 ± 0.08 to 2.25 ± 0.27 mg AE/gdw in brown algae, and 0.23 ± 0.11 to 2.48 ± 0.08 mg AE/gdw in red algae (Table 2).
Algae vary in their total sterol content and in the variety of sterols present [44]. We found that green algae contained the highest sterol content, followed by that in brown algae and then in red algae (Table 2). C. prolifera had the highest value from chlorophytes of 95.35 mg EE/gdw, while D. dichotoma and S. hornschuchii had approximately 90.10 mg EE/gdw. A. taxiformis had 76.45 mg EE/gdw from red seaweeds.
## 4. Conclusion
Seaweeds from the western coast of Libya have similar nutritional values to those found in vegetables and other seaweeds around the world. Hence, we suggest that the studied algal species could be used as alternative nutrient sources for carbohydrate, protein, and lipids for human and animal consumption as these species had a high carbohydrate and low lipid content with an important fraction of protein indicating that marine algae could be considered as healthy food.
## Data Availability
The data that support the findings of this study are available from the corresponding author.
## Conflicts of Interest
The authors have no conflict of interest to declare.
## Authors' Contributions
R.A. conceived, designed, and organized the study. S.A., S.E., M.S., A.E.F., E.A., and F.G. contributed to the conduct of the study. R.A., S.A., M.A., E.Y., N.T., G.S., A.A., W.S.A., and A.A.E performed the experiments. R.A., E.A., A.A.E., G.S., A.A., and W.S.A analyzed the data. R.A., S.A., M.A., S.E., M.S., A.E.F., E.A., E.Y., and N.T drafted the manuscript and critiqued the output for intellectual content. All authors discussed the results and commented on the manuscript.
## References
1. Remya R. R., Samrot A. V., Kumar S. S.. **Bioactive Potential of Brown Algae**. (2022) **2022, article 9104835** 13. DOI: 10.1155/2022/9104835
2. Ali M. S., Jahangir M., Saleem M., Pervez M. K., Hameed S., Ahmad V. U.. **Metabolites of marine algae collected from Karachi-coasts of Arabian Sea**. (2000) **6** 61-65
3. Jibri S. M., Jakada B. H., Umar H. Y., Ahmad T. A. Q.. **Importance of some algal species as a source of food and supplement**. (2016) **5** 186-193. DOI: 10.20546/ijcmas.2016.505.020
4. Stengel D. B., Connan S., Popper Z. A.. **Algal chemodiversity and bioactivity: sources of natural variability and implications for commercial application**. (2011) **29** 483-501. DOI: 10.1016/j.biotechadv.2011.05.016
5. Pati M. P., Sharma S. D., Nayak L., Panda C. R.. **Uses of seaweed and its application to human welfare: a review**. (2016) **8** 12-20. DOI: 10.22159/ijpps.2016v8i10.12740
6. Tanna B., Mishra A.. **Nutraceutical potential of seaweed polysaccharides: structure, bioactivity, safety, and toxicity**. (2019) **18** 817-831. DOI: 10.1111/1541-4337.12441
7. Choudhary B., Chauhan O. P., Mishra A.. **Edible seaweeds: a potential novel source of bioactive metabolites and nutraceuticals with human health benefits**. (2021) **8** 1-17. DOI: 10.3389/fmars.2021.740054
8. Jyoti A., Nehra M., Khan M.. **Algae as a Nutritional and Functional Food Source**. (2022) **7** 189-199
9. Ahmad J., Ahamad J.. **Bioactive phytochemicals: drug discovery to product development**. (2020) **38** 176-213
10. Li Y. X., Wijesekara I., Li Y., Kim S. K.. **Phlorotannins as bioactive agents from brown algae**. (2011) **46** 2219-2224. DOI: 10.1016/j.procbio.2011.09.015
11. Pradhan B., Bhuyan P. P., Patra S.. **Beneficial effects of seaweeds and seaweed-derived bioactive compounds: current evidence and future prospective**. (2022) **39, article 102242**
12. Andarwulan N., Cahyarani Puspita N., Średnicka-Tober D.. **Antioxidants such as flavonoids and carotenoids in the diet of Bogor, Indonesia residents**. (2021) **10** p. 587. PMID: 33920414
13. Widowati R., Handayani S., Suprihatin I. L. R.. **Phytochemicals and antioxidant of methanol extract of**. (2021) **8** 1-12
14. Ben-Dor A., Nahum A., Danilenko M.. **Effects of acyclo-retinoic acid and lycopene on activation of the retinoic acid receptor and proliferation of mammary cancer cells**. (2001) **391** 295-302. PMID: 11437362
15. Samarakoon K. W., Ko J. Y., Lee J. H., Kwon O. N., Kim S. W., Jeon Y. J.. **Apoptotic anticancer activity of a novel fatty alcohol ester isolated from cultured marine diatom, _Phaeodactylum tricornutum_**. (2014) **6** 231-240. DOI: 10.1016/j.jff.2013.10.011
16. Zorofchian Moghadamtousi S., Karimian H., Khanabdali R.. **Anticancer and Antitumor Potential of Fucoidan and Fucoxanthin, Two Main Metabolites Isolated from Brown Algae**. (2014) **2014** 10. DOI: 10.1155/2014/768323
17. Shafay S. E., El-Sheekh M., Bases E., El-Shenody R.. **Antioxidant, antidiabetic, anti-inflammatory and anticancer potential of some seaweed extracts**. (2021) **42**. DOI: 10.1590/fst.20521
18. Harborne A. J.. (1998)
19. 19AOAC
Official Methods of Analysis
1990Washington D.C. 15th, Ed.Association of Official Analytical Chemists. (1990)
20. Okwu D. E., Ndu C. U.. **Evaluation of the phytonutrients, mineral and vitamin contents of some varieties of yam (**. (2006) **2** 199-203
21. Singleton V. L., Orthofer R., Lamuela-Raventós R. M.. **Analysis of total phenols and other oxidation substrates and antioxidants by means of folin-ciocalteu reagent**. (1999) **299** 152-178. DOI: 10.1016/S0076-6879(99)99017-1
22. Zhishen J., Mengcheng T., Jianming W.. **The determination of flavonoid contents in mulberry and their scavenging effects on superoxide radicals**. (1999) **64** 555-559. DOI: 10.1016/S0308-8146(98)00102-2
23. Julkunen-Tiitto R.. **Phenolic constituents in the leaves of northern willows: methods for the analysis of certain phenolics**. (1985) **33** 213-217. DOI: 10.1021/jf00062a013
24. Shamsa F., Monsef H., Ghamooshi R., Verdian-rizi M.. **Spectrophotometric determination of total alkaloids in some Iranian medicinal plants**. (2008) **32** 17-20
25. Sharief N., Srinivasulu A., Rao V. U. M.. **Estimation of alkaloids and total phenol in roots of derris trifoliate and evaluation for antibacterial and antioxidant activity**. (2011) **4** 1-3
26. Buragohain R.. **Carcass traits and sensory characteristics of meat of broiler fed varying levels of**. (2015) **3** 108-112. DOI: 10.11648/j.si.20150306.17
27. Osório A. D. C., Martins J. L. S.. **Determinação de cumarina em extrato fluido e tintura de guaco por espectrofotometria derivada de primeira ordem**. (2004) **40** 481-486. DOI: 10.1590/S1516-93322004000400005
28. Devanaboyina N., Ramalakshmi N., Sudeepthi B. S., Hemachakradhar K., Raju N. P.. **Preliminary phytochemical screening, quantitative estimation and evaluation of antimicrobial activity of**. (2013) **2** 31-39
29. Wan A. H., Davies S. J., Soler-Vila A., Fitzgerald R., Johnson M. P.. **Macroalgae as a sustainable aquafeed ingredient**. (2019) **11** 458-492. DOI: 10.1111/raq.12241
30. Rodrigues D., Freitas A. C., Pereira L.. **Chemical composition of red, brown and green macroalgae from Buarcos bay in Central West Coast of Portugal**. (2015) **183** 197-207. DOI: 10.1016/j.foodchem.2015.03.057
31. Bhuiyan K. A., Qureshi S., Mustafa Kamal A. H., Aftab Uddin S., Siddique A.. **Proximate chemical composition of sea grapes**. (2016) **5**. DOI: 10.4172/2161-0517.1000158
32. Niamnuy C., Devahastin S., Soponronnarit S., Raghavan G. V.. **Kinetics of astaxanthin degradation and color changes of dried shrimp during storage**. (2008) **87** 591-600. DOI: 10.1016/j.jfoodeng.2008.01.013
33. Gupta S., Cox S., Abu-Ghannam N.. **Effect of different drying temperatures on the moisture and phytochemical constituents of edible Irish brown seaweed**. (2011) **44** 1266-1272
34. D’Armas H., Jaramillo C., D’Armas M., Echavarría A., Valverde P.. **Proximate composition of several green, brown and red seaweeds from the coast of Ecuador**. (2019) **67** 61-68. DOI: 10.15517/rbt.v67i1.33380
35. Pirian K., Jeliani Z. Z., Arman M., Sohrabipour J., Yousefzadi M.. **Proximate analysis of selected macroalgal species from the Persian Gulf as a nutritional resource**. (2020) **31** p. 1
36. Lafarga T., Acién-Fernández F. G., Garcia-Vaquero M.. **Bioactive peptides and carbohydrates from seaweed for food applications: natural occurrence, isolation, purification, and identification**. (2020) **48, article 101909**. DOI: 10.1016/j.algal.2020.101909
37. Leandro A., Pacheco D., Cotas J., Marques J. C., Pereira L., Gonçalves A. M.. **Seaweed’s bioactive candidate compounds to food industry and global food security**. (2020) **10** p. 140
38. Sivaramakrishnan T., Biswas L., Shalini B.. **Analysis of proximate composition and in-vitro antibacterial activity of selected green seaweeds from South Andaman Coast of India**. (2017) **6** 1739-1749. DOI: 10.20546/ijcmas.2017.612.197
39. Wells M. L., Potin P., Craigie J. S.. **Algae as nutritional and functional food sources: revisiting our understanding**. (2017) **29** 949-982. DOI: 10.1007/s10811-016-0974-5
40. Munir M., Khan A. M., Qureshi R., Murtaza S., Munazir M.. **Preliminary phytochemical screening, proximate analysis, antioxidant and antibacterial activities of an algal species of Hydrodictyon reticulatum**. (2020) **7** p. 1
41. Stirk W. A., Reinecke D. L., van Staden J.. **Seasonal variation in antifungal, antibacterial and acetylcholinesterase activity in seven South African seaweeds**. (2007) **19** 271-276. DOI: 10.1007/s10811-006-9134-7
42. Schiener P., Black K. D., Stanley M. S., Green D. H.. **The seasonal variation in the chemical composition of the kelp species**. (2015) **27** 363-373. DOI: 10.1007/s10811-014-0327-1
43. Biris-Dorhoi E. S., Michiu D., Pop C. R.. **Macroalgae—a sustainable source of chemical compounds with biological activities**. (2020) **12** p. 3085
44. Holdt S. L., Kraan S.. **Bioactive compounds in seaweed: functional food applications and legislation**. (2011) **23** 543-597. DOI: 10.1007/s10811-010-9632-5
45. McDermid K. J., Stuercke B.. **Nutritional composition of edible Hawaiian seaweeds**. (2003) **15** 513-524. DOI: 10.1023/B:JAPH.0000004345.31686.7f
46. Miyashita K., Mikami N., Hosokawa M.. **Chemical and nutritional characteristics of brown seaweed lipids: a review**. (2013) **5** 1507-1517. DOI: 10.1016/j.jff.2013.09.019
47. Biancarosa I., Belghit I., Bruckner C. G.. **Chemical characterization of 21 species of marine macroalgae common in Norwegian waters: benefits of and limitations to their potential use in food and feed**. (2018) **98** 2035-2042. DOI: 10.1002/jsfa.8798
48. Ganesan A. R., Tiwari U., Rajauria G.. **Seaweed nutraceuticals and their therapeutic role in disease prevention**. (2019) **8** 252-263. DOI: 10.1016/j.fshw.2019.08.001
49. Ortiz J., Uquiche E., Robert P., Romero N., Quitral V., Llantén C.. **Functional and nutritional value of the Chilean seaweeds**. (2009) **111** 320-327. DOI: 10.1002/ejlt.200800140
50. Plaza M., Cifuentes A., Ibáñez E.. **In the search of new functional food ingredients from algae**. (2008) **19** 31-39. DOI: 10.1016/j.tifs.2007.07.012
51. Murray M., Dordevic A. L., Ryan L., Bonham M. P.. **An emerging trend in functional foods for the prevention of cardiovascular disease and diabetes: marine algal polyphenols**. (2018) **58** 1342-1358. DOI: 10.1080/10408398.2016.1259209
52. Gupta S., Cox S., Rajauria G., Jaiswal A. K., Abu-Ghannam N.. **Growth inhibition of common food spoilage and pathogenic microorganisms in the presence of brown seaweed extracts**. (2012) **5** 1907-1916. DOI: 10.1007/s11947-010-0502-6
53. Cotas J., Leandro A., Monteiro P.. **Seaweed phenolics: from extraction to applications**. (2020) **18** p. 384
54. Santos S. A., Félix R., Pais A., Rocha S. M., Silvestre A. J.. **The quest for phenolic compounds from macroalgae: a review of extraction and identification methodologies**. (2019) **9** p. 847
55. Luo H. Y., Wang B., Yu C. G., Su C. L.. **Evaluation of antioxidant activities of five selected brown seaweeds from China**. (2010) **4** 2557-2565
56. Zakaria N. A., Ibrahim D., Sulaiman S. F., Supardy A.. **Assessment of antioxidant activity, total phenolic content and**. (2011) **3** 182-191
57. Chia Y. Y., Kanthimathi M. S., Khoo K. S., Rajarajeswaran J., Cheng H. M., Yap W. S.. **Antioxidant and cytotoxic activities of three species of tropical seaweeds**. (2015) **15** 1-14. DOI: 10.1186/s12906-015-0867-1
58. Farasat M., Khavari-Nejad R. A., Nabavi S. M. B., Namjooyan F.. **Antioxidant activity, total phenolics and flavonoid contents of some edible green seaweeds from northern coasts of the Persian Gulf**. (2014) **13** p. 163. PMID: 24734068
|
---
title: Effects of Low or High Dosages of Dietary Sodium Butyrate on the Growth and
Health of the Liver and Intestine of Largemouth Bass, Micropterus salmoides
authors:
- Yiyang Ge
- Shibin Yao
- Ye Shi
- Chunfang Cai
- Chengrui Wang
- Ping Wu
- Xiamin Cao
- Yuantu Ye
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973142
doi: 10.1155/2022/6173245
license: CC BY 4.0
---
# Effects of Low or High Dosages of Dietary Sodium Butyrate on the Growth and Health of the Liver and Intestine of Largemouth Bass, Micropterus salmoides
## Abstract
The concentration of butyric acid in the intestine increased with the increase in the content of fermentable dietary fibre; however, the potential physiological impact of a high dose of butyric acid on fish has not been sufficiently studied. The aim of this study was to investigate the effect of two dosages of butyric acid on the growth and health of the liver and intestine of the largemouth bass (Micropterus salmoides). Sodium butyrate (SB) was added to the diet at 0 g/kg (CON), 2 g/kg (SB2), and 20 g/kg (SB20), and the juvenile largemouth bass were fed to apparent satiation for 56 days. No significant difference was observed in the specific growth rate or hepatosomatic index among the groups ($P \leq 0.05$). The concentration of β-hydroxybutyric acid in the liver, the activities of alanine aminotransferase, aspartate aminotransferase, and alkaline phosphatase, and the concentrations of triglyceride and total cholesterol in serum increased significantly in the SB20 group compared to the CON group ($P \leq 0.05$). The relative expression of fas, acc, il1b, nfkb, and tnfa in the liver of the SB20 groups was also significantly higher than that of the CON group ($P \leq 0.05$). The above indicators in the group SB2 had similar change tendencies. The expression of nfkb and il1b in the intestine of both the SB2 and SB20 groups was significantly downregulated compared with that in the CON group ($P \leq 0.05$). The size of hepatocytes was enlarged, and the intracellular lipid droplets and the degree of hepatic fibrosis were increased in the SB20 group compared to the CON group. There was no significant difference in intestinal morphology among the groups. The above results indicated that neither 2 g/kg nor 20 g/kg SB had a positive effect on the growth of largemouth bass, while a high dosage of SB induced liver fat accumulation and fibrosis.
## 1. Introduction
After the positive effect of dietary sodium butyrate (SB) on terrestrial animals had been widely reported, studies on aquatic animals have also shown that SB improved growth performance [1–6], liver antioxidant capacity [5], intestinal structure integrity [2, 4–7], and decreased fat accumulation [8]. However, the positive effect of SB is not static [9] or may be dose-dependent [1, 10]. A study in a Caco-2 cell monolayer model showed that excessive butyrate may induce severe intestinal epithelial cell apoptosis and disrupt the intestinal barrier [11]. In mouse model, SB was confirmed to contribute to liver steatosis and fibrosis [12].
It was found that fatty liver [13, 14] and intestinal tissue damage [15] could be caused when fishmeal in the diet was replaced by plant protein sources. Dietary fibre (DF), which is rich in plant ingredients, has been suggested as a potential pathogenic agent in recent years [12, 16]. However, the pathogenic mechanism remains unclear. Butyric acid could be produced by fermentation of DF in the intestine [17, 18], and the yield of butyric acid was prominent when fermentable DF was high in the diet [19]. Therefore, it can be deduced that excessive butyric acid may contribute to liver and intestinal tissue damage in fish.
Farmed fish, including largemouth bass (Micropterus salmoides), suffer from fatty liver disease and enteritis. With largemouth bass as a model animal, the aim of this study was to investigate the effects of different levels of SB on the health of the liver and intestine as well as the growth performance of fish. The results are helpful for better understanding the physiological effects of butyric acid and may also contribute to revealing the antinutritional mechanism of DF, ultimately benefiting fish farming.
## 2.1. Ethical Statement
Procedures in this study were carried out in accordance with the “Guiding Principles in the Care and Use of Animals (China)” and standard operating procedures of Provincial Aquatic Animal Nutrition Key Laboratory of Soochow University. The ethical treatment of animals used in this study was approved by the Animal Welfare Ethics Committee of Soochow University (Approval No. SUDA20220810A01).
## 2.2. Diet
Casein and dextrin were purchased from Henan Gaobao Industrial Co., Ltd. (Zhengzhou, China), and the other diet ingredients were provided by Guangdong Yuehai Feed Co., Ltd. (Zhanjiang, China). According to the daily intake dosage of SB of experimental animals in the study by Lupton [12], the high dosage of SB (guaranteed reagent, purity >$98\%$) in the diet was set at 20 g/kg (SB20 group). The low dosage was set at 2 g/kg (SB2 group) according to Liu et al. [ 5] and Dawood et al. [ 20]. All feed ingredients were ground to pass through a 60 mesh sieve, weighed according to the formulation, and mixed thoroughly. Then, 250 mL/kg distilled water was added and mixed again. The pellets were 2 mm in diameter and 5 mm in length and were made with an assembled machine. After air drying to a moisture content of less than 100 g/kg, the pellets were stored at -20°C. The diet formulation and proximate composition are shown in Table 1.
## 2.3. Feeding and Fish Management
The feeding trial was performed at the Graduate Workstation of Suzhou Yangchenghu National Modern Agricultural Demonstration Zone Development Co., Ltd. Juvenile largemouth bass were purchased from Jinchengfu Fishery Technology Co., Ltd. (Suzhou, China). Fish were reared in indoor cement ponds for 3 weeks and were fed the CON diet during this period. A similar size of 135 fish (average body weight 12.3 g/fish) was selected and randomly divided into 9 polyethylene tanks containing 300 L water, with 15 fish in each tank. The fish were fed the CON diet for another week and weighed again in a fasting state, and the total weight of each tank was adjusted to make the coefficient of variation <$3\%$ among tanks. Then, each experimental diet was randomly fed to three tanks of fish to apparent satiety at 7: 30 and 16: 30. Feed intake was calculated weekly. One-third of the water in the lower layer of the tank as well as the faeces was drawn by a siphon at 9: 00 every day, and then fresh water was immediately input. A natural photoperiod with a light intensity above the water surface of approximately 800 lux was used at noon. The pH of the water was 7.5-7.8, the dissolved oxygen was >6.5 mg/L, and the ammonia nitrogen was <0.1 mg/L. The feeding trial lasted for 56 days.
## 2.4. Sampling and Sample Preparation
After 56 days of feeding, sampling was performed in a state of fasting. The fish were quickly netted, anaesthetized with 200 mg/L eugenol, and individually weighed, and the body length was measured. Three fish from each tank were randomly taken and dissected on ice under sterile conditions. The liver and hindgut tissues were sampled, washed with 0.01 mmol/L PBS (pH 7.2), placed in RNase-free centrifuge tubes, frozen in liquid nitrogen, and stored at -80°C for gene expression analysis. Three more fish were dissected and weighed for liver weight, and then the whole fish were homogenized and stored at -50°C for body composition analysis. The remaining 9 fish were utilized to draw blood. The blood was placed in a 1.5 mL centrifuge tube, stood at 4°C for 6 h, and centrifuged at 4000 rpm at 4°C for 10 min, and the serum was collected and stored at -80°C for subsequent serum biochemical analysis. These 9 fish were dissected on ice, and the liver was collected and weighed. After that, the liver and hindgut of three fish from each tank were taken, washed with 0.01 mmol/L PBS (pH 7.2), absorbed surface moisture with facial tissue, and fixed in $4\%$ formaldehyde solution for histomorphological analysis; the liver and hindgut tissues of the other 6 fish from each tank were placed in a 10 mL tube, frozen with liquid nitrogen, and stored at -80°C for tissue biochemical analysis.
## 2.5.1. Diet and Fish Proximate Composition
Samples of whole fish were ground and freeze-dried (LGJ-18, Sihuangqihang Technology Co., Ltd., Beijing, China) to calculate moisture content. Moisture in the diet was measured by drying at 105°C to constant weight (DHG-9055A, Shanghai Yiheng Scientific Instrument Co., Ltd., China). The contents of crude protein, lipid, and ash in the dry matter were measured by the Kjeldahl method (GB/T 6432-2018, Kjeldahl nitrogen determinator: SKD-1000, Shanghai Peiou Analytical Instruments Co., Ltd., China; Digester: LNK-872, Jiangsu Yixing Science and Education Instrument Research Institute, China), the Soxhlet method (GB/T 6433-2006, glass Soxhlet extractor; thermostatic water bath equipment: DK-S26, Shanghai Jinghong Experimental Equipment Co., Ltd., China), and the burning method (GB/T 6438-2007, Muffle furnace: 8-10TP, Shanghai Huitai Instrument Manufacturing Co., Ltd., China), respectively.
## 2.5.2. Serum Biochemistry and Liver Composition
An Abbott automatic blood biochemical analyser (Abbott c8000, USA) was used to determine the serum aspartate aminotransferase (AST), glutamate pyruvate aminotransferase (ALT), alkaline phosphatase (ALP), total triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), and low density lipoprotein cholesterol (HDL-C) according to the instructions of the kit, which was purchased from Meikang Biotechnology Co., Ltd. (Ningbo, China). The content of β-hydroxybutyric acid (β-HB) in the liver was extracted according to the method reported by Gao and Wu [21] and determined by a kit produced by Suzhou Grete Biomedical Co., Ltd. (Suzhou, China). The protein content of the liver was determined using a kit produced by Beyotime Biotechnology Co., Ltd. (Shanghai, China). The lipids in the liver were extracted and determined in accordance with the method reported by Staessen et al. [ 22]. The collagen fibre content in the liver was determined with Fish COL ELISA Kit (Sanjia, China).
## 2.5.3. Related Expression of Lipid Metabolism and Inflammatory Genes
An appropriate amount of liver and hindgut tissue was thoroughly ground in the presence of liquid nitrogen. Total RNA was extracted following the instructions of the EASYspin plus kit (Beijing Adlai Biotechnology Co., Ltd., China). First-strand cDNA was synthesized using a 5X All-In-One RT MasterMix Kit (with AccuRT Genomic DNA Removal Kit, abmGood) and stored at -20°C. The mRNA expression of fas, cpt1, acc, nfkb, il1b, and tnfa, whose primer sequences are shown in Table 2, was determined by a relative quantitative method following the instructions of Taq Pro Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China). β-Actin was used as an internal reference gene.
## 2.5.4. Histological Morphology Analysis
The liver and hindgut tissues were removed from the formaldehyde solution, and slices were made following routine histological procedures and stained with haematoxylin eosin (H&E). The liver tissue sections were also stained with Masson and oil red O. The histological sections were observed with an optical microscope (Olympus BX51), and pictures were taken with an image acquisition system (SmartV550D, Jiangsu JieDa Technology Development Co., Ltd., China).
## 2.6. Calculation and Statistical Analysis
Weight gain rate (WGR), specific growth rate (SGR), feed conversion rate (FCR), feed intake (FI), feed intake rate (FIR), condition factor (CF), and hepatopancreas somatic indices (HSI) were calculated as follows: [1]Weight gain rate WGR,%=Wf−WiWi×100,Specific growth rate SGR,%d=lnWf−lnWidays×100,Feed conversion rate FCR=WdWf−Wi,FI gfish=WdNo.of fish,Feed intake rate FIR,%d=Wddays×Wf+Wi/2×100,Condition factor CF,%=BWBL3×100,Hepatosomatic index HSI,%=LWBW×100,where Wf (g/fish) is the mean final body weight, Wi (g/fish) is the mean initial body weight, Wd (g) is the weight of the intake diet in each tank, BW (g) is the individual body weight, BL (cm) is the fish body length, and LW is the liver weight (g).
All results are expressed as the mean ± standard deviations. One-way analysis of variance (ANOVA) was used to examine the difference among groups. Tukey's multiple comparison test was used when the variance was homogeneous, and Tamhane's T2 test was used when the variance was nonhomogeneous. Statistical analysis was performed by IBM SPSS 23, and the significance level was set at $P \leq 0.05.$
## 3.1. Growth Performance and Body Condition
As shown in Table 3, there was no significant difference in FIR, WG, SGR, or FCR among the groups ($P \leq 0.05$). The FI of the SB20 group was significantly lower than that of the CON group and the SB2 group ($P \leq 0.05$).
Supplementation with SB showed no significant effects on the body content of moisture, crude protein, lipid, and ash ($P \leq 0.05$) regardless of the dosage (Table 4). The CF decreased while the HSI increased with increasing SB dosage, but not significantly ($P \leq 0.05$).
## 3.2. Biochemical Indices of Serum and Liver
The activities of AST and ALT in the serum increased substantially with increasing SB dosage ($P \leq 0.05$, Figure 1). The activity of ALP in the serum of the SB20 group was considerably higher than that of the SB2 group and the CON group ($P \leq 0.05$). No significant difference was observed between the SB2 group and the CON group ($P \leq 0.05$). The serum TG concentration was $22\%$ and $43\%$ higher than that of the SB2 group and the CON group, respectively, while the TC concentration of the SB20 group was $26\%$ and $65\%$ higher, respectively ($P \leq 0.05$).
With the increase in SB in the diet, the concentration of β-HB in the liver increased significantly ($P \leq 0.05$, Figure 2). The ratio of lipid to protein in the liver showed a trend of increasing, but not significantly ($P \leq 0.05$). The collagen fibre content in the SB20 group was higher, while in the SB2 group lower than that in the CON group, but not significantly ($P \leq 0.05$, Figure 2).
## 3.3. mRNA Expression of Genes Related to Lipid Metabolism and Inflammation
As shown in Figure 3, with the increase in the dosage of SB, the relative expression of fas and acc mRNA in the liver increased significantly. The expression of cpt1 in the SB2 group was higher than that in the CON group and the SB20 group ($P \leq 0.05$), and there was no significant difference between the latter two groups ($P \leq 0.05$).
The mRNA expression of nfkb, tnfa, and il1b in the liver of fish fed a diet containing SB was significantly higher than that of the CON group, and the expression of il-1b increased with increasing SB dosage ($P \leq 0.05$, Figure 4). In intestinal tissue, however, the expression levels of nfkb and il1b in fish fed with a diet containing SB were significantly lower than those in the CON group ($P \leq 0.05$), and there was no significant difference between the SB20 group and the SB2 group ($P \leq 0.05$). No significant difference in tnfa expression in intestinal tissue was observed among the groups ($P \leq 0.05$).
## 3.4. Histological Analysis
Compared to the CON group (Figure 5), the liver sections of the SB20 group showed enlargement of hepatocytes, increased amount of intracellular lipid droplets, blurred cell edges, and lymphocyte infiltration (Figure 5, C1 and C2). The liver cells in the SB2 group showed clear edges and nuclei and no obvious histopathological changes or fat accumulation (Figure 5, B1 and B2). Masson-stained liver tissue sections showed strong blue signals, which showed collagen fibre, appearing only near the portal area but not among hepatocytes in the CON and SB2 groups. In the SB20 group, the blue signals were obvious among hepatocytes (Figure 5 C3). No obvious difference was observed in the integrity of intestinal tissue, the height and width of fold, infiltration of lymphocytes, number of goblet cell, etc. among the groups (data not given).
## 4. Discussion
Studies have shown that fish growth performance could be improved by supplementation of butyrate to the diet; however, the appropriate amount seems to vary among fish species. Jesus et al. [ 3] reported that the growth performance of *Nile tilapia* (Oreochromis niloticus) was improved when 5 g/kg SB was added to the diet. Omosowone et al. [ 23] suggested that 2 g/kg butyric acid in *Clarias gariepinus* and 1.5 g/kg butyric acid in *Oreochromis niloticus* were optimal for growth. Liu et al. [ 5] reported that appropriate dietary supplementation of SB was 2 g/kg to juvenile grass carp (Ctenopharyngodon idella) for growth, while Tian et al. [ 6] recommended 0.1608 g/kg SB (microencapsulated) for the growth of young grass carp. Studies also showed that the growth rate decreased significantly when the dosage of butyrate was beyond the optimal value [1, 4], and no improvement in growth was observed in grass carp (Ctenopharyngodon idella) fed with a diet containing 0.5 g/kg SB [8] or in juvenile giant grouper, Epinephelus lanceolatus, fed with a diet containing 10 g kg−1 butyric acid [9]. In this study, no significant changes in growth performance occurred in either the SB2 or SB20 groups, in which 2 g/kg and 20 g/kg SB was supplied, respectively. The current results, together with the above reports, suggested that the growth-promoting effect of butyric acid might occur only under specific conditions.
Few studies have evaluated the effect of SB as high as 20 g/kg on the growth and physiology of animals, and limited results have shown that the addition of SB up to 50 g/kg has no negative effect on body weights and food intake in mice [24]. No statistically significant change in SGR was noticed in the SB20 group in this study. However, the FI of the SB20 group decreased significantly compared with that of the CON group and the SB2 group. Butyric acid stimulates the secretion of peptide YY (PYY) [25], and PYY acts on the peripheral and central nervous systems through the brain gut axis and contributes to a feeling of satiety [26, 27], which might be the reason for the decline in FI in the SB20 group. The decreased FI further led to decreasing SGR, although not significantly.
Many studies have shown that oral administration of short-chain fatty acids (including butyric acid) can attenuate fat deposition by reducing lipogenesis and enhancing lipolysis in different tissues [8, 28, 29]. However, the effect of butyric acid on lipid metabolism may be completely opposite depending on the dose. Zhao et al. [ 30] noticed that in chicken adipocytes, the fat droplets laden were enlarged accompanied by activation of lipogenic gene expression when butyrate was at a higher concentration (1 mM); however, the opposite response was observed at a lower concentration (0.01 mM). Zhang et al. [ 31] observed in pigs that intravenous SB increased fatty acid synthesis, decreased lipolysis in muscle tissue, and increased lipolysis in adipose tissue. In this study, histological sections showed obviously enhanced lipid deposition in the liver of the SB20 group compared to the CON group. The fat protein ratio, which was used to evaluate liver lipid degeneration [32], also showed an increasing trend in the SB20 group. Liver steatosis is usually accompanied by increases in TC and TG in serum [33, 34]. In this study, the contents of serum TG and TC increased in the SB20 group. All of the above results suggested the induction effect of high-dose SB on fatty liver, which to our knowledge has never been reported in fish. In mice, the direct induction of fatty liver with a high dosage of butyric acid has been reported [12]. fas and acc are key genes for de novo fatty acid synthesis. With the increase in SB in the diet, the mRNA expression of fas and acc in liver tissue was significantly upregulated. These results suggested that liver fat accumulation induced by high-dose SB might occur via de novo hypersynthesis of fatty acids, which has been observed in bovine mammary epithelial cells [35], dairy cows [36], and growing pigs [31].
Generally, butyric acid in the intestine enters the peripheral circulation through the hepatic portal vein and is finally oxidized in the liver to supply energy following the β-oxidation pathway [37, 38]. The current results showed that with increasing SB in the diet, the concentration of β-HB increased significantly, suggesting that butyrate was not completely oxidized under the experimental dosage and that ketone bodies accumulated in the liver. The strong ketogenic capacity of butyrate was also observed in humans [39]. Ketosis is closely associated with increased biomarkers of inflammation [40, 41], and inflammatory reactions induce not only fat accumulation [42, 43] but also fibrosis [44]. In this study, liver fibrosis was also noted in the SB20 group, while the mRNA expression of the inflammatory factor genes nfkb, il1b, and tnfa increased. These results indicated that the pathological changes in liver tissue might be attributed to the chronic inflammatory reaction induced by accumulated β-HB. Similar liver histopathological changes and inflammatory reactions were also observed in mice taking butyrate orally [12].
Hepatic steatosis and fibrosis are often accompanied by an increase in the enzyme activities of AST, ALT, and ALP in serum [45, 46]. In this study, the activity of AST, ALT, and ALP in the SB20 group was increased, which was consistent with the histopathological changes. Unexpectedly, the activity of AST and ALT together with the expression of nfkb, il1b, and tnfa in the SB2 group were also significantly higher than those in the CON group, indicating that liver tissue was also damaged to a certain extent, although the pathological changes in liver tissue were not obvious.
Studies have suggested that an appropriate amount of butyric acid is beneficial in resistance to intestinal tissue damage [2, 4–6] and downregulates the mRNA expression of proinflammatory factor genes [5], and excessive butyrate destroys mucosal barrier function [10]. In this study, there was no obvious abnormality in the intestinal tissue in either the SB2 group or the SB20 group. In addition, the mRNA expression of nfkb and il1b in the intestine was lower than that in the CON group. These results indicated that SB has a certain anti-inflammatory effect on the intestine, even at doses as high as 20 g/kg. Butyric acid can be produced from the fermentation of DF by bacteria in the intestine [18]. Therefore, intestinal epithelial cells may have a high tolerance to high concentrations of butyric acid, which may be the reason for the lack of obvious pathological changes in the intestine in the SB20 group.
In our previous study, it was noticed that high DF led to hepatic steatosis and fibrosis and enteritis in yellow catfish [16, 47]. DF can be fermented by microflora in the intestine and produce butyric acid [18]. The current results showed that a high dosage of SB caused liver fat accumulation accompanied by fibrosis. It can, therefore, be inferred that DF induced liver fat accumulation and tissue damage partly through the fermentation product butyric acid.
## 5. Conclusion
The addition of 2 g/kg and 20 g/kg SB to the diet did not improve the growth performance of largemouth bass, while 20 g/kg SB caused liver lipid accumulation and fibrosis, which might be attributed to de novo hypersynthesis of fatty acids and the inflammatory reaction.
## Data Availability
The data that support the findings of this study are available from the first author, Yiyang Ge ([email protected]).
## Conflicts of Interest
The authors declare no conflicts of interest.
## Authors' Contributions
Yiyang Ge performed the investigation and wrote the original draft of the manuscript in the native language. Shibin Yao organized the experiment, made fish feed, and wrote the draft of the manuscript in English. Ye Shi performed the biochemical and gene expression analysis of the samples. Chunfang Cai reviewed and edited the draft of the manuscript, interpreted the results, supervised the project administration, and acquired the funding. Chengrui Wang was responsible for fish feeding and took part in the sampling. Ping Wu, Xiamin Cao, and Yuantu Ye are laboratory managers who participated in the discussion and formulation of the research plan and assisted in sample analysis and funding acquisition.
## References
1. Zhou C. P., Lin H. Z., Huang Z., Wang J., Wang Y., Yu W.. **Effect of dietary sodium butyrate on growth performance, enzyme activities and intestinal proliferation-related gene expression of juvenile golden pompano**. (2019) **25** 1261-1271. DOI: 10.1111/anu.12940
2. Jesus G. F. A., Owatari M. S., Pereira S. A.. **Effects of sodium butyrate and**. (2021) **117** 62-69. DOI: 10.1016/j.fsi.2021.07.008
3. Jesus G. F. A., Pereira S. A., Owatari M. S.. **Protected forms of sodium butyrate improve the growth and health of Nile tilapia fingerlings during sexual reversion**. (2019) **499** 119-127. DOI: 10.1016/j.aquaculture.2018.09.027
4. Volatiana J. A., Wang L., Gray N., Tong S., Zhang G., Shao Q.. **Tributyrin-supplemented high-soya bean meal diets of juvenile black sea bream,**. (2020) **51** 135-146. DOI: 10.1111/are.14355
5. Liu M. M., Guo W., Wu F., Qu Q., Tan Q., Gong W.. **Dietary supplementation of sodium butyrate may benefit growth performance and intestinal function in juvenile grass carp (**. (2017) **48** 4102-4111. DOI: 10.1111/are.13230
6. Tian L., Zhou X. Q., Jiang W. D.. **Sodium butyrate improved intestinal immune function associated with NF-**. (2017) **66** 548-563. DOI: 10.1016/j.fsi.2017.05.049
7. Lin Y. H., Cheng M. Y.. **Effects of dietary organic acid supplementation on the growth, nutrient digestibility and intestinal histology of the giant grouper _Epinephelus lanceolatus_ fed a diet with soybean meal**. (2017) **469** 106-111. DOI: 10.1016/j.aquaculture.2016.11.032
8. Zhou J. S., Guo P., Yu H. B., Ji H., Lai Z. W., Chen Y. A.. **Growth performance, lipid metabolism, and health status of grass carp (**. (2019) **45** 287-298. DOI: 10.1007/s10695-018-0561-6
9. Yong A. S. K., Zamhari D., Shapawi R., Zhuo L. C., Lin Y. H.. **Physiological changes of giant grouper (**. (2020) **18, article 100499**. DOI: 10.1016/j.aqrep.2020.100499
10. Peng L. Y., He Z. J., Chen W., Holzman I. R., Lin J.. **Effects of butyrate on intestinal barrier function in a Caco-2 cell monolayer model of intestinal barrier**. (2007) **61** 37-41. DOI: 10.1203/01.pdr.0000250014.92242.f3
11. Singh V., Yeoh B. S., Chassaing B.. **Dysregulated microbial fermentation of soluble fiber induces cholestatic liver cancer**. (2018) **175** 679-694.e22. DOI: 10.1016/j.cell.2018.09.004
12. Lupton J. R.. **Microbial degradation products influence colon cancer risk: the butyrate controversy**. (2004) **134** 479-482. DOI: 10.1093/jn/134.2.479
13. Martinez-Llorens S., Baeza-Arino R., Nogales-Merida S., Jover-Cerda M., Tomas-Vidal A.. **Carob seed germ meal as a partial substitute in gilthead sea bream (**. (2012) **338** 124-133. DOI: 10.1016/j.aquaculture.2012.01.029
14. Robaina L., Izquierdo M. S., Moyano F. J.. **Soybean and lupin seed meals as protein sources in diets for gilthead seabream (**. (1995) **130** 219-233. DOI: 10.1016/0044-8486(94)00225-D
15. Romarheim O. H., Zhang C., Penn M.. **Growth and intestinal morphology in cobia (**. (2008) **14** 174-180. DOI: 10.1111/j.1365-2095.2007.00517.x
16. Ren S. J., Cai C. F., Cui G. T.. **High dosages of pectin and cellulose cause different degrees of damage to the livers and intestines of**. (2020) **514, article 734445**. DOI: 10.1016/j.aquaculture.2019.734445
17. Jakobsdottir G., Jadert C., Holm L., Nyman M. E.. **Propionic and butyric acids, formed in the caecum of rats fed highly fermentable dietary fibre, are reflected in portal and aortic serum**. (2013) **110** 1565-1572. DOI: 10.1017/S0007114513000809
18. Martin-Gallausiaux C., Marinelli L., Blottiere H. M., Larraufie P., Lapaque N.. **SCFA: mechanisms and functional importance in the gut**. (2021) **80** 37-49. DOI: 10.1017/S0029665120006916
19. Ocvirk S., Wilson A. S., Appolonia C. N., Thomas T. K., O'Keefe S. J. D.. **Fiber, fat, and colorectal cancer: new insight into modifiable dietary risk factors**. (2019) **21** p. 62. DOI: 10.1007/s11894-019-0725-2
20. Dawood M. A. O., Eweedah N. M., Elbialy Z. I., Abdelhamid A. I.. **Dietary sodium butyrate ameliorated the blood stress biomarkers, heat shock proteins, and immune response of Nile tilapia (**. (2020) **88, article 102500**. DOI: 10.1016/j.jtherbio.2019.102500
21. Gao S. B., Wu T. X.. **The effects of L-carnitine on activities of digestive enzymes in growing-finishing pigs**. (2003) **15** 54-58
22. Staessen T. W. O., Verdegem M. C. J., Nederlof M. A. J., Eding E. H., Schrama J. W.. **Effect of type of dietary non-protein energy source (starch vs. fat) on the body bile acid pool size and composition, faecal bile acid loss and bile acid synthesis in rainbow trout (**. (2021) **27** 865-879. DOI: 10.1111/anu.13231
23. Omosowone O. O., Dada A. A., Adeparusi E. O.. **Comparison of dietary butyric acid supplementation effect on growth performance and body composition of**. (2018) **17** 403-412. DOI: 10.22092/IJFS.2018.115901
24. Chang S., Chen X. L., Huang Z. Q.. **Dietary sodium butyrate supplementation promotes oxidative fiber formation in mice**. (2018) **29** 212-215. DOI: 10.1080/10495398.2017.1358734
25. Schulthess J., Pandey S., Capitani M.. **The short chain fatty acid butyrate imprints an antimicrobial program in macrophages**. (2019) **50** 432-445.e7. DOI: 10.1016/j.immuni.2018.12.018
26. Zac-Varghese S., De Silva A., Bloom S. R.. **Translational studies on PYY as a novel target in obesity**. (2011) **11** 582-585. DOI: 10.1016/j.coph.2011.10.001
27. Rong B. H., Xia T. Y., Zhang T. T.. **Gut microbiota: a potential manipulator for host adipose tissue and energy metabolism**. (2019) **64** 206-217. DOI: 10.1016/j.jnutbio.2018.10.020
28. Jiao A. R., Diao H., Yu B.. **Oral administration of short chain fatty acids could attenuate fat deposition of pigs**. (2018) **13**. DOI: 10.1371/journal.pone.0196867
29. Jiao A. R., Yu B., He J.. **Short chain fatty acids could prevent fat deposition in pigs via regulating related hormones and genes**. (2020) **11** 1845-1855. DOI: 10.1039/C9FO02585E
30. Zhao L. Q., Liu S., Zhang Z. H.. **Low and high concentrations of butyrate regulate fat accumulation in chicken adipocytes via different mechanisms**. (2020) **9** 120-131. DOI: 10.1080/21623945.2020.1738791
31. Zhang H., Ren E. D., Xu R. Y., Su Y.. **Transcriptomic responses induced in muscle and adipose tissues of growing pigs by intravenous infusion of sodium butyrate**. (2021) **10,**. DOI: 10.3390/biology10060559
32. Gilat T., Leikin-Frenkel A., Goldiner I.. **Prevention of diet-induced fatty liver in experimental animals by the oral administration of a fatty acid bile acid conjugate (FABAC)**. (2003) **38** 436-442. DOI: 10.1053/jhep.2003.50348
33. Zhang Z., Wen H. Q., Peng B. J., Weng J., Zeng F. H.. **HFD-induced TRAF6 upregulation promotes liver cholesterol accumulation and fatty liver development via EZH2-mediated miR-429/PPAR**. (2021) **24** 711-727. DOI: 10.1016/j.omtn.2021.01.026
34. Fang D. L., Wang D., Ma G. X.. **_Auricularia polytricha_ noodles prevent hyperlipemia and modulate gut microbiota in high-fat diet fed mice**. (2021) **10** 431-441. DOI: 10.1016/j.fshw.2021.04.005
35. Cheng J., Zhang Y. F., Ge Y. S.. **Sodium butyrate promotes milk fat synthesis in bovine mammary epithelial cells via GPR41 and its downstream signalling pathways**. (2020) **259, article 118375**. DOI: 10.1016/j.lfs.2020.118375
36. Urrutia N., Bomberger R., Matamoros C., Harvatine K. J.. **Effect of dietary supplementation of sodium acetate and calcium butyrate on milk fat synthesis in lactating dairy cows**. (2019) **102** 5172-5181. DOI: 10.3168/jds.2018-16024
37. Xiao S. W., Jiang S., Qian D. W., Duan J. A.. **Modulation of microbially derived short-chain fatty acids on intestinal homeostasis, metabolism, and neuropsychiatric disorder**. (2020) **104** 589-601. DOI: 10.1007/s00253-019-10312-4
38. Astbury S. M., Corfe B. M.. **Uptake and metabolism of the short-chain fatty acid butyrate, a critical review of the literature**. (2012) **13** 815-821. DOI: 10.2174/138920012800840428
39. St-Pierre V., Courchesne-Loyer A., Vandenberghe C., Hennebelle M., Castellano C. A., Cunnane S. C.. **Butyrate is more ketogenic than leucine or octanoate-monoacylglycerol in healthy adult humans**. (2017) **32** 170-175. DOI: 10.1016/j.jff.2017.02.024
40. Abuajamieh M., Kvidera S. K., Fernandez M. V. S.. **Inflammatory biomarkers are associated with ketosis in periparturient Holstein cows**. (2016) **109** 81-85. DOI: 10.1016/j.rvsc.2016.09.015
41. Shen T. Y., Li X. W., Loor J. J.. **Hepatic nuclear factor kappa B signaling pathway and NLR family pyrin domain containing 3 inflammasome is over-activated in ketotic dairy cows**. (2019) **102** 10554-10563. DOI: 10.3168/jds.2019-16706
42. Huang C. Z., Tung Y. T., Hsia S. M., Wu C. H., Yen G. C.. **The hepatoprotective effect of**. (2017) **8** 842-850. DOI: 10.1039/C6FO01585A
43. Li X. W., Huang W. K., Gu J. M.. **SREBP-1c overactivates ROS-mediated hepatic NF-**. (2015) **27** 2099-2109. DOI: 10.1016/j.cellsig.2015.07.011
44. Czaja A. J., Carpenter H. A.. **Progressive fibrosis during corticosteroid therapy of autoimmune hepatitis**. (2004) **39** 1631-1638. DOI: 10.1002/hep.20235
45. Caldwell S. H., Oelsner D. H., Iezzoni J. C., Hespenheide E. E., Battle E. H., Driscoll C. J.. **Cryptogenic cirrhosis: clinical characterization and risk factors for underlying disease**. (1999) **29** 664-669. DOI: 10.1002/hep.510290347
46. Li H., Wang Y. J., Tan K.. **Prevalence and risk factors of fatty liver disease in Chengdu, Southwest China**. (2009) **8** 377-382. PMID: 19666406
47. Cai C., Ren S., Cui G.. **Short-term stress due to dietary pectin induces cholestasis, and chronic stress induces hepatic steatosis and fibrosis in yellow catfish,**. (2020) **516, article 734607**. DOI: 10.1016/j.aquaculture.2019.734607
|
---
title: 'Fish Meal Replacement by Mixed Plant Protein in the Diets for Juvenile Yellow
Catfish Pelteobagrus fulvidraco: Effects on Growth Performance and Health Status'
authors:
- Ya-Kang Han
- Yi-Chuang Xu
- Zhi Luo
- Tao Zhao
- Hua Zheng
- Xiao-Ying Tan
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973144
doi: 10.1155/2022/2677885
license: CC BY 4.0
---
# Fish Meal Replacement by Mixed Plant Protein in the Diets for Juvenile Yellow Catfish Pelteobagrus fulvidraco: Effects on Growth Performance and Health Status
## Abstract
Increasing dietary replacement levels of fish meal by alternative plant proteins are of value for aquaculture. Here, a 10-week feeding experiment was undertaken to explore the effects of fish meal replacement by mixed plant protein (at a 2: 3 ratio of cottonseed meal to rapeseed meal) on growth performance, oxidative and inflammatory responses, and mTOR pathway of yellow catfish Pelteobagrus fulvidraco. Yellow catfish (2.38 ± 0.1 g, mean ± SEM) were randomly divided into 15 indoors fiberglass tanks, 30 fish each tank, and fed five isonitrogenous ($44\%$ crude protein) and isolipidic ($9\%$ crude fat) diets with fish meal replaced by mixed plant protein at $0\%$ (the control), $10\%$ (RM10), $20\%$ (RM20), $30\%$ (RM30), and $40\%$ (RM40), respectively. Among five groups, fish fed the control, and RM10 diets tended to have higher growth performance, higher protein content, and lower lipid content in livers. Dietary mixed plant protein substitute increased hepatic free gossypol content and damaged liver histology and reduced the serum total essential amino acids, total nonessential amino acids, and total amino acid contents. Yellow catfish fed the control, and RM10 diets tended to have higher antioxidant capacity. Dietary mixed plant protein replacement tended to promote proinflammatory responses and inhibited mTOR pathway. Based on the second regression analysis of SGR against mixed plant protein substitutes, the optimal replacement level of fish meal by mixed plant protein was $8.7\%$.
## 1. Introduction
Fish meal (FM) is thought to be a high-quality protein source in aquatic feeds because it possesses good palatability, well-balanced essential amino acid profiles, and essential fatty acids which are required by fish [1]. Therefore, fish meal is most popular protein source in aquatic feeds. However, because of its increasing demand, reduced supplies, and increasing price, scientists and feed manufacturers are searching for alternative dietary protein sources to replace fish meal [2, 3]. Based on the annual yield, cottonseed meal and rapeseed meal are thought to rank the second and third plant protein sources, respectively, and they had relatively high protein content, abundant resources, low price, and convenient processing.
The application of cottonseed meal and rapeseed meal to replace fish meal has been assessed for many fish species [4–7]. But single safe levels of cottonseed meal and rapeseed meal added in aquaculture diets were limited because of the amino acid imbalance and antinutritional factors (ANFs), such as protease inhibitors, phytic acid, tannins, and glucosinolates (GLS), which adversely influence their utilization in aquaculture feed [5, 8, 9]. More and more researches reported that the replacement of multiple plant protein sources for fish meal can partially alleviate amino acid imbalance of single protein source, and accordingly increased the replacement level [10, 11]. Therefore, in present study, we hypothesize that the mixed plant proteins can increase their replacement level for fish meal.
Several antioxidant enzymes, such as superoxide dismutase (SOD) and catalase (CAT), play important roles in oxidative stress. SOD is responsible for the conversion of superoxide radicals to hydrogen peroxide and CAT for hydrogen peroxide to water and oxygen [12]. Furthermore, the antioxidant enzyme activities (including SOD and CA T) are related to their gene transcription, which is controlled by the Kelch-like-ECH-associated protein 1 (Keap1) and the NF-E2-related nuclear factor 2 (Nrf2) [13, 14]. On the other hand, the inflammatory responses were mediated by cytokines and crucial components for the cellular immune response [15]. Tumor necrosis factor-α (TNF-α) and interleukin 8 (IL8) are the important proinflammatory cytokines [16, 17], and transforming growth factor-β (TGF-β) and interleukin 10 (il10) are the important anti-inflammatory cytokines [18]. On the other hand, growth performance is mainly derived from cell proliferation and growth, which are regulated by mammalian target of rapamycin (mTOR) pathway [19]. The mTOR pathway is considered an integration point between the growth and protein metabolism [20]. The pathway regulates protein synthesis via the eukaryotic translation initiation factor 4e-binding protein (4E-BP) and ribosomal protein S6 kinase (S6K1) [21, 22]. Dietary fish meal replacement with a high proportion of cottonseed meal or rapeseed meal could lead to liver inflammation, reduce the antioxidant capacity, and cause tissue damage in fish [23–25]. Studies suggested that high inclusion levels of cottonseed meal and rapeseed meal reduced growth performance and feed utilization [8, 9]. Studies also suggested that plant proteins can significantly inhibit the mTOR pathway [26, 27]. These negative effects greatly limit the level of plant protein in aquafeeds. Therefore, how to improve their inclusion levels in aquaculture feed seems very urgent [10]. Yellow catfish Pelteobagrus fulvidraco is a very important freshwater culture fish in China and other Asian countries [28]. Because of its good fillet quality, yellow catfish has promising market prospects in Asia. The increasing price of fish meal and the shortage of fish meal resources became the main factor limiting its wide use in aquatic feeds. Therefore, it is important to search for the alternative plant protein sources to replace dietary fish meal to reduce feed cost, improve economic benefit, and promote the development of yellow catfish industry. The experiment was to evaluate the effects of fish meal replacement with the mixed plant protein on growth performance, antioxidant and inflammation response, and mTOR pathway of yellow catfish.
## 2.1. Ethic Statement, Diet Preparation, and Fish Culture
The present study on yellow catfish conforms to the ethical guideline of Huazhong Agriculture University and is approved by the Ethics committee of the University.
Five isonitrogenous ($44\%$ protein) and isolipidic ($9\%$ lipid) diets were formulated by replacing $0\%$ (the control), $10\%$ (RM10), $20\%$ (RM20), $30\%$ (RM30), and $40\%$ (RM40) of fish meal protein with mixed plant proteins, respectively (Table 1). The mixed plant proteins were produced at a 3: 2 ratio of rapeseed meal to cottonseed meal, based on Jiang et al. [ 29]. The dry ingredients were ground and sieved through the 40-mesh screen. Then, dry feedstuffs were weighed accurately according to the formula and thoroughly mixed. After addition of oil and water, the compound was pelleted using a meat grinder with 10 mm sieve. The feeds were dried in a forced air circulation oven at 60°C. The dry pellets were placed in plastic bags and kept at −20°C until feeding. The formula and proximate composition of diets are shown in Table 1.
Then, they were mixed thoroughly and cut into pellets with the pellet presser. All five experimental diets were dried at 60°C in the oven and stored at −20°C.
The present study followed the institutional ethics guidelines of the Ethics Committee of Huazhong Agricultural University (HZAU). The experimental protocols were approved by the Ethical Committee of HZAU. Juvenile yellow catfish were purchased from the local fish farm and randomly stocked in 15 tanks (300-L in water volume) for 2 weeks of acclimation. During the acclimatization, yellow catfish were fed to apparent satiation twice a day (08:30 and 16:30) with RM20 diets. When the feeding experiment has begun, yellow catfish were fasted for 24 h before weighing. Then, similar size and healthy yellow catfish (2.38 ± 0.10 g) were stocked to 15 tanks (300 L in water volume), 30 fish per tank. Each diet was assigned to the three tanks. The experimental tanks were provided with the dechlorinated tap water and with continuous aeration to maintain the dissolved oxygen (DO) level above the saturation. The fish were hand fed to the satiation twice daily (0830 and 1630 h). Yellow catfish were weighed every 2 weeks. The feed intake in each tank was recorded daily. The feces were removed before the feeding in the morning. The experimental tanks were cleaned every 2 weeks when yellow catfish were removed for weighing. Mortality was recorded daily. The feeding experiment lasted for 10 weeks. During the feeding trial, water quality was monitored twice every week, and showed below: water temperature from 28.3 to 29.4°C; DO 6.37-6.67 mg/L and NH4-N not higher than 0.1 mg/L.
## 2.2. Growth Performance, Biological Indices, and Sampling
At the end of the feeding trial, yellow catfish were fasted for 24 h before sampling. They were euthanized with MS-222 (100 mg/L water) and weighed to determine weight gain (WG) and specific growth rate (SGR). Then, 6 fish were randomly selected from each tank, and the blood and liver were immediately collected. The livers were frozen in the liquid N2 and stored at −80°C for the RNA and protein isolation. The blood was centrifuged at 3500 g min−1 for 10 min, and the serum was used to determine the free amino acids content. Another three fish per tank were selected, and the livers were collected, and then fixed in $4\%$ paraformaldehyde for histological observation. For analyzing enzyme activity, another six fish per tank were selected, and the liver was sampled, frozen in liquid nitrogen and stored at −80°C for the subsequent analysis.
## 2.3. Proximate Composition and Free Gossypol Content
The diets and liver proximate composition were analyzed by AOAC [30] standard method. Briefly, the dry matter was analyzed by drying the samples at 105°C until the constant weight was achieved. Crude protein and lipid contents were determined using the Kjeldahl method and the Soxhlet ether method, respectively. Ash content was analyzed after the samples were burned for 8 h in the muffle furnace at 550°C.
The contents of free gossypol in diets and liver were determined by the aniline method. Briefly, gossypol was extracted in the presence of a mixture of 2-propanol, 3-amino-1-propanol and hexane. Then, gossypol was converted into the gossypol-aniline. Finally, the absorbance of the compound was measured at the wavelength of 440 nm.
## 2.4. Analysis for Free Amino Acids Contents
Serum samples were deproteinized by mixing thoroughly with $10\%$ sulfosalicylic acid solution, followed by the incubation at 4°C for 1 h. They were then centrifuged at 13000 rpm for 15 min. Finally, the supernatant was passed through a 0.22-μm filter and then used for amino acid analysis via the automated amino acid analyzer (A300-advanced Autoanalyzer, MembraPure, Germany).
## 2.5. Antioxidant Enzymatic Activities and Lipid Peroxidation Analysis
The activities of antioxidant enzymes, such as total T-SOD, CAT and total antioxidant capacity (T-AOC), and lipid peroxidation (MDA) were measured according to our recent study [31]. The liver tissues were homogenized in ice-cold phosphate buffered saline (PBS). The activities of antioxidant enzymes (T-SOD and CAT), total antioxidant capacity (T-AOC), and MDA were determined by commercial reagent kits (Jiancheng, Nanjing, China). One unit of enzyme activity was defined as the amount of enzyme which converts 1 μM substrate to the product per minute at 37°C and expressed as units per milligram of soluble protein. MDA content was determined based on the reaction of the thiobarbituric acid, and the absorbance was detected at 535 nm with the spectrophotometer.
## 2.6. Liver Histology
Hematoxylin-Eosin (H&E) staining was conducted after Wu et al. [ 32]. The sections were imaged by light microscope (Olympus BX53, Tokyo, Japan), and morphological measurements were performed by the ImageJ software (version 1.51, NIH, Maryland, USA).
## 2.7. Real-Time Quantitative PCR Analysis of Gene Expression
Total RNA in the liver tissues was extracted using the TRIzol method. The integrity of RNA was evaluated by the agarose gel electrophoresis. The cDNA synthesis was performed with the UnionScript First-strand cDNA Synthesis Mix (Genesand, SR511). *The* gene-specific primers are given in Table 2. At first, we measured the transcriptional stabilities of 10 housekeeping genes (β-actin, 18S ribosomal RNA, hypoxanthine-guanine phosphoribosyltransferase, ubiquitin C, β2-microglobulin, tubulin-A, glyceraldehyde 3-phosphate dehydrogenase, TATA-binding protein, ribosomal protein L7, and E74-like factor-A) and determined the best combination of two genes by geNorm online tool (https://genorm.cmgg.be/). Finally, we used the 2−ΔΔCt method to calculate the relative expression of genes after we normalized to their geometric mean of two genes.
## 2.8. Western Blotting
Based on the method described in our laboratory [31], we used the western blot to detect mTOR, p-mTOR, S6, p-S6, Nrf2, and Keap1 protein expression levels. Briefly, the liver was prepared with the RIPA buffer (Thermo Fisher Scientific). Then, the proteins (40 μg from each sample) were separated on $8\%$ or $12\%$ SDS–polyacrylamide gel, depending on the molecular weight of proteins. They were transferred to the PVDF membranes (Thermo Fisher Scientific), blocked with $8\%$ (w/v) skimmed milk in the TBST buffer (20 mM Tris–HCl, $0.1\%$ Tween 20, 150 mM sodium chloride, pH 7.5) for 1 h, washed thrice with the TBST buffer for 10 min each, and followed by the incubation with specific primary antibodies, such as rabbit anti-p-mTOR-S2448 (1: 2000, AP0094; Abclonal, Wuhan, China), anti-mTOR (1: 1000, A2445; Abclonal), anti-p-S6- S$\frac{235}{236}$ (1: 1000, AP0538; Abclonal), anti-S6 (1: 1000, A6058; Abclonal), anti-Nrf2 (1: 1000, A0674; Abclonal), and anti-Keap1 (1: 5000, A17061; Abclonal) for overnight at 4°C. They were then incubated with goat anti-rabbit secondary antibody (1: 10000). Immunoreactive bands were visualized via the enhanced chemiluminescence (Cell Signaling Technology) and quantified via the densitometry using ImageJ software (version 1.42, NIH).
## 2.9. Statistical Analysis
The experimental data are presented as mean ± standard errors of means (SEMs). We performed statistical analysis by the Prism 8 software (GraphPad Software, CA, USA). Before the statistical analysis, we verified the normality of the data using the Shapiro-Wilk test and analyzed the homogeneity of variances by Levene's test. Statistical significance was determined by the one-way ANOVA with the Duncan's post hoc test. P value was set at <0.05 for statistically significant differences.
## 3.1. Survival, Growth Performance, and Feed Utilization
In the present study, survival, growth performance and feed utilization are presented in Table 3. Among five groups, no significant difference was observed in survival rate, and WG (weight gain) and SGR (specific growth rate) were the highest for the RM10 group, and FCR (feed conversion ratio) and FI (feed intake) were highest for the RM40 group. Based on the second-regression analysis model between SGR and dietary mixed plant protein replacement levels, their optimal substitution level for fish meal was $8.7\%$ (Figure 1).
## 3.2. Proximate Composition and Free Gossypol Contents
The approximate composition and free gossypol contents in the liver tissue were shown in Table 4. The crude protein content was highest for yellow catfish fed the RM10 diet, followed by the control, and showed no marked discrepancies among other three groups. The crude lipid content was highest for yellow catfish fed the RM40 diet and lowest for the control. The moisture content presented no marked differences among five dietary groups. Hepatic free gossypol content increased with dietary mixed plant protein levels.
## 3.3. Free Amino Acid Profiles in the Serum
Dietary mixed plant protein source replacement significantly influenced the free amino acid profiles in the serum (Table 5). For these essential amino acids, among the five dietary groups, yellow catfish fed the RM10 diet tended to possess higher contents of Met and Leu, and yellow catfish fed the control had higher contents of Lys, Arg, and His; in contrast, yellow catfish fed the RM40 diet had the lowest contents of Met, Lys, Val, His, Leu, Ile, Phe, and Thr. For these nonessential amino acids, among five groups, yellow catfish fed the control had higher contents of Ala, Asn, and Asp, and fish fed the RM10 diet had higher Glu content; in contrast, yellow catfish fed the RM40 diet had lowest contents of Ser, Ala, Gly, Tyr, Asn, Glu, Pro, and Asp. Total NEAA, total EAA, and total amino acid contents declined with increasing mixed plant protein replacement levels.
## 3.4. Histology
Liver histological observation indicated that dietary mixed plant protein replacement tended to increase the vacuolation amounts (Figures 2(a) and 2(b)). Generally, no significant histological changes in the liver were found between the control and RM10 group since these fish between the two groups had the minimal vacuolation and compact hepatocytes with the nuclear in their centers (Figures 2(a) and 2(b)). However, fish fed the RM40 diet resulted in significant pathological changes of liver tissue, such as severe destruction and disarrangement of hepatocytes, and more amounts of vacuolation in hepatocytes with nuclear located beside the cell membrane (Figure 2).
## 3.5. Indicators of Antioxidant Indices in the Liver
Dietary mixed plant protein replacement significantly influenced antioxidant responses in the livers of yellow catfish (Figure 3). Among five groups, yellow catfish fed the control, and RM10 diet had the highest activities of T-SOD and CAT and total antioxidant capacity (Figures 3(a) and 3(b)). In contrast, MDA contents were the lowest for yellow catfish fed the control and RM10 diets and highest for yellow catfish fed RM40 diet (Figure 3(c)). The sod1 mRNA levels were highest for yellow catfish fed the control and RM10 diet and lowest for yellow catfish fed the RM40 diets. The mRNA levels of sod2, cat, and gpx1 were the highest in fish fed the RM10 diet and lower in yellow catfish fed the RM40 diet (Figure 3(d)). The keap1 mRNA expression declined but nrf2 mRNA expression tended to increase with increasing dietary mixed protein replacement levels (Figure 3(e)). Compared to other three groups, yellow catfish fed the control and RM10 diet had lowest Nrf2 protein expression but highest Keap1 protein expression (Figures 3(f)–3(h)).
## 3.6. The mRNA Levels of Inflammation-Related Genes of Yellow Catfish
Dietary mixed plant protein replacement significantly influenced mRNA expression of genes relevant with inflammatory responses (Figure 4). For several proinflammatory factors, the mRNA levels of il1β were the lowest for yellow catfish fed the control and RM10 diet and the highest for yellow catfish fed RM30 and RM40 diets. The il6 mRNA levels were the lowest for yellow catfish fed the control and highest for yellow catfish fed RM30 diet. The il8 and tnfα mRNA levels were lower for yellow catfish fed the control, RM10, and RM20 diets than those for yellow catfish fed the RM30 and RM40 diet. The tnfβ mRNA levels were higher in the RM20 diet group than those in other four groups (Figure 4(a)).
For several anti-inflammatory factors, the mRNA levels of il10 were higher in the RM20 group and presented no marked discrepancies among other four groups, and the tgfβ mRNA level was highest in the control and lowest in the RM20 group (Figure 4(b)).
## 3.7. The Expression of mTOR Pathway-Related Proteins in the Liver
Dietary mixed plant protein replacement significantly influenced mRNA and protein expression of key factors relevant with mTOR signaling pathway (Figure 5). The mTOR mRNA levels declined with the increment of plant protein replacement levels, and the s6 mRNA expression was highest for yellow catfish fed the control and RM10 diets and lowest for yellow catfish fed the RM40 diet (Figure 5(a)). The 4ebp1 mRNA levels increased, and the mRNA levels of s6k1, eif4b and eif4e declined with the increment of replacement levels (Figure 5(b)). Dietary protein replacement did not markedly affect the protein expression of mTOR and S6. However, the ratios of p-mTOR/mTOR and p-S6/S6 reduced with increasing dietary replacement levels (Figures 5(c)–5(f)).
## 4. Discussion
In the results of the present study, survival showed no significant differences among the treatments. Similarly, other studies suggest that high inclusion levels of cottonseed meal with high gossypol levels do not adversely influence the survival in various species [33–35]. The present study indicated that yellow catfish fed the RM10 group possessed the best growth performance and feed utilization. Moreover, compared to the control, the mixture of plant protein replaced $30\%$ fish meal without affecting growth performance and feed utilization of yellow catfish. Many studies explored the optimal fish meal replacement level by an individual cottonseed meal or rapeseed meal in fish [9, 36]. For example, Sun et al. and Zhang et al. [ 9, 36] found that the growth and feed utilization of Hemibagrus wyckioides were reduced with increasing dietary rapeseed meal level. Previous studies found that the optimal dietary fermented cottonseed meal level to replace fish meal was $3.11\%$ for black seabream [36]. However, Lim and Lee [6] demonstrated that the $30\%$ dietary fish meal could be replaced by cottonseed meal for parrot fish. The discrepancies may be due to the species, feeding habitat and the composition in the diets. Different from the previous single plant protein replacement of fish meal, this study used a mixed plant protein (cottonseed meal: rapeseed meal = 2: 3) to replace fish meal in yellow catfish diet. Compared with a single plant protein, mixed plant protein could replace more fish meal without inhibiting growth performance. Studies suggested that mixed plant proteins can alleviate the adverse influences of amino acid imbalance and antinutritional factors in a single plant protein source [10, 11]. Therefore, the mixed plant protein in fish feed was better than that of single plant protein. Studies suggested that the nutritional quality of cottonseed meal and rapeseed meal largely depends on the contents of ANFs, and the ANFs negatively influenced the activities of digestive enzymes and nutrient digestibility and growth performance of fish [9, 35]. Our study also indicated that yellow catfish fed the RM40 diet had the highest FI. Similarly, Zhou et al. [ 37] found that FI increased as the dietary canola meal protein increased, which could be explained by increasing the feed intake to obtain more nutrients with the increasing canola meal levels [38].
In the present study, yellow catfish fed the RM10 diet had highest crude protein content, followed by the control, and the crude protein content showed no marked discrepancies among other three groups. Similarly, studies suggested that dietary plant protein sources addition reduced body protein contents in many fish species [36, 39]. We also found that the crude lipid content was highest for yellow catfish fed the RM40 diet and lowest for yellow catfish fed the control. Alam et al. [ 40] found a higher lipid content in the whole body in flounder fed $75\%$ and $100\%$ cottonseed meal diets compared with fish fed a fish meal-based diet. Potential toxicological effects of dietary ANFs in the plant proteins may impair protein and lipid deposition of fish. Our study indicated that free gossypol content in the liver ranged between 0 and 34.42 mg/kg and increased with dietary mixed plant protein levels. The liver is the major tissue for free gossypol accumulation in fish [41]. Generally speaking, the gossypol accumulation in our study was similar to or slightly higher than those in other studies [6, 36]. However, higher hepatic gossypol contents were also reported in other studies [42, 43]. These differences support the notion that hepatic gossypol accumulation in fish can markedly be influenced by dietary types or the fish species tested. In agreement with our study, other studies suggested a positive relationship between dietary gossypol and hepatic gossypol content [42, 43]. Hardy [44] pointed out that gossypol could induce the formation of ceroid granules. Therefore, pathological changes of liver tissue of yellow catfish with increasing plant protein were likely associated with increased ANFs content.
In order to explore the absorption of dietary amino acids among the groups, we analyzed the free amino acid contents in the serum of yellow catfish. Our study indicated that dietary mixed plant protein source replacement significantly influenced the free amino acid profiles in the serum. Generally speaking, we found that total NEAA, total EAA, and total amino acid contents declined with increasing mixed plant protein replacement levels. Decreased serum free amino acids mean that yellow catfish have limited utilization of mixed plant protein. Similar to our results, Yuan et al. [ 45] demonstrated that $5\%$ replacement of fish meal by cottonseed meal protein hydrolysate in feed significantly reduces the total essential amino acid content in the plasma of blunt snout bream. Yuan et al. [ 45] showed that soybean meal reduced free amino acid contents in the turbot plasma.
The antioxidant defense system was also dependent on nutrition [46]. In our study, dietary mixed plant protein replacement significantly influenced hepatic antioxidant responses of yellow catfish. We found that fish fed the control and RM10 diet possessed higher CAT and T-SOD activities and total antioxidant capacity, the higher sod1, sod2, cat, and gpx1 mRNA levels and the lowest MDA content. SOD and CAT are considered as important defense components against the free radicals in vertebrates [47], and MDA is usually as a biomarker to assess oxidative stress [48]. Similarly, other studies pointed out that high rapeseed meal levels reduced antioxidant capacity and increased oxidative stress in fish [9, 24, 38, 49, 50]. The reduced antioxidant enzyme activities after high plant protein inclusion may be due to the downregulation of mRNA expression of antioxidant genes. The reduced antioxidant enzyme activities will cause lipid peroxidation, which was confirmed by the significant MDA increase in yellow catfish fed increasing mixed plant protein levels. Again, the increased ANFs contents induced by dietary plant protein inclusion are potential stressors which adversely affect oxidative stress of fish. Studies suggested that the expression of antioxidant enzymes was controlled by Nrf2-Keap1 pathway [51]. Meantime, we found that the gene expression of keap1 was declined, but the gene expression of nrf2 was increased with the increment of dietary mixed plant protein levels; moreover, compared to other three groups, yellow catfish fed the control and RM10 diet had lowest Nrf2 protein expression but the highest Keap1 protein expression. Nrf2, an important transcriptional factor, regulates the expression of many antioxidative genes through the direct binding to the antioxidant response element (ARE) of target genes' promoters [12, 52]. Keap1 can bind with Nrf2 protein, prevents its translocation to the nucleus, and promotes its ubiquitination-proteasomal degradation [12, 53]. Studies indicated that phenolic compounds can induce oxidative stress by regulating Keap1 and Nrf2 [9]. Thus, the depressed antioxidant capacity of yellow catfish fed high mixed plant protein diets could be attributable to the presence of ANFs in these ingredients. Downregulation of mRNA expression of the antioxidant genes may result from the inhibition of Nrf2 signaling in fish.
In fish, the immune status is closely linked with inflammation initiation and controlled by the inflammatory cytokines [54]. In the present work, dietary mixed plant protein replacement significantly influenced mRNA expression of genes relevant with inflammatory responses, and high proportion (more than $20\%$) of mixed plant protein in feed significantly increased proinflammatory cytokines of TNF-α, il1β, il6, and il8 mRNA expressions. The TNF-α, il1β, IL6, and IL8 are important proinflammatory cytokines that are used as biomarkers for the activation of inflammatory responses [17]. These cytokines synergistically act to mediate the resistance to infections by controlling pathogen replication within the cells [16, 55]. Studies have shown that the upregulation of TNF-α and IL8 could increase the inflammatory response [56, 57]. Our study indicated that the il10 and tgfβ mRNA expression was variable and could not be linked to dietary treatments. At present, we do not know the exact reason since the TGF-β and il10 could inhibit the excessive activation of the immune responses [58].
The TOR pathway plays the key regulatory roles in protein synthesis in response to nutrients [59, 60]. The mTOR stimulates the protein synthesis through 4EBPs and S6Ks in fish and in mammals [61, 62]. S6Ks is located in the downstream of TOR pathway and control the cell growth via regulating the translation of eukaryotic initiation factor 4B (eIF4B) and ribosomal protein S6 (rpS6) [63]. Previous studies reported that amino acids regulated the gene expression of TOR pathway [64]. However, few reports were published about the relationship between the protein feedstuff and TOR pathway. In the present study, the mRNA levels of mTOR, s6, s6k1, eif4b, and eif4e tended to decline but 4ebp1 mRNA levels tended to increase with increasing replacement levels. The 4E-BP1 is one small-molecular weight translational repressor [63]. Zhou et al. [ 37] found that fish meal replacement with canola meal decreased hepatic TOR mRNA levels and regulated the s6k1 mRNA expression of blunt snout bream. Wacyk et al. [ 65] reported that dietary soybean meal decreased hepatic tor gene expression in rainbow trout. Yuan et al. [ 45] found that dietary $5\%$ and $7\%$ cottonseed meal protein hydrolysate (CPH) decreased S6K1 mRNA expression, further confirming that replacing fish meal with high CPH levels depressed the protein synthesis by inhibiting the TOR pathway. He et al. [ 66] showed that replacing fish meal with high cottonseed protein concentrate decreased the growth performance and inhibited mTOR pathway in largemouth bass. Thus, the tor mRNA level decreased significantly when fish meal was replaced by high levels of mixed plant proteins in diets, which in turn upregulated the gene expression of 4E-BP1, inhibited mRNA translation and cell proliferation, and accordingly inhibited the growth. We found that the ratios of p-mTOR/mTOR and p-S6/S6 reduced with increasing dietary replacement levels, further confirming the inhibition of TOR pathway since studies suggested that these proteins were regulated at the phosphorylation level [67].
## 5. Conclusion
In conclusion, replacing $10\%$ fish meal with mixed plant protein could significantly improve growth performance without adverse effects on feed utilization and health status of yellow catfish. The second-regression analysis based on SGR against dietary mixed plant protein level indicated that their optimal replacement level was $8.7\%$. Higher replacement levels of fish meal by mixed plant protein sources reduced growth performance, damaged the liver histology and induced oxidative stress and inflammatory responses, and inhibited mTOR pathway. These findings provided a reference for the application of cottonseed meal and rapeseed meal in the diets for yellow catfish and other fish species.
## Data Availability
Data will be available with reasonable requirement with corresponding author.
## Conflicts of Interest
The authors declare that there are no conflicts of interest.
## Authors' Contributions
The authors' responsibilities were as follows: X. Y. T. and Y. K. H. designed the experiments; Y. K. H. carried out animal and sample analysis with the help of Y. C. X., Z.L., T. Z., and H. Z.; X. Y. T. and Y. K. H. had primary responsibility for the final content; all the authors read and approved the final manuscript.
## References
1. Choand J. H., Kim I. H.. **Fish meal - nutritive value**. (2011) **95** 685-692. DOI: 10.1111/j.1439-0396.2010.01109.x
2. Alam M. S., Watanabe W. O., Sullivan K. B., Rezek T. C., Seaton P. J.. **Replacement of menhaden fish meal protein by solvent-extracted soybean meal protein in the diet of juvenile black sea bass supplemented with or without squid meal, krill meal, methionine, and lysine**. (2012) **74** 251-265. DOI: 10.1080/15222055.2012.678567
3. Gatlin D. M., Barrows F. T., Brown P.. **Expanding the utilization of sustainable plant products in aquafeeds: a review**. (2007) **38** 551-579. DOI: 10.1111/j.1365-2109.2007.01704.x
4. Chengand Z. J., Hardy R. W.. **Apparent digestibility coefficients and nutritional value of cottonseed meal for rainbow trout (**. (2002) **212** 361-372
5. Liand M. H., Robinson E. H.. **Use of cottonseed meal in aquatic animal diets: a review**. (2006) **68** 14-22. DOI: 10.1577/A05-028.1
6. Limand S. J., Lee K. J.. **Partial replacement of fish meal by cottonseed meal and soybema meal with iron and phytase supplementation for parrot fish**. (2009) **290** 283-289. DOI: 10.1016/j.aquaculture.2009.02.018
7. Wang J., Clark G., Ju M., Castillo S., Gatlin D. M.. **Effects of replacing menhaden fishmeal with cottonseed flour on growth performance, feed utilization and body composition of juvenile red drum**. (2020) **523, article 735217**. DOI: 10.1016/j.aquaculture.2020.735217
8. Luo Y., Ai Q., Mai K., Zhang W., Xu W., Zhang Y.. **Effects of dietary rapeseed meal on growth performance, digestion and protein metabolism in relation to gene expression of juvenile cobia (**. (2012) **368-369** 109-116. DOI: 10.1016/j.aquaculture.2012.09.013
9. Zhang X., Wang H., Zhang J.. **Assessment of rapeseed meal as fish meal alternative in diets for juvenile Asian red-tailed catfish (**. (2020) **18, article 100497**. DOI: 10.1016/j.aqrep.2020.100497
10. Gómez-Requeni P., Mingarro M., Calduch-Giner J. A.. **Protein growth performance, amino acid utilisation and somatotropic axis responsiveness to fish meal replacement by plant protein sources in gilthead sea bream (**. (2004) **232** 493-510. DOI: 10.1016/S0044-8486(03)00532-5
11. Wei Y., Wang J., Zhang X.. **Fish protein hydrolysate supplementation in plant protein based diets for tiger puffer (**. (2021) **20**. DOI: 10.1016/j.aqrep.2021.100720
12. Chen G. H., Song C. C., Pantopoulos K., Wei X. L., Zheng H., Luo Z.. **Mitochondrial oxidative stress mediated Fe-induced ferroptosis via the NRF2-ARE pathway**. (2022) **180** 95-107. DOI: 10.1016/j.freeradbiomed.2022.01.012
13. Chen S., Zou L., Li L., Wu T.. **The protective effect of glycyrrhetinic acid on carbon tetrachloride-induced chronic liver fibrosis in mice via upregulation of Nrf2**. (2013) **8**. DOI: 10.1371/journal.pone.0053662
14. Jiang Z. Y., Lu M. C., Xu L. L.. **Discovery of potent Keap1-Nrf2 protein-protein interaction inhibitor based on molecular binding determinants analysis**. (2014) **57** 2736-2745. DOI: 10.1021/jm5000529
15. Whyte S. K.. **The innate immune response of finfish - a review of current knowledge**. (2007) **23** 1127-1151. DOI: 10.1016/j.fsi.2007.06.005
16. Haugland Ø., Mercy I. S., Romøren K., Torgersen J., Evensen Ø.. **Differential expression profiles and gene structure of two tumor necrosis factor-**. (2007) **44** 1652-1663. DOI: 10.1016/j.molimm.2006.08.015
17. Secombes C., Wang T., Hong S.. **Cytokines and innate immunity of fish**. (2001) **25** 713-723. DOI: 10.1016/S0145-305X(01)00032-5
18. Rymuszka A., Adaszek L.. **Pro- and anti-inflammatory cytokine expression in carp blood and head kidney leukocytes exposed to cyanotoxin stress - an**. (2012) **33** 382-388. DOI: 10.1016/j.fsi.2012.05.021
19. Saxton R. A., Sabatini D. M.. **mTOR signaling in growth, metabolism, and disease**. (2017) **168** 960-976. DOI: 10.1016/j.cell.2017.02.004
20. Dai W., Panserat S., Terrier F., Seiliez I., Skiba-Cassy S.. **Acute rapamycin treatment improved glucose tolerance through inhibition of hepatic gluconeogenesis in rainbow trout (**. (2014) **307** R1231-R1238. DOI: 10.1152/ajpregu.00166.2014
21. Qin Q., Cao X. F., Dai Y. J.. **Effects of dietary protein level on growth performance, digestive enzyme activity, and gene expressions of the TOR signaling pathway in fingerling**. (2019) **45** 1747-1757. DOI: 10.1007/s10695-019-00664-z
22. Shao J., Liu M., Wang B., Jiang K., Wang M., Wang L.. **Evaluation of biofloc meal as an ingredient in diets for white shrimp**. (2017) **479** 516-521. DOI: 10.1016/j.aquaculture.2017.06.034
23. Bu X., Chen A., Lian X.. **An evaluation of replacing fish meal with cottonseed meal in the diet of juvenile Ussuri catfish**. (2017) **479** 829-837. DOI: 10.1016/j.aquaculture.2017.07.032
24. Dossou S., Koshio S., Ishikawa M.. **Growth performance, blood health, antioxidant status and immune response in red sea bream (**. (2018) **75** 253-262. DOI: 10.1016/j.fsi.2018.01.032
25. Xie M., Xie Y., Li Y.. **The effects of fish meal replacement with ultra-micro ground mixed plant proteins (uPP) in practical diet on growth, gut and liver health of common carp (**. (2021) **19, article 100558**. DOI: 10.1016/j.aqrep.2020.100558
26. Wu Z. H., Yu X. J., Guo J. S.. **Effects of replacing fish meal with corn gluten meal on growth performance, intestinal microbiota, mTOR pathway and immune response of abalone**. (2022) **23, article 101007**. DOI: 10.1016/j.aqrep.2022.101007
27. Yu X., Wu Z., Guo J.. **Replacement of dietary fish meal by soybean meal on growth performance, immunity, anti-oxidative capacity and mTOR pathways in juvenile abalone**. (2022) **551** p. 73714. DOI: 10.1016/j.aquaculture.2022.737914
28. Zhang M., Hou C., Li M.. **Modulation of lipid metabolism in juvenile yellow catfish (**. (2018) **45** 115-122. PMID: 30019291
29. Jiang C., Leng X., Li X., Fan Y., Shi S., Luo Y.. **Effects of different proportions of rapeseed meal and cottonseed meal on growth performance, plasma biochemical indices and gossypol residue of**. (2010) **34** 1917-1925
30. 30AOAC
Official Methods of Analysis of Official Analytical Chemists International
199516th edArlington, VA, USAAssociation of Official Analytical Chemists. (1995)
31. Zhao T., Wu K., Hogstrand C.. **Lipophagy mediated carbohydrate-induced changes of lipid metabolism via oxidative stress, endoplasmic reticulum (ER) stress and ChREBP/PPAR**. (2020) **77** 1987-2003. DOI: 10.1007/s00018-019-03263-6
32. Wu K., Luo Z., Hogstrand C., Chen G. H., Wei C. C., Li D. D.. **Zn stimulates the phospholipids biosynthesis via the pathways of oxidative and endoplasmic reticulum stress in the intestine of freshwater teleost yellow catfish**. (2018) **52** 9206-9214. DOI: 10.1021/acs.est.8b02967
33. Anderson A. D., Alam M. S., Watanabe W. O., Carroll P. M., Wedegaertner T. C., Dowd M. K.. **Full replacement of menhaden fish meal protein by low-gossypol cottonseed flour protein in the diet of juvenile black sea bass**. (2016) **464** 618-628. DOI: 10.1016/j.aquaculture.2016.08.006
34. Mbahinzireki G. B., Dabrowski K., Lee K. J., El-Saidy D., Wisner E. R.. **Growth, feed utilization and body composition of tilapia (**. (2000) **7** 189-200
35. Wang X. F., Li X. Q., Leng X. J., Shan L. L., Zhao J. X., Wang Y. T.. **Effects of dietary cottonseed meal level on the growth, hematological indices, liver and gonad histology of juvenile common carp (**. (2014) **428-429** 79-87. DOI: 10.1016/j.aquaculture.2014.02.040
36. Sun H., Tang J., Yao X.. **Partial substitution of fish meal with fermented cottonseed meal in juvenile black sea bream (**. (2015) **446** 30-36. DOI: 10.1016/j.aquaculture.2015.04.020
37. Zhou Q. L., Habte‐Tsion H. M., Ge X.. **Graded replacing fishmeal with canola meal in diets affects growth and target of rapamycin pathway gene expression of juvenile blunt snout bream,**. (2017) **24** 300-309. DOI: 10.1111/anu.12560
38. Cheng Z., Ai Q., Mai K.. **Effects of dietary canola meal on growth performance, digestion and metabolism of Japanese seabass,**. (2010) **305** 102-108. DOI: 10.1016/j.aquaculture.2010.03.031
39. El‐Saidy D. M., Saad A. S.. **Effects of partial and complete replacement of soybean meal with cottonseed meal on growth, feed utilization and haematological indexes for mono-sex male Nile tilapia,**. (2011) **42** 351-359. DOI: 10.1111/j.1365-2109.2010.02629.x
40. Alam M. S., Watanabe W. O., Carroll P. M.. **Evaluation of genetically-improved (glandless) and genetically-modified low- gossypol cottonseed meal as alternative protein sources in the diet of juvenile southern flounder**. (2018) **489** 36-45. DOI: 10.1016/j.aquaculture.2018.02.006
41. Leeand K. J., Dabrowski K.. **Gossypol and gossypolone enantiomers in tissues of rainbow trout fed low and high levels of dietary cottonseed meal**. (2002) **50** 3056-3061. DOI: 10.1021/jf0115387
42. Pham M. A., Lee K. J., Lim S. J., Park K. H.. **Evaluation of cottonseed and soybean meal as partial replacement for fishmeal in diets for juvenile Japanese flounder**. (2007) **73** 760-769. DOI: 10.1111/j.1444-2906.2007.01394.x
43. Yildirim M., Lim C., Wan P., Klesius P. H.. **Growth performance and immune response of channel catfish (**. (2003) **219** 751-768. DOI: 10.1016/S0044-8486(03)00062-0
44. Hardy R. W., Roberts R. J.. **The nutritional pathology of teleosts**. (2012) 421-422
45. Yuan X. Y., Liu M. Y., Cheng H. H.. **Replacing fish meal with cottonseed meal protein hydrolysate affects amino acid metabolism via AMPK/SIRT1 and TOR signaling pathway of**. (2019) **510** 225-233. DOI: 10.1016/j.aquaculture.2019.05.056
46. Martinez-Alvarez R. M., Morales A. E., Sanz A.. **Antioxidant defenses in fish: biotic and abiotic factors**. (2005) **15** 75-88. DOI: 10.1007/s11160-005-7846-4
47. Zhang X. D., Zhang J. W., Wang H. Z.. **Evaluation of soybean meal as alternative to fish meal in diet for juvenile Asian red-tailed catfish (**. (2019) **25** 1036-1049. DOI: 10.1111/anu.12921
48. Liu X. L., Xi Q. Y., Yang L.. **The effect of dietary**. (2011) **30** 495-500. DOI: 10.1016/j.fsi.2010.11.018
49. Bu X. Y., Wang Y. Y., Chen F. Y.. **An evaluation of replacing fishmeal with rapeseed meal in the diet of Pseudobagrus ussuriensis: growth, feed utilization, nonspecific immunity, and growth-related gene expression**. (2018) **49** 1068-1080. DOI: 10.1111/jwas.12470
50. Dossou S., Koshio S., Ishikawa M.. **Effect of partial replacement of fish meal by fermented rapeseed meal on growth, immune response and oxidative condition of red sea bream juvenile,**. (2018) **490** 228-235. DOI: 10.1016/j.aquaculture.2018.02.010
51. Giuliani M. E., Regoli F.. **Identification of the Nrf2-Keap1 pathway in the European eel**. (2014) **150** 117-123. DOI: 10.1016/j.aquatox.2014.03.003
52. Lushchak V. I.. **Environmentally induced oxidative stress in aquatic animals**. (2011) **101** 13-30. DOI: 10.1016/j.aquatox.2010.10.006
53. Ma Q.. **Role of Nrf2 in oxidative stress and toxicity**. (2013) **53** 401-426. DOI: 10.1146/annurev-pharmtox-011112-140320
54. Wang Y., Zhao H., Liu Y.. **Environmentally relevant concentration of sulfamethoxazole-induced oxidative stress-cascaded damages in the intestine of grass carp and the therapeutic application of exogenous lycopene**. (2021) **274, article 116597**. DOI: 10.1016/j.envpol.2021.116597
55. Scapigliati G., Buonocore F., Bird S.. **Phylogeny of cytokines: molecular cloning and expression analysis of sea bass**. (2001) **11** 711-726. DOI: 10.1006/fsim.2001.0347
56. Mulligan M. S., Jones M. L., Bolanowski M. A.. **Inhibition of lung inflammatory reactions in rats by an anti-human IL-8 antibody**. (1993) **150** 5585-5595. DOI: 10.4049/jimmunol.150.12.5585
57. Popivanova B. K., Kitamura K., Wu Y.. **Blocking TNF-alpha in mice reduces colorectal carcinogenesis associated with chronic colitis**. (2008) **118** 560-570. DOI: 10.1172/JCI32453
58. Raida M. K., Buchmann K.. **Bath vaccination of rainbow trout (**. (2008) **26** 1050-1062. DOI: 10.1016/j.vaccine.2007.12.029
59. Holz M. K., Ballif B. A., Gygi S. P., Blenis J.. **mTOR and S6K1 mediate assembly of the translation preinitiation complex through dynamic protein interchange and ordered phosphorylation events**. (2005) **123** 569-580. DOI: 10.1016/j.cell.2005.10.024
60. Lansard M., Panserat S., Plagnes-Juan E., Seiliez I., Skiba-Cassy S.. **Integration of insulin and amino acid signals that regulate hepatic metabolism-related gene expression in rainbow trout: role of TOR**. (2010) **39** 801-810. DOI: 10.1007/s00726-010-0533-3
61. Sarbassov D., Ali S., Sabatini D.. **Growing roles for the mTOR pathway**. (2005) **17** 596-603. DOI: 10.1016/j.ceb.2005.09.009
62. Seiliez I., Gabillard J. C., Skiba-Cassy S.. **An in vivo and in vitro assessment of TOR signaling cascade in rainbow trout (**. (2008) **295** R329-R335. DOI: 10.1152/ajpregu.00146.2008
63. Roux P. P., Topisirovic I.. **Regulation of mRNA translation by signaling pathways**. (2012) **4**. DOI: 10.1101/cshperspect.a012252
64. Feng L., Peng Y., Wu P.. **Threonine affects intestinal function, protein synthesis and gene expression of TOR in jian carp (**. (2013) **8**. DOI: 10.1371/journal.pone.0069974
65. Wacyk J., Powell M., Rodnick K., Overturf K., Hill R. A., Hardy R.. **Dietary protein source significantly alters growth performance, plasma variables and hepatic gene expression in rainbow trout (**. (2012) **356-357** 223-234. DOI: 10.1016/j.aquaculture.2012.05.013
66. He G., Zhang T., Zhou X.. **Effects of cottonseed protein concentrate on growth performance, hepatic function and intestinal health in juvenile largemouth bass,**. (2022) **23, article 101052**. DOI: 10.1016/j.aqrep.2022.101052
67. Wu Y., Li B., Li L.. **Very-low-protein diets lead to reduced food intake and weight loss, linked to inhibition of hypothalamic mTOR signaling, in mice**. (2021) **33** 888-904.e6. DOI: 10.1016/j.cmet.2021.01.017
|
---
title: Dietary Supplementation with Chromium DL-Methionine Enhances Growth Performance
of African Catfish (Clarias gariepinus)
authors:
- Frederik Kaiser
- Michael Schlachter
- Carsten Schulz
- Claudia Figueiredo-Silva
journal: Aquaculture Nutrition
year: 2023
pmcid: PMC9973147
doi: 10.1155/2023/7092657
license: CC BY 4.0
---
# Dietary Supplementation with Chromium DL-Methionine Enhances Growth Performance of African Catfish (Clarias gariepinus)
## Abstract
Sustainable aqua feeds have become an urgent necessity for future-oriented aquaculture sector development, and especially mineral supply could be limited when diets are being prepared with low amounts of animal-based sources. Since knowledge about the efficiency of organic trace mineral supplementation in different species of fish is limited, the effects of chromium DL-methionine in African catfish nutrition were evaluated. Four commercially based diets with increasing chromium DL-methionine supplementation (0, 0.2, 0.4, and 0.6 mg Cr kg−1) in the form of Availa-Cr 1000 were fed to African catfish (*Clarias gariepinus* B., 1822) in quadruplicate groups for 84 days. Growth performance parameters (final body weight, feed conversion ratio, specific growth rate, daily feed intake, protein efficiency ratio, and protein retention efficiency), biometric indices (mortality, hepatosomatic index, spleen somatic index, and hematocrit), and mineral retention efficiency were assessed at the end of the feeding trial. The specific growth rate was significantly increased in fish-fed diets with 0.2 mg Cr kg−1 and 0.4 mg Cr kg−1 supplementation in comparison with control and based on the second-degree polynomial regression analysis; supplementation with 0.33 mg Cr kg−1 was optimal in commercially based diets for African catfish. Chromium retention efficiency was reduced with increasing supplementation levels; however, the chromium content of the whole body was comparable to literature. The results suggest that organic chromium supplementation is a viable and safe supplement for diets to increase the growth performance of African catfish.
## 1. Introduction
Feed is one of the highest cost factors in fish farming, and it has become evident that the utilization of diets for aquatic species has to be as efficient as possible to reduce pollution of the environment [1]. Decade-long research has demonstrated that supplementation of aqua feeds with different nutrients, vitamins, or minerals can be beneficial for fish health, growth, and overall feed efficiency, especially for diets low in fish meal [2].
Chromium is an essential mineral for humans and certain animals [3], although an essentiality could not be demonstrated in fish based on the definition of an essential trace element [4]. However, dietary supplementation with Cr in fish diets resulted in enhanced growth performance as well as improved immune response and stress sensitivity in numerous species of fish [5–15]. Especially trivalent chromium (Cr+3) can support the metabolism of carbohydrates, lipids, and proteins by elevating the activity of digestive enzymes [16] and potentiating the action of insulin [3]. These mechanisms can lead to increased energy and protein utilization and subsequently improved growth performance of fish [8]. However, high amounts of Cr in diets can lead to toxic effects like interrupting cellular integrity and altering several hematological indices [6, 17], and the health status of fish should be monitored when including additional sources of Cr in fish diets. Generally, organic chromium sources are more bioavailable in comparison with inorganic chromium [3] and were therefore preferentially included in aqua feeds. Among different organic sources of chromium, dietary chromium methionine (CrMet) supplementation was recently demonstrated to be nontoxic for fish even at high doses (2 mg kg−1) and to result in improved growth rate and feed efficiency in comparison with chromium oxide and chromium picolinate [18].
Effects on growth performance and health status of fish varied depending on the form and dose of Cr, as well as on experimental duration and fish species [6]. The aim of this study was therefore to investigate the impact of CrMet supplementation in the form of Availa-Cr 1000, a commercially available product containing a chromium DL-methionine complex, on growth performance and health status of African catfish after 84 days of feeding. To enable the highest level of relevance towards a practical application, a commercially based diet with low fish meal content was supplemented with gradually increasing levels of the product under review. African catfish were selected for this study as they are of great importance to the global aquaculture sector [19].
## 2.1. Ethical Considerations
The research site adhered to the guidelines set out in the German Animal Welfare Act and was supervised by an animal welfare officer. The registration number of the trial at the ministry was V242 – $\frac{32390}{2021.}$
## 2.2. Experimental Setup
800 mixed-sex African catfish (54.4 ± 1.2 g, Aquaculture ID, Nederweert, Netherlands) were adapted to the recirculating system of the Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering, Büsum, Germany, for 7 days. During adaptation, fish were fed a commercial catfish diet daily at $2\%$ of their body weight (Aller CLARIA FLOAT 4.5 mm; 42 g 100 g−1 crude protein, 12 g 100 g−1 crude fat, 29.5 100 g−1 NfE, 5.6 100 g−1 ash, 2.9 100 g−1 fiber, and 20.2 MJ kg−1 gross energy; Aller Aqua A/S, Christiansfeld, Denmark) before being starved for two days before the experimental start. Subsequently, quadruplicate groups of catfish consisting of 50 individuals were assigned to 16 aquaria (60 l) in a semirandomized manner to avoid potential tank effects. The initial stocking density was set to 50 kg m−3. After 6 weeks, the tank volume was doubled by pulling a bulkhead.
The aquaria were embedded in a recirculating system (15 m3, water turnover rate 4 h−1) including a drum filter (mesh size 20 μm), moving bed biofilter (6 m3), and UV treatment (3 × 100 W). Water parameters were measured daily to ensure optimal rearing conditions during the growth trial. Oxygen saturation was at 103.9 ± $6.1\%$ (Handy Polaris; Oxy-Guard International A/S, Birkerod, Denmark), the temperature at 27.4 ± 0.9°C, and pH at 7.4 ± 0.3 (GMH 5550, Digital pH-/mV-/Thermometer, Greisinger Electronic, D). Total ammonia nitrogen remained below 0.4 mg l−1 and total nitrate nitrogen below 1 mg l−1 (Microquant test kit for NH4 and NO2; Merck KgaA, Darmstadt, Germany). The photoperiod was set to 24 h of darkness. Red light headlamps were used to check the mortalities and health status of fish during the experiment.
African catfish were fed twice daily (9:00 am and 3:00 pm) to apparent satiation for 84 days. Additionally, a fixed rate of $0.5\%$ of body weight was fed during night hours by automated belt feeders to reduce aggression among fish. To calculate the exact feed intake, an average pellet weight was measured, and excess pellets were collected after the feeding events.
## 2.3. Experimental Diets
Four diets were prepared for the feeding trial (CRTL = control, CR 1 = chromium diet 1, CR 2 = chromium diet 2, and CR 3 = chromium diet 3). A diet reflecting typical commercial catfish feeds in ingredient and nutrient composition served as control (Tables 1 and 2), while the remaining diets were copies of the control, supplemented with increasing levels of Availa-Cr 1000 (200, 400, and 600 mg kg−1, Table 3). Extruded floating pellets were produced by SPAROS, Olhão, Portugal. The analyzed chromium content of the control diet was 2.37 mg kg−1, and the content of supplemented diets exceeded expectations (Tables 3 and 4). However, the analyzed chromium content of diets was still within relevant doses, and a continuous increase in supplementation level persisted among experimental diets. The evaluated product was chromium DL-methionine (Availa-Cr 1000) with batch number HPA19122, provided by Zinpro Corporation in a ready-to-use powder form. The content of chromium methionine in the product was 2 g 100 g−1. The analyzed chromium content of the product was 1180 mg Cr kg−1.
## 2.4. Sampling
The experiment and fish sampling were conducted according to the German Animal Welfare Act as amended 2016 and according to the German Animal Welfare Laboratory Animal Regulation. The trial was registered as number V242 – $\frac{32390}{2021}$ at “Ministerium für Energiewende, Landwirtschaft, Umwelt, Natur und Digitalisierung”.
All fish were starved for 48 hours before sampling. After the initial adaptation phase as well as after 84 days of feeding, three fish per tank were sampled for determination of whole body composition, trace mineral content, hepatosomatic index (HSI), spleen somatic index (SSI), and hematocrit. Fish were anesthetized with a blunt hit on the central nervous system and subsequently euthanized by piercing the heart. Whole body samples were stored at -20°C until being homogenized and freeze-dried (Alpha 1-2 LD plus, Christ, Osterode, Germany) before analysis. For the calculation of hematocrit, blood was sampled with syringes at the caudal vein and subsequently centrifuged (Haematokrit 210, Andreas Hettich GmbH & Co. KG, Tuttlingen, Germany). Values for hematocrit were determined optically with the scale attached to the centrifuge.
Growth performance parameters were calculated including feed conversion ratio (FCR = g feed intake/g weight gain), specific growth rate (SGR = (ln (FBW)–ln (IBW))/feeding days∗ 100), daily feed intake (DFI = FCR∗SGR), protein efficiency ratio (PER = g body weight gain/g crude protein intake), and protein retention efficiency (PRE = g crude protein gained/g crude protein intake∗100).
## 2.5. Analysis of Nutrients and Trace Minerals
Analysis of macronutrients in diets and the whole body was carried out according to the European Commission Regulation (EC) No. $\frac{152}{2009}$ [20]. Analysis of diets and homogenized whole bodies was carried out in duplicates. For the determination of crude lipid content in the whole body, methods according to the Soxhlet protocol (Soxtherm, C. Gerhardt GmbH & Co. KG, Königswinter, Germany) were applied. Crude protein content was determined using standard Kjeldahl methods. Before the determination of ash content via a combustion oven (P300, Nabertherm, Lilienthal, Germany) at 550°C for 12 hours, the dry matter content of samples was ascertained by drying (ED 53, Binder GmbH, Tuttlingen, Germany) for 4 hours at 103°C. Nitrogen-free extracts plus crude fiber content were defined as the remaining portion of macronutrients in diets and the whole body.
Trace minerals (Cr, Cu, Fe, Zn, Mn, and Se) in diets and whole body were analyzed by an external lab (Agrolab LUFA, Kiel, Germany) following the standards for trace mineral analysis [21, 22].
## 2.6. Statistical Analysis
The statistical analyses were performed using SPSS 21 for Windows (SPSS Inc., Chicago, U.S.). Data are presented as mean ± standard deviation (SD) for each treatment and comparisons between treatments. Before the application of one-way analysis of variances (ANOVA), Kolmogorov-Smirnov and Levene tests were applied to determine normal distribution and homogeneity of variances. Tukey's HSD test was used for multiple comparisons when differences among groups were identified. The aggregate type I error was defined at $5\%$ ($P \leq .05$) for each set of comparisons to determine statistical significance.
Additionally, regression analysis between the calculated organic Cr supplementation level as the independent variable and SGR as the dependent variable was calculated by polynomial regressions. The optimum dosage was calculated for slope = 0.
## 3.1. Growth Performance
After 84 days of feeding, growth performance was significantly affected by CrMet supplementation. Final body weight and SGR were significantly increased in dietary groups CR 1 (683.94 ± 44.91 and 3.03 ± 0.06) and CR 2 (670.52 ± 32.92 and 2.99 ± 0.06) compared to control (591.21 ± 17.15 and 2.83 ± 0.03; Table 5). The remaining growth performance parameters were at a similar level among dietary treatments.
The second-degree polynomial regression of dietary organic chromium supplementation and SGR was highly significant ($P \leq 0.001$, Figure 1), which allowed a calculation of the optimal chromium content by performing the first derivation of the equation and calculating x for f (x) = 0. The resulting optimal dietary organic chromium supplementation level was 0.39 mg kg−1 (0.33 mg Cr kg−1 from CrMet).
## 3.2. Body Composition
No significant differences between dietary groups were detected in proximate whole body composition (Table 6), trace mineral content of whole body (Table 7), or trace mineral retention (Table 8) after 84 days of feeding.
## 3.3. Biometric Indices
No significant differences were observed in any of the biometric indices investigated in this study (Table 9).
## 4. Discussion
In the present day, sustainable aqua feeds appear to be an inevitable necessity, and feed additives could contribute significantly by improving the effective utilization of feed ingredients. The present study demonstrated that the supplementation of diets with CrMet could improve the growth performance of African catfish. This effect could have multiple rationales. Firstly, organic minerals are more readily available for fish compared to their inorganic counterparts [3, 9]. Secondly, dietary chromium supplementation has been demonstrated to support the immune response [13] and reduce the stress level of fish [8, 12]. Lastly, diets with chromium addition can improve the energy metabolism of fish by activating various digestive enzymes and enhancing the activity of insulin [2, 3, 8, 10, 18].
According to the results of the current study, dietary organic chromium supplementation appears to have a minor positive effect on both feed conversion and feed intake, which in turn resulted in a significantly improved growth rate of catfish. Multiple earlier studies demonstrated a positive effect on growth rates of various species of fish including hybrid tilapia (*Oreochromis niloticus* L., 1758 x *Oreochromis aureus* S., 1864; [14]; form of chromium supplemented: CrCl36H2O, Na2CrO44H2O, and Cr2O3), *Nile tilapia* (Oreochromis niloticus; [11]; Cr picolinate, 0.6-1.8 mg kg−1), grass carp (*Ctenopharyngodon idella* V., 1844; [23]; Cr picolinate, 0.2-3.2 mg kg−1), common carp (Cyprinus carpio L., 1758; [8, 18]; Cr methionine, 0.31-3.64 mg kg−1; Cr oxide, Cr picolinate, and Cr methionine, 2 mg kg−1), mirror carp (Cyprinus carpio; [5]; Cr chloride, Cr picolinate, and Cr yeast, 0.5-2 mg kg−1), large yellow croaker (*Larimichthys crocea* R., 1846; [15]; Cr polynicotinate, 5-80 mg kg−1), blunt snout bream (*Megalobrama amblycephala* Y., 1955; [24]; Cr picolinate, 0.2-12 mg kg−1), snakehead (*Channa argus* C., 1842; [10]; Cr yeast, 200 mg kg−1), and striped catfish (*Pangasianodon hypophthalmus* S., 1878; [6]; Cr, 2-8 mg kg−1). However, other studies could not show any response from different species of fish to dietary Cr supplementation ([25]; Cr picolinate, 0.8-1.2 mg kg−1; [26]; Cr yeast, 0.8 mg kg−1; [27]; Cr picolinate, 2 mg kg−1; [28]; Cr picolinate, 1.6 mg kg−1). These contradictory results can most likely be explained by different factors influencing the effects of dietary Cr, including form and dose of Cr, duration of experiment, and behavior of concerned species [6].
The low bioavailability of inorganic chromium is caused by a multitude of factors including the formation of nonsoluble Cr oxides, binding to natural chelate-forming compounds in feeds, and interference with ion forms of other minerals [3]. Results suggest that supplementation with organic CrMet is beneficial for the nutritious value of commercially based diets for African catfish. This positive effect is in accordance with Cui et al. [ 18], who demonstrated that dietary CrMet supplementation is superior in comparison with inorganic Cr and Cr picolinate in common carp due to increased absorption efficiency. Dietary CrMet supplementation also improved the growth performance of common carp in earlier research [8] and of different crustacean species [29, 30]. Additionally, organic-chelated minerals could provide a complex that is more stable in the upper digestive tract in comparison with mineral salts, thereby increasing the bioavailability of the minerals [31]. According to Pechova and Pavlata [3], the absorption efficiency of inorganic Cr+3 is inversely proportional to the dietary level. A similar trend was also observed in the present study with organic Cr supplementation, somewhat contradicting observations from Cui et al. [ 18]. However, the analyzed content of Cr in diets differed from the expected values from organic supplementation (Tables 3 and 4), which was most likely caused by contamination with inorganic Cr during feed production. These elevated levels of inorganic Cr could explain the reduced absorption efficiency of Cr. Despite a trend toward reduced absorption of Cr at higher supplementation levels, the content of Cr in the whole body is still comparable to the results of other literature [5].
Due to handling, fish might have been exposed to short periods of stress during the experiment. Stress can increase the demand for Cr in humans and animals [3]. The stress-related secretion of cortisol, which acts as an antagonist for insulin, elevates the blood glucose concentration. Latter elevation results in the mobilization and subsequent excretion of Cr [32]. Multiple studies have shown a reduced sensitivity to different stressors due to dietary supplementation with Cr in various animals including fish [7, 12, 33–35]. This reduced sensitivity to stress can in turn elevate growth performance by enhancing energy utilization, absorption, and allocation [36]. Additionally, Risha et al. [ 13] demonstrated that Cr supports the nonspecific immunity in Nile tilapia, which was also demonstrated in other animals [37–39]. Furthermore, Cr supplementation also supported the antioxidative status of *Nile tilapia* [13]. The immune response and antioxidant status could therefore be enhanced even without stress [13]. Hence, supplementation with bioavailable Cr could result in increased growth performance by reducing stress sensitivity, as well as improving immune response and antioxidant status.
Chromium is also involved in the activation of digestive enzymes and protein stabilization, which are primary steps for the metabolism of carbohydrates, proteins, and lipids [40]. Supplementation with Cr has been shown to improve the activity of glycolytic and lipogenic enzymes in the liver of common carp [8, 16]. Additionally, Cr acts as a cofactor for insulin, enhancing its activity, which potentiates the regulation of glycemia and muscle protein deposition [3]. Increased serum insulin concentrations have been observed previously in fish at a Cr supplementation level of 0.8 mg kg−1 [23], and the anabolic role of this hormone resulted in improved growth performance. Additionally, the enhanced glucose clearance from blood due to higher insulin activity [2] can improve feed intake, since reduced glycemia has been demonstrated to increase feed consumption [41, 42]. Since no analysis of digestive enzymes was conducted, a significant effect was not demonstrated during this study; however, improved activation of digestive enzymes could have contributed to the overall significant effect on growth performance. The overall improvement in feed conversion and feed intake was of a minor extent, indicating an effect of a lesser degree from Cr supplementation on each growth factor. This small effect could partly be explained by diet formulation since the objective of this study was to show the effects of a diet with high relevancy to the industry. The commercially based diet used in this study has a comparatively high crude protein content, which could reduce the effectiveness of a protein-sparing effect from improved carbohydrate and lipid metabolism. This would also be a tangible explanation for the similar protein retention between all dietary groups. The latter observation was contrary to earlier research results demonstrating improved protein retention due to dietary supplementation with Cr in different species of fish [6, 23]. Additionally, the effects of dietary Cr supplementation were more prominent when glucose was included in diets for fish in comparison to starch [14, 43, 44]. More complex carbohydrates tend to lead to a less intense blood glucose peak [2]. Therefore, effects on feed intake due to improved clearance of glucose from the bloodstream could be less prominent considering the current feed formulation in this trial. Despite diet formulation, CrMet was still able to improve the growth performance of African catfish significantly, and it can be expected that this effect would be greater in diets containing less complex carbohydrates or lower amounts of protein. These effects have already been demonstrated in literature for different species of fish [6, 14, 23, 43, 44].
High amounts of Cr supplementation can have toxic effects on fish [17]. Akter et al. [ 6] observed changes in hematological indices, indicating toxic effects for striped catfish at high dietary Cr content (8 mg kg−1). Based on the results of the current study, supplemented levels of CrMet were not toxic for African catfish up to a dietary supplementation level of 0.6 mg Cr kg−1. This is in line with the findings from Akter et al. [ 6] for striped catfish. Additionally, no significant differences were observed in other biometric indices (Table 9), indicating that no negative effects on the liver and spleen occurred due to the supplementation with CrMet. However, it should be noted that the analysis of health parameters was not the main focus of this study and additional conformation for the safe application of CrMet should be collected in future trials with a more comprehensive amount of analyzed health parameters.
Our results demonstrated that supplementing a commercially based diet containing 2.37 mg Cr kg−1 with 330 mg kg−1 CrMet (0.39 mg Cr kg−1) optimizes the growth performance of African catfish. A similar value has been determined for striped catfish (2.82 mg Cr kg−1 total dietary supply; [6]), *Nile tilapia* (3.49 mg Cr kg−1 total dietary supply; [45]), and grass carp (0.8 mg Cr picolinate kg−1 supplementation; [23]).
## 5. Conclusion
Supplementing commercially based diets of African catfish with CrMet significantly improved growth performance at dietary organic Cr supplementation levels of 0.2 and 0.4 mg kg−1. Based on regression analysis, 0.33 mg Cr kg−1 from CrMet supplementation in commercially based diets for African catfish was optimal.
## Data Availability
Data will be made available upon reasonable request.
## Conflicts of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## References
1. 1FAO
The State of World Fisheries and Aquaculture. Towards blue transformation
2022Food and Agriculture Organization of the United Nations. (2022.0)
2. 2National Research Council
Nutrient Requirements of Fish and Shrimp
2011National academies press. (2011.0)
3. Pechova A., Pavlata L.. **Chromium as an essential nutrient: a review**. (2007.0) **52** 1-18. DOI: 10.17221/2010-vetmed
4. Lall S. P., Kaushik S. J.. **Nutrition and metabolism of minerals in fish**. (2021.0) **11** p. 2711. DOI: 10.3390/ani11092711
5. Ahmed A. R., Jha A. N., Davies S. J.. **The efficacy of chromium as a growth enhancer for mirror carp (**. (2012.0) **148** 187-197. DOI: 10.1007/s12011-012-9354-4
6. Akter S., Jahan N., Rohani M. F., Akter Y., Shahjahan M.. **Chromium supplementation in diet enhances growth and feed utilization of striped catfish (**. (2021.0) **199** 4811-4819. DOI: 10.1007/s12011-021-02608-2
7. Castro M. P., Claudiano G. S., Petrillo T. R.. **Acute aerocystitis in Nile tilapia bred in net cages and supplemented with chromium carbochelate and**. (2014.0) **36** 284-290. DOI: 10.1016/j.fsi.2013.11.012
8. Cui P., Yin S., Cheng Z., Qiao X., Zhou Q.. **Effects of dietary chromium methionine on growth performance, hematological characteristics and carbohydrate metabolic enzyme activities of common carp (**. (2018.0) **70**. DOI: 10.46989/001c.20909
9. Gatta P. P., Thompson K. D., Smullen R., Piva A., Testi S., Adams A.. **Dietary organic chromium supplementation and its effect on the immune response of rainbow trout (**. (2001.0) **11** 371-382. DOI: 10.1006/fsim.2000.0323
10. Hou Y., Hou Y., Yao L., Chen S., Fan J., Qian L.. **Effects of chromium yeast, tributyrin and bile acid on growth performance, digestion and metabolism of**. (2019.0) **50** 836-846. DOI: 10.1111/are.13954
11. Li H., Meng X., Wan W.. **Effects of chromium picolinate supplementation on growth, body composition, and biochemical parameters in Nile tilapia**. (2018.0) **44** 1265-1274. DOI: 10.1007/s10695-018-0514-0
12. Liang H., Ge X., Xia D., Ren M., Mi H., Pan L.. **The role of dietary chromium supplementation in relieving heat stress of juvenile blunt snout bream**. (2022.0) **120** 23-30. DOI: 10.1016/j.fsi.2021.11.012
13. Risha E., Ahmed F., Khaled A. A., Hossain F. M. A., Akhtar N., Zahran E.. **Interactive effects of dietary betaine and chromium picolinate on the immunomodulation, antioxidative response and disease resistance of Nile tilapia (**. (2022.0) **53** 3464-3477. DOI: 10.1111/are.15853
14. Shiau S. Y., Chen M. J.. **Carbohydrate utilization by tilapia (**. (1993.0) **123** 1747-1753. DOI: 10.1093/jn/123.10.1747
15. Wang J., Ai Q., Mai K., Xu H., Zuo R.. **Dietary chromium polynicotinate enhanced growth performance, feed utilization, and resistance to Cryptocaryon irritans in juvenile large yellow croaker (**. (2014.0) **432** 321-326. DOI: 10.1016/j.aquaculture.2014.05.027
16. Ahmad A. R., Jha A. N., Davies S. J.. **The effect of dietary organic chromium on specific growth rate, tissue chromium concentrations, enzyme activities and histology in common carp,**. (2012.0) **149** 362-370. DOI: 10.1007/s12011-012-9436-3
17. Aslam S., Yousafzai A. M.. **Chromium toxicity in fish: a review article**. (2017.0) **5** 1483-1488
18. Cui P., Cheng Z., Sun J.. **Effects of different chromium sources on growth performance, serum biochemical, hepatopancreas glycometabolism enzymes activities, IR, GLUT2 and SGLT1 gene expression of common carp (**. (2022.0) **53** 1573-1581. DOI: 10.1111/are.15692
19. Zakaria M. K., Kari Z. A., Van Doan H.. **Fermented soybean meal (FSBM) in African catfish (**. (2022.0) **12** p. 1851. DOI: 10.3390/life12111851
20. **Commission Regulation (EC) No 152/2009 of 27 January 2009 laying down the methods of sampling and analysis for the official control of feed**. (2009.0) **54** 1060-1130
21. 21DIN EN 15621
Futtermittel - Probenahme- und Untersuchungsverfahren - Bestimmung von Calcium, Natrium, Phosphor, Magnesium, Kalium, Schwefel, Eisen, Zink, Kupfer, Mangan und Cobalt nach Druckaufschluss mittels ICP-AES
2017BerlinBeuth Verlag. (2017.0)
22. 22DIN EN 17053
Futtermittel - Probenahme- und Untersuchungsverfahren - Bestimmung von Spurenelementen, Schwermetallen und anderen Elementen in Futtermitteln mittels ICP-MS (Multimethode)
2018BerlinBeuth Verlag. (2018.0)
23. Liu T., Wen H., Jiang M.. **Effect of dietary chromium picolinate on growth performance and blood parameters in grass carp fingerling,**. (2010.0) **36** 565-572. DOI: 10.1007/s10695-009-9327-5
24. Ren M., Mokrani A., Liang H.. **Dietary chromium picolinate supplementation affects growth, whole-body composition, and gene expression related to glucose metabolism and lipogenesis in juvenile blunt snout bream,**. (2018.0) **185** 205-215. DOI: 10.1007/s12011-018-1242-0
25. El-Sayed E. H., Hassanein E. I., Soliman M. H., El-Khatib N. R.. **The effect of dietary chromium picolinate on growth performance, blood parameters and immune status in Nile tilapia,**. 51-63
26. Gatta P. P., Piva A., Paolini M.. **Effects of dietary organic chromium on gilthead seabream (**. (2001.0) **32** 60-69. DOI: 10.1046/j.1355-557x.2001.00005.x
27. Pan Q., Liu S., Tan Y. G., Bi Y. Z.. **The effect of chromium picolinate on growth and carbohydrate utilization in tilapia,**. (2003.0) **225** 421-429. DOI: 10.1016/S0044-8486(03)00306-5
28. Selcuk Z., Tiril S. U., Alagil F.. **Effects of dietary L-carnitine and chromium picolinate supplementations on performance and some serum parameters in rainbow trout (**. (2010.0) **18** 213-221. DOI: 10.1007/s10499-008-9237-z
29. Shi B., Tao X., Betancor M. B.. **Dietary chromium modulates glucose homeostasis and induces oxidative stress in Pacific white shrimp (**. (2021.0) **240, article 105967**. DOI: 10.1016/j.aquatox.2021.105967
30. Zhang Y., Luo J., Zhu T.. **Dietary chromium could improve growth, antioxidant capacity, chromium accumulation in tissues and expression of genes involved into glucose and lipid metabolism in juvenile mud crab**. (2022.0) **23, article 101088**. DOI: 10.1016/j.aqrep.2022.101088
31. Hardy R. W., Kaushik S. J.. (2021.0)
32. Borel J. S., Majerus T. C., Polansky M. M., Moser P. B., Anderson R. A.. **Chromium intake and urinary chromium excretion of trauma patients**. (1984.0) **6** 317-326. DOI: 10.1007/BF02989239
33. Chang X., Mowat D. N.. **Supplemental chromium for stressed and growing feeder calves**. (1992.0) **70** 559-565. DOI: 10.2527/1992.702559x
34. Moonsie-Shageer S., Mowat D. N.. **Effect of level of supplemental chromium on performance, serum constituents, and immune status of stressed feeder calves**. (1993.0) **71** 232-238. DOI: 10.2527/1993.711232x
35. Pechova A., Pavlata L., Illek J.. **Metabolic effects of chromium administration to dairy cows in the period of stress**. (2002.0) **47** 1-7
36. Sadoul B., Vijayan M. M.. **Stress and growth**. (2016.0) **35** 167-205. DOI: 10.1016/B978-0-12-802728-8.00005-9
37. Jain S. K., Rains J. L., Croad J. L.. **Effect of chromium niacinate and chromium picolinate supplementation on lipid peroxidation, TNF-**. (2007.0) **43** 1124-1131. DOI: 10.1016/j.freeradbiomed.2007.05.019
38. Mehrim A. I.. **Physiological, biochemical and histometric responses of Nile tilapia (**. (2014.0) **5** 303-310. DOI: 10.1016/j.jare.2013.04.002
39. Tian Y. Y., Zhang L. Y., Dong B., Cao J., Xue J. X., Gong L. M.. **Effects of chromium methionine supplementation on growth performance, serum metabolites, endocrine parameters, antioxidant status, and immune traits in growing pigs**. (2014.0) **162** 134-141. DOI: 10.1007/s12011-014-0147-9
40. Lashkari S., Habibian M., Jensen S. K.. **A review on the role of chromium supplementation in ruminant nutrition—effects on productive performance, blood metabolites, antioxidant status, and immunocompetence**. (2018.0) **186** 305-321. DOI: 10.1007/s12011-018-1310-5
41. Soengas J. L., Cerdá-Reverter J. M., Delgado M. J.. **Central regulation of food intake in fish: an evolutionary perspective**. (2018.0) **60** R171-R199. DOI: 10.1530/JME-17-0320
42. Tran-Duy A., Smit B., van Dam A. A., Schrama J. W.. **Effects of dietary starch and energy levels on maximum feed intake, growth and metabolism of Nile tilapia,**. (2008.0) **277** 213-219. DOI: 10.1016/j.aquaculture.2008.03.004
43. Shiau S. Y., Liang H. S.. **Carbohydrate utilization and digestibility by tilapia,**. (1995.0) **125** 976-982. DOI: 10.1093/jn/125.4.976
44. Shiau S. Y., Lin S. F.. **Effect of supplemental dietary chromium and vanadium on the utilization of different carbohydrates in tilapia,**. (1993.0) **110** 321-330. DOI: 10.1016/0044-8486(93)90379-D
45. Yuliana Y., Subandiyono S., Hastuti S.. **The effect of dietary chromium on growth and survival rate of tilapia (**. (2022.0) **17** 118-126. DOI: 10.20884/1.oa.2021.17.2.758
|
---
title: Dietary β-Hydroxy-β-Methylbutyrate Supplementation Affects Growth Performance,
Digestion, TOR Pathway, and Muscle Quality in Kuruma Shrimp (Marsupenaeus japonicas)
Fed a Low Protein Diet
authors:
- Hua Mu
- Chenbin Yang
- Yu Zhang
- Shengdi Chen
- Panpan Wang
- Binlun Yan
- Qingqi Zhang
- Chaoqing Wei
- Huan Gao
journal: Aquaculture Nutrition
year: 2023
pmcid: PMC9973151
doi: 10.1155/2023/9889533
license: CC BY 4.0
---
# Dietary β-Hydroxy-β-Methylbutyrate Supplementation Affects Growth Performance, Digestion, TOR Pathway, and Muscle Quality in Kuruma Shrimp (Marsupenaeus japonicas) Fed a Low Protein Diet
## Abstract
An 8-week feeding trial was performed to evaluate the effects of dietary β-hydroxy-β-methylbutyrate (HMB) supplementation on growth performance and muscle quality of kuruma shrimp (Marsupenaeus japonicas) (initial weight: 2.00 ± 0.01 g) fed a low protein diet. The positive control diet (HP) with 490 g/kg protein and negative control diet (LP) with 440 g/kg protein were formulated. Based on the LP, 0.25, 0.5, 1, 2 and 4 g/kg β-hydroxy-β-methylbutyrate calcium were supplemented to design the other five diets named as HMB0.25, HMB0.5, HMB1, HMB2 and HMB4, respectively. Results showed that compared with the shrimp fed LP, the HP, HMB1 and HMB2 groups had significantly higher weight gain and specific growth rate, while significantly lower feed conversion ratio ($p \leq 0.05$). Meanwhile, intestinal trypsin activity was significantly elevated in the above three groups than that of the LP group. Higher dietary protein level and HMB inclusion upregulated the expressions of target of rapamycin, ribosomal protein S6 kinase, phosphatidylinositol 3-kinase, and serine/threonine-protein kinase in shrimp muscle, accompanied by the increases in most muscle free amino acids contents. Supplementation of 2 g/kg HMB in a low protein diet improved muscle hardness and water holding capacity of shrimp. Total collagen content in shrimp muscle increased with increasing dietary HMB inclusion. Additionally, dietary inclusion of 2 g/kg HMB significantly elevated myofiber density and sarcomere length, while reduced myofiber diameter. In conclusion, supplementation of 1-2 g/kg HMB in a low protein diet improved the growth performance and muscle quality of kuruma shrimp, which may be ascribed to the increased trypsin activity and activated TOR pathway, as well as elevated muscle collagen content and changed myofiber morphology caused by dietary HMB.
## 1. Introduction
Aquaculture is the rapidly growing food production sector in the world, with the highest growth rate among animal production sectors [1]. About $70\%$ of global aquaculture (excluding aquatic plants) is dependent on the supply of commercial compound feeds which account for more than half of the aquaculture production costs [2, 3]. Many aquatic animal species require a higher content of dietary protein to maintain the growth and health. The rapid and continuous expansion of world aquaculture has led to a heightened demand and a shortage of supply for protein sources, particularly for prime quality protein fishmeal. Thus, protein, as the most expensive nutrient in diet formulation, is crucial for the sustainable development of aquafeed and aquaculture [4]. Massive research efforts have been devoted in recent years to diminish the expense of feed and reliance on protein sources by reducing dietary protein or fishmeal level [5–7]. However, lower dietary protein or fishmeal level could exert negative impacts on growth, immunity, and muscle quality of farmed aquatic animals [8–11]. Therefore, except for developing novel protein sources, there is an urgent need to exploit safe and eco-friendly feed additives so as to ameliorate the adverse effects of low protein or fishmeal diets and sustain the global aquaculture.
It is well known that protein accretion is the primary determinant of body weight gain in aquatic animals [12]. The rate of protein deposition in the body is determined by the balance between protein synthesis and degradation in tissues (mainly muscle) [4]. Growth will occur when the rate of protein synthesis exceeds the rate of proteolysis in tissues, and protein synthesis, thus, is central to the growth of aquatic animals [13, 14]. Studies in mammals indicated that β-hydroxy-β-methylbutyrate (HMB), as a metabolite of leucine, is more potent than leucine in promoting protein synthesis and inhibiting protein degradation [15–17]. Dietary HMB supplementation has been found to improve the growth performance of Bama Xiang mini-pigs [18] and broiler chickens [19]. Meanwhile, dietary inclusion of HMB improved meat quality of Bama Xiang mini-pigs via manipulation of muscle fiber characteristics [20] and enhanced muscle mass by stimulating protein synthesis of pigs fed low protein diets [21]. Furthermore, the safety profile of HMB is quite unequivocal. For example, HMB has been popular with athletes and bodybuilders due to its function in increasing muscle strength and mass [22]. And plenty of studies showed that HMB supplementation or treatment is effective in preventing exercise-induced muscle damage in healthy subjects as well as muscle loss in pathological conditions characterized by high rates of muscle protein degradation [23–25]. What is more important, HMB is a nitrogen-free nutritional supplement, making the application of HMB in aquaculture eco-friendly.
Consistent with the findings in mammals, our unpublished data demonstrated that dietary HMB inclusion could also improve the growth performance and muscle quality of turbot (*Scophthalmus maximus* L.) and large yellow croaker (Larimichthys crocea), which may be imputable to the effects of HMB supplementation on protein metabolism and muscle fiber characteristics. However, the application efficacies of HMB supplementation in diets of other aquatic animal species (such as shrimp) are still unclear. Moreover, it is unknown whether dietary HMB effectively ameliorates the poor growth performance and muscle quality induced by lower dietary protein or fishmeal level in aquatic animals.
Kuruma shrimp (Marsupenaeus japonicas) is one of the most important cultured shrimps worldwide since its delicious taste, high market value, and excellent resistance to low temperature [26]. Aquaculture has become the best way to meet the market demand of this high-valued seafood due to its decreased wild catches [27, 28]. However, kuruma shrimp is commonly considered to have a higher requirement for dietary protein than other prawn species [29]. Reducing the finite and expensive protein in diet of kuruma shrimp has been shown to impair its growth performance [30]. Therefore, with the above in mind, the objective of this study was to evaluate the effects of dietary HMB supplementation on growth performance and muscle quality in kuruma shrimp fed with a low protein diet. The present finding would provide theoretical basis and guide for the application and research of HMB in prawn feed.
## 2.1. Ethics Statement
The experimental protocols and procedures for animal husbandry and handling were performed according to the Animal Care Committee of Jiangsu Ocean University in the present study.
## 2.2. Experimental Diets
Fishmeal, casein, soybean meal, and wheat gluten were used as the main protein sources, and fish oil and soybean lecithin were used as the main lipid sources to prepare the seven experimental diets. According to the dietary protein requirement (500 g/kg) reported by Teshima et al., [ 30] for kuruma shrimp, the positive control diet (HP) with 490 g/kg crude protein and negative control diet (LP) with 440 g/kg crude protein were formulated. Based on the LP, 0.25, 0.5, 1, 2, and 4 g/kg β-hydroxy-β-methylbutyrate calcium (HMB-Ca) (purity $97\%$, Shanghai Yuanye Bio-Technology Co., Ltd, China) were supplemented to design the other five isonitrogenous (440 g/kg protein) and isolipidic (75 g/kg lipid) diets which were named as HMB0.25, HMB0.5, HMB1, HMB2, and HMB4, respectively. The Table 1 shows the ingredients and proximate compositions, and Table 2 presents the amino acid composition of the experimental diets. Tryptophan was not determined due to the acid hydrolysis.
To prepare the experimental diets, all the dry ingredients were thoroughly crushed and passed through 80-mesh sieve and mixed fully by gradient dilution method according to the dietary formula. After that, fish oil with dissolved soybean lecithin and cholesterol was added and stirred thoroughly. Stiff dough was obtained by adding water and then extruded into pellets by the pellet-making machine (F-26 (II), South China University of Technology, China). Diets were finally dried in a ventilated oven at 40°C and stored at -20°C until use.
## 2.3. Growth Trial
The growth trial was carried out at the Jiangsu Key Laboratory of Marine Biotechnology, Jiangsu Ocean University, Lianyungang, China. Healthy kuruma shrimps were obtained from the Ganyu Jiaxin Fishery Technical Development Co., Ltd. (Lianyungang, Jiangsu Province, China). The shrimps were stocked in three circular tanks (500 L) and fed with the HP for 2 weeks to acclimate the laboratory conditions. After that, a total of 630 shrimp with homogenous size (2.00 ± 0.01 g) were randomly allocated into 21 rectangular polyvinyl chloride tanks (96 L, 30 shrimp per tank) corresponding to triplicate tanks of the seven dietary treatments. A net was used to cover each tank so as to prevent shrimp from jumping out, and sand (approximately 20 mm thickness) was distributed on the bottom of per tank. Shrimp were fed with the experimental diets at $4\%$-$7\%$ of the body weight for 56 days, and daily ration was divided into $30\%$ at 08: 00 and $70\%$ at 20: 00. Before the first feeding, about half of the water in each tank was replaced every second day. Weight gain (WG) and survival were determined every 2 weeks to adjust the feeding rate. Meanwhile, all the tanks including the sand were fully cleaned. During the feeding trial, the monitored water temperature, pH, salinity, dissolved oxygen and ammonia nitrogen were 25.0 ± 1.0°C, 8.1 ± 0.2, 28.0 ± 1.2, 6.1 ± 0.4 mg/L and <0.2 mg/L, respectively. Shrimps were cultured under natural light and dark regime.
## 2.4. Sample Collection
At the end of the growth trial, all shrimp were starved for 24 h. The total number and body weight of shrimp from each tank were recorded to calculate survival, WG, specific growth rate (SGR), and feed conversion ratio (FCR). Six shrimps from each tank were randomly obtained to collect the muscle and intestine samples for the analysis of proximate composition and digestive enzyme activity, respectively. Three shrimps per tank were randomly selected to obtain muscle samples for texture analysis. Another three shrimps from each tank were randomly picked to collect muscle for water holding capacity (WHC) and pH determination. Three muscles from each tank were randomly collected and stored at -80°C to determine the free amino acid (FAA) composition, contents of lactic acid, glycogen, total hydroxyproline (Hyp) and collagen, as well as the gene expressions. Three muscle samples (0.5 cm × 0.5 cm × 0.5 cm) from each tank were collected and soaked in $4\%$ paraformaldehyde for paraffin section analysis. Another three muscles (0.2 cm × 0.2 cm × 0.2 cm) were sampled and kept in fixative solution ($2.5\%$ glutaraldehyde) for transmission electron microscopy analysis.
## 2.5. Analysis of Proximate Composition and Amino Acid in Diets and Muscle
The proximate analysis of diets and muscle was analyzed according to the standard methods [31]. Briefly, moisture content was measured by drying samples at 105°C until constant weight. The contents of crude protein and crude lipid in diets and muscle were determined by a Kjeldahl nitrogen analyzer (Kjeltec 8400, FOSS, Denmark) and a Soxhlet extractor (Soxtec 8000, FOSS, Denmark), respectively. Crude ash content of diets was measured by combusting samples in a muffle furnace at 550°C for 6 h. Amino acid profile in diets was analyzed using an automatic amino acid analyzer (L-8900, Hitachi, Japan) according to the method described by Qu et al. [ 32]. The FAA composition in muscle was detected by an automatic amino acid analyzer (L-8900, Hitachi, Japan) with a lithium high-performance column using the method of Xu et al. [ 33].
## 2.6. Digestive Enzyme Activity of the Intestine
Intestine samples of shrimp were weighed and diluted with normal saline at a ratio of 1: 9, then homogenized on the ice and centrifuged for 10 min at 4°C (2500 r/min) to collect the supernatant for the analysis of digestive enzyme activities. The protein level in the obtained supernatant was measured using Coomassie brilliant blue (CBB) method. The activities of α-amylase (AMS), lipase (LPS), and trypsin (TRS) were detected using the commercial kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China).
## 2.7. Total RNA Extraction and Quantitative Real-Time Polymerase Chain Reaction
Total RNA was extracted from the shrimp muscle using total RNA isolation kit (Sangon Biotech (Shanghai) Co., Ltd., China). The integrity and concentration of RNA were detected by $1\%$ agarose gel electrophoresis and GeneQuant pro (GE Pharmacia, USA), respectively. The isolated RNA was then reversely transcribed to cDNA by reverse transcription kit (Vazyme Biotech Co., Ltd., China).
The amplification reactions for quantitative real-time polymerase chain reaction (qRT-PCR) were performed in 96-well plates using a total volume of 20 μL, containing 0.4 μL of each primer, 4 μL of cDNA template, 10 μL of 2 × ChamQ SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd., China), 0.4 μL of ROX Reference Dye (Vazyme Biotech Co., Ltd., China), and 4.8 μL of sterilized double-distilled water. The qRT-PCR was performed with an ABI StepOnePlus Real-Time PCR System using the following cycle conditions: 95°C for 30 s, followed by 40 cycles of 95°C for10 s, and 60°C for 30 s [34]. Melting curve analysis was carried out to validate that only one PCR product was obtained in these reactions. The expression levels of genes were quantified relative to the expression of β-actin using the comparative CT method (2−△△CT method) [35]. The expression of β-actin was stable among the treatments. *The* gene expression was normalized with the LP group as control. The primer sequences of target genes (target of rapamycin (tor), ribosomal protein S6 kinase (s6k), eukaryotic translation initiation factor 4E (eIF4E)-binding protein 1 (4e-bp1), phosphatidylinositol 3-kinase (pi3k),and serine/threonine-protein kinase (akt)) were designed based on our transcriptome unigenes of kuruma shrimp (Table 3).
## 2.8. Muscle Texture Analysis, Water Holding Capacity (WHC) and pH Determination
The texture profile analyses (TPA) parameters (hardness, cohesiveness, springiness, and chewiness) were determined instrumentally by a texture analyzer (TMS-PRO, FTC, America) equipped with a 5 mm cylinder probe. The measurement conditions were set as double compression cycle test, constant speed of 30 mm/min, $60\%$ deformation of the original thickness, and initial force of 0.1 N [36].
The muscle WHC was detected based on the gravimetric method [37]. In brief, 1 g of shrimp muscle was weighed (M) and wrapped with filter paper, then centrifuged at 4000 × g for 10 min at 4°C. The wet filter paper obtained by centrifugation was weighed (W1) and dried in the oven at 75°C to constant weight (W2). The WHC (%) was calculated as 100 − (W1–W2)/M × 100.
Three points in each muscle sample were selected to measure the pH values by using a portable pH meter (Testo 205, Testo AG, Lenzkirch, Germany).
## 2.9. Determination of Muscle Lactic Acid, Glycogen, Hydroxyproline and Collagen Contents
The contents of lactic acid, glycogen, and total Hyp in shrimp muscle were determined using the commercial kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) following to the instructions. The total collagen content in muscle was calculated by multiplying the Hyp content by 8 [31] because Hyp accounts about $12.5\%$ of collagen.
## 2.10. Muscle Paraffin Section Analysis and Transmission Electron Microscopy Analysis
Muscle paraffin section analysis and transmission electron microscopy analysis were performed according to the method described in a previous study [38]. Briefly, muscle samples (0.5 cm × 0.5 cm × 0.5 cm) were dehydrated with different concentrations of ethanol, embedded in paraffin wax, then sectioned, and stained with hematoxylin-eosin to observe the morphology. At least fifteen images from each muscle sample were captured by a microscope (Olympus, DP72, Tokyo, Japan). The ImageJ Launcher software was employed to count the myofiber number for calculating myofiber density and measure the myofiber diameter. Transmission electron microscopy analysis of muscle samples (0.2 cm × 0.2 cm × 0.2 cm) was carried out by Wuhan Google Biotechnology Co., Ltd. (Wuhan, China). Myofibrillar structure was observed to detect myofibrillar sarcomere length.
## 2.11. Statistical Analysis
All test data were analyzed using one-way analysis of variance (ANOVA) by the SPSS 17.0 software. The Tukey's test was used to assess the differences among seven treatment groups with the level of significance set at $p \leq 0.05.$ Data were expressed as mean ± SE (standard error).
## 3.1. Growth Performance
As shown in Table 4, no remarkable differences were observed in the initial body weight (IBW) and survival among the seven treatment groups ($p \leq 0.05$). After 8 weeks of feeding, the final body weight (FBW) of shrimp showed the highest value in the HP group ($p \leq 0.05$). Meanwhile, the shrimp fed HMB1 and HMB2 exhibited significantly higher FBW compared with the LP group. The HP group had significantly higher WG and SGR than those of the LP and HMB-supplemented groups (excluding HMB1), while no significant differences were found between the HP and HMB1 groups. The WG and SGR significantly increased in the HMB1 and HMB2 groups when compared with the LP group. The FCR in the HP, HMB1 and HMB2 groups was significantly lower than that in the LP group.
## 3.2. Muscle Proximate Analysis
Muscle proximate composition of kuruma shrimp fed with the seven experimental diets is presented in Table 5. Muscle moisture content in the HMB1 group was significantly lower than that in the HP and HMB2 groups. The shrimp fed HMB1 displayed significantly higher crude protein content in muscle when compared with the LP and HMB0.25 groups. The crude lipid content in muscle significantly decreased in the five groups of shrimp fed diets with HMB when compared with the HP and LP groups, with the lowest value in HMB2 group. However, there were no significant differences in contents of crude protein and crude lipid between the HP and LP groups.
## 3.3. Amino Acid Composition in Diets and Muscle
Nine essential amino acids (EAA) and eight nonessential amino acids (NEAA) were determined in the experimental diets (Table 2). The contents of most individual amino acids including leucine (Leu), total EAA, and NEAA in the HP diet were higher than those in the LP diet, while there were no obvious differences among the LP and HMB-supplemented diets.
The content of FAA in muscle of kuruma shrimp fed with the seven experimental diets is given in Table 6. Lower dietary protein level significantly reduced the contents of free threonine (Thr), Leu, valine (Val), phenylalanine (Phe), lysine (Lys), histidine (His), arginine (Arg), serine (Ser), proline (Pro) and glutamic acid (Glu), as well as the contents of total free EAA and total free amino acids (TAA) in shrimp muscle. However, compared with the LP group, the contents of above individual amino acid (excluding Ser), total EAA and TAA in muscle significantly enhanced in the HMB1 group, which were comparable with those of the HP group. Free Ser content in muscle of shrimp fed HMB2 and HMB4 was significantly higher than that in the LP group, although its value in HMB2 and HMB4 groups was still significantly lower than that of the HP group. Meanwhile, compared with the LP group, the HMB2 and HMB4 groups exhibited significantly higher contents of free Val, Phe, Arg, Pro and total EAA in muscle, which were at similar levels as the HP group. However, seven treatments showed no significant effect on the content of total delicious amino acids (glycine + alanine + aspartic acid + Glu) in shrimp muscle.
## 3.4. Digestive Enzyme Activity of the Intestine
As shown in Figure 1, the AMS activity in intestine showed no significant difference among the HP, LP, HMB0.25, HMB0.5 and HMB1 groups. However, its activity in the shrimp fed LP was significantly higher than that in the HMB2 and HMB4 groups. The HMB0.25, HMB0.5 and HMB1 groups had significantly higher LPS activity in intestine than that of the LP group, while no significant difference was found between the HP and other six groups. Intestinal TRS activity showed the highest value in the HP group and the lowest value in the LP, HMB0.25 and HMB0.5 groups. However, compared with the LP, HMB0.25 and HMB0.5 groups, the HMB1, HMB2 and HMB4 groups had significant improvement in the TRS activity.
## 3.5. Expression Levels of TOR Pathway-Related Genes in Muscle
As displayed in Figure 2, the expression level of tor in muscle of shrimp fed LP and HMB0.25 was significantly lower than that in the HP and HMB1 groups. The HP, HMB1 and HMB2 groups had significantly higher expression level of s6k in muscle than that of the LP and HMB0.25 groups. The expression level of 4e-bp1 in muscle significantly increased in the LP group when compared with the HP and HMB-supplemented groups (excluding HMB0.25). The expression of pi3k in muscle of the HP and HMB-supplemented groups (excluding HMB0.25) was significantly higher than that of the LP group. Meanwhile, the LP and HMB0.25 groups had significantly lower expression level of akt in muscle than that of the other five groups, while no significant difference was found either between the LP and HMB0.25 groups or among the other five treatment groups.
## 3.6. Muscle Texture, Water Holding Capacity (WHC) and pH
As shown in Table 7, lower dietary protein level significantly decreased muscle hardness, springiness and chewiness. Compared with the LP group, shrimp fed HMB2 showed significant improvements in muscle hardness and chewiness. However, dietary HMB inclusion did not significantly affect muscle springiness. Meanwhile, all the dietary treatments had no significant effect on muscle cohesiveness. The muscle WHC significantly enhanced in the HMB2 group when compared with the LP, HMB0.25 and HMB0.5 groups. But it showed no significant difference between the HP and other six groups.
As shown in Table 8, there was no significant difference in muscle pH among the seven treatment groups.
## 3.7. Muscle Lactic Acid, Glycogen, Total Hydroxyproline and Collagen Contents
As displayed in Table 8, shrimp fed HMB1 and HMB2 exhibited a significant decrease in muscle lactic acid content compared with the LP group. The contents of total Hyp and collagen in muscle were significantly improved in HMB4 group compared with those in the LP and HMB0.25 groups. However, the contents of lactic acid, total Hyp and collagen in muscle showed no significant differences between the HP and other six groups. Meanwhile, there was no significant difference in muscle glycogen content among the seven treatment groups.
## 3.8. Morphology of Myofiber
The Figure 3 shows the morphology of myofiber in kuruma shrimp fed with four experimental diets (HP, LP, HMB0.5 and HMB2). According to the results from myofiber microstructure of cross sections (Figure 3(a)), a tighter arrangement of myofiber was observed in shrimp fed HMB2. The myofiber microstructure of longitudinal sections (Figure 3(b)) showed that the HP and HMB2 groups had smaller fiber diameters, while fiber diameters of shrimp fed LP were widest. Based on the results from myofiber microstructure of cross and longitudinal sections, significant differences in myofiber density and diameter of shrimp were found among the four treatment groups (Figure 3(c)). The myofiber density significantly increased in the HMB2 group compared with that in the LP group, while there was no significant difference either among the HP, LP and HMB0.5 groups or among the HP, HMB0.5 and HMB2 groups. The HP, HMB0.5 and HMB2 groups had significantly smaller fiber diameter than that of the LP group. But it showed no significant difference among the HP, HMB0.5 and HMB2 groups.
Plentiful irregular myofibrils, complete and clear sarcomeres, sarcoplasmic reticulums, and mitochondria were observed in transmission electron micrographs of myofibrillar structure. The transmission electron micrographs of cross sections (Figure 3(d)) displayed that compared with the shrimp fed LP, the other three groups (especially the HP and HMB2 groups) had tighter myofibrillar structure and narrower intermyofibrillar spaces. From the transmission electron micrographs of longitudinal sections with the alternating dark, light areas and Z lines, the longest and shortest sarcomere length was found in the HP and LP groups, respectively (Figure 3(e)). Figure 3(f) further indicated that sarcomere length in shrimp fed HP and HMB2 was significantly longer than that in the LP group, while no significant difference was observed either between the HP and HMB2 groups or between the LP and HMB0.5 groups.
## 4. Discussion
The rapid development of aquaculture has led to an increasing demand, depressed supply and high price of dietary protein sources. However, a high level of dietary protein has been generally recognized as necessary for good growth of kuruma shrimp due to its low utilization of dietary protein [30, 39]. In the present study, a low protein diet induced poor growth performance and feed utilization of kuruma shrimp, which is in agreement with previous studies in this species [30] and other shrimp [40, 41]. However, the present results suggested that supplementing 1 and 2 g/kg HMB in the low protein diet significantly improved the growth performance and feed utilization of kuruma shrimp. In particular, although the FBW of HP group was significantly higher than that of the other six groups, the shrimp fed a diet with 1 g/kg HMB had similar WG, SGR and FCR as the HP group. Similarly, our unpublished data indicated that 1 g/kg HMB inclusion in diet could also promote the growth performance of turbot and large yellow croaker. Meanwhile, dietary HMB has been proven to promote growth in Bama Xiang mini-pigs [18] and broiler chickens [19].
It was demonstrated that the growth of aquatic animals could be concerned with the digestive enzyme activity [42, 43]. In the present study, the shrimp fed with a low protein diet had significantly lower TRS activity in intestine, which may disturb the protein utilization and further cause the poor growth of shrimp. However, the activities of AMS and LPS in intestine were unchanged by the dietary protein levels, which may be ascribed to the similar levels of dietary carbohydrate and lipid [44]. All of the above observations about digestive enzymes were also demonstrated in oriental river prawn (Macrobrachium nipponense) fed different dietary protein levels [40]. Supplementation with 1, 2, and 4 g/kg HMB in the low protein diet significantly enhanced the intestinal TRS activity of shrimp. Meanwhile, the LPS activity in the shrimp fed a diet with 1 g/kg HMB was also significantly higher than that of the LP group. The increased activities of TRS and LPS possibly promoted digestion and absorption of nutrients and further growth of shrimp, as observed in the HMB1 and HMB2 groups (especially the HMB1 group). In ovo feeding, HMB has been found to increase jejunal nutrient uptake and digestion in turkeys [45]. However, there is no study performed to evaluate the impacts of dietary HMB on the digestion of aquatic animals. More studies are required to confirm the influence and mechanism of dietary HMB on digestion in various aquatic animals.
After nutrient such as protein is digested and resolved in the digestive tract, peptides and amino acids are absorbed by the intestine and then utilized for protein synthesis and other metabolism activities [46]. Protein synthesis generally determines the protein deposition, and the growth of aquatic animals is largely due to the protein deposition in tissues (mainly muscle) [12]. The TOR pathway is considered as a critical pathway which regulates the protein synthesis. Activated TOR stimulates translation initiation through activating s6k and inhibiting 4e-bp1 [47, 48]. The present data indicated that lower dietary protein level decreased the mRNA expression levels of tor and s6k, while enhanced the 4e-bp1 mRNA level in shrimp muscle, which may be related to the shortage of substrates (such as amino acids) for protein synthesis. The activation of the TOR pathway is largely regulated by the availability of amino acids [49, 50]; thus, the deficiency of some amino acids, especially EAA, could downregulate the TOR pathway [51, 52]. In the present study, the contents of most individual FAA, total EAA and TAA in muscle of shrimp fed LP were greatly lower than those of the HP group, which may induce the downregulation of the TOR pathway in the LP group. However, optimal dietary HMB inclusion increased the contents of most individual FAA, total EAA and TAA, and the mRNA expression levels of TOR and s6k, while reduced the 4e-bp1 mRNA level in muscle of shrimp fed with a low protein diet. These results suggested that supplementation with 1 g/kg HMB in a low protein diet could promote protein synthesis by upregulating the TOR pathway in shrimp muscle, which paralleled with the enhanced muscle protein content in the HMB1 group. This may contribute to the improved growth of the kuruma shrimp fed HMB1. A positive correlation between the WG and muscle protein content has been observed in abalone (*Haliotis discus* hannai) [43]. Similarly, HMB supplementation increased muscle mass via activating the TOR pathway in rats [53] and enhanced muscle protein synthesis by stimulating translation initiation in neonatal pigs [54]. It is well known that the TOR pathway is positively regulated by the PI3K/AKT pathway [55, 56]. The present data indicated that higher dietary protein level and dietary HMB supplementation both led to the upregulation of pi3k and akt mRNA levels in shrimp muscle, manifesting that the activated TOR pathway in the HP and HMB-supplemented groups may be partly explained by the upregulated pi3k and akt expressions. This is similar to what has been observed in previous studies, which found that HMB supplementation could result in the AKT activation in muscle of fasting rats [57] and enhance muscle mass by activating AKT signaling of pigs fed low protein diets [21]. Taken together, supplementation with 1 g/kg HMB in a low protein diet could promote muscle protein synthesis via activating the TOR related pathways, thus improving the growth of kuruma shrimp. However, the effect and underlying mechanism of dietary HMB on TOR-related pathways need to be thoroughly explored in more aquatic animals.
In addition to the growth performance, the muscle quality of aquatic animals is receiving the attention of researchers due to a growing global need for healthy, safe and nutritious high-quality meat. The texture parameters (hardness, cohesiveness, springiness, and chewiness) and WHC are some of the most considerable quality indicators, which determine consumer acceptance of aquatic animals [58, 59]. Generally, consumers show no preference for muscle with soft texture and poor WHC which could be improved by dietary nutrition [36, 38, 60]. In the current study, decreased muscle hardness, springiness and chewiness were found in the shrimp fed LP, denoting that lower dietary protein level could induce softer muscle texture in kuruma shrimp. However, we observed that improved muscle hardness, chewiness and WHC of shrimp were achieved at dietary HMB level of 2 g/kg, which is similar to our unpublished results in turbot and large yellow croaker. It has been demonstrated that muscle texture had a negative correlation with the muscle lipid content in aquatic animals [61, 62]. An interesting finding from the present study was that crude lipid content in shrimp muscle significantly decreased in response to dietary HMB supplementation, which may partially contribute to the improved muscle firmness of the HMB2 group. Previous studies reported that dietary HMB could decrease fat deposition in Bama Xiang mini-pigs [63] and broiler chickens [64] through regulating the Bacteroidetes-acetic acid-AMPKα axis and gut microbiota respectively, which needs to be further investigated and verified in aquatic animals.
Apart from the muscle lipid content, the muscle pH and collagen content were proven to influence muscle texture and WHC [65–68]. The muscle pH is mainly determined by the accumulation of muscle lactic acid which is generated by glycogen anaerobic breakdown [69, 70]. A lower muscle pH could decrease connective tissue strength, cause softer muscle texture and increase muscle liquid loss [68, 71]. However, the present data showed that muscle pH and glycogen content remained unaffected by dietary protein levels and HMB treatment, although 1 and 2 g/kg HMB supplementation significantly decreased lactic acid content in shrimp muscle. The total collagen content represented by the total Hyp concentration in muscle has been found to be positively correlated with the muscle hardness in large yellow croaker [36] and abalone [43]. Increased muscle collagen content was also demonstrated to improve muscle hardness in Pacific white shrimp (Litopenaeus vannamei) [38]. The current results indicated that the contents of total Hyp and collagen in shrimp muscle both increased with increasing dietary HMB inclusion, and the highest values were obtained in the 4 g/kg HMB group. The *Hyp is* produced by the hydroxylation of Pro and further used for the collagen biosynthesis [72]. It is interesting that HMB supplementation in the low protein diet also enhanced the muscle Pro content in the present study, which may contribute to the improvements in total Hyp and collagen contents, and texture and WHC of shrimp muscle. Further evidence was found in large yellow croaker, in which increased total Hyp and collagen contents in muscle were accompanied by elevated muscle hardness and lowered water loss in response to dietary Pro inclusion [73]. Taken as a whole, the beneficial effects induced by dietary HMB on muscle texture and WHC might be partially attributed to the enhancement of muscle collagen content in kuruma shrimp. However, the amounts of total Hyp and collagen in muscle were not significantly affected by dietary protein levels, although the shrimp fed HP exhibited relatively greater values.
Besides the abovementioned factors, it has been well-documented that muscle fiber characteristics are one of the pivotal factors affecting muscle texture and WHC [74, 75]. The higher myofiber density, smaller myofiber diameter, tighter myofibrillar arrangement, and longer sarcomere length could improve the muscle firmness and WHC of aquatic animals [38, 76]. Bearing in mind the observed impacts of dietary protein levels and HMB supplementation on muscle texture and WHC, it may be suggested that different dietary treatments affected muscle fiber characteristics in kuruma shrimp. To test this hypothesis, we then selected four treatment groups (HP, LP, HMB0.5 and HMB2) to observe the myofiber morphology of shrimp. As expected, lower dietary protein level decreased myofiber density and elevated myofiber diameter, while 2 g/kg HMB inclusion significantly enhanced myofiber density and reduced myofiber diameter in shrimp. These observations corresponded with the poor muscle texture and WHC of the LP group and improved muscle firmness and WHC of shrimp fed HMB2. In addition, ultrastructural examination revealed that the shrimp fed HP and HMB2 had obviously tighter myofibrillar arrangements, narrower intermyofibrillar spaces, and longer sarcomere lengths. The current results fit well with a previous study founding that dietary HMB could improve meat quality through increasing sarcomere lengths in pigs [20]. A longer sarcomere length could exert a beneficial effect on muscle quality via making a layer structure of muscle and preventing the soluble protein loss [77]. Overall, the low protein diet may induce poor muscle texture and WHC of kuruma shrimp by decreasing myofiber density and sarcomere length, as well as increasing myofiber diameter. Dietary HMB supplementation may improve muscle firmness and WHC via reducing myofiber diameter, elevating myofiber density and sarcomere length of kuruma shrimp.
## 5. Conclusion
In conclusion, the present study demonstrated that HMB could be regarded as a nutrition additive to improve the growth performance and muscle quality of kuruma shrimp fed with a low protein diet (440 g/kg protein), and the recommended level is 1-2 g/kg. The appropriate dietary HMB inclusion level increased the activities of digestive enzymes (TRS and LPS) in intestine and activated the TOR pathway in muscle, thus promoting the growth of shrimp. Meanwhile, optimal dietary HMB level improved muscle firmness and WHC of shrimp fed with a low protein diet, which may be ascribed to the lower muscle lipid content, higher muscle collagen content, reduced myofiber diameter, and increased myofiber density and sarcomere length caused by dietary HMB. Nevertheless, the findings from the current study are suggestive, and further exploration is warranted to validate them. Furthermore, more attention should be paid to the application efficacies of HMB supplementation in diets of other aquatic animal species.
## Data Availability
The data that support the findings of this study are available from the corresponding authors upon reasonable request.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
Hua Mu carried out the conceptualization, data curation, formal analysis, funding acquisition, and writing the original draft. Chenbin Yang was responsible for the feeding, investigation, data curation, and formal analysis. Yu Zhang and Shengdi Chen made the investigation and data curation. Panpan Wang was responsible for the conceptualization and methodology. Binlun Yan carried out the project administration, supervision, writing the review, and editing; Qingqi Zhang was also in charge of the project administration and supervision. Chaoqing Wei made the conceptualization, funding acquisition, methodology, supervision, writing the review, editing, and validation. Huan Gao managed the project administration, supervision, writing the review, and editing. Hua Mu and Chenbin Yang contributed to the work equally.
## References
1. 1FAO
The State of World Fisheries and Aquaculture 2020
2020Rome, ItalyFood and Agriculture Organization of the United Nations. (2020)
2. Béné C., Arthur R., Norbury H.. **Contribution of fisheries and aquaculture to food security and poverty reduction: assessing the current evidence**. (2016) **79** 177-196. DOI: 10.1016/j.worlddev.2015.11.007
3. Li X., Han T., Zheng S., Wu G.. **Nutrition and functions of amino acids in aquatic crustaceans**. (2020) **1285** 169-197. DOI: 10.1007/978-3-030-54462-1_9
4. Li X., Zheng S., Wu G.. **Nutrition and functions of amino acids in fish**. (2020) **1285** 133-168. DOI: 10.1007/978-3-030-54462-1_8
5. Hussain M., Hassan H. U., Siddique M. A. M.. **Effect of varying dietary protein levels on growth performance and survival of milkfish**. (2021) **47** 329-334. DOI: 10.1016/j.ejar.2021.05.001
6. Wei C., Liu C., Wang X., Zhou H., Mai K., He G.. **Dietary arachidonic acid supplementation improves the growth performance and alleviates plant protein-based diet-induced inflammation in juvenile turbot (**. (2021) **27** 533-543. DOI: 10.1111/anu.13204
7. Wei C., Wang X., Li C.. **Replacement of fishmeal with**. (2021) **52** 2110-2120. DOI: 10.1111/are.15063
8. Mu H., Li L., Yang C.. **Effects of dietary fishmeal levels on adenosine triphosphate-related compounds and freshness of raw and cooked muscle in large yellow croaker**. (2022) **26, article 101304**. DOI: 10.1016/j.aqrep.2022.101304
9. Xie S., Wei D., Yin P.. **Dietary replacement of fish-meal impaired protein synthesis and immune response of juvenile Pacific white shrimp,**. (2019) **228** 26-33. DOI: 10.1016/j.cbpb.2018.11.002
10. Xu J., Feng L., Jiang W.. **Different dietary protein levels affect flesh quality, fatty acids and alter gene expression of Nrf2-mediated antioxidant enzymes in the muscle of grass carp (**. (2018) **493** 272-282. DOI: 10.1016/j.aquaculture.2018.05.008
11. Zhang J., Zhao N., Sharawy Z., Li Y., Ma J., Lou Y.. **Effects of dietary lipid and protein levels on growth and physiological metabolism of**. (2018) **495** 458-464. DOI: 10.1016/j.aquaculture.2018.06.004
12. Dumas A., De Lange C. F., France J., Bureau D. P.. **Quantitative description of body composition and rates of nutrient deposition in rainbow trout (**. (2007) **273** 165-181. DOI: 10.1016/j.aquaculture.2007.09.026
13. Fraser K. P., Rogers A. D.. **Protein metabolism in marine animals: the underlying mechanism of growth**. (2007) **52** 267-362. DOI: 10.1016/S0065-2881(06)52003-6
14. Hawkins A.. **Relationships between the synthesis and breakdown of protein, dietary absorption and turnovers of nitrogen and carbon in the blue mussel,**. (1985) **66** 42-49. DOI: 10.1007/BF00378550
15. Duan Y., Li F., Guo Q.. (2018) **66** 170-176. DOI: 10.1021/acs.jafc.7b04841
16. Duan Y., Li F., Li Y.. **The role of leucine and its metabolites in protein and energy metabolism**. (2016) **48** 41-51. DOI: 10.1007/s00726-015-2067-1
17. Fuller J. C., Sharp R. L., Angus H. F., Baier S. M., Rathmacher J. A.. **Free acid gel form of**. (2011) **105** 367-372. DOI: 10.1017/S0007114510003582
18. Zheng C., Song B., Guo Q.. **Alterations of the muscular fatty acid composition and serum metabolome in bama xiang mini-pigs exposed to dietary beta-hydroxy beta-methyl butyrate**. (2021) **11** p. 1190. DOI: 10.3390/ani11051190
19. Tang Z., Song B., Zheng C., Zheng J., Yin Y., Chen J.. **Dietary beta-hydroxy-beta-methyl butyrate supplementation affects growth, carcass characteristics, meat quality, and serum metabolomics profile in broiler chickens**. (2021) **12, article 633964**. DOI: 10.3389/fphys.2021.633964
20. Zheng C., Zhang S., Duan Y.. **Dietary beta-hydroxy-beta-methyl butyrate supplementation improves meat quality of Bama Xiang mini-pigs through manipulation of muscle fiber characteristics**. (2022) **88, article 104885**. DOI: 10.1016/j.jff.2021.104885
21. Duan Y., Li F., Song B.. (2019) **52** 34-42. DOI: 10.1016/j.jff.2018.10.029
22. Molfino A., Gioia G., Rossi Fanelli F., Muscaritoli M.. **Beta-hydroxy-beta-methylbutyrate supplementation in health and disease: a systematic review of randomized trials**. (2013) **45** 1273-1292. DOI: 10.1007/s00726-013-1592-z
23. Aversa Z., Bonetto A., Costelli P.. (2011) **38** 713-720. DOI: 10.3892/ijo.2010.885
24. Rahimi M. H., Mohammadi H., Eshaghi H., Askari G., Miraghajani M.. **The effects of beta-hydroxy-beta-methylbutyrate supplementation on recovery following exercise-induced muscle damage: a systematic review and meta-analysis**. (2018) **37** 640-649. DOI: 10.1080/07315724.2018.1451789
25. Tsuchiya Y., Ueda H., Sugita N., Ochi E.. **Low dose of**. (2021) **40** 211-218. DOI: 10.1080/07315724.2020.1752330
26. Zhao J., Liu X., Liao X.. **Transcriptome analysis reveals the effects of sand substrate removal and body colour change of kuruma shrimp,**. (2021) **52** 577-588. DOI: 10.1111/are.14915
27. Bulbul M., Kader M. A., Koshio S., Ishikawa M., Yokoyama S.. **Effect of replacing fishmeal with canola meal on growth and nutrient utilization in kuruma shrimp**. (2014) **45** 848-858. DOI: 10.1111/are.12026
28. Wang W., Ishikawa M., Koshio S., Yokoyama S., Dawood M. A., Zhang Y.. **Effects of dietary astaxanthin supplementation on survival, growth and stress resistance in larval and post-larval kuruma shrimp,**. (2018) **49** 2225-2232. DOI: 10.1111/are.13679
29. Kanazawa A.. (1990)
30. Teshima S., Koshio S., Ishikawa M., Kanazawa A.. **Protein requirement of the prawn**. (2001) **449** 293-300. DOI: 10.1023/A:1017568615695
31. Cunniff P.. **Official methods of analysis of AOAC international**. (1995) p. 1141
32. Qu L., Xia T., Du X.. **Effects of salinity treatment on muscle quality and off-flavour compounds of grass carp (**. (2022) **53** 4823-4831. DOI: 10.1111/are.15975
33. Xu D., He G., Mai K., Zhou H., Xu W., Song F.. **Postprandial nutrient-sensing and metabolic responses after partial dietary fishmeal replacement by soyabean meal in turbot (**. (2016) **115** 379-388. DOI: 10.1017/S0007114515004535
34. Deng G., Duan J., Mu H.. **Effects of dietary short-chain fatty acid salts on the growth performance, digestive, antioxidant and immune enzyme activities, immune-related gene expression and resistance to**. (2021) **52** 6716-6725. DOI: 10.1111/are.15542
35. Livak K. J., Schmittgen T. D.. **Analysis of relative gene expression data using real-time quantitative PCR and the 2**. (2001) **25** 402-408. DOI: 10.1006/meth.2001.1262
36. Wei Z., Ma J., Pan X.. **Dietary hydroxyproline improves the growth and muscle quality of large yellow croaker**. (2016) **464** 497-504. DOI: 10.1016/j.aquaculture.2016.07.015
37. Gómez-Guillén M., Montero P., Hurtado O., Borderias A.. **Biological characteristics affect the quality of farmed Atlantic salmon and smoked muscle**. (2000) **65** 53-60. DOI: 10.1111/j.1365-2621.2000.tb15955.x
38. Cheng X., Li M., Leng X.. **Creatine improves the flesh quality of Pacific white shrimp (**. (2021) **354, article 129498**. DOI: 10.1016/j.foodchem.2021.129498
39. Deshimaru O., Shigeno K.. **Introduction to the artificial diet for prawn**. (1972) **1** 115-133. DOI: 10.1016/0044-8486(72)90013-0
40. Fan B., Li Y., Huang Y.. **Cloning and expression of chitin deacetylase 1 from**. (2018) **24** 1664-1678. DOI: 10.1111/anu.12801
41. Talukdar A., Dharmendra Deo A., Prasad Sahu N.. **Effects of different levels of dietary protein on the growth performance, nutrient utilization, digestive enzymes and physiological status of white shrimp,**. (2021) **27** 77-90. DOI: 10.1111/anu.13166
42. Kalhoro H., Zhou J., Hua Y.. **Soy protein concentrate as a substitute for fish meal in diets for juvenile**. (2018) **49** 1896-1906. DOI: 10.1111/are.13645
43. Wu Z., Yu X., Guo J.. **Replacement of dietary fish meal with**. (2022) **553, article 738070**. DOI: 10.1016/j.aquaculture.2022.738070
44. Gaxiola G., Cuzon G., García T.. **Factorial effects of salinity, dietary carbohydrate and moult cycle on digestive carbohydrases and hexokinases in**. (2005) **140** 29-39. DOI: 10.1016/j.cbpb.2004.10.018
45. Foye O., Ferket P., Uni Z.. **The effects of in ovo feeding arginine,**. (2007) **86** 2343-2349. DOI: 10.3382/ps.2007-00110
46. Xie S., Wei D., Fang W.. **Survival and protein synthesis of post-larval white shrimp,**. (2020) **263, article 114462**. DOI: 10.1016/j.anifeedsci.2020.114462
47. de la Parra C., Walters B. A., Geter P., Schneider R. J.. **Translation initiation factors and their relevance in cancer**. (2018) **48** 82-88. DOI: 10.1016/j.gde.2017.11.001
48. Seiliez I., Gabillard J.-C., Skiba-Cassy S.. **An in vivo and in vitro assessment of TOR signaling cascade in rainbow trout (**. (2008) **295** R329-R335. DOI: 10.1152/ajpregu.00146.2008
49. Dai W., Panserat S., Plagnes-Juan E., Seiliez I., Skiba-Cassy S.. **Amino acids attenuate insulin action on gluconeogenesis and promote fatty acid biosynthesis via mTORC1 signaling pathway in trout hepatocytes**. (2015) **36** 1084-1100. DOI: 10.1159/000430281
50. Lansard M., Panserat S., Plagnes-Juan E., Dias K., Seiliez I., Skiba-Cassy S.. **L-leucine, L-methionine, and L-lysine are involved in the regulation of intermediary metabolism-related gene expression in rainbow trout hepatocytes**. (2011) **141** 75-80. DOI: 10.3945/jn.110.124511
51. Wang Q., He G., Mai K., Xu W., Zhou H.. **Fishmeal replacement by mixed plant proteins and maggot meal on growth performance, target of rapamycin signalling and metabolism in juvenile turbot (**. (2016) **22** 752-758. DOI: 10.1111/anu.12296
52. Xu D., He G., Mai K.. **Effect of fish meal replacement by plant protein blend on amino acid concentration, transportation and metabolism in juvenile turbot (**. (2017) **23** 1169-1178. DOI: 10.1111/anu.12486
53. Pimentel G. D., Rosa J. C., Lira F. S.. (2011) **8** 1-7. DOI: 10.1186/1743-7075-8-11
54. Wheatley S. M., El-Kadi S. W., Suryawan A.. **Protein synthesis in skeletal muscle of neonatal pigs is enhanced by administration of**. (2014) **306** E91-E99. DOI: 10.1152/ajpendo.00500.2013
55. Testa J. R., Tsichlis P. N.. **AKT signaling in normal and malignant cells**. (2005) **24** 7391-7393. DOI: 10.1038/sj.onc.1209100
56. Vivanco I., Sawyers C. L.. **The phosphatidylinositol 3-kinase-AKT pathway in human cancer**. (2002) **2** 489-501. DOI: 10.1038/nrc839
57. Gerlinger-Romero F., Guimarães-Ferreira L., Yonamine C. Y., Salgueiro R. B., Nunes M. T.. **Effects of beta-hydroxy-beta-methylbutyrate (HMB) on the expression of ubiquitin ligases, protein synthesis pathways and contractile function in extensor digitorum longus (EDL) of fed and fasting rats**. (2018) **68** 165-174. DOI: 10.1007/s12576-016-0520-x
58. Lie Ø.. **Flesh quality–the role of nutrition**. (2001) **32** 341-348. DOI: 10.1046/j.1355-557x.2001.00026.x
59. Zhu B., Dong X., Sun L.. **Effect of thermal treatment on the texture and microstructure of abalone muscle (**. (2011) **20** 1467-1473. DOI: 10.1007/s10068-011-0203-6
60. Valente L. M., Moutou K. A., Conceicao L. E., Engrola S., Fernandes J. M., Johnston I. A.. **What determines growth potential and juvenile quality of farmed fish species?**. (2013) **5** S168-S193. DOI: 10.1111/raq.12020
61. Johnston I. A., Li X., Vieira V. L.. **Muscle and flesh quality traits in wild and farmed Atlantic salmon**. (2006) **256** 323-336. DOI: 10.1016/j.aquaculture.2006.02.048
62. Ma R., Meng Y., Zhang W., Mai K.. **Comparative study on the organoleptic quality of wild and farmed large yellow croaker**. (2020) **38** 260-274. DOI: 10.1007/s00343-019-8353-0
63. Zheng J., Zheng C., Song B.. **HMB improves lipid metabolism of Bama Xiang mini-pigs via modulating the**. (2021) **12, article 736997**. DOI: 10.3389/fmicb.2021.736997
64. Zhang S., Tang Z., Zheng C.. **Dietary beta-hydroxy-beta-methyl butyrate supplementation inhibits hepatic fat deposition via regulating gut microbiota in broiler chickens**. (2022) **10** p. 169. DOI: 10.3390/microorganisms10010169
65. Cheng J., Sun D., Han Z., Zeng X.. **Texture and structure measurements and analyses for evaluation of fish and fillet freshness quality: a review**. (2014) **13** 52-61. DOI: 10.1111/1541-4337.12043
66. Johnsen C. A., Hagen Ø., Adler M.. **Effects of feed, feeding regime and growth rate on flesh quality, connective tissue and plasma hormones in farmed Atlantic salmon (**. (2011) **318** 343-354. DOI: 10.1016/j.aquaculture.2011.05.040
67. Løje H., Jensen K. N., Hyldig G., Nielsen H. H., Nielsen J.. **Changes in liquid-holding capacity, water distribution and microstructure during chill storage of smoked salmon**. (2007) **87** 2684-2691. DOI: 10.1002/jsfa.3031
68. Lavety J., Afolabi O., Love R.. **The connective tissues of fish**. (1988) **23** 23-30. DOI: 10.1111/j.1365-2621.1988.tb00546.x
69. Love R. M.. (1988)
70. Rasmussen R. S.. **Quality of farmed salmonids with emphasis on proximate composition, yield and sensory characteristics**. (2001) **32** 767-786. DOI: 10.1046/j.1365-2109.2001.00617.x
71. Andrés-Bello A., Barreto-Palacios V., García-Segovia P., Mir-Bel J., Martínez-Monzó J.. **Effect of pH on color and texture of food products**. (2013) **5** 158-170. DOI: 10.1007/s12393-013-9067-2
72. Gelse K., Pöschl E., Aigner T.. **Collagens--structure, function, and biosynthesis**. (2003) **55** 1531-1546. DOI: 10.1016/j.addr.2003.08.002
73. Wei Z., Deng K., Zhang W., Mai K.. **Interactions of dietary vitamin C and proline on growth performance, anti- oxidative capacity and muscle quality of large yellow croaker**. (2020) **528, article 735558**. DOI: 10.1016/j.aquaculture.2020.735558
74. Espe M.. (2008). DOI: 10.1533/9781845694920.2.241
75. Lefaucheur L.. **A second look into fibre typing - relation to meat quality**. (2010) **84** 257-270. DOI: 10.1016/j.meatsci.2009.05.004
76. Lin W., Zeng Q., Zhu Z.. **Different changes in mastication between crisp grass carp (**. (2009) **42** 271-278. DOI: 10.1016/j.foodres.2008.11.005
77. Ertbjerg P., Puolanne E.. **Muscle structure, sarcomere length and influences on meat quality: a review**. (2017) **132** 139-152. DOI: 10.1016/j.meatsci.2017.04.261
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---
title: Partial Substitution of Fish Meal with Soy Protein Concentrate on Growth, Liver
Health, Intestinal Morphology, and Microbiota in Juvenile Large Yellow Croaker (Larimichthys
crocea)
authors:
- Xuexi Wang
- Hongjie Luo
- Dejuan Wang
- Yunzong Zheng
- Wenbo Zhu
- Weini Zhang
- Zhengbang Chen
- Xinhua Chen
- Jianchun Shao
journal: Aquaculture Nutrition
year: 2023
pmcid: PMC9973153
doi: 10.1155/2023/3706709
license: CC BY 4.0
---
# Partial Substitution of Fish Meal with Soy Protein Concentrate on Growth, Liver Health, Intestinal Morphology, and Microbiota in Juvenile Large Yellow Croaker (Larimichthys crocea)
## Abstract
The present study investigated the growth performance, feed utilization, intestinal morphology, and microbiota communities of juvenile large yellow croaker (Larimichthys crocea) fed diets containing different proportions of soy protein concentrate (SPC) (0, $15\%$, $30\%$, and $45\%$, namely FM, SPC15, SPC30, and SPC45) as a substitute for fish meal (FM) for 8 weeks. The weight gain (WG) and specific growth rate (SGR) in fish fed SPC45 were significantly lower than those fed FM and SPC15 but not differ with these fed SPC30. The feed efficiency (FE) and protein efficiency ratio (PER) decreased sharply when the dietary SPC inclusion level was higher than $15\%$. The activity of alanine aminotransferase (ALT) and expression of alt and aspartate aminotransferase (ast) were significantly higher in fish fed SPC45 than those fed FM. The activity and mRNA expression of acid phosphatase were opposite. The villi height (VH) in distal intestine (DI) showed a significant quadratic response to increasing dietary SPC inclusion levels and was highest in SPC15. The VH in proximal intestine, middle intestine decreased significantly with increasing dietary SPC levels. The 16S rRNA sequences in intestine revealed that fish fed SPC15 had higher bacterial diversity and abundance of Phylum Firmicutes such as order Lactobacillales and order Rhizobiaceae than those fed other diets. Genus vibrio, family Vibrionaceae and order Vibrionales within phylum Proteobacteria were enriched in fish fed FM and SPC30 diets. Tyzzerella and Shewanella that belongs to phylum Firmicutes and Proteobacteria, respectively, were enriched in fish fed SPC45 diet. Our results indicated that SPC replacing more than $30\%$ FM could lead to lower quality diet, retard growth performance, ill health, disordered intestine structure, and microbiota communities. Tyzzerella could be the bacteria indicator of intestinal in large yellow croaker fed low quality diet due to high SPC content. Based on the quadratic regression analysis of WG, the best growth performance could be observed when the replacement of FM with SPC was $9.75\%$.
## 1. Introduction
Carnivorous fish aquaculture is currently supported by the production of balanced diets, the primary components of which are animal-derived elements [1]. Fish meal (FM) is one of the most important and widely utilized protein sources in aquatic animal feed because of its high-protein quality, generally balanced fatty acids and amino acids profile, abundant vitamins and minerals, and the exceptional palatability. However, as FM is declining due to the overexploitation of pelagic fish and the rapidly rising aquaculture industry, the quest for alternate protein sources is increasingly necessary [2].
Plant proteins have been and continue to be the main protein sources due to their relatively low price and enormous production. The anti-nutritional factors (ANFs) in plant feedstuffs, on the other hand, can impair feed intake, nutrient digestibility, and utilization, as well as modify disease resistance, thus resulting in poor fish growth and development [3–5]. The presence of ANFs is widely acknowledged as one of the major drawbacks of soybean meal (SBM), limiting its inclusion level in aquatic animal diets [6, 7]. In this regard, some researchers have focused their attention on soy protein concentrate (SPC) [8–10], which is produced by extracting defatted soy flakes in aqueous ethanol or methanol to decrease or eliminate ANFs contents [11]. Moreover, SPC have several benefits over SBM, such as a favorable amino acids profile, digestible protein and energy and better palatability [12], whereas relatively lower lysine and methionine contents than FM. Previous study in large yellow croaker (Larimichthys crocea) demonstrates that SPC combined with lysine and methionine could totally replace dietary FM [13]. Given that the potential of SPC is worth to be studied and further clarified. Additionally, the intestine of fish is a complex microbial ecosystem that houses a dynamic consortium of microorganisms related with diet composition and can perform pivotal roles in host health, including nutrition absorption, energy generation, and immune response balance [14, 15]. To the best of our knowledge, there is no study about evaluating the using of SPC without crystal amino acid supplementation in the diet of large yellow croaker (Larimichthys crocea) and its subsequent effects on the fish's intestine so far.
L. crocea is an economically important carnivorous marine fish species in south China because of its excellent taste and important commercial value. The yield of farmed large yellow croaker was 254224 tons according to the China Fishery Statistical Yearbook [16]. Over the past years, researchers have investigated the effects of dietary FM replacement with SBM, fermented soybean meal, corn-gluten meal, meat and bone meal, and Antarctic krill meal on growth performance, tissues composition and intestinal morphology and microbiota in L. crocea [17–21]. These studies demonstrated the possibility of using alternative protein sources in the diets of large yellow croaker. Therefore, this study is aimed at evaluating the effects of replacing FM in the diet by SPC (without crystal amino acid supplementation), as assessed by growth performance, feed utilization, intestinal morphology, and microbiota populations in L. crocea.
## 2.1. Diets, Feeding Trial and Sampling
Five isonitrogenous and isolipidic ($45\%$ and $10\%$ crude protein and lipid, respectively) were formulated to meet the requirement of large yellow croaker [13]. The dietary fish meal was replaced by SPC at: 0, $15\%$, $30\%$, and $75\%$, and named FM, SPC15, SPC30, and SPC45, respectively (Table 1). The amino acid profiles of the experimental diets are shown in Table 2. The experiment diets with uniform pellet size (2.0 × 5.0 mm) were manufactured as described previously [22], after which the diets were stored in a refrigerator at -20°C.
Juvenile fish were obtained from a local commercial farm (Ningde, Fujian, China), and were acclimated in floating cages (4.0 × 2.0 × 2.5 m, length × width × depth) for 2 weeks prior to the experiment. A total of 960 juveniles (19.50 ± 1.62 g) were randomly allocated into 16 floating cages (1.0 × 1.0 × 1.5 m, length × width × depth), and four replicates (60 fish per replicates) were assigned to each treatment. Fish were hand-fed to apparent satiation twice daily (5: 30 and 17: 30) for 8 weeks. During the period of the experiment, the water temperature, dissolved oxygen level and ammonia nitrogen level were 28.0 ± 0.8°C, 6.5 ± 0.7 mg L−1, and 0.20 ± 0.04 mg L−1, respectively.
Following a 24 h fasting period at the end of the feeding trial, large yellow croakers were euthanized (MS-222 at 10 mg L−1), counted and weight from each cage to determine the survival rate (SR), final body weight (FBW), weight gain (WG), specific growth rate (SGR), feed efficiency (FE), and protein efficiency ratio (PER). A total of 10 fish per cage were randomly selected for sampling, 4 fish of which were used to determine morphological parameters including condition factor (CF), viscerosomatic index (VSI), and hepatosomatic index (HSI). Then, the proximal intestine (PI), middle intestine (MI), and distal intestine (DI) from the 4 same fish was stored in $10\%$ neutral formaldehyde for the analysis of morphology. The liver and intestine samples from 6 selected fish per cage were frozen immediately in liquid nitrogen and stored at -80°C. The livers were collected for the enzyme activities and genes expression analysis. The intestine samples were then sent to Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) for 16S rDNA-based microbiota analysis.
## 2.2. Amino Acids Profile
A total of 0.1 g diets with 10 mL of 6 N HCL was digested at 110°C in glass tubes sealed with a nitrogen atmosphere for 24 h. The solution was filtered and flash evaporated to remove any acid. The acid free sample was further made up with 0.05 N HCL and filtered using a 0.2 μm nylon membrane filter (Whatman, Whatman plc, Kent, UK) to remove any residue. The L-8900 amino acid analyzer (Hitachi, Japan) was approached to determine the amino acids profiles of the diets.
## 2.3. Intestinal Histology
Fresh intestine tissue was fixed with $4\%$ paraformaldehyde before paraffin sections were prepared (Servicebio, Hangzhou, China). Briefly, after fixation for at least 24 h, tissue samples were trimmed appropriately in a fume hood before being dehydrated in ethanol with concentration increasing incrementally from $75\%$ to $100\%$. Intestine samples were then embedded in paraffin and sliced into sections of 4 μm using a microtome (HistoCore AUTOCUT, Leica, Germany). They were stained with haematoxylin and eosin (H&E) [23], and images were acquired under a microscope (Nikon Eclipse CI, Tokyo, Japan). Mucosal thickness (MT), villi height (VH), and villi width (VW) were measured with ImagePro Plus6.0 software.
## 2.4. Liver Enzyme Activity Analysis
The liver samples were homogenized on ice in 9 volumes (w: v) of ice cold physiological saline 8.9 g/mL, then centrifugated at 3000 rpm for 10 min at 4°C (Eppendorf centrifuge 5810R, Germany) to collect the supernatant for enzyme activity analysis. The activities of the liver alanine and aspartate aminotransferase (ALT and AST) acid- and alka-line phosphatase (ACP and AKP) were evaluated using commercial assay kits (Jiancheng Bioengineering Institute., Nanjing, China) according a described method [24]. The enzyme specific activity was expressed as unit/g soluble protein. The protein concentration of hepatic homogenates was determined using a protein assay kit (Jiancheng Bioengineering Institute, Nanjing, China).
## 2.5. Real-Time Quantitative PCR (RT-qPCR) Analysis of Liver Health Related Genes
The total RNA of liver was extracted with an Eastep® RNA extraction kit (LS1040, Promega Biotech, China) according to the manufacturer's instruction. The quality of RNA was assessed by a $1.2\%$ agarose gel electrophoresis, and the quantity of total RNA was measured with a spectrophotometer (NP80, IMPLEN, Germany). First- strand cDNA synthesis was performed by using an Eastep® RT Master Mix Kit (LS2050, Promega Biotech, China). β-Actin was used as reference gene after the stability of its expression was confirmed. The specific primers for alkaline phosphatase (akp), acid phosphatase (acp), alanine aminotransferase (alt), and aspartate aminotransferase (ast) were designed by NCBI online tools (Supplementary Table 1). RT-qPCR was performed in a 20 μL reaction volume including 10 μL SYBR Green premix, 0.8 μL cDNA template, 0.4 μL forward and reverse primers (10 μM), and 8.4 μL diethyl pyrocarbonate-treated water. The reaction conditions are as follows: 95°C 30 s, 40 cycles of 95°C 6 s, and 60°C 25 s. After obtaining data, the 2−ΔΔCt method was employed to calculate gene expression level [25] and then subjected to statistical analysis.
## 2.6. DNA Extraction and PCR Amplification
The E.Z.N.A. Soil DNA Kit (Omega Bio-tek Inc., Norcross, USA) was used to extract total DNA from each intestinal sample according to the manufacturer's instructions. A Nano Drop 2000 UV–vis spectrophotometer (Thermo Scientific, Wilmington, USA) was used to assess the final DNA concentration and purification, and DNA quality was evaluated using a $1\%$ agarose gel electrophoresis. With bacterial primers 338F 5′-ACTCCTACGGGAGGCAGCAG-3′ and 806R 5′-GGACTACHVGGGTWTCTAAT-3′, the V3-V4 hypervariable portions of the 16S rRNA gene were amplified. PCR amplification was performed as follows: 95°C for 3 min, 27 cycles of 95°C for 30 s, 55°C for 30 s, and 72°C for 45 s, and 72°C for 10 min 4°C using a Thermocycler PCR system (Gene Amp 9700, ABI, USA). The PCR mixtures contains 4 μL of 5 × TransStart FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of TransStart FastPfu DNA Polymerase, and 10 ng of DHA template and diethyl pyrocarbonate-treated water (to 20 μL). The resulting PCR products of bacteria were extracted from a $2\%$ agarose gel, further purified with the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, USA), and quantified with QuantiFluor-ST (Promega, USA) according to the manufacturer's protocol.
## 2.7. Illumina MiSeq Sequencing and Data Processing
Majorbio Bio-Pharm Technology Co., Ltd. filtered and pooled the bacteria and products in equimolar levels before paired-end sequencing (2 × 300) using an Illumina MiSeq platform (Illumina, San Diego, USA) according to normal protocols (Shanghai, China). Trimmomatic quality-filtered and FLASH merged raw fastq files of bacterial reads received from MiSeq sequencing [26, 27]. The UPARSE (version 7.0) was used to cluster the processed sequences into operational taxonomic units (OTUs) with a minimum of $97\%$ [28]. The RDP Classifier method was used to compare the taxonomy of the bacterial sequence to the SILVA database ($\frac{138}{16}$S-bacteria) with a $70\%$ confidence level. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States was used to anticipate potential functional changes in the bacterial community in intestinal samples (PICRUSt) [29].
The alpha-diversity.py in Quantitative Insights into Ecology (QIIME, Version 1.9.1) was used to calculate the alpha diversity of intestinal bacteria. The bacterial communities in the samples were visualized using vegan package in R3.3.1 through nonmetric multidimensional scaling (NMDS) based on the Bray-Curtis distance. The linear discriminate analysis (LDA) effect size (LEfSe) method was approached to analyze the high-dimensional microbial taxa [30].
## 2.8. Statistical Analysis
Statistical analysis was performed using SPSS 22.0 (SPSS, Inc., Chicago, USA). Data were tested for normal distribution and homogeneity of variance via the Kolmogorov–Smirnov test and Levene's test. Differences in the physicochemical profiles of intestine samples were tested by one-way analysis of variance (ANOVA) with Tukey's test; $P \leq 0.05$ was considered a statistically significant difference. Additionally, orthogonal polynomial contrasts were used to assess the significance of linear or quadratic models, to describe the response of the dependent variable to dietary FM replacement levels. The results of growth performance, intestinal histology, liver enzyme activities, and genes expression were presented as means ± SD ($$n = 4$$). The intestinal microbiota data were presented as means ± SD ($$n = 3$$).
## 3. Results
As shown in Table 3, the SR, HSI, VSI, and CF of large yellow croakers were no affected by the dietary SPC inclusion levels ($P \leq 0.05$). However, the FBW, WG, and SGR of fish fed SPC30 were not different from those fed other diets ($P \leq 0.05$). Compared to fish fed SPC45, these fed FM and SPC15 had higher FBW, WG, and SGR ($P \leq 0.05$). The FE and PER were significantly higher in fish fed SPC15 than those fed SPC30 and SPC45 ($P \leq 0.05$), while no difference was observed between FM and SPC15 group ($P \leq 0.05$). Regression analyses on FBW, WG, SGR, and FE showed the significant negative linear and quadratic responses to increasing dietary SPC inclusion levels ($P \leq 0.05$). Based on the quadratic response of WG to different diets, the best growth performance was observed when the replacement of FM with SPC was $9.75\%$ (Figure 1).
Table 4 showed that the AKP and AST activities in the liver of large yellow croakers were not influenced by dietary FM replacement levels with SPC ($P \leq 0.05$). The ACP activity and decreased significantly ($P \leq 0.05$) and showed negative linear and quadratic responses to increasing dietary SPC inclusion levels ($P \leq 0.05$). The trend of ALT activity was totally opposite ($P \leq 0.05$). The mRNA expression of alt and ast in FM, SPC15, and SPC30 were similar and significantly lower than that in SPC45 ($P \leq 0.05$) (Figure 2). The acp expression level showed a significantly decrease trend with dietary SPC levels increasing from 0 to $45\%$ ($P \leq 0.05$).
The intestinal morphometry of large yellow croaker fed the experimental diets was displayed in Table 5 and Figure 3. The VW and MT in PI, MI and DI were not influenced by the dietary FM replacement level with SPC ($P \leq 0.05$). The VH in PI of fish fed FM, and MI of fish fed FM and SPC15 were significantly higher than those fed SPC30 and SPC45 ($P \leq 0.05$). However, there was no difference between the VH of PI in SPC15 and other groups ($P \leq 0.05$). Fish fed diet SPC15 had significantly higher VH in DI than those fed diet SPC45 ($P \leq 0.05$). However, the VH of DI in FM and SPC30 was not different to SPC15 and/or SPC45. The VH in PI, MI and DI showed negative linear and quadratic responses to increasing dietary SPC inclusion levels ($P \leq 0.05$).
In total, 962 OTUs assigned into 33 different phyla, 302 families, or 499 genera were identified in the intestine samples (Supplementary Table 2). The rarefaction curves tended toward the saturation plateau (Supplementary Figure 1), meanwhile the Good's coverage index revealed that the amounts of obtained bacterial species in five different groups were>$99\%$ (Table 6). The coverage of fish fed diet SPC30 was significantly lower than those fed FM and SPC15 ($P \leq 0.05$). The values of in Sobs, Shannon, Ace, and Chao showed a significantly quadratic response to dietary SPC inclusion levels and were highest in SPC15 group ($P \leq 0.05$). While the Simpson was lowest in SPC15 group, there was no difference among the alpha diversity of FM, SPC30, and SPC45 group ($P \leq 0.05$).
NMDS was conducted to determine the β diversity to analyze the extent of similarities in microbial communities of fish in different groups (Figure 4(b)). The further the components were separated, the greater the difference. The NMDS plot showed that the components of fish in the different experimental groups were clustered and separated from each other. The gut microbiota structure from FM, SPC30, and SPC45 group were similar and clustered within one higher branch, whereas was distinct from SPC15 group and formed another branch (Figure 4(a)). A Venn diagram showed that 44 OTUs were shared by the four different groups (Figure 4(c)). The unique OTUs in FM, SPC15, SPC30, and SPC45 were 39, 487, 139, and 36, respectively. 135, 713, 353, and 156 were the total OTUs in the four groups.
Bacterial community dynamics of gut microbiota in fish fed different experimental diets were shown in Figure 5. At the phylum level, Proteobacteria, Firmicutes, and Fusobacteriota were the predominant bacterial in the intestine of fish among all groups (Figure 5). The dominant phyla in FM group were Proteobacteria ($71\%$), Fusobacteriota ($16\%$), and Firmicutes ($13\%$) while the SPC15 group was dominated by Firmicutes ($45\%$), Proteobacteria ($38\%$), and Fusobacteriota ($4.1\%$). The relative abundance of bacterial phyla in SPC30 group was Proteobacteria ($65\%$), Firmicutes ($31\%$), and Fusobacteriota ($2.5\%$) while in SPC45 group, Firmicutes ($53\%$), Fusobacteriota ($28\%$), and Proteobacteria ($18\%$).
LEfSe analysis was performed to determine the difference in gut microbial community composition of fish fed FM, SPC15, SPC30, and SPC45 diets (Figures 6(a) and 6(b)). In the FM group, phylum Proteobacteria, class Gamma-proteobacteria (class) and genus Photobacterium, Psychrobacter, and vibrio were significantly enriched, Vibrionales (from order to family) and genus Nitratireductor were enriched in SPC30 group, while shewanellaceae (from family to genus), Lachnospiraceae (from order to family), and genus tyzzerella were significantly enriched in the SPC 45 group. More bacteria were significantly enriched in the SPC15 group, including the phylum Verrucomicrobiota, class Vicinamibacteria, and no-rank Vicinamibacteria (from family to genus), class holophagae and Subgoup 7 (from order to genus), order Oscillospirales and its family Ruminoccoccaceae (family and its genus faecalibacterium), Peptostreptoccoccale (from order to genus), Christensenellales (from order to genus), Erysipelotrichales (from order to family), order Lactobacillales, Thermomicrobiales and Rhizobialaes (its families Rhizobiaceae and Beijerinckiaceae), Nocardioidaceae (from family to genus), order Solirubrobacterales and its family 67-14 (from family to genus), family Rikenellaceae, and its genus Alistipes, genus CHKCI001, Eubacterium_hallii_group, unclassified Lachnospiraceae, Blautia.
In Figure 7, significant differences were observed in the KEGG pathways among the five treatments. Biosynthesis of unsaturated fatty acids, PI3K-Akt signaling pathway, AMPK signaling pathway, Glycerophospholipid metabolism, Biosynthesis of secondary metabolites, Biosynthesis of amino acids, Toll, and Imd signaling pathway.
## 4. Discussion
Many studies performed on the replacement of FM showed that SPC could totally replace FM and be good feed ingredient for fish species, such as Atlantic halibut (Hippoglossus hippoglossus) [8], rainbow trout [31], and longfin yellowtail (Seriola rivoliana) [32]. Study in giant grouper (Epinephelus lanceolatus) suggested that at least $40\%$ FM could be replaced by SPC without impairing the growth performance [33]. Moreover, SPC combined with the supplementation of lysine and methionine could replace more than $40\%$ FM in the diet of juvenile starry flounder (Platichthys stellatus) at least [34], whereas $100\%$ FM in the diet of large yellow croaker (initial weight 10.50 ± 0.04 g) as no significant effects on growth performance [13]. In the present study, the SR, HSI, VSI, and CF were not affected by dietary SPC levels. The best growth performance was observed when the dietary SPC inclusion was $9.75\%$, and WG and SGR of fish fed SPC45 were significantly lower than those fed FM and SPC15. These results showed that large yellow croaker could tolerate more than $30\%$ FM replaced by SPC. This is a lower content of SPC than has been shown to be effective in previous study [13]. The discrepancy could be due to different initial weight and the crystal amino acid (methionine and lysine) supplementation, which contributes to the higher tolerance of fish and crustacean as reported in Japanese flounder (Paralichthys olivaceus) [35], juvenile cobia (Rachycentron canadum) [36], and juvenile swimming crab, *Portunus trituberculatus* [10].
AST and ALT in hepatopancreas are the two most important enzymes in the process of metabolism of AAs and key indicators of cellular damage and liver function [37]. The high levels of transaminases often are the sign of ill health. ACP and AKP are affected by nutritional status, environmental changes, diseases and growth stages, and could reflect the health status of animals [38]. In this study, the activity and mRNA expression of ACP in liver decreased significantly, whereas the activity of ALT and expression of alt and ast were opposite with increasing dietary SPC levels. Thus, large yellow croaker fed SPC45 maybe in ill health, which agreed with the lower growth performance described above. Similar results were reported in soft-shelled turtle (Pelodiscus sinensis) [24], darkbarbel catfish (pelteobagrus vachelli) [39], and P. trituberculatus [10].
In the current study, the trend of FE and PER in response to dietary SPC inclusion level was consistent with WG and SGR. Therefore, the effects of dietary SPC replacing FM on the growth of large yellow croaker were significantly related to improvements in nutrient digestion and absorption. Intestine is the primary location of nutrient digestion and absorption in fish, and its structural integrity is critical [40]. Morphological indices such as VH, VW, and MT are indictors of intestinal absorptive capacity [41, 42]. A decrease in intestinal VH and quantity often signifies a corresponding decrease in the absorption area of the digestive tract, and, consequently, a reduction in nutrient absorption [43]. In the current study, the VH of PI, MI, and DI were similar in fish fed FM and SPC15. However, fish fed SPC45 had significantly lower VH in PI, MI and DI when compared with those fed diet FM and/or SPC15. This may contribute to the receiving of the inappropriate diets, which could reduce the functional surface area of the intestinal and lead to lower ability of digestion and absorption [44]. Changes in intestinal morphology could possibly explain the lower FE and growth performance metrics of fish fed diets containing $45\%$ SPC. Similar results were found in juvenile hybrid grouper (E. fuscoguttatus ♀ × E. lanceolatus ♂) fed diets with higher than $55\%$ FM protein being replaced by SPC [45]. Given that the positive correlation between intestine morphology, we analyzed the intestinal microbiota to further investigate the response of large yellow croaker to dietary SPC contents.
Intestinal microbiota modulates host physiological processes and plays a critical role in promoting and maintaining the health of the host. It was reported that the intestinal microbiota of fish was not only closely related to the host but also considerably influenced by the surrounding environment, including water and diets [46–48]. In the present study, the Sobs, Shannon, Ace, and Chao were highest in SPC15 group, while the Simpson was lowest in SPC15 group. No difference was observed among the alpha diversity of FM, SPC30, and SPC45 group. The positive effects in bacterial diversity were also observed in Atlantic salmon (Salmo salar) fed diets containing $5\%$ SPC [49], totoaba (Totoaba macdonaldi) fed diets containing $45\%$ SPC [50], and rainbow trout (Oncorhynchus mykiss) fed diets containing $25\%$ soybean meal [9]. It was reported in adult humans and mammals that a diverse microbiota has frequently been linked with a balanced, well-functioning metabolism [51, 52]. Therefore, our finding indicated that the $15\%$ SPC inclusion could enhance nutrient uptake and metabolism in juvenile large yellow croaker.
In the present study, Proteobacteria, Firmicutes, and Fusobacteriota were the predominant bacterial phyla in the intestine of fish regardless of the dietary SPC inclusion level. Proteobacteria, Fusobacterium, and Firmicutes were also dominant in many other fish species and considered as members of the “core gut microbiota” [53–55]. The existence of similar bacterial taxa in the gut microbiota of a variety of fish species suggests that these bacteria are involved in important host gut functions like digestion, nutrition absorption, and immune response [53]. Interestingly, recent study on L. crocea reported that Fusobacteriota was only identified in the intestinal microbiota of 1-year-old instead of 12-day-old fish [56]. Thus, fish developmental stage may contribute to these different results. The ability of bacteria of the *Fusobacteria phylum* to synthesis vitamins and excrete butyrate is well recognized, and this is important since butyrate has numerous beneficial impacts on the health of the intestinal tract and peripheral tissues in vertebrates [57–59]. To investigate the response of intestinal microbiota to the replacement level of FM, dominant colony was identified subsequently in large yellow croaker fed diets with different SPC contents consequently.
Lactic acid bacteria within order Lactobacillales are among the most used probiotics in aquaculture because these bacteria reproduce rapidly; produce antimicrobial compounds such as organic acids, lactic acid, bacteriocins, and hydrogen peroxide; and stimulate nonspecific immune responses in hosts [60]. Previous study demonstrated that L. vannamei fed the Lactobacillus spp. diet exhibited higher WG and survival, lower abundance of pathogenic bacterial load (e.g., Vibrio) than the shrimp fed control diet (without Lactobacillus spp. addition) [61]. The mixture of lactic acid bacteria in diet led to increasing VH and the abundance of Rhizobium, which were considered to be beneficial effects in *Nile tilapia* (Oreochromis niloticus) [62]. Atlantic salmon (Salmo salar L) fed SPC diet showed a higher abundance of family Lactobacillaceae in proximal intestine and similar growth performance than those fed FM diet [63]. In the study, the enrichment of order Lactobacillales and order Rhizobiaceae (including family Beijerinckiaceae and Rhizobiaceae) in fish fed SPC15 could indicating the beneficial effects of optimal dietary SPC inclusion. However, genus Peptostreptococcaceae was observed to be higher abundance in SPC15 other treatments. Similar trend of genus Lactobacillus and Peptostreptococcaceae abundance was also observed in *Coilia nasus* get infected by Acanthosentis cheni [64]. Peptostreptococcaceae is a group of obligate anaerobe bacteria and is heritable across humans, mice and fish, cooccur, and inhabit the small intestine [65]. The high abundance of Peptostreptococcaceae occurred in human get Crohn's disease (subtype of inflammatory bowel disease) and rectal cancer patients [66, 67]. Moreover, the relative abundance of Peptostreptococcaceae was opposite to beneficial microbiomes such as Enterococcus, Bacteroides, and Prevotella in mice [68]. Thus, Peptostreptococcaceae may also act as pathogen in the intestine of large yellow croaker. Beside order Lactobacillales and Peptostreptococcaceae (from order to genus), a quantity of bacteria belonging to phylum Firmicutes (e.g. class Vicinamibacteria and Christensenellales, order Erysipelotrichales and family Nocardioidaceae) was enriched in intestine of large yellow croaker fed SPC15. Although information on the function of about each genus in fish is limit, Phylum Firmicutes was reported to possess the ability to improve the digestibility and immune status of fish to counteract the effects of pathogenic bacteria [69]. These indicated that SPC replace $15\%$ FM could improve the health status of large yellow croakers.
It was reported that *Vibrio and* Photobacterium are commonly found in carnivores [70]. Photobacterium act as mutualistic bacteria in the host gut aiding with chitin digestion [71]. However, some also produce harmful enzymes such as neuraminidases [72]. It was demonstrated that Photobacterium in intestine of L. crocea was relatively close to *Photobacterium damselae* at the evolutionary level and was associated with pathogenicity [73]. Vibrio is one of the most important bacterial genera in aquaculture, with both pathogenic and probiotic (health-promoting) species [70, 74]. Several *Vibrio bacteria* such as V. harveyi, V. sinaloensi,and V. orientalis are generally disease related [75, 76]. Recent study on the gut microbiota of L. crocea revealed that fish fed commercial feed showed higher *Vibrio abundance* than those fed fresh feed [73]. In terms of pathogenesis associated with high plant protein diets, increased relative abundance of Psychrobacter sp. has been proposed [49]. However, the abundance of Psychrobacter sp. could inhibit pathogenic V. harveyi, V. metschnikovi, and V. alginolyticus in juvenile grouper E. coioides [77]. Shewanella belongs to family shewanellaceae, was reported as opportunistic pathogens exist in Pacific white shrimp (L Vannamei) [78], but as probiotics in several marine fish [70]. Moreover, Tyzzerella has been characterized as a genus that predisposes hosts to diarrhea [79]. Moreover, a higher abundance of *Tyzzerella is* correlated with a higher risk of cardiovascular disease [80] and has been associated with dietary quality in human [81]. In the present study, genus Photobacterium, Psychrobacter, and vibrio belonged to class Gamma-proteobacteria within phylum Proteobacteria were significantly enriched in fish fed FM diet. Vibrionaceae family and Vibrionales order were enriched in fish fed SPC30 diet. Tyzzerella belongs to the Lachnospiraceae family within the phylum Firmicutes, genus Shewanella, and family shewanellaceae within class Gamma-proteobacteria and phylum Proteobacteria were enriched in large yellow croaker fed SPC45 diet. However, compared to fish fed SPC15 diet, those fed other diets had higher abundance of bacteria belonged to phylum Proteobacteria, which was regard as the most stable bacteria in L. vannamei and the abundance of Proteobacteria was not affected by environment or diets [78]. Thus, the abundance of these bacteria is either low or at a certain level that maintains a balance with the number of other microbes in large yellow croaker. Whereas, the higher abundance of Tyzzerella in large yellow croaker fed diet SPC45 could be the response to lower quality diet caused by SPC replacing $45\%$ dietary FM protein, which agreed to the growth performance. Tyzzerella could be the bacteria indicator of intestinal in large yellow croaker fed high SPC content diet.
It has been demonstrated that alternative protein sources can alter the gut microbiome of the host to have a beneficial impact on growth and immunity [82]. In this study, dietary fish oil content increased with increasing dietary SPC inclusion levels due to its lower lipid content than FM, which was also considered to contain relative balanced amino acids profile. Fish oil is richness in the content of long chain polyunsaturated fatty acids, which prefer to deposit in phospholipids. Thus, these vary gut microbiota communities and the enrichment of Toll and Imd signaling pathway, Biosynthesis of unsaturated fatty acids, Glycerophospholipid metabolism, PI3K-Akt signaling pathway, AMPK signaling pathway, Biosynthesis of secondary metabolites, and Biosynthesis of amino acids pathway indicated the regulation of immunity capacity and an acceleration of lipid and protein metabolism, which could be the adjustment of absorbing more SPC in L. crocea and need to be further studied.
## 5. Conclusions
Under this experimental condition, SPC could be good ingredient for large yellow croaker as the growth performance, feed utilization, morphological parameter, and liver health were not affected, the intestinal microbiota communities were well regulated when $15\%$ FM was replaced. Higher FM replacement level than $30\%$ would lead to a decrease in health status, VH, and an addition in the abundance of pathogen such as *Vibrio and* Tyzzerella, and then the reduction of WG, SGR, and FE. Tyzzerella could be the bacteria indicator of intestinal in large yellow croaker fed high SPC content diet. Based on the quadratic regression analysis of WG, the best growth performance could be observed when the replacement of FM with SPC was $9.75\%$. Moreover, the substitution of FM by SPC accelerated the metabolism of lipid, protein, and immunomodulating based on pathway enrichment of the bacterial abundance.
## Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
## Ethical Approval
The animal study was reviewed and approved by Animal Care and Use Committee of Fujian Agriculture and Forestry University.
## Conflicts of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## Authors' Contributions
Xuexi Wang performed the conceptualization, data curation, formal analysis, project administration, investigation, validation, visualization, and writing—original draft. Hongjie Luo worked on the data curation, formal analysis, project administration, investigation, methodology, and validation. Dejuan Wang was tasked in investigation and data curation. Yunzong, Zheng was responsible for investigation and methodology. Wenbo Zhu was focused in resources and investigation. Weini Zhang was responsible for the resources, supervision, writing—review. Zhengbang Chen worked on resources, supervision, and investigation. Jianchun Shao was tasked in concepgtualization, formal analysis, funding acquisition, resources, supervision, writing—review. Xuexi Wang and Hongjie Luo contributed equally to this work.
## References
1. Cashion T., Le Manach F., Zeller D., Pauly D.. **Most fish destined for fishmeal production are food-grade fish**. (2017) **18** 837-844. DOI: 10.1111/faf.12209
2. 2NRC
Nutrient requirements of fish and shrimp
2011National Academies Press. (2011)
3. Daniel N.. **A review on replacing fish meal in aqua feeds using plant protein sources**. (2018) **6** 164-179
4. Taher S., Romano N., Arshad A.. **Assessing the feasibility of dietary soybean meal replacement for fishmeal to the swimming crab,**. (2017) **469** 88-94. DOI: 10.1016/j.aquaculture.2016.11.036
5. Zhang J.-X., Guo L.-Y., Feng L.. **Soybean**. (2013) **8**. DOI: 10.1371/journal.pone.0058115
6. Fuentes-Quesada J. P., Viana M. T., Rombenso A. N.. **Enteritis induction by soybean meal in**. (2018) **495** 78-89. DOI: 10.1016/j.aquaculture.2018.05.025
7. Hossain S., Koshio S., Ishikawa M.. **Substitution of dietary fishmeal by soybean meal with inosine administration influences growth, digestibility, immunity, stress resistance and gut morphology of juvenile amberjack**. (2018) **488** 174-188. DOI: 10.1016/j.aquaculture.2018.01.037
8. Berge G. M., Grisdale-Helland B., Helland S. J.. **Soy protein concentrate in diets for Atlantic halibut (**. (1999) **178** 139-148. DOI: 10.1016/s0044-8486(99)00127-1
9. Green T. J., Smullen R., Barnes A. C.. **Dietary soybean protein concentrate-induced intestinal disorder in marine farmed Atlantic salmon,**. (2013) **166** 286-292. DOI: 10.1016/j.vetmic.2013.05.009
10. Wang X. X., Yuan Y., Li C. C.. **Partial substitution of fish meal with soy protein concentrate in commercial diets for juvenile swimming crab,**. (2020) **259, article 114290**. DOI: 10.1016/j.anifeedsci.2019.114290
11. Lusas E. W., Riaz M. N.. **Soy protein products: processing and use**. (1995) **125** 573S-580S. DOI: 10.1093/jn/125.3_suppl.573s
12. Bauer W., Prentice-Hernandez C., Tesser M. B., Wasielesky W., Poersch L. H.. **Substitution of fishmeal with microbial floc meal and soy protein concentrate in diets for the pacific white shrimp**. (2012) **342-343** 112-116. DOI: 10.1016/j.aquaculture.2012.02.023
13. Wang P., Zhu J., Feng J., He J., Lou Y., Zhou Q.. **Effects of dietary soy protein concentrate meal on growth, immunity, enzyme activity and protein metabolism in relation to gene expression in large yellow croaker**. (2017) **477** 15-22. DOI: 10.1016/j.aquaculture.2017.04.030
14. Semova I., Carten J. D., Stombaugh J.. **Microbiota regulate intestinal absorption and metabolism of fatty acids in the zebrafish**. (2012) **12** 277-288. DOI: 10.1016/j.chom.2012.08.003
15. Wu S., Ren Y., Peng C.. **Metatranscriptomic discovery of plant biomass-degrading capacity from grass carp intestinal microbiomes**. (2015) **91** p. fiv107. DOI: 10.1093/femsec/fiv107
16. 16Fishery Bureau of China Agriculture Department
China Fishery Statistical Yearbook
2022China Agriculture Press. (2022)
17. Ai Q., Mai K., Tan B.. **Replacement of fish meal by meat and bone meal in diets for large yellow croaker,**. (2006) **260** 255-263. DOI: 10.1016/j.aquaculture.2006.06.043
18. He J., Wang P., Feng J., Lou Y.. **Effects of replacing fish meal with corn gluten meal on growth, intestinal histology, and intestinal microbiota of large yellow croaker,**. (2018) **25** 361-372. DOI: 10.3724/SP.J.1118.2018.17180
19. Shui X., Lin K., Zhao X.. **Effects of soy protein concentrate on growth and intestinal microbiota of large yellow croaker, Larimichthys crocea**. (2021) **28** 37-47. DOI: 10.12264/JFSC2020-0100
20. Wei Y., Shen H., Xu W.. **Replacement of dietary fishmeal by Antarctic krill meal on growth performance, intestinal morphology, body composition and organoleptic quality of large yellow croaker**. (2019) **512, article 734281**. DOI: 10.1016/j.aquaculture.2019.734281
21. Wu Z.. (2016)
22. Wang X., Jin M., Cheng X.. **Dietary DHA/EPA ratio affects growth, tissue fatty acid profiles and expression of genes involved in lipid metabolism in mud crab**. (2021) **532, article 736028**. DOI: 10.1016/j.aquaculture.2020.736028
23. Bancroft J. D., Gamble M.. (2008)
24. Zhou F., Wang Y. Q., Ding X. Y., Ng W. K., He F., Xue H. L.. **Partial replacement of fish meal by soy protein concentrates in diets for a new Japanese strain of juvenile soft-shelled turtle, Pelodiscus sinensis**. (2016) **47** 875-886. DOI: 10.1111/are.12548
25. Livak K. J., Schmittgen T. D.. **Analysis of relative Gene Expression data using real-time quantitative PCR and the 2**. (2001) **25** 402-408. DOI: 10.1006/meth.2001.1262
26. Anthony M. B., Marc L., Bjoern U.. **Trimmomatic: a flexible trimmer for Illumina sequence data**. (2014) **30** 2114-2120. DOI: 10.1093/bioinformatics/btu170
27. Tanja M., Steven L. S.. **FLASH: fast length adjustment of short reads to improve genome assemblies**. (2011) **27** 2957-2963. DOI: 10.1093/bioinformatics/btr507
28. Kristi G., Gian Maria Niccolò B., Natalie Vande P., Gregory B.. **CONSTAX: a tool for improved taxonomic resolution of environmental fungal ITS sequences**. (2017) **18** 538-539. DOI: 10.1186/s12859-017-1952-x
29. Huang Y.. **Illumina-based analysis of endophytic bacterial diversity of four**. (2019) **9** 1-11. DOI: 10.1038/s41598-019-51707-7
30. Segata N., Izard J., Waldron L.. **Metagenomic biomarker discovery and explanation**. (2011) **12** p. R60. DOI: 10.1186/gb-2011-12-6-r60
31. Mambrini M., Roem A. J., Carvedi J., Lalles J. P., Kaushik S.. **Effects of replacing fish meal with soy protein concentrate and of DL-methionine supplementation in high-energy, extruded diets on the growth and nutrient utilization of rainbow trout,**. (1999) **77** 2990-2999. DOI: 10.2527/1999.77112990x
32. Kissinger K. R., García-Ortega A., Trushenski J. T.. **Partial fish meal replacement by soy protein concentrate, squid and algal meals in low fish-oil diets containing**. (2016) **452** 37-44. DOI: 10.1016/j.aquaculture.2015.10.022
33. García-Ortega A., Kissinger K. R., Trushenski J. T.. **Evaluation of fish meal and fish oil replacement by soybean protein and algal meal from**. (2016) **452** 1-8. DOI: 10.1016/j.aquaculture.2015.10.020
34. Li P.-Y., Wang J.-Y., Song Z.-D.. **Evaluation of soy protein concentrate as a substitute for fishmeal in diets for juvenile starry flounder (**. (2015) **448** 578-585. DOI: 10.1016/j.aquaculture.2015.05.049
35. Deng J., Mai K., Ai Q.. **Effects of replacing fish meal with soy protein concentrate on feed intake and growth of juvenile Japanese flounder,**. (2006) **258** 503-513. DOI: 10.1016/j.aquaculture.2006.04.004
36. Chi S., Tan B., Dong X., Yang Q., Liu H.. **Effects of supplemental coated or crystalline methionine in low-fishmeal diet on the growth performance and body composition of juvenile cobia**. (2014) **32** 1297-1306. DOI: 10.1007/s00343-014-3276-2
37. Meng Z., Chen Y., Guan X., Zhang H., Liu M.. **Effect of Chinese herb compounds on activities of transaminase in serum and antioxidase in erythrocyte of**. (2010) **41** 75-80
38. Liu S. Q., Jiang X. L., Mou H. J., Wang H. M., Guan H. S.. **Effects of immunopolysaccharide on LSZ, ALP, ACP and pod activities of**. (1999) **30** 278-283
39. Yang Y. H.. (2011)
40. Zhang C., Rahimnejad S., Wang Y.-r.. **Substituting fish meal with soybean meal in diets for Japanese seabass (**. (2018) **483** 173-182. DOI: 10.1016/j.aquaculture.2017.10.029
41. Niu X., Qian X., Feng H.. **Growth and metabolic responses of grouper juveniles (**. (2021) **534, article 736281**. DOI: 10.1016/j.aquaculture.2020.736281
42. Ribeiro L., Moura J., Santos M.. **Effect of vegetable based diets on growth, intestinal morphology, activity of intestinal enzymes and haematological stress indicators in meagre (**. (2015) **447** 116-128. DOI: 10.1016/j.aquaculture.2014.12.017
43. Mahmoud A. O. D., Fawzy I. M., Mohamed E., Mohamed M.. **Impact of yeast fermented poultry by-product meal on growth, digestive enzyme activities, intestinal morphometry and immune response traits of common carp (**. (2020) **20** 939-959. DOI: 10.2478/aoas-2020-0021
44. Bakke-McKellep A., Press C. M., Baeverfjord G., Krogdahl Å., Landsverk T.. **Changes in immune and enzyme histochemical phenotypes of cells in the intestinal mucosa of Atlantic salmon,**. (2000) **23** 115-127. DOI: 10.1046/j.1365-2761.2000.00218.x
45. Wang J., Liang D., Yang Q.. **The effect of partial replacement of fish meal by soy protein concentrate on growth performance, immune responses, gut morphology and intestinal inflammation for juvenile hybrid grouper (**. (2020) **98** 619-631. DOI: 10.1016/j.fsi.2019.10.025
46. Li X., Yu Y., Feng W., Yan Q., Gong Y.. **Host species as a strong determinant of the intestinal microbiota of fish larvae**. (2012) **50** 29-37. DOI: 10.1007/s12275-012-1340-1
47. Liu W., Ren P., He S.. **Comparison of adhesive gut bacteria composition, immunity, and disease resistance in juvenile hybrid tilapia fed two different**. (2013) **35** 54-62. DOI: 10.1016/j.fsi.2013.04.010
48. Paolo F., Carmelo F., Tancred F., Julia C. J., Axel M.. **The gut microbial community of Midas cichlid fish in repeatedly evolved limnetic-benthic species pairs**. (2014) **9**. DOI: 10.1371/journal.pone.0095027
49. Bakke-McKellep A. M., Penn M. H., Salas P. M.. **Effects of dietary soya bean meal, inulin and oxytetracycline on intestinal microbiota and epithelial cell stress, apoptosis and proliferation in the teleost Atlantic salmon (**. (2007) **97** 699-713. DOI: 10.1017/S0007114507381397
50. Larios-Soriano E., Zavala R. C., López L. M.. **Soy protein concentrate effects on gut microbiota structure and digestive physiology of**. (2022) **132** 1384-1396. DOI: 10.1111/jam.15269
51. Ley R. E., Bäckhed F., Turnbaugh P., Lozupone C. A., Knight R. D., Gordon J. I.. **Obesity alters gut microbial ecology**. (2005) **102** 11070-11075. DOI: 10.1073/pnas.0504978102
52. Menni C., Jackson M. A., Pallister T., Steves C. J., Spector T. D., Valdes A. M.. **Gut microbiome diversity and high-fibre intake are related to lower long- term weight gain**. (2017) **41** 1099-1105. DOI: 10.1038/ijo.2017.66
53. Huang Z., Li X., Wang L., Shao Z.. **Changes in the intestinal bacterial community during the growth of white shrimp,**. (2016) **47** 1737-1746. DOI: 10.1111/are.12628
54. Mahdi G., Wolfgang K., Konrad J. D.. **A new view of the fish gut microbiome: advances from next-generation sequencing**. (2015) **448** 464-475. DOI: 10.1016/j.aquaculture.2015.06.033
55. Michl S. C., Ratten J.-M., Beyer M., Hasler M., LaRoche J., Schulz C.. **The malleable gut microbiome of juvenile rainbow trout (**. (2017) **12**. DOI: 10.1371/journal.pone.0177735
56. Wei N., Wang C., Xiao S.. **Intestinal microbiota in large yellow croaker,**. (2018) **49** 256-267. DOI: 10.1111/jwas.12463
57. Canani R. B., Costanzo M. D., Leone L., Pedata M., Meli R., Calignano A.. **Potential beneficial effects of butyrate in intestinal and extraintestinal diseases**. (2011) **17** 1519-1528. DOI: 10.3748/wjg.v17.i12.1519
58. Gábor M., Zsuzsanna N., György C., Anna K., Akos K., Korinna H.. **Effects of orally applied butyrate bolus on histone acetylation and cytochrome P450 enzyme activity in the liver of chicken-a randomized controlled trial**. (2013) **10** 1-10. DOI: 10.1186/1743-7075-10-12
59. Roeselers G., Mittge E. K., Stephens W. Z.. **Evidence for a core gut microbiota in the zebrafish**. (2011) **5** 1595-1608. DOI: 10.1038/ismej.2011.38
60. Merrifield D. L., Carnevali O.. **Probiotic modulation of the gut microbiota of fish**. (2014) **185-222**. DOI: 10.1002/9781118897263.ch8
61. Karthik R., Hussain A. J., Muthezhilan R.. **Effectiveness of lactobacillus sp (AMET 1506) as probiotic against vibriosis in**. (2014) **11** 297-305. DOI: 10.13005/bbra/1423
62. Xia Y., Lu M., Chen G.. **Effects of dietary**. (2018) **76** 368-379. DOI: 10.1016/j.fsi.2018.03.020
63. Hartviksen M., Vecino J., Ringø E.. **Alternative dietary protein sources for Atlantic salmon (**. (2014) **20** 381-398. DOI: 10.1111/anu.12087
64. Min J., Xizhao Z., Yang Y.. **Effects of Acanthosentis cheniinfection on microbiota composition and diversity in the intestine of**. (2019) **26** 577-585. DOI: 10.3724/SP.J.1118.2019.18206
65. Goodrich J. K., Davenport E. R., Waters J. L., Clark A. G., Ley R. E.. **Cross-species comparisons of host genetic associations with the microbiome**. (2016) **352** 532-535. DOI: 10.1126/science.aad9379
66. Ahn J., Sinha R., Pei Z.. **Human gut microbiome and risk for colorectal cancer**. (2013) **105** 1907-1911. DOI: 10.1093/jnci/djt300
67. Wu Z., Sang L., Jiang M., Chang B.. **Research advances in the inflammatory bowel disease dysbiosis**. (2020) **40** 75-78. DOI: 10.19538/j.nk2020010116
68. Tochitani S., Maehara Y., Kawase T.. **Fermented rice bran supplementation ameliorates obesity via gut microbiota and metabolism modification in female mice**. (2022) **70** 160-174. DOI: 10.3164/jcbn.21-96
69. Costantini L., Molinari R., Farinon B., Merendino N.. **Impact of omega-3 fatty acids on the gut microbiota**. (2017) **18** p. 2645. DOI: 10.3390/ijms18122645
70. Egerton S., Culloty S., Whooley J., Stanton C., Ross R. P.. **The gut microbiota of marine fish**. (2018) **9** p. 873. DOI: 10.3389/fmicb.2018.00873
71. Itoi S., Okamura T., Koyama Y., Sugita H.. **Chitinolytic bacteria in the intestinal tract of Japanese coastal fishes**. (2006) **52** 1158-1163. DOI: 10.1139/w06-082
72. Sugita H., Shinagawa Y., Okano R.. **Neuraminidase-producing ability of intestinal bacteria isolated from coastal fish**. (2000) **31** 10-13. DOI: 10.1046/j.1472-765x.2000.00749.x
73. Jing Z.. (2018)
74. Vandenberghe J., Thompson F. L., Gomez-Gil B., Swings J.. **Phenotypic diversity amongst**. (2003) **219** 9-20. DOI: 10.1016/s0044-8486(02)00312-5
75. Soto-Rodriguez S. A., Gomez-Gil B., Lozano R., del Rio-Rodríguez R., Diéguez A. L., Romalde J. L.. **Virulence of**. (2012) **109** 307-317. DOI: 10.1016/j.jip.2012.01.006
76. Zhou Y., Zhang D., Peatman E., Rhodes M. A., Liu J., Davis D. A.. **Effects of various levels of dietary copper supplementation with copper sulfate and copper hydroxychloride on Pacific white shrimp**. (2017) **476** 94-105. DOI: 10.1016/j.aquaculture.2017.04.016
77. Sun Y., Yang H., Ling Z., Chang J., Ye J.. **Gut microbiota of fast and slow growing grouper Epinephelus coioides**. (2009) **3** 637-640. DOI: 10.5897/AJMR.9000353
78. Li E., Xu C., Wang X.. **Gut microbiota and its modulation for healthy farming of Pacific white Shrimp**. (2018) **26** 381-399. DOI: 10.1080/23308249.2018.1440530
79. Zhuang X., Tian Z., Li L., Zeng Z., Chen M., Xiong L.. **Fecal microbiota alterations associated with diarrhea-predominant irritable bowel syndrome**. (2018) **9** p. 1600. DOI: 10.3389/fmicb.2018.01600
80. Kelly T. N., Bazzano L. A., Ajami N. J.. **Gut microbiome associates with lifetime cardiovascular disease risk profile among Bogalusa heart study participants**. (2016) **119** 956-964. DOI: 10.1161/circresaha.116.309219
81. Liu Y., Ajami N. J., El-Serag H. B.. **Dietary quality and the colonic mucosa–associated gut microbiome in humans**. (2019) **110** 701-712. DOI: 10.1093/ajcn/nqz139
82. Foysal J., Fotedar R., Tay C. Y., Gupta S. K.. **Dietary supplementation of black soldier fly (**. (2019) **7, article e6891**. DOI: 10.7717/peerj.6891
|
---
title: Dietary Corn Starch Levels Regulated Insulin-Mediated Glycemic Responses and
Glucose Homeostasis in Swimming Crab (Portunus trituberculatus)
authors:
- Xiangsheng Zhang
- Chaokai Huang
- Yuhang Yang
- Xiangkai Li
- Chen Guo
- Zheng Yang
- Shichao Xie
- Jiaxiang Luo
- Tingting Zhu
- Wenli Zhao
- Min Jin
- Qicun Zhou
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973156
doi: 10.1155/2022/2355274
license: CC BY 4.0
---
# Dietary Corn Starch Levels Regulated Insulin-Mediated Glycemic Responses and Glucose Homeostasis in Swimming Crab (Portunus trituberculatus)
## Abstract
Carbohydrate is the cheapest source of energy among the three major nutrient groups, an appropriate amount of carbohydrates can reduce feed cost and improve growth performance, but carnivorous aquatic animals cannot effectively utilize carbohydrates. The objectives of the present study are aimed at exploring the effects of dietary corn starch levels on glucose loading capacity, insulin-mediated glycemic responses, and glucose homeostasis for Portunus trituberculatus. After two weeks of feeding trial, swimming crabs were starved and sampled at 0, 1, 2, 3, 4, 5, 6, 12, and 24 hours, respectively. The results indicated that crabs fed diet with $0\%$ corn starch exhibited lower glucose concentration in hemolymph than those fed with the other diets, and glucose concentration in hemolymph remained low with the extension of sampling time. The glucose concentration in hemolymph of crabs fed with $6\%$ and $12\%$ corn starch diets reached the peak after 2 hours of feeding; however, the glucose concentration in hemolymph of crabs fed with $24\%$ corn starch attained the highest value after 3 hours of feeding, and the hyperglycemia lasted for 3 hours and decreased rapidly after 6 hours of feeding. Enzyme activities in hemolymph related to glucose metabolism such as pyruvate kinase (PK), glucokinase (GK), and phosphoenolpyruvate carboxykinase (PEPCK) were significantly influenced by dietary corn starch levels and sampling time. Glycogen content in hepatopancreas of crabs fed with $6\%$ and $12\%$ corn starch first increased and then decreased; however, the glycogen content in hepatopancreas of crabs fed with $24\%$ corn starch significantly increased with the prolongation of feeding time. In the $24\%$ corn starch diet, insulin-like peptide (ILP) in hemolymph reached a peak after 1 hour of feeding and then significantly decreased, whereas crustacean hyperglycemia hormone (CHH) was not significantly influenced by dietary corn starch levels and sampling time. ATP content in hepatopancreas peaked at 1 h after feeding and then decreased significantly in different corn starch feeding groups, while the opposite trend was observed in NADH. The activities of mitochondrial respiratory chain complexes I, II, III, and V of crabs fed with different corn starch diets significantly increased first and then decreased. In addition, relative expressions of genes related to glycolysis, gluconeogenesis, glucose transport, glycogen synthesis, insulin signaling pathway, and energy metabolism were significantly affected by dietary corn starch levels and sampling time. In conclusion, the results of the present study reveal glucose metabolic responses were regulated by different corn starch levels at different time points and play an important role in clearing glucose through increased activity of insulin, glycolysis, and glycogenesis, along with gluconeogenesis suppression.
## 1. Introduction
Carbohydrate is not the main source of energy for aquatic animals and the main components of aquafeeds [1]; however, it has a lot of advantages such as its low cost, most widely available, and most abundant of the three energy sources [2]. With the rapid development of aquafeed worldwide, especially in China, the prices of high-quality protein and lipid sources such as fish meal and fish oil have rapidly risen. Under this severe situation, it is the general trend to add an appropriate proportion of carbohydrates to aquafeeds. Appropriate carbohydrate supplementation in aquafeed can not only reduce the cost of feed production, alleviate the contradiction of shortage of feed resources, but also even improve growth and feed utilization capacity of aquatic animals [3–5]. Although the addition of carbohydrates to aquafeed has many advantages, many studies indicated that dietary carbohydrates caused glucose intolerance of aquatic animals, especially carnivorous aquatic animals [1]. Previous studies have demonstrated that it takes 5-8 hours to clear their glucose load for herbivorous or omnivorous fish, such as grass carp (Ctenopharyngodon idellus) [6], tilapia (Oreochromis niloticus) [7], gibel carp (Carassius gibelio) [8]. However, the high blood glucose of carnivorous fish lasts for more than 12 hours after being loaded with glucose or consuming high-carbohydrate aquafeed, such as rainbow trout (Oncorhynchus mykiss) [9], Japanese flounder (Paralichthys olivaceus) [10], and grouper (Epinephelus coioides) [11]. Glucose intolerance, a condition in which the glucose load exceeds the body's ability to clear it, resulted in persistent high blood glucose. Generally, it is judged by a glucose tolerance test; that is, after injecting or taking a high dose of glucose or carbohydrate to the body and observing whether the body has persistent hyperglycemia symptoms, if the body's blood glucose does not return to the basic level within 1-2 hours, it can be judged as impaired glucose tolerance [12]. Only mammals can reach blood basal glucose levels within 1-2.5 hours after a glucose load [13], and glucose intolerance can cause persistent high blood glucose that not only affects normal growth but also harms body health, such as liver injury [4, 14, 15]. The swimming crab owns an open circulatory system, and the injection will have a very high fatality rate, so this experiment simulates the glucose load effect brought by the tolerance test through the feeding test.
The swimming crab, Portunus trituberculatus, is widely distributed in the coastal seawater of China, South Korea, and Japan and has become one of the most important economically farmed species [16]. Due to its rapid growth, delicious meat, and balanced nutrition, it has huge market value and potential. Since the 1990s, the breeding industry of swimming crabs has developed rapidly in the coastal areas in China, and in recent years, the production of swimming crabs has stabilized [17]. According to the 2021 China Fishery Statistical Yearbook, the output of artificially cultured swimming crabs will reach 100,895 tons in 2020 [17]. Therefore, to improve the feed formula of swimming crabs, reduce the cost pressure, and make the swimming crabs grow healthily and rapidly, it is very necessary to find out the tolerance of swimming crabs to carbohydrate substances. An 8-week feeding trial has been carried out to determine the appropriate supplemental level of corn starch in feed [18]. The short-term feeding experiment is to further explore the effects of dietary corn starch levels on glucose tolerance, enzyme activities related to carbohydrate metabolism, hormones, gene expression, and mitochondrial homeostasis of the swimming crab.
## 2.1. Ethics Statement
All experimental procedures were performed in strict accordance with the standard operation procedures (SOPs) of the Guide for Use of Experimental Animals of Ningbo University. The experimental protocol and procedures were approved by the Institutional Animal Care and Use Committee of Ningbo University (NBU-2022-R01).
## 2.2. Experimental Design and Feed Preparation
Four isoproteic ($43\%$ crude protein) and isolipidic ($6\%$ crude lipid) diets were formulated to contain 0, 6, 12, and $24\%$ corn starch, and the actual contents of nitrogen-free extract (NFE) were 2.14, 7.43, 14.00, and $25.30\%$, respectively (Table 1). All ingredients were ground into fine powder by ultrafine grinder, and the vitamin, mineral mixture and other raw materials (less than 100 g) were mixed firstly, then added into the large amount of raw materials. Cold-extruded pellets were produced (F-26, Machine Factory of South China University of Technology) with pellet strands cut into uniform sizes (4 mm diameter pellets) (G-250, Machine Factory of South China University of Technology). Pellets were steamed for 30 min at 90°C and finally air-dried to approximately 100.0 g kg−1 moisture. The dried diets were sealed in vacuum-packed bags and stored at -20°C until used.
## 2.3. Feeding Trial and Experimental Conditions
Swimming crabs were purchased from a local crab breeding farm (Xiangshan, Ningbo, China). Feeding trial was conducted in Ningbo Xiangshan Harbor Aquatic Seed Co. Ltd. Healthy and similar-sized juvenile swimming crabs were acclimated for 2 weeks and fed with a commercial diet ($45\%$ dietary protein and $8\%$ crude lipid, Ningbo Tech-Bank Feed Co., Ltd., Ningbo, China) before the start of the feeding trial. The initial average weight of 240 swimming crab juveniles was 50.87 ± 0.5 g and randomly sorted into 240 individual rectangle plastic baskets (35 × 30 × 35 cm) in three cement pools (6.8 × 3.8 × 1.7 m). Each diet was randomly assigned to three replicates, and each replicate had 20 plastic baskets that were supported with a foam frame so that it does not sink to the bottom. All crabs were fed once at 18:00, and the moulting crab was not fed. Feces, uneaten feed, and crab shells were removed in the morning after the feeding, and about $30\%$ of seawater in the cement pool was daily exchanged to maintain seawater quality. Furthermore, the amounts of uneaten complete pellets (crushed residue feed is not counted) and dead crabs were recorded, weighed, and calculated. In order to continuously supply oxygen, 12 air pipes connected to air stones were placed in the cement pool for continuous aeration. During the experimental period, the temperature of the cement pool was 27-30.5°C, the salinity was 22.5-25.5 g L−1, pH was 7.3-8.0, and ammonia nitrogen was lower than 0.05 mg L−1.
## 2.4. Sample Collection
At the end of the experiment, samples were taken at 0, 1, 2, 3, 4, 5, 6, 12, and 24 hours after feeding, and four crabs were sampled per replicate at each time point. Hemolymph samples from each crab were removed from the pericardial cavity using a 2 mL syringe, sorted into 1.5 mL Eppendorf tubes overnight at 4°C, and centrifuged at 3500 rpm for 10 min (Eppendorf centrifuge 5810R, Germany). Then, the supernatant was collected and stored at -80°C until analysis of glucose concentration and enzyme activities related to carbohydrate metabolism. The hepatopancreas of four crabs after the hemolymph being taken were drawn and immediately collected into 2 mL Eppendorf tubes and 1.5 mL Eppendorf tubes, 2 mL Eppendorf tubes were frozen in liquid nitrogen and stored at -20°C for glycogen, ATP, NADH, and mitochondrial respiratory chain and other indicators of the determination, and 1.5 mL Eppendorf tube was stored at -80°C for the determination of gene expression.
## 2.5. Enzyme Activity in Hemolymph
The glucose concentration and the activities of pyruvate kinase (PK), glucokinase (GK), phosphofructose kinase (PFK), and phosphoenolpyruvate carboxykinase (PEPCK) in hemolymph were determined using detection kits (Nanjing Jiancheng Bioengineering Institute, China). Glucose concentration was determined by the oxidase method. The enzyme activities related to glucose metabolism were determined by the UV method.
GK was measured using a double-antibody one-step sandwich enzyme-linked immunosorbent assay (ELISA). Specimens, standards, and HRP-labeled detection antibodies were added to precoated GK antibody-coated wells, incubated, and washed thoroughly. The color was developed with the substrate TMB. TMB becomes blue under the catalysis of peroxidase and finally yellow under the action of acid. The shade of color is positively correlated with GK in the sample. Measure the absorbance (OD value) at a wavelength of 450 nm with a microplate reader to calculate the sample concentration.
PK can catalyze PEP to produce pyruvate in the presence of ADP, which is then converted into lactate by LDH and NADH into NAD+. PFK catalyzes fructose-6-phosphate and ATP to generate fructose-1,6-bisphosphate and ADP. PK, and LDH further catalyzes NADH to generate NAD+. And the rate of decline of NADH is measured at 340 nm to calculate PFK activity. PEPCK catalyzes the reaction of oxaloacetate to generate phosphoenolpyruvate and carbon dioxide, and pyruvate and lactate dehydrogenase further catalyzes NADH to generate NAD+, and the reduction rate of NADH is measured at 340 nm to calculate PEPCK activity.
## 2.6. Hormone Content in Hemolymph
The contents of insulin-like peptides (ILP) and crustacean hyperglycemic hormones (CHH) in hemolymph were determined using kits (Jiangsu Kete Biotechnology Co., Ltd., China). Insulin-like peptides and hyperglycemic hormones were also measured by double-antibody one-step sandwich enzyme-linked immunosorbent assay (ELISA). The corresponding antibody can be used.
## 2.7. Energy Homeostasis and Mitochondrial Respiratory Chain Complex in Hepatopancreas
Glycogen, ATP (Nanjing Jiancheng Bioengineering Institute, China), and NADH (Shanghai Biyuntian Biotechnology Co., Ltd., China) were determined using kit assays. The activities of hepatopancreatic mitochondrial respiratory chain complexes I, II, III, and V were also determined by kit assay (Jiangsu Kete Biotechnology Co., Ltd., China).
Glycogen and ATP were determined by colorimetry. Under the action of concentrated sulfuric acid, glycogen can generate aldehyde derivatives and then react with anthrone to generate blue compounds, which are compared with the standard glucose solution treated by the same method for colorimetric comparison. ATP and creatine are catalyzed by creatine kinase to generate phosphocreatine, which is determined by phosphomolybdic acid colorimetry.
NAD+ and NADH were determined by WST-8 chromogenic assay. Ethanol is oxidized to acetaldehyde under the action of alcohol dehydrogenase, and NAD+ is reduced to NADH. *The* generated NADH reduces WST-8 to orange-yellow formazan under the action of an electronic coupling reagent, with a maximum absorption peak around 450 nm. The formazan produced in the reaction system is proportional to the total amount of NAD+ and NADH in the sample.
Mitochondrial respiratory chain complexes I, II, III, and V were analyzed to use double-antibody one-step sandwich enzyme-linked immunosorbent assay (ELISA) and use corresponding antibodies.
## 2.8. Real-Time Quantitative PCR
RNA extraction and PCR analysis were referred as the method described by Yuan et al. [ 19]. All specific primers and housekeeping genes were designed by Primer Premier 5.0, synthesized by BGI (Beijing Genomics Research Institute, Shenzhen, China) and verified to be usable (Table 2). In the present study, the expression of genes related to hepatopancreatic carbohydrate metabolism key enzymes, insulin signaling pathway, and transcriptions of mitochondrial function-related genes in swimming crabs was studied. *All* gene expression data were expressed relative to the expression of the $0\%$ corn starch level diet. The fluorescence data were normalized to β-actin and quantified by the 2−ΔΔCt method [20].
## 2.9. Calculations and Statistical Analysis
Data are presented as the means and standard errors of three replicates ($$n = 3$$) and analyzed by one-way ANOVA followed by Tukey's multiple-range test. All statistical analyses were conducted using SPSS 23.0 for Windows.
## 3.1. Glucose Concentrations in Hemolymph
Glucose concentrations in hemolymph of swimming crabs fed with corn starch levels at different time points are presented in Figure 1. The glucose concentration of crabs fed with $0\%$ corn starch diet exhibited a lower level and had no significant change at different time points. Crabs fed diets with 6 and $12\%$ corn starch reached a peak at 2 h after feeding and then decreased to the lowest and remained stable after 12 h. However, crabs fed diet with $24\%$ corn starch, the glucose concentration peaked at 3 hours after feeding and then maintained for 6 h after feeding and then began to decrease significantly, and glucose concentration did not decrease to a minimum and remained stable until 12 h after feeding.
## 3.2. Enzyme Activities Related to Carbohydrate Metabolism in Hemolymph
The effects of different corn starch levels on enzyme activities related to carbohydrate metabolism in hemolymph at different time points are shown in Figure 2. Pyruvate kinase (PK) activity in the hemolymph of crabs fed with different corn starch showed a trend of first increasing and then decreasing with different time points ($P \leq 0.05$), and the PK activity in hemolymph of crabs fed diet with 0, 6, and $12\%$ corn starch significantly increased at 0-2 h and decreased significantly after 2 h after feeding ($P \leq 0.05$); however, PK activity in hemolymph of crabs fed diet with $24\%$ corn starch was significantly increased during the ingestion period of 0-3 h and significantly decreased after 3 h of feeding ($P \leq 0.05$).
In the 0 and $6\%$ corn starch levels, a noticeable change was not found in the glucokinase (GK) activity of swimming crabs ($P \leq 0.05$), while in the 12 and $24\%$ corn starch diets, GK activity reached a peak at 2 hours after feeding and then significantly decreased ($P \leq 0.05$). Phosphofructose kinase (PFK) in hemolymph was not significantly influenced by dietary starch levels at different time points ($P \leq 0.05$). In the 0, 6, 12, and $24\%$ corn starch levels, the activity of phosphoenolpyruvate carboxykinase (PEPCK) in hemolymph decreased first and then increased ($P \leq 0.05$), and the PEPCK activity decreased significantly during the 0-2 h period of ingestion and began to rebound after 2 h ($P \leq 0.05$). The PEPCK activity in hemolymph of crabs fed diet with $24\%$ corn starch reached the peak at 3 hours after feeding and significantly increased after 3 h of feeding ($P \leq 0.05$).
## 3.3. Hormone Concentrations in Hemolymph
The effects of different corn starch levels on hormone concentrations in hemolymph at different time points are shown in Figure 3. There were no significant differences in the insulin-like peptide (ILP) and crustacean hyperglycemic hormone (CHH) concentrations among crabs fed diets with 0, 6, and $12\%$ corn starch ($P \leq 0.05$). However, in the $24\%$ corn starch level, the highest concentration of ILP in hemolymph reached a peak at 1 hour after feeding ($P \leq 0.05$). The lowest CHH concentration occurred at 2 hours after feeding ($P \leq 0.05$).
## 3.4. Hepatopancreatic Energy Homeostasis
The effects of different corn starch levels on hepatopancreatic energy homeostasis of swimming crabs at different time points are presented in Figure 4. In the $0\%$ corn starch level, hepatopancreatic glycogen content was not significantly influenced by dietary corn starch levels at different time points ($P \leq 0.05$). However, in the 6 and $12\%$ corn starch levels, the lowest glycogen content in hepatopancreas was observed at 0 and 1 hour after feeding ($P \leq 0.05$), and the peak value was reached after 6 hours of feeding. However, in the $24\%$ corn starch level, hepatopancreatic glycogen content significantly increased with time points increasing from 0 to 24 hours ($P \leq 0.05$) (Figure 4(a)). In 0, 6, 12, and $24\%$ corn starch levels, the ATP content in hepatopancreas presented a trend of first increasing and then decreasing ($P \leq 0.05$), and the highest hepatopancreatic ATP content occurred at 1 hour after feeding (Figure 4(b)). In the 0, 6, and $12\%$ corn starch diets, the lowest NADH content in hepatopancreas reached at 2 h of feeding; however, in $24\%$ corn starch level, the lowest value was observed at 1 h after feeding (Figure 4(c)). In the diets with 0, 6, 12, and $24\%$ corn starch levels, the activities of the hepatopancreatic mitochondrial respiratory chain complexes I, II, III, and V increased first and then decreased ($P \leq 0.05$) (Figure 4(d)).
## 3.5. Expression Levels of Genes Related to Glucose Metabolism, Transport, and Glycogen Synthesis in Hepatopancreas
The effects of different corn starch levels on the expression of genes related to carbohydrate metabolism in the hepatopancreas at different time points are shown in Figure 5. In the 0, 6, 12, and $24\%$ corn starch diets, the expression levels of gk, pk, g6pase, and pepck were significantly upregulated first and then downregulated ($P \leq 0.05$). There was no significant difference in hk1 of crabs fed diet with $0\%$ corn starch; however, in the 6, 12, and $24\%$ corn starch diets, the expression of hk1 was first downregulated and then upregulated ($P \leq 0.05$), and the lowest hk1 was observed at 6 h after feeding. The expression of fbpase was not significantly influenced by dietary corn starch levels at different time points ($P \leq 0.05$).
In the 0, 6, 12, and $24\%$ corn starch diets, both glut1 and gsk were significantly increased at first and then decreased significantly ($P \leq 0.05$, Figure 6), and the highest glut1 and gsk were presented in 1 h after feeding. The expression of glut2 of crabs fed diets with 0 and $6\%$ corn starch was not significantly influenced in different time points ($P \leq 0.05$, Figure 6), and in the 12 and $24\%$ corn starch diets, the expression of glut2 increased first and then decreased significantly ($P \leq 0.05$, Figure 6), and the highest expression of glut2 was at 2 h after feeding.
## 3.6. Expression Levels of Genes Involved into Hepatopancreas Insulin Signaling Pathway
The effects of different corn starch levels on the expression of genes involved into the insulin signaling pathway in hepatopancreas at different time points are presented in Figure 7. In the 0, 6, 12, and $24\%$ corn starch diets, the expression levels of igf1r, pi3k, akt, foxo, tor, and s6k1 were significantly increased and then significantly decreased ($P \leq 0.05$). The expression level of igf1r and foxo reached the highest at 2 h after feeding, and the expression level of pi3k reached the highest level at 1 h after feeding and then significantly downregulated with increase of time points. Meanwhile, the expression of akt was the highest at 1 h in crabs fed diets with 0, 6, and $12\%$ corn starch levels, and the highest expression of akt was found at 2 h after feeding in crabs fed with $24\%$ corn starch diet. In the $6\%$ corn starch diet, the expression of tor reached the highest at 3 h after feeding, and in the 12 and $24\%$ corn starch diets, the highest expression of tor was at 2 h. In the 0 and $6\%$ corn starch diets, the expression of s6k1 exhibited the highest when ingested for 3 h, and in the $12\%$ corn starch diet, the highest expression of s6k1 occurred at 1 h after feeding; however, in the $24\%$ corn starch diet, the highest expression of s6k1 was observed at 2 h after feeding.
## 3.7. Expression Levels of Genes Related to Energy Metabolism in Hepatopancreas
The effects of different corn starch levels on the expression of genes related to the insulin signaling pathway in hepatopancreas at different time points are presented in Figure 8. In the 0, 6, 12, and $24\%$ diets, the expression levels of nd1, sdhc, cytb, atpase6, sirt1, sirt3, cox1, cox2, and cox3 all showed a trend of significant increase at first and then significant decrease ($P \leq 0.05$). The expressions of nd1 and atpase6 were the highest when ingested for 1 h, and the expression of sirt1 was the highest when ingested for 3 h. In the 0 and $24\%$ corn starch diets, the highest expression of sdhc occurred at 2 h after feeding, and in the 6 and $12\%$ corn starch diets, the highest expression of sdhc occurred at 2 h. The expression of cytb, sirt3, cox1, cox2, and cox3 in hepatopancreas of crabs fed diets with 0, 6, and $12\%$ corn starch reached the peak at 1 h after feeding, and in $24\%$ corn starch level, the expression level of cytb, sirt3, cox1, cox2, and cox3 reached the highest when ingested for 2 h.
## 4. Discussion
Glucose concentrations in blood or hemolymph vary widely not only between species but also under different life histories or feeding strategies of the same breed [21]. McGarry [22] speculated that the blood glucose concentration of fish is also basically maintained at a constant level based on mammals, only fluctuating in a small range. This conclusion may not be entirely accurate. To accurately understand the tolerance of swimming crabs to carbohydrates, it is necessary to carry out glucose tolerance experiments. In the previous study, the optimal corn starch supplementation was estimated to be 8.78-$9.84\%$ for juvenile *Portunus trituberculatus* [18]. The results of present study showed that in the absence of dietary corn starch, there was no significant difference in glucose concentration of the hemolymph for swimming crabs, and a small amount of carbohydrates in other raw materials could not cause the hemolymph glucose levels of swimming crabs to change, but swimming crabs could still grow healthily. The great tolerance of swimming crabs to “low hemolymph glucose” is not possessed by mammals, which is also an interesting research direction [23]. In the 6 and $12\%$ corn starch diets, the glucose concentration in hemolymph reached a peak after 2 hours of feeding, and the glucose concentration decreased to a minimum and remained stable after 12 hours. It was not difficult to conclude that the carbohydrate level of 6-$12\%$ was within the tolerance range of swimming crabs, and no persistent “high hemolymph glucose” phenomenon occurred. However, in the $24\%$ carbohydrate diet, the glucose concentration reached the highest level and the “high hemolymph glucose” state lasted for 3 hours after ingestion for 3 hours, and the glucose concentration returned to the normal level after 12 hours. In the high-carbohydrate diet, the hemolymph glucose peak was delayed by 1 hour, and the hemolymph glucose level remained high. The $24\%$ corn starch level was considered to be excessive in swimming crab commercial diet. The similar findings were frequently reported in carnivorous fish, such as flounder [10], herring (Mylopharyngodon piceus) [24], grouper [25], and Atlantic salmon (Salmo salar L.) [26]. For herbivorous aquatic animals, such as grass carp, the glucose concentration in blood peaked at 3 hours and returned to normal levels after 6 hours [24]. The blood glucose concentration of carp peaked at 3 hours and returned to normal levels after 7 hours [27].
Corresponding fluctuations in carbohydrate metabolism are accompanied by changes in hemolymph glucose levels, suggesting that glucose is an important substrate during various stimuli [28]. Glycolysis under aerobic conditions and gluconeogenesis under anaerobic conditions are important pathways of carbohydrate metabolism. Glucokinase (GK) and hexokinase (HK), phosphofructokinase (PFK), and pyruvate kinase (PK) can all limit the glycolytic pathway and affect the breakdown of glucose for energy. In this study, the enzymatic activities of PK and GK in the hemolymph showed a trend of increasing first and then decreasing. And the expression levels of genes gk and pk in the hepatopancreas also showed a similar trend, but the expression level of hk1 was first downregulated and then upregulated. Many studies indicated that the change of glucose content is not directly related to the expression level of hk1 [29–34]. GK and HK perform similar functions. GK is highly specific for glucose. The increase in glucose content can induce an increase in GK enzyme activity. However, HK is not specific and is inhibited by glucose-6-phosphate and ADP. This may be the reason why the expression level of hk1 is inconsistent with the change in glucose content. Conversely, the gluconeogenesis pathway is the process of synthesizing glucose. The expression levels of genes related to gluconeogenesis such as pepck, fbpase, and g6pase in the hepatopancreas showed a trend of first increasing and then decreasing. Previous studies have demonstrated that the upregulation of glycolysis-related genes is often accompanied by the downregulation of gluconeogenesis-related genes [12, 28]. However, the results of this experiment showed that expression levels of gene related to glycolysis and gluconeogenesis in the hepatopancreas were consistent [18]. The correlation and difference between the carbohydrate metabolism mechanisms of crustaceans and fish remain to be explored and verified.
The balance between glucose storage and production is critical for maintaining glucose homeostasis and depends on the regulation of the activity and gene expression of key enzymes involved in glycolysis, gluconeogenesis, and glycogen synthesis and breakdown [35]. In crustaceans, glucose is primarily stored in the muscle and hepatopancreas as glycogen, and many studies have shown that excess glucose can synthesize more glycogen, which is first consumed when the body provides energy [4, 15, 36]. In the present study, there was no significant change in the hepatopancreatic glycogen concentration of crabs fed with $0\%$ corn starch diet, which was highly consistent with the glucose content. In the 6 and $12\%$ corn starch diets, hepatopancreatic glycogen content first increased and then decreased. And in the $24\%$ corn starch diet, the glycogen content in the hepatopancreas was consistently increased. Obviously, swimming crabs cannot synthesize all the excess carbohydrates into glycogen in a short time. After glucose enters the body, glycogen synthase (GS) plays a key role in the process, and glycogen synthase kinase (GSK) regulates the conversion of glucose to glycogen through the phosphorylation of glycogen synthase [37]. Therefore, the expression level of gsk in the hepatopancreas also showed a trend of increasing first and then decreasing. However, glucose cannot enter the body directly, which is first taken up by the hepatopancreatic epithelial cells of crustaceans and then transported throughout the organism via the hemolymph [5]. Glucose also cannot pass through the lipid bilayer of the hemolymph membrane, and it must enter the cell through the family of glucose transporters (GLUTs) on the cell membrane. GLUT1 is an evolutionarily highly conserved protein responsible for glucose uptake into cells and was the first family of glucose transporters to be cloned [38]. GLUT2 plays a key role in the glucose signaling pathway for intracellular insulin secretion and biosynthesis [39]. In this study, the glucose transporter genes such as glut1 and glut2 were indeed activated after ingesting glucose, and both showed a trend of increasing first and then decreasing.
With the intake of carbohydrates, the glucose concentration in liver or hemolymph increases, and to maintain blood glucose balance, the related hormones that inhibit the increase of glucose will inevitably increase. On the contrary, the hormones that promote the increase of glucose will be inhibited. The effect of insulin on carbohydrate metabolism in fish has been extensively reviewed [23, 40, 41]. Insulin administration inhibits the glucose-sensing system in rainbow trout brains [42–44], and glucagon antagonizes insulin effects in fish, resulting in rapid and wide-ranging hyperglycemia [45, 46]. The types and physiological functions of endocrine hormones in crustaceans are significantly different from those in fish. The researchers found that there is a polypeptide molecule with a similar function to insulin, called insulin-like peptide [47, 48]. In an experiment on the Chinese mitten crab [49], it was proved that insulin-like peptide molecules have a hypoglycemic function, which is mainly expressed in endocrine organs such as hepatopancreas, eye stalk, and thoracic and abdominal nerve groups. Insulin-like peptides and insulin-like growth factor (IGF) polypeptides belong to the same polypeptide family. Gutiérrez et al. [ 50] reported that IGF may be involved in the regulation of carbohydrate metabolism in Pacific white shrimp. Unlike glucagon in higher animals, hyperglycemia hormone (CHH) is the only hormone known to induce significant upregulation of hemolymph glucose in crustaceans. Currently, hormones present in the eye stalk include hyperglycemia hormone, gonadal-stimulating hormone, gonadal-suppressing hormone, and ecdysone-suppressing hormone [49]. A variety of CHH molecules constitute the CHH molecular superfamily. Although the hyperglycemic activity of CHH has been demonstrated, the regulation of its function at the molecular level has been poorly studied [51]. In the present study, no significant differences were found in insulin-like peptides and hyperglycemic hormones at lower corn starch levels (0, 6, and $12\%$), but when the crabs ingested excess corn starch ($24\%$), insulin-like levels increased markedly and regulated the stability of glucose, and hyperglycemia hormones were inhibited. After glucose levels drop, insulin-like levels drop and hyperglycemic hormones rise. At the same time, the increase of glucose concentration will also activate the insulin/IGF signaling pathway [18]. In this study, the expression level of insulin-like growth factor 1 receptor (igf1r) first increased and then decreased, which was highly consistent with the changes in glucose content, while phosphoinositide 3-kinase (pi3k), forkhead box O (foxo), protein kinase B (akt), target of rapamycin (tor), and ribosomal protein S6 kinase 1 (s6k1) all showed the same trend. The results showed that after the activation of the insulin/IGF pathway, the signal may be transmitted from the igf1r genes to the PI3K/AKT signaling pathway, which in turn stimulates genes foxo and tor, and the activation of the tor signaling pathway affects the expression of s6k1. But the specific mechanism has yet to be verified [18].
Endogenous and exogenous factors will change the balance of energy demand and supply in the body. In order to cope with the changes in the balance and maintain energy stability, the body will regulate the production of ATP by regulating the metabolic state of mitochondria. Meanwhile, mitochondrial energy metabolism may be related to β-oxidation [52]. Glucose can only directly generate a small amount of ATP through glycolysis, tricarboxylic acid cycle, and other pathways [28], while most ATP mainly passes through the mitochondrial β-oxidation system, which oxidizes the long-chain acyl-coenzyme entering the mitochondria to acetyl-CoA, and produces reducing substances (FADH2 and NADH), and the electrons of these reducing substances participate in the generation of ATP through the mitochondrial electron transfer respiratory chain [28, 53]. In this study, after swimming crabs ingested carbohydrates, the ATP concentration first increased and then decreased, which was consistent with the glucose content, while NADH was decomposed into ATP, and the NADH concentration first decreased and then increased, just opposite to the concentration of ATP. Similarly, the activity of mitochondrial respiratory chains I, II, III, and V, which play a key role in the process of generating ATP, also showed a trend of first increasing and then decreasing. The previous findings also demonstrated that changes in mitochondrial respiratory chain complex activity can lead to changes in tissue aerobic capacity, thereby affecting aerobic metabolism and ATP production [54]. Previous studies have reported that the expression levels of ATPase (atpase), NADH dehydrogenase (nd), succinate dehydrogenase complex subunit C (sdhc), cytochrome b (cytb), cytochrome c oxidase (cox), and silencing information regulator (sirt) were closely related to the synthesis of mitochondrial complexes [19, 55, 56], and in this study, genes related to energy metabolism all showed a trend of increasing first and then decreasing.
In conclusion, the results of the present study indicated that low carbohydrate in diet resulted that there was no significant difference in hemolymph glucose concentration and hepatopancreatic glycogen. However, dietary excessive carbohydrates ($24\%$ corn starch level) led to the “high hemolymph glucose” which lasted for 3 hours, and the glucose concentration returned to normal levels after 12 hours. At the same time, the glycogen content and insulin-like peptides of crabs fed diet with $24\%$ corn starch significantly increased with an increase of time points after feeding. The results indicated that swimming crabs were unable to convert all glucose into glycogen in a short time and need insulin-like peptides to regulate hemolymph glucose balance. The expressions of genes related to glycolysis, gluconeogenesis, glucose transport, glycogen synthesis, insulin signaling pathway, and energy metabolism were also affected. The results also indicated that the enzymatic machinery related to glucose metabolism could regulate expeditiously to compensate the glucose load generated by feeding different carbohydrate levels for swimming crab. The tolerance to glucose is also reflected by an enhanced use of glucose through glycolysis in the hepatopancreas. Moreover, glucose loading led to a significant disturbance of glucose homeostasis which was confirmed by increased activity of insulin, glycolysis, and glycogenesis, along with gluconeogenesis suppression.
## Data Availability
The data used to support the findings of this study are included within the article.
## Conflicts of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## References
1. 1National Research Council (NRC)
Nutrient Requirements of Fish and Shrimp
2011Washington, DCNational Academies Press. (2011)
2. Azaza M. S., Khiari N., Dhraief M. N., Aloui N., Kraϊem M. M., Elfeki A.. **Growth performance, oxidative stress indices and hepatic carbohydrate metabolic enzymes activities of juvenile Nile tilapia, Oreochromis niloticus L., in response to dietary starch to protein ratios**. (2015) **46** 14-27. DOI: 10.1111/are.12153
3. Li X. F., Wang Y., Liu W. B., Jiang G. Z., Zhu J.. **Effects of dietary carbohydrate/lipid ratios on growth performance, body composition and glucose metabolism of fingerling blunt snout bream Megalobrama amblycephala**. (2013) **19** 701-708. DOI: 10.1111/anu.12017
4. Wang X., Li E., Qin J. G.. **Growth, body composition, and ammonia tolerance of juvenile white shrimp Litopenaeus vannamei fed diets containing different carbohydrate levels at low salinity**. (2014) **33** 511-517. DOI: 10.2983/035.033.0220
5. Wang X., Li E., Chen L.. **A review of carbohydrate nutrition and metabolism in crustaceans**. (2016) **78** 178-187. DOI: 10.1080/15222055.2016.1141129
6. Zhao W., Qin C., Yang G.. **Expression of glut2 in response to glucose load, insulin and glucagon in grass carp (Ctenophcuyngodon idellus)**. (2020) **239** p. 110351. DOI: 10.1016/j.cbpb.2019.110351
7. Chen Y. J., Wang X. Y., Pi R. R.. **Preproinsulin expression, insulin release, and hepatic glucose metabolism after a glucose load in the omnivorous GIFT tilapia Oreochromis niloticus**. (2018) **482** 183-192. DOI: 10.1016/j.aquaculture.2017.10.001
8. Jin J. Y., Yang Y. X., Zhu X. M., Han D., Liu H., Xie S.. **Effects of glucose administration on glucose and lipid metabolism in two strains of gibel carp (Carassius gibelio)**. (2018) **267** 18-28. DOI: 10.1016/j.ygcen.2018.05.023
9. Legate N., Bonen A., Moon T. W.. **Glucose tolerance and peripheral glucose utilization in rainbow trout (Oncorhynchus mykiss), American eel (Anguilla rostrata), and black bullhead catfish (Ameiurus melas)**. (2001) **122** 48-59. DOI: 10.1006/gcen.2001.7620
10. Deng K. Y., Pan M. Z., Liu J. H.. **Chronic stress of high dietary carbohydrate level causes inflammation and influences glucose transport through SOCS3 in Japanese flounder Paralichthys olivaceus**. (2018) **8** p. 7415. DOI: 10.1038/s41598-018-25412-w
11. Liu H. Y., Dong X. H., Chi S. Y.. **Molecular cloning of glucose transporter 1 in grouper Epinephelus coioides and effects of an acute hyperglycemia stress on its expression and glucose tolerance**. (2017) **43** 103-114. DOI: 10.1007/s10695-016-0271-x
12. Moon T. W.. **Glucose intolerance in teleost fish: fact or fiction**. (2001) **129** 243-249. DOI: 10.1016/S1096-4959(01)00316-5
13. Krogdahl A., Hemre G. I., Mommsen T. P.. **Carbohydrates in fish nutrition: digestion and absorption in postlarval stages**. (2005) **11** 103-122. DOI: 10.1111/j.1365-2095.2004.00327.x
14. Sick L. V., Andrews J. W.. **The effect of selected dietary lipids, carbohydrates and proteins on the growth, survival and body composition of P. duorarum**. (1973) **4** 263-276
15. Kallapur V. L., Ramamohanrao Y., Narasubhai A. V.. **Glycolytic enzymes in the premolt field crab Paratelphusa hydrodromus (Milne-Edwards) (Crustacea)**. (1983) **91** 127-132. DOI: 10.3109/13813458309078587
16. Jin M., Zhou Q. C., Zhang W., Xie F. J., ShenTu J. K., Huang X. L.. **Dietary protein requirements of the juvenile swimming crab, Portunus trituberculatus**. (2013) **414-415** 303-308. DOI: 10.1016/j.aquaculture.2013.08.028
17. 17China Fishery Statistical Yearbook
Compiled by Fishery Bureau of China Agriculture Department
2021ChinaAgricultural Press2250. (2021) 22-50
18. Zhang X. S., Jin M., Luo J. X.. **Effects of dietary carbohydrate levels on the growth and glucose metabolism of juvenile swimming crab, Portunus trituberculatus**. (2022) **2022** 1-15. DOI: 10.1155/2022/7110052
19. Yuan Y., Sun P., Jin M., Wang X., Zhou Q.. **Regulation of dietary lipid sources on tissue lipid classes and mitochondrial energy metabolism of juvenile swimming crab, Portunus trituberculatus**. (2019) **10** p. 454. DOI: 10.3389/fphys.2019.00454
20. Livak K. J., Schmittgen T. D.. **Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2**. (2001) **25** 402-408. DOI: 10.1006/meth.2001.1262
21. Hemre G. I., Mommsen T., Krogdahl A.. **Carbohydrates in fish nutrition: effects on growth, glucose metabolism and hepatic enzymes**. (2002) **8** 175-194. DOI: 10.1046/j.1365-2095.2002.00200.x
22. McGarry J.. **What if Minkowski had been ageusic? An alternative angle on diabetes**. (1992) **258** 766-770. DOI: 10.1126/science.1439783
23. Mommsen T. P., Plisetskaya E. M.. **Insulin in fishes and agnathans: history, structure and metabolic regulation**. (1991) **4** 225-259
24. Huang H. Z., Ding L., Song X. H., Wang Y. L., Yang C. G.. **Comparative research on glucose tolerance between black carp Mylopharyngodon piceus and grass carp Ctenopharyngodon idellus**. (2005) **12** 496-500
25. Yang W., Ye J., Wang K.. **Glucose tolerance in grouper (Epinphelus coioides)**. (2012) **36** 563-568
26. Hemre G. I., Torrissen O., Krogdahl A.. **Glucose tolerance in Atlantic salmon, Salmo salar L., dependence on adaption to dietary starch and water temperature**. (1995) **1** 69-75. DOI: 10.1111/j.1365-2095.1995.tb00021.x
27. Chunfang C., Ying L., Liqiao C., Xuehong S., Ping W.. **Metabolic responses of allogynogenetic gbel carp after oral administration of different doses of glucose**. (2003) **27** 602-606
28. Polakof S., Panserat S., Soengas J. L., Moon T. W.. **Glucose metabolism in fish: a review**. (2012) **182** 1015-1045. DOI: 10.1007/s00360-012-0658-7
29. Pansert S., Blin C., Medale F.. **Lack of significant long-term effect of dietary carbohydrates on hepatic glucose-6-phosphatase expression in rainbow trout (Oncorhynchus mykiss)**. (2000) **11** 22-29. DOI: 10.1016/S0955-2863(99)00067-4
30. Lin X. Z., Luo Y. P., Xie X. J.. **Effects of dietary carbohydrate levels on the activity of glycolytic enzymes and serum glucose concentrations in juvenile southern catfish after feeding**. (2006) **3** 304-310
31. Ge X. P., Liu B., Xie J., Yu J. H., Tang Y. K.. **The effect of different carbohydrate levels of dietary on the growth, plasma biochemical indices and hepatic pancreas carbohydrate metabolic enzymes in topmouth culter**. (2007) **30** 88-93
32. Enes P., Panseat S., Kaushik S., Oliva-Teles A.. **Hepatic glucokinase and glucose-6-phosphatase responses to dietary glucose and starch in gilthead sea bream (Sparus aurata) juveniles reared at two temperatures**. (2008) **149** 80-86. DOI: 10.1016/j.cbpa.2007.10.012
33. Enes P., Panseat S., Kaushik S., Oliva-Teles A.. **Growth performance and metabolic utilization of diets with native and waxy maize starch by gilthead sea bream (Sparus aurata) juveniles**. (2008) **274** 101-108. DOI: 10.1016/j.aquaculture.2007.11.009
34. Moreira I. S., Peres H., Couto A., Enes P., Oliva-Teles A.. **Temperature and dietary carbohydrate level effects on performance and metabolic utilisation of diets in European sea bass (Dicentrarchus labrax) juveniles**. (2008) **274** 153-160. DOI: 10.1016/j.aquaculture.2007.11.016
35. Pilkis S. J., Granner D. K.. **Molecular physiology of the regulation of hepatic gluconeogenesis and glycolysis**. (1992) **54** 885-909. DOI: 10.1146/annurev.ph.54.030192.004321
36. Zhan Q., Han T., Li X.. **Effects of dietary carbohydrate levels on growth, body composition, and gene expression of key enzymes involved in hepatopancreas metabolism in mud crab Scylla paramamosain**. (2020) **529** p. 735638. DOI: 10.1016/j.aquaculture.2020.735638
37. Zhang Q. D., Li L.. **Research progress of glycogen synthase kinase-3**. (2007) **2** 85-86
38. Mueckler M., Caruso C., Baldwin S. A.. **Sequence and structure of a human glucose transporter**. (1985) **229** 941-945. DOI: 10.1126/science.3839598
39. Guillam M. T., Dupraz P., Thorens B.. **Glucose uptake, utilization, and signaling in GLUT2-null islets**. (2000) **49** 1485-1491. DOI: 10.2337/diabetes.49.9.1485
40. Navarro I., Capilla E., Castillo J.. **Insulin metabolic effects in fish tissues**. (2006) **1**
41. Caruso M. A., Sheridan M. A.. **New insights into the signaling system and function of insulin in fish**. (2011) **173** 227-247. DOI: 10.1016/j.ygcen.2011.06.014
42. Conde-Sieira M., Aguilar A. J., López-Patiño M. A., Míguez J. M., Soengas J. L.. **Stress alters food intake and glucosensing response in hypothalamus, hindbrain, liver, and Brockmann bodies of rainbow trout**. (2010) **101** 483-493. DOI: 10.1016/j.physbeh.2010.07.016
43. Polakof S., Míguez J. M., Moon T. W., Soengas J. L.. **Evidence for the presence of a glucosensor in hypothalamus, hindbrain, and Brockmann bodies of rainbow trout**. (2007) **292** R1657-R1666. DOI: 10.1152/ajpregu.00525.2006
44. Polakof S., Jm M. i. G., Soengas J. L.. **Changes in food intake and glucosensing function of hypothalamus and hindbrain in rainbow trout subjected to hyperglycemic or hypoglycemic conditions**. (2008) **194** 829-839. DOI: 10.1007/s00359-008-0354-y
45. Mommsen T. P.. **Glucagon-like peptide-1 in fishes: the liver and beyond**. (2000) **40** 259-268. DOI: 10.1093/icb/40.2.259
46. Plisetskaya E. M., Mommsen T. P.. **Glucagon and glucagon-like peptides in fishes**. (1996) **168** 187-257. DOI: 10.1016/S0074-7696(08)60885-2
47. Perillo M., Arnone M. I.. **Characterization of insulin-like peptides (ILPs) in the sea urchin Strongylocentrotus purpuratus: insights on the evolution of the insulin family**. (2014) **205** 68-79. DOI: 10.1016/j.ygcen.2014.06.014
48. Pietri J. E., Pietri E. J., Potts R., Riehle M. A., Luckhart S.. **Plasmodium falciparum suppresses the host immune response by inducing the synthesis of insulin-like peptides (ILPs) in the mosquito Anopheles stephensi**. (2015) **53** 134-144. DOI: 10.1016/j.dci.2015.06.012
49. Wang L.. (2017)
50. Gutiérrez A., Nieto J., Pozo S., Stern S., Schoofs L.. **Effect of insulin/IGF-I like peptides on glucose metabolism in the white shrimp Penaeus vannamei**. (2007) **153** 170-175. DOI: 10.1016/j.ygcen.2007.04.014
51. Chung J. S., Zmora N., Katayama H., Tsutsui N.. **Crustacean hyperglycemic hormone (CHH) neuropeptides family: functions, titer, and binding to target tissues**. (2010) **166** 447-454. DOI: 10.1016/j.ygcen.2009.12.011
52. Bremer J.. **The Role of Carnitine in Cell Metabolism**. (1997) 1-37. DOI: 10.1007/978-1-4615-6005-0_1
53. Li L. Y.. (2020)
54. Eya J. C., Yossa R., Perera D., Okubajo O., Gannam A.. **Combined effects of diets and temperature on mitochondrial function, growth and nutrient efficiency in rainbow trout (Oncorhynchus mykiss)**. (2017) **212** 1-11. DOI: 10.1016/j.cbpb.2017.06.010
55. Zhao M., Luo J. X., Zhou Q. C.. **Influence of dietary phosphorus on growth performance, phosphorus accumulation in tissue and energy metabolism of juvenile swimming crab (Portunus trituberculatus)**. (2021) **20** p. 100654. DOI: 10.1016/j.aqrep.2021.100654
56. Zhang L., Liu W. B., Brown P. B.. **Utilization of raw and gelatinized starch by blunt snout bream Megalobrama amblycephala as evidenced by the glycolipid metabolism, glucose tolerance and mitochondrial function**. (2020) **529** p. 735603. DOI: 10.1016/j.aquaculture.2020.735603
57. Wang X. X., Yuan Y., Li C. C.. **Partial substitution of fish meal with soy protein concentrate in commercial diets for juvenile swimming crab, Portunus trituberculatus**. (2020) **259** p. 114290. DOI: 10.1016/j.anifeedsci.2019.114290
58. Pan L., Yue F., Miao J., Zhang L., Li J.. **Molecular cloning and characterization of a novel c-type lysozyme gene in swimming crab Portunus trituberculatus**. (2010) **29** 286-292. DOI: 10.1016/j.fsi.2010.04.011
|
---
title: Dietary Cinnamaldehyde Enhances Growth Performance, Digestion, Immunity, and
Lipid Metabolism in Juvenile Fat Greenling (Hexagrammos otakii)
authors:
- Yixin Gu
- Jian Han
- Wenjie Wang
- Yu Zhan
- Huijie Wang
- Wenyuan Hua
- Yue Liu
- Yafeng Guo
- Zhuang Xue
- Wei Wang
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973157
doi: 10.1155/2022/2132754
license: CC BY 4.0
---
# Dietary Cinnamaldehyde Enhances Growth Performance, Digestion, Immunity, and Lipid Metabolism in Juvenile Fat Greenling (Hexagrammos otakii)
## Abstract
Fat greenling (Hexagrammos otakii) is a kind of economic fish that is widely consumed by human, and its intensive farming technology is making important progress. However, high-density farming may cause the occurrence of diseases in H. otakii. Cinnamaldehyde (CNE) is a new feed additive for aquatic animals and has a positive effect on disease resistance. In the study, dietary CNE was evaluated on the growth performance, digestion, immune response, and lipid metabolism of juvenile H. otakii (6.21 ± 0.19 g). Six experimental diets were formulated containing CNE at levels of 0, 200, 400, 600, 800, and 1000 mg/kg for 8 weeks. The percent weight gain (PWG), specific growth rate (SGR), survival (SR), and feeding rate (FR) were significantly increased by including CNE in fish diets regardless of the inclusion level ($P \leq 0.05$). The feed conversion ratio (FCR) was significantly decreased among the groups fed CNE supplemented diets ($P \leq 0.05$). A significant decrease in hepatosomatic index (HSI) was observed in fish fed 400 mg/kg-1000 mg/kg CNE compared to the control diet ($P \leq 0.05$). Fish-fed diets containing 400 mg/kg and 600 mg/kg CNE had a higher level of crude protein in muscles than the control diet ($P \leq 0.05$). Moreover, the activities of lipase (LPS) and pepsin (PEP) in the intestinal were markedly increased in juvenile H. otakii-fed dietary CNE ($P \leq 0.05$). Apparent digestibility coefficient (ADC) of dry matter, protein, and lipid was significantly increased with CNE supplement ($P \leq 0.05$). The activities of catalase (CAT) and acid phosphatase (ACP) in the liver were markedly enhanced by including CNE in juvenile H. otakii diets compared with the control ($P \leq 0.05$). The activities of superoxide dismutase (SOD) and alkaline phosphatase (AKP) in the liver were markedly enhanced in juvenile H. otakii treated with CNE supplements 400 mg/kg-1000 mg/kg ($P \leq 0.05$). Additionally, the levels of total protein (TP) in the serum were markedly increased by including CNE in juvenile H. otakii diets compared with the control ($P \leq 0.05$). In the CNE200, CNE400, and CNE600 groups, albumin (ALB) levels in the serum were markedly higher compared with that in the control ($P \leq 0.05$). In the CNE200 and CNE400 groups, the levels of immunoglobulin G (IgG) in the serum were significantly increased compared with that the control group ($P \leq 0.05$). The juvenile H. otakii-fed dietary CNE had lower triglycerides (TG) and total cholesterol (TCHO) levels in the serum than fish-fed CNE-free diets ($P \leq 0.05$). *The* gene expression of peroxisome proliferator-activated receptor alpha (PPAR-α), hormone-sensitive lipase (HSL), and carnitine O-palmitoyltransferase 1 (CPT1) in the liver was significantly increased by including CNE in fish diets regardless of the inclusion level ($P \leq 0.05$). However, fatty acid synthase (FAS), peroxisome proliferator-activated receptor gamma (PPAR-γ), and acetyl-CoA carboxylase alpha (ACCα) in the liver were markedly decreased with CNE supplements 400 mg/kg-1000 mg/kg ($P \leq 0.05$). The glucose-6-phosphate1-dehydrogenase (G6PD) gene expression levels in the liver were markedly decreased compared with the control ($P \leq 0.05$). The optimal supplementation level of CNE was shown by curve equation analysis to be 590.90 mg/kg.
## 1. Introduction
In the modern aquaculture industry, intensive aquaculture system has been widely promoted and applied in grass carp (Ctenopharyngodon idella), carp (Cyprinus carpio), tilapia (Oreochromis niloticus), channel catfish (Ictalurus punctatus), and other fishes, which can create greater economic value for farmers [1–4]. On the other hand, farming of fish under intensive culture system with high densities can trigger a great risk of stressful conditions, which would suppress the immune system and make fish more prone to the diseases resulting in extremely mortalities and significant economic losses [5]. In recent years, a variety of antibiotics, such as flavomycin, bacitracin zinc, salinomycin, and enramycin, have been used to alleviate the pressure of various infectious diseases in farmed fish [6, 7]. However, the long-term use of antibiotics can lead to drug resistance and drug residues in the body of fish [8]. Moreover, beneficial microorganisms such as Lactobacillus and *Vibrio in* the gut of aquatic animals play a key role in stabilizing metabolic balance. Antibiotics can inhibit the production of beneficial microorganisms, which leads to an imbalance in the intestinal microecology and causes intestinal inflammation, intestinal mucosal shedding, and other diseases [9]. Therefore, seeking new feed additives to replace antibiotics has become the focus of fish researchers and nutritionists.
Phytonutrients are considered to be multifunctional and antibiotic-free feed additives, which are beneficial to health in the diets [10]. Cinnamon (Cinnamomum zeylanicum) is one of the phytonutrient species and includes compounds such as polysaccharides, polyphenols, and flavonoids, which are widely used in medicinal materials and the food industry [11]. Cinnamaldehyde (CNE) is the main component of cinnamon, which is an aromatic aldehyde organic compound with the activity of antibacterial, antifungal, anticancer, and antifibrotic [12, 13]. CNE can be used as a flavoring agent in drinks and as an additive in diets [14]. In the field of nutrition research, CNE has been used as a feed additive in the diets of terrestrial animals and birds, and a certain dose of CNE can promote their growth performance and improve disease resistance and antibacterial ability [15–17]. In recent years, CNE has been used in the diet of aquatic animals. The study suggests that CNE at 1000 mg/kg was able to boost the percent weight gain, specific growth rate, and protein efficiency in tongue sole (Cynoglossus semilaevis) [18]. Amer et al. [ 19] have demonstrated that adding 2 mL/kg CNE to the diet of *Nile tilapia* (Oreochromis niloticus) can enhance antioxidant activity and immune status. Bandeira Junior et al. [ 20] pointed out that supplementation of 1 mL/kg cinnamon essential oil increased length and weight gain for 60 days in silver catfish (Rhamdia quelen) and increased the activity of superoxide dismutase in the liver by reducing levels of thiobarbituric acid reactive species.
Fat greenling (Hexagrammos otakii) belongs to Scorpaeniformes, which is a marine carnivorous fish. H. otakii is mainly distributed in the Korean Peninsula, Japan, China, and Russia. H. otakii is an important commercial variety due to its excellent meat quality, rich protein, and nutrition [21]. However, due to higher price and increasing demand of the farming of H. otakii, fish meal (FM) is compelled to search alternative protein sources. Chicken gut meal (CGM) is reasonably priced and nutritious, which can partially or fully replace FM in fish [22, 23]. One of our previous studies showed that replacing FM with $75\%$ CGM in the diet of juvenile H. otakii caused abnormal lipid metabolism (unpublished research). Thence, we need to treat lipid metabolism by improving dietary formula in fish. Since CNE inhibits the release of adrenaline and adrenocorticotropic hormone (ATCH) to fatty acid and facilitates the fat synthesis of glucose in vertebrates, it can be applied as a replacement of insulin to prevent diabetes [24]. Based on nontargeted metabolomics, CNE has been shown to ameliorate disturbances of glucose and lipid metabolism in mice by activating AMP-activated protein kinase (AMPK) [25]. Moreover, according to market research analysis, the price of CNE (￥ 3.2 yuan/kg) and CGM (￥ 7 yuan/kg) is lower than that of FM (￥ 13.1 yuan/kg). Therefore, the combined use of CGM and CNE in juvenile H. otakii diets can save cost and may improve lipid metabolism, which is of great importance for the realization of intensive breeding of H. otakii meaning. To the best of our knowledge, this study is the first to evaluate the effects of dietary CNE supplementation on growth performance, digestion, immune parameters, and relative gene expression of lipid metabolism in juvenile H. otakii and also provided a theoretical reference for the development and utilization of new feed additive in the H. otakii diet.
## 2.1. Experimental Diets and Design
The formulation and proximate composition of the experimental diets are listed in Table 1. FM and CGM were the major protein source; fish oil was the main fat source. Six experimental diets were formulated with CNE at 0 (CNE0), 200 mg/kg (CNE200), 400 mg/kg (CNE400), 600 mg/kg (CNE600), 800 mg/kg (CNE800), and 1000 mg/kg (CNE1000) diet. All of the dry ingredients were finely ground into powder through 60 mesh screens, distilled water was added to mix with them, and the 2 mm pellet feed was made by the granulator, oven-dried at 43°C for approximately 24 h, sealed in polythene bags, and stored at -20°C until used.
## 2.2.1. Experiment Feeding Management
Juvenile H. otakii was obtained from the key laboratory of applied biology and aquaculture of fish (Dalian, China). Select healthy, disease-free 270 juvenile fishes (average initial weight (6.21 ± 0.19) g was randomly assigned to 18 (30 cm × 75 cm) cages in the circulating pool (predisinfection)). Each diet was randomly assigned to three replicate groups of fish. The experimental fish were been acclimated with the experimental diet for one week before the experiment. Feeding was performed twice a day (9:00 and 16:00) and under a natural photoperiod during the 8-week feeding trial. The water temperature was 10 ± 2°C, salinity was 26-30, pH was 7.8 ± 0.4, dissolved oxygen was 6.6 ± 0.7 mg/L, and ammonia nitrogen content < 0.1 mg/L.
## 2.2.2. Sample Collection and Analysis
After the feeding experiment, the fish was starved for 24 h prior to sampling. Then, the 10 juvenile H. otakii in each cage were randomly selected and calculated the percent weight gain (PWG), specific growth rate (SGR), feed conversion ratio (FCR), condition factor (CF), and feeding rate (FR). Juvenile H. otakii were anesthetized with 100 mg/L tricalcium methanesulfonate (MS-222) and subjected to vivisection. Blood was collected from the base of the caudal-fin then centrifuged at a rate of 7000 rpm/min for 10 min at 4°C, and the supernatant was collected to determine the serum biochemical indexes. Superoxide dismutase (SOD), catalase (CAT), malondialdehyde (MDA), acid phosphatase (ACP), alkaline phosphatase (AKP), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were determined from the liver. Fish viscera and whole intestines were collected and measured for lipase (LPS), amylase (AMS), pepsin (PEP), hepatosomatic index (HSI), viscersomatic index (VSI), and intestosomatic index (ISI). Feces of fish were collected by siphon method [26] to determine apparent digestibility coefficient (ADC). Lastly, the muscle of the fish was preserved -20°C to measure the proximate nutritional composition of the muscles.
## 2.2.3. Growth Performance
The growth performance was evaluated using the following parameters. [ 1]Percent weight gain PWG,%=final weight−initial weightinitial weight×100,Specific growth rate SGR,%body weight/day=lnfinal weight–lninitial weightfeeding trial days×100,Feed conversion ratio FCR=total feed consumption wet weight gain,Hepatosomatic index HSI,%=liver wet weightbody wet weight×100,Viscerosomatic index VSI,%=visceral wet weightbody wet weight×100,Intestosomatic index ISI,%=intestine wet weightbody wet weight×100,Condition factor CF,g/cm3=body weightbody length3×100,Survival SR,%=last amount of fishinitial amount of fish×100,Feeding rate FR,%/d=feed intake in dry matterinitial body weight+final body weight/2/feeding trial days ×100.
## 2.2.4. Proximate Composition in Muscles and Diet Analysis
According to the standard method, the approximate composition of diets and muscles was analyzed [27]. Moisture was determined by oven drying at 105°C to constant weight, and ash passed through a muffle furnace at 550°C for 5 h. Crude protein (N × 6.25) was digested with acid and analyzed by the Kjeldahl method. Crude lipid was determined by the petroleum ether extraction.
## 2.2.5. Digestibility Trial
To calculate apparent digestibility coefficients (ADCs) of the experimental diets, a quantity of $0.2\%$ of Cr2O3 was used in each test diet as an inert marker for estimation of apparent digestibility coefficients. According to the standard method, the approximate composition of feces was analyzed [27]. The determination Cr2O3 in diets and feces refers to Zheng et al. [ 28], calculated as follows: [2]ADCdry matter%=100×1−Cr2O3 in the dietCr2O3 in the feces,ADCnutrient%=100×1−nutrient content in the fecesnutrient content in the diet×Cr2O3 in the dietCr2O3 in the feces.
## 2.2.6. Intestinal, Liver, and Serum Biochemical Parameter Measurement
The intestinal digestive enzymes included lipase (LPS) (U/g prot), amylase (AMS, U/mg prot), and pepsin (PEP, U/mg prot) (acid pepsin, pH: 1.5-5); the liver immune and metabolic enzymes included superoxide dismutase (SOD, U/mg prot), catalase (CAT, U/mg prot), malondialdehyde (MDA, nmol/g prot), acid phosphatase (ACP, U/g prot), alkaline phosphatase (AKP, U/g prot), aspartate aminotransferase (AST, U/g prot); and alanine aminotransferase (ALT, U/g prot); the serum immune and metabolic enzymes included total protein (TP, g/L), immunoglobulin G (IgG, g/L), albumin (ALB, g/L), triglycerides (TG, mmol/L), total cholesterol (TCHO, mmol/L), aspartate aminotransferase (AST, U/L), and alanine aminotransferase (ALT, U/L) by using lipase assay kit (colorimetry), amylase assay kit (starch iodine colorimetry), pepsin assay kit (colorimetry), superoxide dismutase assay kit (hydroxy-amine method), catalase assay kit (ammonium molybdate method), malondialdehyde assay kit (TAB method), alkaline phosphatase assay kit (visible light colorimetry), acid phosphatase assay kit (colorimetry), total protein assay kit (Coomassie brilliant blue method), albumin assay kit (colorimetry), immunoglobulin G assay kit (immunoturbidimetry), triglycerides assay kit (colorimetry), total cholesterol assay kit (colorimetry), aspartate aminotransferase assay kit (colorimetry), and alanine aminotransferase assay kit (colorimetry). All the kits were provided by the Jiancheng Bioengineering Institute (Nanjing, China) (http://www.njjcbio.com/).
## 2.2.7. Quantitative Real-Time PCR Analysis
RNA was extracted from the liver of juvenile H. otakii by using the Trizol method [29]. Ultra-microphotometer (Biochrom Technologies, UK) was used to assess the quantity and quality of total RNA. The $\frac{260}{280}$ nm absorbance ratios of all selected samples were ranged from 1.85 to 2.00. Total RNA was used as a template to synthesize cDNA for preservation at -20°C, according to the reverse transcription kit provided by Baisai Biotechnology Co. (Shanghai, China). The primer sequences used are shown in Table 2. β-*Actin* gene was used as a housekeeping gene. The fluorescence quantitative PCR reaction system was 20 μL: 0.6 μL upstream primer, 0.6 μL downstream primer, 10 μL 2× Talent qPCR PreMix, 1 μL cDNA, and 7.8 μL RNase-Free ddH2O. Quantitative real-time PCR (qRT-PCR) analysis was performed in a quantitative thermal cycler (Roche, Light cycler 96, Basel, Switzerland). The cycling conditions of qRT-PCR were used as follows: 95°C for 3 min, 40 cycles for annealing at 60°C for 15 s, and denaturation at 95°C for 5 s. Temperature was increased from 55°C to 95°C to conduct melting curve analysis. Agarose gel electrophoresis of the final product was conducted which confirmed the presence of single amplicons. Standard curves were generated using six different dilutions (in triplicate). The data of expression analysis was analyzed by using the 2−ΔΔCT method [30].
## 2.2.8. Statistical Analysis
The experiment data were analyzed using one-way ANOVA with the software SPSS 19.0 (SPSS, Chicago, Illinois). Data were represented as mean ± standard error of mean (SEM). Prior to statistical analyses, raw data were diagnosed for normality of distribution and homogeneity of variance with the Kolmogorov-Smirnov test and Levene test, respectively. Mathematical transformations were applied if at least one of the assumptions was not verified. Each treatment group was compared with the control group. To adjust for multiple comparisons among the groups, Duncan's method was used, and significant difference was set at $P \leq 0.05.$ A curve equation analysis was conducted to analyze in response to dietary CNE of juvenile H. otakii (Figure 1).
## 3.1. Growth Performances
CNE had a significant positive effect on the growth performance parameters of juvenile H. otakii (Table 3). The best PWG, SGR, FCR, and FR were observed in the CNE400 and CNE600 groups ($P \leq 0.05$). VSI, ISI, and CF have no significant differences among dietary groups ($P \leq 0.05$). HSI was significantly decreased in the CNE400, CNE600, CNE800, and CNE1000 groups compared to the control group ($P \leq 0.05$). Moreover, the groups of supplementation CNE improved SR compared to the control group ($P \leq 0.05$).
A curve analysis was used to estimate the optimal supplementation level of CNE. Based on PWG, curve equations were y = −5718.75x2 + 675.8464x + 108.68643 (R2 = 0.78). The vertices of the lower axis of the curve were at $0.059090\%$.
## 3.2. Muscle Compositions
The muscle compositions of juvenile H. otakii are shown in Table 4. Moisture, crude lipid and ash were not significantly difference among treatments ($P \leq 0.05$). Meanwhile, crude protein was significantly increased in fish groups of CNE400 and CNE600 compared with the control group ($P \leq 0.05$).
## 3.3. Digestive Enzyme Parameters
The digestive enzyme parameters of juvenile H. otakii after an 8-week experimental period are shown in Table 5. Supplementation CNE groups increased the activities of LPS and PEP ($P \leq 0.05$). However, there was no difference in intestinal AMS activity among the groups ($P \leq 0.05$).
## 3.4. Apparent Digestibility Coefficient Parameters
The apparent digestibility coefficient parameters of juvenile H. otakii after an 8-week experimental period are shown in Table 6. The digestibility coefficient of protein, lipid, and dry matter was significantly increased compared with the control group ($P \leq 0.05$).
## 3.5. Liver Biochemical Parameters
Liver biochemical parameters are presented in Table 7. There was no significant difference in MDA, AST, and ALT among all treatments ($P \leq 0.05$). Moreover, the groups of CNE400, CNE600, CNE800, and CNE1000 showed significantly increased SOD and AKP activities compared with the control group ($P \leq 0.05$). Additionally, higher significant CAT and ACP activities were observed in fish groups received diets supplemented with CNE in comparison with the control group ($P \leq 0.05$).
## 3.6. Serum Biochemical Parameters
The serum biochemical parameters of juvenile H. otakii are presented in Table 8. No significant differences were detected in serum AST and ALT among different experimental groups ($P \leq 0.05$). However, IgG was significantly increased in CNE200 and CNE400 groups compared to the control group ($P \leq 0.05$). CNE supplemented fish showed higher significant levels of TP unlike the control group ($P \leq 0.05$). ALB level was significantly increased in CNE200, CNE400, and CNE600 groups ($P \leq 0.05$). Additionally, the levels of TG and TCHO were significantly reduced with CNE supplementation ($P \leq 0.05$).
## 3.7. Expression of Genes Involved in Lipid Metabolism
The relative gene expression involved in lipid metabolism was presented in Figures 2 and 3. Fish-fed diets in groups of CNE400, CNE600, CNE800, and CNE1000 had lower expression of FAS, ACCα, and PPAR-γ than those fed the control diet group ($P \leq 0.05$). The relative gene expression of G6PD was significantly decreased with CNE supplemented ($P \leq 0.05$). Moreover, the groups of supplementations CNE increased gene expression of CPT1, PPAR-α, and HSL as compared to the control group ($P \leq 0.05$).
## 4. Discussion
In high-density farming, several phytonutrients have been shown to increase profits by enhancing fish growth and immunity [31]. Growth performance can directly reflect the response of fish to diet. As a cold-water fish, H. otakii's growth rate and metabolism are much slower than those of warm- and hot-water fish. Therefore, CNE is usually added to fish feed as a growth promoter. There are two reasons for the addition of CNE to the diet to improve fish growth performance. On the one hand, feed intake is one of the factors affecting the growth performance of fish. Appetite enhancement has been documented to be a potential mechanism for the increase in feed intake [32–34]. Plant essential oils are aromatic and volatile compounds that act predators in fish diets. As a kind of plant essential oil, CNE can be used as a food attractant for fish to enhance feed intake [35]. This study indicated that the feeding rate was boosted with CNE supplement in the diets, which is a reason that improved growth performance of juvenile H. otakii. Abd El-Hamid et al. [ 36] indicated that dietary supplementation of CNE improved feed intake and specific growth rate of Nile tilapia. Supplementation of $0.75\%$ cinnamon leaves in the diet enhanced the growth performance and substrate utilization of carp and yellow catfish (Pelteobagrus fulvidraco) [37, 38]. Similar results were also found in pigs, cattle, and growing lambs [39–41]. On the other hand, there is a close relationship between fish digestion and absorption of diet and growth performance [42]. In the present study, the digestive enzymes and apparent digestibility coefficient of fish were improved with CNE supplement. CNE can promote the digestion and absorption of nutrients and improve growth performance by inhibiting intestinal bacteria such as *Escherichia coli* (Gram-negative), Salmonella (Gram-negative), and Shigella (Gram-negative) [43, 44]. Previous studies have shown that CNE can boost growth performance by regulating the activity of broiler chicken gut microbiota [45]. Zhou et al. [ 46] suggested that dietary supplementation with CNE increased the activities of trypsin, amylase, lipase, sodium-potassium-ATPase, and intestinal creatine kinase, which in turn enhanced grass carp's growth performance. In the present study, the percent weight gain and specific growth rate of fish were enhanced following dietary CNE supplementation. These results stem from the fact that the addition of CNE to the diet improved the feed intake and digestibility of fish, which boosted growth performance of fish. Based on curve analysis, the optimal supplementation CNE level is recommended to be 590.90 mg/kg for juvenile H. otakii.
Among fish body indices, CF, HSI, VSI, and ISI reflect the nutritional and physiological status of fish. The results of the present study showed that CF, VSI, and ISI did not change with supplementation of CNE in the diet during the treatment. However, HSI was significantly decreased in the CNE400, CNE600, CNE800, and CNE1000 groups, which was consistent with previous studies performed on striped catfish (Pangasianodon hypophthalmus) [47]. A reduction in the percentage of HSI indicated that dietary CNE supplementation reduces lipid and glycogen content in the liver. CNE maintains insulin hormones by lowering blood sugar and lipid levels, and maintaining healthy liver activity [48].
Muscle nutrient composition may reflect fish response to dietary macronutrients. Fish tissue is composed of many primitive myoblasts, which fuse into multinucleated myotubes and develop into mature muscle tissue [49]. In the present study, moisture, crude lipid, and ash in muscle were not affected by supplementation CNE of diet. However, crude protein was higher in fish groups of CNE400 and CNE600. The amount of muscle protein is closely related to growth. Flavonoids are abundant in cinnamon, which are reported to show a positive effect on muscle quality and muscles cell differentiation as feed additives [50]. Xu et al. [ 51] have demonstrated that addition of $0.06\%$ lotus leaf flavonoids could regulate muscle growth by improving the mRNA expression of fibroblast growth factor 6 b (FGF6b) in grass carp. Villasante et al. [ 52] showed that anthocyanin mixture (120 μM of peonidin chloride, 50 μM of cyanidin chloride, and 40 μM of pelargonidin chloride) could boost cell survival in fish muscle cells by inducing gene expression pattern in accordance with a delay toward terminal differentiation. Therefore, the increase in muscle crude protein in this study was attributed to the improvement of fish muscle cell differentiation by flavonoids in cinnamon.
The antioxidant defenses and nonspecific immune systems in fish are highly linked to their health and immune mechanisms. SOD, CAT, and MAD prevent damage by reactive oxygen species and maintain a balance between free radical production and scavenging [53]. The present study described that the activities of SOD and CAT were increased with CNE supplementation. Some common phytonutrients such as soybean isoflavones and curcumin have major antioxidant action [33, 54]. Studies have shown that phytonutrients can scavenge free radicals through single-electron transfer antioxidant capacity [55]. Moreover, several studies indicated that dietary CNE boosted meat quality by increasing antioxidant capacity in animals, thereby protecting the product from spoilage [56, 57]. The nonspecific immune system is vital for fish, acting as the primary line of protection and driving force of adaptive immunity [58]. The study indicated that CNE as dietary additives trigger toll-like receptors to induce proinflammatory and chemokines, which drive the activation of innate immunity [59]. Faikoh et al. [ 60] found that CNE enhanced nonspecific immunity and survival in zebrafish (Danio rerio) under challenge with *Vibrio vulnificus* or Streptococcus agalactiae. Shan et al. [ 61] also pointed out that yesso scallop (Patinopecten yessoensis) treated with CNE enhanced the activities of ACP and AKP to prevent Vibrio infection. Our work found a significant stimulation in nonspecific immunity, as indicated by the increase in activities of ACP and AKP in fish due to dietary supplementation of CNE.
Serum biochemical markers are considered important to measure overall health of fish and are affected by animal developmental stages and nutritional levels. In this study, the levels of TP, ALB, and IgG were increased in fish fed CNE [62]. TP, ALB, and IgG are indicators for assessing various immunity in the diet, and the increase in their levels is attributed to the improvement of nonspecific immunity. However, TG and TCHO of fish were decreased following dietary supplementation with 200 mg/kg CNE. TG and TCHO are crucial components of animal fat, which are involved in the transport of various lipoprotein particles in plasma. TG and TCHO accumulation in serum could be explained by lipid transport impediment [63]. This study shows that dietary CNE supplementation can improve the fat metabolism status of fish. AST and ALT are one of the most crucial indicators to evaluate the health of liver metabolism. Abnormal protein metabolism can be explained by AST and ALT in the liver entering the blood [64]. In the present study, we notice that dietary CNE did not have a favorable effect on the hepatic protein metabolism status of the juvenile H. otakii. On the other hand, studies showed that CNE could restore the activities of AST and ALT in damaged liver tissue and downregulate the expression of interleukin-6 (IL-6), interleukin-1β (IL-1β), cyclooxygenase-2 (Cox-2), and tumor necrosis factor receptor type-2 (TNFR-2) inflammation-related protein and genes [65]. In addition to difference in species, environmental factors, as well as different metabolic cycles, could be the main reasons for differences in protein metabolism.
Lipids are composed of fatty acids and proteins, which are the main organic constituents of teleost fish. Fatty acids play an important role as a source of metabolic energy in fish growth [66]. However, the abnormal lipid metabolism in fish is a risk factor for diabetes, hyperlipidemia, and metabolic diseases [67, 68]. In recent years, several studies have demonstrated the positive role of CNE in the treatment of lipid metabolism disorders. Kaur et al. [ 69] have investigated the ability of cinnamon to reduce obesity-related metabolic disturbances in a zebrafish by means of blood glucose levels, serum triglyceride analysis, and Oil Red O staining. Setiawati et al. [ 70] pointed out that cinnamon leaf extract and powder more effectively increased high density lipoprotein levels of Asian catfish (Clarias batrachus) and reduced fat in the liver. However, research on lipid metabolism gene expression and CNE is still in its infancy.
Lipid homoeostasis is maintained in fish through a balance of catabolic and anabolic processes [71]. Fatty acids are catabolized in mitochondria or peroxisomes via the β-oxidation pathway [72]. Peroxisome proliferator-activated receptor (PPAR) binds to peroxisome proliferator response elements in the promoter reigns of target genes, which are involved in β-oxidation, such as PPAR-α, CPT1, and HSL. *These* genes are involved in the intracellular transport of fatty acids destined of catabolism [73]. As a subtype of PPAR, PPAR-α induces the intensity of β-oxidation by regulating CPT1. Fang et al. [ 74] pointed out that the expression of PPAR-α and CPT1 was significantly increased by fat deposition in pompano (Trachinotus ovatus). As a downstream gene of PPAR-α, HSL can hydrolyze triglycerides, diglycerides, and monoglycerides [75]. Fish steatohepatitis is accompanied by a significant downregulation of HSL expression. A small amount of HSL is not sufficient to adequately catalyze the release of fatty acids from TG [76].
Conversely, fatty acids can be synthesized de novo by pathways that are activated by sterol regulatory element-binding protein (Srebp). Srebp have many target genes with examples of those in lipid metabolism including FAS, PPAR-γ, ACCα, and G6PD [77]. FAS is a multifunctional enzyme that uses acetyl-CoA as a primer, malonyl-CoA as a two-carbon donor, and nicotinamide adenine dinucleotide phosphate (NADPH) produced by G6PD as a reducing agent to catalyze long-chain fatty acid synthesis [78]. ACCα can be used for the ATP-dependent carboxylation of acetyl-CoA to generate malonyl-CoA, which is involved in the regulation of vertebrate fatty acid synthesis [79]. Both FAS, ACCα, and G6PD are highly expressed in the liver. Hoi et al. [ 80] have demonstrated that CNE inhibition of lipid accumulation was accompanied by downregulation of FAS and ACCα on gene levels, suggesting that FAS and ACCα have a modulating effect on adipogenesis signaling. PPAR-γ is a key transcription factor of fat synthesis genes, which promotes lipid storage and regulates insulin. PPAR-γ induces fibroblasts or preadipocytes to differentiate into adipocytes [81]. This study showed that CNE supplementation in the diet of juvenile H. otakii can improve lipid metabolism by upregulating genes for fat catabolism via β-oxidation (HSL, CPT1, and PPAR-α) and downregulating genes for fatty acid synthesis (FAS, ACCα, G6PD, and PPAR-γ).
## 5. Conclusion
In conclusion, our results proved that supplementation of CNE in the diet of juvenile H. otakii can enhance their growth, digestion, immune, and lipid metabolism status. Based on curve equation analysis, using CNE at the level of 590.90 mg/kg was recommended as a feed additive in the diet of the juvenile H. otakii.
## Data Availability
The data that support the findings of this study are available from the corresponding authors upon reasonable request.
## Conflicts of Interest
The authors declare that there is no competing or financial interests.
## References
1. de Jesus R. B., Gallani S. U., Valladão G. M. R.. **Trypanosomiasis causing mortality outbreak in Nile tilapia intensive farming: identification and pathological evaluation**. (2018) **491** 169-176. DOI: 10.1016/j.aquaculture.2018.02.002
2. Lu J., Li S., He X., Tang R., Li D.. **An in-pond tank culture system for high-intensive fish production: effect of stocking density on growth of grass carp (**. (2022) **549, article 737808**. DOI: 10.1016/j.aquaculture.2021.737808
3. Thurlow C. M., Williams M. A., Carrias A.. (2019) **503** 347-356. DOI: 10.1016/j.aquaculture.2018.11.051
4. Voicea I., Găgeanu I., Vlăduț V., Cujbescu D., Persu C., Matache M.. **Researches on the development of an experimental model of fish pond for intensive fish farming in polyculture system**. (2021) **50** 609-614
5. Dawood M. A. O.. **Nutritional immunity of fish intestines: important insights for sustainable aquaculture**. (2021) **13** 642-663. DOI: 10.1111/raq.12492
6. Lastauskienė E., Valskys V., Stankevičiūtė J.. **The impact of intensive fish farming on pond sediment microbiome and antibiotic resistance gene composition**. (2021) **8, article 673756**. DOI: 10.3389/fvets.2021.673756
7. Opiyo M. A., Marijani E., Muendo P., Odede R., Leschen W., Charo-Karisa H.. **A review of aquaculture production and health management practices of farmed fish in Kenya**. (2018) **6** 141-148. DOI: 10.1016/j.ijvsm.2018.07.001
8. Griboff J., Carrizo J. C., Bonansea R. I., Valdés M. E., Wunderlin D. A., Amé M. V.. **Multiantibiotic residues in commercial fish from Argentina. The presence of mixtures of antibiotics in edible fish, a challenge to health risk assessment**. (2020) **332, article 127380**. DOI: 10.1016/j.foodchem.2020.127380
9. Ming J., Fu Z., Ma Z.. **The effect of sulfamonomethoxine treatment on the gut microbiota of Nile tilapia (**. (2020) **9, article e1116**. DOI: 10.1002/mbo3.1116
10. Feher M., Fauszt P., Tolnai E.. **Effects of phytonutrient-supplemented diets on the intestinal microbiota of**. (2021) **16, article e0248537**. DOI: 10.1371/journal.pone.024853
11. Ghafoor F.. **Importance of herbs in aquaculture; cinnamon a potent enhancer of growth and immunity in fish,**. (2020) **6** 78-92. DOI: 10.29252/ijaah.6.1.78
12. Caipang C. M. A., Mabuhay-Omar J., Gonzales-Plasus M. M.. **Plant and fruit waste products as phytogenic feed additives in aquaculture**. (2019) **12** 261-268
13. Tandel R. S., Dash P., Bhat R. A. H.. **Anti-oomycetes and immunostimulatory activity of natural plant extract compounds against**. (2021) **114** 65-81. DOI: 10.1016/j.fsi.2021.04.018
14. Helal A., Tagliazucchi D.. **Impact of**. (2018) **89** 164-170. DOI: 10.1016/j.lwt.2017.10.047
15. Abbasi R., Abdi-Hachesoo B., Razavi S. M., Namazi F., Nazifi S.. **In vitro and in vivo activity of cinnamaldehyde against**. (2020) **218, article 107978**. DOI: 10.1016/j.exppara.2020.107978
16. İpçak H. H., Alçiçek A.. **Addition of capsicum oleoresin, carvacrol, cinnamaldehyde and their mixtures to the broiler diet II: effects on meat quality**. (2019) **60** 1-11. DOI: 10.7176/JSTR/5-12-01
17. Luo Q., Li N., Zheng Z.. **Dietary cinnamaldehyde supplementation improves the growth performance, oxidative stability, immune function, and meat quality in finishing pigs**. (2020) **240, article 104221**. DOI: 10.1016/j.livsci.2020.104221
18. Wang Y., Wang Q., Xing K., Jiang P., Wang J.. **Dietary cinnamaldehyde and**. (2021) **531, article 735798**. DOI: 10.1016/j.aquaculture.2020.735798
19. Amer S. A., Metwally A. E., Ahmed S. A.. **The influence of dietary supplementation of cinnamaldehyde and thymol on the growth performance, immunity and antioxidant status of monosex Nile tilapia fingerlings (**. (2018) **44** 251-256. DOI: 10.1016/j.ejar.2018.07.004
20. Bandeira Junior G., Bianchini A. E., de Freitas Souza C.. **The use of cinnamon essential oils in aquaculture: antibacterial, anesthetic, growth-promoting, and antioxidant effects**. (2022) **7** p. 133. DOI: 10.3390/fishes7030133
21. Wu Z., Tan X., Wang L.. **Study on artificial induction and early development of gynogenetic fat greenling**. (2022) **22, article 100975**. DOI: 10.1016/j.aqrep.2021.100975
22. Tabinda A. B., Ghazala R., Yasar A., Ashraf M.. **Utilization of chicken intestine as an alternative protein source in the diet for fingerlings of**. (2013) **23** 1603-1608
23. Tabinda A. B., Butt A.. **Replacement of fish meal with poultry by-product meal (chicken intestine) as a protein source in grass carp fry diet**. (2012) **44** 1373-1381
24. Susetyo I.. (2020)
25. Gao J., Zhang M., Niu R.. **The combination of cinnamaldehyde and kaempferol ameliorates glucose and lipid metabolism disorders by enhancing lipid metabolism via AMPK activation**. (2021) **83, article 104556**. DOI: 10.1016/j.jff.2021.104556
26. Mmanda F. P., Lindberg J. E., Norman Haldén A., Mtolera M. S., Kitula R., Lundh T.. **Digestibility of local feed ingredients in tilapia**. (2020) **5** p. 32. DOI: 10.3390/fishes5040032
27. 27AOAC (Association of Analytical Chemists)
Official Methods of Analysis of the Association of Official Analytical Chemists
200017th edAssociation of Official Analytical chemists. (2000)
28. Zheng J. M., Zhang X., Zheng B.. **Study on determination of digestibility of fish diets**. (2002) **2002** 23-27. DOI: 10.3969/j.issn.1008-6137.2002.05.008
29. Simms D., Cizdziel P. E., Chomczynski P.. **TRIzol: a new reagent for optimal single-step isolation of RNA**. (1993) **15** 532-535
30. Livak K. J., Schmittgen T. D.. **Analysis of relative gene expression data using real-time quantitative PCR and the 2**. (2001) **25** 402-408. DOI: 10.1006/meth.2001.1262
31. Justin A. I., Kassam D., Sikawa D. C.. **Integrating piggeries and aquaculture in Malawi: water quality and fish production**. (2022) **79** 1-10. DOI: 10.1080/00207233.2022.2049174
32. Liu X., Feng L., Jiang W. D.. **Dimethyl-**. (2019) **513, article 734421**. DOI: 10.1016/j.aquaculture.2019.734421
33. Pirani F., Moradi S., Ashouri S.. **Dietary supplementation with curcumin nanomicelles, curcumin, and turmeric affects growth performance and silver nanoparticle toxicity in**. (2021) **28** 64706-64718. DOI: 10.1007/s11356-021-15538-2
34. Yeşilayer N., Kaymak I. E.. **Effect of partial replacement of dietary fish meal by soybean meal with betaine attractant supplementation on growth performance and fatty acid profiles of juvenile rainbow trout (**. (2020) **51** 1533-1541. DOI: 10.1111/are.14501
35. Wall E. H., Doane P. H., Donkin S. S., Bravo D.. **The effects of supplementation with a blend of cinnamaldehyde and eugenol on feed intake and milk production of dairy cows**. (2014) **97** 5709-5717. DOI: 10.3168/jds.2014-7896
36. Abd El-Hamid M. I., Ibrahim S. M., Eldemery F.. **Dietary cinnamaldehyde nanoemulsion boosts growth and transcriptomes of antioxidant and immune related genes to fight**. (2021) **113** 96-105. DOI: 10.1016/j.fsi.2021.03.021
37. Liu W., Liu L., Jiang J.. **Effects of attractant four types of plant flavor on**. (2017) **47** 94-100
38. Vinasyiam A.. **Growth performance and flesh quality of common carp,**. (2016) **9** 937-943
39. Chaves A. V., Dugan M. E. R., Stanford K.. **A dose-response of cinnamaldehyde supplementation on intake, ruminal fermentation, blood metabolites, growth performance, and carcass characteristics of growing lambs**. (2011) **141** 213-220. DOI: 10.1016/j.livsci.2011.06.006
40. Williams A. R., Hansen T. V., Krych L.. **Dietary cinnamaldehyde enhances acquisition of specific antibodies following helminth infection in pigs**. (2017) **189** 43-52. DOI: 10.1016/j.vetimm.2017.06.004
41. Yang W. Z., Ametaj B. N., Benchaar C., He M. L., Beauchemin K. A.. **Cinnamaldehyde in feedlot cattle diets: intake, growth performance, carcass characteristics, and blood metabolites**. (2010) **88** 1082-1092. DOI: 10.2527/jas.2008-1608
42. Li M., Zhang M., Ma Y.. **Dietary supplementation with n-3 high unsaturated fatty acids decreases serum lipid levels and improves flesh quality in the marine teleost golden pompano**. (2020) **516, article 734632**. DOI: 10.1016/j.aquaculture.2019.734632
43. Liu Y., Zhang Y., Zhou Y.. **Cinnamaldehyde inhibits type three secretion system in**. (2019) **239, article 108463**. DOI: 10.1016/j.vetmic.2019.108463
44. Patel J., Keelara S., Green J.. **Inactivation of Escherichia coli O157: H7 and Salmonella on fresh herbs by plant essential oils**. (2018) **15** 332-338. DOI: 10.1089/fpd.2017.2377
45. Yang C., Diarra M. S., Choi J.. **Effects of encapsulated cinnamaldehyde on growth performance, intestinal digestive and absorptive functions, meat quality and gut microbiota in broiler chickens**. (2021) **5**. DOI: 10.1093/tas/txab099
46. Zhou Y., Jiang W. D., Zhang J. X.. **Cinnamaldehyde improves the growth performance and digestion and absorption capacity in grass carp (**. (2020) **46** 1589-1601. DOI: 10.1007/s10695-020-00813-9
47. Tartila S. S. Q., Jusadi D., Setiawati M., Fauzi I. A.. **Evaluation of dietary supplementation with cinnamon products on growth, blood composition, liver structure, and meat quality of striped catfish (**. (2021) **29** 2243-2257. DOI: 10.1007/s10499-021-00746-x
48. Hosni A., Abd El-twab S., Abdul-Hamid M.. **Cinnamaldehyde mitigates placental vascular dysfunction of gestational diabetes and protects from the associated fetal hypoxia by modulating placental angiogenesis, metabolic activity and oxidative stress**. (2021) **165, article 105426**. DOI: 10.1016/j.phrs.2021.105426
49. Dugdale H. F., Hughes D. C., Allan R.. **The role of resveratrol on skeletal muscle cell differentiation and myotube hypertrophy during glucose restriction**. (2018) **444** 109-123. DOI: 10.1007/s11010-017-3236-1
50. Rao P. V., Gan S. H.. **Cinnamon: a multifaceted medicinal plant**. (2014) **2014** 12. DOI: 10.1155/2014/642942
51. Xu Y., Tan Q., Hu P., Yao J.. **Characterization and expression analysis of FGF6 (fibroblast growth factor 6) genes of grass carp (**. (2019) **45** 1649-1662. DOI: 10.1007/s10695-019-00655-0
52. Villasante A., Powell M. S., Moutou K.. **Effects of anthocyanidins on myogenic differentiation and antioxidant defense in primary myogenic cells isolated from rainbow trout (**. (2016) **454** 81-89. DOI: 10.1016/j.aquaculture.2015.12.007
53. Mousa A. A., Ramachandran R., Ozdemir O., Karsi A., Abdelhamed H.. **Dietary trans-cinnamaldehyde improves oxidative stress response of channel catfish (**. (2021) **532, article 735985**. DOI: 10.1016/j.aquaculture.2020.735985
54. Zhou C., Lin H., Ge X.. **The Effects of dietary soybean isoflavones on growth, innate immune responses, hepatic antioxidant abilities and disease resistance of juvenile golden pompano**. (2015) **43** 158-166. DOI: 10.1016/j.fsi.2014.12.014
55. Temviriyanukul P., Sritalahareuthai V., Jom K. N.. **Comparison of phytochemicals, antioxidant, and in vitro anti-Alzheimer properties of twenty-seven**. (2020) **25** p. 2600. DOI: 10.3390/molecules25112600
56. Bosetti G. E., Griebler L., Aniecevski E.. **Microencapsulated carvacrol and cinnamaldehyde replace growth-promoting antibiotics: effect on performance and meat quality in broiler chickens**. (2020) **92** p. e20200343. DOI: 10.1590/0001-3765202020200343
57. Naveena B. M., Muthukumar M., Sen A. R., Praveen Kumar Y., Kiran M.. **Use of cinnamaldehyde as a potential antioxidant in ground spent hen meat**. (2014) **38** 1911-1917. DOI: 10.1111/jfpp.12163
58. Harikrishnan R., Devi G., Balasundaram C.. **Impact of cinnamaldehyde on innate immunity and immune gene expression in**. (2021) **117** 1-16. DOI: 10.1016/j.fsi.2021.07.009
59. Youn H. S., Lee J. K., Choi Y. J.. **Cinnamaldehyde suppresses toll-like receptor 4 activation mediated through the inhibition of receptor oligomerization**. (2008) **75** 494-502. DOI: 10.1016/j.bcp.2007.08.033
60. Faikoh E. N., Hong Y. H., Hu S. Y.. **Liposome-encapsulated cinnamaldehyde enhances zebrafish (**. (2014) **38** 15-24. DOI: 10.1016/j.fsi.2014.02.024
61. Shan Z., Wang M., Zhao S., Xie X., Yang D., Liu W.. **Cinnamaldehyde exerts prophylactic and therapeutic effects against**. (2021) **538, article 736588**. DOI: 10.1016/j.aquaculture.2021.736588
62. Nicula M., Bura M., Simiz E.. **Researches concerning reference values assessment of serum biochemical parameters in some fish species from Acipenseridae, Cyprinidae, Esocidae and Salmonidae family**. (2010) **43** 498-505
63. Li G., Zhou X., Jiang W.. **Dietary curcumin supplementation enhanced growth performance, intestinal digestion, and absorption and amino acid transportation abilities in on- growing grass carp (**. (2020) **51** 4863-4873. DOI: 10.1111/are.14777
64. Safari R., Hoseinifar S. H., Imanpour M. R.. **The effects of multi-enzyme and betaine on growth performance, body composition haemato-immunological parameters and expression of growth-related genes in beluga (**. (2022) **549, article 737784**. DOI: 10.1016/j.aquaculture.2021.737784
65. Almoiliqy M., Wen J., Qaed E.. **Protective effects of cinnamaldehyde against mesenteric ischemia-reperfusion-induced lung and liver injuries in rats**. (2020) **2020** 14. DOI: 10.1155/2020/4196548
66. Tocher D. R.. **Metabolism and functions of lipids and fatty acids in teleost fish**. (2003) **11** 107-184. DOI: 10.1080/713610925
67. Abachi S., Pilon G., Marette A., Bazinet L., Beaulieu L.. **Beneficial effects of fish and fish peptides on main metabolic syndrome associated risk factors: diabetes, obesity and lipemia**. (2022) **1-49** 1-49. DOI: 10.1080/10408398.2022.2052261
68. He C., Jia X., Zhang L.. **Dietary berberine can ameliorate glucose metabolism disorder of**. (2021) **47** 499-513. DOI: 10.1007/s10695-021-00927-8
69. Kaur N., Chugh H., Tomar V., Sakharkar M. K., Dass S. K., Chandra R.. **Cinnamon attenuates adiposity and affects the expression of metabolic genes in diet-induced obesity model of zebrafish**. (2019) **47** 2930-2939. DOI: 10.1080/21691401.2019.1641509
70. Setiawati M., Jusadi D., Laheng S., Suprayudi M. A., Vinasyiam A.. **The enhancement of growth performance and feed efficiency of Asian catfish,**. (2016) **9** 1301-1309
71. Torstensen B. E., Nanton D. A., Olsvik P. A., Sundvold H., Stubhaug I.. **Gene expression of fatty acid-binding proteins, fatty acid transport proteins (cd36 and FATP) and**. (2009) **15** 440-451. DOI: 10.1111/j.1365-2095.2008.00609.x
72. Nanton D. A., Lall S. P., Ross N. W., McNiven M. A.. **Effect of dietary lipid level on fatty acid**. (2003) **135** 95-108. DOI: 10.1016/S1096-4959(03)00052-6
73. Houston S. J., Karalazos V., Tinsley J.. **The compositional and metabolic responses of gilthead seabream (**. (2017) **118** 1010-1022. DOI: 10.1017/S0007114517002975
74. Fang H. H., Zhao W., Xie J. J.. **Effects of dietary lipid levels on growth performance, hepatic health, lipid metabolism and intestinal microbiota on**. (2021) **27** 1554-1568. DOI: 10.1111/anu.13296
75. Kurtovic I., Marshall S. N., Zhao X., Simpson B. K.. **Lipases from mammals and fishes**. (2009) **17** 18-40. DOI: 10.1080/10641260802031322
76. Tian J., Wen H., Zeng L. B.. **Changes in the activities and mRNA expression levels of lipoprotein lipase (LPL), hormone-sensitive lipase (HSL) and fatty acid synthetase (FAS) of Nile tilapia (**. (2013) **400-401** 29-35. DOI: 10.1016/j.aquaculture.2013.01.032
77. Zeynep T., Cemal O., Nurhan S., Vijaya J., Kazim S.. **Cinnamon polyphenol extract inhibits hyperlipidemia and inflammation by modulation of transcription factors in high-fat diet-fed rats**. (2017) **2017** 10. DOI: 10.1155/2017/1583098
78. Yu T., Chen Y. K., Chen X. M.. **The effect of oxidized fish oil on lipid metabolism in**. (2020) **17, article 100388**. DOI: 10.1016/j.aqrep.2020.100388
79. Li L., He M., Xiao H., Liu X., Wang K., Zhang Y.. **Acetic acid influences BRL-3A cell lipid metabolism via the AMPK signalling pathway**. (2018) **45** 2021-2030. DOI: 10.1159/000487980
80. Hoi J. K., Lieder B., Liebisch B.. **TRPA1 agonist cinnamaldehyde decreases adipogenesis in 3T3-L1 cells more potently than the non-agonist structural analog cinnamyl isobutyrate**. (2020) **5** 33305-33313. DOI: 10.1021/acsomega.0c05083
81. Naeini Z., Toupchian O., Vatannejad A.. **Effects of DHA-enriched fish oil on gene expression levels of p53 and NF-**. (2020) **30** 441-447. DOI: 10.1016/j.numecd.2019.10.012
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---
title: Molecular Characterization of Grass Carp GIPR and Effect of Nutrition States,
Insulin, and Glucagon on Its Expression
authors:
- Guokun Yang
- Xiaomin Liang
- Yanle Jiang
- Chengquan Li
- Yanmin Zhang
- Xindang Zhang
- Xulu Chang
- Yawei Shen
- Xiaolin Meng
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973162
doi: 10.1155/2022/4330251
license: CC BY 4.0
---
# Molecular Characterization of Grass Carp GIPR and Effect of Nutrition States, Insulin, and Glucagon on Its Expression
## Abstract
GIP plays an important regulatory role in glucose and lipid metabolism. As the specific receptor, GIPR is involved in this physiological process. To assess the roles of GIPR in teleost, the GIPR gene was cloned from grass carp. The ORF of cloned GIPR gene was 1560 bp, encoding 519 amino acids. The grass carp GIPR was the G-protein-coupled receptor which contains seven predicted transmembrane domains. In addition, two predicted glycosylation sites were contained in the grass carp GIPR. The grass carp GIPR expression is in multiple tissues and is highly expressed in the kidney, brain regions, and visceral fat tissue. In the OGTT experiment, the GIPR expression is markedly decreased in the kidney, visceral fat, and brain by treatment with glucose for 1 and 3 h. In the fast and refeeding experiment, the GIPR expression in the kidney and visceral fat tissue was significantly induced in the fast groups. In addition, the GIPR expression levels were markedly decreased in the refeeding groups. In the present study, the visceral fat accumulation of grass carp was induced by overfed. The GIPR expression was significantly decreased in the brain, kidney, and visceral fat tissue of overfed grass carp. In primary hepatocytes, the GIPR expression was promoted by treatment with oleic acid and insulin. The GIPR mRNA levels were significantly reduced by treatment with glucose and glucagon in the grass carp primary hepatocytes. To our knowledge, this is the first time the biological role of GIPR is unveiled in teleost.
## 1. Introduction
Glucose-dependent insulinotropic polypeptide (GIP) is an incretin hormone which is released into the circulation following nutrient ingestion [1]. The crucial role of GIP is stimulating insulin release from pancreatic islet β cells [2, 3]. Moreover, GIP also increases lipogenesis in adipose tissue, promotes bone formation, and induces proliferation of hippocampal progenitor cells [2]. GIP exerts its roles by binding to its specific receptor, namely, GIPR [4]. The GIPR was firstly cloned from the cerebral cortex cDNA library of rat in 1993 [2, 5, 6] and was followed cloned in the hamster [5, 7] and human [5, 8]. The GIPR is a glycoprotein, which belongs to the secretin/vasoactive intestinal peptide (VIP) family of receptors. In this protein family, it includes receptors for glucagon-likepeptide-1 (GLP-1), VIP, secretin, pituitary adenylate cyclase activating polypeptide (PACAP), and glucagon [6].
GIPR is a seven transmembrane protein, which is a member of G-protein-coupled receptor (GPCR) superfamily [2, 5, 9]. As the GPCR, GIPR has a large N-terminal extracellular domain which is vital to receptor activation and high-affinity GIP binding [6, 10]. The C-terminal cytoplasmic domain of GIPR is associated with intracellular signaling [2, 10]. Moreover, the first transmembrane domain of GIPR is essential for cAMP coupling [10]. In addition, the conserved N-glycosylation sequence (N-X-S/T) is located in the N-terminal of GIPR [6]. And the C-terminal and the third cytoplasmic loop of GIPR contain many potential phosphorylation sites [6]. GIPR mRNA expression is a wide range of tissues in the human and mouse [2, 6]. The report reveals that the GIPR is detected in the adipose tissue, kidney, heart, bone, intestine, pancreas, and several regions of the central nervous system (CNS) [2, 6, 11]. In human islets, GIPR expression is detected in α, β, δ, and γ cells [11, 12]. Furthermore, GIPR expression is extensive in the rodent brain, such as the cerebral cortex, hippocampus, brain stem, cerebellum, and olfactory bulb of rats [5, 11]. In mice, GIPR expression level is reduced with an age-increased dependent [13].
The zebrafish GIP (zfGIP) can activate the zebrafish glucagon receptor [14] and human GLP-1 receptor [15]. However, as an endogenous receptor of GIP, GIPR is essential for GIP playing its biological functions. For example, the GIPR signaling deficiency or gain regulates food intake in mice, which mediates by the control of leptin sensitivity [16–18]. Moreover, the diet-induced obesity is alleviated via reducing adipose tissue mass in the mice of GIPR knockout or antagonism [19, 20]. The PI3K/Akt and PKA signaling pathways are involved in the biological roles of GIP binding GIPR. In pancreatic β cells, GIP increases insulin secretion by binding GIPR, in which the intracellular cyclic AMP (cAMP) level promotes and activates PKA signaling pathway [21, 22]. Furthermore, GIP promotes β cell survival by inhibiting apoptotic protein Bax expression which mediates the PI3K/Akt signaling pathway [21, 23]. In adipose tissue, GIP increases glucose transporter 4 (GLUT4) and lipoprotein lipase (LPL) expression and promotes hormone-sensitive lipase (HSL) activity by activating the PKA signaling pathway [21, 24]. In addition, GIPR mediates protein kinase G (PKG) signaling pathway to promote activation and phosphorylation of HSL [21, 25].
As the incretin, GIP plays an important role in lipogenesis, insulin secretion, and bone formation [2], which GIPR is involved in the regulatory functions [21]. Our previous study indicates that GIP takes part in glucose and lipid metabolism of grass carp [22]. However, the roles of fish GIPR have been rarely reported. To investigate the functions of GIPR in fish, the grass carp GIPR was isolated from brain tissue. The tissue-specific expression of GIPR was evaluated by real-time PCR. The effects of OGTT and fast and refeeding on GIPR expression were tested. The visceral fat accumulation of grass carp was induced by overfeeding. The GIPR expression was assessed in the overfed grass carp. In vitro, the effects of glucose, oleic acid, insulin, and glucagon on GIPR mRNA levels were assessed. To our knowledge, this study is the first report of GIPR function of fish.
## 2.1. Animals
In this study, the grass carp was obtained from Yanjin Fishery (Yanjin County, Henan Province). Before the experiment, fish were domesticated for two weeks at indoor tanks. The water quality parameters for fish acclimation were controlled as follows: temperature, 26–28°C; dissolved oxygen concentration, 5.5–6.2 mg/L; and pH 7.2–7.5. The fish were fed commercial pellets (Tongwei, China) of three times per day (8:30, 13:30, and 18:30). All animal experiments were approved by the Animal Care Committee of Henan Normal University.
## 2.2. Molecular Identification and Sequence Analysis of Grass Carp GIPR
The RT-PCR (reverse transcription PCR) was performed to clone grass carp GIPR in this study. Before the experiment, the zebrafish GIPR (XM 005157739.4) sequence was used to blast the predicted sequence of GIPR in the NCBI Transcriptome Shotgun Assembly Sequence database of grass carp (https://www.ncbi.nlm.nih.gov). The specific primers for GIPR cloning were shown in Table 1. Then, the total RNA of grass carp brain was obtained by RNAiso Plus (Takara, Japan). The PrimeScript RT reagent kit was used to synthesize the first-strand cDNA. The PCR program used for GIPR cloning was as follows: 94°C for 3 min, 35 cycles of 94°C for 30 s, 56°C for 30 s, 72°C for 2 min, 72°C for 5 min, and 4°C for infinity. After purifying with E.Z.N.A Gel Extraction Kit (OMEGA, Biotek), the PCR fragments were ligated to the pMD19-T vector (Takara, Japan). The cloned GIPR was analyzed based on sequencing result. The SignalP server-5.0 (http://www.cbs.dtu.dk/services/SignalP/) was used to predict the signal peptide of grass carp GIPR. The glycosylation sites of grass carp GIPR were analyzed by NetNGlyc 1.0 Server (http://www.cbs.dtu.dk/services/NetNGlyc/). The transmembrane domains of grass carp GIPR were analyzed by TMHMM server 2.0 (http://www.cbs.dtu.dk/services/TMHMM/). The protein motif of grass carp GIPR was predicted by the Simple Modular Architecture Research Tool (http://smart.emblheidelberg.de/). The spatial structure of grass carp GIPR was analyzed by Swiss-model (https://swissmodel.expasy.org/). Sequence alignments were performed by ClustalW2 software (http://www.ebi.ac.uk/Tools/msa/clustalo/). The phylogenetic tree of GIPR was constructed with MEGAX by the neighbor-joining method.
## 2.3. Tissue-Specific Expression and Effects of OGTT and Fast and Refeeding on GIPR Expression
In the tissue expression experiment, three grass carp were acclimated for two weeks. Then, fish were anesthetized and sacrificed by decapitation. The experimental samples (the telencephalon, mesencephalon, cerebellum, hypothalamus, pituitary, head kidney, kidney, heart, liver, spleen, foregut, midgut, hindgut, fat, muscle, gonad, and gill) were collected and snap-frozen in liquid nitrogen. The collected samples were stored at −80°C until RNA extraction.
In the OGTT, the experimental process was referred to the previous study [26, 27]. The grass carp were domesticated for two weeks. In the glucose treatment group, fish were performed by gavage glucose solution with the concentration of 1.67 mg/g BW (body weight). In the control group, the fish were performed with PBS. After treatment by gavage for 1, 3, and 6 h, the fish were anesthetized and sacrificed by decapitation. The brain, kidney, and visceral fat were collected and snap-frozen in liquid nitrogen ($$n = 8$$/group). The collected samples were stored at −80°C until RNA extraction.
In the fast and refeeding experiment, the experimental procedure was referred to the previous study [27, 28]. After acclimating for two weeks, the experiment was implemented. In the control group (feeding), fish were fed for 14 days. In the fasting group (fast), the fish were fast for 14 days. In the refeeding group (refeeding), the fish were fast for 14 days and were refed before 6 h for sampling on day 14. By the end of the study, the fish were anesthetized and sacrificed by decapitation. The kidney and visceral fat were quickly collected and snap-frozen in liquid nitrogen ($$n = 12$$/group). The collected samples were stored at −80°C until RNA extraction.
## 2.4. Overfed-Induced Visceral Fat Accumulation of Grass Carp and GIPR Expression
To evaluate the effect of fat accumulation on GIPR mRNA levels in grass carp, the grass carp was induced by overfed. Grass carp were purchased from a fish farm (Yanjin, Henan). Fish were acclimated in a recirculating aquaculture system and fed basic diets for two weeks. Healthy fish were distributed into 6 tanks (150 L) with 20 fish per tank (three tanks per treatment). During the 6-week experimental period, fish were fed commercial feed thrice daily at 08:30, 13:30, and 18:30. In the control group (control), fish were fed at a rate of about $3\%$ body weight every day. In the overfed-induced group (induced), fish were fed until not eating every time. The body weight of fish was recorded every two weeks, and the amount of feed was adjusted based on the body weight.
After 6-week feeding trial, four fish from each tank were chosen to be sampled. Four fish from each tank were chosen to be measured body weight and the weights of visceral adipose tissues to calculate the visceral adipose ratio (VAR) (VAR, % = (final body weights (g)/visceral adipose weight (g) × 100). Blood samples were collected from the caudal vein of each fish. The blood sample was incubated at 4°C at least for 1 h. After centrifugation of 10 min at 7500 g, serum was collected and stored at -80°C for measure contents of glucose and TG. The contents of serum glucose and TG were determined with commercial kits (Jiancheng, China). Then, the kidney, brain, and visceral fat samples were collected from four fish in each tank and quickly frozen in liquid nitrogen for RNA isolation.
## 2.5. Grass Carp Primary Hepatocyte Isolation and Treatments
The experimental method of primary hepatocyte isolation was referred to a previous study [27, 29]. The isolated hepatocytes were cultured in the 24-well plate with 1 mL DMEM/F12 medium contained $10\%$ fetal bovine serum (FBS) with the density of 8 × 105 cells/well. After overnight culture, the cell medium was replaced to fresh DMEM/F12 without FBS. Before treatment, the hepatocytes were cultured for 1 h in the DMEM/F12 without FBS. [ 1] The hepatocytes were treated with glucose (35 mM) or oleic acid (80 μg/mL) for 12 and 24 h. [2] The hepatocytes were treated with human insulin or glucagon at the dose of 0, 10, 100, and 1000 nM for 3 and 6 h. By the end of the study, the hepatocytes were lysed by RNAiso Plus for RNA extraction.
## 2.6. RNA Extraction, Reverse Transcription, and Real-Time PCR
The total RNA of all samples was extracted by the RNAiso Plus. The concentrations of total RNA were detected by the UV spectrophotometer (Nanodrop 2000, Thermo, USA). The gDNA Eraser was used to digest the genomic DNA from 1 μg of total RNA at 42°C for 2 min. Then, the PrimeScript RT reagent kit (PrimeScript RT reagent kit with gDNA Eraser, Takara, China) was used to synthesize the 1st-strand cDNA. In the real-time PCR, the 1st-strand cDNA was used as the template. The used primers were listed in Table 1. Real-time PCR was performed on a LightCycler 480II Sequence Detection System (Roche, Rotkreuz, Switzerland) using the SYBR Green PCR Master Mix (Bimake, Shanghai, China). The procedure of real-time PCR was as follows: 95°C for 5 min, 40 cycles of 95°C for 15 s, 56°C for 15 s, and 72°C for 30 s. 18S rRNA or β-actin was used as the internal reference. The results of gene mRNA levels were normalized to that of internal reference genes using the comparative Ct method [30].
## 2.7. Statistical Analyses
All data of this study are represented as mean ± S.E.M. The SPSS version 18.0 (SPSS Inc., Chicago, IL, USA) was used to perform statistical analysis. The data were analyzed with the unpaired Student t-test (two-group comparisons) or one-way ANOVA (multigroup comparisons) to determine the statistical significance of differences between the groups. It was considered significant that the probability value was of $P \leq 0.05.$
## 3.1. Molecular Characterization of Grass Carp GIPR
The ORF of cloned GIPR was 1560 bp, encoding 519 amino acids (Figure 1(a)). The first 19 amino acid was the predicted signal peptide. The analysis result by TMHMM server 2.0 revealed that the GIPR was the classical GPCR, which had seven transmembrane domains with the intracellular N-terminal and extracellular C-terminal (Figure 1(a)). Moreover, the predicted results of protein motif and spatial structure were indicated that the grass carp GIPR was the seven transmembrane protein (Figures 1(b) and 1(c)). In grass carp GIPR, two predicted N-glycosylation sites were located in the intracellular N-terminal (Figure 1(a)). The result of sequence alignment showed that grass carp GIPR displayed high identities to that of Danio rerio ($92.02\%$), Sinocyclocheilus grahami ($90.56\%$), and Pygocentrus nattereri ($82.27\%$) (Table 2). The phylogenetic tree was constructed with GIPR sequences of various species. The results revealed that the various fishes were clustered into one clade with high bootstrap values (Figure 2).
## 3.2. Tissue-Specific Expression of Grass Carp GIPR
The tissue distribution of GIPR was evaluated by the real-time PCR. The results revealed that the mRNA transcripts of GIPR were detected in all detected tissues of grass carp. The most abundant expression level of GIPR was detected in the kidney, brain regions, and visceral fat tissue of grass carp (Figure 3(a)).
## 3.3. Effects of OGTT and Fast and Refeeding on GIPR Expression
To assess the effects of energy state on the mRNA transcripts of grass carp GIPR, the OGTT and fast and refeeding experiments were performed. In the fast and refeeding experiments, the GIPR mRNA levels were dramatically promoted in the kidney and visceral fat tissue of the fast group. Moreover, the GIPR expression was markedly reduced in the kidney and visceral fat tissue of refeeding group than that in the fed and fast groups (Figures 3(b) and 3(c)). In the OGTT experiment, the GIPR mRNA levels were observably inhibited in the kidney, visceral fat, and brain by treatment with glucose for 1 and 3 h (Figure 4).
## 3.4. Overfed-Induced Visceral Fat Accumulation of Grass Carp and GIPR Expression
As shown in Figure 5, the serum glucose and TG contents were observably promoted in the overfed-induced group (Figures 5(a) and 5(b)). The VAR was also significantly promoted in the induced group (Figure 5(c)). Moreover, the fat was observably accumulated in the abdominal cavity of the induced group (Supplemental Figures 1A, 1B). The GIPR expression in the visceral fat, kidney, and brain tissues was observably reduced in the induced group (Figures 5(d)–5(f)).
## 3.5. Effects of Glucose, Oleic Acid, Insulin, and Glucagon on GIPR Expression in Hepatocytes
In primary hepatocytes, the GIPR expression levels were memorably reduced by treatment with glucose for 12 and 24 h. Moreover, the GIPR mRNA levels were dramatically induced by treatment with oleic acid for 12 and 24 h (Figures 6(a) and 6(b)). By treatment with insulin, the GIPR expression was markedly induced in primary hepatocytes for 6 h. However, the GIPR expression levels were significantly decreased in primary hepatocytes by treatment with glucagon for 3 and 6 h (Figures 6(c) and 6(d)).
## 4. Discussion
As the incretin, GIP is involved in many important physiological functions, in which the GIPR plays important roles [2, 10]. To assess the roles of GIPR in fish, the GIPR was cloned from grass carp brain in our study. The grass carp GIPR is a classical GPCR and is the seven transmembrane proteins with the intracellular N-terminal and extracellular C-terminal. The protein structure of grass carp GIPR is similar to that of mammalian GIRP which is seven transmembrane protein belonging to the VIP/secretin family of receptors [6]. The pervious study indicated that the GIPR had a large N-terminal extracellular domain containing a consensus N-glycosylation sites [6, 10]. The N-terminal domain of the GIPR is vital for high-affinity GIP binding [2, 6, 10]. Moreover, the N-terminal domain of GIPR is necessary for receptor activation and cAMP coupling [2, 6, 10]. In the grass carp GIPR, the intracellular N-terminal is relatively large and also contains two predicted N-glycosylation sites. It speculates that the intracellular N-terminal of grass carp GIPR may take part in GIP binding and receptor activation. The alignment result showed that grass carp GIPR displayed high identities to that of Danio rerio ($92.02\%$), Sinocyclocheilus grahami ($90.56\%$), and Pygocentrus nattereri ($82.27\%$). Furthermore, the phylogenetic tree result revealed that the various fishes were clustered into one clade with high bootstrap values. Based on those results, the cloned sequence in our study is the grass carp GIPR sequence.
The GIPR was firstly identified in transplantable insulinoma and insulin-secreting β cell line of hamster [10]. Subsequently, the rat GIPR was cloned from cerebral cortex cDNA library [5, 10]. In the present study, the grass carp GIPR expression is in multiple tissues. The high transcriptional level of grass carp GIPR was detected in the kidney, brain regions, and fat tissue. The result is similar to previous studies. In mammals, the GIPR expression was also detected in the multiple tissues, including intestine, adipose tissue, pituitary, heart, spleen, kidney, and several regions in the CNS [2, 6, 10]. The results indicate that the GIPR wide expression in multiple tissues is a universal phenomenon.
In the OGTT experiment, the GIPR mRNA level was memorably decreased by treatment with glucose for 1 and 3 h. Furthermore, the results of our previous studies showed that the grass carp serum glucose levels were significantly promoted by treatment with glucose for 1 and 3 h in the OGTT experiment [26, 29, 31]. The previous studies showed that the Zucker diabetic fatty (ZDF) rats were with extreme hyperglycemia, and the mRNA and protein levels of GIP receptor were significantly downregulated [32, 33]. Moreover, the reduced GIPR expression levels in ZDF rats were relieved following normalization of hyperglycemia by phlorizin treatment [32]. In woman, the GIPR expression in the subcutaneous fat was negatively correlated with fasting blood glucose [34]. The researcher suggested that the hyperglycemia-induced downregulation of GIPR expression may be closely associated with ubiquitination [10, 35]. In the present study, the GIPR mRNA level was also memorably inhibited with glucose treatment in grass carp primary hepatocytes. Similarly, GIPR level in INS ($\frac{832}{13}$) cells was strongly decreased by glucose treatment with the time- and concentration-dependent manner [36]. In addition, the protein levels of GIPR were reduced in rat and human islets exposed to glucose [35]. These results reveal that glucose level is the vital regulatory factors of GIPR mRNA and protein expression.
Fasting and refeeding are used to investigate the biological response of teleosts [37]. In the present study, the GIPR mRNA level in the kidney and fat tissue of grass carp was markedly induced by fasting for 14 days. Moreover, the GIPR mRNA level was reduced in the refeeding group. The roles of GIP receptor were closely related to the nutritional status. In the ZDF rats, the levels of GIP receptor mRNA and protein were decreased than that of lean rats [10, 32, 33]. In obese nondiabetic women, the GIPR level was dramatically decreased in adipose tissue [34]. However, the GIPR expression was induced in ECs which were stressed by the removal of serum from the culture media [38]. Furthermore, high-fat diet induces to increased adipocyte mass in normal mice, whereas fed high-fat diets in GIPR(−/−) mice will not induce obese [10]. In addition, the fatty acid (palmitate) markedly promoted the GIPR transcriptional level in the islets isolated from lean Zucker rats, INS ($\frac{832}{13}$) cell line, and BRIN-D11 β cells [36]. Similarly, the GIPR transcriptional level in grass carp hepatocyte was also induced by treatment with fatty acid in our study. The above results indicate that nutritional status plays important role in regulation of GIPR expression.
In our study, the serum glucose, TG, and VAR were significantly promoted in the overfed-induced group. Similarly, the previous studies indicated that the serum glucose and TG levels were also observably promoted in the overfeeding-induced groups [39–42]. Moreover, the numbers of lipid droplets in liver tissue were also increased in the overfeeding zebrafish [39, 41–43]. In addition, the overfeeding-induced zebrafish had more adipocytes accumulated in the abdominal cavity [44, 45]. Furthermore, visceral adipocytes were markedly larger in the obese group [42]. And our present study also showed that the visceral adipocytes were observably larger in the overfed-induced group. Based on those results, the visceral fat accumulation of grass carp was successfully induced with overfed in our study. The GIPR expression was significantly inhibited in the induced group in the present study. The previous studies showed that the GIPR mRNA and protein were observably decreased in the obese rats and women [32–34]. And the GIPR expression was markedly reduced in the hyperglycemic rats [36]. The decreased GIPR expression in the overfed-induced grass carp may be the response to the high serum glucose level in our study.
As the important endocrine cytokine, insulin and glucagon are involved in many physiological processes. In our study, the GIPR transcriptional level in grass carp primary hepatocyte was observably inhibited by treatment with insulin and was significantly induced by treatment with glucagon. A previous study revealed that GIPR(-/-) mice had impaired glucose tolerance and significantly reduced insulin gene expression and secretion compared with wild-type mice [10, 46, 47]. Similarly, the GIPR expression level in visceral fat of postmenopausal nondiabetic women was positively correlated with fasting insulin [34]. However, the culture medium addition of insulin can inhibit GIPR expression in the arterial smooth muscle cells [38]. These results reveal that the insulin is closely related to the GIPR expression level. It is rarely reported that the interactive correlation is between GIPR and glucagon. Glucagon is hyperglycemic in vivo in many fish species and induces glucose production in isolated hepatocytes [48]. The reason of glucagon reduced GIPR expression in our study may be the promoted glucose levels in grass carp hepatocytes by glucagon induction. And the regulation mechanism needs to be elucidated in future study.
In conclusion, the grass carp GIPR was cloned in our study. The GIPR transcriptional level was detected in all detected tissues and with high levels in the kidney, brain regions, and fat tissue of grass carp. A study of OGTT experiment showed that GIPR transcriptional level was dramatically inhibited by glucose treatment. In the fast and refeeding experiment, the GIPR mRNA levels were dramatically induced in the fast groups and were markedly reduced in the refeeding groups. In the overfed-induced grass carp, the GIPR transcriptional level was markedly reduced. In the grass carp hepatocyte, the GIPR transcriptional level was reduced by treatment with glucose and glucagon and was increased by treatment with oleic acid and insulin. To our knowledge, this study is the first biological report of GIPR in teleost.
## Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
## Conflicts of Interest
All the authors declare that they have no conflicts of interest.
## References
1. Musson M. C., Jepeal L. I., Finnerty J. R., Wolfe M. M.. **Evolutionary expression of glucose-dependent-insulinotropic polypeptide (GIP)**. (2011) **171** 26-34. DOI: 10.1016/j.regpep.2011.06.001
2. Baggio L. L., Drucker D. J.. **Biology of incretins: GLP-1 and GIP**. (2007) **132** 2131-2157. DOI: 10.1053/j.gastro.2007.03.054
3. Holst J. J., Rosenkilde M. M.. **Recent advances of GIP and future horizons**. (2020) **125, article 170230**. DOI: 10.1016/j.peptides.2019.170230
4. Yamada Y., Hayami T., Nakamura K.. **Human gastric inhibitory polypeptide receptor: cloning of the gene (GIPR) and cDNA**. (1995) **29** 773-776. DOI: 10.1006/geno.1995.9937
5. Usdin T. B., Mezey E., Button D. C., Brownstein M. J., Bonner T. I.. **Gastric inhibitory polypeptide receptor, a member of the secretin-vasoactive intestinal peptide receptor family, is widely distributed in peripheral organs and the brain**. (1993) **133** 2861-2870. DOI: 10.1210/endo.133.6.8243312
6. Yip R. G., Wolfe M. M.. **GIP biology and fat metabolism**. (2000) **66** 91-103. PMID: 10666005
7. Yasuda K., Inagaki N., Yamada Y., Kubota A., Seino S., Seino Y.. **Hamster gastric inhibitory polypeptide receptor expressed in pancreatic islets and clonal insulin-secreting cells: its structure and functional properties**. (1994) **205** 1556-1562. DOI: 10.1006/bbrc.1994.2844
8. Volz A., Goke R., Lankat-Buttgereit B., Fehmann H. C., Bode H. P., Goke B.. **Molecular cloning, functional expression, and signal transduction of the GIP-receptor cloned from a human insulinoma**. (1995) **373** 23-29. DOI: 10.1016/0014-5793(95)01006-Z
9. Buhren B. A., Gasis M., Thorens B., Muller H. W., Bosse F.. **Glucose-dependent insulinotropic polypeptide (GIP) and its receptor (GIPR): cellular localization, lesion-affected expression, and impaired regenerative axonal growth**. (2009) **87** 1858-1870. DOI: 10.1002/jnr.22001
10. Kim W., Egan J. M.. **The role of incretins in glucose homeostasis and diabetes treatment**. (2008) **60** 470-512. DOI: 10.1124/pr.108.000604
11. Campbell J. E.. **Targeting the GIPR for obesity: To agonize or antagonize? Potential mechanisms**. (2021) **46, article 101139**. DOI: 10.1016/j.molmet.2020.101139
12. Segerstolpe Å., Palasantza A., Eliasson P.. **Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes**. (2016) **24** 593-607. DOI: 10.1016/j.cmet.2016.08.020
13. Garg G., McGuigan F. E., Kumar J., Luthman H., Lyssenko V., Akesson K.. **Glucose-dependent insulinotropic polypeptide (**. (2016) **4** 23-27. DOI: 10.1016/j.bonr.2015.12.001
14. Musson M. C., Jepeal L. I., Mabray P. D., Zhdanova I. V., Cardoso W. V., Wolfe M. M.. **Expression of glucose-dependent insulinotropic polypeptide in the zebrafish**. (2009) **297** R1803-R1812. DOI: 10.1152/ajpregu.00288.2009
15. Graham G. V., Conlon J. M., Abdel-Wahab Y. H., Gault V. A., Flatt P. R.. **Evaluation of the insulinotropic and glucose-lowering actions of zebrafish GIP in mammalian systems: evidence for involvement of the GLP-1 receptor**. (2018) **100** 182-189. DOI: 10.1016/j.peptides.2017.11.007
16. Kaneko K., Fu Y., Lin H. Y.. **Gut-derived GIP activates central Rap1 to impair neural leptin sensitivity during overnutrition**. (2019) **129** 3786-3791. DOI: 10.1172/JCI126107
17. Adriaenssens A. E., Biggs E. K., Darwish T.. **Glucose-dependent insulinotropic polypeptide receptor-expressing cells in the hypothalamus regulate food intake**. (2019) **30** p. 987-+. DOI: 10.1016/j.cmet.2019.07.013
18. Pujadas G., Varin E. M., Baggio L. L.. **The gut hormone receptor GIPR links energy availability to the control of hematopoiesis**. (2020) **39, article 101008**. DOI: 10.1016/j.molmet.2020.101008
19. Miyawaki K., Yamada Y., Ban N.. **Inhibition of gastric inhibitory polypeptide signaling prevents obesity**. (2002) **8** 738-742. DOI: 10.1038/nm727
20. Svendsen B., Capozzi M. E., Nui J.. **Pharmacological antagonism of the incretin system protects against diet-induced obesity**. (2020) **32** 44-55. DOI: 10.1016/j.molmet.2019.11.018
21. Heimburger S. M., Bergmann N. C., Augustin R., Gasbjerg L. S., Christensen M. B., Knop F. K.. **Glucose-dependent insulinotropic polypeptide (GIP) and cardiovascular disease**. (2020) **125** p. 170174. DOI: 10.1016/j.peptides.2019.170174
22. McIntosh C. H. S., Widenmaier S., Kim S. J.. **Glucose-dependent insulinotropic polypeptide signaling in pancreatic**. (2012) **3** 96-106. DOI: 10.1111/j.2040-1124.2012.00196.x
23. Kubota A., Yamada Y., Yasuda K.. **Gastric inhibitory polypeptide activates MAP kinase through the wortmannin- sensitive and -insensitive pathways**. (1997) **235** 171-175. DOI: 10.1006/bbrc.1997.6743
24. Kim S. J., Nian C., McIntosh C. H.. **Activation of lipoprotein lipase by glucose-dependent insulinotropic polypeptide in adipocytes. A role for a protein kinase B, LKB1, and AMP-activated protein kinase cascade**. (2007) **282** 8557-8567. DOI: 10.1074/jbc.M609088200
25. Ussher J. R., Campbell J. E., Mulvihill E. E.. **Inactivation of the glucose-dependent insulinotropic polypeptide receptor improves outcomes following experimental myocardial infarction**. (2018) **27** 450-460.e6. DOI: 10.1016/j.cmet.2017.11.003
26. Yang G., Zhao W., Qin C.. **Igfbp3 in grass carp (**. (2020) **242, article 110394**. DOI: 10.1016/j.cbpb.2019.110394
27. Yang G., Liang X., Jiang Y.. **Myonectin in grass carp (**. (2022) **26, article 101306**. DOI: 10.1016/j.aqrep.2022.101306
28. Yang G. K., Liang X. M., Xu S. Y.. **Molecular identification of Igf3 and its roles in grass carp (**. (2022) **548** p. 737581. DOI: 10.1016/j.aquaculture.2021.737581
29. Yang G. K., Liang X. M., Qin C. B.. (2022) **548** p. 737682. DOI: 10.1016/j.aquaculture.2021.737682
30. Livak K. J., Schmittgen T. D.. **Analysis of relative gene expression data using real-time quantitative PCR and the 2**. (2001) **25** 402-408. DOI: 10.1006/meth.2001.1262
31. Yang G., Zhao W., Qin C.. **Molecular identification of grass carp**. (2020) **46** 1469-1482. DOI: 10.1007/s10695-020-00804-w
32. Piteau S., Olver A., Kim S. J.. **Reversal of islet GIP receptor down-regulation and resistance to GIP by reducing hyperglycemia in the Zucker rat**. (2007) **362** 1007-1012. DOI: 10.1016/j.bbrc.2007.08.115
33. Lynn F. C., Pamir N., Ng E. H., McIntosh C. H., Kieffer T. J., Pederson R. A.. **Defective glucose-dependent insulinotropic polypeptide receptor expression in diabetic fatty Zucker rats**. (2001) **50** 1004-1011. DOI: 10.2337/diabetes.50.5.1004
34. Rudovich N., Kaiser S., Engeli S.. **GIP receptor mRNA expression in different fat tissue depots in postmenopausal non-diabetic women**. (2007) **142** 138-145. DOI: 10.1016/j.regpep.2007.02.006
35. Zhou J., Livak M. F. A., Bernier M.. **Ubiquitination is involved in glucose-mediated downregulation of GIP receptors in islets**. (2007) **293** E538-E547. DOI: 10.1152/ajpendo.00070.2007
36. Lynn F. C., Thompson S. A., Pospisilik J. A.. **A novel pathway for regulation of glucose-dependent insulinotropic polypeptide receptor expression in**. (2003) **17** 91-93. DOI: 10.1096/fj.02-0243fje
37. Sugasawa T., Komine R., Manevich L., Tamai S., Takekoshi K., Kanki Y.. **Gene expression profile provides novel insights of fasting-refeeding response in zebrafish skeletal muscle**. (2022) **14** p. 2239. DOI: 10.3390/nu14112239
38. Berglund L. M., Lyssenko V., Ladenvall C.. **Glucose-dependent insulinotropic polypeptide stimulates osteopontin expression in the vasculature via endothelin-1 and CREB**. (2016) **65** 239-254. DOI: 10.2337/db15-0122
39. Oka T., Nishimura Y., Zang L.. **Diet-induced obesity in zebrafish shares common pathophysiological pathways with mammalian obesity**. (2010) **10** p. 21. DOI: 10.1186/1472-6793-10-21
40. Ran G., Ying L., Li L.. **Resveratrol ameliorates diet-induced dysregulation of lipid metabolism in zebrafish (**. (2017) **12**. DOI: 10.1371/journal.pone.0180865
41. Tainaka T., Shimada Y., Kuroyanagi J.. **Transcriptome analysis of anti-fatty liver action by Campari tomato using a zebrafish diet-induced obesity model**. (2011) **8** p. 88. DOI: 10.1186/1743-7075-8-88
42. Landgraf K., Schuster S., Meusel A.. **Short-term overfeeding of zebrafish with normal or high-fat diet as a model for the development of metabolically healthy versus unhealthy obesity**. (2017) **17** p. 4. DOI: 10.1186/s12899-017-0031-x
43. Turola E., Petta S., Vanni E.. **Ovarian senescence increases liver fibrosis in humans and zebrafish with steatosis**. (2015) **8** 1037-1046. DOI: 10.1242/dmm.019950
44. David C. J., Veena R., Kumaresan G.. **High cholesterol diet induces obesity in zebrafish**. (2016) **8, article e66970**
45. Hasumura T., Shimada Y., Kuroyanagi J.. **Green tea extract suppresses adiposity and affects the expression of lipid metabolism genes in diet-induced obese zebrafish**. (2012) **9** p. 73. DOI: 10.1186/1743-7075-9-73
46. Miyawaki K., Yamada Y., Yano H.. **Glucose intolerance caused by a defect in the entero-insular axis: a study in gastric inhibitory polypeptide receptor knockout mice**. (1999) **96** 14843-14847. DOI: 10.1073/pnas.96.26.14843
47. Pamir N., Lynn F. C., Buchan A. M.. **Glucose-dependent insulinotropic polypeptide receptor null mice exhibit compensatory changes in the enteroinsular axis**. (2003) **284** E931-E939. DOI: 10.1152/ajpendo.00270.2002
48. Moon T. W.. **Glucagon: from hepatic binding to metabolism in teleost fish**. (1998) **121** 27-34. DOI: 10.1016/S0305-0491(98)10108-6
|
---
title: Improvement of Fish Growth and Metabolism by Oligosaccharide Prebiotic Supplement
authors:
- Wei Xu
- Charles Greg Lutz
- Christopher M. Taylor
- Miriam Contin Ortega
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973164
doi: 10.1155/2022/5715649
license: CC BY 4.0
---
# Improvement of Fish Growth and Metabolism by Oligosaccharide Prebiotic Supplement
## Abstract
Finfish aquaculture is expected to continue to benefit from significantly improved fish diets, which are the source of energy to support the growth and health of fish. Strategies to enhance the transformation rate of dietary energy and protein to fish growth are greatly desired by fish culturists. Prebiotic compounds can be used as supplements to human, animal, and fish diets to populate beneficial bacteria in the gut. The goal of the present study is to identify low-cost prebiotic compounds with high efficacy in increasing the absorption of food nutrients by fish. Several oligosaccharides were evaluated as prebiotics in *Nile tilapia* (Oreochromis niloticus), one of the most widely cultured species in the world. Several parameters of the fish on different diets were evaluated, including feed conversion ratios (FCRs), enzymatic activities, expression of growth-related genes, and the gut microbiome. Two age groups of fish (30 days old and 90 days old) were used in this study. The results indicated that the addition of xylooligosaccharide (XOS), galactooligosaccharide (GOS), or XOS and GOS combination to the basic fish diet significantly decreased the feed conversion ratio (FCR) of the fish in both age groups. Both XOS and GOS decreased the FCR of 30-day-old fish by $34.4\%$ compared to the fish on the control diet. In the 90-day-old fish group, XOS and GOS decreased the FCR by $11.9\%$, while the combination of the two prebiotics led to a $20.2\%$ decrease in FCR compared to the control group. The application of XOS and GOS also elevated the production of glutathione-related enzymes and the enzymatic activity of glutathione peroxidase (GPX), indicating the enhancement of antioxidation processes in fish. These improvements were associated with significant changes in the fish gut microbiota. The abundance of Clostridium ruminantium, Brevinema andersonii, Shewanella amazonensis, Reyranella massiliensis, and Chitinilyticum aquatile were upregulated by XOS and GOS supplements. The findings of the present study suggested that the prebiotics would be more effective when they were applied to the younger fish, and the application of multiple oligosaccharide prebiotic compounds could result in a greater growth enhancement. The identified bacteria can be potentially used as probiotic supplements in the future to improve fish growth and feeding efficiency and ultimately reduce the cost of tilapia aquaculture.
## 1. Introduction
Advances in nutrition and feeding play essential roles in the sustained development of finfish aquaculture. How efficiently an aquaculture species can convert nutrients in the feed to body mass is a critical consideration in many perspectives. Maximizing feed conversion ratios (FCRs) reduces the amount of feed required in culture systems and ultimately minimizes the environmental impacts resulting from the unconsumed nutrients released from the system. Many internal and external factors can affect FCR in fish culture, such as feed ingredients, feeding methods, fish strains, fish physiology, and environment [1]. Improving FCR in aquaculture continues to be a priority; however, practical investigations in this area are difficult considering the complexity of internal and external factors.
Improvement of FCR on an individual basis can be achieved by increasing the net utilization of dietary inputs or limiting physical and metabolic activities [2]. Another factor that influences FCR for larger numbers of fish involves the timing and severity of mortality during a production cycle [2, 3].Given that limiting physical and metabolic activities often negatively affects fish health during culture, it is generally more feasible to focus on enhancing the utilization of fish feed. Such efforts are ongoing in many aquaculture fish species. The most common approach is to optimize dietary ingredients so that the digestibility and utilization of key nutrients can be maximized [4, 5]. However, the improvement of FCR through such formulation adjustment is typically marginal. Therefore, the improvement of FCR through enhancing nutrients' gastrointestinal (GI) absorption is increasingly important in aquaculture.
It is known that bacteria colonize internal and external surfaces of all metazoans including fish [6]. A number of metabolic processes in fish, including GI functions, have been shown to be associated with their microbial communities [7–9]. Changing GI microbiomes in individuals can dramatically change their physiological performance. Research previously conducted on human obesity demonstrated that transplantation of GI microbiomes from an obese human patient to the GI of a germ-free mouse resulted in increased body mass and signs of obesity in the mouse [10]. Studies on the influence of GI microbiomes on productivity in livestock and poultry have been widely performed since the development of the next-generation sequencing (NGS) technique. The GI microbiomes in these farmed animals are not only dynamically associated with their diets but also can be used as indicators reflecting the physiological conditions of the animals [11–14].
Compared to studies in mammals, fish microbiome research continues to lag well behind [6]. Most of the studies in fish were performed in zebrafish (Danio rerio) as a biomedical model [15, 16]. Current studies on fish GI microbiomes focus on aquaculture species, such as Siberian sturgeon (Acipenser baerii) [17], grouper (E. coioides) [18], rainbow trout (O. mykiss) [19], and Atlantic salmon (S. salar) [20]. The majority of these fish studies focused on the GI microbiomes with certain types of external or internal stresses. However, mechanistic studies in how GI microbiome changes influence the physiological status of fish are very rare. Therefore, methods to utilize the GI microbiota as tools to improve the quality of aquaculture fish species remain elusive.
Nonetheless, approaches to the manipulation of fish GI microbiomes toward beneficial communities have drawn increasing research interest. Several popular strategies have been used in some aquaculture fish species. Applications of prebiotics for fish health and growth performance have demonstrated great efficiency [21]. Prebiotics has been defined as non-digestible food ingredients that can regulate the growth of certain bacteria in GI tracts and consequently improve host health and growth [22]. The effect of prebiotic application has also been confirmed in Siberian sturgeon, in which the application of arabinoxylooligosaccharide prebiotics successfully stimulated the growth of beneficial bacteria, Lactobacillaceae, in the GI tract [23]. Many more prebiotics have been used in fish production, including insulin, fructooligosaccharides, short-chain fructooligosaccharides, mannanoligosaccharides, transgalactooligosaccharides, galactooligosaccharides, xylooligosaccharides, and isomaltooligosaccharides [24]. These components can be used to control the balances of a variety of bacterial families, which can be beneficial to fish growth and health.
Nile tilapia (Oreochromis niloticus) was used in this study based on its importance as an aquaculture species in the United States and globally. Originally from Africa and the Middle East, tilapias are cultured worldwide and have become the second most farmed fish behind carps. The annual worldwide production of farmed tilapia exceeds 4.8 million tonnes with an estimated market value of over 8.2 billion US dollars [25]. Tilapia production has been leading freshwater aquaculture in many tropical countries and areas, such as China, the Philippines, Indonesia and Thailand, which are major suppliers [25]. The production of tilapia in the United States consistently increased before year 2003; however, dramatically declined since that time. Compared to 2003 when the production of tilapia in the U.S. hit the highest level in history (>320,000 tonnes), this value was reduced by $45\%$ (<180,000 tonnes) in 2013. Low profit caused by the cost of diet in tilapia farming is the main reason causing the reduction of tilapia culture in the U.S.
To improve the utilization rate of fish diet, several oligosaccharides were supplemented to a commercial fish diet as prebiotic components. The impacts of the prebiotic components on the FCR of fish and the dynamics of gut microbiomes were evaluated. Results from this study will help advance an understanding of the importance of the fish gut microbiota in fish growth. These findings may contribute to the development of supplementary products for fish feed to advance the economic and environmental sustainability of finfish aquaculture production.
## 2.1. Diet Preparation
The prebiotic compounds were mixed with the fish diet AquaXcel Starter 5014 (0.8 mm) purchased from Cargill Animal Nutrition (Wayzata, MN). Each of the four tested prebiotic compounds, fructooligosaccharide (FOS), isomaltooligosaccharide (IOS), xylooligosaccharide (XOS), and galactooligosaccharide (GOS), was mixed separately with the diet at $5\%$ (5 g of probiotics in every 100 g diet). Crisco Pure Vegetable Oil (Parsippany, NJ) at $1\%$ (v/w) was used to maintain the attachment of prebiotic powder to the surface of the diet. Accordingly, an additional diet was prepared utilizing vegetable oil without prebiotics as well as a control diet with no oil added, for a total of six diets. All diets were kept at 4 °C at all times except for the time of fish feeding.
Based on initial results, a second trial was conducted utilizing $5\%$ XOS, $5\%$ GOS, and, separately, a combination of XOS and GOS with each at $2.5\%$ by weight, as well as the control diet with vegetable oil.
## 2.2. Animal Housing and Experimental Design
Fingerlings (O. niloticus) were purchased from the Louisiana Specialty Aquafarm (Tangipahoa, LA) and maintained at the Louisiana State University Agricultural Center (LSU AgCenter), Aquaculture Research Station (Baton Rouge, LA). Permit approval for possession of *Nile tilapia* was obtained from the Louisiana Department of Wildlife and Fisheries. The fish housing and handling protocol were reviewed and approved by the LSU Agricultural Center Institutional Animal Care & Use Committee (IACUC).
In the first trial, each of the diets described above was fed to one-month-old O. niloticus fingerlings (2 g average weight) for a period of 90 days. Five replicate 45 L tanks were utilized for each diet, for a total of 30 tanks in a single 1850 L recirculating system with a reservoir sump and a biomechanical floating bead filter. Each tank had independent water supply. Initially, fish were fed to apparent satiation three times daily during the first 14 days, and twice daily thereafter, following the grower's recommendation. Any noticeable unconsumed feed was removed after approximately one hour after each feeding. The total weight of feed fed was recorded daily for each tank, as was any mortality. The temperature was maintained at 27 ± 2°C with a light/dark cycle 12 h:12 h. The water quality was monitored weekly throughout the study to comply with recommended values for *Nile tilapia* culture (pH 7.5-8, 220 + 20 mg/l alkalinity, 280 + 15 mg/l total hardness, and 0.3-0.4 g/l chlorides).
Based on results from the first trial, the second trial was conducted utilizing the four diet treatments described above and 3-month old juvenile fish (90 g average weight) stocked in 280 L tanks. Two 2750 L recirculating systems were used for the growth trial, each with eight tanks, a reservoir sump, and a biomechanical bead filter, and each system included two replicate tanks per treatment. Similar to Trial I, the tanks in Trial II had independent water supplies. The growth trial lasted for 84 days, and as in the first trial, fish were fed to apparent satiation twice daily, and any noticeable unconsumed feed was removed after approximately one hour. The total weight of feed fed was recorded daily for each tank, as was any mortality. The temperature was maintained at 28 ± 2°C, and water quality was monitored and adjusted weekly. The fish larvae from each trial were randomly selected from the fish farm. The first and second trials on fish larvae were independent of each other, and no comparisons were made between the results from the two trials.
## 2.3. Growth Evaluation
At the end of each growth trial, the fish were anesthetized with 50 mg/L tricaine methane-sulfonate (MS-222) buffered with NaHCO3 solution (pH 7.2-7.4). The length (cm) and weight (g) of each fish were measured followed by blood withdrawal. The body mass index (BMI) of each fish is calculated following the equation, BMI = weight (kg)/[height (m)]2. Feed conversion ratio (FCR) is calculated for each tank of fish with the following equation: FCR = total weight of applied feed (g)/gain of fish weight (g). The weight of the diet fed to the fish in each tank was recorded daily, and the total weight of the diet during the experiment was calculated. The total wet weight of the fish in each tank was measured before and after the experiment to calculate weight gain for each replicate tank within a dietary treatment. The total bodyweight of the fish in each tank was measured before and after the experiment to calculate the gain of body weight for the fish in each tank.
## 2.4. Blood and Organ Collections
Upon the completion of the fish measurement, the anesthetized fish was used for blood collection via the caudal vein with 21 Ga syringes [26]. The tubes used for blood collection contained 25 USP unit heparin (50 μL 500 units/mL heparin solution), and the syringes for blood withdrawal were rinsed with 500 units/mL heparin solution briefly. Approximately 1 mL of blood was collected from each fish individual using this method. Thereafter, the fish was dissected and approximate 200 mg of liver was collected and preserved in Trizol reagent (Thermo-Fisher) for RNA extraction. The whole content of GI tract was collected and preserved in the lysis buffer from the DNeasy PowerSoil Pro Kit (Qiagen) followed by the bacterial DNA extraction as outlined in the protocol of the kit.
## 2.5. Gut Microbiome Analysis
The purified gut microbial DNA samples were sent to the Microbial Genomics Resource Group (MGRG) within the LSU Health Sciences Center School of Medicine in New Orleans. The DNAs were first amplified by a pair of commonly used bacterial 16S rRNA gene PCR primers (Supplementary Data 1) targeting the highly variable V4 region across bacterial species [27, 28]. The amplified products were subjected to the Illumina MiSeq high-throughput sequencer for sequencing. Data analyses with the sequences were also performed by the MGRG. Briefly, all the sequences obtained from the Illumina sequencer were preprocessed to remove reads with low-quality, ambiguous bases, and short lengths (<240 bp). The reads passing the quality control were processed through the DADA2 algorithm [29] implemented in QIIME 2 [30].Taxonomic assignment was performed using the SILVA database v138 [31]. Identified bacteria with known names of species, genera, and families were used to construct heatmaps to demonstrate their relative abundance.
## 2.6. Quantification of Growth-Related Transcripts
The transcripts of selected growth-related genes were quantified using quantitative PCR (qPCR) analysis with liver tissues. The RNA isolation was performed using TRIzol followed by DNA removal using TURBO DNA-free™ Kit and RNA cleaning with Qiagen RNeasy Mini Kit. The cDNA of each RNA sample was synthesized using the Invitrogen SuperScript IV Reverse Transcriptase system (Thermo-Fisher). The transcript levels of the genes encoding glutathione S-transferase (gst), glutathione peroxidase (gpx), glutathione-disulfide reductase (gsr), growth hormone receptor II (ghr2), catalase (cat), superoxide dismutase (sod), fatty acid synthase (fas), acetyl-CoA carboxylase β (acacb), and carnitine palmitoyltransferase 1 (cpt1) were analyzed using qPCR on a QuantStudio 3 Real-Time Thermocycler (Applied Biosystems, Waltham, MA) with the following cycling conditions: 50 °C for 2 min; 94 °C for 2 min; and 40 temperature cycles including 30 s of 94 °C, 30 s of 55 °C, and 30 s of 72 °C. The primers for qPCR are listed in Supplementary Data 1. The Ct values of all selected genes in all samples were normalized using the Ct values from a housekeeping gene, glyceraldehyde-3-phosphate dehydrogenase (gapdh), and relative transcript levels of each gene in livers from the fish with different diets were calculated using the 2-ΔΔCt method [32].
## 2.7. Activities of Growth and Immune-Related Enzymes
The enzymatic analyses were performed on four enzymes, including pyruvate kinase, glutathione peroxidase, superoxide dismutase, and catalase, using the EnzyChrom™ enzyme activity kits (BioAssay Systems, Hayward, CA). Blood withdrawn from each fish (1 mL) was centrifuged to separate the serum from the blood cells. 500 mL serum from each blood sample was collected and used for the enzymatic analysis following the manufacturers' instructions for the kits. The activity of each enzyme with treatment was calculated using the standard curves.
## 2.8. Statistical Analyses
Basic descriptive statistics for survival- and growth-related traits in each trial were calculated in Microsoft Excel. Prior to the statistical analyses, all data generated from different qPCR, enzymatic, and microbiome assays were all tested using the Shapiro–Wilk test [33] for the normality tests and Bartlett's test [34] for the homogeneity tests. All data generated from this study were confirmed to be normally distributed with good homogeneities and eligible for the ANOVA tests. Multiple comparisons between treatment and control groups were performed using one-way ANOVA and Tukey's tests with least-squares means. One-way ANOVA tests were performed with the data of fish growth (weight, length, BMI, and FCR), qPCR, and enzymatic analyses. All analyses were done in R.
## 3.1. O. niloticus growth Performance with Prebiotic Supplements
Four prebiotic compounds were tested with one-month-old O. niloticus larvae in the first growth experiment. Vegetable oil was used to mix the prebiotics with the basic fish diet. In the first trial, survival did not differ significantly among treatments ($$p \leq 0.81$$), indicating that FCR and growth values were valid for statistical comparisons and not simply artifacts of density effects resulting from differential survival. There was no significant difference in weight (Figure 1(a)), body length (Figure 1(b)), BMI (Figure 1(c)), or FCR (Figure 1(d)), between the fish larvae fed with the basic diet only and those fed on a basic diet containing vegetable oil. Therefore, the basic diet with vegetable oil was used as the control diet for the fish in the other experiment (Figure 2). Like the vegetable oil, adding FOS, IOS, XOS, or GOS to the fish diet did not significantly improve the fish weight, body, length, or BMI (Figures 1(a)–1(c)). However, the XOS and GOS reduced the FCR from 1.83 ± 0.24 (basic diet + vegetable oil) to 1.20 ± 0.02 ($$p \leq 0.005$$) and 1.20 ± 0.03 ($$p \leq 0.01$$), respectively (Figure 1(d)). The total weight of the diet fed in each tank during the trial showed less diet was used for the fish supplemented by prebiotics. Compared to the control diet, the average weight of FOS, IOS, XOS, and GOS diet for each tank reduced by 12.3, 12.8, 19.3, and $19.0\%$, respectively (*Supplementary data* 2). No significant difference was found between the control and vegetable oil diets.
The combination of XOS and GOS was tested in the second fish growth experiment (Figure 2). The juvenile O. niloticus used in this study were three months old. Similar to the results from one-month-old fish, the three-month juveniles did not gain more bodyweight (Figure 2(a)), length (Figure 2(b)), or BMI (Figure 2(c)) when fed with XOS, GOS, or the combination of the two prebiotics compared to those fed on the control diet. However, XOS, GOS, and their combination reduced the FCR of the basic diet from 1.09 ± 0.03 to 0.96 ± 0.01 ($$p \leq 0.01$$), 0.96 ± 0.03 ($$p \leq 0.01$$), and 0.87 ± 0.02 ($$p \leq 0.0001$$), respectively (Figure 2(d)). Similarly, the application of XOS, GOS, and XOS + GOS to the diet significantly reduced the amount of diet fed per tank during the trial compared to the vegetable oil diet with a decrease of 17.8, 17.3, and $27.9\%$, respectively (*Supplementary data* 2). Direct effects and interactions resulting from the two separate recirculating systems were not statistically significant and were not included in subsequent data analysis. As in the first trial, survival did not differ significantly among treatments ($$p \leq 0.43$$), indicating FCR and growth values were valid for statistical comparisons.
## 3.2. Gene Transcript Analyses
Similar to the fish growth performance results, supplementing vegetable oil to the fish basic diet did not alter the expression level of any genes tested in this study. The expression levels of glutathione S-transferase encoding gene (gst) in livers of fish from the IOS, XOS, and GOS treatment were significantly higher than those with the control diet, with 3.50 ± 0.73 ($$p \leq 0.018$$), 3.34 ± 0.54 ($$p \leq 0.034$$), and 3.70 ± 0.77 ($$p \leq 0.047$$) fold changes, respectively (Figure 3(a)). The transcripts of another glutathione-related protein, glutathione peroxidase (gpx), also increased in the fish fed with XOS and GOS with fold changes 2.32 ± 0.48 ($$p \leq 0.006$$) and 2.08 ± 0.23 ($$p \leq 0.043$$), respectively (Figure 3(b)). In addition, the glutathione-disulfide reductase encoding gene (gsr) was only upregulated by XOS with a 2.91 ± 0.43 fold ($$p \leq 0.045$$) change compared to control (Figure 3(c)). Two other genes encoding growth hormone receptor II (ghr-2) and catalase (cat) were only upregulated by GOS. The transcript of ghr-2 in GOS treated group was 2.63 ± 0.71 ($$p \leq 0.031$$) times of that in the control diet group (Figure 3(d)). The transcription level of cat in GOS group was 3.32 ± 0.60 ($$p \leq 0.007$$) folds of that in control group (Figures 3(e) and 3(f)). Other tested genes did not show notable up- or downregulation in any prebiotic treatment groups compared to the control diet (Figures 3(g)–3(i)).
## 3.3. Enzyme Activities in Fish Sera
Activities of selected enzymes were tested with the serum samples from the three-month juvenile fish in the second trial. The activity of glutathione peroxidase (GPX) in the fish treated with the control diet was 3.49 ± 0.25 U/mL. The GPX activities in XOS and XOS + GOS diet groups were 4.69 ± 0.30 and 4.97 ± 0.44 U/mL, respectively (Figure 4(a)), which were significantly higher than the control group ($$p \leq 0.017$$ and 0.001, respectively). The GOS did not enhance the GPX activity in fish sera compared to the control diet (Figure 4(a)). The pyruvate kinase activity was only enhanced by the diet with addition of XOS + GOS from 0.21 ± 0.03 U/mL (control) to 0.38 ± 0.05 U/mL ($$p \leq 0.001$$) (Figure 4(b)). The two other enzymes, superoxide dismutase (Figure 4(c)) and catalase (Figure 4(d)), were not significantly affected by any diet.
## 3.4. Microbiome Change in Response to Prebiotics
The bacterial species, genera, orders, families, or classes were identified based on the sequencing data. There were 35 bacterial species that were identified from this study (Figure 5(a)). Among these species, five were found to be upregulated by the supplement of XOS, GOS, or XOS + GOS. Clostridium ruminantium was significantly higher in the fish fed with GOS or XOS + GOS (Figure 5(b)). Clostridium ruminantium in control (vegetable oil) was 57 ± 11, while in GOS and XOS + GOS, it was elevated to 299 ± 108 ($$p \leq 0.043$$) and 348 ± 75 ($$p \leq 0.020$$), respectively. Four other bacteria species, including *Brevinema andersonii* (Figure 5(c)), *Shewanella amazonensis* (Figure 5(d)), *Reyranella massiliensis* (Figure 5(e)), and Chitinilyticum aquatile (Figure 5(f)), were found upregulated in XOS + GOS diet treatment compared to the control diet. B. andersonii, S. amazonensis, R. massiliensis, and C. aquatile changed from 18 ± 5, 30 ± 7, 24 ± 9, and 13 ± 3 with control diet to 142 ± 21 ($$p \leq 0.043$$), 91 ± 28 ($$p \leq 0.049$$), 62 ± 12 ($$p \leq 0.044$$), and 45 ± 11 ($$p \leq 0.037$$) with XOS + GOS diet, respectively. In addition, 159 genera, 58 orders, 94 families, and 42 classes were identified from this analysis. The top candidates of genera are shown in Supplementary Data 3.
## 4. Discussion
In recent decades, efforts in the development of prebiotics for aquaculture have been more focused on oligosaccharides, which are often present in plants. The efficacies of some oligosaccharide compounds have been confirmed in many aquacultured fish. Oligosaccharide supplemented diets were reported to cause diarrhea in rainbow trout and Atlantic salmon (Refstie et al.; [ 35]). The application of mannanoligosaccharides (MOS) significantly decreased the apparent digestibility coefficient (ADC) of lipids while increasing the ADCs of protein, organic matter, and carbohydrates in red drum [36]. Results suggest that the performance and application of prebiotics can be improved to minimize negative effects on fish. The application of arabinoxylooligosaccharide prebiotics successfully stimulated the growth of beneficial bacteria, Lactobacillaceae, in the GI tract of Siberian sturgeon [23]. Other oligosaccharides have also been studied in fish production, including FOS, GOS, transgalactooligosaccharides (tGOS), XOS, and IOS with varied results [24].
Four oligosaccharides were evaluated in the present study, FOS, IOS, XOS, and GOS. Results of both fish growth experiments with different age groups of O. niloticus did not indicate significant differences in weight, body length, or BMI between different diet treatments. However, the total feed fed in XOS, GOS, and the combination XOS and GOS treatments was considerably less than in the control diet, resulting in significantly lower FCRs. In one-month-old fingerlings, XOS and GOS decreased FCR by $35.0\%$ and $34.4\%$, respectively. In three-month-old juveniles, both XOS and GOS reduced the FCR by $12.0\%$. The combination of XOS and GOS decreased FCR even more, by $20.0\%$ compared to the control diet. The combination of XOS and GOS improved FCR values compared to each one separately, although the statistical analysis did not indicate a significant difference (p values of XOS + GOS vs. XOS and XOS + GOS vs. GOS were 0.065 and 0.070, respectively). The addition of prebiotic compounds to fish diets demonstrated greater benefits in the first trial with younger fish, suggesting prebiotic supplementation may be of greater value during the early development stages of fish.
The addition of XOS or GOS enhanced the expression levels of several glutathione-related genes including gst, gpx, and gsr, which encode glutathione S-transferase (GST), glutathione peroxidase (GPX), and glutathione-disulfide reductase (GSR), respectively. Since glutathione is an antioxidant compound in animals, protecting cellular components from reactive oxygen species (ROS), the upregulation of those glutathione-related proteins suggested the activation of the antioxidation pathway by these prebiotic compounds. In this pathway, GST promotes the conjugation of glutathione to toxicants. GPX catalyzes the oxidation of glutathione, which is associated with the removal of hydrogen peroxide (H2O2) from cells to reduce the levels of peroxide radicals [37].While GPX removes H2O2, the reduced glutathione (GSH) is oxidated to become glutathione disulfide (GSSG). As a reductase, GSR reduces GSSG to GSH with the hydrogen ion (H+) provided by the nicotinamide adenine dinucleotide phosphate (NADPH). GSH is known to play a key role in detoxification by forming the GS-conjugated construct, which is catalyzed by GST, the third glutathione-related enzyme in this study. The upregulation of the genes encoding these three proteins suggests that XOS and GOS are involved in antioxidation and detoxification in O. niloticus.
The activity of GPX in serum samples of fish from different prebiotic treatment groups was also measured. As a biomarker for antioxidation, GPX demonstrated enhanced activities in fish fed XOS ($34\%$ increase) and XOS + GOS ($42\%$ increase) compared to that in the fish from the control group. This confirmed the function of XOS in antioxidation in O. niloticus, as suggested by the qPCR result. Although GOS did not show a significant effect on the enzyme activity of GPX (Figure 4(a)), the supplement of GOS along with XOS appeared to further increase GPX activity in fish. Interestingly, the activity of pyruvate kinase was also improved by the combination of XOS and GOS supplements (Figure 4(b)). Pyruvate kinase is known to be an enzyme catalyzing the generation of pyruvate and adenosine triphosphate (ATP) from phosphoenolpyruvate (V. [38]) as the last step of glycolysis. Since ATP is the major energy source for cells, the function of pyruvate kinase is considered to be related to energy generation in organisms, which explains the decreased FCR in XOS + GOS treated fish.
The dynamics of fish GI microbiomes are known to be critical in the maintenance of overall fish health [39], metabolism [40], and physiological condition [28]. Among fish GI microbiome studies, many have focused on how diet influences bacterial community composition. Bacterial communities influence the utilization of various nutrients in the feed. More efficient utilization of nutrients will improve FCR values in cultured fish, which consequently enhances environmental and economic sustainability in aquaculture. In addition, the fish with different prebiotic supplement demonstrated some changes in eating behavior. The fish feed was added to each tank progressively with careful observation to avoid overfeeding, which could affect the accuracy of FCR calculation. Fish supplied with the prebiotic compounds consumed less food than the control groups. It is very likely to be the consequence of the shift of gut microbiota caused by the prebiotics. To this end, investigations focusing on the fish GI microbiome are increasingly recognized as critical steps toward the improvement of fish production in aquaculture.
In the present study, the numbers of many bacterial species were found to be enhanced in the gut of the fish with XOS + GOS treatment. The top five bacteria were C. ruminantium, B. andersonii, S. amazonensis, R. massiliensis, and C. aquatile. Clostridium species have been identified as a predominant cluster of gut bacteria in many species [41]. They are also in the guts of many fish species, working as symbionts [42]. A previous study on the prebiotic effects of arabinoxylan oligosaccharides (AXOS) in Siberian sturgeon (Acipenser baerii) showed an upregulation of C. ruminantium in the gut microbial population with AXOS supplementation in a fish diet [17]. B. andersonii was also previously reported as a major gut microbial species in O. niloticus when the fish was fed with probiotics [43]. Similarly, the Atlantic salmon (Salmo salar) fed with an alginate supplemented diet carried more B. andersonii in the gut (S. [44]). B. andersonii is a beneficial bacterial species to many organisms since it is necessary for the production of butyrate, which is a critical short-chain fatty acid for the health of the digestive system (S. [44, 45]).
The other three bacteria species, S. amazonensis, R. massiliensis, and C. aquatile, have not been widely studied in fish digestive tracts and metabolism. Limited studies suggest that Shewanella species may contribute to the synthesis of omega-3 fatty acids in freshwater fish [46] and the protection of fish from infection of the Betanodavirus [47] and Vibrio [48]. The symbiotic growth of *Reyranella bacteria* in the gut of tropical gar, Atractosteus tropicus, was believed to have positive effects on the survival of adult fish in an adverse environment [49]. The potential roles of C. aquatile in fish have not been understood despite the strong chitinolytic activity of Chitinilyticum bacteria. Many *Chitinilyticum bacteria* were isolated from freshwater shrimp ponds, including C. aquatile [50, 51].
The application of selected oligosaccharides in the fish diet improved FCR over the basic diet in O. niloticus. More efforts should be made in the modification of supplemented prebiotics in fish diets. First, to allow ingestion, prebiotic components must be maintained in the feed for a certain amount of time without being dissolved in water. Compared to the usage of prebiotics in humans and livestock, the delivery of prebiotic compounds in aquaculture is challenging due to the unique aquatic environment. Secondly, although the prebiotic compounds in fish diets need to remain integrated without being dissolved in the surrounding water, the compounds have to be soluble in water once the feed is taken by the fish. This is critical to the efficient absorption of prebiotics. Therefore, the solubility of prebiotic compounds in water must also be considered. Finally, prebiotic compounds should remain functional for a relatively long time to provide a consistent and sustainable effect on the host. Considering the complexities of fish GI environments, a prebiotic compound with more resistance to enzymatic digestion and consumption by microbes in the GI tract is desired to provide a more sustained effect on nutrient absorption.
## 5. Conclusions
The present study demonstrated the benefit of prebiotic compounds in the improvement of FCR in O. niloticus culture. Compared to fish in the control diet treatment, fingerling and juvenile O. niloticus demonstrated similar growth performance while consuming a reduced amount of feed supplemented with XOS and GOS prebiotic compounds. The addition of these compounds enhanced the production of glutathione-related proteins, which suggested they contributed to antioxidation and detoxification in O. niloticus. The upregulation of GPX activity in fish sera associated with XOS and GOS supplementation supported the role of these two prebiotics in host detoxification. *These* genetic and physiological changes appear to be attributed to changes in gut microbiota. The bacterial species with enhanced profiles associated with XOS and GOS can potentially be used as probiotic candidates in O. niloticus production in the future.
## Data Availability
The sequencing data used to support the findings of this study are available from the corresponding author upon request.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## References
1. Robinson E. H., Li M. H.. (2015.0)
2. Mengistu S. B., Mulder H. A., Benzie J. A. H., Komen H.. **A systematic literature review of the major factors causing yield gap by affecting growth, feed conversion ratio and survival in Nile tilapia (Oreochromis niloticus)**. (2020.0) **12** 524-541. DOI: 10.1111/raq.12331
3. Besson M., Komen H., Rose G., Vandeputte M.. **The genetic correlation between feed conversion ratio and growth rate affects the design of a breeding program for more sustainable fish production**. (2020.0) **52** p. 5. DOI: 10.1186/s12711-020-0524-0
4. Adeshina I., Jenyo-Oni A., Emikpe B. O., Ajani E. K., Abdel-Tawwab M.. **Stimulatory effect of dietary clove, Eugenia caryophyllata, bud extract on growth performance, nutrient utilization, antioxidant capacity, and tolerance of African catfish, Clarias gariepinus (B.), to Aeromonas hydrophila infection**. (2019.0) **50** 390-405. DOI: 10.1111/jwas.12565
5. Sveier H., Wathne E., Lied E.. **Growth, feed and nutrient utilisation and gastrointestinal evacuation time in Atlantic salmon (Salmo salar L.): the effect of dietary fish meal particle size and protein concentration**. (1999.0) **180** 265-282. DOI: 10.1016/S0044-8486(99)00196-9
6. Llewellyn M. S., Boutin S., Hoseinifar S. H., Derome N.. **Teleost microbiomes: the state of the art in their characterization, manipulation and importance in aquaculture and fisheries**. (2014.0) **5** p. 207. DOI: 10.3389/fmicb.2014.00207
7. Morgan X. C., Tickle T. L., Sokol H.. **Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment**. (2012.0) **13** p. R79. DOI: 10.1186/gb-2012-13-9-r79
8. Perez-Cobas A. E., Gosalbes M. J., Friedrichs A.. **Gut microbiota disturbance during antibiotic therapy: a multi-omic approach**. (2013.0) **62** 1591-1601. DOI: 10.1136/gutjnl-2012-303184
9. Turnbaugh P. J., Ley R. E., Mahowald M. A., Magrini V., Mardis E. R., Gordon J. I.. **An obesity-associated gut microbiome with increased capacity for energy harvest**. (2006.0) **444** 1027-1031. DOI: 10.1038/nature05414
10. Ridaura V. K., Faith J. J., Rey F. E.. **Gut microbiota from twins discordant for obesity modulate metabolism in mice**. (2013.0) **341**. DOI: 10.1126/science.1241214
11. Heinritz S. N., Weiss E., Eklund M.. **Intestinal microbiota and microbial metabolites are changed in a pig model fed a high-fat/low-fiber or a low-fat/high-fiber diet**. (2016.0) **11**. DOI: 10.1371/journal.pone.0154329
12. Hernandez-Sanabria E., Goonewardene L. A., Wang Z., Durunna O. N., Moore S. S., Guan L. L.. **Impact of feed efficiency and diet on adaptive variations in the bacterial community in the rumen fluid of cattle**. (2012.0) **78** 1203-1214. DOI: 10.1128/AEM.05114-11
13. Stanley D., Denman S. E., Hughes R. J.. **Intestinal microbiota associated with differential feed conversion efficiency in chickens**. (2012.0) **96** 1361-1369. DOI: 10.1007/s00253-011-3847-5
14. Stanley D., Hughes R. J., Geier M. S., Moore R. J.. **Bacteria within the gastrointestinal tract microbiota correlated with improved growth and feed conversion: challenges presented for the identification of performance enhancing probiotic bacteria**. (2016.0) **7** p. 187. DOI: 10.3389/fmicb.2016.00187
15. Rawls J. F., Mahowald M. A., Ley R. E., Gordon J. I.. **Reciprocal gut microbiota transplants from zebrafish and mice to germ-free recipients reveal host habitat selection**. (2006.0) **127** 423-433. DOI: 10.1016/j.cell.2006.08.043
16. Roeselers G., Mittge E. K., Stephens W. Z.. **Evidence for a core gut microbiota in the zebrafish**. (2011.0) **5** 1595-1608. DOI: 10.1038/ismej.2011.38
17. Geraylou Z., Souffreau C., Rurangwa E.. **Prebiotic effects of arabinoxylan oligosaccharides on juvenile Siberian sturgeon (Acipenser baerii) with emphasis on the modulation of the gut microbiota using 454 pyrosequencing**. (2013.0) **86** 357-371. DOI: 10.1111/1574-6941.12169
18. Sun Y., Yang H., Ling Z., Chang J., Ye J.. **Gut microbiota of fast and slow growing grouper Epinephelus coioides**. (2009.0) **3** 713-720
19. Huber I., Spanggaard B., Appel K. F., Rossen L., Nielsen T., Gram L.. **Phylogenetic analysis and in situ identification of the intestinal microbial community of rainbow trout (Oncorhynchus mykiss, Walbaum)**. (2004.0) **96** 117-132. DOI: 10.1046/j.1365-2672.2003.02109.x
20. Skrodenyte-Arbaciauskiene V., Sruoga A., Butkauskas D., Skrupskelis K.. **Phylogenetic analysis of intestinal bacteria of freshwater salmon Salmo salar and sea trout Salmo trutta trutta and diet**. (2008.0) **74** 1307-1314. DOI: 10.1111/j.1444-2906.2008.01656.x
21. Gatesoupe F. J.. **Probiotics and other microbial manipulations in fish feeds: prospective health benefits**. (2010.0)
22. Roberfroid M.. **Prebiotics: the concept revisited**. (2007.0) **137** 830S-837S. DOI: 10.1093/jn/137.3.830S
23. Geraylou Z., Souffreau C., Rurangwa E.. **Effects of arabinoxylan-oligosaccharides (AXOS) on juvenile Siberian sturgeon (Acipenser baerii) performance, immune responses and gastrointestinal microbial community**. (2012.0) **33** 718-724. DOI: 10.1016/j.fsi.2012.06.010
24. Ringø E., Dimitroglou A., Hoseinifar S. H., Davies S. J.. **Prebiotics in Finfish: An Update**. (2014.0). DOI: 10.1002/9781118897263.ch14
25. **Fisheries and Aquaculture Information and Statistics Service**
26. Lawrence M. J., Raby G. D., Teffer A. K.. **Best practices for non-lethal blood sampling of fish via the caudal vasculature**. (2020.0) **97** 4-15. DOI: 10.1111/jfb.14339
27. Tamaki H., Wright C. L., Li X.. **Analysis of 16S rRNA amplicon sequencing options on the Roche/454 next-generation titanium sequencing platform**. (2011.0) **6**. DOI: 10.1371/journal.pone.0025263
28. Ye L., Amberg J., Chapman D., Gaikowski M., Liu W. T.. **Fish gut microbiota analysis differentiates physiology and behavior of invasive Asian carp and indigenous American fish**. (2014.0) **8** 541-551. DOI: 10.1038/ismej.2013.181
29. Callahan B. J., McMurdie P. J., Rosen M. J., Han A. W., Johnson A. J., Holmes S. P.. **DADA2: high-resolution sample inference from Illumina amplicon data**. (2016.0) **13** 581-583. DOI: 10.1038/nmeth.3869
30. Bolyen E., Rideout J. R., Dillon M. R.. **Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2**. (2019.0) **37** 852-857. DOI: 10.1038/s41587-019-0209-9
31. Quast C., Pruesse E., Yilmaz P.. **The SILVA ribosomal RNA gene database project: improved data processing and web-based tools**. (2012.0) **41** D590-D596. DOI: 10.1093/nar/gks1219
32. Livak K. J., Schmittgen T. D.. **Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method**. (2001.0) **25** 402-408. DOI: 10.1006/meth.2001.1262
33. David F. N., Johnson N. L.. **The effect of non-normality on the power function of the F-test in the analysis of variance**. (1951.0) **38** 43-57. DOI: 10.1093/biomet/38.1-2.43
34. Bartlett M. S.. **Properties of sufficiency and statistical tests**. (1937.0) **160** 268-282. DOI: 10.1098/rspa.1937.0109
35. Refstie S., Sahlström S., Bråthen E., Baeverfjord G., Krogedal P.. **Lactic acid fermentation eliminates indigestible carbohydrates and antinutritional factors in soybean meal for Atlantic salmon (Salmo salar)**. (2005.0) **246** 331-345. DOI: 10.1016/j.aquaculture.2005.01.001
36. Burr G., Hume M., William H., Neill W. H., Gatlin D. M. I.. **Effects of prebiotics on nutrient digestibility of a soybean-meal-based diet by red drum Sciaenops ocellatus (Linnaeus)**. (2008.0) **39** 1680-1686. DOI: 10.1111/j.1365-2109.2008.02044.x
37. Weydert C. J., Cullen J. J.. **Measurement of superoxide dismutase, catalase and glutathione peroxidase in cultured cells and tissue**. (2010.0) **5** 51-66. DOI: 10.1038/nprot.2009.197
38. Gupta V., Bamezai R. N.. **Human pyruvate kinase M2: a multifunctional protein**. (2010.0) **19** 2031-2044. DOI: 10.1002/pro.505
39. Gómez G. D., Balcázar J. L.. **A review on the interactions between gut microbiota and innate immunity of fish**. (2008.0) **52** 145-154. DOI: 10.1111/j.1574-695X.2007.00343.x
40. Ganguly S., Prasad A.. **Microflora in fish digestive tract plays significant role in digestion and metabolism**. (2012.0) **22** 11-16. DOI: 10.1007/s11160-011-9214-x
41. Nagano Y., Itoh K., Honda K.. **The induction of Treg cells by gut-indigenous Clostridium**. (2012.0) **24** 392-397. DOI: 10.1016/j.coi.2012.05.007
42. Clements K. D., Pasch I. B. Y., Moran D., Turner S. J.. **Clostridia dominate 16S rRNA gene libraries prepared from the hindgut of temperate marine herbivorous fishes**. (2007.0) **150** 1431-1440. DOI: 10.1007/s00227-006-0443-9
43. Maas R. M., Deng Y., Dersjant-Li Y.. **Exogenous enzymes and probiotics alter digestion kinetics, volatile fatty acid content and microbial interactions in the gut of Nile tilapia**. (2021.0) **11** p. 8221. DOI: 10.1038/s41598-021-87408-3
44. Gupta S., Lokesh J., Abdelhafiz Y.. **Macroalga-derived alginate oligosaccharide alters intestinal bacteria of Atlantic Salmon**. (2019.0) **10** p. 2037. DOI: 10.3389/fmicb.2019.02037
45. Li Y., Bruni L., Jaramillo-Torres A., Gajardo K., Kortner T. M., Krogdahl A.. **Differential response of digesta- and mucosa-associated intestinal microbiota to dietary insect meal during the seawater phase of Atlantic salmon**. (2021.0) **3** p. 8. DOI: 10.1186/s42523-020-00071-3
46. Dailey F. E., McGraw J. E., Jensen B. J.. **The microbiota of freshwater fish and freshwater niches contain Omega-3 fatty acid-producing Shewanella species**. (2016.0) **82** 218-231. DOI: 10.1128/AEM.02266-15
47. Wu Y. C., Chu L. S., Cheng C. H., Chi S. C.. **The presence of substances of the Shewanella strain 0409, isolated from grouper intestine, gives antiviral activity against Betanodavirus in cell cultures and protection of fish**. (2020.0) **518** p. 734862. DOI: 10.1016/j.aquaculture.2019.734862
48. Tapia-Paniagua S. T., Vidal S., Lobo C.. **The treatment with the probiotic Shewanella putrefaciens Pdp11 of specimens of Solea senegalensis exposed to high stocking densities to enhance their resistance to disease**. (2014.0) **41** 209-221. DOI: 10.1016/j.fsi.2014.08.019
49. Méndez-Pérez R., García-López R., Bautista-López S.. (2019.0). DOI: 10.1101/557629
50. Chang S. C., Chen W. M., Wang J. T., Wu M. C.. **Chitinilyticum aquatile gen. nov., sp. nov., a chitinolytic bacterium isolated from a freshwater pond used for Pacific white shrimp culture**. (2007.0) **57** 2854-2860. DOI: 10.1099/ijs.0.65168-0
51. Chang S. C., Wu M. C., Chen W. M., Tsai Y. H., Lee T. M.. **Chitinilyticum litopenaei sp. nov., isolated from a freshwater shrimp pond, and emended description of the genus Chitinilyticum**. (2009.0) **59** 2651-2655. DOI: 10.1099/ijs.0.005090-0
|
---
title: Effects of Lysophospholipid Supplementation in Feed with Low Protein or Lipid
on Growth Performance, Lipid Metabolism, and Intestinal Flora of Largemouth Bass
(Micropterus salmoides)
authors:
- Ziye Lu
- Chunfeng Yao
- Beiping Tan
- Xiaohui Dong
- Qihui Yang
- Hongyu Liu
- Shuang Zhang
- Shuyan Chi
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973218
doi: 10.1155/2022/4347466
license: CC BY 4.0
---
# Effects of Lysophospholipid Supplementation in Feed with Low Protein or Lipid on Growth Performance, Lipid Metabolism, and Intestinal Flora of Largemouth Bass (Micropterus salmoides)
## Abstract
The largemouth bass (Micropterus salmoides) were fed diets with three experimental feeds, a control diet (Control, crude protein (CP): $54.52\%$, crude lipid (CL): $11.45\%$), a low-protein diet with lysophospholipid (LP-Ly, CP: $52.46\%$, CL: $11.36\%$), and a low-lipid diet with lysophospholipid (LL-Ly, CP: $54.43\%$, CL: $10.19\%$), respectively. The LP-Ly and LL-Ly groups represented the addition of 1 g/kg of lysophospholipids in the low-protein and low-lipid groups, respectively. After a 64-day feeding trial, the experimental results showed that the growth performance, hepatosomatic index, and viscerosomatic index of largemouth bass in both the LP-Ly and LL-Ly groups were not significantly different compared to those in the Control group ($P \leq 0.05$). The condition factor and CP content of whole fish were significantly higher in the LP-Ly group than those in the Control group ($P \leq 0.05$). Compared with the Control group, the serum total cholesterol level and alanine aminotransferase enzyme activity were significantly lower in both the LP-Ly group and the LL-Ly group ($P \leq 0.05$). The protease and lipase activities in the liver and intestine of both group LL-Ly and group LP-Ly were significantly higher than those of the Control group ($P \leq 0.05$). Compared to both the LL-Ly group and the LP-Ly group, significantly lower liver enzyme activities and gene expression of fatty acid synthase, hormone-sensitive lipase, and carnitine palmitoyltransferase 1 were found in the Control group ($P \leq 0.05$). The addition of lysophospholipids increased the abundance of beneficial bacteria (Cetobacterium and Acinetobacter) and decreased the abundance of harmful bacteria (Mycoplasma) in the intestinal flora. In conclusion, the supplementation of lysophospholipids in low-protein or low-lipid diets had no negative effect on the growth performance of largemouth bass, but increased the activity of intestinal digestive enzymes, enhanced the hepatic lipid metabolism, promoted the protein deposition, and regulated the structure and diversity of the intestinal flora.
## 1. Introduction
The global aquafeed production in 2020 was 51.37 million tonnes, increasing by $3.7\%$ compared with 2019 [1]. Higher dietary lipid and protein contents in aquafeeds not only increase farming cost but also lead to waste and ammonia emissions. However, lower dietary lipid or protein contents could result in negative effects on fish growth performance [2, 3]. Therefore, how to save cost and at the same time to maintain development quality has become an important and urgent issue. Improving the efficiency of nonprotein energy utilization is one of the ways to spare feed protein in aquafeeds.
Lipids have been effectively used to spare protein [4]. Feed additives like phospholipids (PL) have been used to enhance lipid utilization. The PL supplementation in diets of large yellow croaker (Larmichthys crocea) can improve the protein efficiency and protein deposition [5]. After ingestion of phospholipids, large yellow croaker showed increased body protein deposition, enhanced activities of trypsin and amylase, and improved development of digestive tract [6]. In hybrid grouper (*Epinephelus fuscoguttatus* ♀ × E. lanceolatus ♂), a significant increase in liver and whole-body crude protein content was observed when phospholipids were supplemented in the diet [7]. The addition of phospholipids to low fishmeal diets for mud crab (Scylla paramamosain) can significantly increase the protein efficiency and body crude protein content [8].
Lysophospholipid, degraded phospholipid by pancreatic phospholipase A2 [9, 10], not only increased the release of mono- and diglyceride fatty acid esters by emulsifying the lipid [11, 12] but also altered the membrane permeability, which could increase the pore area of intestinal cell membrane [13, 14] and improve the digestion and absorption of dietary fatty acids [15]. It is worth noting that lysophospholipid also plays a key role in a variety of cellular signaling mechanisms. In rats, it promoted the fatty acid catabolism through activation of the AMPKα-ACC-CPT signaling and mitogen-activated protein kinase (MAPK) signaling pathways [16–18]. In turbot, it reduced the hepatic lipid content, plasma triglyceride concentration, and total cholesterol level and increased the plasma free fatty acid contents as well [19]. However, the lysophospholipid synthesis in fish is usually insufficient to meet their metabolic requirements [20, 21]. Diets supplemented with exogenous lysophospholipid could improve protein efficiency ratio and growth of rainbow trout (Oncorhynchus mykiss) [22].
The largemouth bass (Micropterus salmoides) is a typical carnivorous fish species that relies on high level of dietary protein and lipid. Although carbohydrates are the cheapest energy source, this species cannot use carbohydrates efficiently, and high dietary carbohydrate levels often lead to abnormal sugar metabolism and liver dysfunction [23]. The objective of this experiment was to investigate whether three diets could satisfy the growth of largemouth bass and improve the utilization of dietary protein and lipid after reducing the crude protein or crude lipid content of the diets and supplementing with lysophospholipids.
## 2.1. Experimental Diets
Three diets were formulated with crude protein/crude lipid levels of $54.52\%$/$11.45\%$ (Control group), $52.46\%$/$11.36\%$ (LP group), and $54.43\%$/$10.19\%$ (LL group), respectively (Table 1). Lysophospholipid was added into the LP and LL diet at 1 g/kg, to obtain the lysophospholipid-supplemented groups, which was designated as LP-Ly and LL-Ly, respectively. The ingredients were smashed and passed through a 60-mesh sieve and then mixed for 15 minutes with a V-type vertical mixer (JS-14S type, Zhejiang China Electric Co., Ltd.). Oil and water were added into the mixture to form a paste that were put into the extruded machine to make the pellets with a diameter of 3.0 mm. The pellet feeds were dried at room temperature of 25°C with ventilation for 48 h and then sealed and stored at -20°C until use.
## 2.2. Experimental Fish and Feeding Trials
Largemouth bass juveniles were purchased from Zhenghe Fish and Shrimp Hatchery Co. Ltd. (Zhuhai, China), stocked in continuously aerated fiberglass tanks (1000 L), and fed commercial diets (0#, Rongchuan Co. Ltd., Zhuhai, China). After the fish had acclimatized to the experiment environment for 2 weeks, 270 healthy and uniformly sized juvenile largemouth bass (initial weight 6.04 ± 0.04 g) were randomly selected and divided into nine fiberglass buckets (300 L), after being fasted for 24 h. Each experimental group had three replicate tanks. The fish were fed the test feeds at 08:00 and 16:00 daily. The initial feeding rate was $3\%$ according to body weight and then was adjusted to satiation feeding. The daily feed consumption and fish mortality in each experimental group were recorded. The feeding trial was carried out in an indoor hydrostatic system with aeration. The water conditions are as follows: temperature 29-32°C and dissolved oxygen >5 mg/L. The water was changed at 60-$80\%$ every day. The feeding trial lasted 9 weeks.
## 2.3. Sample Collection
At the end of 9 weeks of feeding trial, fish were fasted for 24 h. After anesthetized with eugenol (1: 10000, Sinopharm Chemical Reagent Co., Ltd), fish in each tank were counted and weighed in order to calculate the weight gain rate (WGR), specific growth rate (SGR), feed conversion ratio (FCR), and survival rate (SR). Three largemouth bass were randomly collected. Their body weight and length as well as the liver and visceral mass weight were measured to calculate the condition factor (CF), hepatosomatic index (HSI), and viscerosomatic index (VSI).
## 2.4. Calculations and Statistical Analysis
Weight gain rate (WGR, %) = 100 × (final body weight, g − initial body weight, g)/(initial body weight, g).
Specific growth rate (SGR, %/d) = 100 × (Ln final body weight, g − Ln initial body weight, g)/feeding days.
Survival rate (SR, %) = 100 × final fish number/initial fish number.
Feed conversion ratio (FCR) = (dry weight of feed, g)/(final body weight, g − initial body weight, g).
Hepatosomatic index (HSI, %) = 100 × liver weight, g/body weight, g.
Viscerosomatic index (VSI, %) = 100 × viscera weight, g/body weight, g.
Condition factor (CF) = 100 × body weight, g/body length (cm)3.
Feeding ratio (FR, %) = 100 × dry feed consumed/[days × (final fish number + initial fish number)/2].
Protein deposition ratio (PDR, %) = 100 × [(final body weight (g) × crude protein of end fish (%) − initial body weight (g) × crude protein of the starting fish body)]/(feed protein intake (g) × crude protein of feed (%)).
All data were subjected to independent samples t-test using SPSS 21.0 statistical software, and descriptive statistics were expressed as mean ± standard error (SEM), with $P \leq 0.05$ indicating a significant difference.
## 2.5. Chemical Composition Analysis
The proximate composition of fish body and diets were measured according to the standard AOAC methods [24]. Moisture was assayed by drying the samples at 105°C to a constant weight. Crude protein (N × 6.25) and crude lipid were assayed with the Kjeldahl method (2300-Auto-analyzer, Foss, Sweden) and Soxhlet extraction, respectively. Ash was measured by incineration at 550°C in a muffle furnace.
## 2.6. Biochemical Parameters and Enzyme Activity
Tail vein blood was drawn from 6 fish in each tank, placed in 1.5 mL centrifuge tubes, left for 12 h, and then centrifuged (4000 r/min) for 10 min at 4°C. The serum was separated and stored at -80°C. The liver and intestine were dissected, rinsed with saline, and homogenized with PBS (pH 7.4) at ice bath. The samples were centrifuged for 20 min at 2500 r/min. The supernatants were stored at -80°C for enzyme activity assays.
Serum biochemical indicators were measured using commercial kits (Nanjing Jiancheng Institute of Biological Engineering Co. Ltd., China). Total protein (TP) (A045-2-2), triglycerides (TG) (A110-1-1), total cholesterol (TC) (A111-1-1), high-density lipoprotein cholesterol (HDL-C) (A112-1-1), low-density lipoprotein cholesterol (LDL-C) (A113-1-1), alanine aminotransferase (ALT) (C009-2-1), and aspartate aminotransferase (AST) (C010-2-1) in serum were measured using full-wavelength microplate reader (Thermo Scientific Multiskan GO, America) at 595 nm, 510 nm, 510 nm, 546 nm, 546 nm, 510 nm, 510 nm, and 510 nm, respectively.
Tissue enzyme activities were analyzed using the enzyme-linked immunosorbent assay (ELISA) kit (Shanghai Enzyme Linkage Biotechnology Co. China). The OD values were read for amylase (ml036449), lipase (ml036371), trypsin (ml064285), lipoprotein lipase (LPL) (ml036373), hormone-sensitive lipase (HSL) (ml036437), fatty acid synthase (FAS) (ml036370), acetyl coenzyme A carboxylase (ACC) (ml036379), and carnitine palmitoyltransferase 1 (CPT1) (ml036379) at 450 nm using a microplate reader (Rayto RT-6100, China), strictly following the kits' instructions.
## 2.7. Quantitative Real-Time PCR Analysis
Three fish were randomly selected from each tank and dissected. The liver was immersed in RNAlater (Ambion, USA) and then stored at -80°C for the mRNA expression assays. Total RNA of the samples were extracted using the commercial kit (Beijing All-Style Gold Biotechnology Co., Ltd.). The RNA quality was assessed with agarose gel electrophoresis and ultramicroscopic spectrophotometer (NanoDrop-1000, Wilmington, USA). The cDNA was obtained using the Prime Script™ RT reagent kit (Takara, Japan). Real-time fluorescent quantitative PCR (LightCycler480) was performed using SYBR® Green Master Mix (Takara, Japan). The 10 μL reaction system consisted of 5 μL 2× SYBR Green (Takara, Japan), 1 μL cDNA, 0.5 μL primers, and 3 μL sterile double-distilled water. The processes were as follows: 95°C denaturation step for 30 s, 40 amplification cycles “denaturation at 95°C for 5 s, annealing at 60°C for 30 s,” followed by melt curve analysis and cooling to 4°C. The relative mRNA expression levels of the target genes were calculated according to equation 2-ΔΔCt using β-actin as the housekeeping gene [25]. The primer sequences are presented in Table 2.
## 2.8. High-Throughput Sequencing and Processing
Two fish were randomly selected from each tank. The fish were wiped with $75\%$ alcohol, and the intestines were taken and stored at -80°C until the intestinal flora was assayed. The gut microflora structure was analyzed with 16S rDNA sequencing by Guangzhou Genedenovo Biotechnology Co. Genomic DNA was extracted using the Magen Hipure Soil DNA Kit (Qiagen, Germany). The upstream primer is “CCTACGGGNGGCWGCAG” and the downstream primer is “GGACTACHVGGGTATCTAAT.” After the amplified product was purified (Monarch DNA Gel Extraction Kit, New England Biolabs Ltd., Beijing) and quantified, samples were mixed to a 1: 1 mass ratio. Library construction and sequencing were performed on an Illumina HiSeq sequencing platform (HiSeq 2500, Illumina, USA). Finally, the alpha diversity index of the samples was calculated and analyzed using Mothur (version 1.3.0) software.
## 3.1. Growth Performance
No significant differences were observed in final body weight (FBW), SGR, WGR, and SR between dietary groups ($P \leq 0.05$) (Table 3). The FCR of largemouth bass in groups LP-Ly and LL-Ly was $2\%$ and $4\%$ lower than that in the Control group, respectively ($P \leq 0.05$). No significant differences in HSI and VSI were found ($P \leq 0.05$). Nevertheless, the HSI was $9.52\%$ and $6.12\%$ lower in LP-Ly and LL-Ly compared to Control. The CF was significantly higher in the LP-Ly group than that in the Control group ($P \leq 0.05$).
## 3.2. Whole Fish Composition
There were no significant differences in moisture, crude lipid, and ash content of whole fish ($P \leq 0.05$) (Table 4). The crude lipid content was $3.26\%$ and $4.10\%$ lower in the LP-Ly and LL-Ly groups compared with the Control group. The crude protein of whole fish was significantly higher in the LP-Ly group than that in the Control group ($P \leq 0.05$).
## 3.3. Serum Biochemical Indicators
There were no significant ($P \leq 0.05$) differences in serum TP, TG, and HDL-C between groups (Table 5). The TC levels in the LP-Ly and LL-Ly groups were significantly lower than those in the Control group ($P \leq 0.05$). The LDL-C level in the Control group was $2.88\%$ higher than that in the LP-Ly group without significance ($P \leq 0.05$) difference and was significantly higher than that in the LL-Ly group ($P \leq 0.05$). The ALT activity was significantly higher in the Control group than in the LP-Ly and LL-Ly groups ($P \leq 0.05$). The AST activity was significantly lower in the LP-Ly group than in the Control group ($P \leq 0.05$), but was $6.15\%$ lower in the LL-Ly group than in the Control group ($P \leq 0.05$).
## 3.4. Digestive Enzyme Activity
The protease and lipase activities in the liver and intestine were significantly higher in the LP-Ly and LL-Ly groups compared to the Control group ($P \leq 0.05$) (Table 6). The amylase activity in the liver was significantly higher in the LL-Ly group compared to the Control group ($P \leq 0.05$), while it was $27.41\%$ higher in the LP-Ly group compared to the Control group ($P \leq 0.05$). Compared to the Control group, the amylase activity in the intestine was not significantly different in the LP-Ly and LL-Ly groups ($P \leq 0.05$).
## 3.5.1. Sequencing Results and Quality Control
A total of 961,428 high-quality sequences with an average length of 441 bp were obtained in this study (Figure 1). The highest and lowest number of unique OTUs was observed in the Control and LP-Ly groups, respectively. The OTU number in the LP-Ly, LL-Ly, and Control groups was 467, 713, and 607, respectively. The OTU number shared by the samples from each treatment group was 433.
## 3.5.2. α-Diversity of Microbial Community Richness
The ACE, Chao1, Shannon, and Simpson were significantly higher in the Control group than in the LP-Ly group ($P \leq 0.05$) (Table 7). The Shannon and Simpson parameters in the LL-Ly group were not significantly different from those in the Control group ($P \leq 0.05$), but the ACE and Chao1 parameters in the LL-Ly group were significantly lower than those in the Control group ($P \leq 0.05$).
The top 10 dominant phylum of largemouth bass gut microbes were Proteobacteria, Tenericutes, Fusobacteria, Cyanobacteria, Actinobacteria, Firmicutes, Bacteroidetes, Planctomycetes, Acidobacteria, and Gemmatimonadetes (Figure 2(a)). The top 4 phyla of the fish intestinal flora were further analyzed (Figure 2(b)). Compared to the Control group, the LP-Ly group showed a significant increase in Fusobacteria abundance ($P \leq 0.05$), but a significant decrease in the abundance of Proteobacteria, Tenericutes, and Cyanobacteria ($P \leq 0.05$). Compared to the Control group, the LL-Ly group showed a significant increase in Cyanobacteria abundance ($P \leq 0.05$), while the abundance of Fusobacteria, Proteobacteria, and Tenericutes was not significantly different between groups ($P \leq 0.05$).
The top 10 dominant genera in the intestine were Mycoplasma, Cetobacterium, Acinetobacter, Stenotrophomonas, Klebsiella, Bifidobacterium, Pseudomonas, Paracoccus, Lactobacillus, and Aeromonas (Figure 3(a)). Further analysis of the top 4 species in the intestinal flora is showed in Figure 3(b). Compared to the Control group, the LP-Ly group showed significantly reduced abundance of Mycoplasma and Stenotrophomonas ($P \leq 0.05$), but significantly higher Cetobacterium abundance ($P \leq 0.05$). Compared to the Control group, the abundance of Acinetobacter was significantly higher ($P \leq 0.05$), but the abundance of Mycoplasma was significantly lower ($P \leq 0.05$) in the LL-Ly group.
## 3.5.3. Functional Prediction of the Intestinal Flora
The KEGG functional predictions of the intestinal flora of largemouth bass fed the three diets were analyzed using Tax4Fun from SILVA annotations of 16s sequences (Figure 4). Both lipid and amino acid metabolisms in groups LP-Ly and LL-Ly were significantly higher than those in the Control group ($P \leq 0.05$).
## 3.6. Hepatic Lipid Metabolism-Related Enzymes
The activities of LPL, HSL, CPT-1, FAS, and ACC in the liver of largemouth bass fed the LP-Ly and LL-Ly diets were significantly higher than those in the Control group ($P \leq 0.05$) (Table 8).
## 3.7. Hepatic Lipid Metabolism-Related Gene Expression
The expression of lipoprotein lipase gene (lpl), hormone-sensitive lipase gene (hsl), fatty acid synthase gene (fas), acetyl coenzyme A carboxylase gene (acc), and carnitine palmitoyltransferase 1 gene (cpt-1) was significantly higher in the LP-Ly group than in the Control group ($P \leq 0.05$) (Figure 5). Compared to the Control group, the expression of hsl, fas, acc, and cpt-1 was significantly higher ($P \leq 0.05$) in the LL-Ly group ($P \leq 0.05$).
## 4. Discussion
Juvenile animals may secrete insufficient bile salts and lipase, which results in a low capacity of lipid digestion and absorption. Supplementing exogenous emulsifiers in the diet is one of the strategies to improve lipid and energy utilization of the young animals. Previous studies have revealed that the dietary phospholipids significantly increased the growth performance of common carp larvae (Cyprinus carpio) [26], blunt snout bream fingerlings [27], large yellow croaker larvae [6], and turbot (Scophthalmus maximus) [19, 28]. Dietary lysophospholipid supplementation also promoted the growth performance and carcass yield of the broilers [13]. In this experiment, adding 1 g·kg−1 lysophospholipids in low-crude lipid or low-crude protein diets did not significantly affect the growth performance of largemouth bass. There was a decrease trend in body lipid content and an increase trend in crude protein content with lysophospholipid supplementation. This result was similar to that observed in heterozygous silver carp (*Carassius auratus* gibelio) [29], amberjack (Seriola dumerili) [30], large yellow croaker [6], and blunt snout bream (Megalobrama amblycephala) [27]. It has been reported that dietary lysophospholipid induced an increase in protein synthesis, increased cell size, increased atrial natriuretic factor (ANF) expression, and activated mitogen-activated protein (MAP) kinases [31, 32]. Addition of lysophospholipids to turbot feed aided digestion of dietary lipid and promoted the lipolytic gene expression [33].
Studies with red-spotted grouper (Epinephelus akaara) showed that reductions in serum TG and TC levels were found to be associated with reductions in protein and lipid content in the diet [34]. In this experiment, there were no significant differences in serum TG level between the groups, suggesting that a relative decrease in protein or lipid content in feeds supplemented with lysophospholipids did not affect the serum TG level. HDL-C plays an important role in transporting TC and free fatty acids (FFA) from peripheral tissues to hepatocytes for metabolism, while LDL-C transfers TC to peripheral tissue cells [35, 36]. Following lysophospholipid supplementation, the HDL-C level was not affected compared to the control, while the LDL-C level was significantly lower in the LL-Ly group. In this experiment, the lower serum LDL-C and TC levels in group LL-Ly may suggest that lysophospholipid facilitated the transport of LDL-C to liver metabolism. Notably, the structural specificity of lysophospholipids shows a number of nutritional advantages. It affects the composition and function of lipoproteins in vivo through different pathways [37]. Studies have shown that n-3 PUFA-rich phospholipids from krill oil can reduce TC, LDL-C, and TG in nonhuman primates [38]. Lysophospholipid helps to improve digestion, transport, and absorption of dietary lipids, thereby improving lipid deposition and energy efficiency and reducing serum levels of TC and LDL-C [39]. In addition, phospholipids can interact with the membranes of intestinal cells and reduce their ability to absorb cholesterol [40]. Lower activities of AST and ALT in serum usually indicate healthy status of the liver [41]. In this study, the lower AST and ALT activities were found in the serum of fish fed the LP-Ly or LL-Ly diets, suggesting that lysophospholipids could maintain the liver health in largemouth bass consuming diets with low protein or lipid levels.
In the present experiment, significantly higher amylase, lipase, and protease activities in the liver and intestine were found in the LP-Ly and LL-Ly groups, which was consistent with the results in carp [42] and Caspian brown trout (*Salmo trutta* Caspius) [43]. Lysophospholipid with the better emulsification can stimulate bile secretion from the gallbladder and directly increase the contact area between celiac particles and digestive enzymes, which relatively increases the substrate concentration and then enhances the digestive enzyme activity.
The complex microbial ecosystem in the gut exhibits a vital role in the nutrition and health of the host [44, 45]. Indeed, the composition of the intestinal flora can be influenced by factors such as feed and environment [46, 47]. Iced trash fish and artificial diets could affect the diversity and composition of gut microbes in largemouth bass [48, 49]. This study showed that reducing feed protein levels with addition of lysophospholipids significantly reduced the diversity of the gut flora of largemouth bass, which however could not be necessarily negative for the fish growth performance.
Proteobacteria, Fusobacteria, and Firmicutes are widespread in the gut of aquatic organisms such as *Nile tilapia* (Oreochromis niloticus) [50], Atlantic salmon (Salmo salar) [51], and hybrid grouper [52]. Fusobacteria produces the short-chain fatty acid butyrate [53], which provides energy to gastrointestinal cells, increases mucus secretion, and has some growth-promoting effects [54]. In this experiment, there was no significant change in growth performance in the LP-Ly group compared to the Control group, probably due to the fact that lysophospholipids contribute to the Fusobacteria proliferation and make more butyrate in the intestine of largemouth bass. Cyanobacteria are potentially pathogenic, but Cyanobacteria in low abundance promotes growth and in high abundance produces hepatotoxic microcystins that may be harmful to aquatic animals [55, 56]. The cyanobacteria abundance in this experiment showed a significant decrease in the LP-Ly group but a significant increase in the group LL-Ly. According to the growth performance of these two groups, cyanobacteria could not show negative effects. Mycoplasma spp. can cause inflammatory responses [57]. The abundance of Mycoplasma was significantly lower in the LP-Ly and LL-Ly groups compared to the Control group. This suggests that the addition of lysophospholipids at the low-protein or low-lipid diets may reduce inflammation in fish to some extent by reducing the abundance of Mycoplasma in the intestine. The present study showed that fish fed the LP-Ly diet had increased abundance of Cetobacterium, which is protease-producing bacteria that are thought to be involved in peptone fermentation and protein metabolism [58, 59]. Interestingly, this is corroborated by the higher protein content of fish body in group LP-Ly. In addition, Acinetobacter has been associated with disease infections in tilapia and humans [60, 61], and the results of the present study showed that its abundance was significantly higher in the LL-Ly group, which may increase the risk of disease infection in fish.
The addition of lysophospholipids can promote protein metabolism and energy metabolism in broilers [62]. In the present experiment, functional predictions of the gut microbiota also indicate that the amino acid metabolism and lipid metabolism were significantly upregulated in the LP-Ly and LL-Ly groups compared to the Control group. Lysophospholipid mechanically promotes the germination of encapsulated vesicles (COPII), which are required for amino acid penetration to be packaged into transport vesicles in vitro [63, 64]. Functional diversity of gut microbiota can reflect the influence of gut microbiota on the metabolic processes of host organism [25]. The results of hepatic lipid metabolizing enzyme activity and the relevant gene expression in largemouth bass also indicate that low-nutrient diets supplemented with lysophospholipid contributed to enhancement of hepatic lipid metabolism. The function of intestinal flora is coordinated with the nutrient metabolism in the fish liver.
Previous studies have demonstrated that dietary phospholipids can affect lipid metabolism in fish at the transcriptional level [5]. Therefore, the expression of genes involved in the regulation of lipid metabolism was investigated, in order to explore the mechanisms by which phospholipids induce lipid metabolism in the liver. LPL and HSL are two key enzymes and serve an important role in hepatic lipolysis catabolism of fish [65], of which the activities generally increase with the increase of dietary lipid content [65, 66]. However, the present study showed that the activities and mRNA expression of LPL and HSL increased with the addition of lysophospholipid when the dietary lipid or protein content decreased, which is similar to the findings in broiler chickens [67]. These changes may be related to the increased emulsification of lipids by lysophospholipids [68, 69], which accelerates lipolysis, provides energy, and allows better protein deposition in the body. CPT1 is the rate-limiting enzyme in fatty acid oxidation and the activity of CPT1 is closely related to lipid catabolism [70]. The addition of phospholipids had no significant effect on CPT1 enzyme activity in large yellow croaker [71], but a certain amount of egg yolk lecithin could significantly increase ACC enzyme activity and gene expression in green mud crab [72]. In this study, the enzyme activity and mRNA expression of CPT1 and ACC were significantly higher in low dietary protein and lipid groups supplemented with lysophospholipids. This may be because that lysophospholipids can activate the AMPKα-ACC-CPT signaling pathway and increase fatty acid β-oxidation in hepatocytes, thus promoting fatty acid catabolism [18]. The fas gene had higher expression level in the LP-Ly and LL-Ly groups, showing that lysophospholipid affected the hepatic lipid metabolism in multiple ways. The addition of phospholipids to the diet significantly reduces the lipid accumulation in mammals, which is caused by the downregulation of fas expression [73, 74], and similar results have been observed in large yellow croaker [5] and hybrid snakehead [75]. However, the expression of adipogenesis-related genes (such as fas and acc) of the hybrid grouper was not significantly affected by the dietary phospholipids [7]. In this study, the hepatic lipolysis may be affected at higher levels than lipid synthesis, which facilitates energy supply. These results corresponded to the decrease in body lipid content and increase in body protein content, as well as functional predictions of the gut microbiota.
In conclusion, under the conditions of this feeding trial, supplementation of lysophospholipids in diets with reduced levels of crude protein or crude lipid had no negative impact on the growth performance of largemouth bass compared to diets with normal protein and lipid levels. However, it enhanced the lipid metabolism, alleviated the liver damage, and regulated the structure and diversity of the intestinal flora.
## Data Availability
The processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
Chunfeng Yao has an equal contribution to the first author.
## References
1. **Alltech 2021 Global Feed Survey**. (2021)
2. Sankian Z., Khosravi S., Kim Y.-O., Lee S.-M.. **Effect of dietary protein and lipid level on growth, feed utilization, and muscle composition in golden mandarin fish Siniperca scherzeri**. (2017) **20** 1-6
3. Sheng Z., Turchini G. M., Xu J.. **Functional properties of protein hydrolysates on growth, digestive enzyme activities, protein metabolism, and intestinal health of larval largemouth bass (**. (2022) **13**. DOI: 10.3389/fimmu.2022.913024
4. Watanabe T.. **Lipid nutrition in fish**. (1982) **73** 3-15. DOI: 10.1016/0305-0491(82)90196-1
5. Cai Z., Mai K., Ai Q.. **Regulation of hepatic lipid deposition by phospholipid in large yellow croaker**. (2017) **118** 999-1009. DOI: 10.1017/S000711451700294X
6. Zhao J., Ai Q., Mai K., Zuo R., Luo Y.. **Effects of dietary phospholipids on survival, growth, digestive enzymes and stress resistance of large yellow croaker,**. (2013) **410-411** 122-128. DOI: 10.1016/j.aquaculture.2013.05.018
7. Huang Y., Xu J., Sheng Z.. **Effects of dietary phospholipids on growth performance, fatty acid composition, and expression of lipid metabolism related genes of juvenile hybrid grouper (**. (2022) **22, article 100993**. DOI: 10.1016/j.aqrep.2021.100993
8. Kader M., Bulbul M., Asaduzzaman M.. **Effect of phospholipid supplements to fishmeal replacements on growth performance, feed utilization and fatty acid composition of mud crab, Scylla paramamosain (Estampador 1949)**. (2017) **2017** 47-61
9. Joshi A., Paratkar S. G., Thorat B. N.. **Modification of lecithin by physical, chemical and enzymatic methods**. (2006) **108** 363-373. DOI: 10.1002/ejlt.200600016
10. Tocher D. R., Bendiksen E. Å., Campbell P. J., Bell J. G.. **The role of phospholipids in nutrition and metabolism of teleost fish**. (2008) **280** 21-34. DOI: 10.1016/j.aquaculture.2008.04.034
11. Schwarzer K., Adams C. A.. **The influence of specific phospholipids as absorption enhancer in animal nutrition**. (1996) **98** 304-308. DOI: 10.1002/lipi.19960980905
12. Van Nieuwenhuyzen W., Tomás M. C.. **Update on vegetable lecithin and phospholipid technologies**. (2008) **110** 472-486. DOI: 10.1002/ejlt.200800041
13. Chen X., Wang Q., Guo Z.. **Effects of dietary oxidized fish oil on growth performance and antioxidant defense mechanism of juvenile**. (2019) **512, article 734368**. DOI: 10.1016/j.aquaculture.2019.734368
14. Zhao P. Y., Kim I. H.. **Effect of diets with different energy and lysophospholipids levels on performance, nutrient metabolism, and body composition in broilers**. (2017) **96** 1341-1347. DOI: 10.3382/ps/pew469
15. Niu J., Liu Y. J., Lin H. Z.. **Effects of dietary chitosan on growth, survival and stress tolerance of postlarval shrimp,**. (2011) **17** e406-e412. DOI: 10.1111/j.1365-2095.2010.00775.x
16. Davidsen J., Mouritsen O. G., Jørgensen K.. **Synergistic permeability enhancing effect of lysophospholipids and fatty acids on lipid membranes**. (2002) **1564** 256-262. DOI: 10.1016/S0005-2736(02)00461-3
17. Paoletti L., Domizi P., Marcucci H.. **Lysophosphatidylcholine drives neuroblast cell fate**. (2016) **53** 6316-6331. DOI: 10.1007/s12035-015-9528-0
18. Thomas G., Betters J. L., Lord C. C.. **The serine hydrolase ABHD6 is a critical regulator of the metabolic syndrome**. (2013) **5** 508-520. DOI: 10.1016/j.celrep.2013.08.047
19. Li B., Li Z., Sun Y., Wang S., Huang B., Wang J.. **Effects of dietary lysolecithin (LPC) on growth, apparent digestibility of nutrient and lipid metabolism in juvenile turbot**. (2019) **4** 61-66. DOI: 10.1016/j.aaf.2018.11.003
20. Kanazawa A., Teshima S., Sakamoto M.. **Effects of dietary bonito-egg phospholipids and some phospholipids on growth and survival of the larval ayu, Plecoglossus altivelis**. (1985) **4** 165-170
21. Noordin N. M., Zeng C., Southgate P. C.. **Progress in diet development for blue swimmer crab, Portunus pelagicus, juveniles: effect of dietary phospholipid on survival, development, growth and resistance to osmotic shock**. (2018) **24** 1834-1844. DOI: 10.1111/anu.12822
22. Taghavizadeh M., Shekarabi S. P. H., Mehrgan M. S., Islami H. R.. **Efficacy of dietary lysophospholipids (Lipidol**. (2020) **525** p. 735315. DOI: 10.1016/j.aquaculture.2020.735315
23. Feng Z., Zhong Y., He G.. **Yeast culture improved the growth performance, liver function, intestinal barrier and microbiota of juvenile largemouth bass (**. (2022) **120** 706-715. DOI: 10.1016/j.fsi.2021.12.034
24. Lee M. H.. **Official methods of analysis of AOAC International (16th edn)**. (1995) **6** p. 382
25. Wang Q. Q., Long S. F., Hu J. X., Li M., Pan L., Piao X. S.. **Effects of dietary lysophospholipid complex supplementation on lactation performance, and nutrient digestibility in lactating sows**. (2019) **251** 56-63. DOI: 10.1016/j.anifeedsci.2018.12.009
26. Geurden I., Radünz-Neto J., Bergot P.. **Essentiality of dietary phospholipids for carp (**. (1995) **131** 303-314. DOI: 10.1016/0044-8486(94)00344-N
27. Li Y., Gao J., Huang S.. **Effects of different dietary phospholipid levels on growth performance, fatty acid composition, PPAR gene expressions and antioxidant responses of blunt snout bream Megalobrama amblycephala fingerlings**. (2015) **41** 423-436. DOI: 10.1007/s10695-014-9994-8
28. Chen C., Jung B., Kim W. K.. **Effects of lysophospholipid on growth performance, carcass yield, intestinal development, and bone quality in broilers**. (2019) **98** 3902-3913. DOI: 10.3382/ps/pez111
29. Li H. X., Liu W. B., Li X. F., Wang J. J., Liu B., Xie J.. **Effects of dietary choline-chloride, betaine and lysophospholipids on the growth performance,fat metabolism and blood indices of crucian carp (**. (2010) **34** 292-299. DOI: 10.3724/SP.J.1231.2010.06416
30. Uyan O., Koshio S., Ishikawa M.. **The influence of dietary phospholipid level on the performances of juvenile amberjack, Seriola dumerili, fed non-fishmeal diets**. (2009) **15** 550-557. DOI: 10.1111/j.1365-2095.2008.00621.x
31. Goetzl E. J., Lee H., Azuma T., Stossel T. P., Turck C. W., Karliner J. S.. **Gelsolin binding and cellular presentation of lysophosphatidic acid**. (2000) **275** 14573-14578. DOI: 10.1074/jbc.275.19.14573
32. Hilal-Dandan R., Means C. K., Gustafsson A. B.. **Lysophosphatidic acid induces hypertrophy of neonatal cardiac myocytes via activation of Gi and Rho**. (2004) **36** 481-493. DOI: 10.1016/j.yjmcc.2003.12.010
33. Xu H., Luo X., Bi Q.. **Effects of dietary lysophosphatidylcholine on growth performance and lipid metabolism of juvenile turbot**. (2022) **2022, article 3515101** 1-12. DOI: 10.1155/2022/3515101
34. Wang J. T., Han T., Li X. Y.. **Effects of dietary protein and lipid levels with different protein-to-energy ratios on growth performance, feed utilization and body composition of juvenile red-spotted grouper,**. (2017) **23** 994-1002. DOI: 10.1111/anu.12467
35. Iwata T., Hoshi S., Takehisa F., Tsutsumi K., Furukawa Y., Kimura S.. **The effect of dietary safflower phospholipid and soybean phospholipid on plasma and liver lipids in rats fed a hypercholesterolemic diet**. (1992) **38** 471-479. DOI: 10.3177/jnsv.38.471
36. Lewis-McCrea L. M., Lall S. P.. **Effects of moderately oxidized dietary lipid and the role of vitamin E on the development of skeletal abnormalities in juvenile Atlantic halibut (**. (2007) **262** 142-155. DOI: 10.1016/j.aquaculture.2006.09.024
37. Lordan R., Tsoupras A., Zabetakis I.. **Phospholipids of animal and marine origin: structure, function, and anti-inflammatory properties**. (2017) **22** p. 1964. DOI: 10.3390/molecules22111964
38. Hals P. A., Wang X., Xiao Y. F.. **Effects of a purified krill oil phospholipid rich in long-chain omega-3 fatty acids on cardiovascular disease risk factors in non-human primates with naturally occurring diabetes type-2 and dyslipidemia**. (2017) **16** 1-16. PMID: 28056980
39. Cohn J. S., Wat E., Kamili A., Tandy S.. **Dietary phospholipids, hepatic lipid metabolism and cardiovascular disease**. (2008) **19** 257-262. DOI: 10.1097/MOL.0b013e3282ffaf96
40. Cohn J. S., Kamili A., Wat E., Chung R. W., Tandy S.. **Dietary phospholipids and intestinal cholesterol absorption**. (2010) **2** 116-127. DOI: 10.3390/nu2020116
41. Wang L., Cui Z., Ren X., Li P., Wang Y.. **Growth performance, feed cost and environmental impact of largemouth bass**. (2021) **20, article 100757**. DOI: 10.1016/j.aqrep.2021.100757
42. Abdel-Latif H. M., Abdel-Tawwab M., Khafaga A. F., Dawood M. A.. **Dietary oregano essential oil improved the growth performance via enhancing the intestinal morphometry and hepato-renal functions of common carp (**. (2020) **526** p. 735432. DOI: 10.1016/j.aquaculture.2020.735432
43. Kenari A. A., Sotoudeh E., Rezaei M. H.. **Dietary soybean phosphatidylcholine affects growth performance and lipolytic enzyme activity in Caspian brown trout (**. (2011) **42** 655-663. DOI: 10.1111/j.1365-2109.2010.02587.x
44. Coyte K. Z., Schluter J., Foster K. R.. **The ecology of the microbiome: networks, competition, and stability**. (2015) **350** 663-666. DOI: 10.1126/science.aad2602
45. Sommer F., Anderson J. M., Bharti R., Raes J., Rosenstiel P.. **The resilience of the intestinal microbiota influences health and disease**. (2017) **15** 630-638. DOI: 10.1038/nrmicro.2017.58
46. Arias C. R., Ray L., Cai W., Willmon E.. **Fish are not alone: characterization of the gut and skin microbiomes of largemouth bass (**. (2019) **2** 1-15. DOI: 10.25177/JAFFS.2.2.RA.459
47. Liu Q., Lai Z., Gao Y.. **Connection between the gut microbiota of largemouth bass (**. (2021) **9** p. 1770. DOI: 10.3390/microorganisms9081770
48. Ermeng Y., Zhennan Z., Wei X., Jun X., Guangjun W., Deguang Y.. **Effects of different diets on intestinal microflora of largemouth bass (**. (2015) **39** 118-126
49. Xie S., Yin P., Tian L., Liu Y., Tan B., Niu J.. **Interactions between dietary lipid levels and chronic exposure of legal aquaculture dose of sulfamethoxazole in juvenile largemouth bass**. (2020) **229, article 105670**. DOI: 10.1016/j.aquatox.2020.105670
50. Deng Y., Verdegem M. C. J., Eding E., Kokou F.. **Effect of rearing systems and dietary probiotic supplementation on the growth and gut microbiota of Nile tilapia (**. (2022) **546, article 737297**. DOI: 10.1016/j.aquaculture.2021.737297
51. Weththasinghe P., Rocha S. D. C., Oyas O.. **Modulation of Atlantic salmon (**. (2022) **4** p. 9. DOI: 10.1186/s42523-021-00161-w
52. He Y., Guo X., Tan B.. **Replacing fish meal with fermented rice protein in diets for hybrid groupers (**. (2021) **19, article 100603**. DOI: 10.1016/j.aqrep.2021.100603
53. Di Maiuta N., Schwarzentruber P., Schenker M., Schoelkopf J.. **Microbial population dynamics in the faeces of wood-eating loricariid catfishes**. (2013) **56** 401-407. DOI: 10.1111/lam.12061
54. Hamer H. M., Jonkers D. M., Bast A.. **Butyrate modulates oxidative stress in the colonic mucosa of healthy humans**. (2009) **28** 88-93. DOI: 10.1016/j.clnu.2008.11.002
55. Jia J., Chen Q., Lauridsen T. L.. **A systematic investigation into the environmental fate of microcystins and the potential risk: study in Lake Taihu**. (2016) **8** p. 170. DOI: 10.3390/toxins8060170
56. Kang H. S., Sturdy M., Krunic A.. **Minutissamides E-L, antiproliferative cyclic lipodecapeptides from the cultured freshwater cyanobacterium cf.**. (2012) **20** 6134-6143. DOI: 10.1016/j.bmc.2012.08.017
57. Hao Y. T., Wu S. G., Xiong F.. **Succession and fermentation products of grass carp (**. (2017) **8** p. 1585. DOI: 10.3389/fmicb.2017.01585
58. Larsen A. M., Mohammed H. H., Arias C. R.. **Characterization of the gut microbiota of three commercially valuable warmwater fish species**. (2014) **116** 1396-1404. DOI: 10.1111/jam.12475
59. Tsuchiya C., Sakata T., Sugita H.. **Novel ecological niche of Cetobacterium somerae, an anaerobic bacterium in the intestinal tracts of freshwater fish**. (2008) **46** 43-48. PMID: 17944860
60. Adewoyin M. A., Okoh A. I.. **Seasonal shift in physicochemical factors revealed the ecological variables that modulate the density of acinetobacter species in freshwater resources**. (2020) **17** p. 3606. DOI: 10.3390/ijerph17103606
61. Grande Burgos M. J., Romero J. L., Perez Pulido R., Cobo Molinos A., Galvez A., Lucas R.. **Analysis of potential risks from the bacterial communities associated with air-contact surfaces from tilapia (**. (2018) **160** 385-390. DOI: 10.1016/j.envres.2017.10.021
62. Boontiam W., Hyun Y. K., Jung B., Kim Y. Y.. **Effects of lysophospholipid supplementation to reduced energy, crude protein, and amino acid diets on growth performance, nutrient digestibility, and blood profiles in broiler chickens**. (2019) **98** 6693-6701. DOI: 10.3382/ps/pex005
63. Kuehn M. J., Schekman R., Ljungdahl P. O.. **Amino acid permeases require COPII components and the ER resident membrane protein Shr3p for packaging into transport vesicles in vitro**. (1996) **135** 585-595. DOI: 10.1083/jcb.135.3.585
64. Melero A., Chiaruttini N., Karashima T.. **Lysophospholipids facilitate COPII vesicle formation**. (2018) **28** 1950-1958.e6. DOI: 10.1016/j.cub.2018.04.076
65. Tian J., Wen H., Zeng L. B.. **Changes in the activities and mRNA expression levels of lipoprotein lipase (LPL), hormone-sensitive lipase (HSL) and fatty acid synthetase (FAS) of Nile tilapia (**. (2013) **400-401** 29-35. DOI: 10.1016/j.aquaculture.2013.01.032
66. Galan X., Llobera M., Ramirez I.. **Lipoprotein lipase and hepatic lipase in Wistar and Sprague-Dawley rat tissues. Differences in the effects of gender and fasting**. (1994) **29** 333-336. DOI: 10.1007/BF02537186
67. Haetinger V. S., Dalmoro Y. K., Godoy G. L.. **Optimizing cost, growth performance, and nutrient absorption with a bio- emulsifier based on lysophospholipids for broiler chickens**. (2021) **100**. DOI: 10.1016/j.psj.2021.101025
68. Reis P., Holmberg K., Watzke H., Leser M. E., Miller R.. **Lipases at interfaces: a review**. (2009) **147-148** 237-250. DOI: 10.1016/j.cis.2008.06.001
69. Taylor J. F., Martinez-Rubio L., del Pozo J.. **Influence of dietary phospholipid on early development and performance of Atlantic salmon (**. (2015) **448** 262-272. DOI: 10.1016/j.aquaculture.2015.06.012
70. Houten S. M., Violante S., Ventura F. V., Wanders R. J.. **The biochemistry and physiology of mitochondrial fatty acid**. (2016) **78** 23-44. DOI: 10.1146/annurev-physiol-021115-105045
71. Feng S., Cai Z., Zuo R., Mai K., Ai Q.. **Effects of dietary phospholipids on growth performance and expression of key genes involved in phosphatidylcholine metabolism in larval and juvenile large yellow croaker,**. (2017) **469** 59-66. DOI: 10.1016/j.aquaculture.2016.12.002
72. Liu T., Xu H., Han T., Wang J., Yin F., Wang C.. **Effect of dietary egg yolk lecithin levels on survival, growth, lipid metabolism, and antioxidant capacity of early juvenile green mud crab**. (2021) **540, article 736706**. DOI: 10.1016/j.aquaculture.2021.736706
73. Liu X., Xue Y., Liu C.. **Eicosapentaenoic acid-enriched phospholipid ameliorates insulin resistance and lipid metabolism in diet-induced-obese mice**. (2013) **12** p. 109. DOI: 10.1186/1476-511X-12-109
74. Liu Y., Shi D., Liu Y.. **Eicosapentaenoic acid-containing phosphatidylcholine alleviated lipid accumulation in orotic acid-induced non-alcoholic fatty liver**. (2016) **23** 294-305. DOI: 10.1016/j.jff.2016.02.041
75. Lin S. M., Li F. J., Yuangsoi B., Doolgindachbaporn S.. **Effect of dietary phospholipid levels on growth, lipid metabolism, and antioxidative status of juvenile hybrid snakehead (**. (2018) **44** 401-410. DOI: 10.1007/s10695-017-0443-3
|
---
title: The Adaptive Characteristics of Cholesterol and Bile Acid Metabolism in Nile
Tilapia Fed a High-Fat Diet
authors:
- Rui-Xin Li
- Yi-Fan Qian
- Wen-Hao Zhou
- Jun-Xian Wang
- Yan-Yu Zhang
- Yuan Luo
- Fang Qiao
- Li-Qiao Chen
- Mei-Ling Zhang
- Zhen-Yu Du
journal: Aquaculture Nutrition
year: 2022
pmcid: PMC9973220
doi: 10.1155/2022/8016616
license: CC BY 4.0
---
# The Adaptive Characteristics of Cholesterol and Bile Acid Metabolism in Nile Tilapia Fed a High-Fat Diet
## Abstract
Since high-fat diet (HFD) intake elevates liver cholesterol and enhanced cholesterol-bile acid flux alleviates its lipid deposition, we assumed that the promoted cholesterol-bile acid flux is an adaptive metabolism in fish when fed an HFD. The present study investigated the characteristic of cholesterol and fatty acid metabolism in *Nile tilapia* (Oreochromis niloticus) after feeding an HFD ($13\%$ lipid level) for four and eight weeks. Visually healthy *Nile tilapia* fingerlings (average weight 3.50 ± 0.05 g) were randomly distributed into four treatments (4-week control diet or HFD and 8-week control diet or HFD). The liver lipid deposition and health statue, cholesterol/bile acid, and fatty acid metabolism were analyzed in fish after short-term and long-term HFD intake. The results showed that 4-week HFD feeding did not change serum alanine transaminase (ALT) and aspartate transferase (AST) enzyme activities, along with comparable liver malondialdehyde (MDA) content. But higher serum ALT and AST enzyme activities and liver MDA content were observed in fish fed 8-week HFD. Intriguingly, remarkably accumulated total cholesterol (mainly cholesterol ester, CE) was observed in the liver of fish fed 4-week HFD, along with slightly elevated free fatty acids (FFAs) and comparable TG contents. Further molecular analysis in the liver showed that obvious accumulation of CE and total bile acids (TBAs) in fish fed 4-week HFD was mainly attributed to the enhancement of cholesterol synthesis, esterification, and bile acid synthesis. Furthermore, the increased protein expressions of acyl-CoA oxidase $\frac{1}{2}$ (Acox1 and Acox2), which serve as peroxisomal fatty acid β-oxidation (FAO) rate-limiting enzymes and play key roles in the transformation of cholesterol into bile acids, were found in fish after 4-week HFD intake. Notably, 8-week HFD intake remarkably elevated FFA content (about 1.7-fold increase), and unaltered TBAs were found in fish liver, accompanied by suppressed Acox2 protein level and cholesterol/bile acid synthesis. Therefore, the robust cholesterol-bile acid flux serves as an adaptive metabolism in *Nile tilapia* when fed a short-term HFD and is possibly via stimulating peroxisomal FAO. This finding enlightens our understanding on the adaptive characteristics of cholesterol metabolism in fish fed an HFD and provides a new possible treatment strategy against metabolic disease induced by HFD in aquatic animals.
## 1. Introduction
Currently, to obtain the protein-sparing effect, high-fat diets (HFD) have been extensively formulated to meet the energy demand of fish growth. However, HFD usually leads to severe accumulation of lipids, mainly triglyceride (TG), in fish [1, 2]. Also, the elevation of cholesterol is a common phenomenon in fish body. In fact, cholesterol is an essential lipid involved in many biological processes for providing a precursor for the biosynthesis of bile acids, vitamin D, and steroid hormones in animals. However, excessive free cholesterol (FC) is the major risk factor for nonalcoholic fatty liver disease (NAFLD) [3–5]. One of the mechanisms by which FC causes lipotoxicity is the generation of reactive oxygen species production and mitochondrial damage [6, 7]. In fish, limited studies focus on the mechanism linking HFD-induced obesity to elevated cholesterol in the liver, while the physiological role of cholesterol in HFD-fed fish is not fully understood.
There is a strong link between cholesterol and fatty acids as previous studies reported that free fatty acids (FFAs) stimulate hepatic cholesterol biosynthesis [8, 9]. It is well-known that cholesterol biosynthesis is a complex enzymatic biochemical process that initializes with substrate acetyl coenzyme A (acetyl-CoA). The acetyl-CoA for cholesterol biosynthesis mainly derives from the following two sources: one is generated by citrate cleavage, and the other is from acetate produced by fatty acid β-oxidation (FAO) [10, 11]. A recent study about the mechanism of diabetes-induced hypercholesterolemia in mice revealed that stimulated liver peroxisomal FAO generated considerable free acetate, a precursor for cholesterol biosynthesis [12]. In a normal physiological condition, cholesterol homeostasis is tightly regulated in animals based on the cellular cholesterol level [13, 14], and high cholesterol levels always promote cholesterol esterification and elimination to prevent lipotoxicity [15, 16]. Previous reports in obese mice demonstrated that the enhancement of cholesterol metabolism, including cholesterol and bile acid efflux, alleviated the deposition of lipids such as cholesterol, FFA, and TG in the liver [17–19]. Therefore, to some extent, enhancing the conversion of FFA to cholesterol and bile acids is an important metabolism pathway for reducing FFA accumulation in the liver.
Previous researches believed that fish could grow normally without exogenous dietary cholesterol supplementation [20, 21], indicating that robust regulation of cholesterol metabolism exists in fish and is enough to meet body cholesterol requirements compared with mammals. Additionally, although HFD feeding would disturb lipid homeostasis, resulting in excessive lipid accumulation and severe lipotoxicity in fish, there is a protective mechanism against the lipotoxicity of FFA in response to temporary HFD intake [22]. However, such a protective mechanism has not been well illustrated in fish. We assumed that its robust regulation of cholesterol metabolism is an efficient process to alleviate HFD-induced lipotoxicity. Therefore, further studies are required to demonstrate the physiological role of cholesterol in HFD-fed fish. Such knowledge helps to give insight into the role of cholesterol metabolism on metabolic disorders and provides a new possible treatment strategy against metabolic disease in aquatic animals. The present study mainly investigated the changes in cholesterol and fatty acid metabolism after short-term (4 weeks) or long-term (8 weeks) HFD feeding in *Nile tilapia* (Oreochromis niloticus), which is a global farmed fish widely used in metabolic studies [22, 23]. Previous studies in *Nile tilapia* showed that approximately $6\%$ lipid level was the optimal dietary lipid level [24–26], whereas $13\%$ lipid level showed a negative impact on growth rates and feed utilization [27]. After the feeding trials, organic index, biochemical indicators, and gene/protein expression involved in cholesterol and fatty acid metabolism were analyzed.
## 2.1. Experimental Animals and Diet and Sample Collection
The animal study was conducted strictly conforming to the procedures approved by the Committee on Ethics of Animal Experiments of East China Normal University (approval number F20190101). Nile tilapia were acclimated into tanks (water volume at 200 liters each tank within an indoor circulating water system) and fed a commercial diet (including $35\%$ protein and $5\%$ lipid, Tongwei, Co. Ltd., Chengdu, China) twice daily (09:00 h and 17:00 h) for two weeks. After acclimatization, visually healthy *Nile tilapia* fingerlings (average weight 3.50 ± 0.05 g) were randomly distributed into tanks (30 fish in each tank, three replicates, and four treatments). The experimental diets were formulated to contain two different lipid levels ($6\%$ and $13\%$; control diet and HFD, respectively), along with approximately $39\%$ protein and $32\%$ carbohydrate levels according to previous studies [24–27]. All dry ingredients were finely ground through 60-mesh screen and thoroughly mixed, and then, soybean oil and distilled water were added to thoroughly blend for producing 2 mm diameter pellets with a twin-screw extruder (South China University of Technology, Guangzhou, China). All the experimental diets were air-dried and stored at −20°C until used. The fish were fed with control diet and HFD (see Table 1) twice daily (09:00 h and 17:00 h) at $4\%$ of their body weight per day for four weeks or eight weeks. The weight of fish in each tank was recorded to adjust diet intake each week. During the whole feeding trial, water temperature, total ammonia-nitrogen, and dissolved oxygen ranged from 27.00 to 29.00°C, 0 to 0.20 mg/L, and 5.00 to 6.50 mg/L, respectively.
At the end of the 4-week or 8-week feeding trial, all fish of control and HFD (three tanks from each treatment) were deprived of food overnight, then anesthetized with MS-222 (0.1 g/L, Sigma, USA), counted, and bulk weighed to analyze the survival rate and growth performance. Four fish were randomly collected from each tank and individually weighed and measured to calculate the condition factor (CF). Then, blood samples were collected from the fish caudal vein using 1 mL syringes. Serum samples were obtained for biochemical analyses after the centrifugation at 3000 rpm for 10 min at 4°C. Additionally, the fish were dissected to obtain mesenteric fat and liver tissues. The weights of these tissues were noted to determine the mesenteric fat index (MFI) and hepatosomatic index (HSI). Serum, liver, and intestinal content samples were immediately frozen into liquid nitrogen and stored at −80°C until further analysis.
## 2.2. Biochemical and Histological Analyses
The total lipid in the liver was extracted by using chloroform/methanol (2: 1, v/v) according to previously described [28]. Liver tissue and intestinal content were homogenized in phosphate-buffered saline (PBS) at a ratio of 1: 10 (w: v) and then centrifuged (4°C, 3000 rpm, 10 min). The supernatants were collected and used for biochemical assays. The total cholesterol (TC) and free cholesterol (FC) were determined using specific commercial assay kits (Solarbio, Beijing, China) according to the method of enzymatic assays [29]. Briefly, the esterified cholesterol was hydrolyzed to free cholesterol by cholesterol esterase; then, the free cholesterol was oxidized to Δ4-cholesterone and H2O2 by cholesterol oxidase. Finally, the H2O2 was oxidized by peroxidase in the presence of 4-aminoantipyrine and phenol to form red quinones, which could be measured at 500 nm by using spectrophotometers. TG, free fatty acids (FFAs), total bile acids (TBAs), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in serum, liver, or intestinal content were measured using specific commercial assay kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) according to the manufacturer's instructions. Briefly, TG was measured with glycerol phosphate oxidase/peroxidase and hydrogen peroxide (GPO/PAP) method. FFA was detected by acyl CoA synthetase and acyl CoA oxidase (ACS-ACOD) method. TBA was detected in the presence of Thio-NAD and 3-α-hydroxysteroid dehydrogenase (3-α-HSD), which converted bile acids to 3-keto steroids and Thio-NADH. LDL-C and HDL-C were determined with cholesterol esterase and cholesterol oxidase after being selectively solubilized by different specific surfactants (see instructions for details). Very low-density lipoprotein cholesterol (VLDL-C) was assessed by ELISA (enzyme-linked immunosorbent assay) kit (Hengyuan Biotech Co., China) according to the manufacturer's protocols. The activities of aspartate transferase (AST) and alanine transaminase (ALT) were detected following the method [30]. Briefly, AST catalyzed α-ketoglutaric acid and aspartate to produce glutamic acid and oxaloacetic acid. Oxaloacetic acid was further decarboxylated to form pyruvate, which react with 2,4-dinitrophenylhydrazine to produce 2,4-dinitrophenylhydrazone. ALT facilitated the conversion of alanine and α-ketoglutarate to glutamate and pyruvate, which reacted with 2,4-dinitrophenylhydrazine to form a yellow pyruvate phenylhydrazone. Superoxide dismutase (SOD) activity and malondialdehyde (MDA) content were assessed according to the methods described previously [31, 32]. SOD catalyzed the dismutation of the superoxide anion into hydrogen peroxide and O2, and superoxide anions acted on WST-1 to produce a water-soluble formazan dye. MDA reacted with thiobarbituric acid (TBA) to generate an MDA-TBA adduct, which could be quantified colorimetrically. Liver tissues were fixed in $4\%$ paraformaldehyde solution and processed for histology in paraffin wax and then cut into 5 μm thick sections for neutral lipid staining following the procedure [33].
## 2.3. Quantitative Real-Time PCR
The quantitative real-time PCR was conducted according to our previous study [34]. Total RNA was extracted from the liver using TRIzol reagent (Takara, Dalian, China) according to the manufacturer's protocol. Briefly, liver tissues (about 30 mg) were homogenized and centrifuged in lysis buffer. The supernatant was mixed with isopropanol for the retention of RNA. After the sediment (RNA) was washed appropriately, it was eluted in 50 μL of RNAse-free H2O. The concentrations of the total RNA obtained were measured using NanoDrop 2000 Spectrophotometer. Next, cDNA was obtained by using the HiScript III RT SuperMix for qPCR (with gDNase, Vazyme Biotech Co., Ltd., Nanjing, China) according to the manufacturer's protocols. Briefly, after eliminating genomic DNA (gDNA) with DNase, the cDNA was synthesized in the presence of HiScript III RT SuperMix. The reaction mixture was incubated under the following condition: 37°C for 15 min, 85°C for 5 s, and 4°C forever. Finally, the qPCR was performed in the CFX96 Real-Time RCR system (Bio-Rad, CA) with ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China) following the manufacturer's instructions. The region of interest (ROI), background, and pure dye spectra of qPCR instrument were regularly calibrated according to the instrument's manual. The amplification condition applied to qPCR was as follows: 95°C for 3 min, followed by 40 cycles of 95°C for 10 s and 60°C for 30 s. The housekeeping (β-actin and elongation factor 1 alpha (EF1α), both of which stably expressed in different dietary groups) and target gene primers used for qPCR are present in Table 2. The amplification efficiencies of the candidate genes ranged from 90 to $105\%$. The expression levels of target genes were determined by using the 2-∆∆Ct method [35].
## 2.4. Western Blotting
The Western blotting was performed following the method described previously [36]. Liver total proteins were obtained by using the ice-cold RIPA lysis buffer supplemented with protease inhibitors (Beyotime Biotechnology, China), and their concentrations were determined following the protocol of total protein quantification assay (New Cell & Molecular Biotech, China). The supernatant protein was mixed with 5× SDS loading buffer and boiled for 15 min. The obtained protein samples were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto nitrocellulose membranes. Immunoblots were further incubated with $5\%$ BSA in phosphate-buffered saline with Tween 20 (TBST) buffer. The target proteins of membrane were incubated with primary antibodies (anti-ACAT1, 1: 800, A13273, ABclonal; anti-ACAT2, 1: 1000, A1399, ABclonal; anti-CYP7A1, 1: 800, A10615, ABclonal; anti-CPT1a, 1: 800, 15184-1-AP, Proteintech; anti-ACLY, 1: 800, ab40793, Abcam; ACOX1, 1: 800, 10957-1-AP, Proteintech; ACOX2, 1: 800, A12796, ABclonal; anti-α-Tubulin, 1: 1000, M-1051-1, Huabio; and anti-β-ACTIN, AB0035, Abways) overnight at 4°C. After washing in TBST, bolts were incubated with secondary antibodies for 1 h at room temperature. The protein images were visualized by using an Odyssey CLx Imager (Licor, USA).
## 2.5. Statistical Analyses
The obtained data were analyzed by two-way ANOVA after testing the normality and homogeneity of variances using the Shapiro-Wilk test and Levene's test, respectively. Significant differences (P ≤ 0.05) were determined by Tukey's HSD post hoc test. Asterisks indicate the differences between the C and HFD groups in the same feeding period. Hash symbols indicate the differences between the 4-week and 8-week feeding trials of the same lipid level diet. All the obtained experimental data were analyzed by using the SPSS Statistics 23.0 software (IBM, Michigan Avenue, USA). All values in the results are presented as the means ± SD (standard deviation).
## 3.1. HFD Feeding Caused Hyperlipidemia
HFD intake (4 or 8 weeks) did not affect survival rate of fish (Figure 1(b)), but after the 4-week HFD feeding trial, although no significant difference in body weight was found between the control and HFD groups (Figure 1(a)), higher CF and MFI were observed in the HFD-fed *Nile tilapia* (Figures 1(c) and 1(d)). However, 8 weeks of HFD intake significantly increased the body weight of fish (Figure 1(a)) and caused remarkably higher CF, MFI, and HSI than the control (Figures 1(c)–1(e)). Furthermore, 4-week HFD intake significantly elevated serum TG (Figure 1(f)), TC (Figure 1(h)), and FC (Figure 1(i)) levels, which further accumulated after 8 weeks of HFD feeding. Notably, 4-week HFD feeding neither changed serum FFA level (Figure 1(g)) nor serum ALT and AST enzyme activities (Figures 1(k) and 1(l)), two important indicators of liver function. But higher serum FFA levels (Figure 1(g)), along with elevated ALT and AST (Figures 1(k) and 1(l)) enzyme activities were observed in the 8-week HFD-fed fish. Additionally, the 4-week HFD feeding elevated serum LDL-C content, which conversely diminished in fish after 8-week HFD intake (Figure 1(j)). Generally, short-term (4 weeks) HFD intake caused hyperlipidemia that was aggravated after long-term (8 weeks) HFD feeding and is accompanied by impaired liver functions in fish.
## 3.2. Biochemical Changes in Lipid Metabolism in the Fish Fed HFD
Furthermore, oil red O staining and biochemical analysis showed that lipid accumulation was evident in the liver of fish fed an HFD for 4 weeks and became more severe in fish fed 8-week HFD (Figures 2(a) and 2(d)). Accordingly, although 4-week HFD intake has no effect on liver MDA content and SOD enzyme activity in fish (Figures 2(b) and 2(c)), increased liver MDA content and decreased SOD enzyme activity were found in the fish after 8-week HFD feeding. Moreover, significantly elevated FFA (Figure 2(f)) rather than TG (Figure 2(e)) contents were observed in the liver of fish fed 4-week HFD. However, remarkably accumulated TC (increased by onefold) was found in fish liver (Figure 2(g)). The increased TC was mainly CE (Figure 2(i)) while FC elevated slightly (Figure 2(h)). In addition, liver LDL-C, HDL-C, and VLDL contents were not changed in fish after the 4-week feeding trial (Figures 2(j)–2(l)). However, liver lipid accumulation caused by 8-week HFD intake was mainly reflected in elevated TG (increased by about 1.4-fold, Figure 2(e)) and considerably accumulated FFA (increased by about 1.5-fold, Figure 2(f)). In addition, there was significant accumulation of TC (Figure 2(g)), which was mainly in FC (Figure 2(h)) rather than CE (Figure 2(i)) in the liver. Liver LDL-C and VLDL contents were significantly increased in HFD-fed fish for 8 weeks (Figures 2(j) and 2(l)). Furthermore, although no significantly altered TC content was detected in the intestinal content of fish fed HFD (Figure 2(n)), the fish exhibited remarkably increased TBA content in the liver and intestinal content after 4-week feeding trial (Figures 2(m) and 2(o)). However, after 8-week feeding trial, both of these parameters were unchanged in fish between the control and HFD groups (Figures 2(m) and 2(o)). These results indicated that 4-week HFD intake caused a high accumulation of CE rather than TG in fish liver, but elevated TG and FFA contents primarily accounted for the liver lipid deposition of fish fed 8-week HFD.
## 3.3. Molecular Changes in Cholesterol Metabolism in the Fish Fed HFD
Further molecular analysis in the liver showed that 4-week HFD intake markedly upregulated the expression of genes associated with cholesterol synthesis (3-hydroxy-3-methylglutaryl-coenzyme A reductase, hmgcr; squalene monooxygenase, sqle; lanosterol synthase, lss; methylsterol monooxygenase 1, msmol; Figure 3(a)), cholesterol esterification (acetyl-CoA acetyltransferase 1, acat1; acetyl-CoA acetyltransferase 2, acat2; Figure 3(b)), and bile acid synthesis (cholesterol 7alpha-hydroxylase, cyp7a1; Figure 3(c)) but did not affect gene expressions related to cholesterol uptake (low-density lipoprotein receptor, ldlr; Figure 3(d)) and efflux (ATP binding cassette subfamily A member 1, abca1; abcg1, ATP binding cassette subfamily G member 1; ATP binding cassette subfamily G member 5, abcg5; ATP binding cassette subfamily G member 8, abcg8; Figure 3(e)). Accordingly, protein expression of Acat1 (Figure 3(g)), Acat2 (Figure 3(h)), and Cyp7a1 (Figure 3(i)) was significantly upregulated in the liver of fish after 4-week HFD feeding (Figure 3(f)). However, 8-week HFD feeding significantly inhibited the expression of genes related to cholesterol synthesis (hmgcr, sqle, and lss; Figure 3(a)) and protein related to cholesterol esterification (Acat1 and Acat2; Figures 3(g) and 3(h)). In addition, this long-term HFD feeding did not affect the expression of genes related to cholesterol efflux (abca1, abcg1, abcg5, and abcg8; Figure 3(e)) and bile acid synthesis (cyp7a1; Figure 3(c)) but enhanced cholesterol uptake-related gene ldlr (Figure 3(d)). These results indicated that the evident accumulation of cholesterol and bile acids in the liver of fish fed 4-week HFD was mainly due to the enhancement of cholesterol synthesis, esterification, and bile acid synthesis. Eight-week HFD intake-induced cholesterol accumulation was linked to the suppressed hepatic LDL-C uptake.
## 3.4. Molecular Changes in TG and FA Metabolism in the Fish Fed HFD
Because cholesterol metabolism is closely related to fatty acid, we further performed the molecular analysis on fatty acid metabolism. The results showed that 4-week HFD intake significantly inhibited the gene expressions associated with fatty acid synthesis (fasn, fatty acid synthase; acly, ATP citrate lyase; accɑ, acetyl-CoA carboxylase alpha; Figure 3(a)) and lipolysis (atgl, adipose triglyceride lipase; Figure 4(b)) but did not alter the expression of genes related to autophagy (atg$\frac{5}{7}$, autophagy-related gene $\frac{5}{7}$ and beclin; Figure 4(c)). Interestingly, peroxisome proliferator-activated receptor α (pparα, a key transcription factor of fatty acid β-oxidation; Figure 4(d)) and Acox$\frac{1}{2}$ (acyl-CoA oxidase $\frac{1}{2}$; peroxisomal β-oxidation rate-limiting enzymes; Figures 4(f), 4(i), and 4(j)) were significantly upregulated in gene and/or protein levels, but Cpt1a (carnitine palmitoyl transferase 1a, a mitochondrial β-oxidation key transporter protein; Figures 4(f) and 4(g)) and Acly (Figures 4(f) and 4(h)) protein expressions were significantly inhibited in the liver of fish fed 4-week HFD. However, 8-week HFD intake significantly stimulated the gene expressions related to lipolysis (atgl and hsl; Figure 4(b)) and suppressed the peroxisomal β-oxidation (Acox$\frac{1}{2}$; Figures 4(i) and 4(j)), but it did not affect fatty acid synthesis (fasn, acly, accα, and srebp1; Figures 4(a) and 4(d)), autophagy (atg$\frac{5}{7}$ and beclin; Figures 4(c)), and mitochondrial β-oxidation (Cpt1a; Figures 4(e) and 4(g)). These results suggested that liver peroxisomal β-oxidation was activated after 4-week HFD feeding but was inhibited when fed HFD for 8 weeks.
## 4.1. Short-Term HFD Feeding Shows an Adaptive Mechanism against Lipotoxicity in the Liver
It is well-known that HFD leads to lipid accumulation in most animals. In fish, obviously increased mass of visceral adipose tissue, elevated plasma TG, FFA, cholesterol levels, and liver lipid content were observed after HFD intake [37, 38]. It should be pointed out that in response to short-term HFD intake, high levels of circulating FFAs are primarily stored in the form of TG in the adipose tissue of fish. This process has been identified as an adaptive mechanism to ease the FFA-induced lipotoxicity in animals [39]. For instance, our previous study in *Nile tilapia* reported that the increased number of adipocytes is the primary strategy to cope with HFD intake at the early stages of the progression of fatty liver, along with relatively stable TG content in the liver [22]. Consistently, higher CF and MFI but not HSI were also observed in the fish fed with HFD for 4 weeks in the present study. Although hyperlipidemia occurred in fish after 4 weeks of HFD intake, there is only a slight elevation of FFA in the fish liver while the TG content remains stable. The liver is the central metabolic organ, where dysregulation of lipid metabolism is a critical factor in liver steatosis [40]. In the present study, 4-week HFD intake significantly inhibited fatty acid synthesis (fasn, acly, and srebp1) and lipolysis (atgl) processes. These results might partially explain why short-term HFD feeding did not cause liver damage, as evident by comparable serum ALT and AST enzyme activities, liver MDA content, and liver SOD enzyme activity in fish. We proposed that liver FFA level was tightly controlled within normal fluctuation levels so that the liver would not be impaired in fish, which supported the protective mechanism against lipotoxicity in the liver of fish after short-term HFD intake.
## 4.2. Short-Term HFD Feeding Triggers Cholesterol-Bile Acid Flux to Eliminate FFA-Derived Cholesterol
Cholesterol biosynthesis begins with acetyl-CoA, which could be supplied by FAO, a critical lipid catabolism process involved in the degradation of FFA [10, 11]. This might explain the previous finding that there is a strong link between cholesterol and FFA [8, 9]. In the present study, short-term HFD feeding caused cholesterol accumulation (mainly CE) rather than TG in fish liver, accompanied by enhanced cholesterol synthesis (hmgcr, sqle, lss, and msmo1) and esterification (Acat1 and Acat2). The obvious elevation of CE along with slightly increased FC content might be explained by the fact that FC, as a polar lipid with lipotoxicity, can be efficiently converted to CE by ACATs, and then, CE is subsequently stored in lipid droplets [16]. Also, the liver eliminates the excessive cholesterol within cells mainly via the transformation of cholesterol to bile acids, which are then being excreted into the gallbladder and intestine [41, 42]. Bile acid synthesis exclusively occurs in the liver and is the only quantitatively significant cholesterol catabolic mechanism [41, 42]. In the present study, short-term HFD feeding increased TBA content in the liver and intestinal content, along with promoted bile acid synthesis (such as Cyp7a1, the first rate-limiting enzyme) in fish liver. These results indicated that the enhanced bile acid synthesis and excretion processes also served as a protective mechanism for eliminating FFA-derived cholesterol in the liver of fish after short-term HFD intake.
Of note, the gross energy is different between the control and high-fat diets in the present study. Thus, it raises an interesting question that whether the adaptive metabolic characteristics of cholesterol and bile acid also exist in *Nile tilapia* fed a high-energy diet. In fact, previous study showed that the high-carbohydrate diet intake also led to high level of hepatic cholesterol and bile acids [43], indicating the robust cholesterol and bile acid metabolism in the liver of fish under the high carbohydrate nutrition. Therefore, we hypothesized that fish could alleviate the metabolic disorders caused by the high-energy diet through enhancing cholesterol and bile acid metabolism. Future studies are necessary to assess the variety and content of bile acids in the gallbladder of fish and reveal their physiological functions in fish under the high energy nutrition.
## 4.3. Short-Term HFD Feeding Enhanced Peroxisomal FAO-Mediated Cholesterol and Bile Acid Synthesis
In mammals, it was reported that increased FAO activity in the liver is an efficient adaptive metabolism to prevent lipid accumulation and liver disease progression [44]. The process of FAO occurs in peroxisomes and mitochondria [45–47], both of which cooperate intimately [48]. For example, mitochondrial FAO was stimulated to alleviate FFA accumulation as a compensatory mechanism when peroxisomal FAO is suppressed [49]. On the contrary, stimulation of peroxisomal FAO inhibited mitochondrial FAO by promoting malonyl-CoA formation in the liver of mice fed an HFD [50]. In the present study, 4-week HFD feeding increased Acox1 and Acox2 (peroxisomal FAO rate-limiting enzymes) but inhibited Cpt1a (mitochondrial FAO rate-limiting enzyme) protein expressions in fish liver. This result suggests a similar cooperative relationship between mitochondrial and peroxisomal FAO in fish. Notably, substrate specificity studies in mammals uncovered that ACOX1 is involved in the oxidation of straight-chain fatty acids, but ACOX2 is responsible for the degradation of the branched-chain fatty acids and uniquely plays an important role in the transformation of cholesterol into bile acid [51]. In fact, it was estimated that the proportion of peroxisomal FAO is about $30\%$ of total FAO under normal conditions in mice. However, this proportion elevated significantly in mice under the diabetic condition as liver peroxisomal FAO was stimulated in diabetes. Therefore, it was demonstrated that peroxisomal FAO plays an important role in regulating cholesterol synthesis, and enhanced peroxisomal FAO contributed to high cholesterol level in diabetes mice [12]. Also, in the present study, the stimulated peroxisomal FAO might primarily contribute to the enhanced cholesterol and bile acid synthesis in the liver of fish fed a short-term HFD, thereby reducing FFA accumulation and alleviating liver injury caused by lipotoxicity. Since, long-chain fatty acids as endogenous ligands for PPARα, elevated hepatic FFA level in diabetic mice activated PPARα and resulted in promoting the transcription of genes related to peroxisomal FAO [52, 53]. Our previous study in *Nile tilapia* also indicated that the activation of PPARα by fenofibrate significantly promoted mitochondrial and peroxisomal FAO in the liver [54]. In the present study, 4-week HFD feeding induced pparα gene expression, which possibly contributed to the enhanced peroxisomal FAO. These results indicated that short-term HFD feeding enhanced peroxisomal FAO-mediated cholesterol and bile acid synthesis (see Figure 5). Therefore, peroxisomal FAO may also play a critical role in regulating cholesterol and bile acid synthesis in fish.
## 4.4. Long-Term HFD Intake Suppressed Conversion of FFA to Cholesterol
Animals possess an adaptive metabolism during the short-term HFD intake. The enlargement of adipocytes, which contain many lipid droplets, is the normal physiological consequence and protective strategy against lipotoxicity from high FFA [22, 55]. However, the size or numbers of adipocytes would not continuously increase with the duration of HFD intake; thereby, the maintenance of lipid homeostasis would be disrupted after long-term HFD intake. This would result in pathological consequences such as excessive lipid deposition in other tissues such as the liver, accompanied by high levels of oxidative and inflammatory factors in animals, including fish [1, 56]. The increased reactive oxygen species (ROS) produced by lipid peroxidation in hepatocytes would seriously impair lipid metabolism and further increase lipid accumulation and cause liver damage [57]. In the present study, 8-week HFD intake further elevated serum TG and TC levels and caused higher serum ALT and AST enzyme activities, indicating impaired liver in fish after long-term HFD intake. Also, more severe lipid accumulation was observed in fish liver, as is evident by obviously accumulated TG (1.4-fold increase) and FFA (1.7-fold increase). Consistently, elevated MDA content and diminished SOD activity were observed in the liver of fish fed 8-week HFD in the present study. These results might be partially due to perturbed lipid metabolism, as HFD stimulated lipolysis and suppressed peroxisomal β-oxidation (Acox2) protein expression in fish liver, although it did not affect the fatty acid synthesis, autophagy, and mitochondrial β-oxidation. Additionally, the inhibited cholesterol synthesis might link to deficient peroxisomal FAO, which provided substrate acetyl-CoA in fish liver. The disturbance of cholesterol metabolism caused by HFD intake is a critical factor for FC accumulation [2, 58]. Previous study showed that rats with HFD-induced obesity presented cholesterol accumulation in the liver via inhibiting CYP7A1 gene expression, which remarkably decreased the conversion of cholesterol to bile acids [58]. In Nile tilapia, long-term HFD feeding caused hepatic cholesterol accumulation mainly due to impaired assembly of VLDL and HLD particles [2]. However, in the present study, 8-week HFD feeding significantly elevated liver FC content that might be associated with compromised esterification of cholesterol. Additionally, LDL-C content and its receptor ldlr gene expression were increased, whereas cholesterol efflux and bile acid synthesis did not change in the liver of fish fed HFD. Accordingly, declined serum LDL-C level was observed in fish after 8-week HFD intake, accompanied by unaltered TBA levels in the liver and intestinal content. These results suggested that accumulated hepatic cholesterol resulted from the increased LDL-C uptake from the blood, and long-term HFD intake also caused the disturbance of cholesterol metabolism in fish. The contradictory conclusions from various studies might be due to the discrepancy between species and their nutrition status. Of note, the remarkably elevated FFA content (about 1.7-fold increase) was found in the liver of fish, along with suppressed Acox2 protein level and downregulated cholesterol/bile acid synthesis. Hence, the deficient liver peroxisomal FAO might block the conversion of FFA to cholesterol and bile acids, thereby failing to alleviate FFA accumulation in fish after 8-week HFD intake. Therefore, the elevated FFA and FC contents primarily accounted for liver lipotoxicity and injury in fish fed a long-term HFD (see Figure 5).
## 5. Conclusion
The present study revealed that short-term HFD intake triggered protective mechanisms against FFA accumulation and liver injury. Of note, short-term HFD feeding caused cholesterol accumulation (mainly CE) rather than TG in fish liver, along with increased TBA content in liver and intestinal content. Furthermore, the stimulated hepatic peroxisomal FAO, which provides substrate acetyl-CoA for the biosynthesis of cholesterol, might primarily contribute to cholesterol and bile acid synthesis and efflux, thereby alleviating FFA accumulation in the liver of fish during the short-term HFD intake. However, long-term HFD intake caused evident elevation of TG and FFA, along with accumulated FC content in fish liver. The elevated FC content resulted from the enhancement of LDL-C uptake indicating long-term HFD intake also caused the disturbance of cholesterol metabolism. Of note, long-term HFD intake suppressed peroxisomal FAO and cholesterol/bile acid synthesis, indicating the blocked conversion of FFA to cholesterol and bile acids, thereby failing to alleviate FFA accumulation in fish liver. Therefore, the robust cholesterol-bile acid flux serves as an adaptive metabolism in *Nile tilapia* fed an HFD, possibly via stimulating peroxisomal FAO pathway. This finding enlightens our understanding of the adaptive characteristics of cholesterol metabolism in fish fed an HFD and provides a new possible treatment strategy against metabolic disease induced by HFD in aquatic animals.
## Data Availability
The datasets generated during the current study are available from the corresponding author on reasonable request.
## Conflicts of Interest
All authors declare no conflicts of interests.
## References
1. Du Z. Y., Clouet P., Huang L. M.. **Utilization of different dietary lipid sources at high level in herbivorous grass carp (**. (2008) **14** 77-92. DOI: 10.1111/j.1365-2095.2007.00507.x
2. Jia R., Cao L.-P., Du J.-L.. **Effects of high-fat diet on steatosis, endoplasmic reticulum stress and autophagy in liver of tilapia (**. (2020) **7** p. 363. DOI: 10.3389/fmars.2020.00363
3. Kim E. J., Kim B. H., Seo H. S.. **Cholesterol-induced non-alcoholic fatty liver disease and atherosclerosis aggravated by systemic inflammation**. (2014) **9**. DOI: 10.1371/journal.pone.0097841
4. Min H.-K., Kapoor A., Fuchs M.. **Increased hepatic synthesis and dysregulation of cholesterol metabolism is associated with the severity of nonalcoholic fatty liver disease**. (2012) **15** 665-674. DOI: 10.1016/j.cmet.2012.04.004
5. Musso G., Gambino R., Cassader M.. **Cholesterol metabolism and the pathogenesis of non-alcoholic steatohepatitis**. (2013) **52** 175-191. DOI: 10.1016/j.plipres.2012.11.002
6. Anavi S., Hahn-Obercyger M., Madar Z., Tirosh O.. **Mechanism for HIF-1 activation by cholesterol under normoxia: a redox signaling pathway for liver damage**. (2014) **71** 61-69. DOI: 10.1016/j.freeradbiomed.2014.03.007
7. Poli G., Biasi F., Leonarduzzi G.. **Oxysterols in the pathogenesis of major chronic diseases**. (2013) **1** 125-130. DOI: 10.1016/j.redox.2012.12.001
8. Gu Y., Yin J.. **Saturated fatty acids promote cholesterol biosynthesis: effects and mechanisms**. (2020) **18, article 100201**. DOI: 10.1016/j.obmed.2020.100201
9. Schäfer H. F., Kattermann R.. **Fatty acid modulation of HepG2 cell cholesterol biosynthesis and esterification**. (1992) **25** 325-330. DOI: 10.1016/0009-9120(92)80008-5
10. Dupont J.. **Fatty acid oxidation in relation to cholesterol biosynthesis in rats**. (1966) **1** 415-421. DOI: 10.1007/BF02532545
11. Cenedella R. J., Allen A.. **Differences between the metabolism of linoleic and palmitic acid: utilization for cholesterol synthesis and oxidation to respiratory CO2**. (1969) **4** 155-158. DOI: 10.1007/BF02531936
12. Zhang X., Wang Y., Yao H.. **Peroxisomal**. (2022) **298**. DOI: 10.1016/j.jbc.2022.101572
13. Goedeke L., Fernández-Hernando C.. **Regulation of cholesterol homeostasis**. (2012) **69** 915-930. DOI: 10.1007/s00018-011-0857-5
14. Luo J., Yang H., Song B.-L.. **Mechanisms and regulation of cholesterol homeostasis**. (2020) **21** 225-245. DOI: 10.1038/s41580-019-0190-7
15. Kennedy M. A., Barrera G. C., Nakamura K.. **ABCG1 has a critical role in mediating cholesterol efflux to HDL and preventing cellular lipid accumulation**. (2005) **1** 121-131. DOI: 10.1016/j.cmet.2005.01.002
16. Chang T.-Y., Li B.-L., Chang C. C. Y., Urano Y.. **Acyl-coenzyme a:cholesterol acyltransferases**. (2009) **297** E1-E9. DOI: 10.1152/ajpendo.90926.2008
17. Ma D., Liu W., Wang Y.. **ApoA-I or ABCA1 expression suppresses fatty acid synthesis by reducing 27-hydroxycholesterol levels**. (2014) **103** 101-108. DOI: 10.1016/j.biochi.2014.04.010
18. Su K., Sabeva N. S., Wang Y.. **Acceleration of biliary cholesterol secretion restores glycemic control and alleviates hypertriglyceridemia in obese db/db mice**. (2014) **34** 26-33. DOI: 10.1161/ATVBAHA.113.302355
19. Su K., Sabeva N. S., Liu J.. **The ABCG5 ABCG8 sterol transporter opposes the development of fatty liver disease and loss of glycemic control independently of phytosterol accumulation**. (2012) **287** 28564-28575. DOI: 10.1074/jbc.M112.360081
20. Jobling M.. **National Research Council (NRC): nutrient requirements of fish and shrimp**. (2012) **20** 601-602. DOI: 10.1007/s10499-011-9480-6
21. Sealey W. M., Craig S. R., Gatlin D. M.. **Dietary cholesterol and lecithin have limited effects on growth and body composition of hybrid striped bass (**. (2001) **7** 25-31. DOI: 10.1046/j.1365-2095.2001.00159.x
22. He A. Y., Ning L. J., Chen L. Q.. **Systemic adaptation of lipid metabolism in response to low- and high-fat diet in Nile tilapia (**. (2015) **3**. DOI: 10.14814/phy2.12485
23. Zhang H., Wen J.-J., Zhang Y.-N.. **Forskolin reduces fat accumulation in Nile tilapia (**. (2019) **230** 7-15. DOI: 10.1016/j.cbpa.2018.12.011
24. Eid A. E., Ahmed R. A., Baghdady E. S., Abd El_Naby A. S.. **Dietary lipids requirement for nile tilapia (**. (2019) **22** 463-469. DOI: 10.21608/ejnf.2019.79439
25. Abdel-Tawwab M., Ahmad M. H., Khattab Y. A. E., Shalaby A. M. E.. **Effect of dietary protein level, initial body weight, and their interaction on the growth, feed utilization, and physiological alterations of Nile tilapia, Oreochromis niloticus (L.)**. (2010) **298** 267-274. DOI: 10.1016/j.aquaculture.2009.10.027
26. Boonanuntanasarn S., Jangprai A., Kumkhong S.. **Adaptation of Nile tilapia (Oreochromis niloticus) to different levels of dietary carbohydrates: new insights from a long term nutritional study**. (2018) **496** 58-65. DOI: 10.1016/j.aquaculture.2018.07.011
27. Abdel-Ghany H. M., Mohamed El-S S., Ezzat A. A.. **Effects of different levels of dietary lipids on growth performance, liver histology and cold tolerance of Nile tilapia (**. (2021) **96, article 102833**. DOI: 10.1016/j.jtherbio.2020.102833
28. Folch J., Lees M., Stanley G. H. S.. **A simple method for the isolation and purification of total lipides from animal tissues**. (1957) **226** 497-509. DOI: 10.1016/S0021-9258(18)64849-5
29. Allain C. C., Poon L. S., Chan C. S. G., Richmond W., Fu P. C.. **Enzymatic determination of total serum cholesterol**. (1974) **20** 470-475. DOI: 10.1093/clinchem/20.4.470
30. Reitman S., Frankel S.. **A colorimetric method for the determination of serum glutamic oxalacetic and glutamic pyruvic transaminases**. (1957) **28** 56-63. DOI: 10.1093/ajcp/28.1.56
31. Misra H. P., Fridovich I.. **The role of superoxide anion in the autoxidation of epinephrine and a simple assay for superoxide dismutase**. (1972) **247** 3170-3175. DOI: 10.1016/S0021-9258(19)45228-9
32. Buege J. A., Aust S. D.. **[30] Microsomal lipid peroxidation**. (1978) 302-310. DOI: 10.1016/S0076-6879(78)52032-6
33. Lillie R. D., Fullmer H.. (1976)
34. Li R. X., Zhou W. H., Ren J.. **Dietary sodium lactate promotes protein and lipid deposition through increasing energy supply from glycolysis in Nile tilapia (**. (2022) **550, article 737858**. DOI: 10.1016/j.aquaculture.2021.737858
35. Livak K. J., Schmittgen T. D.. **Analysis of relative gene expression data using real-time quantitative PCR and the 2**. (2001) **25** 402-408. DOI: 10.1006/meth.2001.1262
36. Han S.-L., Wang J., Zhang Y.-X.. **Inhibited autophagy impairs systemic nutrient metabolism in Nile tilapia**. (2019) **236, article 110521**. DOI: 10.1016/j.cbpa.2019.06.021
37. Du Z. Y., Liu Y. J., Tian L. X., Wang J. T., Wang Y., Liang G. Y.. **Effect of dietary lipid level on growth, feed utilization and body composition by juvenile grass carp (**. (2005) **11** 139-146. DOI: 10.1111/j.1365-2095.2004.00333.x
38. Wang J.-T., Liu Y.-J., Tian L.-X.. **Effect of dietary lipid level on growth performance, lipid deposition, hepatic lipogenesis in juvenile cobia (Rachycentron canadum)**. (2005) **249** 439-447. DOI: 10.1016/j.aquaculture.2005.04.038
39. Boden G.. **Obesity, insulin resistance, type 2 diabetes and free fatty acids**. (2006) **1** 499-505. DOI: 10.1586/17446651.1.4.499
40. Nguyen P., Leray V., Diez M.. **Liver lipid metabolism**. (2008) **92** 272-283. DOI: 10.1111/j.1439-0396.2007.00752.x
41. Li T., Chiang J. Y.. **Bile acid signaling in metabolic disease and drug therapy**. (2014) **66** 948-983. DOI: 10.1124/pr.113.008201
42. Wang Y., Ding W.-X., Li T.. **Cholesterol and bile acid-mediated regulation of autophagy in fatty liver diseases and atherosclerosis**. (2018) **1863** 726-733. DOI: 10.1016/j.bbalip.2018.04.005
43. Chen P., Zhu Y., Wu X., Gu X., Xue M., Liang X.. **Metabolic adaptation to high-starch diet in largemouth bass (**. (2022) 1-14. DOI: 10.1017/S0007114522001180
44. Barbier-Torres L., Fortner K. A., Iruzubieta P.. **Silencing hepatic MCJ attenuates non-alcoholic fatty liver disease (NAFLD) by increasing mitochondrial fatty acid oxidation**. (2020) **11** p. 3360. DOI: 10.1038/s41467-020-16991-2
45. Houten S. M., Wanders R. J. A.. **A general introduction to the biochemistry of mitochondrial fatty acid**. (2010) **33** 469-477. DOI: 10.1007/s10545-010-9061-2
46. Wanders R. J., Ferdinandusse S., Brites P., Kemp S.. **Peroxisomes, lipid metabolism and lipotoxicity**. (2010) **1801** 272-280. DOI: 10.1016/j.bbalip.2010.01.001
47. Wanders R. J., Waterham H. R.. **Biochemistry of mammalian peroxisomes revisited**. (2006) **75** 295-332. DOI: 10.1146/annurev.biochem.74.082803.133329
48. Crockett E. L., Sidell B. D.. **Peroxisomal beta-oxidation is a significant pathway for catabolism of fatty acids in a marine teleost**. (1993) **264** R1004-R1009. DOI: 10.1152/ajpregu.1993.264.5.r1004
49. Zeng J., Deng S., Wang Y., Li P., Tang L., Pang Y.. **Specific inhibition of Acyl-CoA oxidase-1 by an acetylenic acid improves hepatic lipid and reactive oxygen species (ROS) metabolism in rats fed a high fat diet**. (2017) **292** 3800-3809. DOI: 10.1074/jbc.M116.763532
50. Chen X., Shang L., Deng S.. **Peroxisomal oxidation of erucic acid suppresses mitochondrial fatty acid oxidation by stimulating malonyl-CoA formation in the rat liver**. (2020) **295** 10168-10179. DOI: 10.1074/jbc.RA120.013583
51. Ferdinandusse S., Denis S., van Roermund C. W. T.. **A novel case of ACOX2 deficiency leads to recognition of a third human peroxisomal acyl-CoA oxidase**. (2018) **1864** 952-958. DOI: 10.1016/j.bbadis.2017.12.032
52. Reddy J. K., Hashimoto T.. **Peroxisomal**. (2001) **21** 193-230. DOI: 10.1146/annurev.nutr.21.1.193
53. Thomassen M. S., Norseth J., Christiansen E. N.. **Long-term effects of high-fat diets on peroxisomal beta-oxidation in male and female rats**. (1985) **20** 668-674. DOI: 10.1007/BF02534385
54. Ning L.-J., He A.-Y., Li J.-M.. **Mechanisms and metabolic regulation of PPAR**. (2016) **1861** 1036-1048. DOI: 10.1016/j.bbalip.2016.06.005
55. Weyer C., Foley J. E., Bogardus C., Tataranni P. A., Pratley R. E.. **Enlarged subcutaneous abdominal adipocyte size, but not obesity itself, predicts type II diabetes independent of insulin resistance**. (2000) **43** 1498-1506. DOI: 10.1007/s001250051560
56. Posey K. A., Clegg D. J., Printz R. L.. **Hypothalamic proinflammatory lipid accumulation, inflammation, and insulin resistance in rats fed a high-fat diet**. (2009) **296** E1003-E1012. DOI: 10.1152/ajpendo.90377.2008
57. Jin Y., Tan Y., Chen L., Liu Y., Ren Z.. **Reactive oxygen species induces lipid droplet accumulation in HepG2 cells by increasing perilipin 2 expression**. (2018) **19** p. 3445. DOI: 10.3390/ijms19113445
58. Duan Y., Zhang F., Yuan W.. **Hepatic cholesterol accumulation ascribed to the activation of ileum Fxr-Fgf15 pathway inhibiting hepatic Cyp7a1 in high-fat diet-induced obesity rats**. (2019) **232, article 116638**. DOI: 10.1016/j.lfs.2019.116638
|
---
title: 'Quercetin Application for Common Carp (Cyprinus carpio): I. Effects on Growth
Performance, Humoral Immunity, Antioxidant Status, Immune-Related Genes, and Resistance
against Heat Stress'
authors:
- Kobra Armobin
- Ehsan Ahmadifar
- Hossein Adineh
- Mahsa Naderi Samani
- Naser Kalhor
- Sevdan Yilmaz
- Seyed Hossein Hoseinifar
- Hien Van Doan
journal: Aquaculture Nutrition
year: 2023
pmcid: PMC9973228
doi: 10.1155/2023/1168262
license: CC BY 4.0
---
# Quercetin Application for Common Carp (Cyprinus carpio): I. Effects on Growth Performance, Humoral Immunity, Antioxidant Status, Immune-Related Genes, and Resistance against Heat Stress
## Abstract
This study was done to evaluate the effect of different quercetin levels on growth performance, immune responses, antioxidant status, serum biochemical factors, and high-temperature stress responses in common carp (Cyprinus carpio). A total number of 216 common carp with an average weight of 27.21 ± 53 g were divided into 12 tanks (four treatments × three replications) and fed 0 mg/kg quercetin (T0), 200 mg/kg quercetin (T1), 400 mg/kg quercetin (T2), and 600 mg/kg quercetin (T3) for 60 days. There were significant differences in growth performance, and the highest final body weight (FBW), weight gain (WG), specific growth rate (SGR), and feed intake (FI) were observed in T2 and T3 ($P \leq 0.05$). Different quercetin levels significantly increased complement pathway activity (ACH50) and lysozyme activity both before and after heat stress ($P \leq 0.05$). Catalase (CAT), glutathione peroxidase (GPx), and malondialdehyde (MDA) were significantly increased in fish exposed to heat stress, but fish fed with a supplemented diet with quercetin showed the lowest levels both before and after heat stress ($P \leq 0.05$). Superoxide dismutase (SOD) levels were significantly enhanced in fish fed diets supplemented with quercetin in both phases ($P \leq 0.05$). Different quercetin levels led to a significant decrease in alanine aminotransferase (ALT) and aspartate aminotransferase (AST) before and after the challenging test ($P \leq 0.05$). Glucose and cortisol levels were significantly higher in the control group compared to the other treatments in both phases ($P \leq 0.05$). The expression of glutathione peroxidase (GPx) and lysozyme was markedly upregulated in fish fed with quercetin-supplemented diets ($P \leq 0.05$). No marked effects were observed for growth hormone (GR) and interleukin-8 (IL8) ($P \leq 0.05$). In conclusion, dietary quercetin supplementations (400-600 mg/kg quercetin) improved growth performance, immunity, and antioxidant status and increased tolerance to heat stress.
## 1. Introduction
Aquaculture is developing incrementally in parallel with global population growth. Nevertheless, intensive production activities, sudden temperature changes, abnormal climate, and other environmental conditions as such cause stress to fish and hamper production quality and yield by triggering immune depression and increased infectious epidemics. One of the most important environmental items in the aquaculture industry is temperature because it can affect the metabolism, feeding behavior, growth performance, survival, and disease resistance of aquatic animals [1–3]. Temperature changes decreased the growth, feed efficiency, physiological function, immune response, and oxidative status in the organs of aquatic animals ([3–5].
To combat such challenges, the involvement of functional feed additives in the fish diet is environment-friendly and efficient. These additives can potentially increase the growth and reproductive performance of fish, fillet quality, and resistance to diseases and environmental conditions.
Polyphenols and polyphenol-rich additives are added to aquatic animal feed to increase growth performance, immune response, antioxidant status, and disease resistance [6, 7]. Being considered the main class of flavonoids (flavonols), a family of polyphenolic compounds, quercetin is abundant in many fruits and vegetables, including tea, onions, apples, strawberries, cabbage, nuts, and cauliflower.
It is widely acknowledged that quercetin inhibits biofilm production of many pathogenic bacteria such as Enterococcus faecalis, Staphylococcus aureus, Streptococcus mutans, Escherichia coli, and *Pseudomonas aeruginosa* with its features such as suppression of quorum-sensing pathways, inhibition of efflux pumps, disruption or alteration of the plasma membrane, and blocking nucleic acid synthesis [8]. Moreover, quercetin has many beneficial properties such as being an anticancer [9], antioxidant, and anti-inflammatory [10].
Previous findings indicated that dietary quercetin had a positive effect on the antioxidant status [11, 12], growth performance [13, 14], flesh quality [15], hematological and immune parameters [16], disease resistance [17, 18], and grouper iridovirus defense properties [19–21]. Some researches showed that dietary quercetin enhanced the growth performance of chicken [22] and boosted sensory scores such as color, texture, and overall acceptability of goat loin [23]. In aquatic animals, quercetin above 400 mg/kg improved the growth performance of tilapia (Oreochromis spp.) [ 24].
Previous studies have reported that dietary herbal supplementations reduce stress markers in some fish and/or shellfish species like *Macrobrachium rosenbergii* [25], *Megalobrama amblycephala* [26], and Oncorhynchus mykiss [27] subjected to high temperature. However, no information is available as to the effect of dietary quercetin on aquatic animals exposed to high-temperature stress. Therefore, the present study was carried out to compare the effect of dietary quercetin supplementations on growth performance, some immune parameters, antioxidant status, some serum biochemical variables, and high-temperature stress responses in fish.
## 2.1. Experimental Diets
Four experimental diets with different quercetin (>$95\%$ purity, Sigma Chemical Co., USA) levels were formulated (Table 1). Briefly, the dry ingredients were mixed thoroughly. After the liquid ingredients were diffused into the mixture, deionized water was added (250 ml per kg of diet). The prepared mixture was extruded in an electric meat grinder (MG1400R, Pars Khazar, Tehran, Iran), and feeds were passed off based on the fish's mouth size (1 mm in diameter). Finally, four experimental diets were prepared including 0 mg/kg quercetin (T0), 200 mg/kg quercetin (T1), 400 mg/kg quercetin (T2), and 600 mg/kg quercetin (T3). The pellets were dried in a drying cabinet and then stored at 4°C until use. The chemical composition, moisture, crude protein, lipids, and ash contents were confirmed by following the standard methods [28].
## 2.2. Fish and Experimental Condition
A total number of 216 common carp with an average weight of 27.21 ± 53 g were obtained from a private fish farm and moved to the research hall of the University of Gonbad (Gonbad, Iran). They were stored in 12 tanks with a density of 18 fish per tank and acclimated to experimental conditions for 10 days. During the acclimation period, common carp were fed a basal diet twice a day. At the end of adaptation time, these tanks were randomly nominated to 4 treatments with 3 replicates, and fish were fed at a rate of $2.5\%$ of body weight twice a day for 60 days. Fish waste and half of the aquarium water were siphoned daily and replaced with well-aerated water. Water quality parameters such as temperature, dissolved oxygen, and pH were regularly measured and kept at standard levels for common carp (temperature 24.1 ± 0.5C, dissolved oxygen 6.8 ± 0.2mg/l, pH7.36 ± 0.1, and photoperiod 12D: 12L) [29].
## 2.3. Biometry and Blood Sample Collection
After a 24 h fasting period, all fish in each tank were anesthetized using 200 mg l−1 clove powder, and the weight of each fish was recorded [30]. The growth performance and survival rate were calculated using a common formula [7]. Three fish per tank (9 fish per treatment) were randomly selected to collect the blood sample. Blood samples were collected via the caudal vein of each fish using a sterile syringe and introduced into tubes. The tubes were centrifuged (5000 rpm for 10 min at 4°C) [29], and then, the supernatant was separated and stored at -20°C.
## 2.4. Heat Challenge
After 60 days of feeding, a heat stress challenge was administered according to the Dawood et al. [ 30] study in triplicate. First, feeding was stopped during the test, and seven starved fish per tank (21 fish per treatment) were held at a similar condition with a high temperature (32°C) for 48 hours for heat stress. Aquarium heaters were used to increase the temperature up to 32°C, and the water temperature was checked frequently by portable multiparameter meters (YSI, China).
## 2.5. Blood and Tissue Sampling
After the challenging test, all fish in each tank were anesthetized using 200 mg l−1 clove powder, and three fish per tank (9 fish per treatment) were selected to collect blood samples. Blood samples were collected via the caudal vein of each fish using a sterile syringe and introduced into tubes. The tubes were centrifuged (5000 rpm for 10 min at 4°C) [29], and then, the supernatant was separated and stored at -20°C.
## 2.6. Biochemistry Assay
Before and after challenging tests, glucose, alanine aminotransferase (ALT), aspartate aminotransferase (AST), catalase (CAT), glutathione peroxidase (GPx), malondialdehyde (MDA), and superoxide dismutase (SOD) were assayed using commercial kits (Pars Azmoon, Iran). Serum lysozyme activity was assayed using the method of Saurabh and Sahoo [31] with turbidimetry. First, a suspension of *Micrococcus lysodeikticus* was prepared by dissolving 0.2 mg ml−1 in a 0.05 M sodium phosphate buffer (pH 6.2). Then, 50 μl serum was added to a 2 ml suspension of Micrococcus lysodeikticus, and the reaction mixture was divided into a 96-well microtitre plate (Hiperion, Germany), and initial OD was measured spectrophotometrically at 450 nm immediately. The final OD was measured after incubation of the reaction mixture at 24°C for 1 h. A unit of lysozyme activity was defined as the sample amount causing a decrease in absorbance of 0.001/min lysozyme of the sample calibrated using a standard curve determined with hen's egg white lysozyme (Sigma) in PBS. Serum alternative complement (ACH50) activity was measured based on Yano et al. [ 32], in which rabbit red blood cells (RBCs) were used as a target. Serum cortisol levels were measured by the competitive ELISA method using a commercial kit (IBL Co., Gesellschaft für Immunchemie und Immunbiologie).
## 2.7. Gene Expression
Total RNA of fish livers collected after a challenging test was isolated using the GeneAll Kit (GeneAll Biotechnology, Seoul, Korea). After evaluating RNA quality by electrophoresis agarose gels, 2 μg of total RNA was treated with DNase I and used for first-strand cDNA synthesis using oligo (dT) primers, 10 mM dNTPs, and reverse transcriptase according to the manufacturer's instructions (Thermo Scientific, Germany). Gene-specific primers were mentioned for RT-PCR analysis in Table 2. The beta-actin gene was used as an internal control. qPCR was carried out using RealQ Plus Master Mix Green (AMPLIQONIII) following the manufacturer's instructions. The reaction consisting of 10 μl SYBR green mix, 1 μl of diluted cDNA, 0.5 μl of each primer, and Millipore water was added to a final volume of 20 μl. The following program was used for the reaction: 10 min at 95°C, denaturation at 95°C for 10 s, annealing at 60°C for 40 s, and extension at 72°C for 35 s for 40 cycles. The RNA level was calculated based on the 2-ΔΔCT method [33]. Three biological repeats were done for each experiment.
## 2.8. Statistical Analysis
Data (mean ± SD) were analyzed by one-way analysis of variance (ANOVA) followed by Duncan's multiple range test to compare the means between groups. Before analyses, data were assessed for normality and homogeneity of variance with the Shapiro-Wilk test and Levene's test, respectively. Two-way ANOVA was also used to test the effects of the diet, heat stress, and their interactions. Statistical analyses were conducted using SPSS software version 23, and $P \leq 0.05$ was the accepted significance level.
## 3.1. Growth Performance
There were significant differences in growth performance, and the highest FBW, WG, SGR, and FI were seen in T2 and T3 (Table 3, $P \leq 0.05$). FCR was significantly decreased in fish fed quercetin (Table 3, $P \leq 0.05$).
## 3.2. Serum Immune Response
Lysozyme and ACH50 activity were remarkably decreased when common carps were exposed to heat stress, and the lowest activity was observed in fish fed a control diet and fish fed with supplemented diets, representing the highest levels (Table 4, $P \leq 0.05$). Before and after heat stress, different quercetin levels significantly increased ACH50 and lysozyme activity, and the highest activities were seen in fish fed 400 (T2) and 600 (T3) mg/kg quercetin (Table 4, $P \leq 0.05$).
## 3.3. Antioxidant Enzymes
Catalase, MDA, and GPx were significantly raised in fish exposed to heat stress, with the highest level in fish fed with a control diet and fish fed with a supplemented diet with quercetin showing the lowest levels (Table 4, $P \leq 0.05$). SOD levels decreased significantly in fish exposed to heat stress (when all treatments were combined); also, there was a significant difference in the levels of SOD amongst the treatments before and after heat challenge, and the highest level was recorded in T2 in both phases (Table 5, $P \leq 0.05$).
## 3.4. Stress Parameters
Before and after heat stress, glucose and cortisol levels were significantly higher in the control group compared to quercetin supplementation treatments (Table 6, $P \leq 0.05$). Exposure of common carp to high temperatures led to a significant increase in AST and ALT in all treatments, but fish fed with 600 mg/kg quercetin had the lowest levels (Table 6, $P \leq 0.05$). Moreover, before and after the challenge, the highest and lowest activities of ALT and AST were seen in the control group and T3, respectively (Table 6, $P \leq 0.05$).
## 3.5. Gene Expression
After heat stress exposure, the expression of GPx (Figure 1(a)) was markedly upregulated, and the highest RNA levels were observed in fish fed 400 mg/kg quercetin ($P \leq 0.05$). No marked effects were observed for GR (Figure 1(b)) and IL8 (Figure 1(c)), but they were higher in fish fed with quercetin-supplemented feeds ($P \leq 0.05$). The lysozyme gene expression was markedly higher in common carp fed with quercetin-supplemented feeds than in the control group (Figure 1(d)).
## 4. Discussion
In this study, the dietary incorporation of quercetin significantly improved growth and feed efficiency in common carp. Accordingly, the best WG, SGR, FI, and FCR were obtained in the fish fed with 400 and 600 mg/kg quercetin-supplemented diets. Previous findings indicated that dietary quercetin had a positive effect on the WG, SGR, and FCR in *Nile tilapia* (Oreochromis niloticus) [34], grass carp (Ctenopharyngodon idella) (), and common carp [13]. The promoting effect of quercetin on fish growth performance may be related to their better efficacy in stimulating the secretion of digestive enzymes like protease, amylase, and lipase [13, 34], decreasing the abundance of harmful bacteria of the intestine and promoting the populations of beneficial bacteria [35], ameliorating the intestinal barrier function, and improving the surface area of intestinal villi and goblet cells [36].
ACH50 activity is a significant parameter that benefitted in the evaluation of humoral nonspecific immune response in fish. A decrease in serum ACH50 levels is reported in different stress situations that fish are exposed to. In the present study, serum ACH50 activity significantly increased in the group supplemented with the doses of 400 and 600 mg/kg quercetin before the stress and after the stress compared to the control group. In conclusion, as the liver is the main source of complement proteins, the increase in ACH50 levels can be explained by improving the health and function of the liver. Our results in terms of increasing ACH50 activity are in full agreement with findings of earlier studies in common carp fed diets incorporated with 200 or 800 mg/kg quercetin [16]. Moreover, the results are in line with previous studies showing exogenous feed additives successful to prevent poststress complement activity reduction [37, 38].
Lysozyme is an important antimicrobial peptide of the innate immune system invading pathogens. A significant decrease in serum lysozyme activity was found in the control group of fish exposed at 32°C for 48 hours. A similar decrease in serum lysozyme activity at higher temperatures was observed in Nile tilapia, *Oreochromis niloticus* [39]. In the present study, serum lysozyme activity was significantly increased in all quercetin-supplemented groups before the stress and after the stress compared to the control group. This suggests that quercetin supplementation inhibits post-high-temperature stress immunosuppression.
Parallel with our study, lysozyme levels have also been increased in some fish species fed with quercetin-containing feeds. For instance, C. carpio fed with a diet containing 200 or 800 mg kg−1 quercetin showed an increase in lysozyme levels in serum, intestine, and mucus [16]. Wang et al. [ 18] added 0.01, 0.1, 1, 10, 100, and 1000 μg/l quercetin to the rearing water of zebrafish (Danio rerio) and reported that serum lysozyme activity was significantly higher in the 1 μg/l quercetin group than in the control group.
It is well known that serum glucose is used as a nonspecific stress index in fish studies [40, 41]. In this study, quercetin supplementations decreased serum glucose content significantly in the prestress group. Some reports are available on the hypoglycaemic effects of quercetin [42, 43]. This was supported in a recent study, where quercetin decreases serum glucose levels in common carp [16]. However, unlike our study, serum glucose levels in silver catfish, Rhamdia quelen [12], and blunt snout bream, *Megalobrama amblycephala* [44], fed with feed containing quercetin remained unchanged.
Superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) are the main enzymes that scavenge reactive oxygen species (ROS) and detoxification of H2O2 and hydroperoxides, respectively. In addition, malondialdehyde (MDA) is frequently used as a biomarker of lipid peroxidation. Our results in terms of increasing SOD and decreasing MDA activity are in full agreement with findings of earlier studies in common carp, C. carpio, fed diets incorporated with 800 mg/kg quercetin. Similarly, Ibrahim et al. [ 45] reported the increased SOD and GPx and decreased MDA effect of quercetin in Nile tilapia, Oreochromis niloticus. Moreover, SOD mRNA expression [46] and brain, gill, liver, and/or muscle SOD and CAT activity levels [12] were higher in fish fed with quercetin diets. SOD, CAT, MDA, and GPx parameters play a significant role in antioxidant response in different stress conditions like pesticides [47], metals [48], high temperature [49, 50], hypoxia [50], and cold temperature [51]. However, no study is available on the effects of quercetin on antioxidant capacity in fish under high-temperature stress conditions. In the present study, decreased CAT, MDA, and GPx levels and increased SOD levels observed in post-high-temperature stress conditions indicate a significant antioxidant effect of quercetin. Similarly, the dietary anthraquinone extract especially at 0.1-$0.2\%$ levels significantly increasing SOD and decreasing MDA levels in the liver was reported in M. rosenbergii under high-temperature stress [25].
Serum enzyme activities are accepted as important indicators of tissue damage; for example, AST and ALT are indicators of liver damage and lactate dehydrogenase (LDH) for liver or muscle damage in fish [52]. Therefore, increases in these enzymes in blood could be attributed to liver and muscle tissue damage. Previous studies showed that the serum ALT and AST activities provided decreased when fish were fed with quercetin [13, 16]. No study on the effects of quercetin under high-temperature stress on fish serum enzyme levels has been found in the literature so far. However, [25] reported that AST and ALT activities in freshwater prawn (M. rosenbergii) fed a diet containing anthraquinone extract from R. officinale Bail under high-temperature stress were significantly lower than those of the control fish. In this study, serum AST and ALT (except for 200 mg/kg) decreased in fish fed with quercetin-supplemented feeds before high-temperature stress. This trend was sustained after high-temperature stress as well.
The low serum glucose levels in the high-temperature stressed fish may be the result of undernourished conditions, liver disorders, and inflammation [25, 50, 53]. This can be related to the increases in glucose use in peripheral tissues, probably related to the enhancement of energy demand generated by the stress response [54]. A similar reduction in the serum glucose level at a higher temperature (21°C) was observed in rainbow trout, Oncorhynchus mykiss [50]. In contrast to our findings, however, increased levels of glucose in blood were reported in Wuchang bream, M. amblycephala, under high-temperature stress [26]. These different results might be associated with differences in rearing conditions, exposure time, fish species, fish size, and/or age.
Blood cortisol is one of the most important indexes of the response to heat stress in fish [55]. In this study, cortisol levels were significantly lower in fish fed different quercetin levels before heat stress. Also, fish fed with diets supplemented with quercetin displayed significantly lower levels of cortisol after heat stress, highlighting the protective potential of quercetin supplementation on the metabolism of common carp. Similarly, *Nile tilapia* fed with dietary sodium butyrate showed a decrease in cortisol levels when exposed to heat stress [30]. In this study, cortisol was significantly lower during heat stress in fish fed quercetin as compared to the control group. These results may be related to antistress effects of quercetin that inhibited the domain of raised cortisol.
In line with these findings, it can be suggested that quercetin (400-600 mg/kg) may be a useful substance in the treatment and protection of cells against possible oxidative stress.
## Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## References
1. Wu F., Yang C., Wen H.. **Improving low-temperature stress tolerance of tilapia, Oreochromis niloticus: a functional analysis of Astragalus membranaceus**. (2019) **50** 749-762. DOI: 10.1111/jwas.12586
2. Abd El-Hack M. E., Abdelnour S. A., Taha A. E.. **Herbs as thermoregulatory agents in poultry: an overview**. (2020) **703, article 134399**. DOI: 10.1016/j.scitotenv.2019.134399
3. Liu C., Shen W., Hou C.. **Low temperature-induced variation in plasma biochemical indices and aquaglyceroporin gene expression in the large yellow croaker Larimichthys crocea**. (2019) **9** 1-10. PMID: 30626917
4. Qiang J., Yang H., Wang H., Xu P., Qi Z., He J.. **Studies on blood biochemical indices and expression of hepatic HSP70 mRNA of different tilapia strains artificially challenged with**. (2012) **36** 958-968. DOI: 10.3724/SP.J.1231.2012.27883
5. Vicente I. S., Fleuri L. F., Carvalho P. L.. **Orange peel fragment improves antioxidant capacity and haematological profile of Nile tilapia subjected to heat/dissolved oxygen-induced stress**. (2019) **50** 80-92. DOI: 10.1111/are.13870
6. Ahmadifar E., Yousefi M., Karimi M.. **Benefits of dietary polyphenols and polyphenol-rich additives to aquatic animal health: an overview**. (2021) **29** 478-511. DOI: 10.1080/23308249.2020.1818689
7. Ahmadifar E., Eslami M., Kalhor N.. **Effect of a diet enriched with sodium propionate on growth performance, antioxidant property, innate-adaptive immune response, and growth-related genes expression in critically endangered beluga sturgeon (**. (2022) **125** 101-108. DOI: 10.1016/j.fsi.2022.04.031
8. Memariani H., Memariani M., Ghasemian A.. **An overview on anti-biofilm properties of quercetin against bacterial pathogens**. (2019) **35**. DOI: 10.1007/s11274-019-2719-5
9. Baghel S. S., Shrivastava N., Baghel R. S., Agrawal P., Rajput S.. **A review of quercetin: antioxidant and anticancer properties**. (2012) **1** 146-160
10. Lesjak M., Beara I., Simin N.. **Antioxidant and anti-inflammatory activities of quercetin and its derivatives**. (2018) **40** 68-75. DOI: 10.1016/j.jff.2017.10.047
11. Pês T. S., Saccol E. M., Londero É. P.. **Protective effect of quercetin against oxidative stress induced by oxytetracycline in muscle of silver catfish**. (2018) **484** 120-125. DOI: 10.1016/j.aquaculture.2017.10.043
12. Pês T. S., Saccol E. M., Ourique G. M.. **Quercetin in the diet of silver catfish: effects on antioxidant status, blood parameters and pituitary hormone expression**. (2016) **458** 100-106. DOI: 10.1016/j.aquaculture.2016.02.020
13. Ghafarifarsani H., Hoseinifar S. H., Javahery S., Yazici M., Van Doan H.. **Growth performance, biochemical parameters, and digestive enzymes in common carp (Cyprinus carpio) fed experimental diets supplemented with vitamin C, thyme essential oil, and quercetin**. (2022) **21** 291-302. DOI: 10.1080/1828051X.2021.1965923
14. Zhai S. W., Liu S. L.. **Effects of dietary quercetin on growth performance, serum lipids level and body composition of tilapia (Oreochromis niloticus)**. (2013) **12**. DOI: 10.4081/ijas.2013.e85
15. Xu Z., Li X., Yang H., Liang G., Gao B., Leng X.. **Dietary quercetin improved the growth, antioxidation, and flesh quality of grass carp (Ctenopharyngodon idella)**. (2019) **50** 1182-1195. DOI: 10.1111/jwas.12663
16. Ghafarifarsani H., Hoseinifar S. H., Javahery S., Van Doan H.. **Effects of dietary vitamin C, thyme essential oil, and quercetin on the immunological and antioxidant status of common carp (**. (2022) **553, article 738053**. DOI: 10.1016/j.aquaculture.2022.738053
17. Shameem M. S., Mayavan K., Venkatachalam R.. **Influence of quercetin loaded mesoporous silica nanoparticles (QMSNs) on immunity and diseases resistance in Nile tilapia (**. (2021) 1-20. DOI: 10.1080/10454438.2021.2000921
18. Wang J., Zhang C., Zhang J.. **The effects of quercetin on immunity, antioxidant indices, and disease resistance in zebrafish (Danio rerio)**. (2020) **46** 759-770. DOI: 10.1007/s10695-019-00750-2
19. Fu J., Xu N., Sun H., Xuan J., Lu L.. **Quercetin protects rare minnow Gobiocypris rarus from infection of genotype II grass carp reovirus**. (2021) **52** 4867-4873. DOI: 10.1111/are.15321
20. Huang L., Li M., Wei H.. **Research on the indirect antiviral function of medicinal plant ingredient quercetin against grouper iridovirus infection**. (2022) **124** 372-379. DOI: 10.1016/j.fsi.2022.04.013
21. Liu M., Yu Q., Xiao H.. **The inhibitory activities and antiviral mechanism of medicinal plant ingredient quercetin against grouper iridovirus infection**. (2020) **11, article 586331**. DOI: 10.3389/fmicb.2020.586331
22. Kim D. W., Hong E. C., Kim J. H.. **Effects of dietary quercetin on growth performance, blood biochemical parameter, immunoglobulin and blood antioxidant activity in broiler chicks**. (2015) **42** 33-40. DOI: 10.5536/KJPS.2014.42.1.33
23. Sung K. C., Cheorun J., Samuel J.. **Effects of dietary quercetin on the feed utilization, blood parameters, and meat quality in Korean native goats**. (2010) **52** 297-304. DOI: 10.5187/JAST.2010.52.4.297
24. Li J., Liu S. L., Shi Q. C., Zhai S. W.. **Effects of dietary quercetin on growth performance, serum biochemical indices, non-specific immune indices of juvenile tilapia (GIFT Oreochromis niloticus)**. (2014) **35** 21-25
25. Liu B., Xie J., Ge X.. **Effects of anthraquinone extract from**. (2010) **29** 49-57. DOI: 10.1016/j.fsi.2010.02.018
26. Liu B., Xie J., Ge X.. **Comparison study of the effects of anthraquinone extract and emodin from Rheum officinale Bail on the physiological response, disease resistance of Megalobrama amblycephala under high temperature stress**. (2012) **12**
27. Akyüz A. P., Dernekbașı S., Karayücel İ.. **The effect of cherry laurel (Laurocerasus officinalis Roem) leaf extract on the growth, survival and some antioxidant enzymes of rainbow trout (Oncorhynchus mykiss Walbaum, 1792) exposed to oxidative stress induced by high temperature**. (2018) **35** 131-139. DOI: 10.12714/egejfas.2018.35.2.05
28. 28AOAC
Official Methods of Analysis of AOAC International
200917th ed.Arlington, VAAssoc. Official Analytical Chemists. (2009)
29. Adineh H., Naderi M., Yousefi M., Khademi Hamidi M., Ahmadifar E., Hoseini S. M.. **Dietary licorice (Glycyrrhiza glabra) improves growth, lipid metabolism, antioxidant and immune responses, and resistance to crowding stress in common carp, Cyprinus carpio**. (2021) **27** 417-426. DOI: 10.1111/anu.13194
30. Dawood M. A., Eweedah N. M., Elbialy Z. I., Abdelhamid A. I.. **Dietary sodium butyrate ameliorated the blood stress biomarkers, heat shock proteins, and immune response of Nile tilapia (**. (2020) **88, article 102500**. DOI: 10.1016/j.jtherbio.2019.102500
31. Saurabh S., Sahoo P. K.. **Lysozyme: an important defence molecule of fish innate immune system**. (2008) **39** 223-239. DOI: 10.1111/j.1365-2109.2007.01883.x
32. Yano T., Hatayama Y., Matsuyama H., Nakao M.. **Titration of the alternative complement pathway activity of representative cultured fishes**. (1988) **54** 1049-1054. DOI: 10.2331/suisan.54.1049
33. Livak K. J., Schmittgen T. D.. **Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2**. (2001) **25** 402-408. DOI: 10.1006/meth.2001.1262
34. Liu S. L., Zhai S. W.. **Effects of dietary quercetin on the growth performance, digestive enzymes and antioxidant potential in the hepatopancreas of tilapia (Oreochromis niloticus)**. (2014) **66, article 20770**
35. Shabbir U., Rubab M., Daliri E. B. M., Chelliah R., Javed A., Oh D. H.. **Curcumin, quercetin, catechins and metabolic diseases: the role of gut microbiota**. (2021) **13** p. 206. DOI: 10.3390/nu13010206
36. Shi T., Bian X., Yao Z., Wang Y., Gao W., Guo C.. **Quercetin improves gut dysbiosis in antibiotic-treated mice**. (2020) **11** 8003-8013. DOI: 10.1039/D0FO01439G
37. Hoseini S. M., Mirghaed A. T., Mazandarani M., Zoheiri F.. **Serum cortisol, glucose, thyroid hormones' and non-specific immune responses of Persian sturgeon, _Acipenser persicus_ to exogenous tryptophan and acute stress**. (2016) **462** 17-23. DOI: 10.1016/j.aquaculture.2016.04.031
38. Varghese T., Kumar V. R., Gopan A.. **Dietary arginine modulates nonspecific immune responses in Indian Major Carp, _Cirrhinus mrigala_ exposed to hypoxia**. (2020) **529, article 735613**. DOI: 10.1016/j.aquaculture.2020.735613
39. Dominguez M., Takemura A., Tsuchiya M.. **Effects of changes in environmental factors on the non-specific immune response of Nile tilapia, Oreochromis niloticus L**. (2005) **36** 391-397. DOI: 10.1111/j.1365-2109.2005.01220.x
40. Heath A. G.. (1995)
41. Yılmaz S., Ergün S.. (2018) **78** 140-157. DOI: 10.1016/j.fsi.2018.04.034
42. Kobori M., Masumoto S., Akimoto Y., Takahashi Y.. **Dietary quercetin alleviates diabetic symptoms and reduces streptozotocin- induced disturbance of hepatic gene expression in mice**. (2009) **53** 859-868. DOI: 10.1002/mnfr.200800310
43. Youl E., Bardy G., Magous R.. **Quercetin potentiates insulin secretion and protects INS-1 pancreatic**. (2010) **161** 799-814. DOI: 10.1111/j.1476-5381.2010.00910.x
44. Jia E., Yan Y., Zhou M.. **Combined effects of dietary quercetin and resveratrol on growth performance, antioxidant capability and innate immunity of blunt snout bream (**. (2019) **256, article 114268**. DOI: 10.1016/j.anifeedsci.2019.114268
45. Ibrahim D., Kishawy A. T., Khater S. I.. **Interactive effects of dietary quercetin nanoparticles on growth, flesh antioxidant capacity and transcription of cytokines and Aeromonas hydrophila quorum sensing orchestrating genes in Nile tilapia (**. (2021) **119** 478-489. DOI: 10.1016/j.fsi.2021.10.034
46. Shin H. S., Yoo J. H., Min T. S., Lee J., Choi C. Y.. **Effect of quercetin on the activity and mRNA expression of antioxidant enzymes and physiological responses in olive flounder (Paralichthys olivaceus) exposed to cadmium**. (2010) **23** 742-749. DOI: 10.5713/ajas.2010.10006
47. Slaninova A., Smutna M., Modra H., Svobodova Z.. **A review: oxidative stress in fish induced by pesticides**. (2009) **30** 2-12. PMID: 20027135
48. Sevcikova M., Modra H., Slaninova A., Svobodova Z.. **Metals as a cause of oxidative stress in fish: a review**. (2011) **56** 537-546. DOI: 10.17221/4272-VETMED
49. Cheng C. H., Yang F. F., Liao S. A.. **High temperature induces apoptosis and oxidative stress in pufferfish (**. (2015) **53** 172-179. DOI: 10.1016/j.jtherbio.2015.08.002
50. Jiang X., Dong S., Liu R.. **Effects of temperature, dissolved oxygen, and their interaction on the growth performance and condition of rainbow trout (**. (2021) **98, article 102928**. DOI: 10.1016/j.jtherbio.2021.102928
51. Yilmaz S., Ergün S., Çelik E. Ş., Banni M., Ahmadifar E., Dawood M. A.. **The impact of acute cold water stress on blood parameters, mortality rate and stress-related genes in**. (2021) **100, article 103049**. DOI: 10.1016/j.jtherbio.2021.103049
52. Yılmaz S., Ergun S., Şanver Çelik E., Yigit M., Bayizit C.. **Dietary trans‐cinnamic acid application for rainbow trout (Oncorhynchus mykiss): II. Effect on antioxidant status, digestive enzyme, blood biochemistry and liver antioxidant gene expression responses**. (2019) **25** 1207-1217. DOI: 10.1111/anu.12935
53. Liu E., Zhao X., Li C.. **Effects of acute heat stress on liver damage, apoptosis and inflammation of pikeperch (**. (2022) **106, article 103251**. DOI: 10.1016/j.jtherbio.2022.103251
54. Rodríguez-Quiroga J. J., Otero-Rodiño C., Suárez P.. **Differential effects of exposure to parasites and bacteria on stress response in turbot Scophthalmus maximus simultaneously stressed by low water depth**. (2017) **91** 242-259. DOI: 10.1111/jfb.13338
55. Mazeaud M. M., Mazeaud F., Donaldson E. M.. **Primary and secondary effects of stress in fish: some new data with a general review**. (1977) **106** 201-212. DOI: 10.1577/1548-8659(1977)106<201:PASEOS>2.0.CO;2
|
---
title: cotH Genes Are Necessary for Normal Spore Formation and Virulence in Mucor
lusitanicus
authors:
- Csilla Szebenyi
- Yiyou Gu
- Teclegiorgis Gebremariam
- Sándor Kocsubé
- Sándor Kiss-Vetráb
- Olivér Jáger
- Roland Patai
- Krisztina Spisák
- Rita Sinka
- Ulrike Binder
- Mónika Homa
- Csaba Vágvölgyi
- Ashraf S. Ibrahim
- Gábor Nagy
- Tamás Papp
journal: mBio
year: 2023
pmcid: PMC9973265
doi: 10.1128/mbio.03386-22
license: CC BY 4.0
---
# cotH Genes Are Necessary for Normal Spore Formation and Virulence in Mucor lusitanicus
## ABSTRACT
Mucormycosis is an invasive fungal infection caused by certain members of the fungal order of Mucorales. The species most frequently identified as the etiological agents of mucormycosis belong to the genera Rhizopus, Lichtheimia, and Mucor. The frequency of systemic mucormycosis has been increasing, mainly because of increasing numbers of susceptible patients. Furthermore, Mucorales display intrinsic resistance to the majority of routinely used antifungal agents (e.g., echinocandins and short-tailed azoles), which limits the number of possible therapeutic options. All the above-mentioned issues urge the improvement of molecular identification methods and the discovery of new antifungal targets and strategies. Spore coat proteins (CotH) constitute a kinase family present in many pathogenic bacteria and fungi and participate in the spore formation in these organisms. Moreover, some of them can act as virulence factors being receptors of the human GRP78 protein during Rhizopus delemar-induced mucormycosis. We identified 17 cotH-like genes in the *Mucor lusitanicus* genome database. Successful disruption of five cotH genes in Mucor was performed using the CRISPR-Cas9 system. The CotH3 and CotH4 proteins play a role in adaptation to different temperatures as well as in developing the cell wall structure. We also show CotH4 protein is involved in spore wall formation by affecting the total chitin content and, thus, the composition of the spore wall. The role of CotH3 and CotH4 proteins in virulence was confirmed in two invertebrate models and a diabetic ketoacidosis (DKA) mouse model.
## INTRODUCTION
Mucormycosis is a life-threatening opportunistic fungal infection caused by several members of the order Mucorales [1]. Rhizopus, Lichtheimia, and Mucor species have most often been isolated from such infections as the causative agents (2–6). These invasive infections, which can manifest as rhino-orbito-cerebral, pulmonary, gastrointestinal, cutaneous, or disseminated diseases, are known for their aggressive progression and high mortality rates (i.e., 30 to $90\%$, depending on the manifestation, the underlying condition of the patient, and the therapy) [5, 7, 8]. They most frequently occur in patients with an immunocompromised status due to immunosuppression (i.e., primarily for solid organ or hematopoietic stem cell transplantation) or hematological malignancies. Uncontrolled diabetes (with or without ketoacidosis), elevated levels of free iron in the blood, and severe trauma can also be risk factors for mucormycosis [6, 7, 9, 10]. The frequency of systemic mucormycosis has been increasing, mainly because of the increasing numbers of susceptible populations. Furthermore, Mucorales fungi display intrinsic resistance to the majority of routinely used antifungal agents (e.g., echinocandins and short-tailed azoles), which also limits the number of possible therapeutic options [4, 11]. Recently, an increasing number of mucormycosis cases have been reported among COVID-19 patients treated with corticosteroids and with underlying diabetes (12–14).
Spore coat protein H (CotH) was first discovered in the endospore-forming bacterium *Bacillus subtilis* where it participates in the formation of the endospore coat [15]. It proved to be an atypical protein kinase, which has an essential role in endospore formation by phosphorylating other structural proteins, such as CotB and CotG. Knockout of the encoding cotH gene had a pleiotropic effect on the structure of the outer spore coat, as well as the development of a germination-deficient phenotype (16–18).
CotH proteins not only occur in endospore-forming bacteria but also are present in many Mucoromycota species, such as Mucor lusitanicus, Lichtheimia corymbifera, Cunninghamella bertholletiae, Rhizopus delemar, Saksanaea vasiformis, Syncephalastrum monosporum, Mortierella alpina, and *Umbelopsis isabellina* [19, 20]. In the mucormycosis-causing species R. delemar (synonym R. oryzae), several CotH proteins, including CotH1 (RO3G_05018 [EIE80313.1]), CotH2 (RO3G_08029 [EIE83324.1]), CotH3 (RO3G_11882 [EIE87171.1]), CotH4 (RO3G_09277 [EIE84567.1]), CotH5 (RO3G_01139 [EIE76435.1]), CotH6 (IGS-990-880_03186), CotH7 (IGS-990-880_09445), and CotH8 (IGS-990-880_11474), were previously identified [20, 21]. Among them, CotH2 and CotH3 were found to mediate the interaction of the fungus with the glucose-regulated protein 78 (GRP78) expressed on the surface of endothelial cells [19], while CotH7 was found to mediate invasion of alveolar epithelial cells via interaction with integrin α3β1 [22]. This interaction proved to be crucial for the fungal invasion of the host. Specifically, the level of GRP78 molecules significantly increases in sinuses and lungs during diabetic ketoacidosis (DKA), causing vulnerability toward the fungal infection [23]. Moreover, IgG antibodies produced against a peptide of Rhizopus CotH3 protein and conserved among CotH7 protein protected mice with DKA and neutropenia from mucormycosis [19]. Thus, anti-CotH3 antibodies were proposed as promising candidates for immunotherapy of human mucormycosis [1]. Finally, due to the universal presence of cotH genes in Mucorales and the lack of their presence in other pathogens, they proved to be appropriate biomarkers for diagnosis through a PCR-based assay allowing fungal DNA detection in human urine samples [24].
M. lusitanicus (formerly Mucor circinelloides f. lusitanicus) [25] is a frequently used model organism for studying morphogenesis and pathogenesis of mucormycosis (26–31). In the genome of this fungus, we found 17 possible cotH genes, of which the function and role in the pathogenicity or other mechanisms are yet unknown. This high number of genes lets us consider the possibility that the CotH family may be a diverse group of proteins having a role in various biological processes. The present study aimed to investigate the function and possible role in pathogenicity of five cotH genes of M. lusitanicus.
## In silico analysis of the CotH proteins.
In the M. lusitanicus genome database (DoE Joint Genome Institute; M. lusitanicus CBS277.49v3.0; [http://genome.jgi-psf.org/Mucci3/Mucci3.home.html]), 17 potential CotH-like protein-coding genes were found by a similarity search using the amino acid sequence of R. delemar 99–880 CotH1 (EIE80313.1), CotH2 (EIE83324.1), and CotH3 (EIE87171.1), which were named CotH1 to CotH17, respectively (Table 1). In silico analysis of the M. lusitanicus putative CotH proteins’ amino acid sequences indicated that all of them carry the CotH kinase domain (Pfam ID PF08757), while there are 15 genes in the Rhizopus delemar genome that carry the CotH domain (Pfam ID PF08757) [15, 16]. Furthermore, the M. lusitanicus CotH proteins are associated with the presence of a signal peptide, while only CotH2, CotH4, CotH10, and CotH12 contained sequences predicted to encode glycosylphosphatidylinositol (GPI)-anchored proteins. Analysis of the sequences suggests that CotH proteins can be predominantly extracellular in nature. Among the 17 putative proteins, only the amino acid sequences of CotH4, CotH5, and CotH13 carry the special (M/Q/A-E/M/A-QTNDGAY-I/K-D-T/Y/G-N/A/E-E/N/T-N) motif previously described in R. delemar CotH2 and CotH3 proteins as a ligand for the GRP78 receptor (Fig. 1) [18], thereby suggesting that these proteins might play a role in the virulence of M. lusitanicus.
**FIG 1:** *CotH motif-carrying proteins in Mucorales based on in silico analysis. The tree shows clade 1 of the full phylogeny based on the available fungal genomic sequences presented in Fig. S1.* TABLE_PLACEHOLDER:TABLE 1 All publicly available fungal genomic sequences were used for phylogenetic analysis of CotH proteins. The simplified view of the resulting tree is shown in Fig. 2, while the whole version can be found in Fig. S1 in the supplemental material. The collapse of the phylogenetic tree was based on the merging of clades of genes from predominantly closely related species. Interestingly, CotH proteins were found only in two phyla, Neocallimastigomycota and Mucoromycota, and within the latter, they were detected in two subphyla, Mucoromycotina and Mortierellomycotina. In the Mucoromycota linage, 12 well-supported clades with Mucorales CotH proteins occurred (Fig. 2). The remaining isolates, all of which were Neocallimastigomycetes, formed a distinct clade. Mortierellomycetes fungi are located in a self-clade (clade 2), integrated between clade 1 and clade 3, formed by Mucor and Rhizopus isolates, respectively.
**FIG 2:** *Collapsed version of the maximum likelihood tree showing phylogenetic relationships of cotH gene sequences. The simplified view of the maximum likelihood tree was based on the merging of clades of genes of predominantly closely related species. Only bootstrap values of >95% from maximum likelihood analyses are shown above the branches.*
Mucor CotH4, CotH5, and CotH13, which carry the special amino acid sequence described as a ligand for the GRP78 receptor [18], were localized in the same clade (clade 1) together with the Rhizopus CotH2 and CotH3 proteins, which have been described previously for their essential role in virulence (Fig. 1) [18]. Clade 1 also includes the R. delemar CotH7 (IGS-990-880_09445 [protein ID 9765]), which appears to be the major ligand mediating binding to integrin α3β1 of alveolar epithelial cells [22]. Clade 3, clade 4, clade 5, clade 11, and clade 12 include both Rhizopus and Mucor CotH proteins, while some CotH proteins are segregated into clades formed exclusively by Mucor species (clade 7 and clade 8). Uniquely, in clade 1, some CotH homologues found in M. lusitanicus (i.e., CotH4, CotH5, and CotH13) are most closely related to the R. delemar and P. blakesleeanus orthologues than to their paralogs.
## Transcription analysis of the M. lusitanicus cotH genes.
Transcription of the 17 cotH genes was analyzed by real-time quantitative reverse transcription-PCR (qRT-PCR) on the second day of cultivation at 28°C (Fig. S2), where cotH2 and cotH4 showed the highest expression. Most cotH genes reached their maximum transcript abundance on the second day of cultivation at 28°C, the optimum temperature of the fungus. Interestingly, the transcription level of cotH4 increased dramatically at higher glucose concentrations and in the presence of human serum (Fig. S2). Based on the qRT-PCR analysis, five genes (i.e., cotH1 to cotH5) were selected for disruption and functional analysis.
## Knockout of five cotH genes using the CRISPR-Cas9 system.
Disruption of cotH1, cotH2, cotH3, cotH4, and cotH5 was performed using a CRISPR-Cas9 system, and the resulting mutants were named MS12-ΔcotH1+pyrG, MS12-ΔcotH2+pyrG, MS12-ΔcotH3+pyrG, MS12-ΔcotH4+pyrG, and MS12-ΔcotH5+pyrG, respectively. For further analysis two independently derived mutants were selected. In each case, a template DNA containing the pyrG gene as a selection marker and two fragments homologous to the target site served as the deletion cassette. Transformation and genome editing frequencies are presented in Table S1A. In the experiments performed to disrupt the cotH5 gene, transformed protoplasts died before the colony development or colonies could be maintained only temporarily, indicating that the disruption of this gene may be lethal for the fungus. It is also possible that the mutation causes a defect in protoplast regeneration; thus, transformants could not be recovered even though the mutant would be viable under normal growth conditions. Investigation of this possibility is hampered by the fact that currently only transformation of protoplasts of the fungus offers the possibility of producing stable genetic mutants of M. lusitanicus.
## Characterization of the knockout mutants.
(i) Growth ability of the mutants. The growth ability of the mutants was examined at 20, 28, and 35°C for 4 days. Growth analysis was performed with two independently derived mutants for each cotH knockout. No significant differences in the growth of MS12-ΔcotH1+pyrG, MS12-ΔcotH2+pyrG, and the control strain were observed at the optimum growth temperature of the fungus (28°C) (Fig. 3A). However, the colony diameter of MS12-ΔcotH3+pyrG significantly increased, while that of MS12-ΔcotH4+pyrG significantly decreased compared to the control strain at this temperature (Fig. 3A). Apart from the growth intensity of these two strains, the morphology of the mutants did not differ from that of the original strain. Cultivation at lower temperature did not affect the growth of the strains differently compared to the control (Fig. 3B). At 35°C, growth of the cotH4 disruption mutant was significantly less affected by the increased temperature than those of the cotH1, cotH2, and cotH3 mutants (Fig. 3C). Complementation of the cotH3 or cotH4 gene was achieved by an autosomally replicating plasmid construction, where the complementing gene construct was maintained extrachromosomally. After complementation of the cotH3 and cotH4 gene, the resulting MS12-ΔcotH4+cotH4+leuA strain showed the growth characteristics of the control (MS12+pyrG) (Fig. 3).
**FIG 3:** *Growth of cotH mutants at different temperatures and effect of different stressors. (A) Colony diameter of the cotH mutant strains on leucine-supplemented minimal medium for 4 days at 28°C; (B) effect of the lower temperature on growth of cotH mutant strains on leucine-supplemented minimal medium for 4 days at 20°C; (C) effect of the higher temperature on growth of cotH mutant strains on leucine-supplemented minimal medium for 4 days at 35°C. The effects of the lower (B) and higher (C) temperatures on the growth of cotH mutant strains are represented by comparing the differences of the colony diameters measured after cultivation under the control conditions and those obtained in the cultivation under 20°C and 35°C. (D) Effect of Congo Red (CR) on the growth of cotH mutant strains; (E) effect of calcofluor white (CFW) cell wall stressor on the growth of cotH mutant strains; (F) effect of SDS membrane stressor on the growth of cotH mutant strains. The effects of the different stressors on the growth of the mutants are represented by comparing the differences of the colony diameters measured after cultivation under the control conditions and those obtained in the presence of the stressor. The values presented are from three independent cultivations (error bars indicate standard deviation). The strains were grown at 28°C in the dark for 4 days, and colony diameters were measured daily. The effects of the different temperatures (A to C) and stressors (D to F) were then plotted in millimeters, and significance was calculated based on the effect of the temperatures or stressors on MS12+pyrG. P values were calculated according to the two-sample paired t test statistical method. Values indicated by asterisks are significantly different from the value of the MS12+pyrG strain measured on the same day (*, P ≤ 0.05; **, P ≤ 0.01).*
Cell wall stressors Congo Red (CR) and calcofluor white (CFW) had significant effects on the growth of all strains compared to cultivation on normal medium (Fig. 3). Difference of the colony diameter of each mutant from that of the control strain on normal, untreated medium (Fig. 3A) (i.e., increased or decreased growth ability of the mutants) was taken into account during the evaluation. Using CR as a stressor, no difference in the growth intensity of the deletion mutants MS12-ΔcotH1+pyrG and MS12-ΔcotH2+pyrG was observed compared to MS12+pyrG (i.e., the control strain). MS12-ΔcotH3+pyrG displayed an increased sensitivity, while MS12-ΔcotH4+pyrG showed higher resistance to CR than the control strain (Fig. 3D). CFW exerted a significant effect on the growth of all cotH mutants. MS12-ΔcotH1+pyrG, MS12-ΔcotH2+pyrG, and MS12-ΔcotH3+pyrG was more sensitive, while MS12-ΔcotH4+pyrG was more resistant to CFW than MS12+pyrG (Fig. 3E). The MS12-ΔcotH4+pyrG proved to be more resistant to SDS than the control strain. The effect of SDS was observed from the third cultivation day in the case of MS12-ΔcotH1+pyrG, MS12-ΔcotH2+pyrG, and MS12-ΔcotH3+pyrG (Fig. 3F). For these strains, an increased sensitivity was observed. After complementation of the cotH3 and cotH4 gene, the resulting MS12-ΔcotH3+cotH3+leuA and MS12-ΔcotH4+cotH4+leuA strains showed the growth characteristics of the control (MS12+pyrG) (Fig. 3).
Under aerobic conditions, M. lusitanicus displays a hyphal growth, while it grows in a yeast-like form under anaerobiosis. This feature, named as morphological dimorphism is regarded as an important property of pathogenicity [27]. Gene disruption did not affect the anaerobic growth of any of the mutants compared to the control strain, and yeast-like cells could be formed in the absence of the tested cotH genes. However, under microaerophilic conditions, MS12-ΔcotH4+pyrG produced more elongated hyphae than the control strain (Fig. S3).
(ii) Transmission electron microscopic analysis of the spores of the mutant strains. To determine the effect of the gene disruptions on the spore wall structure of M. lusitanicus spores of the mutants and the control strain (MS12+pyrG) were subjected to transmission electron microscopic (TEM) image analysis (Fig. 4). Spore wall of MS12-ΔcotH4+pyrG showed a characteristic phenotype as it was abnormally thickened (Fig. 4B and D and Data Set S1). After complementation of the cotH4 gene disruption, the cell wall of MS12-ΔcotH4+cotH4+leuA spores was restored and the cell wall thickness of the complemented spores did not differ significantly from those of the control (MS12+pyrG) (Fig. S4).
**FIG 4:** *Cell wall changes of cotH mutants observed by TEM. (A) Schematic representation of the outer, inner, and middle layers of the cell wall of spores. TEM measurements represent the thickness of different cell wall layers, as shown in the right panel enlargement of the red box in the left panel of panel A: 1, outer spore wall layer; 2, middle spore wall layer; 3, inner spore wall layer. (B) Total spore wall thickness of layers 1 to 3 compared to the control strain. (C to E) Spores of the cotH mutants MS12-ΔcotH3+pyrG (C), MS12-ΔcotH4+pyrG (D), and MS12+pyrG (control) (E). Significance was calculated based on a two-sample t test (**, P ≤ 0.001). Error bars indicate standard deviation. For each mutant, spore wall thickness was determined by measuring the thickness of five different spores at 10 different points on the spore wall. Scale bars, 2 μm.*
(iii) *Surface analysis* of fungal spores with fluorescent dyes. By monitoring the spores stained with CWF, we found that the dye-emitted intensity of the spores of MS12-ΔcotH4+pyrG significantly increased compared to the control (Fig. S5) suggesting a chitin accumulation in the spore wall. At the same time, staining for mannosyl derivatives using concanavalin A-fluorescein isothiocyanate (ConA-FITC) did not reveal differences among the mutants and the control strain. Intensity of the cotH4-complemented (i.e., MS12-ΔcotH4+cotH4+leuA) spores did not differ significantly from those of the control (MS12+pyrG) (Fig. S5).
(iv) In vitro interaction with macrophages. To examine whether recognition and internalization of the fungal spores by macrophages were affected by the disruption of the cotH genes in M. lusitanicus, the phagocytosing capacity (Fig. S6) and the phagosome maturation of J774.2 cells exposed to the MS12+pyrG and the mutant strains were tested (Fig. S7). Subsequently, J774.2 cells were coincubated for 3 h with the labeled spores, and then the ratio of pHrodo Red+ macrophages was examined by imaging flow cytometry (see Fig. S7A and B in the supplemental material). No significant differences were found between the mutant and the control strains in neither the phagocytic index values (Fig. S6C). Similarly, testing the survival of fungal spores cocultured for 3 h with murine J774.2 cells did not detect differences between the control and the mutant strains (Fig. S6D). Moreover, our results suggested that the viability of spores did not decrease in the presence of macrophages at all. Furthermore, acidification of phagosomes is not affected by the lack of the examined CotH proteins (Fig. S7B) and the absence of CotH proteins did not affect the survival of spores after in vitro interaction with macrophages.
(v) In vivo study of virulence of cotH mutant strains. To investigate the role of CotH proteins in pathogenicity of M. lusitanicus, Drosophila melanogaster and wax moth larvae (Galleria mellonella) were used as nonvertebrate animal models in in vivo virulence studies.
In D. melanogaster (Fig. 5A), the pathogenicities of the control strain MS12+pyrG and the wild-type (WT) strain CBS277.49 did not differ significantly. However, lack of the cotH3 and cotH4 genes significantly decreased the virulence in the mutants compared to the controls. In case of the Galleria model Fig. 5B, disruption of the cotH4 gene resulted in significantly decreased virulence of the mutant strain. In an intratracheally infected DKA mouse model, all the mice infected with the wild-type M. lusitanicus strain CBS277.4 died before the third day postinoculation (Fig. 5C). In this model, MS12-ΔcotH3+pyrG and MS12-ΔcotH4+pyrG showed significantly decreased virulence. After complementation of the cotH3 and cotH4 genes, the virulence of the resulting MS12-ΔcotH3+cotH3+leuA and MS12-ΔcotH4+cotH4+leuA strains showed the characteristics of the control (CBS277.49) (Fig. 5).
**FIG 5:** *Virulence studies with cotH mutants. (A) Survival of Drosophila melanogaster (n = 60) infected with the cotH mutants and the control M. lusitanicus MS12+pyrG and CBS277.49 strains. Survival curves followed by asterisks were significantly different from the control strain (MS12+pyrG) according to the Mantel-Cox log rank test (*, P ≤ 0.05; **, P ≤ 0.001). The results from 3 independent experiments are summarized. (B) Survival of Galleria mellonella (n = 20) infected with the cotH mutants and the control M. lusitanicus MS12+pyrG and CBS277.49 strains. Survival curves followed by asterisks were significantly different from the control strain according to the Mantel-Cox log rank test (**, P ≤ 0.001). The results from 3 independent experiments are summarized. (C) Virulence of cotH mutants in a DKA mouse model following intratracheal infection. DKA male ICR (CD-1) outbred mice (≥20 g) (Envigo) (n = 8) were infected intratracheally with 2.5 × 106 fresh spores (1 × 108 spores/mL) in 25 μL PBS. The results summarize the results of 2 independent experiments. Survival curves followed by asterisks were significantly different from the control strain according to the Mantel-Cox log rank test (*, P ≤ 0.05; **, P ≤ 0.01).*
## DISCUSSION
CotH protein-encoding genes are widely present in Mucorales fungi [19]. Although present in other organisms, Mucorales cotH genes have divergent sequences from their orthologs in Bacillus species. However, the function of cotH genes has been verified in only a few of them (e.g., B. subtilis, *Bacillus cereus* and R. delemar) [15, 16, 19, 32]. For instance, the Rhizopus CotH3 carries the amino acid sequence MGQTNDGAYRDPTDNN, which is assumed to be a key factor in the specific interaction between the fungal cells and the host's endothelial cells via the GRP78 molecule [19]. This interaction was shown to be critical in enhancing R. delemar virulence through promotion of hematogenous dissemination [18]. Equally important, the ability of R. delemar spores to invade and damage nasal epithelial cells was directly proportional to the expression of GRP78 on nasal epithelial cells triggered by host conditions mimicking hyperglycemia and ketoacidosis, thus explaining the increased susceptibility of diabetics in ketoacidosis to the rhino-orbito-cerebral form of mucormycosis (ROCM) [22]. Concordant with these findings, is the increased number of mucormycosis cases (mainly ROCM) detected among COVID-19 patients treated with corticosteroids and with underlining diabetes (i.e., COVID-19-associated mucormycosis [CAM]) [33]. Interestingly, significantly higher serum GRP78 levels in COVID-19 patients have been reported and could explain the increased incidents of CAM through CotH/GRP78-mediated invasion of host tissues [34]. Thus, some members of the CotH protein family serving as ligands for GRP78 receptors may be considered potential therapeutic targets [1].
In a previous study, several different cotH transcripts were reported to be expressed in M. lusitanicus using the domain profile PF08757 [34]. Moreover, it was also noted that the number of cotH transcripts was 2 times higher in the transcriptome of Mucor strains considered to be pathogenic than in Mucor strains used in cheese production [35]. This study used the so-called “CotH motif” described previously for Rhizopus [35] to report on the presence of three CotH proteins in M. lusitanicus. In our study, we found 17 genes in M. lusitanicus that contain the PF08757 domain, of which three genes (i.e., cotH4, cotH5, and cotH13) harbor the CotH motif. In the phylogeny of the cotH genes, M. lusitanicus cotH4, cotH5, and cotH13 localized in the same clade (clade 1) together with the R. delemar cotH2 and cotH3 genes. Only the genes positioned in clade 1 of this phylogeny contained the CotH motif, suggesting that these genes may have a role in the CotH-GRP78 interaction.
Expression of the M. lusitanicus CotH3 protein proved to be essential for the virulence in the DKA mouse model. However, the encoding gene localizes to clade 7 along with the genes for other CotH proteins and does not carry the CotH motif predicted to interact with GRP78. This result suggests that host targets other than GRP78 are involved in mucormycosis pathogenesis due to M. lusitanicus. Indeed, integrin α3β1 has been reported to be the target for R. delemar CotH7, which results in activation of the epidermal growth factor receptor [22, 36].
Phylogenetic analysis indicated that CotH proteins show a strong dominance in the Mucoromycotina fungi within the fungal kingdom. Clade 4, clade 5, clade 11, and clade 12 include both Rhizopus and Mucor cotH genes, while some cotH genes are segregated into clades formed exclusively by Mucor species (clade 7 and clade 8), suggesting various gene duplication events within the Mucoromycota clade. Distribution of the genes in the phylogeny also suggests that several duplication events may occur in the zygomycete ancestor before the divergence of species. The presence of this large number of genes and their phylogenetic analysis led us to consider the possibility that the CotH family is a diverse group of proteins, which may be involved in many biological processes and not exclusively in the virulence.
Since orthologs of CotH proteins are mostly considered spore surface or spore envelope structural elements [35], the spore wall composition of the fungus and its mutants generated in this study was monitored. TEM images revealed the separation of the spore wall and the cell membrane of the cotH4 disruption mutant. Analysis using fluorescent dyes specifically binding to the cell wall components [37, 38] indicated a chitin accumulation between the two structures. Changes in the cell wall composition can alter the susceptibility to cell wall stressors, such as CFW and CR [37], and may influence the fungal virulence [39]. Indeed, disruption of the cotH4 gene decreased the sensitivity to CR and caused resistance to other stressors (i.e., CFW and SDS) at the same time, MS12-ΔcotH3+pyrG displayed an increased sensitivity to CR. The ability of pathogenic fungi to respond to stress is crucial in adapting to the host environment [40]. Our results suggest that the proteins encoded by the cotH3 and cotH4 genes are involved in the determination of the spore wall composition and structure.
Chemical components, such as the surface molecules, and physical properties, such as the size and shape, of the fungal propagules greatly influence pathogen recognition and further removal of the fungus by the immune cells [41]. Changes in the spore wall of the cotH4 disruption mutant, as well as the absence of the CotH proteins, did not affect their recognition by the macrophages; also, no significant difference was found in the number of fungal cells phagocytosed by a macrophage. The ability of the spores to germinate and penetrate the cells determines the further outcome of the infection [41]. It was previously described that Mucor spores within the phagosome are exposed to a cytotoxic environment, which includes acidification, nutrient starvation, oxidation, and the presence of antimicrobial proteins [41]. We examined whether the acidification of phagosomes could be affected by the CotH proteins. In our experiments, macrophages were equally capable of killing the spores of the cotH knockout mutants, so neither the recognition nor the killing mechanism was impaired or altered. Despite the involvement of CotH proteins in cell wall remodeling and/or integrity, there is no indication that CotH1 to -4 proteins would be involved in phagosome maturation blockade in Mucor, as suggested by a previous study [41]. It should be noted, however, that of the 17 CotH proteins encoded in the Mucor genome, two other proteins (i.e., ID 76509 [CotH8] and ID 166651 [CotH17]) are suggested to play a role in macrophage interactions [41].
Although the information on the role of genes or proteins involved in infection can be obtained through in vitro experiments, the function of systemic infection can only be elucidated in an in vivo model [42]. Disruption of both the cotH3 and the cotH4 genes caused reduced virulence in the Drosophila infection model, while the disruption of cotH1 and cotH2 genes did not affect the virulence of the fungus. The Drosophila pattern recognition receptors after the recognition of conserved microbial patterns can activate a cellular and humoral response that is specific to a particular microorganism [43]. The wax moth, G. mellonella, is also a widely used nonvertebrate model organism to examine the pathogenicity of different filamentous fungi such as Mucorales species [26, 31, 44]. In vivo viability studies in G. mellonella also confirmed the role of the CotH4 protein in virulence. The pathological alterations that resemble human mucormycosis are limited if we would like to use Drosophila or Galleria, but each of these is an appropriate model for testing larger numbers of mutants [42]. Furthermore, there one limitation on the use of invertebrate hosts is whether the results obtained in these models can be adapted to mammals and the human body because of fundamental differences, such as the lack of an adaptive immune system and specific organs [42].
Inhalation of Mucorales spores is the most common route of infection. Thus, intratracheal instillation or intranasal inhalation is the most widely used models to mimic pulmonary infection. Disseminated mucormycosis can also be induced by intravenous inoculation of the fungus, usually into the tail vein and mimicking direct inoculation into the blood as the infection occurs in severe trauma [45]. Therefore, the role of cotH genes in R. delemar was investigated using a DKA mouse model, in which pulmonary mucormycosis was induced by intratracheal instillation of fungal spores [19]. This model is preferably used to investigate the pathomechanism of mucormycosis, as the most common underlying condition of this fungal disease is diabetic ketoacidosis in addition to immunodeficiency [3, 30, 38]. For M. lusitanicus, an intratracheal mouse infection model had not been previously used, so the infectivity of the wild type (WT) (CBS277.4) in mice was first established [31]. Viability studies in DKA mice demonstrated that deletion of either the CotH3 or CotH4 gene attenuates the pathogenicity of M. lusitanicus. Importantly, the cotH3 mutant did not show reduced virulence in a *Galleria mellonella* model but did in a DKA mouse model with elevated GRP78 receptor expression, although the protein does not carry the characteristic motif determined earlier for R. delemar. The data suggest that cotH mutants exhibit altered cell wall composition or organization, with the cotH4 mutant showing a significant loss of cell wall integrity and cell wall chitin composition. Host recognition of the fungal cell wall often determines the outcome in the host and plays an important role in the pathomechanism of the infection. Based on these findings, it seems conceivable that in M. lusitanicus, CotH3 and CotH4 proteins mediate the process of fungal infection in a cell-wall-dependent manner.
Spore size dimorphism is linked to virulence of M. lusitanicus species [24]. However, considering that the mutant lacking the CotH3 protein showed reduced virulence in the pathogenicity studies, but no change in spore size, it can be assumed that spore size is not the only determining factor of virulence. Interestingly, interaction with the GRP78 receptor is unlikely in case of this mutant, as the CotH3 protein does not carry the CotH motif. Alternatively, other motifs that are yet to be identified bind to GRP78. Due to its sequence similarity to the Rhizopus CotH3 and the presence of the CotH motif, CotH4 could be a potential ligand for the GRP78 receptor, which requires further investigation. Given that CotH proteins are involved not only in pathogenicity but also in spore structure, it is important to consider the role of CotH protein family members not only as virulence factors but also in spore formation and other physiological roles.
## Strains, media, and growth conditions.
Strain MS12 of *Mucor lusitanicus* (formerly known as Mucor circinelloides f. lusitanicus), which is auxotrophic to leucine and uracil (leuA− and pyrG−) and was derived from strain CBS277.49 by chemical mutagenesis [46], was used in the transformation experiments. As the lack of a functional pyrG gene slightly affects the growth and virulence of M. lusitanicus [47], the strain MS12+pyrG, was used as a control during the characterization of mutants. In this strain, uracil auxotrophy was complemented by expressing the pyrG gene [47]. In certain experiments, CBS277.49 was also involved as a control. Growth analysis was performed with two independently derived mutants for each of the cotH mutations.
For quantitative PCR (qPCR) experiments, human serum was isolated from venous blood of the same donors taken into serum separation blood collection tubes (BD Vacutainer, Becton Dickinson, Franklin Lakes, NJ, USA). Then, coagulation tubes were centrifuged at 300 × g for 15 min at room temperature and the serum was collected and added at $10\%$, and then cultivation was performed for 2 days at 28°C in liquid minimal medium.
For nucleic acid extractions, spores were plated onto yeast nitrogen base (YNB) solid minimal medium consisting of 10 g/L glucose, 0.5 g/L yeast nitrogen base without amino acids (BD Difco, Becton, Dickinson, Franklin Lakes, NJ, USA), 1.5 g/L (NH4)2SO4, 1.5 g/L sodium glutamate, and 20 g/L agar, supplemented with leucine and/or uracil (0.5 mg/mL) if required, and incubated at 28°C for 4 days. To test the mitotic stability of the transformants, malt extract agar (MEA) (10 g/L glucose, 5 g/L yeast extract, 10 g/L malt extract, and 20 g/L agar) was used as a complete, nonselective medium. To examine the effect of the temperature on the growth, 104 spores were plated on solid YNB and incubated at 20, 28, and 35°C. Anaerobic growth was performed in a BBL GasPak anaerobic system (Becton, Dickinson) with Anaerocult A (Merck, Darmstadt, Germany) at 28°C. Microaerophile growth was performed in a BBL GasPak anaerobic system (Becton, Dickinson) with Anaerocult C (Merck, Darmstadt, Germany) at 28°C, where 104 spores were plated on solid YNB and incubated at 28°C for 2 days. Fungi grown on YNB under anaerobiosis were sampled on the second day of culture, and the morphology of the fungal cells was examined by light microscopy. To determine the effect of membrane and cell wall stressors, 104 spores were point inoculated at the center of YNB with or without the stressors, which were SDS (4 mg/mL), Congo red (CR) (2 mg/mL), and calcofluor white (CFW) (0.1 mg/mL). For growth tests, plates were incubated at 28°C for 4 days in the dark, and colony diameter was measured daily. In each case, the difference between the colony diameters of the mutant strains and that of the control was determined under the control conditions (i.e., when the strains were grown at 28°C on YNB) and in the presence of the stressor. The effect of the different temperatures and stressors on the growth of the mutants was represented by comparing the differences in the colony diameters measured after cultivation under the control conditions and those obtained in the cultivation under stressors. The effect of the different temperatures was then plotted in millimeters, and significance was calculated based on the cultivation under different temperatures on MS12+pyrG.
## Sequence and phylogenetic analysis of CotH proteins.
Motifs, domains, and main features of the CotH proteins were predicted using the tools available at the Expasy Bioinformatics Resource Portal (http://www.expasy.ch), such as Compute pI/Mw, MyHits, PROSITE, and ProtScale [48]. For the phylogenetic analysis, a blastp search was conducted on the JGI MycoCosm portal (https://mycocosm.jgi.doe.gov/mycocosm/home) [49] with 17 sequences of *Mucor lusitanicus* CBS277.49 containing the CotH domain. All retrieved sequences were scanned for CotH domains with InterProScan 5.48–83.0 [50] based on the Pfam database [51]. Only those sequences were retained that contained CotH domains solely. Filtered sequences were clustered by using MMseq2 v.bbd564172bd55d9e6acd1170e59790c37157a21b [52] with default settings. Multiple-sequence alignment was conducted using MAFFT v.7.453 [53] with the E-INS-i iterative refinement method, including sequences from all clusters that contained the CotH domain based on the clustering results. Phylogenetic reconstruction was carried out by using IQ-TREE v.1.6.12 [54] with the LG4M+R7 model determined by the inbuilt ModelFinder tool [55]. Statistical support of the best tree was calculated with ultrafast bootstrap approximation [56] in 5,000 replicates.
## General molecular techniques.
Genomic DNA and total RNA were isolated using the ZR Fungal/Bacterial DNA MiniPrep (Zymo Research, Irvine, CA, USA) and the Direct-zol RNA MiniPrep (Zymo Research, Irvine, CA, USA) kits, respectively, according to the instructions of the manufacturer. To amplify genes or gene fragments from genomic DNA, Phusion high-fidelity DNA polymerase (Thermo Fisher Scientific, Waltham, MA, USA) was used according to the manufacturer's recommendations. PCR products were isolated and concentrated using the Zymoclean large-fragment DNS recovery kit (Zymo Research, Irvine, CA, USA) and DNA Clean & Concentrator-5 (Zymo Research, Irvine, CA, USA). Restriction digestions and ligations were carried out according to the commonly used methods [57]. To clone the PCR fragments, the pJET1.2/blunt vector (CloneJET PCR cloning kit; Thermo Fisher Scientific, Waltham, MA, USA) was used according to the manufacturer’s instructions. Plasmid purification was carried out using the GeneJET plasmid miniprep kit (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's recommendations. Sequencing of the cloned fragments was commercially performed by LGC Genomics (Berlin, Germany). The sequences obtained were aligned by using the BioEdit 7.2 sequence editor program [58] and analyzed using the Basic Local Alignment Search Tool (BLAST) at the site of the National Center for Biotechnology Information (NCBI) (https://blast.ncbi.nlm.nih.gov/Blast.cgi). Oligonucleotide sequences were designed based on the sequence data available in the M. lusitanicus CBS277.49 v.3.0 genome database (DoE Joint Genome Institute [http://genome.jgi-psf.org/Mucci2/Mucci3.home.html]) [59]. Primers used in the study are listed in Table S1B in the supplemental material.
## qRT-PCR analysis.
Reverse transcription was carried out with the Maxima H minus first-strand cDNA synthesis kit (Thermo Fisher, Waltham, MA, USA) using random hexamer and oligo(dT)18 primers according to the manufacturer's recommendations. qRT-PCR experiments were performed in a CFX96 real-time PCR detection system (Bio-Rad) using the Maxima SYBR green qPCR master mix (Thermo Fisher Scientific, Waltham, MA, USA), and the primers are presented in Table S1B. Relative quantification of copy number and gene expression was performed by the threshold cycle (2−ΔΔCT) method using the M. lusitanicus actin gene (CBS277.49 v.2.0 genome database, scaffold_07:2052804–2054242) as a reference [60]. Amplification conditions involved 95°C for 3 min followed by 40 cycles at 95°C for 15 s, 60°C for 30 s, and 72°C for 30 s. Melting curve analysis was performed at 65 to 95°C with a 0.5°C increment. All experiments were performed in biological and technical triplicates.
## Knockout of the cotH genes using the CRISPR-Cas9 method.
Knockout mutants were constructed by partially exchanging the coding regions of the cotH genes to a functional pyrG gene (CBS277.49. v2.0 genome database ID Mucci1.e_gw1.3.865.1), which complements the uracil auxotrophy of the applied strain. *This* gene replacement was carried out by homology-driven repair (HDR) following a CRISPR-Cas9 strategy described previously [29, 61]. Protospacer sequences designed to target the DNA cleavage in the cotH1, cotH2, cotH3, cotH4, and cotH5 genes are presented in Table S2A. Using these sequences, Alt-R CRISPR RNA (crRNA) and Alt-R CRISPR-Cas9 transactivating crRNA (tracrRNA) molecules were designed and purchased from Integrated DNA Technologies (IDT, Coralville, IA, USA). To form the crRNA:tracrRNA duplexes (i.e., the guide RNAs [gRNAs]), IDT nuclease-free duplex buffer (IDT, Coralville, IA, USA) was used according to the instructions of the manufacturer. Deletion cassettes functioning also as the template DNAs for the HDR were constructed by PCR using the Phusion Flash high-fidelity PCR master mix (Thermo Fisher Scientific, Waltham, MA, USA). First, two fragments upstream and downstream from the protospacer sequence of the corresponding cotH gene and the entire pyrG gene along with its promoter and terminator sequences were amplified using the primers listed in Table S1B. The amplified fragments were fused in a subsequent PCR using nested primers (Table S1B) where the ratio of concentrations of the fragments was 1:1:1. For each transformation procedure, 5 μg template DNA, 10 μM gRNA, and 10 μM Cas9 nuclease enzyme (Alt-R S.p. Cas9 nuclease; IDT, Coralville, IA, USA) were introduced together into the M. lusitanicus MS12 strain by polyethylene glycol (PEG)-mediated protoplast transformation [29, 62]. Potential mutant colonies were selected on solid YNB medium by complementing the uracil auxotrophy of the MS12 strain. From each primary transformant, monosporangial colonies were formed under selective conditions. Disruption of the cotH genes and the presence of the integrated pyrG gene were proven by PCR using the primers listed in Table S1B and sequencing of the amplified fragment. Sequencing and PCR revealed that the CRISPR-Cas9-mediated HDR caused the expected modification (i.e., disruption of the cotH genes by the integration of the pyrG) in the targeted sites. Real-time quantitative reverse transcription-PCR (qRT-PCR) analysis indicated the lack of cotH transcripts in all transformants.
## Complementation.
To complement the knockout of the cotH3 and the cotH4 genes, autonomously replicating vectors were used, where the complementing gene constructs were maintained episomally. cotH3 and cotH4 with their promoter and terminator sequences were amplified by PCR using the Phusion Flash high-fidelity PCR master mix (Thermo Scientific) and the primer pairs McCotH3_compl_fw and McCotH3_compl_rev and McCotH4_compl_fw and McCotH4_compl_rev, respectively, and were ligated separately into pJet1.2 cloning vectors (Thermo Scientific), resulting in the constructs pJet1.2-McCotH3 and pJet1.2-McCotH4, respectively. We further ensured that a unique rare-cutting restriction site is present in both plasmids (NotI), which should facilitate easy insertion of complementing genes. We have therefore combined in a single vector the auxotrophic selection marker leuA, allowing primary selection of numerous transformants with our gene of interest, resulting in plasmids pMCcotH3leuAcomp and pMCcotH4leuAcomp. The plasmid construct was introduced to the MS12-ΔcotH3+pyrG and MS12-ΔcotH4+pyrG disruption strains by PEG-protoplast transformation. In experiments with a plasmid for complementation of cotH3 and cotH4 gene deletion, 3 μg DNA was added to the protoplasts in a transformation reaction mixture. Transformants were transferred to minimal media (YNB), considering the phenomenon of complementation of auxotrophy after successful transformation, and confirmed by PCR and qPCR. The selection method is based on the complementation of the leucine auxotrophy. The created knockout and complemented M. lusitanicus strains are listed in Table S2B.
## Electron microscopy and quantitative analysis of the wall thickness.
Pellets from isolated spores were immersed into a $2\%$ paraformaldehyde (Sigma, St. Louis, MO, USA) and $2.5\%$ glutaraldehyde (Polysciences, Warrington, PA, USA) containing modified Karnovsky fixative in phosphate buffer. The pH of the solution was adjusted to 7.4. Samples were fixed overnight at 4°C, then briefly rinsed in distilled water (pH 7.4) for 10 min and fixed in $2\%$ osmium tetroxide (Sigma-Aldrich, St. Louis, MO, USA) in distilled water (pH 7.4) for 60 min. After osmification, samples were rinsed in distilled water for 10 min again then dehydrated using a graded series of ethanol (Molar, Halasztelek, Hungary) from $50\%$ to $100\%$ for 10 min in each. Afterwards, all spore pellets were proceeded through in propylene oxide (Molar, Halasztelek, Hungary) and then embedded in an epoxy-based resin, Durcupan ACM (Sigma-Aldrich, St. Louis, MO, USA). After polymerization for 48 h at 56°C, resin blocks were etched, and 50-nm-thick ultrathin sections were cut on an Ultracut UCT ultramicrotome (Leica, Wetzlar, Germany). Sections were mounted on a single-hole, Formvar-coated copper grid (Electron Microscopy Sciences, Hatfield, PA, USA). For a better signal-to-noise ratio, $2\%$ uranyl acetate (Electron Microscopy Sciences) (in $50\%$ ethanol from Molar, Halasztelek, Hungary) and $2\%$ lead citrate (in distilled water from Electron Microscopy Sciences, Hatfield, PA, USA) were used. Ultrathin sections from the pellets were screened at a magnification of ×1,000 to ×3,000 on a JEM-1400 Flash transmission electron microscope (JEOL, Tokyo, Japan) until 70 individual spore cross sections were identified from each sample. For quantitative measurements of the major and minor axes, images with area and circularity of a ×1,000 to ×3,000 magnification were used. For the thickness measurements of the different layers of the spore wall (Fig. 5D), images were recorded at ×10,000 magnification using a 2k × 2k Matataki (JEOL, Tokyo, Japan) scientific complementary metal oxide-semiconductor camera. All the quantitative data were determined using the built-in measurements module of the TEM Center software (JEOL, Tokyo, Japan).
## Fluorescence staining.
Four-day-old fungal spores were collected from MEA and washed three times with 1× sterile phosphate buffered saline (PBS) (137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 2 mM KH2PO4 [pH 7.4]) and were collected by centrifugation for 10 min at 4°C at 2,000 × g followed by another centrifugation step using the same conditions for 5 min. The pellet was resuspended in 0.5 mL of $1\%$ (wt/vol) bovine serum albumin (BSA) (Gibco) solution and incubated for 30 min at room temperature with constant rotation. Fungal spores (107/mL) were washed three times with 1× PBS and then stained for 45 min in PBS containing 5 μg/mL CFW solution or 100 μg/mL ConA-FITC solution. After staining, samples were washed five times with PBS. For analysis, collected samples were centrifuged at 2,000 × g for 15 min and resuspended in 200 μL PBS supplemented with $0.05\%$ Tween 20 (Reanal, Budapest, Hungary). Fluorescence images of the stained fungal cells were taken with a Zeiss Axioscope 40 microscope and an Axiocam Mrc camera. Filters at 350-nm excitation and 432-nm emission were used to detect chitin, while filters at 495-nm excitation and 515-nm emission were used after ConA-FITC staining. Samples were also measured with a FlowSight imaging flow cytometer (Amnis ImageStream X Mk II imaging flow cytometer; Amnis, Austin, TX, USA), and the associated IDEAS 6.2 software [63] was used for evaluation. For samples prepared by fluorescence microscopy, the intensity of the dyes for the different strains was determined using ImageJ2 (Fiji) [64]. The mean fluorescence intensity of CFW was determined by microscopy. Significance was determined by unpaired t test.
## Interactions of M. lusitanicus with macrophages.
The murine macrophage cell line J774.2 was cultivated in Dulbecco's minimal essential medium (DMEM) (Lonza, Basel, Switzerland) supplemented with $10\%$ heat-inactivated fetal bovine serum (FBS) (Biosera, Kansas City, MO, USA) and $1\%$ penicillin–streptomycin solution (Sigma-Aldrich, St. Louis, MO, USA) at 37°C, $5\%$ CO2, and $100\%$ relative humidity. The same medium was used in all the interaction experiments. Four hours before the experiment, J774.2 cells (2 × 105 cells/mL) were freshly harvested and stained with CellMask deep red plasma membrane stain (Thermo Fisher Scientific, Waltham, MA, USA) following the instructions of the manufacturer, and then seated on a 24-well plate. Fungal spores were freshly collected from 1-week-old MEA cultures and stained with Alexa Fluor 488, carboxylic acid, and succinimidyl ester (Invitrogen, Waltham, MA, USA). Labeled macrophages and spores were coincubated at a multiplicity of infection (MOI) of 5:1 for 90 min at 37°C and $5\%$ CO2. For analysis, collected samples were centrifuged with 1,000 × g for 10 min and then resuspended in 200 μL PBS supplemented with $0.05\%$ Tween 20. Interaction and phagocytosis were measured using a FlowSight imaging flow cytometer (Amnis ImageStream X Mk II imaging flow cytometer; Amnis, Austin, TX, USA) and evaluated with IDEAS software (Amnis ImageStream X Mk II imaging flow cytometer; Amnis, Austin, TX, USA). Data from 10,000 events per sample were collected and analyzed. The number of engulfed cells was determined by examining 200 images of individual macrophages, while the phagocytic index (PI) was determined using the following formula: PI = (mean spore count per phagocytosing cell) × (% of phagocytosing cells containing at least one fungal spore).
To analyze the phagosome acidification of J774.2 cells by flow cytometry, fungal spores were labeled with pHrodo Red succinimidyl ester (Invitrogen, Waltham, MA, USA), according to the manufacturer’s instructions. CellMask Deep Red Plasma Membrane stain (Thermo Ficher Scientific, Waltham, MA, USA) labeled macrophages and FITC labeled spores were coincubated at an MOI of 5:1, for 120 min at 37°C and $5\%$ CO2. The ratio of the phagocytosing cells was also determined at 120 min under the same conditions of interactions, except the fungal spores were stained with CFW. Phagosome acidification was calculated as follows: [(% of pHrodo Red+ cells)/(% of phagocytosing cells)] × 100.
To assay the survival of the fungal spores, the interaction of J774.2 cells and the spores was performed at an MOI of 5:1 for 180 min. After the interaction, cells and spores were collected and macrophages were lysed with sterile distilled water. Serial dilutions were prepared from the spore suspensions and plated on MEA to quantify the CFU. Survival of spores was calculated using the following formula: survival = (CFUinteraction × 100)/CFUcontrol, where CFUinteraction is the CFU of samples coincubated with macrophages, while CFUcontrol is the CFU of control samples, incubated under the same conditions but without macrophages.
## Survival assay in Drosophila melanogaster.
Drosophila stocks were raised and kept following the infection on standard cornmeal agar medium at 28°C. Spore suspensions were prepared in sterile PBS from 7-day-old cultures grown on YNB plates (supplemented with 0.5 g/L leucine, if required) at 28°C. The Drosophila Oregon R strain, originally obtained from the Bloomington Drosophila Stock Center (Bloomington, IN, USA), was used throughout the experiments. Infection was performed by dipping a thin needle in a suspension of fungal conidia (107 conidia/mL) or PBS as the uninfected control, and subsequently, the thorax of the anaesthetized fly was collected. Flies were counted at different times to monitor survival. Flies were moved into fresh vials every other day. Each experiment was performed with 60 flies. The results shown are representative of at least three independent experiments.
## Survival assay in Galleria mellonella larvae.
Spores were resuspended in insect physiological saline (IPS) (50 mM NaCl, 5 mM KCl, 10 mM EDTA and 30 mM sodium citrate in 0.1 M Tris-HCl [pH 6.9]) [65]. G. mellonella larvae (TruLarv; BioSystems Technology) were inoculated with 105 fungal cells in 20 μL IPS via the last proleg using 29-gauge insulin needles (BD Micro-Fine). For each M. lusitanicus strain, 20 larvae were infected. For IPS-treated (uninfected) and witness controls (no injections, uninfected), 20 animals were utilized too. Larvae were maintained at 28°C, and their survival was monitored daily for 6 days. The results shown are representative of at least three independent experiments.
## In vivo virulence studies in diabetic ketoacidotic (DKA) mouse model.
Male ICR (CD-1) outbred mice (≥20 g) were all purchased from Envigo and housed in groups of 8 each. Mice were rendered DKA with a single intraperitoneal injection of 190 mg/kg streptozocin in 0.2 mL citrate buffer (pH 4.2) 10 days before the fungal challenge. Glycosuria and ketonuria were confirmed in all mice 7 days after streptozocin treatment. Furthermore, on days −2 and +3 relative to infection, mice were given a dose of cortisone acetate (250 mg/kg subcutaneously [s.c.]). Mice were given 50 ppm enrofloxacin (Baytril; Bayer) added to the drinking water from day −3 to day 0 ad libitum. Ceftazidine antibiotics were added (5 mg/dose/0.2 mL s.c.) from day 0 and continued until day +8. DKA mice were infected intratracheally with a target inoculum of 2.5 × 106 fresh spores (1 × 108 spores/mL) in 25 μL PBS after sedation with isoflurane gas.
## Ethics statement.
All animal studies were approved by the Institutional Animal Care and Use Committee (IACUC) of the Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center according to the NIH guidelines for animal housing and care (project 11671).
## Statistical analysis.
All measurements were performed in at least two technical and three biological replicates. Statistical significance was analyzed by t tests or one-way analysis of variance (ANOVA) followed by Dunnett’s multiple-comparison test using Microsoft Excel of the Microsoft Office package or GraphPad Prism 7.00 (GraphPad Software, La Jolla, California USA) as appropriate. P values of <0.05 were considered statistically significant. In in vivo survival experiments, differences between the pathogenicity of the fungal strains were compared by the log rank test. P values of <0.05 were considered statistically significant.
## References
1. Gebremariam T, Alkhazraji S, Soliman SSM, Gu Y, Jeon HH, Zhang L, French SW, Stevens DA, Edwards JE, Filler SG, Uppuluri P, Ibrahim AS. **Anti-CotH3 antibodies protect mice from mucormycosis by prevention of invasion and augmenting opsonophagocytosis**. *Sci Adv* (2019) **5**. DOI: 10.1126/sciadv.aaw1327
2. Walther G, Wagner L, Kurzai O. **Outbreaks of Mucorales and the species involved**. *Mycopathologia* (2020) **185** 765-781. DOI: 10.1007/s11046-019-00403-1
3. Prakash H, Chakrabarti A. **Global epidemiology of mucormycosis**. *J Fungi (Basel)* (2019) **5** 26. DOI: 10.3390/jof5010026
4. Skiada A, Lass-Floerl C, Klimko N, Ibrahim A, Roilides E, Petrikkos G. **Challenges in the diagnosis and treatment of mucormycosis**. *Med Mycol* (2018) **56** 93-101. DOI: 10.1093/mmy/myx101
5. Jeong W, Keighley C, Wolfe R, Lee WL, Slavin MA, Kong DCM, Chen SC-A. **The epidemiology and clinical manifestations of mucormycosis: a systematic review and meta-analysis of case reports**. *Clin Microbiol Infect* (2019) **25** 26-34. DOI: 10.1016/j.cmi.2018.07.011
6. Riley TT, Muzny CA, Swiatlo E, Legendre DP. **Breaking the mold: a review of mucormycosis and current pharmacological treatment options**. *Ann Pharmacother* (2016) **50** 747-757. DOI: 10.1177/1060028016655425
7. Lanternier F, Dannaoui E, Morizot G, Elie C, Garcia-Hermoso D, Huerre M, Bitar D, Dromer F, Lortholary O. **A global analysis of mucormycosis in France: the RetroZygo study (2005–2007)**. *Clin Infect Dis* (2012) **54** S35-S43. DOI: 10.1093/cid/cir880
8. Garcia A, Vellanki S, Lee SC. **Genetic tools for investigating Mucorales fungal pathogenesis**. *Curr Clin Microbiol Rep* (2018) **5** 173-180. DOI: 10.1007/s40588-018-0097-7
9. Gomes MZR, Lewis RE, Kontoyiannis DP. **Mucormycosis caused by unusual Mucormycetes, non-**. *Clin Microbiol Rev* (2011) **24** 411-445. DOI: 10.1128/CMR.00056-10
10. Skiada A, Lanternier F, Groll AH, Pagano L, Zimmerli S, Herbrecht R, Lortholary O, Petrikkos GL. **Diagnosis and treatment of mucormycosis in patients with hematological malignancies: guidelines from the 3rd European Conference on Infections in Leukemia (ECIL 3)**. *Haematologica* (2013) **98** 492-504. DOI: 10.3324/haematol.2012.065110
11. Reid G, Lynch JP, Fishbein MC, Clark NM. **Mucormycosis**. *Semin Respir Crit Care Med* (2020) **41** 99-114. DOI: 10.1055/s-0039-3401992
12. do Monte Junior ES, dos Santos MEL, Ribeiro IB, Luz GdO, Baba ER, Hirsch BS, Funari MP, de Moura EGH. **Rare and fatal gastrointestinal mucormycosis (zygomycosis) in a COVID-19 patient: a case report**. *Clin Endosc* (2020) **53** 746-749. DOI: 10.5946/ce.2020.180
13. Mehta S, Pandey A. **Rhino-orbital mucormycosis associated with COVID-19**. *Cureus* (2020) **12**. DOI: 10.7759/cureus.10726
14. Werthman-Ehrenreich A. **Mucormycosis with orbital compartment syndrome in a patient with COVID-19**. *Am J Emerg Med* (2021) **42** 264.e5-264.e8. DOI: 10.1016/j.ajem.2020.09.032
15. Naclerio G, Baccigalupi L, Zilhao R, De Felice M, Ricca E. *J Bacteriol* (1996) **178** 4375-4380. DOI: 10.1128/jb.178.15.4375-4380.1996
16. Nguyen KB, Sreelatha A, Durrant ES, Lopez-Garrido J, Muszewska A, Dudkiewicz M, Grynberg M, Yee S, Pogliano K, Tomchick DR, Pawłowski K, Dixon JE, Tagliabracci VS. **Phosphorylation of spore coat proteins by a family of atypical protein kinases**. *Proc Natl Acad Sci USA* (2016) **113** E3482-E3491. DOI: 10.1073/pnas.1605917113
17. Scott JD, Newton AC. **Bacterial spore coat protein kinases: a new twist to an old story**. *Proc Natl Acad Sci USA* (2016) **113** 6811-6812. DOI: 10.1073/pnas.1607004113
18. Freitas C, Plannic J, Isticato R, Pelosi A, Zilhão R, Serrano M, Baccigalupi L, Ricca E, Elsholz AKW, Losick R, Henriques AO. **A protein phosphorylation module patterns the**. *Mol Microbiol* (2020) **114** 934-951. DOI: 10.1111/mmi.14562
19. Gebremariam T, Liu M, Luo G, Bruno V, Phan QT, Waring AJ, Edwards JE, Filler SG, Yeaman MR, Ibrahim AS. **CotH3 mediates fungal invasion of host cells during mucormycosis**. *J Clin Invest* (2014) **124** 237-250. DOI: 10.1172/JCI71349
20. Chibucos MC, Soliman S, Gebremariam T, Lee H, Daugherty S, Orvis J, Shetty AC, Crabtree J, Hazen TH, Etienne KA, Kumari P, O'Connor TD, Rasko DA, Filler SG, Fraser CM, Lockhart SR, Skory CD, Ibrahim AS, Bruno VM. **An integrated genomic and transcriptomic survey of mucormycosis-causing fungi**. *Nat Commun* (2016) **7** 12218. DOI: 10.1038/ncomms12218
21. Ma L-J, Ibrahim AS, Skory C, Grabherr MG, Burger G, Butler M, Elias M, Idnurm A, Lang BF, Sone T, Abe A, Calvo SE, Corrochano LM, Engels R, Fu J, Hansberg W, Kim J-M, Kodira CD, Koehrsen MJ, Liu B, Miranda-Saavedra D, O'Leary S, Ortiz-Castellanos L, Poulter R, Rodriguez-Romero J, Ruiz-Herrera J, Shen Y-Q, Zeng Q, Galagan J, Birren BW, Cuomo CA, Wickes BL. **Genomic analysis of the basal lineage fungus**. *PLoS Genet* (2009) **5**. DOI: 10.1371/journal.pgen.1000549
22. Alqarihi A, Gebremariam T, Gu Y, Swidergall M, Alkhazraji S, Soliman SSM, Bruno VM, Edwards JE, Filler SG, Uppuluri P, Ibrahim AS. **GRP78 and integrins play different roles in host cell invasion during mucormycosis**. *mBio* (2020) **11**. DOI: 10.1128/mBio.01087-20
23. Liu M, Spellberg B, Phan QT, Fu Y, Fu Y, Lee AS, Edwards JE, Filler SG, Ibrahim AS. **The endothelial cell receptor GRP78 is required for mucormycosis pathogenesis in diabetic mice**. *J Clin Invest* (2010) **120** 1914-1924. DOI: 10.1172/JCI42164
24. Baldin C, Soliman SSM, Jeon HH, Alkhazraji S, Gebremariam T, Gu Y, Bruno VM, Cornely OA, Leather HL, Sugrue MW, Wingard JR, Stevens DA, Edwards JE, Ibrahim AS. **PCR-based approach targeting Mucorales-specific gene family for diagnosis of mucormycosis**. *J Clin Microbiol* (2018) **56**. DOI: 10.1128/JCM.00746-18
25. Wagner L, Stielow JB, de Hoog GS, Bensch K, Schwartze VU, Voigt K, Alastruey-Izquierdo A, Kurzai O, Walther G. **A new species concept for the clinically relevant**. *Persoonia* (2020) **44** 67-97. DOI: 10.3767/persoonia.2020.44.03
26. Li CH, Cervantes M, Springer DJ, Boekhout T, Ruiz-Vazquez RM, Torres-Martinez SR, Heitman J, Lee SC. **Sporangiospore size dimorphism is linked to virulence of**. *PLoS Pathog* (2011) **7**. DOI: 10.1371/journal.ppat.1002086
27. Lee SC, Li A, Calo S, Heitman J. **Calcineurin plays key roles in the dimorphic transition and virulence of the human pathogenic zygomycete**. *PLoS Pathog* (2013) **9**. DOI: 10.1371/journal.ppat.1003625
28. Lee SC, Li A, Calo S, Inoue M, Tonthat NK, Bain JM, Louw J, Shinohara ML, Erwig LP, Schumacher MA, Ko DC, Heitman J. **Calcineurin orchestrates dimorphic transitions, antifungal drug responses and host-pathogen interactions of the pathogenic mucoralean fungus**. *Mol Microbiol* (2015) **97** 844-865. DOI: 10.1111/mmi.13071
29. Nagy G, Szebenyi C, Csernetics Á, Vaz AG, Tóth EJ, Vágvölgyi C, Papp T. **Development of a plasmid free CRISPR-Cas9 system for the genetic modification of**. *Sci Rep* (2017) **7** 16800. DOI: 10.1038/s41598-017-17118-2
30. Chang Z, Heitman J. **Drug-resistant epimutants exhibit organ-specific stability and induction during murine infections caused by the human fungal pathogen**. *mBio* (2019) **10**. DOI: 10.1128/mBio.02579-19
31. Vellanki S, Garcia AE, Lee SC. **Interactions of FK506 and rapamycin with FK506 binding protein 12 in opportunistic human fungal pathogens**. *Front Mol Biosci* (2020) **7** 588913. DOI: 10.3389/fmolb.2020.588913
32. Zilhão R, Naclerio G, Henriques AO, Baccigalupi L, Moran CP, Ricca E. **Assembly requirements and role of CotH during spore coat formation in**. *J Bacteriol* (1999) **181** 2631-2633. DOI: 10.1128/JB.181.8.2631-2633.1999
33. Prakash H, Skiada A, Paul RA, Chakrabarti A, Rudramurthy SM. **Connecting the dots: interplay of pathogenic mechanisms between COVID-19 disease and mucormycosis**. *J Fungi (Basel)* (2021) **7** 616. DOI: 10.3390/jof7080616
34. Sabirli R, Koseler A, Goren T, Turkcuer I, Kurt O. **High GRP78 levels in Covid-19 infection: a case-control study**. *Life Sci* (2021) **265** 118781. DOI: 10.1016/j.lfs.2020.118781
35. Lebreton A, Meslet-Cladière L, Morin-Sardin S, Coton E, Jany J-L, Barbier G, Corre E. **Comparative analysis of five**. *Genomics* (2019) **111** 1306-1314. DOI: 10.1016/j.ygeno.2018.09.003
36. Watkins TN, Gebremariam T, Swidergall M, Shetty AC, Graf KT, Alqarihi A, Alkhazraji S, Alsaadi AI, Edwards VL, Filler SG, Ibrahim AS, Bruno VM. **Inhibition of EGFR signaling protects from mucormycosis**. *mBio* (2018) **9**. DOI: 10.1128/mBio.01384-18
37. Ram AFJ, Klis FM. **Identification of fungal cell wall mutants using susceptibility assays based on calcofluor white and Congo Red**. *Nat Protoc* (2006) **1** 2253-2256. DOI: 10.1038/nprot.2006.397
38. Rodrigues J, Fonseca FL, Schneider RO, Godinho RM, Firacative C, Maszewska K, Meyer W, Schrank A, Staats C, Kmetzsch L, Vainstein MH, Rodrigues ML. **Pathogenic diversity amongst serotype C VGIII and VGIV**. *Sci Rep* (2015) **5** 11717. DOI: 10.1038/srep11717
39. Garcia-Rubio R, de Oliveira HC, Rivera J, Trevijano-Contador N. **The fungal cell wall:**. *Front Microbiol* (2019) **10** 2993. DOI: 10.3389/fmicb.2019.02993
40. Bahn YS, Xue C, Idnurm A, Rutherford JC, Heitman J, Cardenas ME. **Sensing the environment: lessons from fungi**. *Nat Rev Microbiol* (2007) **5** 57-69. DOI: 10.1038/nrmicro1578
41. Pérez-Arques C, Navarro-Mendoza MI, Murcia L, Lax C, Martínez-García P, Heitman J, Nicolás FE, Garre V. *mBio* (2019) **10**. DOI: 10.1128/mBio.02765-18
42. Jacobsen ID. **Animal models to study mucormycosis**. *J Fungi (Basel* (2019) **5** 27. DOI: 10.3390/jof5020027
43. Gottar M, Gobert V, Matskevich AA, Reichhart J-M, Wang C, Butt TM, Belvin M, Hoffmann JA, Ferrandon D. **Dual detection of fungal infections in**. *Cell* (2006) **127** 1425-1437. DOI: 10.1016/j.cell.2006.10.046
44. Maurer E, Hörtnagl C, Lackner M, Grässle D, Naschberger V, Moser P, Segal E, Semis M, Lass-Flörl C, Binder U. *Med Mycol* (2019) **57** 351-362. DOI: 10.1093/mmy/myy042
45. López-Fernández L, Sanchis M, Navarro-Rodríguez P, Nicolás FE, Silva-Franco F, Guarro J, Garre V, Navarro-Mendoza MI, Pérez-Arques C, Capilla J. **Understanding**. *Virulence* (2018) **9** 707-720. DOI: 10.1080/21505594.2018.1435249
46. Benito EP, Díaz-Mínguez JM, Iturriaga EA, Campuzano V, Eslava AP. **Cloning and sequence analysis of the**. *Gene* (1992) **116** 59-67. DOI: 10.1016/0378-1119(92)90629-4
47. Ibragimova S, Szebenyi C, Sinka R, Alzyoud EI, Homa M, Vágvölgyi C, Nagy G, Papp T. **CRISPR-Cas9-based mutagenesis of the mucormycosis-causing fungus**. *Int J Mol Sci* (2020) **21** 3727. DOI: 10.3390/ijms21103727
48. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A, Walker JM. *The proteomics protocols handbook.* (2005) 571-607
49. Grigoriev IV, Nikitin R, Haridas S, Kuo A, Ohm R, Otillar R, Riley R, Salamov A, Zhao X, Korzeniewski F, Smirnova T, Nordberg H, Dubchak I, Shabalov I. **MycoCosm portal: gearing up for 1000 fungal genomes**. *Nucleic Acids Res* (2014) **42** D699-D704. DOI: 10.1093/nar/gkt1183
50. Quevillon E, Silventoinen V, Pillai S, Harte N, Mulder N, Apweiler R, Lopez R. **InterProScan: protein domains identifier**. *Nucleic Acids Res* (2005) **33** W116-W120. DOI: 10.1093/nar/gki442
51. Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, Heger A, Holm L, Sonnhammer ELL, Eddy SR, Bateman A, Finn RD. **The Pfam protein families database**. *Nucleic Acids Res* (2012) **40** D290-D301. DOI: 10.1093/nar/gkr1065
52. Steinegger M, Söding J. **MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets**. *Nat Biotechnol* (2017) **35** 1026-1028. DOI: 10.1038/nbt.3988
53. Katoh K, Standley DM. **MAFFT multiple sequence alignment software version 7: improvements in performance and usability**. *Mol Biol Evol* (2013) **30** 772-780. DOI: 10.1093/molbev/mst010
54. Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. **IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies**. *Mol Biol Evol* (2015) **32** 268-274. DOI: 10.1093/molbev/msu300
55. Kalyaanamoorthy S, Minh BQ, Wong TK, von Haeseler A, Jermiin LS. **ModelFinder: fast model selection for accurate phylogenetic estimates**. *Nat Methods* (2017) **14** 587-589. DOI: 10.1038/nmeth.4285
56. Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. **UFBoot2: improving the ultrafast bootstrap approximation**. *Mol Biol Evol* (2018) **35** 518-522. DOI: 10.1093/molbev/msx281
57. Sambrook J, Fritsch EF, Maniatis T. *Molecular cloning: a laboratory manual* (1989)
58. Alzohairy A. **BioEdit: an important software for molecular biology**. *GERF Bull of Biosciences* (2011) **2** 60-61
59. Corrochano LM, Kuo A, Marcet-Houben M, Polaino S, Salamov A, Villalobos-Escobedo JM, Grimwood J, Álvarez MI, Avalos J, Bauer D, Benito EP, Benoit I, Burger G, Camino LP, Cánovas D, Cerdá-Olmedo E, Cheng J-F, Domínguez A, Eliáš M, Eslava AP, Glaser F, Gutiérrez G, Heitman J, Henrissat B, Iturriaga EA, Lang BF, Lavín JL, Lee SC, Li W, Lindquist E, López-García S, Luque EM, Marcos AT, Martin J, McCluskey K, Medina HR, Miralles-Durán A, Miyazaki A, Muñoz-Torres E, Oguiza JA, Ohm RA, Olmedo M, Orejas M, Ortiz-Castellanos L, Pisabarro AG, Rodríguez-Romero J, Ruiz-Herrera J, Ruiz-Vázquez R, Sanz C, Schackwitz W. **Expansion of signal transduction pathways in fungi by extensive genome duplication**. *Curr Biol* (2016) **26** 1577-1584. DOI: 10.1016/j.cub.2016.04.038
60. Livak KJ, Schmittgen TD. **Analysis of relative gene expression data using real-time quantitative PCR and the 2**. *Methods* (2001) **25** 402-408. DOI: 10.1006/meth.2001.1262
61. Nagy G, Vaz AG, Szebenyi C, Takó M, Tóth EJ, Csernetics Á, Bencsik O, Szekeres A, Homa M, Ayaydin F, Galgóczy L, Vágvölgyi C, Papp T. **CRISPR-Cas9-mediated disruption of the HMG-CoA reductase genes of**. *Fungal Genet Biol* (2019) **129** 30-39. DOI: 10.1016/j.fgb.2019.04.008
62. van Heeswijck R, Roncero MIG. **High frequency transformation of**. *Carlsberg Res Commun* (1984) **49** 691-702. DOI: 10.1007/BF02907500
63. Dominical V, Samsel L, McCoy JP. **Masks in imaging flow cytometry**. *Methods* (2017) **112** 9-17. DOI: 10.1016/j.ymeth.2016.07.013
64. Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, Preibisch S, Rueden C, Saalfeld S, Schmid B, Tinevez J-Y, White DJ, Hartenstein V, Eliceiri K, Tomancak P, Cardona A. **Fiji: an open-source platform for biological-image analysis**. *Nat Methods* (2012) **9** 676-682. DOI: 10.1038/nmeth.2019
65. Bergin D, Reeves EP, Renwick J, Wientjes FB, Kavanagh K. **Superoxide production in**. *Infect Immun* (2005) **73** 4161-4170. DOI: 10.1128/IAI.73.7.4161-4170.2005
|
---
title: Guild-Level Microbiome Signature Associated with COVID-19 Severity and Prognosis
authors:
- Mingquan Guo
- Guojun Wu
- Yun Tan
- Yan Li
- Xin Jin
- Weiqiang Qi
- Xiaokui Guo
- Chenhong Zhang
- Zhaoqin Zhu
- Liping Zhao
journal: mBio
year: 2023
pmcid: PMC9973266
doi: 10.1128/mbio.03519-22
license: CC BY 4.0
---
# Guild-Level Microbiome Signature Associated with COVID-19 Severity and Prognosis
## ABSTRACT
Coronavirus disease 2019 (COVID-19) severity has been associated with alterations of the gut microbiota. However, the relationship between gut microbiome alterations and COVID-19 prognosis remains elusive. Here, we performed a genome-resolved metagenomic analysis on fecal samples from 300 in-hospital COVID-19 patients, collected at the time of admission. Among the 2,568 high quality metagenome-assembled genomes (HQMAGs), redundancy analysis identified 33 HQMAGs which showed differential distribution among mild, moderate, and severe/critical severity groups. Co-abundance network analysis determined that the 33 HQMAGs were organized as two competing guilds. Guild 1 harbored more genes for short-chain fatty acid biosynthesis, and fewer genes for virulence and antibiotic resistance, compared with Guild 2. Based on average abundance difference between the two guilds, the guild-level microbiome index (GMI) classified patients from different severity groups (average AUROC [area under the receiver operating curve] = 0.83). Moreover, age-adjusted partial Spearman’s correlation showed that GMIs at admission were correlated with 8 clinical parameters, which are predictors for COVID-19 prognosis, on day 7 in hospital. In addition, GMI at admission was associated with death/discharge outcome of the critical patients. We further validated that GMI was able to consistently classify patients with different COVID-19 symptom severities in different countries and differentiated COVID-19 patients from healthy subjects and pneumonia controls in four independent data sets. Thus, this genome-based guild-level signature may facilitate early identification of hospitalized COVID-19 patients with high risk of more severe outcomes at time of admission.
## INTRODUCTION
Coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a worldwide pandemic with a heavy toll on human health and the economy. Over 576 million people have been infected by SARS-CoV-2, with over 6 million deaths globally [1]. Angiotensin-converting enzyme 2 (ACE-2), which is distributed in multiple tissues and widely expressed on the luminal surface of the gut, has been identified as a vital entry receptor of SARS-CoV-2 for promoting viral infection and replication [2]. This can impair gut barrier and induce inflammation, which may disrupt the gut microbiome, contributing to cytokine storm and sepsis in already compromised patients with COVID-19 [2].
Recent studies have shown that dysbiosis of the gut microbiome and its related metabolites is closely associated with COVID-19 disease. These studies reveal the overall difference in the gut microbial composition between COVID-19 patients and healthy controls (3–11), association of microbial taxa and metagenomic functions with disease severity [3, 8, 9, 11] and persistent dysbiosis of the gut microbiota after recovery [3]. The enrichment of pathobionts and depletion of beneficial microbes have been reported to be related to disease severity in COVID-19 [4, 7]. However, these studies have suffered from small sample sizes and lack of cross-study validation and have missed microbiome signatures at admission for COVID-19 prognosis in hospitalized patients (4, 8–11). In addition, the reported findings are constrained due to analyzing the microbiome at low-resolution levels, such as species, genus, or even phylum, or broad metagenomic functional categories (3–11). In the gut microbial ecosystem, the strains/genomes are the minimum responding units to environmental perturbations and their response and contributions to the host are not constrained by taxonomy, even in the same species [12].
In this study, we obtained high-quality metagenome-assembled genomes (HQMAGs), which had completeness > $95\%$, contamination < $5\%$, and strain heterogeneity = 0, from metagenomically sequenced fecal samples collected from 300 in-hospital COVID-19 patients with mild, moderate, severe, and critical disease severities at the time of admission. We identified a guild-level microbiome signature of 33 HQMAGs. This signature classified patients with different severities, associating them with clinical parameters related to prognosis after 1 week in hospital and the death/discharge outcomes of critical patients. The capacity of this signature for classifying COVID-19 patients with different levels of severity and differentiating COVID-19 patients from pneumonia control and healthy individuals was validated in four independent data sets.
## Overall structural changes of the gut microbiome were associated with disease severity in COVID-19 patients at admission.
From May to September 2020, we collected 330 stool samples from 300 in-hospital patients with COVID-19 confirmed by positive SARS-CoV2-2 reverse transcription-quantitative PCR (RT-qPCR) result. Among the 330 samples, 297 were collected from 297 patients at admission and 33 were collected from 29 patients during their hospitalization (Table S1 at https://github.com/nightkid03/SHCOVID-19). To profile the gut microbiome, metagenomic sequencing was performed on all 330 stool samples. To achieve strain/subspecies-level resolution, we reconstructed 2,568 nonredundant HQMAGs (two HQMAGs were collapsed into one if the average nucleotide identity [ANI] between them was > $99\%$) from the metagenomic data set. The HQMAGs accounted for more than $77.17\%$ ± $0.23\%$ (mean ± standard error of the mean [SEM]) of the total reads and were used as the basic variables for the subsequent microbiome analysis.
The 296 patients with a metagenomic data set at admission (one sample was discarded due to low mapping rate of the reads against HQMAGs) were classified into the ($$n = 88$$), moderate ($$n = 196$$), severe ($$n = 5$$), and critical ($$n = 7$$) groups based on their symptoms. Due to the limited sample sizes for severe and critical patients, we combined these two groups into one for the following analysis. There were significant differences in age between the patients with mild, moderate, and severe/critical symptoms (Kruskal-Wallis test, $$P \leq 1.6$$ × 10−14); i.e., the more severe symptoms the patients had, the older they were (Fig. S1). There was no difference in gender among the 3 groups (chi-square test, $$P \leq 0.22$$).
At admission, in the context of beta-diversity based on Bray-Curtis distance, principal coordinate analysis (PCoA) revealed separations of the gut microbiota along PC1, which was in accordance with the severity of symptoms (Fig. 1A to C). A single-factor permutational multivariate analysis of variance (PERMANOVA) test showed that both age (R2 = 0.0059, $$P \leq 0006$$) and disease severity (R2 = 0.014, $$P \leq 0.001$$) were significantly associated with the overall gut microbial composition. However, a marginal PERMANOVA test showed that when controlling for age, disease severity was still significantly associated with the overall gut microbial composition (R2 = 0.012, $$P \leq 0.0002$$), but age was insignificant (R2 = 0.0033, $$P \leq 0.5$$) when controlling for disease severity. This showed that in our data set, part of the variation in gut microbiota was uniquely associated with disease severity, which was independent of age. Pairwise comparisons between the 3 different severity groups via PERMANOVA showed that the gut microbial composition of the patients was significantly different from each other (mild versus moderate: R2 = 0.0081, $$P \leq 0.0001$$; mild versus severe/critical: R2 = 0.026, $$P \leq 0.0001$$; moderate versus severe/critical: R2 = 0.0079, $$P \leq 0.0099$$). The distance between the mild and moderate groups was significantly smaller than that between the mild and the severe/critical groups (Fig. S2), which showed that the gut microbiota of severe/critical group was more different from the mild group compared with the moderate group. In regards to alpha-diversity, the Shannon index was highest in the mild group, followed by the moderate group and lowest in the severe/critical group (Fig. 1D, mild versus moderate: $$P \leq 0.0046$$, mild versus severe/critical: $$P \leq 0.0046$$, moderate versus severe/critical: $$P \leq 0.086$$), which showed a continuous reduction in gut microbial diversity with increasing symptom severity. These results showed that the overall gut microbial structure was associated with symptom severity in COVID-19 patients.
**FIG 1:** *The overall structural variations of gut microbiota at admission are associated with disease severity in hospitalized COVID-19 patients. (A) Principal coordinate analysis (PCoA) based on Bray-Curtis calculated from abundance of the 2,568 genomes. (B) and (C) Comparison of the PC1 and PC2. (D) Comparison of alpha-diversity as indicated by Shannon index. Boxes show medians and interquartile ranges (IQRs); whiskers denote the lowest and highest values that were within 1.5× the IQR from the first and third quartiles, outliers are shown as individual points. Kruskal-Wallis test followed by Dunn’s post hoc test (two-sided) was used to compare groups. Compact letters indicate the significance of the post hoc test (P < 0.05 is significant). Mild, n = 88; moderate, n = 196; severe/critical, n = 12.*
## Two competing guilds were associated with disease severity of hospitalized COVID-19 patients at admission.
Specific HQMAGs that were associated with the COVID-19 symptom severity were identified by redundancy analysis (RDA) (Fig. S3). Out of the 2,568 HQMAGs, we found that 48 had at least $5\%$ of their variability explained by the constraining variable, i.e., the three severity groups. Among the 48 HQMAGs, 17 were significantly higher in the mild group compared to the moderate and severe/critical groups, and these showed a continuous decrease alongside the symptom severity. These 17 HQMAGs included 5 from Faecalibacterium prausnitzii, 3 from Romboutsia timonensis, 2 each from Ruminococcus and Clostridium, and 1 each from Acutalibacteraceae, Allisonella histaminiformans, Coprococcus, Lachnospiraceae, and Negativibacillus (Fig. 2A). The abundance of 31 out of the 48 HQMAGs identified by RDA was higher in the severe/critical group compared with the mild and the moderate groups. Among these 31 HQMAGs, 16 showed significant differences between the three groups. These 16 HQMAGs included 4 from Enterococcus, 2 from Lactobacillus, and 1 each from Acutalibacteraceae, Akkermansia muciniphila, Anaerotignum, Barnesiella intestinihominis, Clostridium bolteae, Dore, Intestinibacter bartlettii, Lachnospiraceae, Phascolarctobacterium faecium, and Ruthenibacterium lactatiformans (Fig. 2A). We then focused on the 17 mild group-enriched HQMAGs and 16 severe/critical group-enriched HQMAGs because they were all identified by RDA analysis and were significantly different between the 3 severity groups.
**FIG 2:** *Two competing guilds composed of differentially abundant gut microbial genomes are associated with symptom severity in COVID-19 patients. (A) Heatmap of 33 high-quality metagenome-assembled genomes (HQMAGs) identified by redundancy analysis (RDA) and showing differences between the 3 severity groups. RDA analysis was conducted based on the Hellinger transformed abundance of all HQMAGs and used three symptom severity groups as environmental variables. HQMAGs with at least 5% of the variability in their abundance explained by constrained axes were selected. A Kruskal-Wallis test followed by Dunn’s post hoc test (two-sided) was used to test the differences between the 3 severity groups. Compact letters indicate the significance of the post hoc test (P < 0.05 is significant). Heatmap shows the mean abundance of each HQMAGs in each group. Abundance was scaled across each row. (B) Co-abundance network of the HQMAGs reflects two competing guilds. The co-abundance correlation between the HQMAGs were calculated using Fastspar (n = 296 subject). All significant correlations with Benjamini-Hochberg (BH)-adjusted P < 0.05 were included. Edges between nodes represent correlations. Red and blue colors indicate positive and negative correlations, respectively. Node size indicates the average abundance of the HQMAGs in 296 samples. Genomes were clustered into two guilds based on co-abundance correlation and complete linkage followed by weighted correlation network analysis (WGCNA) analysis. Node color indicates guild: Guild 1, orange; Guild 2, purple. (C) Comparison of guild-level microbiome index (GMI). Data points which do not share common compact letters are significantly different from each other (P < 0.05). Boxes show medians and IQRs; whiskers denote the lowest and highest values that lie within 1.5× the IQR from the first and third quartiles, outliers are shown as individual points. A Kruskal-Wallis test followed by Dunn’s post hoc test (two-sided) was applied to compare the groups. Compact letters indicate the significance of the test (P < 0.05). Mild, n = 88; moderate, n = 196; severe/critical, n = 12. (D) GMI supports classification of different COVID-19 symptom severities. AUROC (area under the receiver operating characteristic curve) is shown.*
Because bacteria in the gut ecosystem are not independent but rather form coherent functional groups (aka “guilds”) to interact with each other and affect host health [13], we applied co-abundance analysis to these 33 HQMAGs to explore the interactions between them and find potential guild structures with hierarchical clustering and weighted correlation network analysis (WGCNA) [14]. Interestingly, the 33 HQMAGs organized themselves into two guilds. The 17 HQMAGs with significantly higher abundance in mild group were positively interconnected with each other and formed Guild 1. The 16 severe/critical group-enriched HQMAGs were positively correlated with each other as Guild 2. Meanwhile, there were only negative correlations between the two guilds, suggesting a potentially competitive relationship between them (Fig. 2B).
To explore the genetic basis underlying the associations between the two guilds and symptom severities, we performed a genome-centric analysis of the metagenomes of the two competing guilds. A previous study showed that a lack of short-chain fatty acids (SCFAs) is significantly correlated with disease severity in COVID-19 patients [7]. For the terminal genes for the butyrate biosynthetic pathways (i.e., but, buk, atoA/D, and 4Hbt) [15], 7 HQMAGs in Guild 1 harbored the but gene, while only 1 HQMAGs in Guild 2 possessed this gene (Fisher’s exact test, $$P \leq 0.039$$) (Fig. S4). Four HQMAGs in Guild 1 harbored the buk gene, while no HQMAGs in Guild 2 had this gene (Fisher’s exact test, $$P \leq 0.10$$). The other butyrate biosynthetic terminal genes were not found in the HQMAGs in either guild. The numbers of HQMAGs encoding genes for acetate and propionate production were similar in the two guilds (Fig. S4). From a pathogenicity perspective, both guilds had 12 HQMAGs encoding virulence factor (VF) genes. However, Guild 1 had 17 VF genes from 3 VF categories, while Guild 2 had 58 VF genes from 5 VF categories (Fig. S5A). In terms of antibiotic resistance genes (ARGs), 3 genomes in Guild 1 encoded 10 ARGs and 5 genomes in Guild 2 encoded 14 ARGs (Fig. S5B). Taken together, these data showed that the two competing guilds had different genetic capacities, with Guild 1 being more beneficial and Guild 2 more detrimental. Thus, the genetic difference between the two guilds may help explain their associations with disease severity in COVID-19 patients.
We then calculated the guild-level microbiome index (GMI) based on the average abundance difference between guilds 1 and 2 to reflect the dominance of Guild 1 over Guild 2. At admission, the GMI was highest in the mild group, followed by the moderate group, and was lowest in the severe/critical group (Fig. 3C; mild versus moderate: $$P \leq 2.46$$ × 10−7; mild versus severe/critical: $$P \leq 6.57$$ × 10−9; moderate versus severe/critical: $$P \leq 1.59$$ × 10−4). The GMI reached an AUROC (area under the receiver operating characteristic curve) of 0.7 and an AUPRC (area under the precision-recall curve) of 0.8, with a baseline of 0.69 to differentiate between the mild and moderate groups; an AUROC of 0.94 and AUPRC of 0.59 with a baseline of 0.12 to differentiate between the mild and severe/critical groups; and an AUROC of 0.86 and AUPRC of 0.32 with a baseline of 0.06 to differentiate between the moderate and severe/critical groups (Fig. 3D and Fig. S6A–C). This result indicates the feasibility of using the GMI as a biomarker to differentiate between different symptom severity groups of COVID-19 patients.
**FIG 3:** *The two competing guilds at admission are associated with COVID-19 severity in hospitalized patients on day 7 after admission and with endpoint in critical patients. (A) Bar plot shows the correlations between the GMI at admission and clinical parameters of COVID-19 in hospitalized patients on day 7. Age-adjusted partial Spearman’s correlation was calculated. Correlations with BH-adjusted P < 0.1 are shown. Blue bar, biochemical indicators; green bar, coagulation indicators; red bar, immune indicators. (B) GMI at admission associated with death/discharge outcomes of critical COVID-19 patients. A two-sided Mann-Whitney test was used to determine significance. *, P < 0.05. Death, n = 3; discharge, n = 4.*
## Gut microbiome signature was associated with the COVID-19 prognosis of hospitalized patients.
To explore whether our microbiome signature at admission is associated with the prognosis of COVID-19 patients during hospitalization, we calculated correlations between GMI at admission and 72 different clinical parameters on day 7 of hospitalization. Two and six clinical parameters on day 7 showed significantly (partial Spearman’s correlation; Benjamini-Hochberg [BH]-adjusted $P \leq 0.1$) positive and negative correlations, respectively, with the GMI values at admission after adjusting for age (Fig. 3A). Regarding immune indicators, interleukin (IL)-5 is secreted chiefly by Th2 cells and is essentially anti-inflammatory but also involved in several allergic responses [16]. Some studies have revealed higher levels of IL-5 in severe cases than in mild cases [17, 18]. However, others have shown that IL-5 levels have no correlations with COVID-19 and showed no differences between different severity groups [19, 20]. Here, we found positive correlations between the GMI at admission and IL-5 levels after 1 week. The effects of the microbiome on particular cytokines and its subsequent influences on COVID-19 require further study. Coagulation disorder occurred during the early stage of COVID-19 infection [21]. D-dimer and fibrin degradation product (FDP) levels increased in COVID-19 patients and were correlated with clinical classification [21, 22]. Moreover, elevated D-dimer and FDP levels are significant indicators of severe COVID-19 and poor prognosis (21–24). Here, a higher GMI at admission was correlated with lower D-dimer and FDP levels after 1 week. Regarding biochemical indicators, compared with health subjects, total cholesterol (TC) was significantly lower in COVID-19 patients and decreased with increasing severity [25, 26]. A meta-analysis showed that a reduction in TC was significantly associated with increased mortality in COVID-19 patients, and TC may assist with early risk stratification [26]. Hypocalcemia has been reported to be common in COVID-19 patients [27]. Higher total bilirubin (TBIL) was associated with a significant increase in the severity of COVID-19 infection [28]. Moreover, COVID-19 patients with an elevated TBIL at admission had a higher mortality rate [28]. In addition to TBIL, increased direct bilirubin (DBIL) has been reported as an independent indicator of complications and mortality in COVID-19 patients [29]. In particular, DBIL levels on day 7 of hospitalization are advantageous for predicting the prognosis of COVID-19 in severe/critical patients [29]. Lactate dehydrogenase (LDH) has been associated with worse outcomes in viral infection. One meta-analysis showed that LDH could be used as a COVID-19 severity marker and a predictor of survival [30]. Here, the GMI at admission was positively correlated with TC and negatively correlated with TBIL, DBIL, and LDH after 1 week. These results suggest that the gut microbiome signature in early stages of the disease may reflect the clinical outcomes of COVID-19 in hospitalized patients.
Moreover, in our cohort, 3 patients died, all of which were in the critical group at admission. Compared with the other 4 discharged critical patients, the 3 dead patients were significantly younger (Fig. S7). The GMIs of the 3 dead patients at admission were significantly lower than those of the 4 discharged critical patients (Fig. 3B). This suggests an association between the microbiome signature and the final outcome in critical hospitalized COVID-19 patients. Although interesting, this result should be interpreted with caution given the small sample size.
## The microbiome signature was validated in independent studies.
We then asked whether this genome-based microbiome signature would be applicable in other COVID-19 cohorts. To answer this question, we used the genomes of the 33 HQMAGs as references to perform read-recruitment analysis, a common method for estimating the abundances of reference genomes from metagenomes [31, 32]. In an independent study, which included 24 mild/moderate and 14 severe/critical COVID-19 patients from China [9], we validated the associations between the microbiome signature and different COVID-19 severities. In this validation data set, the two patient groups had even distributions of age, gender, and comorbidities, preventing potential biases for our validation. On average, the 33 HQMAGs accounted for $4.39\%$ ± $0.90\%$ (mean ± SEM) of the total abundance of the gut microbial community. In the context of beta-diversity, as measured via Bray-Curtis distance, the composition of the microbiome signature significantly differed between mild/moderate and severe/critical COVID-19 patients (Fig. 4A). GMI and abundance of Guild 1 were significantly higher in the mild/moderate patients, while the abundance of Guild 2 was significantly higher in the severe/critical patients (Fig. 4B). Moreover, GMI had a discriminatory power of AUROC = 0.72 and AUPRC = 0.63 with a baseline of 0.37 to differentiate the two severity groups (Fig. 4C and Fig. S6D).
**FIG 4:** *Genome-based microbiome signature enables classification of COVID-19 patients from different severity groups in an independent Chinese cohort. (A) PCoA based on Bray-Curtis distance calculated from the abundance of the 33 HQMAGs. Permutational multivariate analysis of variance (PERMANOVA) test showed significant differences in the composition of the 33 HQMAGs between the two groups. (B) Significant differences in GMI and abundances of guilds 1 and 2 between mild/moderate and severe/critical COVID-19 patients. Bar plot summarizes the mean and standard error of the mean (SEM). Mann-Whitney test (two-sided) was used to compare groups. Mild/moderate, n = 24; severe/critical, n = 14. **, P < 0.01; *, P < 0.05. (C) GMI supports classification according to different COVID-19 symptom severities.*
To further test the applicability of the microbiome signature in different geographies, we included metagenomic sequencing data from 18 moderate and 9 severe COVID-19 patients from the United States [33] and applied the same validation process. On average, the 33 HQMAGs accounted for $4.18\%$ ± $0.59\%$ (mean ± SEM) of the total abundance of the gut microbial community. Although the composition of the microbiome signature between the moderate and severe COVID-19 patients was not significantly different based on Bray-Curtis distance (Fig. 5A), the GMI and abundance of Guild 1 were significantly higher in the moderate patients, while the abundance of Guild 2 was significantly higher in the severe patients (Fig. 5B). GMI had a discriminatory power of AUROC = 0.9 and AUPRC = 0.89 with a baseline of 0.33 to differentiate the two severity groups (Fig. 5C and Fig. S6E). These results validated our findings of the associations between genome-resolved microbiome signature and COVID-19 disease severity in different geographies.
**FIG 5:** *Genome-based microbiome signature enables classification of COVID-19 patients from different severity groups in an independent American cohort. (A) PCoA based on Bray-Curtis distance calculated from the abundance of the 33 HQMAGs. PERMANOVA test showed significant differences in the composition of the 33 HQMAGs between the two groups. (B) Significant differences in GMI and abundances of guilds 1 and 2 between moderate and severe COVID-19 patients. Bar plot summarizes mean and SEM. Mann-Whitney test (two-sided) was applied to compare groups. Moderate, n = 18; severe, n = 9. ***, P < 0.001; **, P < 0.01. (C) GMI supports classification according to different COVID-19 symptom severities.*
Because this microbiome signature was associated with COVID-19 disease and was able to classify COVID-19 severity, we were interested in determining whether it could classify COVID-19 and non-COVID-19 controls as well. We first included metagenomic sequencing data from 66 COVID-19 patients (first sample after admission), of which 47 were mild/moderate, and 9 community-acquired pneumonia controls which were negative for COVID-197. The genomes of the 33 HQMAGs were used as reference genomes to perform read-recruitment analysis. On average, the 33 HQMAGs accounted for $3.75\%$ ± $0.74\%$ (mean ± SEM) of the total abundance of the gut microbial community. In the context of beta-diversity as measured via Bray-Curtis distance, the composition of the microbiome signature between the two groups was significantly different (Fig. 6A). The GMI and abundance of Guild 1 were significantly higher in the COVID-19 group, while abundance of Guild 2 was higher in the pneumonia control group (Fig. 6B). GMI had a discriminatory power of AUROC = 0.75 and AUPRC = 0.32 with a baseline of 0.12 to differentiate the two groups (Fig. 6C and Fig. S6F).
**FIG 6:** *Genome-based microbiome signature enabled distinction between COVID-19 and pneumonia subjects in an independent data set. (A) PCoA based on Bray-Curtis distance calculated from the abundance of the 33 HQMAGs. (B) Significant differences in GMI and abundances of guilds 1 and 2 between COVID-19 and pneumonia subjects. Bar plot summarizes the mean and SEM. Mann-Whitney test (two-sided) was applied to compare groups. COVID-19, n = 66; pneumonia, n = 9. **, P < 0.01; *, P < 0.05. (C) GMI supports classification between COVID-19 and pneumonia control group.*
Next, we included metagenomic sequencing data from 46 COVID-19 patients and 19 age- and sex-matched healthy controls from the study conducted by Li et al. [ 34]. On average, the 33 HQMAGs accounted for $1.61\%$ ± $0.12\%$ (mean ± SEM) of the total abundance of the gut microbial community. Based on the Bray-Curtis distance, the PCoA plot revealed a separation between the COVID-19 patients and healthy subjects (Fig. 7A). Compared with the healthy controls, COVID-19 patients had a significantly lower GMI and abundance of Guild 1 but a higher abundance of Guild 2 (Fig. 7B). These results suggest that SARS-CoV-2 infection is associated with altered composition of the 33 HQMAGs. GMI had a discriminatory power of AUROC = 0.75 and AUPRC = 0.9 with a baseline of 0.71 to differentiate the COVID-19 patients and healthy controls (Fig. 7C and Fig. S6G). These showed that the microbiome signature was related to host health and could be used as a biomarker to differentiate the COVID-19 subjects from the pneumonia controls and healthy subjects.
**FIG 7:** *Genome-based microbiome signature enables distinction between COVID-19 subjects and heathy controls in an independent data set. (A) PCoA based on Bray-Curtis distance calculated from the abundance of the 33 MAGs. PERMANOVA test showed significant differences in the composition of the 33 MAGs between the two groups. (B) Significant differences in GMI and abundances of guilds 1 and 2 between COVID-19 and healthy subjects. Bar plot summarizes the mean and SEM. Mann-Whitney test (two-sided) was applied to compare groups. COVID-19, n = 46; healthy control, n = 19. *, P < 0.05; **, P < 0.01. (C) GMI supports classification of COVID-19 and healthy control subjects.*
## DISCUSSION
In the current study, a genome-based microbiome signature, composed of 33 HQMAGs at the time of admission, was found to be associated with the severity and prognosis of COVID-19 in hospitalized patients. With these 33 genomes as a reference, we were also able to validate the microbiome signature in data sets collected from four independent studies.
We arrived at this finding by way of a unique analytical strategy for the microbiome data set. Previous studies relied on reference databases to profile gut microbial composition at taxonomic levels and explored the relationships between different taxa and COVID-193–11. Our strategy used a reference-free discovery approach which does not need any prior knowledge. This allowed us to keep the novel part of the data set intact. In addition, the use of high-quality draft genomes in our study ensured the highest possible resolution for identifying microbiome signatures associated with COVID-19, overcoming the pitfalls of taxon-based analysis [13]. In previous studies based on taxon-level analysis, Enterococcus faecium, Enterococcus avium, and *Akkermansia muciniphila* have been reported to be enriched in severe/critical COVID-19 patients and positively correlated with symptom severity [3, 9]. In our results, a total of 28 A. muciniphila, 2 E. avium, and 5 E. faecium HQMAGs were assembled in our data set, but only 3 strains of E. faecium and 1 each of A. muciniphila and E. avium were enriched in the severe/critical group, suggesting that not all strains from these 3 species were associated with COVID-19 severity. Another example is that Faecalibacterium prausnitzii, a key producer of SCFAs, is consistently depleted in COVID-19 patients and negatively correlated with disease severity [3, 4]; however, in our results, only half of the F. prausnitzii HQMAGs in our data set were negatively associated with COVID-19 symptom severity. These results indicate that the associations between gut microbiota and COVID-19 are strain/genome-specific. This means that even species-level analysis may not provide the necessary resolution to reveal associations of gut microbiome with COVID-19.
In addition to identifying COVID-19 associated gut microbiota at the genome level, we used guild-based analysis to reveal potential interactions among key gut bacteria via a co-abundance network. We found that the genomes enriched in the mild/moderate group and the genomes enriched in the severe/critical group formed two guilds, Guild 1 and Guild 2, respectively. The genomes in Guild 1 had higher SCFA-producing genetic capacity, while the Guild 2 genomes contained more VF- and ARG-encoding genes. Reduced abundance of SCFA-producing pathways has been correlated with more adverse clinical outcomes in COVID-19 patients [7]. The expression levels of VF and ARG, as measured by metatranscriptomic sequencing, were significantly higher in COVID-19 patients compared with the healthy and non-COVID-19 pneumonia controls [35]. Higher abundance of Guild 1 and lower abundance of Guild 2 were associated with reduced severity in our COVID-19 patients. Such a two-competing-guilds structure, in which one beneficial guild and one detrimental guild compete with each other and influence host health, has been reported as a core microbiome signature associated with various chronic diseases [36]. Our findings suggest that such two competing guild microbiome signatures may also be applicable to infectious diseases.
In our study cohort, GMI based on the average abundance difference between the two guilds was able to discriminate between different symptomatic severity groups of COVID-19 patients at admission. This capacity of GMI to discriminate COVID-19 symptom severities has been further validated in independent cohorts from China and United States [9, 33]. Moreover, GMI had the capacity to distinguish COVID-19 subjects from pneumonia controls and healthy subjects in two other independent studies [7, 34]. These indicate the feasibility of using this microbiome signature risk stratification for COVID-19 patients. It is worth validating the applicability of the microbiome signature in COVID-19 diagnosis in cohorts across additional ethnicities and geographies.
A recent mouse-model study showed that SARS-COV-2 infection alone caused gut microbiome dysbiosis and gut epithelial cell alterations, with an increased number of goblet cells and a decreased number of Paneth cells [37]. This dysbiotic gut microbiome may play a role in modulating host immune responses and outcomes of COVID-19 patients by translocating potential pathogens or their antigens into systemic circulation [37] and decreasing production of metabolites such as SCFAs and l-isoleucine [7]. In the current study, we found that the microbiome signature, which was related to COVID-19 severity at admission, was associated with COVID-19 prognosis. The two competing guilds’ microbiome signatures at admission were associated with several coagulation, hemogram, and biochemical indicators of hospitalized patients after 1 week. These indicators included D-dimer, FDP, TC, TBIL, DBIL, and LDH, which have been reported to play essential roles in the host response to COVID-19 infection and disease progression (21–30). The microbiome signature may serve as an early predictor of COVID-19 prognosis because it was positively associated with bio-clinical parameters that have inverse relationships with poor prognosis and negatively associated with those that have direct relationships with poor prognosis. More importantly, early time-point variations of the two competing guilds’ microbiome signatures were correlated with later changes in these prognosis related bio-clinical parameters. These results suggest that dysbiosis of the gut microbiota may play a pivotal role in triggering more severe symptoms after patients are infected with SARS-CoV-2. More mechanistic studies, such as time-series experiments involving transplanting gut microbiota from COVID-19 patients with different disease severities or transplanting different combinations of the isolates from the two competing guilds into germ-free mice with or without SARS-COV-2 infection, and more gut microbiota-targeted intervention studies on COVID-19 patients, are needed to further understand the relationships between gut microbiota and COVID-19 prognoses.
Early identification and treatment of high-risk patients is critical for improving COVID-19 prognosis when the end of the pandemic is not yet in sight due to emerging SARS-CoV-2 variants such as Omicron. Screening hospitalized COVID-19 patients at the time of admission using our genome-based guild-level microbiome signature may facilitate early identification of patients at high risk of more severe outcomes so they can be put under intensive surveillance and preventive care.
## Ethics statement.
This study procedure was reviewed and approved by the Ethics Committee of Shanghai Public Health Clinical Center (SHPHC, no. YJ-2020-S080-02), and informed written consent were obtained from all subjects according to the Declaration of Helsinki. All experimental procedures were performed in strict accordance with the biosafety operation guidelines of the SARS-CoV-2 Laboratories of the National Health and Family Planning Commission (no. 2020 [70]) and the Shanghai Municipal Health and Family Planning Commission (no. 2020 [8]). Table S1 at https://github.com/nightkid03/SHCOVID-19 lists sample collection and disease severity information.
## Subject recruitment and sample collection.
This study was retrospectively conducted at the Shanghai Public Health Clinical Center, a designated hospital for COVID-19 treatment in East China. In total, 337 COVID-19 patients were recruited for this study; all patients were typed and grouped based on clinical symptoms by senior clinicians in strict accordance with the criteria of the Diagnosis and Treatment Plan for SARS-CoV-2 (trial version 7) issued by the General Office of the National Health Commission. The clinical data of the study subjects, including patient epidemiology (age, gender, disease classification, length of hospital stay, duration of disease, clinical outcomes) and respective clinical laboratory test results (hematologic, clinical chemistry, coagulation, immune inflammatory indices, and radiographic indications) were stored in a computerized database in the hospital medical record system. Stool samples were collected within 48 h of admission from all patients from May to September 2020, ensuring that all patients did not receive antiviral, antibiotic, probiotic, hormone, or other drug interventions. About 100 mg of each patient’s feces was collected in a stool collection tube and frozen immediately at −80°C until processing.
## Clinical laboratory examination and data collection.
All laboratory tests were conducted at the department of laboratory medicine in the Shanghai Public Health Clinical Center. A Sysmex XN-1000 automated hematology analyzer (Hisense Meikang Medical Electronics, Shanghai Co., Ltd.) and its supporting test reagents were used to analyze blood routine tests, including white blood cell count, lymphocyte count, platelet count, %neutrophils, %monocytes, %lymphocytes, hemoglobin, hypersensitive C-reactive protein, etc. Biochemical parameters such as albumin, amylase, cholinesterase, lactate, lactate dehydrogenase, alkaline phosphatase, glucose, creatinine, uric acid, and prealbumin were measured by a biochemical immunoassay workstation (ARCHITECT 3600J, Abbott Laboratories Co., USA). Urine routine (pH value, specific gravity, urobilinogen, leukocyte esterase, nitrite, urine protein, glucose, ketone body, bilirubin, and occult blood) was measured by a Cobas 6500 urine dry chemical analysis system and supporting test strips (Roche, Switzerland). For the coagulation indicators, an STA Compact Max was used to measure fibrinogen, D-dimer, fibrinogen degradation products, prothrombin time, activated partial thromboplastin time, thrombin time, etc.
## Plasma cytokine measurements.
A FACS Canto II Flow cytometer (BD Biosciences, USA) was used for lymphocyte analysis, CD3+ cell counts, CD4+ cell counts, CD8+ cell counts, CD19+ cell counts, CD16+ CD56+ cell counts, and to determine CD4+/CD8+ percentage. Plasma cytokine-related parameters, including IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12P70, IL-17, tumor necrosis factor alpha, interferon alpha, and interferon gamma, were measured using a microsphere array kit and a FACS Canto II cytometer (Raisecare Biotechnology, China).
## Gut microbiome analysis.
(i) DNA extraction and metagenomic sequencing. The laboratory procedures in this section were performed by trained laboratory personnel under the condition of tertiary protection in a biosafety level 2 (BSL2)-qualified laboratory. DNA was extracted from fecal samples using the bead-beating method as previously described [38]; a QIAamp PowerFecal Pro DNA kit (Qiagen, Germany) was used to perform DNA extraction according to the manufacturer’s instructions. Briefly, fecal samples (~100 mg) were dissolved by Powerlyzer lysate in a PowerBead Pro Tube, followed by vigorous shaking for 10 min and centrifugation. Total genomic DNA was captured on a silica membrane in a spin-column. DNA was then washed and eluted. An A260/A280 ratio of ~1.8, concentration, and curve observations were used to assess the quality of DNA extraction. Qualified DNA samples were ready for downstream application. Metagenomic sequencing was performed using an Illumina HiSeq 3000 at GENEWIZ Co. (Beijing, China). *Cluster* generation, template hybridization, isothermal amplification, linearization, and blocking, denaturing, and hybridization of the sequencing primers were performed according to the workflow specified by the service provider. Libraries were constructed with an insert size of approximately 500 bp followed by high-throughput sequencing to obtain paired-end reads with 150 bp in the forward and reverse directions. Table S2 at https://github.com/nightkid03/SHCOVID-19 shows the number of raw reads for each sample.
(ii) Data quality control. Trimmomatic [39] was used to trim low-quality bases from the 3′ end, remove low quality reads, and remove reads of <60 bp, with the parameters leading:6 trailing:6, slidingwindow:4:20 minlen:60. Reads that could be aligned to the human genome (H. sapiens, UCSC hg19) were removed (aligned with Bowtie2 [40] using -reorder -no-hd -no-contain -dovetail). Table S2 at https://github.com/nightkid03/SHCOVID-19 shows the number of high-quality reads of each sample for further analysis.
(iii) De novo assembly, abundance calculation, and taxonomic assignment of genomes. De novo assembly was performed for each sample using MEGAHIT [41] (-min-contig-len 500, -presets meta-large). The assembled contigs were further binned using MetaBAT 2 [42] and MaxBin 2 [43]. A refinement step was then performed using the bin_refinement module from MetaWRAP [44] to combine and improve the results generated by the 2 binners. The quality of the bins was assessed using CheckM [45]. Bins with completeness > $95\%$, contamination < $5\%$, and strain heterogeneity = 0 were retained as high-quality draft genomes (Table S3 at https://github.com/nightkid03/SHCOVID-19). The assembled high-quality draft genomes were further dereplicated by using dRep [46]. DiTASiC [47], which applies kallisto for pseudo-alignment [48] and a generalized linear model for resolving shared reads among genomes, was used to calculate the abundance of the genomes in each sample, estimated counts with $P \leq 0.05$ were removed, and all samples were downsized to 30 million reads (one sample at admission with a read-mapping ratio of ~$32\%$, which could not be well represented by the high-quality genomes, were removed in further analyses). Taxonomic assignment of the genomes was performed using GTDB-Tk [49] (Table S4 at https://github.com/nightkid03/SHCOVID-19).
(iv) Gut microbiome functional analysis. Prokka [50] was used to annotate genomes. KEGG Orthologue (KO) IDs were assigned to the predicted protein sequences in each genome by HMMSEARCH against KOfam using KofamKOALA [51]. Antibiotic resistance genes were predicted using ResFinder [52] with the default parameters. Identification of virulence factors was based on the core set of the Virulence Factors of Pathogenic Bacteria Database (VFDB [53], downloaded July 2020). Predicted protein sequences were aligned to the reference sequence in VFDB using BLASTP (best hist with E value < 1e-5, identity > $80\%$, and query coverage > $70\%$). Genes encoding formate-tetrahydrofolate ligase, propionyl-CoA:succinate-CoA transferase, propionate CoA-transferase, 4Hbt, AtoA, AtoD, Buk, and But were identified as described previously [54].
(v) Gut microbiome co-abundance network construction and analysis. Fastspar [55], a rapid and scalable correlation estimation tool for microbiome study, was used to calculate the correlations between the genomes with 1,000 permutations at each time point based on the abundances of the genomes across all patients, and correlations with BH-adjusted $P \leq 0.05$ were retained for further analysis. The co-abundance network was visualized using Cytoscape v3.8.1 [56]. Complete linkage based on the co-abundance correlations followed by WGCNA analysis [14] was used to identify the guilds.
(vi) Definition of guild-level microbiome index. We defined the GMI using the abundance of the 33 QHMAGs and their relationships. For each individual sample, the GMI of sample j, denoted as GMIj, was calculated as follows: [1]Ijguild1=∑i∈NAij [2]Ijguild2=∑i∈MAij [3]GMIj=Ijguild1|N|−Ijguild2|M| Where *Aij is* the relative abundance of HQMAG i in sample j; N and M are subsets of HQMAGs in guilds 1 and 2, respectively; and |N| and |M| are the sizes of these two sets. GMI = 0 indicates equality between guilds 1 and 2. Theoretically, the range of GMI is −100 to 100.
(vii) Validation in an independent cohort. Metagenomic sequencing data from 24 mild/moderate and 14 severe/critical COVID-19 patients were downloaded from the European Nucleotide Archive (ENA) database under PRJNA792726 [9] (Table S5 at https://github.com/nightkid03/SHCOVID-19). The metagenomic sequencing data from 18 moderate and 9 were downloaded from ENA under PRJNA660883 [33] (Table S5). The metagenomic sequencing data from 66 COVID-19 patients (first sample after admission) and 9 community-acquired pneumonia controls that were negative for COVID-19 were downloaded from ENA under PRJNA689961 [7] (Table S5). The metagenomic sequencing data from 46 COVID-19 patients and 19 healthy controls were downloaded from ENA under PRJEB43555 [34] (Table S5). KneadData (https://huttenhower.sph.harvard.edu/kneaddata/) was applied to perform quality control of the raw reads with the following parameters: -decontaminate-pairs strict, -run-trim-repetitive, -bypass-trf, -trimmomatic-options = “slidingwindow:4:20 minlen:60.” Reads that could be aligned to the human genome were identified and removed in KneadData by aligning reads against the Homo sapiens hg37 genome. The abundance of the 33 MAGs were estimated by using Coverm v0.6.1 (https://github.com/wwood/CoverM) with the following parameters: coverm genome –min-read-aligned-percent = 90 -min-read-percent-identity = 99 -m relative_abundance.
## Statistical Analysis.
Statistical analysis was performed in R version 4.1.1. A Kruskal-Wallis test followed by Dunn’s post hoc test (two-sided) was used to compare the different severity groups. Redundancy analysis was conducted based on the Hellinger transformed abundance to find specific gut microbial members associated with COVID-19 severity. Both single-factor and marginal PERMANOVA tests including both age and symptom severity were used to compare overall gut microbial composition. AUROC and AUPRC were used to evaluate the capacity of GMI to discriminate between groups using the R packages pROC and PRROC [57], respectively. AUROC considers the trade-offs between sensitivity and specificity and compares the performance of classifiers with a baseline value of 0.5 for a random classifier. AUPRC, which considers the trade-offs between precision and recall with a baseline that equals the proportion of positive cases in all samples, was used as a complementary assessment, particularly for highly imbalanced data sets.
## Code availability.
Parameters of the bioinformatic tools applied in the study are listed in Materials and Methods. Scripts and command lines related to the current study can be found at https://github.com/nightkid03/SHCOVID-19.
## Ethics and inclusion statement.
We have carefully considered research contributions and authorship criteria when involved in multi-region collaborations involving local researchers to promote greater equity in research collaborations.
## Data availability.
The metagenomic sequencing data for the current study have been deposited into the CNGB Sequence Archive (CNGB) of the China National GenBank Database (CNGBdb) [58] under accession no. CNP0003849. Supplementary tables can be found at https://github.com/nightkid03/SHCOVID-19.
## References
1. **COVID-19 dashboard**
2. Penninger JM, Grant MB, Sung JJY. **The role of angiotensin converting enzyme 2 in modulating gut microbiota, intestinal inflammation, and coronavirus infection**. *Gastroenterology* (2021.0) **160** 39-46. DOI: 10.1053/j.gastro.2020.07.067
3. Yeoh YK, Zuo T, Lui GC-Y, Zhang F, Liu Q, Li AY, Chung AC, Cheung CP, Tso EY, Fung KS, Chan V, Ling L, Joynt G, Hui DS-C, Chow KM, Ng SSS, Li TC-M, Ng RW, Yip TC, Wong GL-H, Chan FK, Wong CK, Chan PK, Ng SC. **Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19**. *Gut* (2021.0) **70** 698-706. DOI: 10.1136/gutjnl-2020-323020
4. Zuo T, Zhang F, Lui GCY, Yeoh YK, Li AYL, Zhan H, Wan Y, Chung ACK, Cheung CP, Chen N, Lai CKC, Chen Z, Tso EYK, Fung KSC, Chan V, Ling L, Joynt G, Hui DSC, Chan FKL, Chan PKS, Ng SC. **Alterations in gut microbiota of patients with COVID-19 during time of hospitalization**. *Gastroenterology* (2020.0) **159** 944-955.e8. DOI: 10.1053/j.gastro.2020.05.048
5. Schult D, Reitmeier S, Koyumdzhieva P, Lahmer T, Middelhoff M, Erber J, Schneider J, Kager J, Frolova M, Horstmann J, Fricke L, Steiger K, Jesinghaus M, Janssen K-P, Protzer U, Neuhaus K, Schmid RM, Haller D, Quante M. **Gut bacterial dysbiosis and instability is associated with the onset of complications and mortality in COVID-19**. *Gut Microbes* (2022.0) **14** 2031840. DOI: 10.1080/19490976.2022.2031840
6. Ren Z, Wang H, Cui G, Lu H, Wang L, Luo H, Chen X, Ren H, Sun R, Liu W, Liu X, Liu C, Li A, Wang X, Rao B, Yuan C, Zhang H, Sun J, Chen X, Li B, Hu C, Wu Z, Yu Z, Kan Q, Li L. **Alterations in the human oral and gut microbiomes and lipidomics in COVID-19**. *Gut* (2021.0) **70** 1253-1265. DOI: 10.1136/gutjnl-2020-323826
7. Zhang F, Wan Y, Zuo T, Yeoh YK, Liu Q, Zhang L, Zhan H, Lu W, Xu W, Lui GCY, Li AYL, Cheung CP, Wong CK, Chan PKS, Chan FKL, Ng SC. **Prolonged impairment of short-chain fatty acid and L-isoleucine biosynthesis in gut microbiome in patients with COVID-19**. *Gastroenterology* (2021.0) **162** 548-561.e4. DOI: 10.1053/j.gastro.2021.10.013
8. Wu Y, Cheng X, Jiang G, Tang H, Ming S, Tang L, Lu J, Guo C, Shan H, Huang X. **Altered oral and gut microbiota and its association with SARS-CoV-2 viral load in COVID-19 patients during hospitalization**. *NPJ Biofilms Microbiomes* (2021.0) **7** 61. DOI: 10.1038/s41522-021-00232-5
9. Xu X, Zhang W, Guo M, Xiao C, Fu Z, Yu S, Jiang L, Wang S, Ling Y, Liu F, Tan Y, Chen S. **Integrated analysis of gut microbiome and host immune responses in COVID-19**. *Front Med* (2022.0) **16** 263-275. DOI: 10.1007/s11684-022-0921-6
10. Cao J, Wang C, Zhang Y, Lei G, Xu K, Zhao N, Lu J, Meng F, Yu L, Yan J, Bai C, Zhang S, Zhang N, Gong Y, Bi Y, Shi Y, Chen Z, Dai L, Wang J, Yang P. **Integrated gut virome and bacteriome dynamics in COVID-19 patients**. *Gut Microbes* (2021.0) **13** 1-21. DOI: 10.1080/19490976.2021.1887722
11. Sun Z, Song Z-G, Liu C, Tan S, Lin S, Zhu J, Dai F-H, Gao J, She J-L, Mei Z, Lou T, Zheng J-J, Liu Y, He J, Zheng Y, Ding C, Qian F, Zheng Y, Chen Y-M. **Gut microbiome alterations and gut barrier dysfunction are associated with host immune homeostasis in COVID-19 patients**. *BMC Med* (2022.0) **20** 24. DOI: 10.1186/s12916-021-02212-0
12. Zhang C, Zhao L. **Strain-level dissection of the contribution of the gut microbiome to human metabolic disease**. *Genome Med* (2016.0) **8** 41. DOI: 10.1186/s13073-016-0304-1
13. Wu G, Zhao N, Zhang C, Lam YY, Zhao L. **Guild-based analysis for understanding gut microbiome in human health and diseases**. *Genome Med* (2021.0) **13** 22. DOI: 10.1186/s13073-021-00840-y
14. Langfelder P, Horvath S. **WGCNA: an R package for weighted correlation network analysis**. *BMC Bioinformatics* (2008.0) **9** 559. DOI: 10.1186/1471-2105-9-559
15. Vital M, Howe AC, Tiedje JM. **Revealing the bacterial butyrate synthesis pathways by analyzing (meta)genomic data**. *mBio* (2014.0) **5**. DOI: 10.1128/mBio.00889-14
16. Van Averbeke V, Berkell M, Mysara M, Rodriguez-Ruiz JP, Xavier BB, De Winter FHR, Jongers B’, Jairam RK, Hotterbeekx A, Goossens H, Cohen ES, Malhotra-Kumar S, Kumar-Singh S. **Host immunity influences the composition of murine gut microbiota**. *Front Immunol* (2022.0) **13** 828016. DOI: 10.3389/fimmu.2022.828016
17. Zhang Z, Ai G, Chen L, Liu S, Gong C, Zhu X, Zhang C, Qin H, Hu J, Huang J. **Associations of immunological features with COVID-19 severity: a systematic review and meta-analysis**. *BMC Infect Dis* (2021.0) **21** 738. DOI: 10.1186/s12879-021-06457-1
18. Cabaro S, D’Esposito V, Di Matola T, Sale S, Cennamo M, Terracciano D, Parisi V, Oriente F, Portella G, Beguinot F, Atripaldi L, Sansone M, Formisano P. **Cytokine signature and COVID-19 prediction models in the two waves of pandemics**. *Sci Rep* (2021.0) **11** 20793. DOI: 10.1038/s41598-021-00190-0
19. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, Cheng Z, Yu T, Xia J, Wei Y, Wu W, Xie X, Yin W, Li H, Liu M, Xiao Y, Gao H, Guo L, Xie J, Wang G, Jiang R, Gao Z, Jin Q, Wang J, Cao B. **Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China**. *Lancet* (2020.0) **395** 497-506. DOI: 10.1016/S0140-6736(20)30183-5
20. Stephens DS, McElrath MJ. **COVID-19 and the path to immunity**. *JAMA* (2020.0) **324** 1279-1281. DOI: 10.1001/jama.2020.16656
21. Long H, Nie L, Xiang X, Li H, Zhang X, Fu X, Ren H, Liu W, Wang Q, Wu Q. **D-dimer and prothrombin time are the significant indicators of severe COVID-19 and poor prognosis**. *Biomed Res Int* (2020.0) **2020** 6159720. DOI: 10.1155/2020/6159720
22. Ponti G, Maccaferri M, Ruini C, Tomasi A, Ozben T. **Biomarkers associated with COVID-19 disease progression**. *Crit Rev Clin Lab Sci* (2020.0) **57** 389-399. DOI: 10.1080/10408363.2020.1770685
23. Nugroho J, Wardhana A, Mulia EP, Maghfirah I, Rachmi DA, A’yun MQ, Septianda I. **Elevated fibrinogen and fibrin degradation product are associated with poor outcome in COVID-19 patients: a meta-analysis**. *Clin Hemorheol Microcirc* (2021.0) **77** 221-231. DOI: 10.3233/CH-200978
24. Sui J, Noubouossie DF, Gandotra S, Cao L. **Elevated plasma fibrinogen is associated with excessive inflammation and disease severity in COVID-19 patients**. *Front Cell Infect Microbiol* (2021.0) **11** 734005. DOI: 10.3389/fcimb.2021.734005
25. Wei X, Zeng W, Su J, Wan H, Yu X, Cao X, Tan W, Wang H. **Hypolipidemia is associated with the severity of COVID-19**. *J Clin Lipidol* (2020.0) **14** 297-304. DOI: 10.1016/j.jacl.2020.04.008
26. Zinellu A, Paliogiannis P, Fois AG, Solidoro P, Carru C, Mangoni AA. **Cholesterol and triglyceride concentrations, COVID-19 severity, and mortality: a systematic review and meta-analysis with meta-regression**. *Front Public Health* (2021.0) **9** 705916. DOI: 10.3389/fpubh.2021.705916
27. Bennouar S, Cherif AB, Kessira A, Bennouar DE, Abdi S. **Vitamin D deficiency and low serum calcium as predictors of poor prognosis in patients with severe COVID-19**. *J Am Coll Nutr* (2021.0) **40** 104-110. DOI: 10.1080/07315724.2020.1856013
28. Parohan M, Yaghoubi S, Seraji A. **Liver injury is associated with severe coronavirus disease 2019 (COVID-19) infection: a systematic review and meta-analysis of retrospective studies**. *Hepatol Res* (2020.0) **50** 924-935. DOI: 10.1111/hepr.13510
29. Chen W, Liu H, Yang G, Wang W, Liu Q, Huang C, Zou Z, Liu Y, Zhuang G, Zhang L. **Effect of direct bilirubin level on clinical outcome and prognoses in severely/critically ill patients with COVID-19**. *Front Med (Lausanne)* (2022.0) **9** 843505. DOI: 10.3389/fmed.2022.843505
30. Szarpak L, Ruetzler K, Safiejko K, Hampel M, Pruc M, Kanczuga- Koda L, Filipiak KJ, Jaguszewski MJ. **Lactate dehydrogenase level as a COVID-19 severity marker**. *Am J Emerg Med* (2021.0) **45** 638-639. DOI: 10.1016/j.ajem.2020.11.025
31. Nayfach S, Roux S, Seshadri R, Udwary D, Varghese N, Schulz F, Wu D, Paez-Espino D, Chen I-M, Huntemann M, Palaniappan K, Ladau J, Mukherjee S, Reddy TBK, Nielsen T, Kirton E, Faria JP, Edirisinghe JN, Henry CS, Jungbluth SP, Chivian D, Dehal P, Wood-Charlson EM, Arkin AP, Tringe SG, Visel A, Woyke T, Mouncey NJ, Ivanova NN, Kyrpides NC, Eloe-Fadrosh EA. **A genomic catalog of Earth’s microbiomes**. *Nat Biotechnol* (2021.0) **39** 499-509. DOI: 10.1038/s41587-020-0718-6
32. Reji L, Cardarelli EL, Boye K, Bargar JR, Francis CA. **Diverse ecophysiological adaptations of subsurface Thaumarchaeota in floodplain sediments revealed through genome-resolved metagenomics**. *ISME J* (2021.0) **16** 1140-1152. DOI: 10.1038/s41396-021-01167-7
33. Britton GJ, Chen-Liaw A, Cossarini F, Livanos AE, Spindler MP, Plitt T, Eggers J, Mogno I, Gonzalez-Reiche AS, Siu S, Tankelevich M, Grinspan LT, Dixon RE, Jha D, van de Guchte A, Khan Z, Martinez-Delgado G, Amanat F, Hoagland DA, tenOever BR, Dubinsky MC, Merad M, van Bakel H, Krammer F, Bongers G, Mehandru S, Faith JJ. **Limited intestinal inflammation despite diarrhea, fecal viral RNA and SARS-CoV-2-specific IgA in patients with acute COVID-19**. *Sci Rep* (2021.0) **11** 13308. DOI: 10.1038/s41598-021-92740-9
34. Li S, Yang S, Zhou Y, Disoma C, Dong Z, Du A, Zhang Y, Chen Y, Huang W, Chen J, Song D, Chen Z, Liu P, Li S, Zheng R, Liu S, Razzaq A, Chen X, Tao S, Yu C, Feng T, Liao W, Peng Y, Jiang T, Huang J, Wu W, Hu L, Wang L, Li S, Xia Z. **Microbiome profiling using shotgun metagenomic sequencing identified unique microorganisms in COVID-19 patients with altered gut microbiota**. *Front Microbiol* (2021.0) **12** 712081. DOI: 10.3389/fmicb.2021.712081
35. Wu C, Xu Q, Cao Z, Pan D, Zhu Y, Wang S, Liu D, Song Z, Jiang W, Ruan Y, Huang Y, Qin N, Lu H, Qin H. **The volatile and heterogeneous gut microbiota shifts of COVID-19 patients over the course of a probiotics-assisted therapy**. *Clin Transl Med* (2021.0) **11**. DOI: 10.1002/ctm2.643
36. Wu G, Xu T, Zhao N, Lam YY, Ding X, Wei D, Fan J, Shi Y, Li X, Li M, Ji S, Wang X, Fu H, Zhang F, Peng Y, Shi Y, Zhang C, Zhao L. **Two competing guilds as a core microbiome signature for chronic diseases**. *bioRxiv* (2022.0)
37. Bernard-Raichon L, Venzon M, Klein J, Axelrad JE, Zhang C, Sullivan AP, Hussey GA, Casanovas-Massana A, Noval MG, Valero-Jimenez AM, Gago J, Putzel G, Pironti A, Wilder E, Thorpe LE, Littman DR, Dittmann M, Stapleford KA, Shopsin B, Torres VJ, Ko AI, Iwasaki A, Cadwell K, Schluter J. **Gut microbiome dysbiosis in antibiotic-treated COVID-19 patients is associated with microbial translocation and bacteremia**. *Nat Commun* (2022.0) **13** 5926. DOI: 10.1038/s41467-022-33395-6
38. Liu H, Chen X, Hu X, Niu H, Tian R, Wang H, Pang H, Jiang L, Qiu B, Chen X, Zhang Y, Ma Y, Tang S, Li H, Feng S, Zhang S, Zhang C. **Alterations in the gut microbiome and metabolism with coronary artery disease severity**. *Microbiome* (2019.0) **7** 68. DOI: 10.1186/s40168-019-0683-9
39. Bolger AM, Lohse M, Usadel B. **Trimmomatic: a flexible trimmer for Illumina sequence data**. *Bioinformatics* (2014.0) **30** 2114-2120. DOI: 10.1093/bioinformatics/btu170
40. Langmead B, Salzberg SL. **Fast gapped-read alignment with Bowtie 2**. *Nat Methods* (2012.0) **9** 357-359. DOI: 10.1038/nmeth.1923
41. Li D, Liu CM, Luo R, Sadakane K, Lam TW. **MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph**. *Bioinformatics* (2015.0) **31** 1674-1676. DOI: 10.1093/bioinformatics/btv033
42. Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, Wang Z. **MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies**. *PeerJ* (2019.0) **7**. DOI: 10.7717/peerj.7359
43. Wu YW, Simmons BA, Singer SW. **MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets**. *Bioinformatics* (2016.0) **32** 605-607. DOI: 10.1093/bioinformatics/btv638
44. Uritskiy GV, DiRuggiero J, Taylor J. **MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis**. *Microbiome* (2018.0) **6** 158. DOI: 10.1186/s40168-018-0541-1
45. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. **CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes**. *Genome Res* (2015.0) **25** 1043-1055. DOI: 10.1101/gr.186072.114
46. Olm MR, Brown CT, Brooks B, Banfield JF. **dRep: A tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication**. *ISME J* (2017.0) **11** 2864-2868. DOI: 10.1038/ismej.2017.126
47. Fischer M, Strauch B, Renard BY. **Abundance estimation and differential testing on strain level in metagenomics data**. *Bioinformatics* (2017.0) **33** I124-I132. DOI: 10.1093/bioinformatics/btx237
48. Bray NL, Pimentel H, Melsted P, Pachter L. **Near-optimal probabilistic RNA-seq quantification**. *Nat Biotechnol* (2016.0) **34** 525-527. DOI: 10.1038/nbt.3519
49. Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. **GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database**. *Bioinformatics* (2019.0) **36** 1925-1927. DOI: 10.1093/bioinformatics/btz848
50. Seemann T. **Prokka: rapid prokaryotic genome annotation**. *Bioinformatics* (2014.0) **30** 2068-2069. DOI: 10.1093/bioinformatics/btu153
51. Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, Ogata H. **KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold**. *Bioinformatics* (2020.0) **36** 2251-2252. DOI: 10.1093/bioinformatics/btz859
52. Zankari E, Hasman H, Cosentino S, Vestergaard M, Rasmussen S, Lund O, Aarestrup FM, Larsen MV. **Identification of acquired antimicrobial resistance genes**. *J Antimicrob Chemother* (2012.0) **67** 2640-2644. DOI: 10.1093/jac/dks261
53. Liu B, Zheng D, Jin Q, Chen L, Yang J. **VFDB 2019: a comparative pathogenomic platform with an interactive web interface**. *Nucleic Acids Res* (2019.0) **47** D687-D692. DOI: 10.1093/nar/gky1080
54. Zhao L, Zhang F, Ding X, Wu G, Lam YY, Wang X, Fu H, Xue X, Lu C, Ma J, Yu L, Xu C, Ren Z, Xu Y, Xu S, Shen H, Zhu X, Shi Y, Shen Q, Dong W, Liu R, Ling Y, Zeng Y, Wang X, Zhang Q, Wang J, Wang L, Wu Y, Zeng B, Wei H, Zhang M, Peng Y, Zhang C. **Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes**. *Science* (2018.0) **359** 1151-1156. DOI: 10.1126/science.aao5774
55. Watts SC, Ritchie SC, Inouye M, Holt KE. **FastSpar: rapid and scalable correlation estimation for compositional data**. *Bioinformatics* (2019.0) **35** 1064-1066. DOI: 10.1093/bioinformatics/bty734
56. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. **Cytoscape: a software environment for integrated models of biomolecular interaction networks**. *Genome Res* (2003.0) **13** 2498-2504. DOI: 10.1101/gr.1239303
57. Grau J, Grosse I, Keilwagen J. **PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R**. *Bioinformatics* (2015.0) **31** 2595-2597. DOI: 10.1093/bioinformatics/btv153
58. Chen FZ, You LJ, Yang F, Wang LN, Guo XQ, Gao F, Hua C, Tan C, Fang L, Shan RQ, Zeng WJ, Wang B, Wang R, Xu X, Wei XF. **CNGBdb: China National GeneBank DataBase**. *Yi Chuan* (2020.0) **42** 799-809. DOI: 10.16288/j.yczz.20-080
|
---
title: Microbiome Features Differentiating Unsupervised-Stratification-Based Clusters
of Patients with Abnormal Glycometabolism
authors:
- Ting Xu
- Xuejiao Wang
- Yu Chen
- Hui Li
- Liping Zhao
- Xiaoying Ding
- Chenhong Zhang
journal: mBio
year: 2023
pmcid: PMC9973283
doi: 10.1128/mbio.03487-22
license: CC BY 4.0
---
# Microbiome Features Differentiating Unsupervised-Stratification-Based Clusters of Patients with Abnormal Glycometabolism
## ABSTRACT
The alteration of gut microbiota structure plays a pivotal role in the pathogenesis of abnormal glycometabolism. However, the microbiome features identified in patient groups stratified solely based on glucose levels remain controversial among different studies. In this study, we stratified 258 participants (discovery cohort) into three clusters according to an unsupervised method based on 16 clinical parameters involving the levels of blood glucose, insulin, and lipid. We found 67 cluster-specific microbiome features (i.e., amplicon sequence variants [ASVs]) based on 16S rRNA gene V3-V4 region sequencing. Specifically, ASVs belonging to Barnesville and Alistipes were enriched in cluster 1, in which participants had the lowest blood glucose levels, high insulin sensitivity, and a high-fecal short-chain fatty acid concentration. ASVs belonging to Prevotella copri and *Ruminococcus gnavus* were enriched in cluster 2, which was characterized by a moderate level of blood glucose, serious insulin resistance, and high levels of cholesterol and triglyceride. Cluster 3 was characterized by a high level of blood glucose and insulin deficiency, enriched with ASVs in P. copri and Bacteroides vulgatus. In addition, machine learning classifiers using the 67 cluster-specific ASVs were used to distinguish individuals in one cluster from those in the other two clusters both in discovery and testing cohorts ($$n = 83$$). Therefore, microbiome features identified based on the unsupervised stratification of patients with more inclusive clinical parameters may better reflect microbiota alterations associated with the progression of abnormal glycometabolism.
## INTRODUCTION
A linkage between gut microbiota dysbiosis and type 2 diabetes (T2D) has been extensively explored (1–3). The alterations of the gut microbiota of patients with T2D are characterized by a decrease in the abundance of beneficial bacteria (e.g., Akkermansia muciniphila, the genera of Bifidobacterium and Roseburia spp.) and an increase in opportunistic pathogens, including *Clostridium bolteae* and Desulfovibrio sp. ( 1, 4–6). The loss of potential butyrate-producing bacteria, such as Faecalibacterium prausnitzii, and the deficiency in butyrate production are the most common findings in patients with T2D, as well as in patients with prediabetes (1, 3, 5–7). Nonetheless, the association of particular bacteria with T2D has varied among studies. For instance, Zhang et al. found that A. muciniphila was less abundant, whereas Clostridiales sp. strain SS$\frac{3}{4}$ was more abundant in patients with T2D [4], but the abundances of these two taxa showed the opposite result from those reported in the study by Qin [1]. This confusion has led to difficulty in using bacterial features to elucidate the mechanism of gut microbiota in the development of T2D for developing new biomarkers and therapies.
One possible reason for the challenge in identifying gut microbial characteristics in patients with T2D and prediabetes is that all of the previous studies have solely used blood glucose levels as the criteria to stratify their cohorts. Clinical investigation, however, has shown that people within the same blood glucose range, as defined by the American Diabetes Association (ADA) and World Health Organization (WHO) criteria, are heterogeneous in insulin sensitivity and islet β-cell function [8, 9]. Insulin resistance and islet β-cell dysfunction are the two pathogeneses of abnormal glycometabolism and occur much early than the onset of hyperglycemia [10]. In addition, the condition of dyslipidemia, a risk factor of T2D and diabetic complications, is varied in people within the same blood glucose range. For example, in the Framingham Heart Study, $19\%$ of the men and $17\%$ of the women with diabetes had increased total plasma triglyceride levels, and the prevalence of increased total plasma triglyceride levels in men and women with normal blood glucose levels were 9 and $8\%$ [11]. Recently, apart from blood glucose levels, additional variables related to insulin resistance and sensitivity, as well as dyslipidemia, have been proposed for use in glycometabolism classifications (12–14). In addition to blood glucose levels, a study classified individuals, including healthy people and people with prediabetes, into six distinct clusters by including parameters such as insulin secretion, insulin resistance, and high-density lipoprotein-cholesterol (HDL), and monitored them for 4.1 years and 16.3 years (mean follow-up years in two cohorts, respectively) [12]. Longitudinal follow-up revealed that different clusters had different risks of diabetes and diabetic complications. Individuals in one of the clusters experienced slower progression to overt T2D than those in other clusters but had a higher risk of nephropathy. In addition to the hyperglycemia, the disorder of gut microbiota has been shown to have a causative role in the progression of obesity and insulin resistance both in mouse models and in human studies by gut microbiota transplantation. For example, the *Enterobacter cloacae* B29 strain isolated from a morbidly obese human’s gut induced obesity and insulin resistance in germfree mice [15]. A gut microbiota transplantation study on humans revealed that gut microbiota from lean donors increased the insulin sensitivity in obese recipients with metabolic syndrome [16]. In addition, the gut microbiota could regulate insulin resistance by producing metabolites, such as imidazole propionate, which could directly impair insulin signaling at the level of insulin receptor substrate [17]. Furthermore, a study in germfree and conventionally raised mice showed that the gut microbiota had an effect on the host’s serum lipidomes, especially the triglyceride levels [18]. In obese mice fed a high-fat diet, supplementation with probiotics, such as Lactobacillus curvatus, alone or together with L. plantarum, reduced cholesterol in plasma; supplementation with Bifidobacterium spp. decreased the levels of circulating triglycerides and low-density lipoprotein-cholesterol (LDL) and increased the HDL level (19–21). Therefore, it is essential to consider insulin and lipid levels in the criteria for cohort stratification when studying the association of glycometabolism and gut microbiota.
In this study, to identify the microbiome features that correlate with progression of abnormal glycometabolism, we classified 258 individuals into three glycometabolism clusters according to variables related to blood glucose level, insulin resistance, and dyslipidemia. We found that the gut microbiota structure was different among the three glycometabolism clusters, and the characteristics of gut microbiota were associated with metabolic phenotypes. Moreover, we identified cluster-specific microbiome features and further validated their association with glycometabolism with a testing cohort.
## Unsupervised-stratification-based clusters of patients with abnormal glycometabolism.
During a survey of patients with T2D conducted at Sijing Community Health Service Center of Shanghai Songjiang District, we recruited 267 participants as a discovery cohort and examined 33 bioclinical parameters of these participants. According to ADA criteria [8], 84 participants had normal glucose tolerance (NGT), 52 had isolated impaired fasting glucose (IFG), 34 had isolated impaired glucose tolerance (IGT), 38 had combined glucose intolerance (CGI), and 59 had T2D. We observed that the coefficients of variation (CV) of insulin levels for the oral glucose tolerance test (OGTT), homeostatic model assessment for insulin resistance (HOMA-IR), β-cell function (HOMA-β), serum triglyceride level, and cholesterol levels (total cholesterol, HDL, and LDL) were high in participants with the same classification based on ADA criteria (Fig. 1A). We evaluated insulin resistance and insulin secretion using the HOMA-IR and HOMA-β index, which also showed significant variations in participants within the same glucose range (Fig. 1B; see also Fig. S1 in the supplemental material). These results confirmed that the subjects with same glucose level had high heterogeneity of metabolic state.
**FIG 1:** *Metabolic characteristics of the unsupervised-stratification-based clusters. (A, top panel) Coefficients of variation (CV) of clinical variables in each ADA group. The size of the circle indicates the CV value. (Bottom panel) Heatmap of clinical variables. The values were scale transformed by column. (B) Variations of HOMA-IR among members within the same blood glucose range. (C) Silhouette coefficient corresponding to the number of clusters from 2 to 20. (D) The number of participants in each cluster, with colors indicating glycemic categories (NGT, normal glucose tolerance; IFG, impaired fasting glycemia; IGT, impaired glucose tolerance; CGI, combined impaired fasting glycemia and impaired glucose tolerance; T2D, type 2 diabetes). (E to G) Comparisons of HbA1c (E), HOMA-IR (F), and HOMA-β (G) among clusters. Boxes show the medians and the interquartile ranges (IQRs), the whiskers denote the lowest and highest values that were within 1.5 times the IQR from the first and third quartiles, and the outliers are shown as individual points. The Kruskal-Wallis test P value is shown at the bottom of each plot. A Wilcoxon rank sum test was used for comparisons between two clusters (adjusted by FDR). Clusters with common characters were not significantly different (FDR > 0.05). (H to J) Radar charts show the median values of clinical parameters related to blood glucose levels (H), blood insulin levels (I), and lipometabolism (J). Each spoke in the chart represents one cluster.*
Then, we reclassified the same group of participants in the discovery cohort according to variables, including HbA1c, OGTT-derived glucose levels (five-time point blood glucose levels during OGTT), and insulin levels (five-time point blood insulin levels during OGTT), and anthropometric variables (body mass index [BMI], waist circumference, hip circumference), as well as variables related to dyslipidemia and insulin resistance (fasting triglyceride, HDL cholesterol). We standardized the variables before the clustering procedure. We excluded nine participants (NGT = 1, IFG = 2, IGT = 2, CGI = 1, T2D = 3) with at least one outlier variable. By using the K-Mediods clustering algorithm [22], we classified 258 participants into three clusters that were determined according to the maximum silhouette coefficient (silhouette coefficient = 0.21) (Fig. 1C and D). The participants with NGT and prediabetes were clustered only into clusters 1 and 2. Specifically, $80.7\%$ participants with NGT were clustered into cluster 1 and the rest with NGT were clustered into cluster 2. In addition, $68\%$ with IFG, $37.5\%$ with IGT, and $45.9\%$ with CGI were clustered into cluster 1 and the remainder were clustered into cluster 2. In contrast, most of T2D ($53.6\%$) were clustered into cluster 3, only 3.6 and $42.9\%$ were clustered into clusters 1 and 2, respectively. Finally, the Jaccard coefficient means for the three clusters were greater than 0.7 (0.78, 0.71, and 0.83, respectively), which indicated the three clusters were stable.
Compared to cluster 1, clusters 2 and 3 showed more serious disruptions in glycometabolism and lipometabolism and an increased inflammatory state (Table 1). The levels of HbA1c, FBG (fasting blood glucose), 0.5-h PBG (postprandial blood glucose), 1-h PBG, 2-h PBG, and 3-h PBG were lowest in cluster 1 and highest in cluster 3 (Fig. 1E and H and Table 1). The insulin levels during OGTT except 3-h insulin level were significantly higher in cluster 2 than in clusters 1 and 3 (Fig. 1I and Table 1), and the HOMA-IR index was significantly higher in clusters 2 and 3 than in cluster 1 (Fig. 1F), indicating that cluster 1 had the highest insulin sensitivity, and cluster 2 had compensatory secretion insulin, whereas the cluster 3 was insulin deficiency (Fig. 1G). In addition, BMI, waist-to-hip ratio (WHR), and systolic blood pressure (SBP) were significantly higher in cluster 2 than those in the other two clusters (Fig. 1J and Table 1). The levels of total cholesterol, triglyceride, and leptin were significantly higher in clusters 2 and 3 than those in cluster 1 (Fig. 1J and Table 1). The lipopolysaccharide (LPS)-binding protein (LBP) level, an indicator of chronic inflammation, was lowest in cluster 1 and highest in cluster 3 (Table 1).
**TABLE 1**
| Category | Mean ± SEM | Mean ± SEM.1 | Mean ± SEM.2 | P |
| --- | --- | --- | --- | --- |
| Category | Cluster 1 (n = 132) | Cluster 2 (n = 96) | Cluster 3 (n = 30) | P |
| Glycemic category (ADA criteria) | | | | |
| NGT (n) | 67 | 16 | 0 | / |
| IFG (n) | 34 | 16 | 0 | / |
| IGT (n) | 12 | 20 | 0 | / |
| CGI (n) | 17 | 20 | 0 | / |
| T2D (n) | 2 | 24 | 30 | / |
| General information | | | | |
| Age (yrs) | 57.42 ± 0.57A (131) | 57.59 ± 0.62A (95) | 60.83 ± 1.01B (30) | 9.2E–03 |
| Gender (no. of females/no. of males) | 68/64 | 53/43 | 11/19 | NS |
| Glycometabolism indicators | | | | |
| HbA1c (%) | 5.49 ± 0.03A (132) | 5.76 ± 0.05B (96) | 7.1 ± 0.19C (30) | 4.6E–16 |
| FBG (mmol/L) | 5.47 ± 0.05A (132) | 5.7 ± 0.08B (96) | 8.45 ± 0.28C (30) | 4.5E–18 |
| 0.5-h PBG (mmol/L) | 9.22 ± 0.15A (129) | 10.1 ± 0.23B (91) | 13.06 ± 0.5C (26) | 3.9E–11 |
| 1-h PBG (mmol/L) | 8.77 ± 0.2A (129) | 10.84 ± 0.32B (91) | 16.29 ± 0.57C (26) | 6.4E–18 |
| 2-h PBG (mmol/L) | 6.66 ± 0.14A (132) | 8.76 ± 0.27B (96) | 15.21 ± 0.52C (30) | 6.6E–24 |
| 3-h PBG (mmol/L) | 4.41 ± 0.11A (125) | 5.44 ± 0.21B (87) | 10.12 ± 0.5C (26) | 2.4E–16 |
| Glucose AUC (mmol/L/min) | 1253.99 ± 23.93A (132) | 1480.7 ± 43.71B (96) | 2213.3 ± 131.75C (30) | 3.7E–14 |
| HOMA-IR | 0.8 ± 0.04A (130) | 1.6 ± 0.11B (91) | 1.33 ± 0.23B (28) | 6.7E–09 |
| HOMA-IS | 1.69 ± 0.09A (130) | 1.02 ± 0.08B (91) | 1.28 ± 0.15B (28) | 6.7E–09 |
| HOMA-β | 38.09 ± 2.66A (130) | 63.18 ± 4.77B (91) | 14.71 ± 2.19C (28) | 4.1E–14 |
| Fasting insulin (μU/mL) | 3.32 ± 0.16A (130) | 6.24 ± 0.41B (91) | 3.45 ± 0.53A (28) | 3.6E–09 |
| 0.5-h insulin (μU/mL) | 20.88 ± 1.3A (127) | 31.8 ± 2.35B (87) | 6.62 ± 0.84C (28) | 5.6E–15 |
| 1-h insulin (μU/mL) | 24.51 ± 1.29A (127) | 42.33 ± 2.95B (87) | 11.18 ± 1.54C (28) | 7.6E–13 |
| 2-h insulin (μU/mL) | 19.64 ± 1.38A (131) | 43.44 ± 3.77B (91) | 13.15 ± 1.74A (28) | 1.6E–12 |
| 3-h insulin (μU/mL) | 5.07 ± 0.44A (127) | 14.44 ± 1.51B (87) | 8.67 ± 1.19B (28) | 7.3E–12 |
| Insulin AUC (μU/mL/min) | 3028.32 ± 156.09A (130) | 5745.88 ± 377.77B (91) | 1802.82 ± 197.32C (28) | 1.2E–15 |
| Fasting C-peptide (nmol/L) | 1.19 ± 0.05A (131) | 1.96 ± 0.11B (91) | 1.32 ± 0.17A (28) | 2.6E–09 |
| 0.5-h C-peptide (nmol/L) | 3.67 ± 0.17A (127) | 5.24 ± 0.32B (87) | 1.86 ± 0.22C (28) | 1.2E–11 |
| 1-h C-peptide (nmol/L) | 4.91 ± 0.23A (127) | 7.28 ± 0.44B (87) | 2.67 ± 0.3C (28) | 7.6E–12 |
| 2-h C-peptide (nmol/L) | 4.81 ± 0.24A (131) | 8.09 ± 0.42B (91) | 4.08 ± 0.54A (28) | 3.3E–12 |
| 3-h C-peptide (nmol/L) | 2.41 ± 0.13A (127) | 4.63 ± 0.29B (87) | 3.47 ± 0.37C (28) | 1.7E–11 |
| Anthropometric markers | | | | |
| BMI (kg/m2) | 23.75 ± 0.18A (132) | 27.86 ± 0.28B (96) | 25.17 ± 0.58A (30) | 6.4E–21 |
| Ht (cm) | 160.62 ± 0.67 (132) | 161.81 ± 0.86 (96) | 163.83 ± 1.06 (30) | 9.9E–02 |
| Wt (kg) | 61.39 ± 0.68A (132) | 72.88 ± 0.87B (96) | 67.71 ± 1.89C (30) | 5.7E–17 |
| Waist circumference (cm) | 80.79 ± 0.51A (132) | 91.57 ± 0.67B (96) | 84.82 ± 1.63A (30) | 9.9E–23 |
| Hip circumference (cm) | 92.54 ± 0.34A (132) | 99.5 ± 0.57B (96) | 96.04 ± 1.03C (30) | 1.5E–17 |
| Waist-to-hip ratio | 0.87 ± 0A (132) | 0.92 ± 0B (96) | 0.88 ± 0.01A (30) | 8.9E–11 |
| SBP (mm Hg) | 128.62 ± 1.1A (132) | 133.58 ± 1.41B (96) | 127.13 ± 2.19A (30) | 3.2E–03 |
| DBP (mm Hg) | 81.58 ± 0.68 (132) | 83.21 ± 0.74 (96) | 82 ± 1.55 (30) | 9.4E–02 |
| Lipometabolism indicators | | | | |
| Total cholesterol (mmol/L) | 1.84 ± 0.1A (127) | 2.41 ± 0.16B (91) | 1.8 ± 0.25AB (27) | 1.1E–02 |
| Triglyceride (mmol/L) | 0.66 ± 0.04A (127) | 1.3 ± 0.16B (91) | 1.05 ± 0.25AB (27) | 8.5E–04 |
| HDL (mmol/L) | 0.63 ± 0.04 (127) | 0.7 ± 0.04 (91) | 0.56 ± 0.08 (27) | 1.2E–01 |
| LDL (mmol/L) | 1.06 ± 0.1 (127) | 1.11 ± 0.1 (91) | 0.8 ± 0.12 (27) | 2.4E–01 |
| Leptin (ng/mL) | 0.34 ± 0.03A (95) | 0.51 ± 0.04B (78) | 0.36 ± 0.05AB (27) | 1.1E–02 |
| Inflammatory indicators | | | | |
| LBP (μg/mL) | 12.55 ± 0.53A (77) | 14.12 ± 0.51B (64) | 16.51 ± 0.92C (24) | 1.2E–04 |
Taken together, participants in cluster 1 showed high insulin sensitivity and the lowest glucose levels and serum lipids. Participants in cluster 2 had serious insulin resistance and high serum lipids levels. Participants in cluster 3 had insulin deficiency, with the highest levels of blood glucose and chronic inflammation.
## Differences in gut microbiota in unsupervised-stratification-based clusters.
To investigate the differences in the gut microbiota among three unsupervised-stratification-based clusters, we performed 16S rRNA gene V3-V4 region sequencing on fecal samples collected from all of the participants. Although there were no significant differences in diversity and richness of the gut microbiota (see Fig. S2), the PCA of phylogenetic-ILR (PhILR; phylogenetic-isometric log ratio transformation)-transformed Euclidean distances and score plots of the linear discriminant analysis (LDA) showed that the structure and composition of the gut microbiota differed significantly among the three clusters (Fig. 2A and B). Moreover, the distance between cluster 1 and cluster 3 was larger than it was between cluster 1 and cluster 2 (Fig. 2C).
**FIG 2:** *Characterization of the gut microbiota in unsupervised-stratification-based clusters. (A) PCA of PhILR-transformed Euclidean-distance based on the abundance of ASVs. The circles and error bars indicate the means and standard errors of the mean (SEM). Comparisons of gut microbiota structures among three clusters were tested by permutational multivariate analysis of variances (PERMANOVA; permutations = 9,999). (B) LDA score plot of the gut microbiota structure of the three clusters. (C) Between-sample Bray-Curtis distances of the gut microbiota of three clusters. Kruskal-Wallis test (***, P < 0.001). (D) Visualization of constructed networks based on Pearson correlation coefficient. The first six modules with a large number of nodes are shown in different colors, and the other modules are shown in gray. (E) Degree centralities of networks from three clusters. Kolmogorov-Smirnov tests were used to test the differences in cumulative distributions. (F and G) Comparisons of fecal butyric acid (F) and fecal acetic acid (G) concentrations among clusters. Boxes, whiskers, and outliers denote values as described for Fig. 1E. A Wilcoxon rank sum test was used for comparisons between two clusters (adjusted by FDR). Clusters with common characters were not significantly different (FDR > 0.05).*
Then we constructed a coabundance network of prevalent ASVs (shared by more than $20\%$ of the samples) in each cluster based on Pearson correlation coefficient to explore the ecological relationship of the members in gut microbial community (Fig. 2D). We calculated the topological parameters of networks in three clusters to explore whether any differences existed in complexity among the microbial networks. The total numbers of nodes were similar (168, 153, and 158) among the three networks, whereas the total numbers of edges varied relatively (638, 420, and 332); in particular, the network in cluster 3 had the lowest number of edges. The network density, which is defined as the ratio of the number of actual edges and the number of possible edges, decreased progressively in the three clusters (the density values in clusters 1, 2, and 3 were 0.045, 0.036, and 0.027, respectively). Moreover, the network degree centrality, a measure of the relative connectivity of each node in a network, decreased from cluster 1 to cluster 3 (Fig. 2E). These results suggested that cluster 1 had more microbial interactions than the other two clusters. Since bacteria act as functional groups (guilds) in the gut ecosystem [23], we next clustered the 188 nodes (i.e., ASVs) of the three networks into 29 coabundance groups (CAGs). Correlation analysis between CAGs and clinical parameters showed significant correlation between gut microbiota and glycometabolism, insulin secretion, and lipid levels (see Fig. S3).
Moreover, we measured the content of short-chain fatty acids (SCFAs) in the fecal samples of all participants. SCFAs are important microbiota-derived metabolites and have been proved to be associated with glycometabolism. We found that cluster 1 had the highest concentration of fecal butyric acid, whereas cluster 3 had the lowest (Fig. 2F). The difference of acetic acid concentration among three clusters was similar with butyric acid (Fig. 2G). We further examined the genes involved in the production of butyric and acetic acid, e.g., buk for butyrate and fhs for acetate production. *The* gene abundances showed a pattern similar to that of the fecal butyric and acetic acid concentration among three clusters (see Fig. S4).
Taken together, the β-diversity of gut microbiota, the gut microbial network topology, and the capacity for producing butyrate and acetate were significantly different among these three clusters.
## Features of gut microbiota in unsupervised-stratification-based clusters.
To identify the cluster-specific microbiome features of three clusters, we compared the abundance of ASVs among the three clusters using Wilcoxon rank sum test (false discovery rate [FDR] < 0.05, |log2-fold change| > 1). We found that 67 ASVs were significantly different between at least two clusters (Fig. 3, top panel; see Table S1 in the supplemental material). Only 14 of the 67 ASVs showed significantly altered abundance between clusters 1 and 2, and 36 ASVs were significantly altered between clusters 1 and 3, whereas the abundance of 43 ASVs changed significantly between clusters 2 and 3. β-*Diversity analysis* based on the Bray-Curtis distance showed significant correlations between the profiles of 67 cluster-specific ASVs and all ASVs (Mantel test, $R = 0.4$, $$P \leq 0.001$$; Fig. S5, Procrustes analysis, $P \leq 0.001$). In addition, the Mantel statistic based on Euclidean distance of 67 cluster-specific ASVs and Euclidean distance of 16 clinical variables used for unsupervised clustering, showed that the alteration pattern of microbiome features was significantly associated with the clinical phenotype (Mantel test, $R = 0.14$, $$P \leq 0.003$$).
**FIG 3:** *Cluster-specific microbiome features identified based on unsupervised-stratification-based clusters. (Top panel) The colors of circles indicate the scale-transformed mean abundance of the 67 ASVs in each cluster. These ASVs were clustered with a Spearman correlation coefficient and ward linkage based on their scale-transformed abundance values. (Middle panel) Association between ASVs and clinical variables. The colors denote the correlation coefficients. P values were adjusted by Benjamini-Hochberg procedure. #, Adjusted P < 0.25 was considered to be statistically significant based on the instruction of MaAslin2. Age and gender were considered to be covariates. Red text on the right indicates the variables used for classification. (Bottom panel) Taxonomy of ASVs. Colors represent the phyla.*
We subsequently assessed the correlation between the cluster-specific ASVs and all of the host clinical variables based on a modified general linear model (Fig. 3). Among the 67 cluster-specific ASVs, we found that 50 ASVs were correlated with at least one clinical variable. Four ASVs (three belonged to Clostridia: *Eubacterium xylanophilum* ASV2293, Eubacterium coprostanoligenes ASV5556, and Clostridia UCG-014 ASV4831; one belonged to Bacteroidia: *Alistipes shahii* ASV0472), which were significantly higher in cluster 1, were negatively correlated with the parameters related to glucose intolerance. Most of the ASVs (39 ASVs) enriched in cluster 3 were significantly positively correlated with parameters related to glucose intolerance, and five of them were positively correlated with parameters related to lipid metabolism. Among them, two ASVs showed significant correlation with insulin-related variables. ASV3403, belonging to Blautia, was negatively correlated with HOMA-β, but ASV4033 enriched in cluster 3, was positively correlated with HOMA-IS. One of the ASVs enriched in cluster 2, *Bacteroides stercoris* ASV4844, was positively correlated with insulin and C-peptide levels. Other ASVs (Paeniclostridium ASV1761, Coprococcus ASV2125, Muribaculaceae ASV2262, and *Parabacteroides distasonis* ASV4677) enriched in cluster 2 were positively correlated with parameters related to glucose intolerance or hip circumference. Taken together, the results indicated a significant correlation between cluster-specific microbiome features and clinical phenotypes.
## Stratification in testing cohort based on cluster-specific microbiome features.
We next developed machine-learning classifiers based on a random forest algorithm by a leave-one-out cross-validation to distinguish individuals in one cluster from those in other two clusters using the 67 cluster-specific ASVs. Receiver operating characteristic curve analysis suggested that the models had high prediction power with area under the curve (AUC) ranging from 0.88 to 0.94 (Fig. 4A). We then tested whether these features of gut microbiota could distinguish the subjects from different glycometabolism clusters in the testing cohort, who were the survey participants at the same community 2 years ago. We assigned the participants in the testing cohort to the nearest one of the three clusters based on Euclidean distance of the clinical variables that had been used for clustering in the discovery cohort except for the 3-h glucose level and 3-h insulin level (because these two variables were not available for the testing cohort) (Fig. 4B). The principal-component analysis (PCA) based on clinical variables, which we used for classification, revealed a separation among the three clusters of testing cohort (Fig. 4B). HOMA-IR and HOMA-β were significantly higher in cluster 2 than in the other two clusters (Fig. 4C and D), which was similar to the differences identified among the clusters in the discovery cohort. In addition, cluster 1 showed the lowest levels of glucose AUC and cluster 3 showed the highest levels (see Table S2), which suggested that cluster 1 had the best glucose metabolism. The gut microbiota structure of the three clusters in the testing cohort were clearly separated from each other (Fig. 4E), and the score plot of the LDA showed that the gut microbiota structure was different among clusters and not cohorts (see Fig. S6). To explore whether the participants in the different clusters in the testing cohort could be distinguished by the microbial profile, we developed machine-learning classifiers again using the abundance of the 67 cluster-specific ASVs in the testing cohort. Receiver operating characteristic curve analysis suggested that the models had a moderate prediction power with an AUC ranging from 0.87 to 0.94 (Fig. 4F). The classification for the testing cohort suggested that the cluster-specific ASVs reflected the microbiota alterations associated with abnormal glycometabolism.
**FIG 4:** *Classification based on cluster-specific microbiome features in the testing cohort. (A) Receiver-operating characteristic (ROC) curves for classification of individuals in one cluster from the other two clusters in the discovery cohort. The random forest classifier was constructed based on leave-one-out cross-validation using the 67 cluster-specific ASVs. (B) The number of participants in each cluster in the testing cohort, with colors indicating glycemic categories according to ADA criteria, and PCA plot showing the different clinical phenotype of three clusters. (C and D) Comparisons of HOMA-IR (C) and HOMA-β (D) among clusters. The Kruskal-Wallis test P value is shown at the bottom of each plot. Boxes, whiskers, and outliers denote values as described for Fig. 1E. A Wilcoxon rank sum test was used for comparisons between two clusters (adjusted by FDR). Clusters with common characters were not significantly different (FDR > 0.05). (E) LDA score plot of the three clusters based on the abundance of ASVs. (F) ROC curves for classification of individuals in one cluster from the other two clusters in the testing cohort using the 67 cluster-specific microbial features identified in the discovery cohort.*
## DISCUSSION
In the current population-based cross-sectional study, we showed that unsupervised stratification of patients with abnormal glycometabolism based on more inclusive clinical parameters could help identify microbiome features more robustly associated with glycometabolism. We confirmed the association between identified microbiome features and glycometabolism in a validation cohort.
The microbiome composition of individuals with T2D has been controversial among studies [24]. Bifidobacterium and Bacteroides were the most reported genera containing microbes related with T2D. Bifidobacterium has been reported to be potentially protective against T2D in most studies, whereas only one study has reported conflicting result (2, 25–28). Bacteroides has been reported to be negatively correlated with T2D in five cross-sectional studies and positively correlated with T2D in three studies that had involved some type of treatment [4, 5, 24, 29]. One of the reasons for this unreliable relationship between gut microbiota and T2D is that individuals within the same blood glucose range are heterogeneous in insulin sensitivity and secretion as well as lipid metabolism, which are also associated with gut microbiota [21]. In our study, based on 16 variables that combined blood glucose levels and parameters related to insulin resistance and dyslipidemia, we classified 258 individuals into three clusters with unique metabolic characteristics: cluster 1 was characterized by the lowest blood glucose levels, insulin sensitivity, and lowest lipid levels; cluster 2 was characterized by a moderate level of blood glucose, serious insulin resistance, and high levels of cholesterol and triglyceride; and cluster 3 was characterized by the highest blood glucose levels and insulin deficiency. The Swedish All New Diabetics study reported that clusters identified based on more inclusive indexes and an unsupervised method showed different risk of diabetic complications [13]. This result suggested that the stratification based on more inclusive clinical parameters was better than that based only on blood glucose levels, because it not only could separate people within different glucose levels and insulin levels but also could predict the risk of diabetic complications. Based on the unsupervised-stratification-based clusters, we identified cluster-specific microbiome features that not only were related to glycometabolism but also were available for population classification in another general cohort. This finding implied an association between these cluster-specific microbiome features and glycometabolism. Thus, our research findings suggested that the stratification combining the blood glucose levels and indicators related to insulin resistance and dyslipidemia could make it possible to identify the microbiome features associated with abnormal glycometabolism.
The identified cluster-specific microbiome features may contribute to the progression of abnormal glycometabolism. For example, we found that two ASVs belonging to Prevotella copri were enriched in clusters 2 and 3, respectively, and one ASV belonging to *Bacteroides vulgatus* was enriched in cluster 3. Both cluster 2 and cluster 3 were characterized by the most resistance to insulin. One study found that P. copri and B. vulgatus were the strongest driver species for the positive association between HOMA-IR and microbial branched-chain amino acids (BCAAs) biosynthesis in Danish individuals without diabetes and further found that P. copri caused insulin resistance and impaired glucose intolerance by changing the circulating serum levels of BCAAs in mice [30]. Transplantation of B. vulgatus resulted in insulin resistance in recipient mice [31]. Therefore, these two bacteria may have contributed to a high level of insulin resistance in clusters 2 and 3 in our work. Furthermore, we also found a high abundance of *Ruminococcus gnavus* ASV4377 in clusters 2 and 3. Studies reported that R. gnavus is a mucin-degrading bacterium that may directly break the integrity of gut barrier and is associated with inflammatory bowel diseases (32–35). The disruption of gut barrier may lead to the translocation of endotoxins produced by gut bacteria to the host. In our study, the level of LBP, a load marker of gut-derived antigens, was higher in clusters 2 and 3, which suggested an increased level of plasma endotoxin load produced by gut bacteria. The increased circulating endotoxin load would induce chronic inflammation, which is a driving factor for insulin resistance and dyslipidemia [36]. Thus, the disordered gut microbiome, such as increased levels of R. gnavus, may contribute to the insulin resistance and dyslipidemia by disrupting gut barrier, elevating circulated endotoxin load, and inducing chronic inflammation. Moreover, cluster 1 enriched the ASVs belonging to Barnesiella. Some species of Barnesiella have been reported to produce acetate [37]. In addition, high levels of acetate and butyrate concentration, as well as the functional genes involved in the production of these metabolites were observed in cluster 1. Studies have revealed that acetate suppresses body fat accumulation and inflammation in obese or diabetic rodents through multiple mechanisms (38–41). Butyrate also has been shown to improve gut integrity by increasing the tight junction assembly [42], inducing mucin synthesis [43], and decreasing gut bacterial transport across the epithelium [44]. Thus, the individuals in cluster 1 may have benefited from the integrity of the intestinal barrier, which was protected by higher acetate/butyrate concentration, and may have avoided chronic inflammation that can be induced by elevated endotoxin load. Taken together, the cluster-specific microbiome features found in the present study may have contributed to the distinct glycometabolism phenotype of the three clusters. The contribution and mechanism of these bacteria in the progression of glycometabolism disorder need to be further experimentally verified.
In this study, we showed that unsupervised stratification based on blood glucose, insulin, and lipid levels led to the identification of cluster-specific gut microbiome features associated with glycometabolism. A well-stratified cohort is a prerequisite for the identification of bacteria associated with glycometabolism. Upon further validation in larger cohorts and follow-up with a longer duration, the elucidation of the mechanism of these identified cluster-specific microbiome features in the progression of abnormal glycometabolism may lead to the future development of biomarkers for early diagnosis and therapeutic treatment.
## Ethical approval.
The protocols for both studies were approved by the Human Research Ethics Committee of Shanghai General Hospital (2009KY037, 2013KY083) before the procedure of enrollment. This clinical trial was registered in the Chinese Clinical Trial Registry under number ChiCTR-IPC-14005346. All of the participants signed an informed consent form before sample collection.
## Overview of the cohorts.
Patients at the Sijing Community Health Service Center of Songjiang District participated in a survey about type 2 diabetes (T2D). We recruited 267 individuals from a diabetes survey taken in 2014, which was considered to be the discovery cohort in the analysis. To test our findings in the discovery cohort, we recruited 86 individuals from a diabetes survey taken in 2012 as the testing cohort.
The participants were asked to fast overnight (more than 10 h) to collect the fasting venous blood. After physical examination and fasting venous blood collection, we performed a 3-h OGTT (75 g glucose) and collected venous blood samples at 30, 60, 120, and 180 min. The blood samples were set at room temperature for 30 min and then centrifuged to obtain the serum. The serum of fasting venous blood was divided into two parts: one was used to evaluate the fasting blood glucose, blood lipid, and inflammation, and the other was immediately stored at −80°C for quantification of LBP and leptin. Stool samples were collected on the day of the physical examination and stored at −80°C quickly until fecal DNA extraction.
## Biochemical assays.
The levels of HbA1c, serum glucose, serum insulin, serum C-peptide, triglyceride, total cholesterol, HDL cholesterol, and LDL cholesterol were determined at Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine. Enzyme-linked immunosorbent assays (ELISAs) were used to quantify the levels of LBP (Hycult Biotech, PA, USA) and leptin (DL Develop, Wuhan, China) in the lab of Shanghai Jiao Tong University.
## Unsupervised stratification.
We used 16 clinical variables (HbA1c, five-time point blood glucose levels and insulin levels during OGTT, BMI, waist circumference, hip circumference, triglyceride, HDL) to stratify the discover cohort. We performed unsupervised stratification in an R environment (version 3.6.1). Euclidean distances were calculated (the vegdist function in the “vegan” package) for the standardized clinical variables (scaled to a mean of 0 and a standard deviation of 1) to complete the clustering analysis. Individuals with outlier variables (absolute standardized levels of ≥5) were excluded from the clustering analysis. We performed the K-Mediods clustering algorithm using the pam function in the “cluster” package to complete the clustering procedure. The silhouette-width was calculated using the silhouette function in the “cluster” package. Clusterboot algorithm from the “fpc” package was used to assess the stability of clusters. Finally, other than 9 participants who had at least one outlier variable, 258 participants were clustered into three clusters.
We used the median values of the 14 selected clinical variables in the discovery cohort, except for the 3-h glucose and 3-h insulin levels (because these two variables were not available for the testing cohort), to assign participants to clusters in the testing cohort. We took the nearest neighbors of the three cluster centers based on Euclidean distances. After we removed three participants with outlier variables, 83 participants were used in the testing cohort.
## Statistical analysis of clinical data.
Statistical analyses of clinical data were performed in an R environment (version 3.6.1). The difference in the clinical variables among clusters was tested by Kruskal-Wallis test. For differences between two clusters, a Wilcoxon rank sum test was used (adjusted by FDR). FDR values were converted into a character-based display in which common characters represented clusters that were not significantly different (the multcompLetters function in the “multcompView” package). The Pearson chi-square test was performed to compare the differences in categorical data. $P \leq 0.05$ (for Kruskal-Wallis test) and FDR < 0.05 (for Wilcoxon rank sum test) were considered to have a significant difference.
## Fecal DNA extraction and 16S rRNA gene V3-V4 region sequencing.
We extracted fecal microbial DNA based on the previously published method [45]. A total of 353 samples were sequenced in four batches on Illumina Miseq system (Illumina Inc., USA). The sequencing library of 16S rRNA gene V3-V4 regions was prepared as previously described [46], according to a modified version of the manufacturer’s instructions.
We used QIIME2 software (v2018.11) [47] to process and analyze the 16S rRNA gene read pairs. The raw sequence data were demultiplexed, denoised, and filtered for chimeric reads with the DADA2 plugin [48] to obtain the frequency table and representative sequence file of amplicon sequence variants (ASVs). After we removed the ASVs considered to be contaminants [49], the decontamination table composed of 353 sample and 5,448 ASVs was downsized to 10,000,000 to standardize sequence depth. We used the representative sequence file for taxonomic annotation using the SILVA database (version 138).
## Functional gene prediction.
The functional genes for producing butyrate (but, butyrate kinase) and acetate (fhs, formate-tetrahydrofolate ligase) were predicted based on 16S rRNA gene information by using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) [50].
## Bioinformatics and statistical analysis of microbiota data.
The following analyses of microbiota were performed by R (version 3.6.1). The α-diversity of each sample was calculated with Shannon index, Simpson index, Observed ASVs, and Faith’s phylogenetic diversity (PD whole tree) (R-packages “picante,” “phyloseq,” and “ape”). Structural differences in gut microbiota were assessed by β-diversity based on PhILR-transformed Euclidean distance using the R-packages “phyloseq” and “philr.” A random forest model was trained to distinguish the individuals in one cluster from those in other two clusters using the train function in the R-package “caret.” Wilcoxon rank sum test (adjusted by FDR) was used to analyze the difference of α-diversity index and identify the significantly different ASVs between two clusters. ASVs were considered to be significantly different between two clusters when the FDR was <0.05 and the absolute value of the logarithmic (base 2) fold change (|log2-fold change|) in relative abundance was >1. Permutational multivariate analysis of variance (PERMANOVA; permutations = 9,999) was used to assess the structural difference between different clusters.
## Network construction.
In each cluster, prevalent ASVs shared by more than $20\%$ of the samples were used to construct the microbial association network. Networks of clusters 1, 2, and 3 were generated based on Pearson correlations of the prevalent ASVs using the R-package “WGCNA.” The correlations with $P \leq 0.05$ (adjusted by Benjamini-Hochberg procedure) were retained for further analysis. The layout of nodes and edges was determined by the Fruchterman-Reingold layout algorithm using the correlation efficient as weight. The topological characteristics calculation and visualization of the networks were performed using the R-package “igraph.” Kolmogorov-Smirnov test (the ks.test function in the “stats” package) was used to compare the network’s topological characteristics between clusters. $P \leq 0.05$ was considered to have a significant difference.
Next, the ASVs from the three networks were clustered using the “ward. D2” (the hclust function in the “vegan” package) based on the correlation distance which were converted from correlation values. Permutational MANOVA (permutations = 9,999, $P \leq 0.01$) was used to determine whether the two clades of the cluster tree were not significantly different and clustered into one CAG.
## Association of microbiome features and clinical phenotypes.
To calculate the associations between microbiome features, as well as CAGs and clinical phenotypes, we used a modified general linear model as implemented by MaAsLin2 (Multivariate microbial Association with Linear Models), which combines an arcsine square root transformed analysis of relative abundances in a standard multivariable linear model while adjusting for gender and age. The P values were adjusted by using the Benjamini-Hochberg procedure. According to the instructions of the “Maaslin2” package, adjusted P value lower than 0.25 was considered to be significant.
## Quantification of fecal short-chain fatty acids.
Fecal SCFAs were quantified by gas chromatography/mass spectrometry as previously described [2]. A Wilcoxon rank sum test was used to analyze the difference of SCFAs between clusters.
## Data availability.
The raw sequence data reported have been deposited (PRJCA013291) in the Genome Sequence Archive (GSA) database under accession number CRA008952.
## References
1. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, Peng Y, Zhang D, Jie Z, Wu W, Qin Y, Xue W, Li J, Han L, Lu D, Wu P, Dai Y, Sun X, Li Z, Tang A, Zhong S. **A metagenome-wide association study of gut microbiota in type 2 diabetes**. *Nature* (2012) **490** 55-60. DOI: 10.1038/nature11450
2. Zhao L, Zhang F, Ding X, Wu G, Lam YY, Wang X, Fu H, Xue X, Lu C, Ma J, Yu L, Xu C, Ren Z, Xu Y, Xu S, Shen H, Zhu X, Shi Y, Shen Q, Dong W, Liu R, Ling Y, Zeng Y, Wang X, Zhang Q, Wang J, Wang L, Wu Y, Zeng B, Wei H, Zhang M, Peng Y, Zhang C. **Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes**. *Science* (2018) **359** 1151-1156. DOI: 10.1126/science.aao5774
3. Wu H, Tremaroli V, Schmidt C, Lundqvist A, Olsson LM, Kramer M, Gummesson A, Perkins R, Bergstrom G, Backhed F. **The gut microbiota in prediabetes and diabetes: a population-based cross-sectional study**. *Cell Metab* (2020) **32** 379-390. DOI: 10.1016/j.cmet.2020.06.011
4. Zhang X, Shen D, Fang Z, Jie Z, Qiu X, Zhang C, Chen Y, Ji L. **Human gut microbiota changes reveal the progression of glucose intolerance**. *PLoS One* (2013) **8**. DOI: 10.1371/journal.pone.0071108
5. Karlsson FH, Tremaroli V, Nookaew I, Bergstrom G, Behre CJ, Fagerberg B, Nielsen J, Backhed F. **Gut metagenome in European women with normal, impaired and diabetic glucose control**. *Nature* (2013) **498** 99-103. DOI: 10.1038/nature12198
6. Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E, Sunagawa S, Prifti E, Vieira-Silva S, Gudmundsdottir V, Pedersen HK, Arumugam M, Kristiansen K, Voigt AY, Vestergaard H, Hercog R, Costea PI, Kultima JR, Li J, Jørgensen T, Levenez F, Dore J, Nielsen HB, Brunak S, Raes J, Hansen T, Wang J, Ehrlich SD, Bork P, Pedersen O. **Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota**. *Nature* (2015) **528** 262-266. DOI: 10.1038/nature15766
7. Allin KH, Tremaroli V, Caesar R, Jensen BAH, Damgaard MTF, Bahl MI, Licht TR, Hansen TH, Nielsen T, Dantoft TM, Linneberg A, Jorgensen T, Vestergaard H, Kristiansen K, Franks PW, Consortium I-D, Hansen T, Backhed F, Pedersen O. **Aberrant intestinal microbiota in individuals with prediabetes**. *Diabetologia* (2018) **61** 810-820. DOI: 10.1007/s00125-018-4550-1
8. **Diagnosis and classification of diabetes mellitus**. *Diabetes Care* (2010) **33** S62-S69. DOI: 10.2337/dc10-S062
9. Alberti KG, Zimmet PZ. **Definition, diagnosis, and classification of diabetes mellitus and its complications. 1. Diagnosis and classification of diabetes mellitus: provisional report of a WHO consultation**. *Diabet Med* (1998) **15** 539-553. DOI: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S
10. Kahn SE. **The relative contributions of insulin resistance and beta-cell dysfunction to the pathophysiology of type 2 diabetes**. *Diabetologia* (2003) **46** 3-19. DOI: 10.1007/s00125-002-1009-0
11. Kannel WB. **Lipids, diabetes, and coronary heart disease: insights from the Framingham Study**. *Am Heart J* (1985) **110** 1100-1107. DOI: 10.1016/0002-8703(85)90224-8
12. Wagner R, Heni M, Tabák AG, Machann J, Schick F, Randrianarisoa E, Hrabě de Angelis M, Birkenfeld AL, Stefan N, Peter A, Häring H-U, Fritsche A. **Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes**. *Nat Med* (2021) **27** 49-57. DOI: 10.1038/s41591-020-1116-9
13. Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, Vikman P, Prasad RB, Aly DM, Almgren P, Wessman Y, Shaat N, Spégel P, Mulder H, Lindholm E, Melander O, Hansson O, Malmqvist U, Lernmark Å, Lahti K, Forsén T, Tuomi T, Rosengren AH, Groop L. **Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables**. *Lancet Diabetes Endocrinol* (2018) **6** 361-369. DOI: 10.1016/S2213-8587(18)30051-2
14. Stidsen JV, Henriksen JE, Olsen MH, Thomsen RW, Nielsen JS, Rungby J, Ulrichsen SP, Berencsi K, Kahlert JA, Friborg SG, Brandslund I, Nielsen AA, Christiansen JS, Sorensen HT, Olesen TB, Beck-Nielsen H. **Pathophysiology-based phenotyping in type 2 diabetes: a clinical classification tool**. *Diabetes Metab Res Rev* (2018) **34**. DOI: 10.1002/dmrr.3005
15. Fei N, Zhao L. **An opportunistic pathogen isolated from the gut of an obese human causes obesity in germfree mice**. *ISME J* (2013) **7** 880-884. DOI: 10.1038/ismej.2012.153
16. Vrieze A, Van Nood E, Holleman F, Salojärvi J, Kootte RS, Bartelsman JFWM, Dallinga–Thie GM, Ackermans MT, Serlie MJ, Oozeer R, Derrien M, Druesne A, Van Hylckama Vlieg JET, Bloks VW, Groen AK, Heilig HGHJ, Zoetendal EG, Stroes ES, de Vos WM, Hoekstra JBL, Nieuwdorp M. **Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome**. *Gastroenterology* (2012) **143** 913-916. DOI: 10.1053/j.gastro.2012.06.031
17. Koh A, Molinaro A, Stahlman M, Khan MT, Schmidt C, Manneras-Holm L, Wu H, Carreras A, Jeong H, Olofsson LE, Bergh PO, Gerdes V, Hartstra A, de Brauw M, Perkins R, Nieuwdorp M, Bergstrom G, Backhed F. **Microbially produced imidazole propionate impairs insulin signaling through mTORC1**. *Cell* (2018) **175** 947-961. DOI: 10.1016/j.cell.2018.09.055
18. Velagapudi VR, Hezaveh R, Reigstad CS, Gopalacharyulu P, Yetukuri L, Islam S, Felin J, Perkins R, Borén J, Oresic M, Bäckhed F. **The gut microbiota modulates host energy and lipid metabolism in mice**. *J Lipid Res* (2010) **51** 1101-1112. DOI: 10.1194/jlr.M002774
19. An HM, Park SY, Lee DK, Kim JR, Cha MK, Lee SW, Lim HT, Kim KJ, Ha NJ. **Antiobesity and lipid-lowering effects of**. *Lipids Health Dis* (2011) **10** 116. DOI: 10.1186/1476-511X-10-116
20. Yoo SR, Kim YJ, Park DY, Jung UJ, Jeon SM, Ahn YT, Huh CS, McGregor R, Choi MS. **Probiotics L. plantarum and L. curvatus in combination alter hepatic lipid metabolism and suppress diet-Induced obesity**. *Obesity (Silver Spring)* (2013) **21** 2571-2578. DOI: 10.1002/oby.20428
21. Aron-Wisnewsky J, Warmbrunn MV, Nieuwdorp M, Clément K. **Metabolism and metabolic disorders and the microbiome: the intestinal microbiota associated with obesity, lipid metabolism, and metabolic health—pathophysiology and therapeutic strategies**. *Gastroenterology* (2021) **160** 573-599. DOI: 10.1053/j.gastro.2020.10.057
22. Reynolds AP, Richards G, de la Iglesia B, Rayward-Smith VJ. **Clustering rules: a comparison of partitioning and hierarchical clustering algorithms**. *J Mathemat Model Algorithms* (2006) **5** 475-504. DOI: 10.1007/s10852-005-9022-1
23. Zhang C, Yin A, Li H, Wang R, Wu G, Shen J, Zhang M, Wang L, Hou Y, Ouyang H, Zhang Y, Zheng Y, Wang J, Lv X, Wang Y, Zhang F, Zeng B, Li W, Yan F, Zhao Y, Pang X, Zhang X, Fu H, Chen F, Zhao N, Hamaker BR, Bridgewater LC, Weinkove D, Clement K, Dore J, Holmes E, Xiao H, Zhao G, Yang S, Bork P, Nicholson JK, Wei H, Tang H, Zhang X, Zhao L. **Dietary modulation of gut microbiota contributes to alleviation of both genetic and simple obesity in children**. *EBioMedicine* (2015) **2** 968-984. DOI: 10.1016/j.ebiom.2015.07.007
24. Gurung M, Li Z, You H, Rodrigues R, Jump DB, Morgun A, Shulzhenko N. **Role of gut microbiota in type 2 diabetes pathophysiology**. *EBioMedicine* (2020) **51** 102590. DOI: 10.1016/j.ebiom.2019.11.051
25. Sedighi M, Razavi S, Navab-Moghadam F, Khamseh ME, Alaei-Shahmiri F, Mehrtash A, Amirmozafari N. **Comparison of gut microbiota in adult patients with type 2 diabetes and healthy individuals**. *Microb Pathog* (2017) **111** 362-369. DOI: 10.1016/j.micpath.2017.08.038
26. Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R, Manneras-Holm L, Stahlman M, Olsson LM, Serino M, Planas-Felix M, Xifra G, Mercader JM, Torrents D, Burcelin R, Ricart W, Perkins R, Fernandez-Real JM, Bäckhed F. **Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug**. *Nat Med* (2017) **23** 850-858. DOI: 10.1038/nm.4345
27. Xu J, Lian F, Zhao L, Zhao Y, Chen X, Zhang X, Guo Y, Zhang C, Zhou Q, Xue Z, Pang X, Zhao L, Tong X. **Structural modulation of gut microbiota during alleviation of type 2 diabetes with a Chinese herbal formula**. *ISME J* (2015) **9** 552-562. DOI: 10.1038/ismej.2014.177
28. Sasaki M, Ogasawara N, Funaki Y, Mizuno M, Iida A, Goto C, Koikeda S, Kasugai K, Joh T. **Transglucosidase improves the gut microbiota profile of type 2 diabetes mellitus patients: a randomized double-blind, placebo-controlled study**. *BMC Gastroenterol* (2013) **13** 81. DOI: 10.1186/1471-230X-13-81
29. Sun L, Xie C, Wang G, Wu Y, Wu Q, Wang X, Liu J, Deng Y, Xia J, Chen B, Zhang S, Yun C, Lian G, Zhang X, Zhang H, Bisson WH, Shi J, Gao X, Ge P, Liu C, Krausz KW, Nichols RG, Cai J, Rimal B, Patterson AD, Wang X, Gonzalez FJ, Jiang C. **Gut microbiota and intestinal FXR mediate the clinical benefits of metformin**. *Nat Med* (2018) **24** 1919-1929. DOI: 10.1038/s41591-018-0222-4
30. Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BA, Forslund K, Hildebrand F, Prifti E, Falony G. **Human gut microbes impact host serum metabolome and insulin sensitivity**. *Nature* (2016) **535** 376-381. DOI: 10.1038/nature18646
31. Qi X, Yun C, Sun L, Xia J, Wu Q, Wang Y, Wang L, Zhang Y, Liang X, Wang L, Gonzalez FJ, Patterson AD, Liu H, Mu L, Zhou Z, Zhao Y, Li R, Liu P, Zhong C, Pang Y, Jiang C, Qiao J. **Gut microbiota–bile acid–interleukin-22 axis orchestrates polycystic ovary syndrome**. *Nat Med* (2019) **25** 1225-1233. DOI: 10.1038/s41591-019-0509-0
32. Png CW, Lindén SK, Gilshenan KS, Zoetendal EG, McSweeney CS, Sly LI, McGuckin MA, Florin TH. **Mucolytic bacteria with increased prevalence in IBD mucosa augment**. *Am J Gastroenterol* (2010) **105** 2420-2428. DOI: 10.1038/ajg.2010.281
33. Tailford LE, Owen CD, Walshaw J, Crost EH, Hardy-Goddard J, Le Gall G, de Vos WM, Taylor GL, Juge N. **Discovery of intramolecular trans-sialidases in human gut microbiota suggests novel mechanisms of mucosal adaptation**. *Nat Commun* (2015) **6** 7624. DOI: 10.1038/ncomms8624
34. Crost EH, Tailford LE, Le Gall G, Fons M, Henrissat B, Juge N. **Utilization of mucin glycans by the human gut symbiont**. *PLoS One* (2013) **8**. DOI: 10.1371/journal.pone.0076341
35. Henke MT, Kenny DJ, Cassilly CD, Vlamakis H, Xavier RJ, Clardy J. **Ruminococcus gnavus, a member of the human gut microbiome associated with Crohn’s disease, produces an inflammatory polysaccharide**. *Proc Natl Acad Sci USA* (2019) **116** 12672-12677. DOI: 10.1073/pnas.1904099116
36. Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, Neyrinck AM, Fava F, Tuohy KM, Chabo C, Waget A, Delmee E, Cousin B, Sulpice T, Chamontin B, Ferrieres J, Tanti JF, Gibson GR, Casteilla L, Delzenne NM, Alessi MC, Burcelin R. **Metabolic endotoxemia initiates obesity and insulin resistance**. *Diabetes* (2007) **56** 1761-1772. DOI: 10.2337/db06-1491
37. Sakamoto M, Lan PTN, Benno Y. *Int J Syst Evol Microbiol* (2007) **57** 342-346. DOI: 10.1099/ijs.0.64709-0
38. Sakakibara S, Yamauchi T, Oshima Y, Tsukamoto Y, Kadowaki T. **Acetic acid activates hepatic AMPK and reduces hyperglycemia in diabetic KK-A(y) mice**. *Biochem Biophys Res Commun* (2006) **344** 597-604. DOI: 10.1016/j.bbrc.2006.03.176
39. Yamashita H, Fujisawa K, Ito E, Idei S, Kawaguchi N, Kimoto M, Hiemori M, Tsuji H. **Improvement of obesity and glucose tolerance by acetate in type 2 diabetic Otsuka Long-Evans Tokushima Fatty (OLETF) rats**. *Biosci Biotechnol Biochem* (2007) **71** 1236-1243. DOI: 10.1271/bbb.60668
40. Kondo T, Kishi M, Fushimi T, Kaga T. **Acetic acid upregulates the expression of genes for fatty acid oxidation enzymes in liver to suppress body fat accumulation**. *J Agric Food Chem* (2009) **57** 5982-5986. DOI: 10.1021/jf900470c
41. Carvalho BM, Guadagnini D, Tsukumo DML, Schenka AA, Latuf-Filho P, Vassallo J, Dias JC, Kubota LT, Carvalheira JBC, Saad MJA. **Modulation of gut microbiota by antibiotics improves insulin signalling in high-fat fed mice**. *Diabetologia* (2012) **55** 2823-2834. DOI: 10.1007/s00125-012-2648-4
42. Peng L, Li ZR, Green RS, Holzman IR, Lin J. **Butyrate enhances the intestinal barrier by facilitating tight junction assembly via activation of AMP-activated protein kinase in Caco-2 cell monolayers**. *J Nutr* (2009) **139** 1619-1625. DOI: 10.3945/jn.109.104638
43. Burger-van Paassen N, Vincent A, Puiman PJ, van der Sluis M, Bouma J, Boehm G, van Goudoever JB, van Seuningen I, Renes IB. **The regulation of intestinal mucin MUC2 expression by short-chain fatty acids: implications for epithelial protection**. *Biochem J* (2009) **420** 211-219. DOI: 10.1042/BJ20082222
44. Lewis K, Lutgendorff F, Phan V, Soderholm JD, Sherman PM, McKay DM. **Enhanced translocation of bacteria across metabolically stressed epithelia is reduced by butyrate**. *Inflamm Bowel Dis* (2010) **16** 1138-1148. DOI: 10.1002/ibd.21177
45. Godon JJ, Zumstein E, Dabert P, Habouzit F, Moletta R. **Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis**. *Appl Environ Microbiol* (1997) **63** 2802-2813. DOI: 10.1128/aem.63.7.2802-2813.1997
46. Zhang Q, Wu Y, Wang J, Wu G, Long W, Xue Z, Wang L, Zhang X, Pang X, Zhao Y, Zhao L, Zhang C. **Accelerated dysbiosis of gut microbiota during aggravation of DSS-induced colitis by a butyrate-producing bacterium**. *Sci Rep* (2016) **6** 27572-27511. DOI: 10.1038/srep27572
47. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C. **Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2**. *Nat Biotechnol* (2019) **37** 852-857. DOI: 10.1038/s41587-019-0209-9
48. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. **DADA2: high-resolution sample inference from Illumina amplicon data**. *Nat Methods* (2016) **13** 581-583. DOI: 10.1038/nmeth.3869
49. Moossavi S, Fehr K, Khafipour E, Azad MB. **Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota**. *Microbiome* (2021) **9** 41. DOI: 10.1186/s40168-020-00998-4
50. Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, Huttenhower C, Langille MGI. **PICRUSt2 for prediction of metagenome functions**. *Nat Biotechnol* (2020) **38** 685-688. DOI: 10.1038/s41587-020-0548-6
|
---
title: A Manganese-independent Aldolase Enables Staphylococcus aureus To Resist Host-imposed
Metal Starvation
authors:
- Paola K. Párraga Solórzano
- Talina S. Bastille
- Jana N. Radin
- Thomas E. Kehl-Fie
journal: mBio
year: 2023
pmcid: PMC9973326
doi: 10.1128/mbio.03223-22
license: CC BY 4.0
---
# A Manganese-independent Aldolase Enables Staphylococcus aureus To Resist Host-imposed Metal Starvation
## ABSTRACT
The preferred carbon source of *Staphylococcus aureus* and many other pathogens is glucose, and its consumption is critical during infection. However, glucose utilization increases the cellular demand for manganese, a nutrient sequestered by the host as a defense against invading pathogens. Therefore, bacteria must balance glucose metabolism with the increasing demand that metal-dependent processes, such as glycolysis, impose upon the cell. A critical regulator that enables S. aureus to resist nutritional immunity is the ArlRS two-component system. This work revealed that ArlRS regulates the expression of FdaB, a metal-independent fructose 1,6-bisphosphate aldolase. Further investigation revealed that when S. aureus is metal-starved by the host, FdaB functionally replaces the metal-dependent isozyme FbaA, thereby allowing S. aureus to resist host-imposed metal starvation in culture. Although metal-dependent aldolases are canonically zinc-dependent, this work uncovered that FbaA requires manganese for activity and that FdaB protects S. aureus from manganese starvation. Both FbaA and FdaB contribute to the ability of S. aureus to cause invasive disease in wild-type mice. However, the virulence defect of a strain lacking FdaB was reversed in calprotectin-deficient mice, which have defects in manganese sequestration, indicating that this isozyme contributes to the ability of this pathogen to overcome manganese limitation during infection. Cumulatively, these observations suggest that the expression of the metal-independent aldolase FdaB allows S. aureus to alleviate the increased demand for manganese that glucose consumption imposes, and highlights the cofactor flexibility of even established metalloenzyme families.
## INTRODUCTION
For many bacterial pathogens, including Staphylococcus aureus, glucose is the preferred carbon source for growth, with reductions in the ability to consume glucose ablating virulence (1–9). The value of glucose consumption to invaders is highlighted by the increased risk of infection with S. aureus, Escherichia coli, Streptococcus pneumoniae, Mycobacterium tuberculosis, Klebsiella pneumoniae, and Candida albicans that is faced by diabetic and hyperglycemic individuals (10–12). While glucose consumption is necessary for and enhances infections, many glycolytic enzymes require metals, such as manganese (Mn) and zinc (Zn), to function, and this increases the cellular demand for these essential nutrients (2, 3, 13–19). This creates a conflict that pathogens must resolve as the host restricts the availability of metals during infection [20, 21]. Thus, bacteria must balance their need to consume glucose with the increased demand for metals that glycolysis places upon the cell. How S. aureus and other pathogens balance these competing demands remains largely unknown.
Glycolysis increases the staphylococcal demand for Mn [13, 22, 23]. In a nutrient-replete environment, the increased cellular demand for metals that glycolysis places on the cell does not pose a challenge. However, during infection, essential metals, including Mn, iron (Fe), and Zn, are withheld from invaders by the host (24–28). The impact of this defense, known as nutritional immunity, is far-reaching, with $50\%$ of enzymes and $30\%$ of proteins being predicted to require a metal cofactor for proper function [29, 30]. Differing from organic molecules that can frequently be synthesized as needed, inorganic nutrients, such as transition metals, must be obtained from the environment. As a result, nutritional immunity disrupts a wide range of cellular processes in pathogens, including those involved in metabolism and virulence (13, 14, 27, 31–34). A critical component of the host’s nutrient withholding response is the immune effector calprotectin (CP) [21, 26, 35]. The loss of CP ablates the ability of the host to restrict metals during infection and renders mice more susceptible to disease by multiple bacterial and fungal pathogens, including S. aureus, A. baumannii, and K. pneumoniae (23, 26, 28, 36–38). CP is the most abundant protein in the cytosol of neutrophils, and its concentration can exceed 1 mg/mL in areas of infection [39, 40]. CP is a heterodimer of S100A8 and S100A9 and possesses two metal-binding sites that can tightly bind Mn, Zn, and other transition metals (41–45). In addition to inhibiting processes that are necessary for optimal growth, such as glycolysis, CP-imposed metal starvation also inhibits the activity of enzymes that are essential for bacteria to survive the onslaught of the immune response, such as Mn-dependent superoxide dismutases (SODs) [13, 27].
To successfully overcome nutritional immunity, pathogens rely on the expression of high-affinity metal uptake systems and adaptation [46]. S. aureus, for instance, expresses two Mn transporters: MntH, a natural resistance-associated macrophage protein (NRAMP) family member, and MntABC, an ATP-binding cassette (ABC) permease [22, 23, 47]. The adaptations that facilitate success in the face of nutritional immunity include the reduced utilization of metal-dependent processes and the activation of alternative pathways and enzymes that do not rely on the restricted metal (13, 48–55). In addition to glucose, S. aureus can use amino acids as an energy source and can thus reduce its cellular demand for Mn [13]. However, S. aureus must retain the ability to consume glucose to cause infection [2, 56]. S. aureus and other bacteria frequently coordinate their responses to metal limitation by using the metal-sensing regulators Fur (Fe), Zur (Zn), and MntR (Mn) [46, 47, 57, 58]. However, other regulatory systems that do not directly sense metal availability, such as the staphylococcal two-component system (TCS) ArlRS, also contribute to overcoming Mn starvation and nutritional immunity [14]. ArlRS directly and indirectly controls the expression of numerous virulence determinants, including toxins, exoenzymes, immune modulators, and cell surface proteins involved in clumping and adherence (59–65). This TCS appears to sense the alterations in metabolic flux that occur in the latter half of glycolysis, which can be caused by Mn limitation and elevated pyruvate concentration [14, 62]. Upon activation, ArlRS facilitates a metabolic shift toward amino acid utilization, thereby reducing the cellular demand for Mn [13]. Consistent with its expansive regulatory network, the loss of ArlRS reduces the ability of S. aureus to cause disease in several animal models of infection (13, 61, 65–67). Notably, when the host cannot sequester Mn, the need for ArlRS during infection is ablated [13]. Cumulatively, this indicates that ArlRS significantly contributes to coordinating the staphylococcal metabolic response to Mn limitation during infection.
The current studies seek to better understand how ArlRS contributes to coordinating the metabolic response of S. aureus to nutritional immunity. This work reveals that this TCS controls the expression of a metal-independent variant of aldolase (FdaB) that functionally replaces a metal-dependent isozyme (FbaA) when S. aureus is metal-starved by the host. While metal-dependent aldolases classically rely on Zn for activity, further investigation revealed that FbaA utilizes Mn and that FdaB enables S. aureus to survive host-imposed Mn limitation.
## Gene regulation upon ArlRS activation.
Two prior studies have elucidated the ArlRS regulon [60, 64]. However, these studies were conducted prior to the identification of the signals that activate ArlRS [14, 62]. Thus, these studies only compared gene expression differences between wild-type bacteria and strains lacking the ArlRS system in standard culture conditions. Both prior studies compared gene expression between the wild-type and a strain lacking a functional ArlRS system, following growth in TBS to mid-exponential phase, with Liang et al. harvesting RNA from WCUH29 (OD600 of 0.4) and Crosby et al. harvesting RNA from USA300 LAC (OD600 of 1.5). The absence of glucose was recently observed to activate ArlRS [14, 68]. This finding was leveraged to better understand the ArlRS regulon by comparing gene expression between wild-type bacteria and ΔarlRS, following growth in the presence and absence of glucose. The use of glucose limitation as an activating signal allows for the activation of ArlRS while minimizing of the impact of the activating stimulus on the growth of the wild-type bacteria and ΔarlRS. This analysis, which harvested RNA from exponentially growing S. aureus Newman at an OD600 of 0.1, revealed that, in the presence of glucose, 183 genes are downregulated and 217 are upregulated in the ΔarlRS mutant, compared to the wild-type S. aureus Newman (Table S1). The apparent activity in the absence of an activating signal is consistent with the prior observation that even in the absence of ArlS, ArlR can drive the expression of the mgrA P2 promoter [68]. In the absence of glucose, when ArlS is active, 202 genes are downregulated and 452 are upregulated in ΔarlRS, compared to the wild-type strain (Table S2). In total, using a 2-fold change in transcript abundance, 614 genes whose expression is modulated by ArlRS were unique to the current analysis (Fig. 1). Notably, despite this increased number of ArlRS-regulated genes in the current data set, it did not fully encompass all of the genes identified by prior analyses [60, 64]. 39 genes were shared between all three studies, whereas 137 genes were shared when only data sets generated using RNA-seq were considered (Table S3) [60, 64]. Prior work observed that the genes whose expression is influenced by ArlRS are dependent on the specific activating stimulus [69]. Thus, it seems likely that the difference between the current work and previous work can be attributed to a combination of assaying gene expression in the presence and absence of an activating signal, differences in culture medium, and the growth phase, with the shared genes perhaps representing a core regulon. Regardless, the current analysis further establishes ArlRS as a significant modulator of staphylococcal gene expression.
**FIG 1:** *Gene regulation upon ArlRS activation. Venn diagram depicting the genes regulated by ArlRS in the absence and presence of glucose and overlap with ArlRS regulons obtained in previous studies (60, 64).*
## FdaB promotes resistance to calprotectin.
Among the genes regulated by ArlRS is fdaB, whose expression decreases 4-fold in ΔarlRS when glucose is present. This result is similar to previous observations by Crosby et al. [ 64] and was confirmed using qRT-PCR (Fig. 2A). FdaB is a fructose 1,6-bisphosphate aldolase that does not require a metal ion for activity and has previously been implicated in the facilitation of gluconeogenesis [70]. S. aureus also possesses a putatively Zn-dependent aldolase, FbaA [71], which is associated with glycolytic flux. During glycolysis, aldolase converts fructose 1,6-bisphosphate into glyceraldehyde 3-phosphate and dihydroxyacetone 3-phosphate, and it performs the reverse reaction during gluconeogenesis. Given this prior connection to glycolytic flux, the individual contributions of the aldolases to S. aureus growth were evaluated in a TSB-based, metal-replete, glucose-containing medium. While the loss of FdaB did not affect the ability of S. aureus to grow under these conditions, a ΔfbaA mutation resulted in a pronounced growth defect in both strain Newman and the USA300 strain JE2 (Fig. 2B and C). The ectopic expression of FbaA from a plasmid reversed the growth defect in both strains (Fig. 2D). These findings are consistent with potential unique roles in glycolysis and/or gluconeogenesis, and they suggest that FbaA is necessary in metal-replete environments. However, the presence of metal-dependent and independent variants of aldolase is reminiscent of the Mn-dependent and Mn-independent phosphoglycerate mutase (PGM) isozymes that are possessed by S. aureus and *Salmonella enterica* Typhimurium. Whereas the PGM isozymes were previously thought to contribute to glycolysis and gluconeogenesis, respectively, the Mn-independent variants of PGM enable both of these pathogens to maintain glycolytic flux when Mn-starved by the host [72]. Therefore, the contribution of FdaB to resistaning host-imposed metal starvation was evaluated. For these and subsequent assays with CP, an NRPMI-based medium was used. This medium contains glucose and was supplemented with Mn, Zn, and Fe such that it would be metal-replete in the absence of CP. The loss of FdaB reduced the ability of S. aureus Newman and USA300 JE2 to grow in the presence of CP (Fig. 2E and F). The constitutive expression of FdaB from a plasmid reversed the phenotype (Fig. 2G and H). Overall, these observations indicate that FdaB is necessary for S. aureus to resist CP-imposed metal starvation.
**FIG 2:** *FdaB promotes resistance to calprotectin. (A) Wild-type S. aureus Newman was grown in TSB with glucose in the presence and absence of 240 μg/mL of CP, and the transcript levels of fbaA and fdaB were assessed via qRT-PCR. *, P ≤ 0.05 relative to the wild-type by a two-way ANOVA with Tukey’s multiple-comparison test. #, P ≤ 0.05 relative to the same strain in the absence of CP by a two-way ANOVA with Tukey’s multiple-comparison test. (B and C) S. aureus Newman, USA300 JE2 wild-type, ΔfbaA, and ΔfdaB were grown overnight in TSB, subcultured 1:100 in medium containing 38% TSB and 62% CP buffer, and grown in the absence of calprotectin. Growth was assessed via the measurement of the optical density, with panel C showing the OD600 values at t = 6 h. *, P ≤ 0.05 relative to the wild-type by a one-way ANOVA with Tukey’s multiple-comparison test. (D) Wild-type bacteria and ΔfbaA containing either an empty pOS1 plgt (pVC) or pOS1 plgt:fbaA (pFbaA) were grown as described in panels B and C. Growth was assessed at 6 h via the measurement of the optical densitiy. *, P ≤ 0.05 for the indicated comparison by a one-way ANOVA with Sidak’s multiple-comparison test. (E and F) S. aureus Newman, USA300 wild-type, ΔfdaB, and (G and H) S. aureus wild-type and ΔfdaB pOS1 plgt (pVC) or pOS1 plgt:fdaB (pFdaB) were assessed for CP sensitivity following growth in NRPMI containing 1 μM Zn, 1 μM Mn, and 1 μM Fe. Growth was assessed via the measurement of the absorbance at OD600 (F, G, and H) at t = 6 h. *, P ≤ 0.05 relative to the wild-type by a two-way ANOVA with Tukey’s multiple-comparison test. n ≥ 3. Error bars indicate the SEM.*
## ArlRS is not needed to induce FdaB in response to host-imposed metal starvation.
Having established that FdaB contributes to the resistance against metal starvation, the necessity of ArlRS for FdaB expression in the presence of CP was also assessed. In the presence of CP, a similar level of transcript was observed in ΔarlRS, compared to wild-type bacteria (Fig. 2A). This suggests the existence of another regulator that can induce the expression of fdaB in a metal-deplete medium. The expression of fbaA was also assessed and was observed to decrease in response to CP in both the wild-type bacteria and in a ΔarlRS mutant (Fig. 2A). This observation raised the possibility that the need for FdaB is due to the reduced expression of FbaA in the presence of CP. However, the constitutive expression of FbaA from a plasmid does not rescue the growth of ΔfdaB in metal-deplete medium (Fig. 3A). This suggests that the need for FdaB is not simply due to the reduced expression of FbaA. The sensitivity of ΔfdaB, ΔarlRS, and ΔarlRSΔfdaB to CP was also assessed. While both ΔfdaB and ΔarlRS were more sensitive to CP than were the wild-type bacteria, the growth defect upon the loss of ArlRS was more pronounced than the loss of FdaB (Fig. 3B and C). However, the growth of ΔarlRSΔfdaB was not further impaired, compared to the ΔarlRS mutant (Fig. 3B and C). Cumulatively, these results suggest that ArlRS induces the expression of fdaB. However, this TCS is not necessary to express the metal-independent aldolase isozyme in response to CP.
**FIG 3:** *ArlRS is not needed to induce FdaB in response to host-imposed metal starvation. (A) S. aureus Newman wild-type, ΔfdaB containing pOS1 plgt (pVC), pOS1 plgt:fdaB (pFdaB), or pOS1 plgt:fbaA (pFbaA), and (B and C) Newman wild-type and ΔfdaB, ΔarlRS, or ΔfdaBΔarlRS were assessed for CP sensitivity in NRPMI containing 1 μM Zn, 1 μM Mn, and 1 μM Fe. Growth was assessed via the measurement of the absorbance at OD600 at (A and C) t = 6 h. (A) *, P ≤ 0.05 relative to the wild-type containing an empty vector control at the same CP concentration by a two-way ANOVA with Tukey’s multiple-comparison test. (C) *, P ≤ 0.05 relative to the wild-type at the same CP concentration by a two-way ANOVA with Tukey's multiple-comparison test. n ≥ 3. Error bars indicate the SEM.*
## FdaB facilitates growth in manganese-limited environments.
While FbaA is a putatively Zn-dependent enzyme, CP can bind multiple first-row transition metals using two binding sites (26, 73–75). The first site (S1) can bind Mn, Zn, and *Fe via* six histidines, whereas the second site (S2) binds Zn, but not Mn or Fe, using three histidines and one aspartic acid [43, 44, 76, 77]. To elucidate whether CP-imposed Zn limitation renders FdaB necessary for growth, wild-type CP and its ΔS1 and ΔS2 mutants, which have altered metal-binding properties, were leveraged. The ΔS1 mutant cannot bind Mn or Fe but retains the ability to bind Zn, whereas the ΔS2 variant binds all three metals. Surprisingly the Newman and USA300 JE2 ΔfdaB mutants have growth defects in the presence of ΔS2 but not in the presence of ΔS1 (Fig. 4A). This suggests that Zn limitation is not driving the necessity of FdaB for growth in the presence of CP. Given the unexpected nature of this finding, the ability of wild-type S. aureus and ΔfdaB to grow in a metal-defined medium, namely, NRPMI, which was lacking Mn, Zn, and Fe, was evaluated. Consistent with FdaB enabling S. aureus to survive metal starvation, the loss of FdaB ablated the growth of S. aureus Newman and USA300 JE2 in media to which Mn, Zn, and Fe were not added (Fig. 4B–F). The addition of Mn, but not Fe or Zn, reversed the growth defect (Fig. 4B, C, E, and F). Notably, differing from the results obtained using the TSB-based medium (Fig. 2B), ΔfbaA grew equivalent to the wild-type in NRPMI to which Mn, Zn, or Fe had been individually added back. This suggests that either all three metals must be present for the loss of FbaA to impact growth or that another component of the growth medium dictates the importance of FbaA. The ectopic expression of FdaB reversed the growth defect of the ΔfdaB mutant in Newman (Fig. 4D). Cumulatively, these results suggest that FdaB promotes resistance to Mn starvation. The loss of both S. aureus Mn transporters, MntABC and MntH, reduces intracellular Mn levels, compared to the wild-type [22]. To further test the idea that FdaB is important for responding to Mn starvation, the impact that the loss of the staphylococcal Mn transporters has on ΔfdaB growth was investigated. Compared to ΔfdaB and ΔmntCΔmntH, ΔfdaBΔmntCΔmntH is more sensitive to CP (Fig. 4G and H), with the double mutant having a growth defect, even in the absence of CP. This finding indicates that losing the ability to import Mn intensifies the impact of losing the metal-independent aldolase. While unexpected, cumulatively, these results suggest that FdaB is necessary for S. aureus to resist Mn limitation.
**FIG 4:** *FdaB facilitates growth in manganese-limited environments. (A) S. aureus Newman and USA300 (JE2) wild-type and ΔfdaB mutants were assessed for sensitivity to 240 μg/mL of wild-type CP or the ΔS1 and ΔS2 variants following growth in NRPMI containing 1 μM Zn, 1 μM Mn, and 1 μM Fe. Growth was assessed via the measurement of the absorbance at OD600 at t = 6 h. *, P ≤ 0.05 relative to the wild-type with the same CP variant by a two-way ANOVA with Tukey’s multiple-comparison test. (B and C) S. aureus Newman wild-type, ΔfbaA, ΔfdaB, as well as (D) Newman wild-type and ΔfdaB containing pOS1 plgt (pVC) or pOS1 plgt:fdaB (pFdaB) and (E and F) USA300 JE2 wild-type, ΔfbaA and ΔfdaB were grown in NRPMI containing 1 μM of the indicated metal. Growth was assessed via the measurement of the absorbance at OD600 (C, D, and F) at t = 8 h. (C and F) *, P ≤ 0.05 relative to the wild-type at the same growth condition by a two-way ANOVA with Tukey’s multiple-comparison test. (D) *, P ≤ 0.05 relative to the wild-type containing an empty vector by a one-way ANOVA with Tukey’s multiple-comparison test. (G & H) S. aureus Newman wild-type, ΔfdaB, ΔmntHΔmntC, and ΔfdaBΔmntHΔmntC were assessed for CP sensitivity in NRPMI containing 1 μM Zn, 1 μM Mn, and 1 μM Fe. Growth was assessed via the measurement of the absorbance at OD600 (H) at t = 6 h. *, P ≤ 0.05 relative to the wild-type strain at the CP concentration by a two-way ANOVA with Tukey’s multiple-comparison test. #, P ≤ 0.05 relative to the parental strain at the same CP concentration by a two-way ANOVA with Tukey’s multiple-comparison test. n ≥ 3. Error bars indicate the SEM.*
## FbaA activity is dependent on manganese.
The unexpected finding that FdaB is necessary for the ability of S. aureus to resist Mn starvation called into question the Zn dependency of FbaA. To evaluate the metal dependency of FbaA, ΔfdaB was used to eliminate the activity of the metal-independent aldolase. As the ΔfdaB mutant grows poorly in NRPMI lacking Mn, aldolase activity was initially assessed following growth in medium supplemented with Mn or with Mn and Zn. Regardless of whether the medium contained Zn, similar levels of aldolase activity were observed in the ΔfdaB mutant (Fig. 5). To more directly test the metal dependency of FbaA, the cell lysates were treated with EDTA and then supplemented with either Mn or Zn. Treatment with EDTA eliminated the aldolase activity of the ΔfdaB mutant. The addition of excess Mn restored aldolase activity to comparable levels to those of untreated cell lysates (Fig. 5). In contrast, the addition of Zn resulted in a minimal increase in aldolase activity. Taken together, these observations suggest that FbaA requires Mn for activity.
**FIG 5:** *The activity of FbaA is Mn-dependent. S. aureus Newman ΔfdaB was grown in NRPMI containing 1 μM of the indicated metal. The aldolase activity was assessed. When indicated, the cell lysates were treated with EDTA, and Mn or Zn was added to the reaction. *, P ≤ 0.05 relative to the untreated by a two-way ANOVA with Tukey’s multiple-comparison test. n ≥ 3. Error bars indicate the SEM.*
## Both staphylococcal aldolases contribute to infection.
The current results suggest that FdaB enables S. aureus to resist Mn starvation in culture but that FbaA may be critical in metal-replete, glucose-containing environments. To determine the importance of the aldolase isozymes in vivo, a retro-orbital systemic model of staphylococcal infection was used. Initially, wild-type C57BL/6 mice were infected with wild-type S. aureus, ΔfdaB, and ΔfbaA. The bacterial burdens of mice infected with ΔfdaB and ΔfbaA were significantly decreased in the heart and liver, compared to those infected with wild-type S. aureus (Fig. 6A and B). These results suggest that both the metal-dependent and metal-independent aldolases are necessary for S. aureus to cause infection. Differing from the heart and liver, the loss of neither aldolase reduced the bacterial burdens in the kidney (Fig. 6B), suggesting that either aldolase is sufficient in this tissue. The expression of either FbaA or FdaB from a plasmid largely reversed the virulence defect of ΔfbaA or ΔfdaB, respectively (Fig. 6C). Next, CP-deficient mice, which fail to remove Mn from the liver during infection [9], were infected to determine whether the virulence defect of ΔfdaB is associated with an inability to cope with host-imposed Mn starvation. CP-deficient mice infected with wild-type S. aureus, ΔfbaA, or ΔfdaB had comparable bacterial burdens in the liver (Fig. 6A), indicating that FdaB is necessary to resist Mn starvation during infection. Cumulatively, these findings highlight the importance of both aldolases during infection and indicate that FdaB contributes to the ability of S. aureus to overcome Mn starvation.
**FIG 6:** *FdaB is necessary for the ability of S. aureus to cause infection. 9-week-old mice or C57BL/6J or CP-deficient mice (C57BL/6J S100A9−/−), were retro-orbitally infected with 1 × 107 of S. aureus Newman wild-type, ΔfbaA, or ΔfdaB. After 4 days, the bacterial burdens in the (A) liver, (B) heart, and kidney were enumerated. (C) Newman wild-type, ΔfbaA, or ΔfdaB carrying either empty pKK30 (pEmpty), pKK30:fbaA (pFbaA), or pKK30:fdaB (pFdaB) were used to infect the mice, and after 4 days, the bacterial burdens were assessed. Statistical significance was evaluated via a Mann-Whitney U test. Specific P values for relevant comparisons are indicated. Bars indicate the median.*
## DISCUSSION
Nutrients, such as glycolic substrates and metals, are critical for pathogens during infection (2–5, 21, 25, 78–80). Glucose is the preferred carbon source for many invading pathogens, but it can also increase the cellular demand for Mn [13]. This creates a challenge for S. aureus and for other pathogens, as glycolysis contributes to their ability to survive the assault by the immune system, but metal availability is also restricted at the sites of infections [46, 81]. Therefore, to successfully cause infection, pathogens must balance rerouting metabolism to consume alternative energy sources, such as amino acids, with strategies that maximize their ability to retain glycolytic flux. The present work reveals that the metabolic regulator ArlRS, which has been implicated in the promotion of amino acid consumption [13], also regulates the expression of FdaB, a metal-independent aldolase that promotes resistance to host-imposed Mn starvation. This finding is unexpected, as the metal-dependent staphylococcal aldolase was predicted to depend on Zn, as do most metal-dependent versions of this enzyme (71, 82–87). Further investigation revealed that, differing from most previously characterized metal-dependent aldolases, which are Zn-dependent, FbaA utilizes Mn as a cofactor. Thus, the current work reveals aldolase as a target of nutritional immunity, a mechanism used by pathogens to preserve aldolase activity, and emphasizes the plasticity of enzyme metal specificity across organisms.
The surprising observation that FdaB is necessary to resist Mn starvation is not simply explained by the reduced expression of FbaA in Mn-deplete conditions, as the constitutive expression of FbaA does not reverse the growth defect of ΔfdaB in the presence of CP (Fig. 3A). Further investigation revealed that FbaA requires Mn for full activity (Fig. 5). Taken together, these observations suggest that S. aureus FbaA uses Mn, not Zn, as a cofactor. Whereas, to the best of our knowledge, S. aureus is the first pathogen to be identified to possess a Mn-dependent aldolase, Deinococcus radiodurans and *Bacillus methanolicus* also possess Mn-dependent class II fructose-1,6-bisphosphate aldolases [88, 89]. Notably, all of these bacteria have intrinsically high intracellular Mn concentrations. Despite these prior findings and the similarity of class II aldolases to FbaA in S. aureus, class II aldolases are, by default, presumed to be Zn-dependent. This current work highlights the need to carefully evaluate metal dependency including consideration of the natural metal content of the host species, even for enzyme classes that have been extensively studied.
Aldolase is not the only staphylococcal glycolytic enzyme with two isoforms. PGM also has metal-dependent and metal-independent variants [72]. Similar to the current observations, the metal-independent PGM isozyme, GpmA, contributes to resisting nutritional immunity [72]. Notably, metal-independent glycolytic isozymes of PGM also promote resistance to Mn starvation in Salmonella [72]. Differing from the staphylococcal PGMs, for which the metal-dependent isozyme appears to be dispensable in a systemic model of infection [72], both FdaB and FbaA are necessary for invasive disease in the heart and liver. This is despite the fact that, similar to PGM, both aldolases carry out the same chemical reaction. The classical explanation for the possession of two glycolytic enzymes is that one preferentially functions in glycolysis and the other preferentially functions in gluconeogenesis. This is one potential explanation for the nonredundancy of the staphylococcal aldolases, and this idea is supported by the observation that the loss of FbaA results in a growth defect in metal-replete media containing glucose. However, FdaB is capable of promoting S. aureus growth in glucose-containing media if *Mn is* limited. This suggests that environmental conditions, such as metal availability and regulatory cues, may drive the nonredundancy of the two staphylococcal aldolases, rather than specific roles in glycolysis and gluconeogenesis. Alternatively, metal-dependent aldolases from Neisseria meningitidis, Mycoplasma hyopneumoniae, and *Francisella novicida* have been associated with moonlighting functions, such as acting as transcriptional regulators and adhesins (90–92). While both aldolases are necessary for the infection of the liver and heart, either is sufficient in the kidneys. This suggests that in some tissues, they serve the same purpose or that despite the kidney being a Mn-restricted environment, the need for aldolase activity is reduced to a point that residual activity from FbaA is sufficient. Alternatively, it is possible that aldolase activity is dispensable in the kidney. Regardless of the rationale for why FbaA contributes to S. aureus infection, the current results establish an important role for the metal-independent isozyme FdaB in resisting Mn starvation during infection.
There are two different classes of aldolases: class-I and class-II, where class-I are metal-independent and class-II are metal-dependent, and bacteria can possess one or more of these enzymes [90]. When bacteria express a single aldolase, it is most commonly metal-dependent, belonging to class II [90]. However, certain bacteria, including S. aureus, Escherichia coli, and *Mycobacterium tuberculosis* express two aldolases, with one belonging to class I and the other to class II (93–96). Similar to that of S. aureus, the metal-independent aldolase of E. coli has been suggested to favor gluconeogenesis [70]. However, the current work raises the possibility that possessing a second metal-independent aldolase may also facilitate the ability of bacteria to survive metal starvation or potentially other stresses. Although, in the case of E. coli, it seems more likely that the metal-independent aldolase would promote resistance to Zn limitation, as the metal-dependent isozyme relies on that metal for function [97]. Interestingly, *Bacillus methanolicus* possesses two apparently metal-dependent aldolases [98], with the molecular rationale remaining unknown. The observation that not all aldolases are Zn-dependent raises the possibility that the second aldolase in this species may also promote resistance to metal limitation if the two isozymes have differing metal specificities.
The current observations suggest that ArlRS also aids in the preservation of glycolytic flux by modulating the expression of one of the staphylococcal aldolases. While ArlRS is not necessary to induce the expression of fdaB in response to CP, the loss of this TCS does ablate FdaB expression (Fig. 2A). This observation suggests the existence of additional regulators that modulate FdaB expression in response to metal limitation. It is tempting to speculate that the upregulation of FdaB by ArlRS might enhance gluconeogenesis and thereby allow for the production of essential biosynthetic precursors that lay upstream of aldolase. This role for ArlRS would also be consistent with its apparent ability to sense the accumulation of metabolites from the latter half of the glycolytic pathway and with the reported role of FdaB in gluconeogenesis [14, 99]. In addition to withholding essential metals, the host has other defenses that can disrupt glycolytic flux, including the production of itaconate, which can inhibit FbaA, among other enzymes [100]. Given the importance of aldolase activity to glycolysis and the multiple ways by which the host can target this pathway, it is perhaps unsurprising that multiple regulators can control the expression of fdaB. While multiple host defenses can target FbaA, the observation that the loss of CP reverses the virulence defect of ΔfdaB suggests that this alternative isozyme is important for resting nutritional immunity.
Adaptation to host-imposed environmental challenges is critical for bacterial survival. The expression of two different aldolases with different biochemical properties supports the importance of the redundancy of certain enzymes that are critical in coping with the different stresses that pathogens encounter during infection. It also highlights this pathogen’s remarkable metabolic plasticity and ability to adapt to hostile host milieu.
## Ethics statement.
All experiments involving animals were approved by the Institutional Animal Care and Use Committee of the University of Illinois at Urbana-Champaign (IACUC license number 15059) and were performed according to NIH guidelines, the Animal Welfare Act, and U.S. federal law.
## Strains and growth conditions.
S. aureus strains were grown at 37°C in tryptic soy broth with glucose (TSB) on a roller drum or on tryptic soy agar (TSA) plates for the performance of routine culturing or for genetic manipulation. E. coli strains were routinely cultivated at 37°C in Luria broth (LB) with shaking or on Luria agar plates. As needed for plasmid maintenance in E. coli and S. aureus, 100 μg/mL of ampicillin and 10 μg/mL of chloramphenicol were added to the growth media, respectively. Both bacterial species were stored at −80°C in a growth medium that contained $30\%$ glycerol.
S. aureus Newman or USA300 (JE2) and derivatives were used for all of the experiments. For the overnight cultures, the bacteria were grown in 5 mL of either tryptic soy broth with glucose (TSB) or Chelex-treated RPMI plus $1\%$ Casamino Acids (NRPMI) supplemented with 1 mM MgCl2, 100 μM CaCl2, and 1 μM FeCl2 in 15 mL conical tubes at 37°C on a roller drum [13, 23]. 10 μg/mL of chloramphenicol was added as need for plasmid maintenance. The hemolytic activity of all staphylococcal strains was confirmed via plating on blood agar plates. The strains used in this study are listed in Table 1. The fbaA::erm and fdaB::erm alleles were obtained from the Nebraska Transposon Mutant Library (NTML) and were introduced into S. aureus Newman and USA300 JE2 via phage transduction. Plasmids in the pOS1plgt background were constructed with the indicated primers (Table 2) via restriction cloning, whereas those in the pKK30 background were constructed via Gibson assembly.
## Transcriptome profiling.
S. aureus Newman wild-type and ΔarlRS were grown in TSB with glucose overnight. Then, the cultures were diluted 1:100 into 96-well round-bottom plates containing 100 μL of growth medium ($38\%$ TSB [with or without glucose] and $62\%$ calprotectin buffer [20 mM Tris pH 7.5, 100 mM NaCl, 3 mM CaCl2, 10 mM β-mercaptoethanol]). The growth medium was supplemented with 1 μM MnCl2 and 1 μM ZnSO4. Bacteria were harvested during log-phase growth (OD600 of approximately 0.1), and an equal volume of ice-cold 1:1 acetone-ethanol was then added to the cultures before freezing at −80°C until RNA extraction. RNA was extracted, and cDNA was generated as previously described (101–103). Purified RNA was submitted for RNA-seq preparation and sequencing at the Roy J. Carver Biotechnology Center (CBC) at the University of Illinois Urbana-Champaign.
## Expression analysis.
To assess the expression of fbaA and fdaB, S. aureus Newman was grown in TSB with glucose overnight. Then, the cultures were diluted 1:100 into 96-well round-bottom plates containing 100 μL of growth medium ($38\%$ TSB with glucose and $62\%$ calprotectin buffer [20 mM Tris pH 7.5, 100 mM NaCl, 3 mM CaCl2, 10 mM β-mercaptoethanol]) in the presence and absence of 240 μg/mL of CP. The growth medium was supplemented with 1 μM MnCl2 and 1 μM ZnSO4. Bacteria were harvested, RNA was extracted, and cDNA was prepared as indicated above for transcriptome profiling. Gene expression was assessed via quantitative reverse transcription-PCR (qRT-PCR), using the indicated primers (Table 2), with 16S being used as a normalizing control.
## Calprotectin growth assays.
CP assays were largely performed as described previously [13, 27, 43]. Overnight cultures grown in TSB with glucose were diluted 1:50 into 5 mL of fresh medium and were then incubated for 1 h or, if the strain contained a plasmid, 2 h at 37°C on a roller drum. The cultures were then back-diluted 1:100 in 96-well round-bottom plates containing 100 μL of growth medium ($38\%$ 3 × NRPMI and $62\%$ calprotectin buffer [20 mM Tris pH 7.5, 100 mM NaCl, 3 mM CaCl2]) in the presence of various concentrations of CP. The growth medium was supplemented with 1 μM MnCl2, 1 μM FeCl2, and 1 μM ZnSO4. For all assays, the bacteria were incubated with orbital shaking (180 rpm) at 37°C, and growth was measured by assessing the optical density (OD600) every 1 to 2 h. Prior to the measurement of the optical density, the 96-well plates were vortexed.
## Metal starvation growth assays.
For the growth assays using Chelex-treated medium to impose metal limitation, overnight cultures grown in NRPMI that contained 1 mM MgCl2 and 100 μM CaCl2, were diluted 1:10 in fresh medium that lacked metals before being further diluted 1:100 in 96-well round-bottom plates containing NRPMI supplemented with 1 mM MgCl2 and 100 μM CaCl2. As specified, 1 μM MnCl2, 1 μM ZnSO4, and 1 μM FeCl2 were also added. The bacteria were incubated with orbital shaking (180 rpm) at 37°C, and growth was measured by assessing the optical density (OD600) every 1 to 2 h. Prior to the measurement of the optical density, the 96-well plates were vortexed.
## Aldolase activity assays.
Overnight cultures grown in NRPMI containing 1 mM MgCl2, 100 μM CaCl2, and 1 μM FeCl2 were diluted 1:10 in fresh medium before being further diluted 1:100 in 96-well round-bottom plates containing NRPMI supplemented with 1 mM MgCl2, 100 μM CaCl2, and 1 μM FeCl2. Additionally, 1 μM MnCl2 and/or 1 μM ZnSO4 were added as specified. Bacteria were harvested during logarithmic-phase growth ($t = 6$ h), with approximately 8 mL of cell culture per sample being harvested via centrifugation. The bacterial pellets were washed with 10 mL of 50 mM Tris-HCl (pH 7.5), before resuspension in 1 mL of this buffer. Prior to assaying aldolase activity, the cells were homogenized twice in a FastPrep-24 Beadbeater at 6 m/s for 45 s cycles with 5 min of incubation on ice in between. The cell lysates were centrifuged at 4°C in a microcentrifuge at 14,000 × g for 10 min. The supernatants were collected and used for the aldolase activity assay, which was performed as described by Zhang, et al., with a few modifications [88]. Briefly, aldolase activity was determined by mixing untreated or EDTA-treated supernatants, 2 mM hydrazine, and 2.4 mM fructose-1,6-bisphosphate in 50 mM Tris-HCl (pH 7.5). Glyceraldehyde-3-phosphate produced from fructose-1,6-bisphosphate reacts with hydrazine to form an aldehyde-hydrazone, the production of which was measured via the tracking of the absorbance at 240 nm after 1 h of incubation at 25°C. Supernatants treated with 0.67 nM EDTA were incubated for 10 min at 25°C prior to their use for the aldolase activity assay. When indicated, 1 mM MnCl2 or 1 mM ZnSO4 was added to the reaction. For normalization, the total protein was determined using a BCA assay. Activity was defined as the change of the absorbance at 240 nm per minute per mg of total protein.
## Animal experiments.
All animal infections were performed as previously described [13, 48, 104]. 9-week-old female C57BL/6 or S100A9−/− mice were retro-orbitally infected with approximately 1 × 107 CFU suspended in 100 μL of sterile PBS. The infection was allowed to proceed for 4 days, after which the mice were sacrificed. The liver, heart, and kidneys were collected. These organs were homogenized, and the bacterial burdens were determined via the plating of serial dilutions.
## Data availability.
Transcriptional profiling data were deposited in the NCBI Gene Expression Omnibus (GEO) repository (accession number: GSE202268).
## References
1. Gotz A, Goebel W. **Glucose and glucose 6-phosphate as carbon sources in extra- and intracellular growth of enteroinvasive Escherichia coli and Salmonella enterica**. *Microbiology (Reading)* (2010) **156** 1176-1187. DOI: 10.1099/mic.0.034744-0
2. Vitko NP, Grosser MR, Khatri D, Lance TR, Richardson AR. **Expanded glucose import capability affords Staphylococcus aureus optimized glycolytic flux during infection**. *mBio* (2016) **7**. DOI: 10.1128/mBio.00296-16
3. Vitko NP, Spahich NA, Richardson AR. **Glycolytic dependency of high-level nitric oxide resistance and virulence in Staphylococcus aureus**. *mBio* (2015) **6**. DOI: 10.1128/mBio.00045-15
4. Bowden SD, Rowley G, Hinton JC, Thompson A. **Glucose and glycolysis are required for the successful infection of macrophages and mice by Salmonella enterica serovar typhimurium**. *Infect Immun* (2009) **77** 3117-3126. DOI: 10.1128/IAI.00093-09
5. Tchawa Yimga M, Leatham MP, Allen JH, Laux DC, Conway T, Cohen PS. **Role of gluconeogenesis and the tricarboxylic acid cycle in the virulence of Salmonella enterica serovar Typhimurium in BALB/c mice**. *Infect Immun* (2006) **74** 1130-1140. DOI: 10.1128/IAI.74.2.1130-1140.2006
6. Inada T, Kimata K, Aiba H. **Mechanism responsible for glucose-lactose diauxie in Escherichia coli: challenge to the cAMP model**. *Genes Cells* (1996) **1** 293-301. DOI: 10.1046/j.1365-2443.1996.24025.x
7. Deutscher J, Francke C, Postma PW. **How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria**. *Microbiol Mol Biol Rev* (2006) **70** 939-1031. DOI: 10.1128/MMBR.00024-06
8. Deutscher J, Kuster E, Bergstedt U, Charrier V, Hillen W. **Protein kinase-dependent HPr/CcpA interaction links glycolytic activity to carbon catabolite repression in gram-positive bacteria**. *Mol Microbiol* (1995) **15** 1049-1053. DOI: 10.1111/j.1365-2958.1995.tb02280.x
9. Liebeke M, Dorries K, Zuhlke D, Bernhardt J, Fuchs S, Pane-Farre J, Engelmann S, Volker U, Bode R, Dandekar T, Lindequist U, Hecker M, Lalk M. **A metabolomics and proteomics study of the adaptation of Staphylococcus aureus to glucose starvation**. *Mol Biosyst* (2011) **7** 1241-1253. DOI: 10.1039/c0mb00315h
10. Kourany WM, Miro JM, Moreno A, Corey GR, Pappas PA, Abrutyn E, Hoen B, Habib G, Fowler VG, Sexton DJ, Olaison L, Cabell CH, Investigators IM. **Influence of diabetes mellitus on the clinical manifestations and prognosis of infective endocarditis: a report from the International Collaboration on Endocarditis-Merged Database**. *Scand J Infect Dis* (2006) **38** 613-619. DOI: 10.1080/00365540600617017
11. Lipsky BA, Tabak YP, Johannes RS, Vo L, Hyde L, Weigelt JA. **Skin and soft tissue infections in hospitalised patients with diabetes: culture isolates and risk factors associated with mortality, length of stay and cost**. *Diabetologia* (2010) **53** 914-923. DOI: 10.1007/s00125-010-1672-5
12. Casqueiro J, Casqueiro J, Alves C. **Infections in patients with diabetes mellitus: a review of pathogenesis**. *Indian J Endocrinol Metab* (2012) **16 Suppl 1** S27-36. DOI: 10.4103/2230-8210.94253
13. Radin JN, Kelliher JL, Parraga Solorzano PK, Kehl-Fie TE. **The two-component system ArlRS and alterations in metabolism enable Staphylococcus aureus to resist calprotectin-induced manganese starvation**. *PLoS Pathog* (2016) **12**. DOI: 10.1371/journal.ppat.1006040
14. Parraga Solorzano PK, Yao J, Rock CO, Kehl-Fie TE. **Disruption of glycolysis by nutritional immunity activates a two-component system that coordinates a metabolic and antihost response by Staphylococcus aureus**. *mBio* (2019) **10**. DOI: 10.1128/mBio.01321-19
15. Kehres DG, Maguire ME. **Emerging themes in manganese transport, biochemistry and pathogenesis in bacteria**. *FEMS Microbiol Rev* (2003) **27** 263-290. DOI: 10.1016/S0168-6445(03)00052-4
16. Bond CS, White MF, Hunter WN. **High resolution structure of the phosphohistidine-activated form of Escherichia coli cofactor-dependent phosphoglycerate mutase**. *J Biol Chem* (2001) **276** 3247-3253. DOI: 10.1074/jbc.M007318200
17. Chander M, Setlow B, Setlow P. **The enzymatic activity of phosphoglycerate mutase from gram-positive endospore-forming bacteria requires Mn2+ and is pH sensitive**. *Can J Microbiol* (1998) **44** 759-767. DOI: 10.1139/cjm-44-8-759
18. Crow VL, Pritchard GG. **The effect of monovalent and divalent cations on the activity of Streptococcus lactis C10 pyruvate kinase**. *Biochim Biophys Acta* (1977) **481** 105-114. DOI: 10.1016/0005-2744(77)90142-5
19. Yamada T, Carlsson J. **Glucose-6-phosphate-dependent pyruvate kinase in Streptococcus mutans**. *J Bacteriol* (1975) **124** 562-563. DOI: 10.1128/jb.124.1.562-563.1975
20. Juttukonda LJ, Skaar EP. **Manganese homeostasis and utilization in pathogenic bacteria**. *Mol Microbiol* (2015) **97** 216-228. DOI: 10.1111/mmi.13034
21. Kehl-Fie TE, Skaar EP. **Nutritional immunity beyond iron: a role for manganese and zinc**. *Curr Opin Chem Biol* (2010) **14** 218-224. DOI: 10.1016/j.cbpa.2009.11.008
22. Radin JN, Zhu J, Brazel EB, McDevitt CA, Kehl-Fie TE. **Synergy between nutritional immunity and independent host defenses contributes to the importance of the MntABC manganese transporter during Staphylococcus aureus infection**. *Infect Immun* (2019) **87**. DOI: 10.1128/IAI.00642-18
23. Kehl-Fie TE, Zhang Y, Moore JL, Farrand AJ, Hood MI, Rathi S, Chazin WJ, Caprioli RM, Skaar EP. **MntABC and MntH contribute to systemic Staphylococcus aureus infection by competing with calprotectin for nutrient manganese**. *Infect Immun* (2013) **81** 3395-3405. DOI: 10.1128/IAI.00420-13
24. Bullen JJ. **The significance of iron in infection**. *Rev Infect Dis* (1981) **3** 1127-1138. DOI: 10.1093/clinids/3.6.1127
25. Cassat JE, Skaar EP. **Iron in infection and immunity**. *Cell Host Microbe* (2013) **13** 509-519. DOI: 10.1016/j.chom.2013.04.010
26. Corbin BD, Seeley EH, Raab A, Feldmann J, Miller MR, Torres VJ, Anderson KL, Dattilo BM, Dunman PM, Gerads R, Caprioli RM, Nacken W, Chazin WJ, Skaar EP. **Metal chelation and inhibition of bacterial growth in tissue abscesses**. *Science* (2008) **319** 962-965. DOI: 10.1126/science.1152449
27. Kehl-Fie TE, Chitayat S, Hood MI, Damo S, Restrepo N, Garcia C, Munro KA, Chazin WJ, Skaar EP. **Nutrient metal sequestration by calprotectin inhibits bacterial superoxide defense, enhancing neutrophil killing of Staphylococcus aureus**. *Cell Host Microbe* (2011) **10** 158-164. DOI: 10.1016/j.chom.2011.07.004
28. Weinberg ED. **Iron availability and infection**. *Biochim Biophys Acta* (2009) **1790** 600-605. DOI: 10.1016/j.bbagen.2008.07.002
29. Waldron KJ, Rutherford JC, Ford D, Robinson NJ. **Metalloproteins and metal sensing**. *Nature* (2009) **460** 823-830. DOI: 10.1038/nature08300
30. Andreini C, Bertini I, Cavallaro G, Holliday GL, Thornton JM. **Metal ions in biological catalysis: from enzyme databases to general principles**. *J Biol Inorg Chem* (2008) **13** 1205-1218. DOI: 10.1007/s00775-008-0404-5
31. Papp-Wallace KM, Maguire ME. **Manganese transport and the role of manganese in virulence**. *Annu Rev Microbiol* (2006) **60** 187-209. DOI: 10.1146/annurev.micro.60.080805.142149
32. Bolick DT, Kolling GL, Moore JH, de Oliveira LA, Tung K, Philipson C, Viladomiu M, Hontecillas R, Bassaganya-Riera J, Guerrant RL. **Zinc deficiency alters host response and pathogen virulence in a mouse model of enteroaggregative Escherichia coli-induced diarrhea**. *Gut Microbes* (2014) **5** 618-627. DOI: 10.4161/19490976.2014.969642
33. Ogunniyi AD, Mahdi LK, Jennings MP, McEwan AG, McDevitt CA, Van der Hoek MB, Bagley CJ, Hoffmann P, Gould KA, Paton JC. **Central role of manganese in regulation of stress responses, physiology, and metabolism in Streptococcus pneumoniae**. *J Bacteriol* (2010) **192** 4489-4497. DOI: 10.1128/JB.00064-10
34. Price SL, Vadyvaloo V, DeMarco JK, Brady A, Gray PA, Kehl-Fie TE, Garneau-Tsodikova S, Perry RD, Lawrenz MB. **Yersiniabactin contributes to overcoming zinc restriction during Yersinia pestis infection of mammalian and insect hosts**. *Proc Natl Acad Sci USA* (2021) **118**
35. Zackular JP, Chazin WJ, Skaar EP. **Nutritional immunity: S100 proteins at the host-pathogen interface**. *J Biol Chem* (2015) **290** 18991-18998. DOI: 10.1074/jbc.R115.645085
36. Hood MI, Mortensen BL, Moore JL, Zhang Y, Kehl-Fie TE, Sugitani N, Chazin WJ, Caprioli RM, Skaar EP. **Identification of an Acinetobacter baumannii zinc acquisition system that facilitates resistance to calprotectin-mediated zinc sequestration**. *PLoS Pathog* (2012) **8**. DOI: 10.1371/journal.ppat.1003068
37. Achouiti A, Vogl T, Urban CF, Rohm M, Hommes TJ, van Zoelen MA, Florquin S, Roth J, van 't Veer C, de Vos AF, van der Poll T. **Myeloid-related protein-14 contributes to protective immunity in gram-negative pneumonia derived sepsis**. *PLoS Pathog* (2012) **8**. DOI: 10.1371/journal.ppat.1002987
38. Bianchi M, Niemiec MJ, Siler U, Urban CF, Reichenbach J. **Restoration of anti-Aspergillus defense by neutrophil extracellular traps in human chronic granulomatous disease after gene therapy is calprotectin-dependent**. *J Allergy Clin Immunol* (2011) **127** 1243-1252. DOI: 10.1016/j.jaci.2011.01.021
39. Clohessy PA, Golden BE. **Calprotectin-mediated zinc chelation as a biostatic mechanism in host defence**. *Scand J Immunol* (1995) **42** 551-556. DOI: 10.1111/j.1365-3083.1995.tb03695.x
40. Gebhardt C, Nemeth J, Angel P, Hess J. **S100A8 and S100A9 in inflammation and cancer**. *Biochem Pharmacol* (2006) **72** 1622-1631. DOI: 10.1016/j.bcp.2006.05.017
41. Brophy MB, Hayden JA, Nolan EM. **Calcium ion gradients modulate the zinc affinity and antibacterial activity of human calprotectin**. *J Am Chem Soc* (2012) **134** 18089-18100. DOI: 10.1021/ja307974e
42. Brophy MB, Nakashige TG, Gaillard A, Nolan EM. **Contributions of the S100A9 C-terminal tail to high-affinity Mn(II) chelation by the host-defense protein human calprotectin**. *J Am Chem Soc* (2013) **135** 17804-17817. DOI: 10.1021/ja407147d
43. Damo SM, Kehl-Fie TE, Sugitani N, Holt ME, Rathi S, Murphy WJ, Zhang Y, Betz C, Hench L, Fritz G, Skaar EP, Chazin WJ. **Molecular basis for manganese sequestration by calprotectin and roles in the innate immune response to invading bacterial pathogens**. *Proc Natl Acad Sci USA* (2013) **110** 3841-3846. DOI: 10.1073/pnas.1220341110
44. Hayden JA, Brophy MB, Cunden LS, Nolan EM. **High-affinity manganese coordination by human calprotectin is calcium-dependent and requires the histidine-rich site formed at the dimer interface**. *J Am Chem Soc* (2013) **135** 775-787. DOI: 10.1021/ja3096416
45. Korndorfer IP, Brueckner F, Skerra A. **The crystal structure of the human (S100A8/S100A9)2 heterotetramer, calprotectin, illustrates how conformational changes of interacting alpha-helices can determine specific association of two EF-hand proteins**. *J Mol Biol* (2007) **370** 887-898. DOI: 10.1016/j.jmb.2007.04.065
46. Chandrangsu P, Rensing C, Helmann JD. **Metal homeostasis and resistance in bacteria**. *Nat Rev Microbiol* (2017) **15** 338-350. DOI: 10.1038/nrmicro.2017.15
47. Horsburgh MJ, Wharton SJ, Cox AG, Ingham E, Peacock S, Foster SJ. **MntR modulates expression of the PerR regulon and superoxide resistance in Staphylococcus aureus through control of manganese uptake**. *Mol Microbiol* (2002) **44** 1269-1286. DOI: 10.1046/j.1365-2958.2002.02944.x
48. Garcia YM, Barwinska-Sendra A, Tarrant E, Skaar EP, Waldron KJ, Kehl-Fie TE. **A superoxide dismutase capable of functioning with iron or manganese promotes the resistance of Staphylococcus aureus to calprotectin and nutritional immunity**. *PLoS Pathog* (2017) **13**. DOI: 10.1371/journal.ppat.1006125
49. Smaldone GT, Revelles O, Gaballa A, Sauer U, Antelmann H, Helmann JD. **A global investigation of the Bacillus subtilis iron-sparing response identifies major changes in metabolism**. *J Bacteriol* (2012) **194** 2594-2605. DOI: 10.1128/JB.05990-11
50. Masse E, Gottesman S. **A small RNA regulates the expression of genes involved in iron metabolism in Escherichia coli**. *Proc Natl Acad Sci USA* (2002) **99** 4620-4625. DOI: 10.1073/pnas.032066599
51. Gaballa A, Antelmann H, Aguilar C, Khakh SK, Song KB, Smaldone GT, Helmann JD. **The Bacillus subtilis iron-sparing response is mediated by a Fur-regulated small RNA and three small, basic proteins**. *Proc Natl Acad Sci USA* (2008) **105** 11927-11932. DOI: 10.1073/pnas.0711752105
52. Merchant SS, Helmann JD. **Elemental economy: microbial strategies for optimizing growth in the face of nutrient limitation**. *Adv Microb Physiol* (2012) **60** 91-210. DOI: 10.1016/B978-0-12-398264-3.00002-4
53. Yost FJ, Fridovich I. **An iron-containing superoxide dismutase from Escherichia coli**. *J Biol Chem* (1973) **248** 4905-4908. DOI: 10.1016/S0021-9258(19)43649-1
54. Sankaran B, Bonnett SA, Shah K, Gabriel S, Reddy R, Schimmel P, Rodionov DA, de Crecy-Lagard V, Helmann JD, Iwata-Reuyl D, Swairjo MA. **Zinc-independent folate biosynthesis: genetic, biochemical, and structural investigations reveal new metal dependence for GTP cyclohydrolase IB**. *J Bacteriol* (2009) **191** 6936-6949. DOI: 10.1128/JB.00287-09
55. Natori Y, Nanamiya H, Akanuma G, Kosono S, Kudo T, Ochi K, Kawamura F. **A fail-safe system for the ribosome under zinc-limiting conditions in Bacillus subtilis**. *Mol Microbiol* (2007) **63** 294-307. DOI: 10.1111/j.1365-2958.2006.05513.x
56. Richardson AR. **Virulence and metabolism**. *Microbiol Spectr* (2019) **7**. DOI: 10.1128/microbiolspec.GPP3-0011-2018
57. Lindsay JA, Foster SJ. **zur: a Zn(2+)-responsive regulatory element of Staphylococcus aureus**. *Microbiology (Reading)* (2001) **147** 1259-1266. DOI: 10.1099/00221287-147-5-1259
58. Horsburgh MJ, Ingham E, Foster SJ. **In Staphylococcus aureus, fur is an interactive regulator with PerR, contributes to virulence, and is necessary for oxidative stress resistance through positive regulation of catalase and iron homeostasis**. *J Bacteriol* (2001) **183** 468-475. DOI: 10.1128/JB.183.2.468-475.2001
59. Fournier B, Hooper DC. **A new two-component regulatory system involved in adhesion, autolysis, and extracellular proteolytic activity of Staphylococcus aureus**. *J Bacteriol* (2000) **182** 3955-3964. DOI: 10.1128/JB.182.14.3955-3964.2000
60. Liang X, Zheng L, Landwehr C, Lunsford D, Holmes D, Ji Y. **Global regulation of gene expression by ArlRS, a two-component signal transduction regulatory system of Staphylococcus aureus**. *J Bacteriol* (2005) **187** 5486-5492. DOI: 10.1128/JB.187.15.5486-5492.2005
61. Walker JN, Crosby HA, Spaulding AR, Salgado-Pabon W, Malone CL, Rosenthal CB, Schlievert PM, Boyd JM, Horswill AR. **The Staphylococcus aureus ArlRS two-component system is a novel regulator of agglutination and pathogenesis**. *PLoS Pathog* (2013) **9**. DOI: 10.1371/journal.ppat.1003819
62. Harper L, Balasubramanian D, Ohneck EA, Sause WE, Chapman J, Mejia-Sosa B, Lhakhang T, Heguy A, Tsirigos A, Ueberheide B, Boyd JM, Lun DS, Torres VJ. **Staphylococcus aureus responds to the central metabolite pyruvate to regulate virulence**. *mBio* (2018) **9**. DOI: 10.1128/mBio.02272-17
63. Crosby HA, Kwiecinski J, Horswill AR. **Staphylococcus aureus aggregation and coagulation mechanisms, and their function in host-pathogen interactions**. *Adv Appl Microbiol* (2016) **96** 1-41. DOI: 10.1016/bs.aambs.2016.07.018
64. Crosby HA, Tiwari N, Kwiecinski JM, Xu Z, Dykstra A, Jenul C, Fuentes EJ, Horswill AR. **The Staphylococcus aureus ArlRS two-component system regulates virulence factor expression through MgrA**. *Mol Microbiol* (2020) **113** 103-122. DOI: 10.1111/mmi.14404
65. Kwiecinski JM, Crosby HA, Valotteau C, Hippensteel JA, Nayak MK, Chauhan AK, Schmidt EP, Dufrene YF, Horswill AR. **Staphylococcus aureus adhesion in endovascular infections is controlled by the ArlRS-MgrA signaling cascade**. *PLoS Pathog* (2019) **15**. DOI: 10.1371/journal.ppat.1007800
66. Benton BM, Zhang JP, Bond S, Pope C, Christian T, Lee L, Winterberg KM, Schmid MB, Buysse JM. **Large-scale identification of genes required for full virulence of Staphylococcus aureus**. *J Bacteriol* (2004) **186** 8478-8489. DOI: 10.1128/JB.186.24.8478-8489.2004
67. Burgui S, Gil C, Solano C, Lasa I, Valle J. **A systematic evaluation of the two-component systems network reveals that ArlRS is a key regulator of catheter colonization by Staphylococcus aureus**. *Front Microbiol* (2018) **9** 342. DOI: 10.3389/fmicb.2018.00342
68. Parraga Solorzano PK, Shupe AC, Kehl-Fie TE. **The sensor histidine kinase ArlS is necessary for Staphylococcus aureus to activate ArlR in response to nutrient availability**. *J Bacteriol* (2021) **203**. DOI: 10.1128/JB.00422-21
69. Crosby HA, Schlievert PM, Merriman JA, King JM, Salgado-Pabon W, Horswill AR. **The Staphylococcus aureus global regulator MgrA modulates clumping and virulence by controlling surface protein expression**. *PLoS Pathog* (2016) **12**. DOI: 10.1371/journal.ppat.1005604
70. Stribling D, Perham RN. **Purification and characterization of two fructose diphosphate aldolases from Escherichia coli (Crookes' strain)**. *Biochem J* (1973) **131** 833-841. DOI: 10.1042/bj1310833
71. Capodagli GC, Lee SA, Boehm KJ, Brady KM, Pegan SD. **Structural and functional characterization of methicillin-resistant Staphylococcus aureus's class IIb fructose 1,6-bisphosphate aldolase**. *Biochemistry* (2014) **53** 7604-7614. DOI: 10.1021/bi501141t
72. Radin JN, Kelliher JL, Solorzano PKP, Grim KP, Ramezanifard R, Slauch JM, Kehl-Fie TE. **Metal-independent variants of phosphoglycerate mutase promote resistance to nutritional immunity and retention of glycolysis during infection**. *PLoS Pathog* (2019) **15**. DOI: 10.1371/journal.ppat.1007971
73. Nakashige TG, Zhang B, Krebs C, Nolan EM. **Human calprotectin is an iron-sequestering host-defense protein**. *Nat Chem Biol* (2015) **11** 765-771. DOI: 10.1038/nchembio.1891
74. Nakashige TG, Zygiel EM, Drennan CL, Nolan EM. **Nickel sequestration by the host-defense protein human calprotectin**. *J Am Chem Soc* (2017) **139** 8828-8836. DOI: 10.1021/jacs.7b01212
75. Besold AN, Gilston BA, Radin JN, Ramsoomair C, Culbertson EM, Li CX, Cormack BP, Chazin WJ, Kehl-Fie TE, Culotta VC. **Role of calprotectin in withholding zinc and copper from Candida albicans**. *Infect Immun* (2018) **86**. DOI: 10.1128/IAI.00779-17
76. Clark HL, Jhingran A, Sun Y, Vareechon C, de Jesus Carrion S, Skaar EP, Chazin WJ, Calera JA, Hohl TM, Pearlman E. **Zinc and manganese chelation by neutrophil S100A8/A9 (calprotectin) limits extracellular Aspergillus fumigatus hyphal growth and corneal infection**. *J Immunol* (2016) **196** 336-344. DOI: 10.4049/jimmunol.1502037
77. Gagnon DM, Brophy MB, Bowman SE, Stich TA, Drennan CL, Britt RD, Nolan EM. **Manganese binding properties of human calprotectin under conditions of high and low calcium: X-ray crystallographic and advanced electron paramagnetic resonance spectroscopic analysis**. *J Am Chem Soc* (2015) **137** 3004-3016. DOI: 10.1021/ja512204s
78. Porcheron G, Garenaux A, Proulx J, Sabri M, Dozois CM. **Iron, copper, zinc, and manganese transport and regulation in pathogenic Enterobacteria: correlations between strains, site of infection and the relative importance of the different metal transport systems for virulence**. *Front Cell Infect Microbiol* (2013) **3** 90. DOI: 10.3389/fcimb.2013.00090
79. Palmer LD, Skaar EP. **Transition metals and virulence in bacteria**. *Annu Rev Genet* (2016) **50** 67-91. DOI: 10.1146/annurev-genet-120215-035146
80. Neumann W, Hadley RC, Nolan EM. **Transition metals at the host-pathogen interface: how Neisseria exploit human metalloproteins for acquiring iron and zinc**. *Essays Biochem* (2017) **61** 211-223. DOI: 10.1042/EBC20160084
81. Becker KW, Skaar EP. **Metal limitation and toxicity at the interface between host and pathogen**. *FEMS Microbiol Rev* (2014) **38** 1235-1249. DOI: 10.1111/1574-6976.12087
82. Berry A, Marshall KE. **Identification of zinc-binding ligands in the class II fructose-1,6-bisphosphate aldolase of Escherichia coli**. *FEBS Lett* (1993) **318** 11-16. DOI: 10.1016/0014-5793(93)81317-s
83. Blom NS, Tetreault S, Coulombe R, Sygusch J. **Novel active site in Escherichia coli fructose 1,6-bisphosphate aldolase**. *Nat Struct Biol* (1996) **3** 856-862. DOI: 10.1038/nsb1096-856
84. Hall DR, Leonard GA, Reed CD, Watt CI, Berry A, Hunter WN. **The crystal structure of Escherichia coli class II fructose-1, 6-bisphosphate aldolase in complex with phosphoglycolohydroxamate reveals details of mechanism and specificity**. *J Mol Biol* (1999) **287** 383-394. DOI: 10.1006/jmbi.1999.2609
85. Cooper SJ, Leonard GA, McSweeney SM, Thompson AW, Naismith JH, Qamar S, Plater A, Berry A, Hunter WN. **The crystal structure of a class II fructose-1,6-bisphosphate aldolase shows a novel binuclear metal-binding active site embedded in a familiar fold**. *Structure* (1996) **4** 1303-1315. DOI: 10.1016/S0969-2126(96)00138-4
86. Kobes RD, Simpson RT, Vallee RL, Rutter WJ. **A functional role of metal ions in a class II aldolase**. *Biochemistry* (1969) **8** 585-588. DOI: 10.1021/bi00830a018
87. Harris CE, Kobes RD, Teller DC, Rutter WJ. **The molecular characteristics of yeast aldolase**. *Biochemistry* (1969) **8** 2442-2454. DOI: 10.1021/bi00834a029
88. Zhang YM, Liu JK, Shouri MR, Wong TY. **Characterization of a Mn-dependent fructose-1,6-bisphosphate aldolase in Deinococcus radiodurans**. *Biometals* (2006) **19** 31-37. DOI: 10.1007/s10534-005-4320-7
89. Stolzenberger J, Lindner SN, Wendisch VF. **The methylotrophic Bacillus methanolicus MGA3 possesses two distinct fructose 1,6-bisphosphate aldolases**. *Microbiology (Reading)* (2013) **159** 1770-1781. DOI: 10.1099/mic.0.067314-0
90. Pirovich DB, Da'dara AA, Skelly PJ. **Multifunctional fructose 1,6-bisphosphate aldolase as a therapeutic target**. *Front Mol Biosci* (2021) **8** 719678. DOI: 10.3389/fmolb.2021.719678
91. Ziveri J, Tros F, Guerrera IC, Chhuon C, Audry M, Dupuis M, Barel M, Korniotis S, Fillatreau S, Gales L, Cahoreau E, Charbit A. **The metabolic enzyme fructose-1,6-bisphosphate aldolase acts as a transcriptional regulator in pathogenic Francisella**. *Nat Commun* (2017) **8** 853. DOI: 10.1038/s41467-017-00889-7
92. Yu Y, Liu M, Hua L, Qiu M, Zhang W, Wei Y, Gan Y, Feng Z, Shao G, Xiong Q. **Fructose-1,6-bisphosphate aldolase encoded by a core gene of Mycoplasma hyopneumoniae contributes to host cell adhesion**. *Vet Res* (2018) **49** 114. DOI: 10.1186/s13567-018-0610-2
93. Tunio SA, Oldfield NJ, Berry A, Ala'Aldeen DA, Wooldridge KG, Turner DP. **The moonlighting protein fructose-1, 6-bisphosphate aldolase of Neisseria meningitidis: surface localization and role in host cell adhesion**. *Mol Microbiol* (2010) **76** 605-615. DOI: 10.1111/j.1365-2958.2010.07098.x
94. Allen AE, Moustafa A, Montsant A, Eckert A, Kroth PG, Bowler C. **Evolution and functional diversification of fructose bisphosphate aldolase genes in photosynthetic marine diatoms**. *Mol Biol Evol* (2012) **29** 367-379. DOI: 10.1093/molbev/msr223
95. Shams F, Oldfield NJ, Wooldridge KG, Turner DP. **Fructose-1,6-bisphosphate aldolase (FBA)-a conserved glycolytic enzyme with virulence functions in bacteria: 'ill met by moonlight'**. *Biochem Soc Trans* (2014) **42** 1792-1795. DOI: 10.1042/BST20140203
96. Bai NJ, Pai MR, Murthy PS, Venkitasubramanian TA. **Effect of oxygen tension on the aldolases of Mycobacterium tuberculosis H37Rv**. *FEBS Lett* (1974) **45** 68-70. DOI: 10.1016/0014-5793(74)80812-4
97. Szwergold BS, Ugurbil K, Brown TR. **Properties of fructose-1,6-bisphosphate aldolase from Escherichia coli: an NMR analysis**. *Arch Biochem Biophys* (1995) **317** 244-252. DOI: 10.1006/abbi.1995.1159
98. Schultenkamper K, Gutle DD, Lopez MG, Keller LB, Zhang L, Einsle O, Jacquot JP, Wendisch VF. **Interrogating the role of the two distinct fructose-bisphosphate aldolases of Bacillus methanolicus by site-directed mutagenesis of key amino acids and gene repression by CRISPR interference**. *Front Microbiol* (2021) **12** 669220. DOI: 10.3389/fmicb.2021.669220
99. Hecker M, Mader U, Volker U. **From the genome sequence via the proteome to cell physiology - pathoproteomics and pathophysiology of Staphylococcus aureus**. *Int J Med Microbiol* (2018) **308** 545-557. DOI: 10.1016/j.ijmm.2018.01.002
100. Tomlinson KL, Lung TWF, Dach F, Annavajhala MK, Gabryszewski SJ, Groves RA, Drikic M, Francoeur NJ, Sridhar SH, Smith ML, Khanal S, Britto CJ, Sebra R, Lewis I, Uhlemann AC, Kahl BC, Prince AS, Riquelme SA. **Staphylococcus aureus induces an itaconate-dominated immunometabolic response that drives biofilm formation**. *Nat Commun* (2021) **12** 1399. DOI: 10.1038/s41467-021-21718-y
101. Collins JA, Irnov I, Baker S, Winkler WC. **Mechanism of mRNA destabilization by the glmS ribozyme**. *Genes Dev* (2007) **21** 3356-3368. DOI: 10.1101/gad.1605307
102. Grossoehme N, Kehl-Fie TE, Ma Z, Adams KW, Cowart DM, Scott RA, Skaar EP, Giedroc DP. **Control of copper resistance and inorganic sulfur metabolism by paralogous regulators in Staphylococcus aureus**. *J Biol Chem* (2011) **286** 13522-13531. DOI: 10.1074/jbc.M111.220012
103. Kehl-Fie TE, Porsch EA, Miller SE, St Geme JW. **Expression of Kingella kingae type IV pili is regulated by sigma54, PilS, and PilR**. *J Bacteriol* (2009) **191** 4976-4986. DOI: 10.1128/JB.00123-09
104. Grim KP, San Francisco B, Radin JN, Brazel EB, Kelliher JL, Parraga Solorzano PK, Kim PC, McDevitt CA, Kehl-Fie TE. **The metallophore staphylopine enables Staphylococcus aureus to compete with the host for zinc and overcome nutritional immunity**. *mBio* (2017) **8**. DOI: 10.1128/mBio.01281-17
105. Duthie ES, Lorenz LL. **Staphylococcal coagulase; mode of action and antigenicity**. *J Gen Microbiol* (1952) **6** 95-107. DOI: 10.1099/00221287-6-1-2-95
106. Krute CN, Kraus KL, Markiewicz MA, Joyner JA, Pokhrel S, Hall PR, Bose JL. **Generation of a stable plasmid for in vitro and in vivo studies of Staphylococcus species**. *Appl Environ Microbiol* (2016) **82** 6859-6869. DOI: 10.1128/AEM.02370-16
|
---
title: The IRE1α-XBP1 Signaling Axis Promotes Glycolytic Reprogramming in Response
to Inflammatory Stimuli
authors:
- Bevin C. English
- Hannah P. Savage
- Scott P. Mahan
- Vladimir E. Diaz-Ochoa
- Briana M. Young
- Basel H. Abuaita
- Gautam Sule
- Jason S. Knight
- Mary X. O’Riordan
- Andreas J. Bäumler
- Renée M. Tsolis
journal: mBio
year: 2022
pmcid: PMC9973330
doi: 10.1128/mbio.03068-22
license: CC BY 4.0
---
# The IRE1α-XBP1 Signaling Axis Promotes Glycolytic Reprogramming in Response to Inflammatory Stimuli
## ABSTRACT
Immune cells must be able to adjust their metabolic programs to effectively carry out their effector functions. Here, we show that the endoplasmic reticulum (ER) stress sensor Inositol-requiring enzyme 1 alpha (IRE1α) and its downstream transcription factor X box binding protein 1 (XBP1) enhance the upregulation of glycolysis in classically activated macrophages (CAMs). The IRE1α-XBP1 signaling axis supports this glycolytic switch in macrophages when activated by lipopolysaccharide (LPS) stimulation or infection with the intracellular bacterial pathogen Brucella abortus. Importantly, these different inflammatory stimuli have distinct mechanisms of IRE1α activation; while Toll-like receptor 4 (TLR4) supports glycolysis under both conditions, TLR4 is required for activation of IRE1α in response to LPS treatment but not B. abortus infection. Though IRE1α and XBP1 are necessary for maximal induction of glycolysis in CAMs, activation of this pathway is not sufficient to increase the glycolytic rate of macrophages, indicating that the cellular context in which this pathway is activated ultimately dictates the cell’s metabolic response and that IRE1α activation may be a way to fine-tune metabolic reprogramming.
## INTRODUCTION
It is becoming increasingly evident that the metabolism of immune cells is closely tied to their effector functions; thus, immune cells must be able to alter their metabolic programs in response to different stimuli. Macrophages have different activation states and associated metabolic programs that enable them to carry out different physiological roles. While there are likely many different activation profiles in vivo, one activation state that has been studied extensively are classically activated macrophages (CAMs). These CAMs, sometimes referred to as M1 macrophages, have a glycolysis-driven metabolism, allowing for the rapid production of ATP and antimicrobial products, such as reactive oxygen and nitrogen species [1]. A variety of stimuli can induce CAMs, including certain cytokines and bacterial pathogens or products, such as lipopolysaccharide (LPS) [1] and the intracellular pathogen *Brucella abortus* (2–4).
The endoplasmic reticulum (ER) is an organelle that plays a key role in maintaining cellular homeostasis. When ER function is perturbed, the cell experiences ER stress and initiates the unfolded protein response (UPR), a collection of linked signaling cascades, to overcome the initiating stress and return to homeostasis. The most evolutionarily conserved branch is that of the ER stress sensor IRE1α. Upon activation, IRE1α oligomerizes and transautophosphorylates, activating its RNase activity [5]. One key function of activated IRE1α is the excision of a noncanonical intron from the unspliced XBP1 transcript (XBP1u), resulting in the spliced XBP1 transcript (XBP1s), which encodes a transcription factor that regulates a wide range of genes involved in a variety of cellular processes [6, 7].
UPR signaling is closely linked to the immune system. IRE1α signaling leads to activation of JNK [8], NF-κB [9, 10], and NOD1 and NOD2 [11, 12], while XBP1 directly regulates the expression of proinflammatory cytokines [13, 14]. The UPR is activated in many different immune cells after stimulation, including T cells [15], natural killer (NK) cells [16], and macrophages (17–19). Many intracellular pathogens induce ER stress in their host cells [20, 21], including Brucella spp., which use their type IV secretion systems (T4SS) to interact extensively with the ER [22], ultimately leading to UPR activation (23–27). IRE1α has been shown to be phosphorylated upon Brucella infection [23, 28] and to form puncta throughout infected cells [23]. Though it is well established that IRE1α plays an important role in the development and effector functions of immune cells, the links between IRE1α activation and innate immunity remain poorly understood. Thus, we set out to determine how IRE1α influences the activation of macrophages in response to inflammatory stimuli.
## IRE1α supports lactate production and CAM gene expression during B. abortus infection or LPS stimulation in macrophages.
During in vitro infection with B. abortus, macrophages shift their metabolism to be more glycolysis-driven (2–4). Consistent with this, we observed that RAW 264.7 macrophage-like cells acidify the culture media during infection, as indicated by the yellowing of the pH indicator phenol red in the media. However, we noticed that the media on IRE1α knockout (KO) RAW 264.7 cells [29] was not yellowing to the same extent as the media on wild-type (WT) cells during infection; thus, we hypothesized that the IRE1α-deficient cells were producing less lactate. Indeed, the IRE1α KO RAW 264.7 cells produce less lactate after B. abortus infection compared to WT cells (Fig. 1A). We also assessed the expression of two genes involved in glycolysis, Glut1, which encodes a glucose importer, and Pfkfb3, which encodes a glycolytic enzyme, as well as Irg1 (also called Acod1), which is a marker of CAMs [30]. Similar to what we observed with lactate levels, the IRE1α KO RAW cells show an impaired induction of these genes (Fig. 1B), suggesting that IRE1α supports the Brucella-induced glycolytic switch in macrophages.
**FIG 1:** *IRE1α supports lactate production and glycolytic gene expression during B. abortus infection or LPS stimulation in macrophages. Wild-type (WT) and IRE1α knockout (KO) RAW 264.7 cells were infected with B. abortus (Ba) for 48 h (A and B) or stimulated with 100 ng/mL Salmonella LPS for 24 h (C and D). Supernatant lactate was quantified (A and C), and the relative expression of the indicated genes normalized to uninfected controls was assessed by reverse transcription-quantitative PCR (RT-qPCR) (B and D). (E to H) Same as panels A to D, but with WT (LysM-Cre−
Ern1fl/fL) or IRE1α KO (LysM-Cre+
Ern1fl/fL) BMDMs. The data are presented as means of triplicate wells ± the standard deviations (SD). *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001; ns, no statistical difference (Student two-tailed t test).*
It has been reported previously that IRE1α contributes to the intracellular replication of Brucella (23, 25, 26, 31–33), and we also observed that IRE1α-deficient macrophages had a slight reduction in bacterial burden during infection (see Fig. S1A and B in the supplemental material). Thus, to ensure that the reduced glycolytic shift in IRE1α KO macrophages was not secondary to reduced bacterial burden, we tested an additional stimulus. LPS is commonly used to polarize CAMs and activates IRE1α through Toll-like receptor 4 (TLR4) signaling [13, 34]. When treated with LPS from *Salmonella enterica* serotype Typhimurium, a potent TLR4 agonist, IRE1α KO RAW 264.7 cells had a reduced glycolytic response (Fig. 1C and D), consistent with what we observed with B. abortus infection.
Because RAW 264.7 cells are a murine cancer-derived cell line, we wanted to confirm our findings in primary cells. To this end, we tested bone marrow-derived macrophages (BMDMs) from IRE1α conditional knockout animals (LysM-Cre+/− Ern1afl/fL) and their WT littermate controls (LysM-Cre−/− Ern1afl/fL [35]). Consistent with our observations with RAW 264.7 cells, the IRE1α-deficient BMDMs also showed reduced lactate production and glycolytic gene expression after B. abortus infection or LPS treatment (Fig. 1E to H). Together, these data demonstrate that IRE1α supports macrophage glycolytic reprogramming in response to inflammatory stimuli.
## XBP1 supports lactate production and CAM gene expression during B. abortus infection or LPS stimulation in macrophages.
We then wanted to determine how IRE1α was promoting glycolysis in CAMs. IRE1α is both a kinase and RNase, and we wondered which of these enzymatic functions was influencing macrophage metabolism. Treatment of macrophages with 4μ8c, which inhibits the RNase activity of IRE1α without affecting its kinase activity [36], led to reduced expression of glycolytic genes after B. abortus infection or LPS stimulation (see Fig. S2). There are two major outcomes of IRE1α endonuclease activity: splicing of the unspliced XBP1 mRNA (XBP1u), forming the spliced XBP1 transcript (XBP1s), which encodes a transcription factor, and regulated IRE1α-dependent decay (RIDD), a process where specific RNA species are degraded [21]. Because XBP1 regulates different metabolic states in a variety of cells [15, 16, 18, 19, 37], we chose to focus on XBP1. We used CRISPR/Cas9 to generate XBP1 KO RAW 264.7 cells (see Fig. S3). These cells had reduced expression of Il6, a direct XBP1s target [13], after Brucella infection or LPS stimulation, further demonstrating that this pathway is activated under these inflammatory conditions (Fig. 2B and D). Like IRE1α KO macrophages, these XBP1 KO macrophages also had a reduced glycolytic response to B. abortus infection (Fig. 2A and B) or LPS stimulation (Fig. 2C and D). Thus, the IRE1α-XBP1 signaling axis promotes the glycolytic switch in macrophages in response to inflammatory stimuli.
**FIG 2:** *XBP1 promotes lactate production and the expression of glycolytic and inflammatory genes during B. abortus infection or LPS treatment. WT and XBP1 KO RAW 264.7 cells were infected with B. abortus (Ba) for 48 h (A and B) or treated with 100 ng/mL Salmonella LPS for 24 h (C and D). Supernatant lactate was quantified (A and C) and relative expression of the indicated genes was assessed by RT-qPCR (B and D). Expression levels of Glut1, Pfkfb3, and Irg1 were normalized to uninfected controls. Because it is not detected in unstimulated cells, IL-6 expression was normalized to the infected or LPS-stimulated WT cells. The data are presented as means of triplicate wells ± the SD. *, P ≤ 0.05; **, P ≤ 0.01; ns, no statistical difference (Student two-tailed t test).*
## Glucose import of infected macrophages correlates with bacterial burden and is reduced in IRE1α or XBP1 KO macrophages.
While infection leads to increased lactate and expression of glycolytic genes (Fig. 1 and 2), the magnitude of this increase was small in some cases, leading us to hypothesize that uninfected cells in our bulk assays, such as reverse transcription-quantitative PCR (RT-qPCR) and lactate measurements, may be masking the specific effect of B. abortus on the metabolic state of infected cells. To look at glycolysis on a single-cell level, we used 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl) amino]-2-deoxy-d-glucose (2-NBDG), an unmetabolizable fluorescent glucose analog that accumulates inside cells proportionately to their glucose import rate and thus can be used to assess their glycolytic rate [38]. To assess the bacterial burden of individual cells, we used a WT B. abortus strain that expresses mCherry [4]. We observed that the glucose import rate of cells correlated with bacterial burden (Fig. 3A and B), suggesting that increased glycolysis is a cell-intrinsic effect of infection. Indeed, the glucose import of mock-infected cells was equivalent to that of uninfected bystander cells (Fig. 3B). These data suggest that B. abortus acts directly on infected cells to promote glycolysis and that increased glycolysis is not simply a result of paracrine signaling from infected cells.
**FIG 3:** *Glucose import of infected macrophages correlates with bacterial burden and is reduced in IRE1α or XBP1 KO macrophages. (A and B) WT RAW 264.7 cells were mock infected or infected with mCherry (mChe)-expressing B. abortus for 48 h and then stained with fluorescent glucose analog 2-NBDG. Cells were then gated based on mCherry signal. (A) Representative fluorescence-activated cell sorting plots showing mock or infected RAW 264.7 cells, gated on all live cells. RAW 264.7 cells infected with a wild-type non-mCherry-expressing strain is shown as an mCherry-negative control. (B) The MFI of 2-NBDG was calculated within the indicated populations. (C and D) RAW 264.7 cells of the indicated genotypes were mock infected or infected with mCherry-expressing B. abortus for 48 h before 2-NBDG staining. (Left) 2-NBDG MFIs of mock-infected cells. (Right) 2-NBDG MFI for the mCherry-high populations after binning based on mCherry signal. Dots represent individual wells, columns are means, and error bars are the SD. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001; ns, no statistical difference (as determined by a Student two-tailed t test, except for the left side of panel B) which was analyzed using one-way analysis of variance [ANOVA] with Tukey’s post hoc test.*
We hypothesized that IRE1α-XBP1 signaling contributed to glucose import during B. abortus infection, as this signaling pathway was necessary for maximal expression of the glucose importer Glut1 (Fig. 1 and 2). Consistent with our previous data, mock-infected IRE1α and XBP1 KO macrophages had comparable glucose import compared to wild-type cells (Fig. 3C and D). When assessing the glucose import of highly infected cells, we wanted to ensure we were comparing cells with comparable bacterial burdens. Because IRE1α supports the replication of B. abortus (23, 25, 26, 31–33) (see Fig. S1), we were concerned that any observed reduction in glucose import by the IRE1α knockout macrophages could be due to reduced bacterial burden. To overcome this limitation, we binned the data across the range of mCherry signal, resulting in comparable mean fluorescence intensities (MFIs) and thus comparable bacterial burdens within each bin. We then compared the 2-NBDG signal within each bin. Across the bins, the glucose import rate of WT macrophages was higher than that of the IRE1α and the XBP1 KO macrophages (Fig. 3C and D). Together, these data provide more evidence that IRE1α-XBP1 signaling promotes glycolysis during B. abortus infection.
## IRE1α and XBP1 are required for maximal glycolytic flux after LPS stimulation.
Though we demonstrated that IRE1α and XBP1 contribute to lactate accumulation, glycolytic gene expression, and glucose import after macrophage stimulation, these are indirect measurements of glycolysis, and we wanted to directly measure glycolytic flux of stimulated macrophages in real time. To this end, we assessed the extracellular acidification rate (ECAR) of our IRE1α- and XBP1-deficient macrophages after LPS stimulation. However, factors other than glycolytic rate, such as mitochondrial production of CO2, can contribute to ECAR; thus, we also assessed the proton efflux rate from glycolysis (glycoPER) specifically. Consistent with our previous data, the KO macrophages showed reduced ECAR and glycoPER after LPS stimulation (Fig. 4), further demonstrating that the IRE1α-XBP1 signaling axis promotes glycolytic flux.
**FIG 4:** *IRE1α and XBP1 support glycolysis after LPS stimulation. (A to C) WT or IRE1α KO RAW 264.7 cells (A), WT (LysM-Cre−
Ern1fl/fL) or IRE1α KO (LysM-Cre+
Ern1fl/fL) BMDMs (B), and WT or XBP1 KO RAW 264.7 cells (C) were stimulated with 100 ng/mL Salmonella LPS for 6 h before the assessing extracellular acidification rate (ECAR), with rotenone/antimycin A (Rot/AA) and 2-doxyglucose (2-DG) treatments, as indicated (left). The proton efflux rate from glycolysis (glycoPER) was calculated as a more specific assessment of glycolytic flux (right).*
## TLR4 supports glycolysis in macrophages but is not required for IRE1α activation during B. abortus infection.
We then wondered if both Salmonella LPS and B. abortus were activating the IRE1α-XBP1 signaling pathway in the same manner. LPS activates IRE1α via TLR4 [13, 34], and Salmonella LPS is a strong TLR4 agonist. Though Brucella spp. have a modified LPS that is a weak TLR4 agonist [39, 40] and encode an effector that downregulates TLR4 signaling during infection [41, 42], TLR4 has been shown to play a role in the response to Brucella infection [43, 44]. Thus, we generated BMDMs from WT and TLR4 KO mice. As expected, TLR4 KO BMDMs show a severely attenuated glycolytic response to LPS stimulation (Fig. 5C and D). After B. abortus infection, TLR4 KO BMDMs also show a decreased upregulation of glycolysis (Fig. 5A and B), which is intriguing since B. abortus reduces activation of TLR4 by its LPS during infection [45, 46].
**FIG 5:** *TLR4 supports glycolysis but not activation of IRE1α during B. abortus infection. BMDMs from WT or TLR4 KO mice were infected with B. abortus (Ba) for 48 h, treated with 100 ng/mL Salmonella LPS for 24 h, or treated with 250 nM thapsigargin (Tg) for 24 h. (A and C) Supernatant lactate was quantified. (B and D) Relative expression of the indicated genes normalized to uninfected controls was assessed by RT-qPCR. (E) XBP1 splicing was assessed via nonquantitative RT-PCR. The densitometry of the XBP1s band relative to uninfected samples for each genotype is reported below. (F) IRE1α protein levels were assessed by Western blotting. Densitometry of the IRE1α band normalized to the GAPDH band and relative to uninfected for each genotype is reported below. Lactate and expression data are presented as means of triplicate wells ± the SD. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001 (Student two-tailed t test).*
While these data demonstrate that TLR4 supports the induction of glycolysis in classically activated macrophages, we next wanted to determine whether TLR4 was activating the IRE1α-XBP1 signaling axis or acting in a parallel pathway. We examined IRE1α activation by assessing XBP1 splicing and IRE1α levels, since IRE1α activation leads to IRE1α upregulation in a positive-feedback loop [47]. As expected, LPS treatment of TLR4 KO macrophages failed to induce significant XBP1 splicing or IRE1α upregulation. However, TLR4 KO macrophages showed robust XBP1 splicing and modest IRE1α upregulation during B. abortus infection (Fig. 5E and F), demonstrating that TLR4 is not required for IRE1α activation during B. abortus infection.
## Maximal glucose import by macrophages is dependent on the type IV secretion system during B. abortus infection.
Because TLR4 is not required for the IRE1α-mediated induction of glycolysis during B. abortus infection, we then interrogated how B. abortus was promoting glycolysis in macrophages. B. abortus uses its type IV secretion system (T4SS) to interact extensively with the host cell ER, resulting in robust intracellular replication and the induction of ER stress and subsequent IRE1α activation [22]. Thus, we investigated the role of the T4SS in Brucella-induced glycolytic induction in macrophages. We expressed mCherry in our virB2 mutant bacteria, which lack the T4SS, and in the complemented strain [48]. Because the T4SS is required for intracellular replication, we increased the multiplicity of infection (MOI) used for the T4SS mutant, since we have previously observed that this increased MOI results in macrophages containing a high burden of the T4SS mutant [49].
Even though all strains express the same level of fluorescence (see Fig. S4A), the mCherry signal of cells infected with the T4SS mutant at a high MOI did not match that of macrophages infected with the complemented strain (see Fig. S4B), which was equivalent to that of wild type (see Fig. S4C). For the mCherry-high population of RAW cells, the mCherry MFI was significantly higher for the macrophages infected with the complemented strain (see Fig. S4B). This suggests that increasing the MOI cannot fully compensate for the intracellular replication defect of the T4SS mutant. Thus, 2-NBDG uptake by all cells highly infected with either the mutant or complemented strain could not be compared, since 2-NBDG uptake is correlated with the level of infection (Fig. 3B). To overcome this difference, we compared cells infected with either the mutant or the complemented strain in two ways. First, we compared glucose uptake among mCherry-low cells. For the mCherry-low cells, the mCherry MFI was significantly higher for the cells infected with the virB2 mutant than those infected with the complemented strain. However, despite this disparity in bacterial burdens, the macrophages showed equivalent glucose uptake, suggesting that the T4SS promotes the glycolytic switch in infected macrophages (Fig. 6A). Next, for the more highly infected cells, we again binned the data across the range of mCherry signal. Consistent with our previous observation, as bacterial burden increased, the 2-NBDG signal increased. For all bins, the glucose import rate of macrophages infected with the complemented strain was consistently higher than that of macrophages infected with the T4SS mutant (Fig. 6B). Together, these data demonstrate that the T4SS contributes to the glycolytic switch of infected macrophages.
**FIG 6:** *The type IV secretion system promotes glucose import by macrophages during B. abortus infection. RAW 264.7 cells were infected with the T4SS-deficient mCherry-expressing virB2 mutant at an MOI of 2,000 or the mCherry-expressing complemented virB2 strain at an MOI of 100 and then stained with 2-NBDG after 48 h. (A) MFIs of mCherry and 2-NBDG for the mCherry-low population of RAW 264.7 cells infected with the indicated B. abortus strains. (B) 2-NBDG MFI for the mCherry-high populations infected with the indicated B. abortus strains after binning based on mCherry signal. Dots represent individual wells, columns are means, and error bars are the SD. **, P ≤ 0.01; ns, no statistical difference (Student two-tailed t test).*
## Impaired induction of glycolysis does not necessarily impact intracellular replication of B. abortus.
We and others have shown that IRE1α contributes to the intracellular replication of Brucella (23, 25, 26, 31–33) (see also Fig. S1). Intriguingly, it has also been shown that glycolysis in the infected macrophage and lactate catabolism by Brucella also support intracellular replication [2]. We wondered if IRE1α was promoting intracellular replication by increasing glycolysis. B. abortus showed no replication defect in XBP1 or TLR4 KO macrophages (see Fig. S5A and B), despite those cells’ reduced glycolytic induction. Thus, reducing the glycolytic rate of infected cells does not necessarily impair the intracellular growth of B. abortus, suggesting that IRE1α promotes the intracellular replication of B. abortus independently of its role in the glycolytic switch.
## Activation of the IRE1α-XBP1s pathway is not sufficient to increase glycolysis.
Having established that the IRE1α-XBP1 signaling axis promotes glycolysis in CAMs, we then investigated whether activation of this pathway was sufficient to cause increased glycolysis. We treated RAW 264.7 cells with the chemical ER stress inducers tunicamycin and thapsigargin, which led to robust XBP1 splicing (Fig. 7A). However, neither of these treatments led to an upregulation of glycolytic genes (Fig. 7B), suggesting that IRE1α activation is not sufficient to increase glycolysis.
**FIG 7:** *Activation of the IRE1α-XBP1 signaling axis is not sufficient to increase glycolysis in macrophages. (A and B) RAW 264.7 cells were treated with 200 ng/mL tunicamycin (Tm) or 50 nM thapsigargin (Tg) for 24 h. XBP1 splicing was assessed by nonquantitative RT-PCR (A), and expression of the indicated genes normalized to untreated controls was assessed by RT-qPCR (B). (C and D) Two independent XBP1s overexpression (o/e) RAW 264.7 cell lines were generated. XBP1s protein levels were assessed by Western blotting (C), and expression of the indicated genes normalized to wild-type RAW 264.7 was assessed by RT-qPCR (D). The data are means of triplicate wells ± the SD.*
Tunicamycin and thapsigargin are potent ER stress inducers that activate all three branches of the UPR. To look more specifically at the IRE1α-XBP1 signaling axis, we overexpressed XBP1s in two independently generated RAW 264.7 cell lines (Fig. 7C). As we observed with chemical IRE1α activation, overexpression of XBP1s was not sufficient to upregulate glycolytic genes or the CAM marker Irg1 (Fig. 7D). Together, these data demonstrate that activation of the IRE1α-XBP1s signaling pathway is not sufficient to increase glycolysis in macrophages.
## DISCUSSION
A cell must utilize the right metabolic pathways to optimally perform its effector functions, and the ability to modulate metabolic processes is essential for cells that must respond to different stimuli, especially immune cells. Here, we show that the ER stress sensor IRE1α and its downstream regulator XBP1s contribute to metabolic reprogramming of macrophages by promoting glycolysis in response to inflammatory stimuli. This occurs when IRE1α is activated in a TLR4-dependent manner, such as with LPS, or a TLR4-independent manner, such as with B. abortus (see Fig. S6). Although TLR4 is not required for IRE1α activation during B. abortus infection, TLR4-deficient macrophages show a reduction in glycolytic flux during infection, suggesting that TLR4 can support glycolysis via IRE1α-dependent and IRE1α-independent mechanisms. TLR4 signaling has been shown to lead to the accumulation of HIF-1α, a key transcriptional regulator of CAMs [50], and the IRE1α-XBP1 signaling axis enhances HIF-1α transcriptional activity without affecting HIF-1α protein levels in cancer cells [37].
In this study, we chose to focus on the IRE1α-XBP1 signaling axis; however, because IRE1α activation has effects other than XBP1 splicing, we cannot rule out a role for these other IRE1α functions in metabolic reprogramming. Indeed, while IRE1α-deficient cells showed no increase in lactate production after LPS stimulation or Brucella infection, the XBP1-deficient cells produced an intermediate level of lactate, suggesting that IRE1α may also promote glycolysis via XBP1-independent mechanisms (Fig. 2A and C). For example, IRE1α phosphorylation leads to JNK activation [8], and JNK signaling promotes the Warburg effect in cancer cells [51]. In addition to splicing the XBP1 transcript, activated IRE1α also degrades specific RNA species in a process called regulated IRE1α-dependent decay, or RIDD. Intriguingly, RIDD, which contributes to the intracellular survival of Brucella [28], influences the metabolism of cancer cells [52] and thus may also be affecting CAM metabolism.
While our results demonstrate that the IRE1α-XBP1 signaling axis supports glycolysis in CAMs, it is unclear whether this pathway is also involved in other metabolic changes during macrophage activation. Brucella infection leads to the production of mitochondrial reactive oxygen species (ROS) [53, 54], mitochondrial fragmentation [55], and decreased mitochondrial metabolism [2], while IRE1α activation leads to increased mitochondrial ROS during infection with an attenuated B. abortus strain [53] or multidrug-resistant *Staphylococcus aureus* [29]. However, during B. abortus infection, ROS production and subsequent IL-1β production are XBP1-independent [53], but we show here that XBP1 contributes to glycolysis. On the other hand, XBP1 inhibits mitochondrial function in tumor-associated T cells [15]. Future studies will investigate how the IRE1α-XBP1 signaling axis affects mitochondrial function during CAM polarization.
One interesting aspect of this study is the observation that reduced glycolytic induction in macrophages is not sufficient to alter B. abortus intracellular replication. While IRE1α, XBP1, and TLR4 KO macrophages all showed reduced glycolysis during infection, only the IRE1α-deficient macrophages showed a reduced bacterial burden, suggesting that IRE1α supports B. abortus replication independently of enhanced glycolysis. Irg1 has been implicated in the control of Brucella in vivo [56], but we did not observe enhanced replication in macrophages with reduced Irg1 expression. It has also been reported that inhibition of host cell glycolysis and lactate production with potent small molecule inhibitors impairs Brucella replication [2]. However, it is worth noting that IREα and XBP1 KO macrophages are still upregulating glycolytic genes (Fig. 1B, D, F and H; Fig. 2B and D), glucose import (Fig. 3C and D), and glycolytic flux (Fig. 4) under inflammatory stimuli, just to a lesser extent than WT cells. Lactate utilization is required for robust intracellular replication during in vitro infection [18]. However, during chronic infection in vivo, B. abortus favors alternatively activated macrophages (AAM), which have markedly different metabolism compared to CAMs, due to increased glucose availability [4]. We believe the ability of B. abortus to replicate in cells with different metabolic states contributes to its success as a pathogen. Indeed, different metabolic states of the host cell contribute to the replication of other intracellular pathogens, including *Chlamydia trachomatis* [57], *Salmonella enterica* [58], and *Legionella pneumophila* [59].
It is clear the ER plays a central role in both sensing and directing different metabolic processes and that IRE1α activation can have profound effects on cellular metabolism [60]. However, these effects are very context dependent. In NK cells, IRE1α-XBP1 signaling during viral infection drives oxidative phosphorylation mediated by c-Myc [16], while XBP1s inhibits mitochondrial function in tumor-infiltrating T cells [15]. In breast cancer cells, XBP1s cooperates with HIF-1α to directly regulate many glycolytic genes, including the glucose importer Glut1 [37]. In obese mice, the IRE1α-XBP1 axis represses AAM polarization [18], and in nonobese mice, it contributes to the mixed phenotype of tumor-associated macrophages, regulating the expression of both CAM and AAM markers [19]. By demonstrating that IRE1α-XBP1 signaling is required for robust glycolytic induction in macrophages in response to different inflammatory stimuli, we have provided another context in which this critical signaling pathway plays an important role in immunometabolism.
## Bacterial strains and culture conditions.
Bacterial strains in this study are the virulent wild-type B. abortus 2308; its isogenic mCherry+ strain MX2 [4]; the T4SS-deficient virB2 mutant ADH3 [48]; its isogenic mCherry+ BCE4; the virB2 complemented strain ADH8 [48]; and its isogenic mCherry+ BCE5. MX2, BCE4, and BCE5 each have an insertion of the pKSoriT-bla-kan-PsojA-mCherry plasmid [61]. BCE4 and BCE5 were generated via conjugation with S17 *Escherichia coli* bearing the mCherry plasmid; clones that were kanamycin resistant and fluorescent were selected, and the insertion site was validated by multiplex PCR (the primers are listed in Table S1 in the supplemental material).
All B. abortus strains were cultured on blood agar plates (UC Davis Veterinary Medicine Biological Media Services) for 3 days at 37°C with $5\%$ CO2. B. abortus was then cultured overnight at 37°C with aeration in tryptic soy broth (TSB; BD Difco), then subcultured in acidic EGY (pH 5.5) for 4 h at 37°C with aeration prior to macrophage infections. To confirm equivalent fluorescent signals between MX2, BCE4, and BCE5, each strain was grown in triplicate overnight cultures in TSB, then mCherry fluorescence was measured on a GloMax Explorer Microplate Reader (Promega) and viable cells enumerated by CFU counting after plating on tryptic soy agar (BD Difco) plates and incubation at 37°C with $5\%$ CO2 for 3 days. All work with B. abortus was performed at biosafety level 3 and was approved by the Institutional Biosafety Committee at the University of California—Davis.
## Mammalian cell culture.
RAW 264.7 murine macrophage-like cells (TIB-71; ATCC) and their derivatives were cultured in RPMI 1640 media (Gibco) supplemented with $10\%$ heat-inactivated fetal bovine serum (FBS) at 37°C and $5\%$ CO2. Bone marrow-derived macrophages (BMDMs) were generated as previously described [4]. Briefly, bone marrow cells from femurs and tibiae from 6- to 8-week-old female C57BL/6J (Jackson Laboratory, stock 000664), TLR4 KO (Jackson Laboratory, stock 029015), LysM-Cre+ Ern1fl/fL, and LysM-Cre− Ern1fl/fL [35] mice were isolated and maintained in RPMI 1640 supplemented with $10\%$ FBS (Gibco), $30\%$ L929 cell supernatant, and GlutaMAX (Gibco) at 37°C and $5\%$ CO2 for 7 days before use in in vitro assays. The same medium was used for all subsequent BMDM experiments. Lipopolysaccharides from *Salmonella enterica* serotype Typhimurium (Sigma) and tunicamycin (Sigma) were reconstituted in d-PBS (Gibco). Thapsigargin (Sigma) and 4μ8c (Sigma) were reconstituted in dimethyl sulfoxide. 2-NBDG [(2-N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino)-2-deoxyglucose] (Thermo) was reconstituted in $100\%$ ethanol.
## Generation of XBP1 knockout and overexpression cell lines.
*To* generate XBP1 knockout (KO) cells, complementary oligonucleotides (see Table S1 in the supplemental material) forming the nontargeting control (NTC) sgRNA (5′-TCCTGCGCGATGACCGTCGG-3′) and the XBP1-targeting sgRNA (5′-CGGCCTTGTGGTTGAGAACC-3′) were phosphorylated, annealed, and ligated into BsmBI-digested pXPR_001 [62], resulting in pBCE44 and pBCE41, respectively. *To* generate lentiviral particles, pBCE44 or pBCE41 were cotransfected with psPAX2 (Addgene, plasmid 12260) and pMD2.G (Addgene, plasmid 12259) using Xfect (TaKaRa Bio) into HEK-293T cells (CRL-2316, ATCC) grown in Dulbecco modified *Eagle medium* (DMEM; Gibco) with $10\%$ FBS (Gibco). Lentiviral particles were concentrated from clarified supernatants by using a Lenti-X Concentrator (TaKaRa Bio). RAW 264.7 cells were transduced with the lentiviral particles and then selected with 4 μg/mL puromycin (Gibco). After 5 days of selection, single cells were plated by serial dilution in 96-well plates for clonal selection, and gDNA was extracted from the remaining pool by using a DNeasy kit (Qiagen). The XBP1 locus was amplified by PCR and sequenced by Sanger sequencing using the primers listed in Table S1; the cutting efficiency was estimated by TIDE analysis [63]. After confirming efficient disruption, gDNA was prepared from clonal lines using QuickExtract (Lucigen), and the XBP1 locus was amplified by PCR and sequenced by Sanger sequencing. Putative knockouts were further validated by Western blot analysis and by measuring expression of ERdJ4, an XBP1s-specific target, after thapsigargin treatment.
*To* generate XBP1 overexpression (o/e) lines, XBP1s was amplified from cDNA generated from RAW 264.7 cells treated with thapsigargin and cloned into a modified pENTR1A (64; Addgene, plasmid 17398) using Gibson Assembly Master Mix (New England BioLabs). After sequence validation by Sanger sequencing, XBP1s was cloned into the mammalian expression vector pLENTI CMV Puro Dest (Addgene, plasmid 17452) using LR Clonase II (Invitrogen). Lentiviral particles were generated, and RAW 264.7 cells were transduced as described above such that two separate XBP1s overexpression lines were generated. XBP1s overexpression was confirmed by Western blot analysis.
## Macrophage infections.
For most infections, 1 day prior to infection, RAW 264.7 cells were seeded in 24-well plates at 5 × 104 cells per well, and BMDMs were seeded at 1.5 × 105 cells per well in 24-well tissue culture plates. For flow cytometry experiments, RAW 264.7 cells were seeded at 2 × 105 cells per well in 6-well tissue culture plates. For infection of RAW 264.7 cells, B. abortus was opsonized for 30 min at room temperature with $20\%$ antiserum in PBS++ prepared from male C57BL/6J mice infected with B. abortus 2308 for 2 weeks. For inoculum preparation, the bacteria were washed in d-PBS (Gibco), diluted in the appropriate cell culture media, and added to the macrophages at an MOI of 100 unless otherwise indicated. The tissue culture plates were then centrifuged at 210 × g for 5 min to synchronize infection. After a phagocytosis period of 30 min at 37°C in $5\%$ CO2, the cells were washed twice with d-PBS and then incubated with 50 μg/mL gentamicin (Gibco) in the appropriate culture media for 30 min at 37°C in $5\%$ CO2; the medium was then replaced with gentamicin-free media. To examine intracellular replication by CFU, infected macrophages were lysed in $0.5\%$ Tween 20 at the indicated time points. The lysates were serially diluted in d-PBS and spread on TSA plates, which were then incubated at 37°C and $5\%$ CO2 for 3 to 5 days before colony enumeration. For lactate quantification, cell culture supernatants were sterile-filtered through 0.22-μm-pore size filters and stored at −80°C until use. Lactate levels were measured using a Lactate Colorimetric Assay Kit II (BioVision) according to the manufacturer’s protocol.
## 2-NBDG assay.
RAW 264.7 cells of the indicated genotypes were infected as described above at an MOI of 100 for 2308, ADH8, MX2, and BCE5 or at an MOI of 2,000 for ADH3 and BCE4. After 48 h, the cells were washed three times with d-PBS and collected by scraping. Viable cells were counted on a hemacytometer using trypan blue. One million viable cells were incubated with 300 nM 2-NBDG in glucose-free DMEM (Gibco) with $10\%$ FBS for 45 min, stained with Live/Dead Fixable Aqua (Thermo Fisher) in d-PBS for 15 min, and fixed in CytoFix (BD Biosciences) for 30 min. The cells were then run on a CytoFLEX flow cytometer (Beckman Coulter), and data were analyzed using FlowJo (v10.8.0). Nonfluorescent B. abortus strains were used to inform gating strategies. When indicated for the mCherry-high cells, the mCherry signal was binned by equal units within each log across the population (e.g., 1 × 106, 2 × 106, 3 × 106, etc.).
## Seahorse analysis.
Totals of 2 × 104 RAW 264.7 cells or 3 × 104 BMDMs were seeded in Seahorse XF96 cell culture microplates. The next day, the cells were treated with 100 ng/mL LPS for 6 h in the appropriate media. The cells were assessed on a Seahorse XFe96 Analyzer (Agilent) using a Seahorse XF glycolytic rate assay kit according to the manufacturer’s instructions, with Seahorse XF RPMI (pH 7.4) supplemented with 1 mM pyruvate, 2 mM glutamine, and 10 mM glucose. GlycoPER was calculated using the glycolytic rate assay report generator. All reagents were from Agilent.
## RNA isolation and RT-PCR.
For RNA isolation, cells were washed with d-PBS and collected in TRI Reagent (Molecular Research Center). After the addition of chloroform, total RNA was isolated from the aqueous phase using Econo-Spin columns (Epoch Life Science) and subjected to on-column PureLink DNase (Invitrogen) digestion. *To* generate cDNA, 1 μg of total RNA was reverse transcribed with MultiScribe reverse transcriptase (Applied Biosystems) with random hexamers (Invitrogen) and RNaseOUT (Invitrogen). Real-time PCR was performed using SYBR green (Applied Biosystems) and the primers listed in Table S1 on a ViiA 7 real-time PCR system (Applied Biosystems) with the following cycling parameters: 50°C (2 min), 95°C (10 min), 40 cycles of 95°C (15 s) and 60°C (1 min), followed by dissociation curve analysis. Data were analyzed using QuantiStudio Real-Time PCR software v1.3 (Applied Biosystems) and analyzed using the ΔΔCT method. Isoforms of XBP1 were detected using nonquantitative RT-PCR with the primers listed in Table S1 and Phusion High-Fidelity PCR Master Mix (Thermo Fisher) under the following cycling conditions: 98°C for 30 s, 35 cycles of 98°C (10 s), 65°C (30 s), and 72°C (30 s), followed by 72°C for 10 min. The resulting amplicons were separated and visualized on a $2.5\%$ agarose gel containing SYBR Safe (Invitrogen).
## Protein isolation and Western blots.
For the TLR4 KO BMDMs, proteins were extracted from samples collected in TRI Reagent (Molecular Research Center) according to a modified protocol [65]. For validation of the XBP1 KO and overexpression lines, proteins were extracted using radioimmunoprecipitation assay buffer (50 mM Tris, 150 mM NaCl, $0.1\%$ sodium dodecyl sulfate (SDS), $0.5\%$ sodium deoxycholate, and $1\%$ Triton X-100) with Protease Inhibitor Cocktail Set III, Animal-Free (EMD Millipore). Insoluble debris was removed by centrifugation. Protein concentrations were determined using a Pierce MicroBCA protein assay kit (Thermo Fisher). Equivalent amounts of protein were separated by SDS-PAGE and transferred to Immobilon-P polyvinylidene difluoride membrane (Millipore). Membranes were incubated with antibodies per the manufacturer’s suggestions. Blots were developed with Western Lightning Plus ECL (Perkin-Elmer). The following antibodies were used: XBP1s (Cell Signaling Technology D2C1F, catalog no. 12782), IRE1α (Cell Signaling Technology 14C10, catalog no. 3294), GAPDH (Cell Signaling Technology 14C10, catalog no. 2118), and goat anti-rabbit horseradish peroxidase (Jackson ImmunoResearch). Images were processed with Adobe Photoshop, which was utilized on occasion to change the order of lanes in the image to group appropriate samples together.
## Data analysis.
Data were analyzed with Microsoft Excel (Microsoft) and Prism (GraphPad) using the statistical tests indicated in the figure legends. Densitometry was measured with ImageJ (version 1.53 [66]). Data presented here are from a minimum of triplicate measurements from representative experiments.
## References
1. Viola A, Munari F, Sanchez-Rodriguez R, Scolaro T, Castegna A. **The Metabolic signature of macrophage responses**. *Front Immunol* (2019) **10** 1462. DOI: 10.3389/fimmu.2019.01462
2. Czyż DM, Willett JW, Crosson S. *J Bacteriol* (2017) **199**. DOI: 10.1128/JB.00227-17
3. Gomes MTR, Guimaraes ES, Marinho FV, Macedo I, Aguiar E, Barber GN, Moraes-Vieira PMM, Alves-Filho JC, Oliveira SC. **STING regulates metabolic reprogramming in macrophages via HIF-1alpha during**. *PLoS Pathog* (2021) **17**. DOI: 10.1371/journal.ppat.1009597
4. Xavier MN, Winter MG, Spees AM, den Hartigh AB, Nguyen K, Roux CM, Silva TM, Atluri VL, Kerrinnes T, Keestra AM, Monack DM, Luciw PA, Eigenheer RA, Baumler AJ, Santos RL, Tsolis RM. **PPARγ-mediated increase in glucose availability sustains chronic**. *Cell Host Microbe* (2013) **14** 159-170. DOI: 10.1016/j.chom.2013.07.009
5. Adams CJ, Kopp MC, Larburu N, Nowak PR, Ali MMU. **Structure and molecular mechanism of ER stress signaling by the unfolded protein response signal activator IRE1**. *Front Mol Biosci* (2019) **6** 11. DOI: 10.3389/fmolb.2019.00011
6. Yoshida H, Matsui T, Yamamoto A, Okada T, Mori K. **XBP1 mRNA is induced by ATF6 and spliced by IRE1 in response to ER stress to produce a highly active transcription factor**. *Cell* (2001) **107** 881-891. DOI: 10.1016/s0092-8674(01)00611-0
7. Cox JS, Walter P. **A novel mechanism for regulating activity of a transcription factor that controls the unfolded protein response**. *Cell* (1996) **87** 391-404. DOI: 10.1016/s0092-8674(00)81360-4
8. Urano F, Wang X, Bertolotti A, Zhang Y, Chung P, Harding HP, Ron D. **Coupling of stress in the ER to activation of JNK protein kinases by transmembrane protein kinase IRE1**. *Science* (2000) **287** 664-666. DOI: 10.1126/science.287.5453.664
9. Hu P, Han Z, Couvillon AD, Kaufman RJ, Exton JH. **Autocrine tumor necrosis factor alpha links endoplasmic reticulum stress to the membrane death receptor pathway through IRE1alpha-mediated NF-κB activation and down-regulation of TRAF2 expression**. *Mol Cell Biol* (2006) **26** 3071-3084. DOI: 10.1128/MCB.26.8.3071-3084.2006
10. Hu R, Warri A, Jin L, Zwart A, Riggins RB, Fang HB, Clarke R. **NF-κB signaling is required for XBP1 (unspliced and spliced)-mediated effects on antiestrogen responsiveness and cell fate decisions in breast cancer**. *Mol Cell Biol* (2015) **35** 379-390. DOI: 10.1128/MCB.00847-14
11. Keestra-Gounder AM, Byndloss MX, Seyffert N, Young BM, Chavez-Arroyo A, Tsai AY, Cevallos SA, Winter MG, Pham OH, Tiffany CR, de Jong MF, Kerrinnes T, Ravindran R, Luciw PA, McSorley SJ, Baumler AJ, Tsolis RM. **NOD1 and NOD2 signalling links ER stress with inflammation**. *Nature* (2016) **532** 394-397. DOI: 10.1038/nature17631
12. Pei G, Zyla J, He L, Moura-Alves P, Steinle H, Saikali P, Lozza L, Nieuwenhuizen N, Weiner J, Mollenkopf HJ, Ellwanger K, Arnold C, Duan M, Dagil Y, Pashenkov M, Boneca IG, Kufer TA, Dorhoi A, Kaufmann SH. **Cellular stress promotes NOD1/2-dependent inflammation via the endogenous metabolite sphingosine-1-phosphate**. *EMBO J* (2021) **40**. DOI: 10.15252/embj.2020106272
13. Martinon F, Chen X, Lee AH, Glimcher LH. **TLR activation of the transcription factor XBP1 regulates innate immune responses in macrophages**. *Nat Immunol* (2010) **11** 411-418. DOI: 10.1038/ni.1857
14. Zeng L, Liu YP, Sha H, Chen H, Qi L, Smith JA. **XBP-1 couples endoplasmic reticulum stress to augmented IFN-beta induction via a**. *J Immunol* (2010) **185** 2324-2330. DOI: 10.4049/jimmunol.0903052
15. Song M, Sandoval TA, Chae CS, Chopra S, Tan C, Rutkowski MR, Raundhal M, Chaurio RA, Payne KK, Konrad C, Bettigole SE, Shin HR, Crowley MJP, Cerliani JP, Kossenkov AV, Motorykin I, Zhang S, Manfredi G, Zamarin D, Holcomb K, Rodriguez PC, Rabinovich GA, Conejo-Garcia JR, Glimcher LH, Cubillos-Ruiz JR. **IRE1α-XBP1 controls T cell function in ovarian cancer by regulating mitochondrial activity**. *Nature* (2018) **562** 423-428. DOI: 10.1038/s41586-018-0597-x
16. Dong H, Adams NM, Xu Y, Cao J, Allan DSJ, Carlyle JR, Chen X, Sun JC, Glimcher LH. **The IRE1 endoplasmic reticulum stress sensor activates natural killer cell immunity in part by regulating c-Myc**. *Nat Immunol* (2019) **20** 865-878. DOI: 10.1038/s41590-019-0388-z
17. Yan D, Wang HW, Bowman RL, Joyce JA. **STAT3 and STAT6 signaling pathways synergize to promote cathepsin secretion from macrophages via IRE1α activation**. *Cell Rep* (2016) **16** 2914-2927. DOI: 10.1016/j.celrep.2016.08.035
18. Shan B, Wang X, Wu Y, Xu C, Xia Z, Dai J, Shao M, Zhao F, He S, Yang L, Zhang M, Nan F, Li J, Liu J, Liu J, Jia W, Qiu Y, Song B, Han JJ, Rui L, Duan SZ, Liu Y. **The metabolic ER stress sensor IRE1alpha suppresses alternative activation of macrophages and impairs energy expenditure in obesity**. *Nat Immunol* (2017) **18** 519-529. DOI: 10.1038/ni.3709
19. Batista A, Rodvold JJ, Xian S, Searles SC, Lew A, Iwawaki T, Almanza G, Waller TC, Lin J, Jepsen K, Carter H, Zanetti M. **IRE1α regulates macrophage polarization, PD-L1 expression, and tumor survival**. *PLoS Biol* (2020) **18**. DOI: 10.1371/journal.pbio.3000687
20. Choi JA, Song CH. **Insights into the role of endoplasmic reticulum stress in infectious diseases**. *Front Immunol* (2019) **10** 3147. DOI: 10.3389/fimmu.2019.03147
21. Celli J, Tsolis RM. **Bacteria, the endoplasmic reticulum and the unfolded protein response: friends or foes?**. *Nat Rev Microbiol* (2015) **13** 71-82. DOI: 10.1038/nrmicro3393
22. Celli J. **The intracellular life cycle of**. *Microbiol Spectr* (2019) **7**. DOI: 10.1128/microbiolspec.BAI-0006-2019
23. Taguchi Y, Imaoka K, Kataoka M, Uda A, Nakatsu D, Horii-Okazaki S, Kunishige R, Kano F, Murata M. **Yip1A, a novel host factor for the activation of the IRE1 pathway of the unfolded protein response during**. *PLoS Pathog* (2015) **11**. DOI: 10.1371/journal.ppat.1004747
24. Li P, Tian M, Bao Y, Hu H, Liu J, Yin Y, Ding C, Wang S, Yu S. **Brucella rough mutant induce macrophage death via activating IRE1α pathway of endoplasmic reticulum stress by enhanced T4SS secretion**. *Front Cell Infect Microbiol* (2017) **7** 422. DOI: 10.3389/fcimb.2017.00422
25. Smith JA, Khan M, Magnani DD, Harms JS, Durward M, Radhakrishnan GK, Liu YP, Splitter GA. *PLoS Pathog* (2013) **9**. DOI: 10.1371/journal.ppat.1003785
26. Guimaraes ES, Gomes MTR, Campos PC, Mansur DS, Dos Santos AA, Harms J, Splitter G, Smith JA, Barber GN, Oliveira SC. *J Immunol* (2019) **202** 2671-2681. DOI: 10.4049/jimmunol.1801233
27. de Jong MF, Starr T, Winter MG, den Hartigh AB, Child R, Knodler LA, van Dijl JM, Celli J, Tsolis RM. **Sensing of bacterial type IV secretion via the unfolded protein response**. *mBio* (2013) **4** e00418-12-e00412. DOI: 10.1128/mBio.00418-12
28. Wells KM, He K, Pandey A, Cabello A, Zhang D, Yang J, Gomez G, Liu Y, Chang H, Li X, Zhang H, Feng X, da Costa LF, Metz R, Johnson CD, Martin CL, Skrobarczyk J, Berghman LR, Patrick KL, Leibowitz J, Ficht A, Sze SH, Song J, Qian X, Qin QM, Ficht TA, de Figueiredo P. **Brucella activates the host RIDD pathway to subvert BLOS1-directed immune defense**. *Elife* (2022) **11**. DOI: 10.7554/eLife.73625
29. Abuaita BH, Schultz TL, O’Riordan MX. **Mitochondrion-derived vesicles deliver antimicrobial reactive oxygen species to control phagosome-localized**. *Cell Host Microbe* (2018) **24** 625-636.e5. DOI: 10.1016/j.chom.2018.10.005
30. O’Neill LAJ, Artyomov MN. **Itaconate: the poster child of metabolic reprogramming in macrophage function**. *Nat Rev Immunol* (2019) **19** 273-281. DOI: 10.1038/s41577-019-0128-5
31. Liu N, Li Y, Dong C, Xu X, Wei P, Sun W, Peng Q. **Inositol-requiring enzyme 1-dependent activation of AMPK promotes**. *J Bacteriol* (2016) **198** 986-993. DOI: 10.1128/JB.00868-15
32. Pandey A, Lin F, Cabello AL, da Costa LF, Feng X, Feng HQ, Zhang MZ, Iwawaki T, Rice-Ficht A, Ficht TA, de Figueiredo P, Qin QM. **Activation of host IRE1α-dependent signaling axis contributes the intracellular parasitism of**. *Front Cell Infect Microbiol* (2018) **8** 103. DOI: 10.3389/fcimb.2018.00103
33. Qin QM, Pei J, Ancona V, Shaw BD, Ficht TA, de Figueiredo P. **RNAi screen of endoplasmic reticulum-associated host factors reveals a role for IRE1α in supporting**. *PLoS Pathog* (2008) **4**. DOI: 10.1371/journal.ppat.1000110
34. Qiu Q, Zheng Z, Chang L, Zhao YS, Tan C, Dandekar A, Zhang Z, Lin Z, Gui M, Li X, Zhang T, Kong Q, Li H, Chen S, Chen A, Kaufman RJ, Yang WL, Lin HK, Zhang D, Perlman H, Thorp E, Zhang K, Fang D. **Toll-like receptor-mediated IRE1α activation as a therapeutic target for inflammatory arthritis**. *EMBO J* (2013) **32** 2477-2490. DOI: 10.1038/emboj.2013.183
35. Sule G, Abuaita BH, Steffes PA, Fernandes AT, Estes SK, Dobry C, Pandian D, Gudjonsson JE, Kahlenberg JM, O’Riordan MX, Knight JS. **Endoplasmic reticulum stress sensor IRE1α propels neutrophil hyperactivity in lupus**. *J Clin Invest* (2021) **131**. DOI: 10.1172/JCI137866
36. Cross BC, Bond PJ, Sadowski PG, Jha BK, Zak J, Goodman JM, Silverman RH, Neubert TA, Baxendale IR, Ron D, Harding HP. **The molecular basis for selective inhibition of unconventional mRNA splicing by an IRE1-binding small molecule**. *Proc Natl Acad Sci USA* (2012) **109** E869-E878. DOI: 10.1073/pnas.1115623109
37. Chen X, Iliopoulos D, Zhang Q, Tang Q, Greenblatt MB, Hatziapostolou M, Lim E, Tam WL, Ni M, Chen Y, Mai J, Shen H, Hu DZ, Adoro S, Hu B, Song M, Tan C, Landis MD, Ferrari M, Shin SJ, Brown M, Chang JC, Liu XS, Glimcher LH. **XBP1 promotes triple-negative breast cancer by controlling the HIF1α pathway**. *Nature* (2014) **508** 103-107. DOI: 10.1038/nature13119
38. Zou C, Wang Y, Shen Z. **2-NBDG as a fluorescent indicator for direct glucose uptake measurement**. *J Biochem Biophys Methods* (2005) **64** 207-215. DOI: 10.1016/j.jbbm.2005.08.001
39. Goldstein J, Hoffman T, Frasch C, Lizzio EF, Beining PR, Hochstein D, Lee YL, Angus RD, Golding B. **Lipopolysaccharide (LPS) from**. *Infect Immun* (1992) **60** 1385-1389. DOI: 10.1128/iai.60.4.1385-1389.1992
40. Moreno E, Berman DT, Boettcher LA. **Biological activities of**. *Infect Immun* (1981) **31** 362-370. DOI: 10.1128/iai.31.1.362-370.1981
41. Jakka P, Bhargavi B, Namani S, Murugan S, Splitter G, Radhakrishnan G. **Cytoplasmic linker protein CLIP170 negatively regulates TLR4 signaling by targeting the TLR adaptor protein TIRAP**. *J Immunol* (2018) **200** 704-714. DOI: 10.4049/jimmunol.1601559
42. Jakka P, Namani S, Murugan S, Rai N, Radhakrishnan G. **The**. *J Biol Chem* (2017) **292** 20613-20627. DOI: 10.1074/jbc.M117.815878
43. Lee JJ, Kim DH, Kim DG, Lee HJ, Min W, Rhee MH, Cho JY, Watarai M, Kim S. **Toll-like receptor 4-linked Janus kinase 2 signaling contributes to internalization of**. *Infect Immun* (2013) **81** 2448-2458. DOI: 10.1128/IAI.00403-13
44. Campos MA, Rosinha GM, Almeida IC, Salgueiro XS, Jarvis BW, Splitter GA, Qureshi N, Bruna-Romero O, Gazzinelli RT, Oliveira SC. **Role of Toll-like receptor 4 in induction of cell-mediated immunity and resistance to**. *Infect Immun* (2004) **72** 176-186. DOI: 10.1128/IAI.72.1.176-186.2004
45. Salcedo SP, Marchesini MI, Degos C, Terwagne M, Von Bargen K, Lepidi H, Herrmann CK, Santos Lacerda TL, Imbert PR, Pierre P, Alexopoulou L, Letesson JJ, Comerci DJ, Gorvel JP. **BtpB, a novel**. *Front Cell Infect Microbiol* (2013) **3** 28. DOI: 10.3389/fcimb.2013.00028
46. Conde-Alvarez R, Arce-Gorvel V, Iriarte M, Mancek-Keber M, Barquero-Calvo E, Palacios-Chaves L, Chacon-Diaz C, Chaves-Olarte E, Martirosyan A, von Bargen K, Grillo MJ, Jerala R, Brandenburg K, Llobet E, Bengoechea JA, Moreno E, Moriyon I, Gorvel JP. **The lipopolysaccharide core of**. *PLoS Pathog* (2012) **8**. DOI: 10.1371/journal.ppat.1002675
47. Walter F, O’Brien A, Concannon CG, Dussmann H, Prehn JHM. **ER stress signaling has an activating transcription factor 6α (ATF6)-dependent “off-switch.”**. *J Biol Chem* (2018) **293** 18270-18284. DOI: 10.1074/jbc.RA118.002121
48. den Hartigh AB, Sun YH, Sondervan D, Heuvelmans N, Reinders MO, Ficht TA, Tsolis RM. **Differential requirements for VirB1 and VirB2 during**. *Infect Immun* (2004) **72** 5143-5149. DOI: 10.1128/IAI.72.9.5143-5149.2004
49. Hiyoshi H, English BC, Diaz-Ochoa VE, Wangdi T, Zhang LF, Sakaguchi M, Haneda T, Tsolis RM, Baumler AJ. **Virulence factors perforate the pathogen-containing vacuole to signal efferocytosis**. *Cell Host Microbe* (2022) **30** 163-170.e6. DOI: 10.1016/j.chom.2021.12.001
50. Blouin CC, Page EL, Soucy GM, Richard DE. **Hypoxic gene activation by lipopolysaccharide in macrophages: implication of hypoxia-inducible factor 1α**. *Blood* (2004) **103** 1124-1130. DOI: 10.1182/blood-2003-07-2427
51. Papa S, Choy PM, Bubici C. **The ERK and JNK pathways in the regulation of metabolic reprogramming**. *Oncogene* (2019) **38** 2223-2240. DOI: 10.1038/s41388-018-0582-8
52. Almanza A, Mnich K, Blomme A, Robinson CM, Rodriguez-Blanco G, Kierszniowska S, McGrath EP, Le Gallo M, Pilalis E, Swinnen JV, Chatziioannou A, Chevet E, Gorman AM, Samali A. **Regulated IRE1α-dependent decay (RIDD)-mediated reprograming of lipid metabolism in cancer**. *Nat Commun* (2022) **13** 2493. DOI: 10.1038/s41467-022-30159-0
53. Bronner DN, Abuaita BH, Chen X, Fitzgerald KA, Nunez G, He Y, Yin XM, O’Riordan MX. **Endoplasmic reticulum stress activates the inflammasome via NLRP3- and caspase-2-driven mitochondrial damage**. *Immunity* (2015) **43** 451-462. DOI: 10.1016/j.immuni.2015.08.008
54. Gomes MT, Campos PC, Oliveira FS, Corsetti PP, Bortoluci KR, Cunha LD, Zamboni DS, Oliveira SC. **Critical role of ASC inflammasomes and bacterial type IV secretion system in caspase-1 activation and host innate resistance to**. *J Immunol* (2013) **190** 3629-3638. DOI: 10.4049/jimmunol.1202817
55. Lobet E, Willemart K, Ninane N, Demazy C, Sedzicki J, Lelubre C, De Bolle X, Renard P, Raes M, Dehio C, Letesson JJ, Arnould T. **Mitochondrial fragmentation affects neither the sensitivity to TNFα-induced apoptosis of**. *Sci Rep* (2018) **8** 5173. DOI: 10.1038/s41598-018-23483-3
56. Demars A, Vitali A, Comein A, Carlier E, Azouz A, Goriely S, Smout J, Flamand V, Van Gysel M, Wouters J, Abendroth J, Edwards TE, Machelart A, Hoffmann E, Brodin P, De Bolle X, Muraille E. **Aconitate decarboxylase 1 participates in the control of pulmonary**. *PLoS Pathog* (2021) **17**. DOI: 10.1371/journal.ppat.1009887
57. Rajeeve K, Vollmuth N, Janaki-Raman S, Wulff TF, Baluapuri A, Dejure FR, Huber C, Fink J, Schmalhofer M, Schmitz W, Sivadasan R, Eilers M, Wolf E, Eisenreich W, Schulze A, Seibel J, Rudel T. **Reprogramming of host glutamine metabolism during**. *Nat Microbiol* (2020) **5** 1390-1402. DOI: 10.1038/s41564-020-0762-5
58. Eisele NA, Ruby T, Jacobson A, Manzanillo PS, Cox JS, Lam L, Mukundan L, Chawla A, Monack DM. **Salmonella require the fatty acid regulator PPARδ for the establishment of a metabolic environment essential for long-term persistence**. *Cell Host Microbe* (2013) **14** 171-182. DOI: 10.1016/j.chom.2013.07.010
59. Escoll P, Song OR, Viana F, Steiner B, Lagache T, Olivo-Marin JC, Impens F, Brodin P, Hilbi H, Buchrieser C. *Cell Host Microbe* (2017) **22** 302-316.e7. DOI: 10.1016/j.chom.2017.07.020
60. Huang S, Xing Y, Liu Y. **Emerging roles for the ER stress sensor IRE1α in metabolic regulation and disease**. *J Biol Chem* (2019) **294** 18726-18741. DOI: 10.1074/jbc.REV119.007036
61. Copin R, Vitry MA, Hanot Mambres D, Machelart A, De Trez C, Vanderwinden JM, Magez S, Akira S, Ryffel B, Carlier Y, Letesson JJ, Muraille E. **In situ microscopy analysis reveals local innate immune response developed around**. *PLoS Pathog* (2012) **8**. DOI: 10.1371/journal.ppat.1002575
62. Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelson T, Heckl D, Ebert BL, Root DE, Doench JG, Zhang F. **Genome-scale CRISPR-Cas9 knockout screening in human cells**. *Science* (2014) **343** 84-87. DOI: 10.1126/science.1247005
63. Brinkman EK, Chen T, Amendola M, van Steensel B. **Easy quantitative assessment of genome editing by sequence trace decomposition**. *Nucleic Acids Res* (2014) **42**. DOI: 10.1093/nar/gku936
64. Campeau E, Ruhl VE, Rodier F, Smith CL, Rahmberg BL, Fuss JO, Campisi J, Yaswen P, Cooper PK, Kaufman PD. **A versatile viral system for expression and depletion of proteins in mammalian cells**. *PLoS One* (2009) **4**. DOI: 10.1371/journal.pone.0006529
65. Kopec AM, Rivera PD, Lacagnina MJ, Hanamsagar R, Bilbo SD. **Optimized solubilization of TRIzol-precipitated protein permits Western blotting analysis to maximize data available from brain tissue**. *J Neurosci Methods* (2017) **280** 64-76. DOI: 10.1016/j.jneumeth.2017.02.002
66. Schneider CA, Rasband WS, Eliceiri KW. **NIH Image to ImageJ: 25 years of image analysis**. *Nat Methods* (2012) **9** 671-675. DOI: 10.1038/nmeth.2089
67. Bell SL, Lopez KL, Cox JS, Patrick KL, Watson RO. **Galectin-8 senses phagosomal damage and recruits selective autophagy adapter TAX1BP1 to control**. *mBio* (2021) **12**. DOI: 10.1128/mBio.01871-20
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